Different types of indicators used for different countries.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"3361",leadTitle:null,fullTitle:"Regenerative Medicine and Tissue Engineering",title:"Regenerative Medicine and Tissue Engineering",subtitle:null,reviewType:"peer-reviewed",abstract:"Few events in science have captured the same level of sustained interest and imagination of the nonscientific community as Stem Cells, Tissue Engineering, and Regenerative Medicine. The fundamental concept of Tissue Engineering and Regenerative Medicine is appealing to scientists, physicians, and lay people alike: to heal tissue or organ defects that the current medical practice deems difficult or impossible to cure. Tissue engineering combines cells, engineering, and materials methods with suitable biochemical and physiochemical factors to improve or replace biologic functions. Regenerative medicine is a new branch of medicine that attempts to change the course of chronic disease, in many instances regenerating failing organ systems lost due to age, disease, damage, or congenital defects. The area is rapidly becoming one of the most promising treatment options for patients suffering from tissue failure. This book of Regenerative Medicine and Tissue Engineering fairly reflects the state of the art of these two disciplines at this time as well as their therapeutic application. It covers numerous topics, such as stem cells, cell culture, polymer synthesis, novel biomaterials, drug delivery, therapeutics, and the creation of tissues and organs. The goal is to have this book serve as a reference for graduate students, post-docs, teachers, scientists and physicians, and as an explanatory analysis for executives in biotech and pharmaceutical companies.",isbn:null,printIsbn:"978-953-51-1108-5",pdfIsbn:"978-953-51-4248-5",doi:"10.5772/46192",price:169,priceEur:185,priceUsd:219,slug:"regenerative-medicine-and-tissue-engineering",numberOfPages:868,isOpenForSubmission:!1,isInWos:1,isInBkci:!0,hash:"fe914d49a96b3dcd00d27292ae23536e",bookSignature:"Jose A. 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Andrades completed his doctorate in Cellular Biology by the University of Málaga (Spain). He has been a Post-Doctoral fellow and Visiting Professor at Children’s Hospital Los Angeles, University of Southern California (Los Angeles, CA), as well as at the Skeletal Research Center, Case Western Reserve University (Cleveland, OH). He currently has a faculty position at the Department of Cell Biology, Genetics and Physiology (Laboratory of Bioengineering and Tissue Regeneration) at the University of Málaga. Dr. Andrades leads grants, and is author of scientific publications, focused on the study of the chondro-osteo-tendinogenesis by using mesenchymal stem cells, growth factors with specific molecular domains, and different biomaterials, for skeletal tissue engineering. The group has developed patents on cellular procedures that allow regeneration in bone/articular cartilage lesions of live animals. Currently, Dr. Andrades is coordinator of the Cellular Therapy Program NACRE (New Approaches for Cartilage Regeneration) in the CIBER-BBN. He belongs to the TerCel Network, and to the NanomedSpain, in which consortia develops collaborations with several groups of physicians for the clinical translation of their studies.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Malaga",institutionURL:null,country:{name:"Spain"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"980",title:"Tissue Engineering and Regenerative Medicine",slug:"tissue-engineering-and-regenerative-medicine"}],chapters:[{id:"44123",title:"Placenta-Derived Stem Cells as a Source for Treatment of Lung and Liver Disease in Cystic 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Sensor technology in the agriculture domain provides excellent support and offers the farmers to map their fields easily. Around the globe, the researchers of the agriculture domain strongly depending on the sensor technologies for both plant phenotyping and soil quality by using the latest technologies, including multispectral cameras, satellite imagery and drones, with the aid of internet of things (IoT) and cloud computing [1, 2]. The achievement of increment in the production level of agriculture outcome by introducing sensor technologies which offer the improvement in crop and soil quality, safety of food, sustainability, and profitability [2]. It helps farmers to understand the crops on the microscale. Sensors-based techniques used to provide appropriate tools to achieve the goals mentioned above [2]. Different sensing phenomena adopted for the agriculture field, and few of the selective sensors and their functionality.
The technological advances and development facilities to attain the implementations on the agriculture domain by breaking the barriers to the basic needs of the farmers. Many sensing technologies that were already identified for precision agriculture by monitoring and optimising the crops [2]. Few of the sensors are listed below, which can offer the best solution for this precise farming.
This technology supports the proper application of agrochemicals and can safeguard water quality. Around 82 per cent of the implementation of the fertiliser can be uniform and appropriate by using a human resource controlled or lightbar guidance system [3]. Determination of longitude, altitude, and latitude by using the signals received from signals; these sensors can monitor the accurate position or location of the crop (Figure 1).
G.P.S. system.
The G.P.S. systems used to measure the distances to the precisely located G.P.S. satellites to find positions on earth. Radio signals broadcasted from the G.P.S. satellites monitored by receivers [3]. A GPS position is usually determined by simultaneously measuring the distance to at least three satellites. The time taken for a radio signal which travels from the satellite to the G.P.S. receiver determines the length. For the calculation of positions, the information collected from the radio signals, which includes broadcasting time and satellite information, has to be processed.
This technology relatively inexpensive and also helps with parallel tracking devices, which assists the operators for the visualisation of the position concerning previous passes and to recognise the need for steering adjustments. Commonly, these aids are coming with different configurations. G.P.S. technology was used for monitoring yield or mapping the field and also soil sampling [3, 4]. The G.P.S. navigation system can increase the efficiency of the farm and improve the aspects of agribusiness by reducing environmental impacts. This system can also reduce the operator’s fatigue and anxiety regarding fertiliser and pesticide application. The use of this technology can demonstrate to the non-agricultural community that advanced technology used for farming efficiently and safely sampling [4].
In the last decades, farming implemented by several technological transformations and becoming more industrialised and driven by the latest technology. Introduction of smart agriculture gadgets which helps farmers for gaining best control on the process of crops growth and maintaining livestock as well with excellent efficiency. Internet of Things (IoT), based devices started to occupy every part of our life, from health care, automation, automotive and logistics, to smart cities and industrialisation (Figure 2). The Internet of Things creates up an era of precision agriculture sampling [5].
IoT system.
Precision agriculture is a basic term for all the services based on digital systems and inventions on technical things for the fulfilment of the modern farmer’s needs for the yield optimisation, reduction of wastage, and maintaining the quality of environment [5, 6]. IoT sensors installed in the crop can support the farmers for allotting the pesticides and fertilisers in the right way along with the following support:
Harvesting time optimisation
The health of the crop
Temperature, light and humidity level monitoring in greenhouses
Soil quality and moisture level measurement
Many smartphone applications identified to incorporate with the Internet of Things (IoT) ideals, aggregation of data, and speed of the process, which may bring the data up to date, information can be provided to the small farmers like watering, seeding, fertilising and weeding. These applications are collecting the data from these sensors, especially from remote sensors and weather stations [6]. It helps in an in-depth analysis of data and provides valuable recommendations too.
Seeding is not guesswork after the innovation and application of IoT technologies. The programmed smart device can find the exact place for a seed to be planted and grown in a possible way. The collection of crops by the smart tractors with more exceptional efficiency and care when the harvest is ripe. Presently, the percentage of energy needed for the cultivation of crop by repairing the tractor damage itself goes around 80 to 90. By using the G.P.S. controlled steering system and route planning based on the input data, we can:
Minimising erosion by tracking vehicle path
Fuel cost reduction
Improvement in accuracy on the operations
The applications developed for small-scaled farmers may support them in multiple ways. The diagnosis of the diseases on plants identified and forwarded to the experts to rectify. The number of nutrients needed by the fertilisers by the determination of leaf colour and soil quality [7]. Also, the pH value of the soil and other conditions can be measured. From the observations on leaves, the water needs of the plants determined. The readiness on the crop harvesting with the aid of U.V. and white light-based photos can aid in the prevention of ripeness [7].
The optical sensors are used to collect and record the data about crop field and soil quality by the collection of light reflected from the growing plants. The application of nitrogen to the plants indicated to the users according to the health of the plants [8]. As this technology is not depending on the atmospheric light, the optical sensors used day and night. It uses external light to analyse the properties of soil. Measurement of light reflectance frequencies is carried out by the sensors in near and mid-infrared and polarised light spectrums. Optical sensors can be easily placed or integrated on vehicles or drones or even satellites too. The aggregation of data, collected from optical sensors, can be processed further. Determination of the organic matter, clay, and soil moisture level content can also be analysed by optical sensors (Figure 3).
Optical system.
According to the data collected using various platforms, like satellites, aerial (aeroplanes, UAVs and drones) and ground-based, the reflectance recorded. The collection of images from satellites, aircraft, and UAV’s using cameras where the optical sensors installed in the ground are able to collect the reflectance data as a text file. According to the operation, these ground sensors classified either active or passive. The passive sensors are in need of an external source of light, like the sun. However, the active sensors are operated by their source of view of different wavelengths or a specific wavelength [9]. The relationship between the visible light and the chlorophyll content provides plant details. From this analysis, we could identify healthy plants as green. The mesophyll cells are reflecting the near-infrared light, which is invisible to the human eye, found that more than chlorophyll content, the quantity in a plant, results in the highest reflectance than the visible lights. Biomass production and evaluation of colour classified by analysing both wavelengths. Sensor position may affect the field measurements, like the crop distance, light source dependency, leaves may cover by snow dews, and also because of other factors that may cause the plant stress. The moderated distance between the target and the sensor kept avoiding noise in the captured signal. It will lead to overcoming the limitations of the sensor output. It is essential to monitor the leaves, which should not be covered by water molecules or dews, which may change the reflectance [9].
Among different domains and their development like the Internet of Things (IoT) supported farming, the electrochemical sensor system is playing a vital role by detecting single or multiple soil components effectively, selectivity, and efficiently for soil quality measurements. It can be done either remotely by sharing the data and in-situ like the direct point of care on soil health. This perspective is aimed for the description of the state of art sensor technology based on the electrochemical mechanism for the measurement of soil quality by considering present scenarios. The electrochemical sensing mechanism explored its applications in many fields and even for a point of use. Mainly, lab-based methods like an ion-selective membrane, impedance spectroscopy, and amperometric sensors are in use to detect the nutrients of the soil and other parameters of agriculture (Figure 4) [10].
Amperometric sensor.
One of the attractive methods is to combine the electrochemical sensing technique by using ion-selective membrane transducers, which can easily monitor the parameters of soil like phosphate, nitrate, potassium, and others. Electrochemical sensing techniques are not so complicated like spectroscopy or any optical complexity and deployed directly to measure soil nutrients. These sensors are consisting of two electrodes of a working electrode, which can detect the target and another one of a reference electrode, which supplies a constant potential. The difference in potential between these two electrodes is either proportional or inversely proportional to the target according to its nature, either anions or cations. The working principle of this sensor governed by the Nernst equation. By relating the change in working electrode potential, which is compared with the potential of a reference electrode, based on the linearity of the activity of the sensed ion. The electrochemical sensors to deploy for in-situ measurements are expecting the electronic circuits embedded with the sensor (Figure 5) [11].
Electromechanical system.
The microelectromechanical system (MEMS) based sensors embedded with electrochemical sensing units, which gains excellent potential for the analysis of soil quality because of their portability, rapidity, real-time measurement, and in-field deployability [12]. The ability of electrochemical soil sensors to sense different soil parameters, needed to be present in those systems as a basic and essential part for smart farming. This micro-scaled sensing system with the high potential for soil analysis is the much need for next-generation agriculture. MEMS-based sensors can save the data easily due to their affordability & sharing, on-time analysis, and accuracy in the decision [12].
These sensors used to estimate the mechanical resistance of the soil. The penetration or cutting through the land to measure the force using individual devices like strain gauges or load cells is the basic phenomenon of these sensors (Figure 6).
Mechanical sensor.
The developed prototypes by the researchers can map the soil resistance continuously in a feasible way. Unfortunately, these prototypes are not available commercially. A new technique called the “traction control” system on tractors based on drift sensors is using a similar method to control the three-point hitch on the way [13].
Dielectric sensors are used for measuring the soil moisture levels by the utilisation of the dielectric constant of the material. It defined as the electrical property, which is getting changed according to the content of soil moisture (Figure 7).
Dielectric sensor.
These sensors embedded with rain gauge stations and arranged around the farm. While the vegetation level goes down, the observation on soil moisture conditions can be performed by them. Also, the soil moisture sensors used the soil’s dielectric constant to justify the content of the volume of water and the transmission of electricity based on the soil’s capability depending on its dielectric constant. The dielectric constant land’s water is larger compare with air, so that, if the water content of the soil increases, the increment of the dielectric constant of the soil will also be recorded. So, the constant dielectric measurement provides a fair observation of water content.
Airflow sensors used to measure the permeability of air of the soil. The amount of pressure needed to pressurise a certain volume of air to some depth on the land, which is used to compare the multiple properties of soil (Figure 8).
Airflow sensor.
From multiple experiments, it is possible to distinguish between various soil types and soil structure, moisture levels and compaction. These measurements can be made not only at a single location, while in motion too dynamically. The expected outcome is the need for pressure to allow a particular amount of air to the ground in the wanted level of depth. By using such unique sensors, we can study various types of soil properties, including soil type, compaction, moisture level and structure, which produces unique identified signatures.
Agriculture sensors can increase the food demand because of the utilisation of minimum resources like water, seeds, and fertilisers. These sensors fulfil the above basic requirements by resource conservation and field mapping. Also, these sensors easily installed and used efficiently. They are cost-effective too. Along with the usage in agriculture, these sensors can also serve for the prevention of pollution and global warming. With the advantages of communication protocols, these sensors controlled remotely.
Precision agriculture and IoT technology are expecting flawless internet connectivity, which is a significant constraint and not available in many of the developing countries like I.N.D.I.A. there is a presumption among the customers that they may not be ready to utilise the present IoT devices integrated with agriculture sensors. Another significant impact on the infrastructure requirements like traffic systems, smart grids, and communication towers is not available everywhere, which also hinders the growth of the use of agriculture sensors.
Challenges and ideas to overcome limitations:
According to the expert’s vision, precision agriculture has a standard potential to meet the increment in food demand around the globe. Even though the field has good growth and scope, still this has not robust as expected earlier. This domain has several challenges that we need to overcome.
The technology following the standards is not uniform and the same, which gets changed often. Precision agriculture expected, to a large extent. The challenge depends on converting smart devices like sensors and gateways to farmer-friendly platforms.
Setting up the architecture for IoT technology is needed to be implemented. Knowledge of precision farming must be reached the farmers and enrich them to operate the sensors/tools independently so that the loss of the workforce prevented.
Providing continuous internet connectivity is mandatory, and network performance like the speed of bandwidth closely monitored.
All the crops are not going to produce the same products. So the product functioning must be defined correctly. Dividing their land as small zones for proper management may also derive the right results.
To prevent the mechanical damage of the sensor/device, continuous monitoring of the operation of these devices is a must. So, food safety cant is compromised. Upgradation of the tools is also essential. E-waste of these devices should adequately evacuate.
One of the formidable global challenges is to feed the huge population soon. It predicted that the population could increases to 9.73 billion people by 2050 and estimated that it would require 70% additional food production in comparison to the present scenario [14]. The conventional agriculture practices resulted in a decline in the total productivity, causing poor ecological diversity, reduce the pollination services, affects carbon sequestration, causes soil and water pollution, soil erosion and food security [15, 16]. It is in dare need to use newly emerged modern sensing and controlling digital technology for effective agriculture. The agricultural sector is not just about maximising productivity it has shifted to the spectrum of other activities like optimising landscape management, development of rural, protection of the environment and social justice outcomes [17, 18]. Precision farming is one of the innovative methods practised, it incepted in the early 1980s, and with the past few years, it has become more common. It is a concept of “right practice at the right location at the right time at the right intensity”. Precision agriculture uses electronic information and other digital technologies to collect data and analyse spatial/temporal data to improve the efficiency, productivity, and sustainability of agricultural operations [19]. Site-specific crop management practised from earlier decades like grid soil sampling and spot application of fertiliser and lime to optimise soil nutrient levels [20]. Global positioning systems (G.P.S.) initiated for civilian use in 1983, and in 1990’s Global Navigation Satellite Systems (GNSS) enabled to develop equipment for variable rate fertiliser application for soil sampling and yield monitoring [21]. Incorporating digital management and surveillance technologies in farming automates the farming with integrated crop management to maximise the effectiveness of crop and yield [22, 23, 24]. The mechanical digitisation encompasses farm machinery for the sowing of seedling, fertilisers, cultivation, harvesting and the implication of satellites and tractors to drones, using Geographic Information Systems (G.I.S.), Global Positioning System includes yield mapping, remote sensing, variable rate irrigation, automatic tractor navigation, and robotics, proximal sensing of soils and crops, and profitability and adoption of precision farming (Figure 9). The details of the machinery discussed in the below sections. It is essential to understand the soil quality, functions and the role of indicators.
Precision farming cycle.
Soil is a vigorous component for crop production, and it plays a critical role in delivering ecosystem services. Like water and air, soils contribute a major carrier for biodiversity. The concept infers the capacity of soil to perform a specific function as a store, recycle and energy balance, that reflects the living and dynamic nature of the soil within the ecosystem boundary for multiple uses [25, 26]. The diverse potential of land uses to understand the quality of soil for ensuring the sustainability of the environment [27]. In the context of agriculture, good quality of soil has the fitness to support crop growth with enhanced productivity resulting in abundant and high quality of crops [28]. Generally, the soil has two parts viz., intrinsic, and dynamic. Intrinsic soils have the nature or inherent capacity for crop growth, which depends upon the parent material and topography. These soils are almost static, and the characteristics of these soils are permanent and do not change easily [29, 30]. Dynamic soil quality depends on its agronomic practices managed. The soil property encompasses soil texture, depth, permeability, soil organic matter, biological activity, water-and nutrient-holding capacity and soil structure. The organic matter changes from years to decades, pH changes from months to years, few properties can change from hours to days like microbial biomass and populations, soil respiration, nutrient mineralisation rates, and macroporosity [29, 31].
The primary function of soil is to nurture and sustain crop growth. Due to the dive’s potential of land use, each soil performs a specific function for sufficient crop growth. Regulation of partition of water flow and storage helps for plant root penetration, and water infiltration for the crop growth [27, 32, 33]. The natural fertility of the soil increases by nutrient availability and has the adequate cation-exchange capacity, decreases acidity, maintains a proper buffer, and helps to remove the toxicants [34]. It also reduces the compaction risk like water retention, water infiltration, cohesion workability/trafficability [35, 36, 37]. The soil also reduces the contamination risk, leaching potential, toxic absorption, and toxic mobility. However, overuse exploitation of the earth can deteriorate the soil quality temporarily or permanently based on its usage. Soil erosion is widespread and estimated that approximately 75 billion tons of fertile soil is lost from world agricultural systems every year, consequently reduces the productivity of all-natural ecosystems [38, 39, 40, 41]. Soil organic carbon (S.O.C.) observed and depleted 30–40% in cropland soils when compared to natural or semi-natural vegetation due to cultivation [42, 43]. Other threats like soil compaction, salinisation, waterlogging, nutrient imbalance, floods, and landslides and soil sealing, have both natural and human-induced causes [40, 41, 44, 45, 46]. This threat posses both agricultural production and terrestrial ecosystem. It reported that nearly 11.9–13.4% of the global agricultural supply lost due to soil degradation. Hence it is essential to protect soil degradation at different levels to safeguard food security, ecological health, and also for global sustainable development [47].
Soil indicators fill the gap of traditional soil testing because merely measuring and reporting individual parameters is no longer sufficient; it requires an in-depth understanding of soil quality by inferring various parameters. U.S.D.A. classified the soil into four classes, such as visual, physical, chemical, and biological indicators. Visual was mostly observed to be a conventional type and mainly analysed by farmers through local knowledge and also obtained through photographic interpretation, subsoil exposure, erosion, presence of weeds and colour. The physical indicators connected to the organisation of the particles and pores like particle-size distribution, aggregate stability, max. Root depth, penetration resistance, hydraulic conductivity, infiltration rate, water holding capacity, water content, porosity, soil depth, particle density, water-dispersible clay, shear strength, stone content, clay mineralogy, total surface area¸ soil odour [48, 49, 50, 51, 52, 53, 54, 55, 56]. The chemical property such as pH; T.O.C. or organic matter, Nutrient Availability electrical conductivity; selected heavy metals, organic pollutants, particulate matter [55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66] Soil respiration; N. mineralisation, earthworms, nematodes respiration, urease activity enzyme activities, total species number, fungal biomass functional diversity, bacterial biomass, potential denitrification activity, potential amonium oxidation, mycorrhiza populations root health, soil fauna diversity, phosphatase activity, microbial diversity are the biological indicators that measure the quality of soil [49, 54, 67, 68, 69, 70, 71, 72, 73, 74]. The selection of these indicators needs to ensure that they are sensitive and responsive to pressure and change in land use management. Table 1 infers that indicators measured for different countries (Table 1). Soil indicators refer to the capacity of soil to perform crop production that used in response to the dynamic changes in an agroecosystem.
Indicators | Values | Description | References |
---|---|---|---|
pH (CaCl2) | 4.0 ± 0.37 | Physical and chemical properties of soil in Araucaria forest (N.F.), Brazil | Pereira et al. [75] |
Organic-C (g kg−1) | 33 ± 12.9 | ||
Bulk density (g cm−3) | 1.08 ± 0.2 | ||
Macroporosity (m3 m−3) | 0.16 ± 0.07 | ||
Microporosity (m3 m−3) | 0.41 ± 0.06 | ||
Sand (g kg−1) | 459.0 ± 157 | ||
Silt (g kg−1) | 87.3 ± 40 | ||
Clay (g kg−1) | 453.8 ± 136.5 | ||
Organic matter | 10–20 g kg−1 | Lal [76, 77] | |
Nitrogen | 1.6–2.4 g kg−1 | Adeoye and Agboola [78] | |
Active carbon | 6–15 g kg−1 | Adeyolanu [79] | |
Cation exchange capacity | 3.5–6.0 c mol kg−1 | Adeoye and Agboola [78] | |
Wet stable aggregate | 0.40–0.75 kg kg−1 | Adeyolanu [79] | |
Mean weight diameter | 0.53–2.00 mm | Adeyolanu [79] | |
Available moisture content | 8–20% | Lal [76, 77] | |
Bulk density | 1.3–1.5 g cm−3 | Lal [76, 77] | |
Macroporosity | 0.15–0.18 m3 m−3 | Lal [76, 77] | |
Soil strength | 60–120 kPa | Adeyolanu [79] | |
Infiltration capacity | 7–21 cm hr.−1 | Adeyolanu [79] | |
Saturated hydraulic conductivity | 0.2–3 cm hr.−1 | Adeyolanu [79] | |
Organic matter content(%) | 4.3 | Benchmark soil, for natural Pampa Region, Argentina | de la Rosa and Sobral |
Respiration rate (kg C ha−1 d−1) | 83 | ||
Aggregate stability (%) | 70 | ||
Infiltration (mm h−1) | 44 | ||
Compaction (Mpa) | 3.7 | ||
O.M. (%) | 2.65 ± 0.96 | Soil water retention and soil resistance to penetration curves of Argentina | Imhoff et al. |
Clay (%) | 27 ± 10 | ||
Sand (%) | 18 ± 18 | ||
Silt (%) | 55 ± 15 | ||
Bd (g cm−3) | 1.37 ± 0.09 | ||
B.D. (g cm−3) | 1.5 | Soil quality indicators, baseline limits used for in northern Ethiopia. | Harris et al. [80] |
MWHC (%) | 30 | Gregory et al. [81] | |
OCe (%) | 3.5 | Kay and Anger [82] | |
SAS (%) | 30 | Harris et al. [80] | |
Zn (mg kg−1) | 18 | Mausbach and Seybold [83] | |
Fe (mg kg−1) | 40 | Harris et al. [80] |
Different types of indicators used for different countries.
Ml is a technology that aims to build an intelligent model that makes an accurate prediction without the intervention of human beings. The conventional machine learning approach depicted in Figure 1. It constructs various algorithms to make effective decisions in the problem domain. The primary step is to select the data on the problem under investigation and to select the parameters for the examination. The model is trained by a sample set of data (termed as training data) to gain experience in the environment and make the model fit. Later, the model evaluated using a sample set of data (termed as test data). So this is the primary step involved in any machine learning model, i.e., Train-Test-Predict. Usually, the data set was divided into two viz., training (70%) and testing (30%). Testing data is kept separate and not used in the preparation. The conventional machine learning approach depicted in Figure 9.
The dataset with many alternatives is collected and pre-processed using any normalisation or standardisation methods. The pre-processed data set was divided as train and test data set. The machine algorithms take the train data as input to train the model or to learn for the historical information. The trained model is evaluated with test data. The data visualisation tools are used for visualising the prediction or classification results. Algorithms involved in machine learning are supervised and unsupervised learning. In supervised learning, the model is trained with input data and mapped it into the known results whereas, in unsupervised learning, the model is trained, validated with input data and finds all type of unknown patterns.
The most familiar learning models that fall under these two categories are clustering, regression, classification, and dimensionality reduction. Machine learning utilises a secondary dataset (termed as validation data) for training the model further to avoid the overfitting of the model by the trained data. If the model generates more error on validation data, that means the model overfitted with the prepared data so that training stopped. Now the data split can be done like 60, 10, and 30 per cent of training, validation, and testing, respectively. Machine learning employed in almost all scientific applications such as health care, home automation, smart city, robotics, aquaculture, digital marketing, financial solutions, enterprises, climatology, food safety, agriculture, and more.
As Agriculture forms the major economy for most of the countries, better assistance speeding up each stage of agricultural crop production is mandatory. ML and the Internet of Things (IoT) serve this platform more effectively. IoT devices such as sensors, actuators through wireless communication protocols continuously monitor the crop, soil, water and communicate their health to remote devices either by message or log data or buzzer to alert the agriculturalist to take necessary actions. The data from these devices will make meaningful predictions and recommendations to the user exclusively farmers through machine learning algorithms.
Machine learning models trained by the historical data of the agricultural field through which it gains experience and makes wise decisions for the data signals received from the IoT devices. The data collected from these IoT devices must be secured and ensure confidentiality for accurate prediction results. Precision Agriculture is a strategy adopted to integrate heterogeneous information (Spatio-temporal data) for making precise and effective managerial decisions for global sustainable agricultural practices. Most of the parts of our country are adopting this strategy to improvise agrarian production in a brief span. Application of machine learning in precision agriculture has reshaped the plan such as field-based crop suggestion, fertiliser recommendation, water supply prediction, harvest prediction, thereby controlling the water usage by assisting the agriculturalists or farmers for better yield in a smart way.
Digital agriculture (a term coined by use of Precision Agriculture and Remote sensing) evolved to increase agricultural productivity with a minimised impact on environmental factors. Digital agriculture uses the data (crop, soil, and weather) sensed from the IoT devices to make effective decisions on nutrient demand-based fertiliser recommendation, water supply through proper irrigation, soil nourishment, pest or weed control, and crop protection from intruders. Digital agriculture focuses on the best-of-breed optimisation algorithms fro crop production and its protection during growth. Multi-cropping is a technique adopted in Digital agriculture or smart farming, which allows the cultivation of more than one crop in a single cultivable land.
Digital agriculture has to take more precautionary steps while feeding these different crops with weeds and fertilisers as the mixed plant has a different nutritional requirement and water supply. So it takes into account inter-variability and intra-variability among the crops before feeding the fertilisers. It adopts the techniques like in-row treatment to spray fertiliser for each plant separately, sensor-equipped drones to track the weed, automated sensing of fertiliser details from the barcode label for a correct proportionate mix of pesticides, drift reduction techniques and integration of these applications with global positioning system and comprehensive information system for periodic relay to the agriculturalists.
The application of Machine learning in different stages of agricultural crop production are depicted in Figure 10. The necessary steps involved in crop cultivation are Land suitability analysis, appropriate crop selection, crop production, crop protection, nutrient supply, water supply, crop health monitoring (pest and weed control), human and animal attack detection, yield management, and post-harvesting.
Machine learning approach.
Although these steps are common for all types of crops, soil nourishment value and chemical composition determine the techniques adopted in each level. Also, this paves a significant consideration of fertiliser supply when multi-cropping is selected. This multi-cropping technique has been in evolution decades back and done explicitly in the hill areas with meagre farming areas yielding better productivity.
Land suitability analysis has done for any barren land before permitting any residential plots to be constructed on that land. By ensuring better land use analysis, most of the agricultural land not converted into residential buildings or industrial areas. It will reduce the cultivable land area and air pollution. Cultivating a crop without suitability analysis may lead to an enormous waste of time, more fertiliser supply, abnormal and water requirements. Therefore, Land suitability analysis for the cultivation of crops is an essential factor in ensuring sustainable agriculture yielding better production. Geographic Information System (G.I.S.) provides more significant support in aiding the suitability analysis of the land. Multiple factors considered for analysing the land suitability attained from advanced G.I.S. systems. Some of the factors considered for land suitability analysis are soil quality parameters (pH, organic carbon content, salinity, texture, slope), topography, water availability, essential nutrients, socio-economic factors.
Mokkaram et al. have implemented an ensemble classifier method, namely RotBoost, an integration of Rotation forest and AdaBoost algorithms for land suitability analysis. Benjamin et al. have assessed the suitability of land for cultivation of a different variety of rice crops in rural Thailand using species presence only prediction method. They proved that the MaxEnt model outperforms and provides better crop suitability on particular land. A land with a higher suitability index for the cultivation of a crop selected for farming. Support Vector Machines (SVM) preferred for classifying the suitable area for agriculture of rainfed wheat based on thirteen factors relating to property, topography, climate, and soil.
Senagi et al. have applied Parallel Random Forest (PRF), SVM, Linear Regression (L.R.), K.N.N., Linear Discriminant Analysis (LDA), and Gaussian-Naïve Bayesian to ensure the land suitability for sorghum crop cultivation. PRF provides better accuracy than others when evaluated using ten cross-fold validation. One of the most important attributes that contribute to suitability analysis is soil quality. The moisture content in the soil helps to determine the suitability of growing a particular crop in a land. Typically the dryness or wetness level of the earth can be determined by considering the same at other locations, which has similar soil type and hydroclimate.
Coopersmith et al. recommend that land suitability analysis will be more accurate in the sandier soil (with more drainage) than poorly drained soils. They have used K.N.N., Boosted perceptron, and classification tree for soil dryness estimate at a site in Urbana. Perhaps, K.N.N. shows best results than Boosted Perceptron when evaluated with farmer’s assessments. Soil fertility levels should be periodically monitored and maintained at appropriate levels for the continuous nourishment of crop production in agricultural land. Gholab applied the decision tree classification model for building the predictive model. All these approaches use the data obtained through remote sensing and IoT devices. A better understanding of the land suitability of the agricultural field under consideration will assist in selecting suitable crops as well as supplying fertiliser to make it better nourished for growing the required plants. It followed by crop production, water supply, and Nutrient management.
Crop Production and management include crop selection, soil preparation based on suitability analysis, sowing seeds, application of manure & fertiliser, water management through proper irrigation mechanisms, and harvesting. Machine learning in agriculture crop production links various participants in the food chain or agricultural chain. Machine learning helps the agriculturalists in making better decisions in crop quality determination, yield prediction, plant species determination, crop disease prediction, and harvesting techniques (Figure 11).
Machine learning in agricultural crop cultivation.
The machine learning algorithms data acquired from IoT sensors in the agricultural field. Once the data feed, ML algorithms train the model using history and can make predictions at any stage of production to determine the different features required to predict the yield. It will help to improve the nutritional value (if deficient in the current return predicted) in the next production. Consequently, the crop production price will show a dramatic improvement in the upcoming yield. Application of A.I. in agriculture will enable the farmers to get up to minute information about current production, suggestions on next production, plant species identification, and quality improvement.
Once Land suitability analysis for cultivation is done, crop species selection has to be done based on suitability. Based on the nourishing factor in the soil and nutrient capability, a crop can be selected appropriately. Multi-criteria decision-making models used to get land suitability analysis. Image processing techniques integrated with machine learning suggested for plant species identification for the given crop image. Patil et al. analysed the various ML techniques used for crop selection based on environmental parameters and live market. They have used the K.N.N. classifier for the data obtained through multiple IoT sensors and prices based on entries in National Commodity and Derivative Exchange.
Land specific yield prediction by considering Crop yield prediction using topological algorithms like ANN, backpropagation, and Multi-layered perceptron through the implementation of a neural network. Support vector regression (S.V.R.) a variant of SVM used for crop yield prediction. As nitrogen is an essential component for photosynthesis, nitrogen management is mandatory as the yield prediction. The various decision support systems provide agricultural decisions, the agriculturalist has to deal with enormous heterogeneous data for making wise decisions, so Machine learning plays a vital role. Chlingaryan et al., 2018 have analysed the various ML approaches and signal processing methods used for crop yield prediction and optimised techniques for nitrogen management. They reviewed that B.P.N.N. provides best accurate crop yield estimation (by considering the importance of vegetative indices), CNN with Gaussian Process is best for feature extraction, best Multi-class crop estimation by M5 Prime R.T., Least Squares SVM for Nitrogen management and Fuzzy cognitive map for representing the expert’s opinion.
A comparative analysis of ML algorithms M5-Prime, K.N.N., S.V.R., ANN, and Multi-linear regression model was carried out on prediction of crop yield and suggested that M5-Prime outperforms others followed by K.N.N., S.V.R., ANN, and the last Multi-Linear Regression. It was evaluated based on the accuracy metrics (Normalised Mean Absolute Error, Root Relative Squared Error, Root mean square error, and Correlation Factor). Corn yield prediction predicted by Back Propagation Neural Network whose efficiency tested on green vegetation index, Normalised Difference Vegetation Index, perpendicular vegetation index, and soil adjusted vegetation index. Also, Deepa learning showed the most stable results on corn yield prediction at the particular region (Iowa state) when compared with Estimated Randomised Trees, Random Forest, and SVM. Deepa learning overcomes the overfitting problem prevalent in most of the ML algorithms.
One or more stages of crop cultivation will give information to other steps and vice versa. Depending on soil test results done during land suitability and crop health monitoring, the fertilisers will be recommended. Consequently, water and nutrient management carried out. The ML approaches work best for fertiliser recommendation. Water management is M.L.P. neural network with Backpropagation algorithm based on soil nutrient content, Gradient boosting and Random forest for soil nutrient assessment and Multivariate Relevance Vector Machine and Multilayer Perceptron for estimating the water requirement based on evapotranspiration and climatic data. Periodic Drought assessment is essential for crop maintenance and water management. Machine learning approaches used for drought assessment are Random Forest, Cubist, boosted regression trees, support vector regression, coupled wavelet ANNs, and ANN. Drought assessment is done based on the drought factors (land surface-related) and drought index.
Crop protection implies the protection of crops from weeds (unwanted plants that grow in the land), pests (insects, bugs), and intruders (an animal which intends to graze the crops and human for theft). K-Means clustering, Support vector machines, and Neural networks are more prominent machine learning techniques employed in Precision Agriculture for crop protection. The weeds may cause a significant loss to the crop yield. Weedicides are applied (weeding) before the crop seeding stage and flowering stage. The weedicides, instead of any common herbicide, have to be explicitly asked to avoid the devastation of the desirable crop in the field. Accurate detection of weeds is more significant and done using Machine learning algorithms integrated with sensor data.
One of the most undesirable weeds, which causes a significant loss to crop and very difficult to detect and abolish, is Silabynum marianum. Pantazi XE et al., have suggested a weed detection method by multispectral imagery obtained through a camera mounted on Unmanned Aircraft Vehicle (UAV) using Counter Propagation ANN, XY-Fusion Network and Supervised Kohonen Network (S.F.N.) to detect Silabynum marianum from other crops. Furthermore, a weed detection system that accurately classifies the weeds was designed based on hyperspectral images through the camera mounted on a robot using an active learning machine learning model. This model designed using a class neural network classifier (one class mixture of Gaussians) for novelty detection and one self-organising class map. This active learning model provides 100% accuracy on the classification of the crop, whereas different weed species detection accuracy varied from 34 to 98%.
The different weed species detected using this model are
Some pests may infect weeds, and that might be contagious to the crops, so pest detection is one of the essential stages in crop protection. Thus weeds serve as hosts for pests and diseases consuming all the resources supplied to the plants. It is done using machine learning algorithms and followed by the recommendation of pesticides for pests. The images acquired through the optical sensors attached to UAV help in detecting the pests. CNN provides better results in this classification of pests from images. D. C. Corrales et al. have suggested a list of supervised machine learning algorithms used for crop protection in terms of diseases and pests. The are SVM, K.N.N., ANN, Decision trees, and Bayesian Network. Decision trees, SVN, and ANN, are best for prediction and classification of pests, whereas Bayesian Networks and K.N.N. are excellent in training. These pests have a devastating effect on the crop storage, precautionary measures taken by identifying the categories of pests and their nature of the occurrence. Crop Image analysis used to categorise the type of pests using computer vision.
Cheng et al., have implemented a deep residual learning model for classifying the pest image and it outperforms the Back-Propagation Neural Network and SVM in the accuracy of the pest image recognition. Also, it provides better performance than deep CNN (Alexnet). Tomato Whitefly classification using deep CNN, Paddy crop pests classification using deep CNN [84] and banana pest and disease detection using deep CNN are some of the successful CNN based crop pest classification models outperforming the traditional approaches. Therefore integration Image processing or computer vision and machine learning CNN algorithms provide the best classification of crop pests and diseases.
Animal intrusion detection is one of the threats to the agricultural crop. These intrusions identified and detected to avoid loss of crop production. IoT sensors provide periodic alerts on the detection of an animal object like rats, cow, sheep, elephant, and other wild animals. It can be detected effectively and prevented through wireless sensors alerts to farmers mobile and machine learning algorithms can be used for object classification. Also, Machine learning algorithms used to predict the animal or human object entry apriori by training the model with past data from IoT sensors.
Livestock management is essential for animal husbandry, and wellbeing of rural people as this frames a significant economic factor for rural beings and sustainable agricultural practices. Livestock species used for varied purposes such as employment for the community, food supply, nourishing the family nutrition, significant income to few families, soil enrichment, believed ritual events. Livestock management includes vaccination for cattle species, health monitoring of livestock, managing livestock during drought, feed schedule, grazing, milk quality management, ketosis for dairy animals, ear tagging, production, and castration. The machine learning approaches used for animal welfare are Bagging with decision trees for classification of cattle behaviour-based features like grazing, walking, sleeping, ruminating, classification of chewing patterns in calf using decision tree/C4.5 based on chewing signals while dieting ryegrass, supplements, hay, rumination and during sleep, behavioural changes monitoring and tracking of pigs using Gaussian Mixture Model based on 3D motion information, ANN for determination of rumen fermentation, CNN for face recognition of pigs, estimation of beef’s carcass weight using S.V.R. models, SVM models for early evaluation of egg production in hen and bovine weight estimation in cattle.
Several machine learning approaches have become popular for achieving superior and precision agriculture [85, 86]. The following sub-section discusses certain machine learning approaches that have been deployed for achieving enhanced agricultural benefits. In the perspective of machine learning, supervised learning is a phenomenon that encompasses both the input and the sought after target values. Besides, both the input and target data are in labelled form, which offers a learning platform for processing data in the future. Further, when this model is offered a new test dataset (with a similar background) since the model is already trained, it generates the accurate output for the test data. Kaur et al. review the scheme of plant disease diagnosis and taxonomy employing leaf images with the aid of computer vision technologies [87].
Belief Networks also referred to as Bayesian Networks, are probabilistic graphical models, which are utilised for building models from data or through specialists’ outlook. Further, these networks can be a beneficial approach for evaluation and effective decision-making process in the case of agrarian problems. The Belief Networks are built using the Bayes theorem, which in turn supports in computing the input data’s posterior likelihood. Belief Networks are more suitable for agrarian applications owing to their capability to reason with inadequate data, and further, they also add new evidence data. Further, Aguilera et al., [88] evaluate the quality of the groundwater by deploying the probabilistic clustering supported by the hybrid Bayesian networks via Mixtures of Truncated Exponentials. Huang et al. [89] established a Bayesian driven averaging technique for offering a trustworthy forecast of maise yields in China. Besides, Cornet et al. [90] established a Bayesian network model for identifying the initial growth and yam yield interactions. Zhu et al. [91] established the Bayesian networks based model to characterise the connections between the symptoms and harvest maladies. De Rainville et al. [92] devised the naive-Bayesian classifier combined with the Gaussian mixture clustering approach for classifying the weeds from the actual row crops. Stanaway et al. [93] discussed the hierarchical Bayesian framework for the early diagnosis of exotic plant pests attacks and infectious plant diseases. Russo et al. [94] established a Bayesian model for estimating the hydrologic characteristics and irrigation needs in order to devise a sustainable water management scheme for the agrarian lands in Punjab, India.
The classification and regression trees (CART) are usually referred to as decision trees. Besides, they act as a decision support tool, which deploys a tree-like graph or a decision model and their probable consequences. In a decision tree, each internal node signifies a test on a feature, each branch characterises a result of the test, and each terminal node embraces a class label. There are several applications of the decision tree in agriculture, such as disease diagnosis and classification, crop monitoring and weed classification. Waheed et al. [95] devised a CART algorithm for categorising hyper-spectral information of the corn plots into different classes based on water stress, weeds’ existence, and nitrogen application rates. Xueli Liu et al. [96] established a decision tree model for assessing grain loss due to various factors involved in grain storage. Bosma et al. [97] discussed the decision tree model for estimating and modelling the decision-making process of the agriculturists on assimilating aquaculture into agronomy in Vietnam. Moonjun et al. [98] concerted on deploying the G.I.S. assisted decision tree and artificial neural network-based model for assessing the landscape-soil relationship in inaccessible areas of Thailand. Kim et al. [99] established the decision-tree assisted model combined with the geographical information system for forecasting and mapping the variety of bacteria in the soil. Rossi Neto et al. [100] elucidated a decision tree-based approach for categorising the biometric attributes with the highest impact on the sugarcane productivity under the distinct arrangement of plants and edaphoclimatic settings.
Connectionist systems also referred to as an artificial neuron network (ANN) is a computation based archetypal relying on the structure and functions of the human brain. Moreover, the connectionist systems are known to possess the neurons that are interconnected to one another in numerous layers of the networks. Also, such neurons are referred to as nodes. Connectionist systems consist of input and output layers, as well as a hidden layer comprising of units, which converts the input into unique values that the output layer can use. Besides, such systems are exceptional methods for determining complicated patterns. Also, brain-inspired systems have an arithmetical value that can accomplish more than one task, concurrently. Priyanka et al. [101] discussed the deployment of the neural networks combined with satellite imageries for monitoring crops and also for estimating the agricultural produce. Daniel et al. [102] established a review on ANN modelling for Agroecology application. Jha et al. [103] investigated various the usage of ANN/Artificial intelligence techniques combined with the internet of things and wireless systems for classifying plants and flowers, in order to accomplish sustainable development in the agricultural domain. Kaul et al. [104] deliberated about the deployment of the ANN models for forecasting the corn and soybean produces under distinctive climatic settings in Maryland, U.S.A. Thomas et al. deployed the multilayer neural networks along with genetic algorithms for detecting the viruses in plants via data collected using biosensors. Were et al. [105] employed the ANN approach for forecasting and mapping soil organic carbon stocks in Kenya. Besides, this model was validated by means of independent testing data. Nahvi et al. [106] deployed a self-adaptive evolutionary model for forecasting the everyday temperatures of the soil, at six diverse depths and validated the results through genetic programming and ANN models.
Random forests (R.F.) algorithm is a supervised learning approach that is deployed for real-world or simulated applications (both classification and regression problems). Besides, it is similar to the bootstrapping algorithm combined with the CART model. Moreover, in this algorithm, the decision trees on data samples get created, followed by the forecast from each of these trees, and lastly, chooses the best solution via voting. Further, it is an ensemble technique that performs superior to a solitary decision tree, since it lessens the over-fitting by averaging the outcome. Fukuda et al. [107] devised an R.F. model for forecasting the yield of the mangoes in retort to the supply of the water in diverse irrigation systems. Philibert et al. [108] designed an R.F. model for forecasting the N2O discharge depending on local data for ranking environmental and crop management attributes. Further, they also established the impact of these attributes on N2O emission. Rhee et al. [109] elucidated an RF-based high-resolution drought estimation system for ungauged expenses by deploying the long-range climate estimation and remote sensing information. Inacio et al. [110] developed a system for identifying weeds in sugarcane fields by deploying the Unmanned Aerial Vehicle for capturing images and later classifying these images via an RF-based classification scheme. Saussure et al. [111] demonstrated the harms caused in maise crops due to wireworms in several agricultural fields across France. Besides, they deployed the R.F. technique for imputing the missing values. Everingham et al. [112] devised an R.F. model for categorising the different types of sugarcane and crop cycle with the aid of imagery acquired via hyperspectral sensors.
A support vector machine (SVM) is a comprehensive supervised learning approach, which is generally deployed for mostly solving two-class categorisation problems. Besides, the SVM can also be utilised for analysing the data for classification and regression scenarios. Further, SVM employs the kernel phenomenon for transforming the data and then depending upon these transformations; it determines an optimal borderline among the likely outcomes. Moreover, the decision boundary between the two classes on a graph needs to be widespread. SVM builds an optimal borderline that splits the new data point and assigns it to the correct category. Therefore, this optimal borderline is also known as the hyperplane. Misra et al. [113] investigated the deployment of SVM techniques for stimulating run-off and sediment produces from the watersheds, via the support of the monsoon-period information. Kovačević et al. [114] developed an SVM model for classifying soil types based on the assessment of the physical and chemical characteristics of the soil. Huang et al. [115] devised a machine vision-driven SVM system for diagnosing the borer diseases in the sugarcane plant. Kawamura et al. [116] devised an SVM model for classifying the diverse inflorescence types by making use of an artificial dataset. Liu et al. [84] developed an SVM-based system for classifying the urban soil based on quality attributes, such as the soil toxicity due to heavy-metals, soil richness, and potency. Singh et al. [11] reviewed the deployment of SVM based model for the assessment of the plants undergoing high-throughput stress phenol-typing, with the aid of sensors.
In this chapter, smart sensor-based approaches are presented for precision agriculture. The use of remote sensors like temperature, humidity, soil moisture, water level sensors and pH value, will provide an idea to on productive farming, which will show accuracy as well as practical agriculture to deal with challenges in the field. This advancement could empower agricultural management systems to handle farm data in an orchestrated manner and increase the agribusiness by formulating effective strategies. The evolutions of Machine Learning (ML) and the Internet of Things (IoT) established methods offered to help researchers to implement these methods in agriculture to support farmers. These will support farmers to improve throughput, effective utilisation of field and manage pests. This paper presents to contribute to an overview of the modern sensor technologies deployed to precision agriculture and suggests an abstract of the present and essential applications and presents the challenges and feasible solutions and applications.
Peroxisomes are single membrane-bound organelles found in all eukaryotic cells, with diverse functions according to the cell type and metabolic conditions. The study of peroxisomes, their processes, and regulation activity has taken flight in the last decade, thanks to the introduction of novel cellular methodologies. Simultaneously, sequencing and human genetics methods have strongly improved, and a growing number of metabolic diseases have been associated with genetic variations in peroxisomal genes. However, a deep understanding of the coordinated workings of peroxisomal genes in health and disease is still lacking.
Peroxisomes play a key role in cell metabolism and homeostasis. For example, they are involved in the β-oxidation of fatty acids, the formation of specific ether phospholipids, and in the dissipation of damage caused by reactive oxygen species (ROS). At the same time, peroxisomes are very dynamic organelles that use creative solutions in their biogenesis and assembly, import of large proteins, morphological changes by fusion and fission, and complex interactions with other organelles and lipid droplets. Several insightful review articles summarize the current challenges of the metabolism and biology of peroxisomes [1, 2]. This chapter explores the use of state-of-the-art computational approaches and methods for the study of peroxisome biology at various levels. First, we discuss the basics of deep learning and machine learning algorithms. Then, we explore how deep-learning-augmented cell imaging explores peroxisomal biology. Advances in microscopy have yielded a trove of high-resolution, high-throughput imaging data from living cells that require and enable a more advanced level of analysis. We then zoom in to a higher-level resolution, focusing on structural analysis and integrative modeling of peroxisomal proteins and their protein complexes. Next, we explore high throughout methods to study the interactions between proteins and lipids at the level of the proteome and lipidome. Finally, we discuss genetic approaches considering peroxisomal dysfunction and pathology. We highlight the role of computational and bioinformatics-based approaches to major open questions in the field of peroxisome biology.
Observation remains one of the central pillars of biological research and cell biology in particular. Throughout the second half of the 20th century, electron microscopy (EM) images were fundamental to unveil the intracellular structures and organelles of a static cell at nanoscale resolution. The field of cell biology was revived by merging molecular biology techniques with
Current microscopy technology enables imaging at exceptional resolution, in the xy-plane varying from about 200 nm in confocal microscopy to about 10 nm in single-molecule localization microscopy [3]. Since peroxisome size ranges from 100 to 1000 nm in size, high-resolution microscopy is required for accurate and detailed imaging of protein expression and cellular localization. With the advances in high-resolution microscopy linked to automated, robotic lab support, the experimental results produce unprecedented big data of images and videos of cell dynamics. Although each technique has its advantages and limitations, the level of detail they reveal requires advanced data processing, analysis, and storage solutions. In past years, advanced algorithms, and especially deep-learning algorithms, developed for application to the biological domain, have skyrocketed.
Supervised deep-learning algorithms require an annotated dataset, training on that dataset, and the use of the trained model on unseen, new data. In imaging applications, a dataset can be augmented and diversified by providing the model with edited (e.g., zoomed or rotated) images. The model is then trained on the dataset. The classification task aims to construct a function that takes this array as input to predict a label. In neural networks, which are usually used for classification problems, the learning task then aims to minimize the “loss function” (i.e., error), by optimizing a set of parameters, or weights, that multiply input data to obtain the output data that is passed on to the next layer in the neural network. A common loss function is cross-entropy to measure the difference between a true label and the predicted one. The algorithm architecture should be chosen to minimize overfitting, that is, when the model performs better on the training data than on a validation dataset. In the case of underfitting, the model performs poorer on the training set, resulting in suboptimal performance. For an elaborated review of the use of deep learning for cellular image analysis see ref. [4] and references within.
The field of cellular and molecular biology had benefited from the availability of analytical tools for classical microscopy images. Major tools include CellProfiler, Microscopy Image Browser, OMERO, Fiji, and others [4]. Applying cell imaging tools according to unified standards led to the success of large-scale resources, such as the Human Proteome Atlas (HPA). HPA compiled enormous amounts of microscopical confocal images for annotating the subcellular information by using specific antibodies for most human coding genes across several human cell lines [5].
Recent years have seen the publication of updates of these tools with added components of deep-learning algorithms. Important applications of these tools include image classification, image segmentation, object tracking, and augmented microscopy. Specific examples of image analysis for the study of organelles are briefly discussed. Figure 1 shows a scheme for addressing open questions in peroxisome biology using a large set of raw microscopic images (static and dynamic) and state-of-the-art methods of deep learning for the task of deciphering cross-organelle interactions [6, 7].
A scheme for addressing open questions in peroxisome biology using a large set of raw microscopic images (static and dynamic). The image data is transformed by a convolutional neural network (CNN) whose output provides insights on large-scale cell biological challenges such as the task of organelles interactions, shape, and their dynamic crosstalk. Mito, mitochondrion; LD, lipid droplet: PhagoM, phagophore membrane; Lys, lysozyme, ER, endoplasmic reticulum; Cyt, polymerized cytoskeleton fiber (e.g., actin). Peroxisomes are colored red and membranes are colored yellow.
In image classification, labels can be added to images, for example, to identify if a fluorophore-labeled protein is localized to a specific organelle or resides in the cytoplasm. For this purpose, machine-learning-based image classification has long focused on the generation of classifiers to identify changes in morphology following exposure to compounds or growth conditions or to identify changes in cell state.
A popular software package called ilastik provides an addition to classical cell imaging tools with workflows for image segmentation, object classification, counting, and tracking. The pixel classification workflow produces semantic segmentation of images, attaching a user-defined class label to each pixel of the image. This step also forms the first step for object classification with morphological object features or may be used as initial input for the carving workflow. For example, ilastik was used for the reconstruction of 3D data from focused ion beam scanning EM (FIB-SEM) to segment the ER, using a pseudo-automated approach [8].
In a study by Li et al. [9], deep learning was used to classify organelle morphology of chloroplasts, mitochondria, and peroxisomes in the plant model Arabidopsis. The authors described a deep-learning framework, DeepLearnMOR (Deep Learning of the Morphology of Organelles), that identifies organelle morphology abnormalities at 97% accuracy. In the study, a dataset of 47,000 confocal fluorescence microscopy images from Arabidopsis wild-type and mutant plants with abnormal division in chloroplasts, mitochondria, or peroxisomes, was used to train the model. The dataset was augmented by using rotated, flipped, and split images. The model is based on both transfer learning and convoluted neural networks and significantly outperformed conventional machine-learning methods. In deep learning, transfer learning entails training a model on a large dataset and then fine-tuning the model for a different task using a new, smaller dataset. In this framework, the model distinguished well between mitochondria and peroxisomes, despite the overlap in their sizes. The framework can be used to study subtle morphological changes to classify intact and aberrant human peroxisome morphology.
In another example, a multi-scale convolutional neural network approach was developed and trained on eight publicly available cellular imaging datasets [10]. Following training, both the binary phenotype classification task as well as a multilabel classification task, performed at least as good as state-of-the-art architectures, saving time on the manual adjustment of parameters for segmentation and feature selection, that is needed for conventional image analysis pipelines. The datasets included images of stains for various organelles and cell types. Although peroxisomes were not included in the datasets used, this approach can be used for expanding the classification to cover peroxisome phenotypes.
An additional classic classification question regards the observation that proteins are localized into multiple subcellular compartments. About half of human proteins exist in more than one organelle simultaneously, and these multi-locational proteins are likely to play critical roles in cellular functions [5]. To deal with these multilabel proteins, most existing methods converted the multilabel classification problem into L binary problems, L being the number of classes. However legitimate, such simplified approaches ignored label dependencies that actually exist among subcellular locations [11]. The difficulty of the classification part mainly lies in the multilabel nature of protein localization, as is also exemplified in ref. [10]. Even in a simplified setting of a cell line in culture, cells are at different stages of cell division and density, yielding nonuniform localization profiles.
Image segmentation is the task of identifying multiple objects or features within an image, for example, cell counting. LysoQuant is a deep-learning approach for the detection and segmentation of organelles and is available as an ImageJ plugin. Its efficacy was demonstrated using the ER as a model organelle and a polymerogenic α1-antitrypsin Z (ATZ) variant as a model disease-causing aberrant protein [12]. The model’s performance was validated on the quantification of catabolic pathways that maintain cellular homeostasis and proteostasis. The model was tested on two cell types and on the ER as a model organelle, but it may very well be applicable for use in other cell types and for the study of peroxisomes.
Due to the advancement of classification accuracy and the availability of high computational power, cell image segmentation approaches are often based on deep convoluted neural networks. One of the disadvantages of deep-learning approaches is the large amount of training data required to train them. Although software packages, such as CellProfiler, already use deep-learning models, they do not support retraining on new data, thus restricting their application domain to an available set of datasets. In contrast, U-Net is pretrained on a diverse set of data and for every new task needs only a few (<10) annotated images [13].
In addition, sequential images often differ one from the other in the sense that the number of objects belonging to each class differs from image to image, leading to an imbalance in the class weights. One approach to tackle this problem is by automatically updating the weights of the imbalanced classes by constructing a new objective function. In one recent study, a U-Net-like convoluted neural networks model is used with two updated loss functions to improve segmentation of cell organelles, including cytoplasm, plastics, nucleus, mitochondrion, and peroxisome [14]. This demonstrates the importance of adapting the loss function in deep-learning approaches for improving the success of segmentation tasks.
The challenges in imaging described so far assume cells to be rather static objects. Obviously, the dynamics and heterogeneity of cells define their biology. In object tracking, objects are followed through a series of time-lapse images. This requires two tasks—object detection and object linkage. Single-particle tracking (SPT) is often the rate-limiting step in live-cell imaging studies of subcellular dynamics. Many object tracking models are based on a tracking algorithm that addresses the principal challenges of SPT, namely high particle density, particle motion heterogeneity, temporary disappearance of particles, and merging and splitting of particles. The algorithm first links particles between consecutive frames and then links the resulting track segments into complete trajectories [15]. This approach forms the basis for software such as CellProfiler and TrackMate.
TrackMate is a software that offers several detections and tracking modules that allow combining manual and automated particle tracking approaches. An openly available tool, it is available as an extension of ImageJ. Moreover, the capabilities of the software can be tailored by the user through the addition of specific tracking, detection, visualization, or analysis modules. Its data model makes it a useful tool for a wide range of tracking applications, ranging from single-particle tracking of subcellular organelles to cell lineage analysis. Importantly, the TrackMate study stresses the importance of avoiding photoinduced stress due to the continuous or repetitive illumination required for fluorescence microscopy [16].
An interesting example of the use of TrackMate for tracking, along with other image analysis methods, is presented in ref. [17]. While current imaging techniques are constrained by the small number of distinctive fluorescent labels within a single image, the use of confocal and lattice light sheet (LLS) fluorescence microscopy combined with computational sophistication allowed to track globular organelles and propose dynamic inter-organelle contacts. The study describes the frequency and locality of two- to five-way interactions among major membrane-bound organelles (ER, Golgi, lysosome, peroxisome, mitochondria, and lipid droplet) and shows how these relationships change over time.
Augmented microscopy is the extraction of latent information from biological images, such as the identification of the locations of cellular nuclei in bright-field images. Many augmented microscopies approach train neural networks to translate between label-free (bright-field, phase, differential interference contrast, and transmission EM) and labeled (fluorescence) images of the same cells. The ability to predict fluorescence images from grayscale data is advantageous for increased imaging speed and improved time-lapse imaging. Moreover, this neural network methodology was adapted from the classical field of imaging processing and its implementation to organelle biology takes advantage of the overwhelming amounts of grayscale images already produced by standard biology labs [18]. In this study, the prediction performance across organelles and other subcellular structures reach an accuracy of detection between 70 and 90% for cellular compartments, such as nucleoli, nuclear envelope, mitochondria, and ER [18, 19]. Undoubtedly, the model can be trained and tested for the study of peroxisomes as well.
Protein structure modeling and prediction are experiencing exciting times. The “protein folding problem,” which has puzzled scientists for many decades, asks to predict a protein’s structure from its primary amino acid sequence. It is thus not surprising that artificial intelligence (AI)-driven protein structure software, which includes both AlphaFold and RoseTTAFold, were announced as the Science breakthrough of the year 2021. In this chapter, we will discuss these models, followed by a discussion of how protein prediction models may be integrated into classical 3D experimental methods, such as cryo-EM, X-ray crystallography, mass spectrometry (MS), and nuclear magnetic resonance (NMR). We will then discuss approaches for modeling protein–protein interactions (PPI) and protein complexes.
The Critical Assessment of protein Structure Prediction (CASP) was initiated in 1994 as a biennial open competition for advancing methods for 3D protein structure prediction. Until 2016, the average prediction accuracy score across multiple approaches was bound to 30–40 (on a scale of 100). In 2018, the AlphaFold model developed by the DeepMind company (owned by Google) scored ~55, and in 2020, AlphaFold2 reached an astonishing score of ~92. For reference, a score > 90 is roughly equivalent to the variation monitored from repeated experiments for determining protein structure [20].
The input of the AlphaFold model is based on the primary amino acid sequence of a protein and the sequence alignment compilation of known homologs. Specifically, the amino acid sequence is used to build a multiple sequence alignment (MSA) from similar sequences found in protein sequence databases. Assuming an accurate MSA, the amino acid pairs that were co-mutated along the evolution path can be detected. A crude structure representation, or “pair representation” is then produced based on structural templates and on the paired amino acids that are likely in contact with each other. The templates and MSA are then passed through a transformer termed “Evoformer” that takes the MSA representation and the pair representation, to refine these representations. In the final step, the “structure module” takes the refined MSA representation and pair representation to construct a refined 3D structure model of heavy atoms [21].
The predicted structures contain atomic coordinates and per-residue confidence estimates on a scale from 0 to 100, with higher scores corresponding to higher confidence. This per-residue confidence measure, pLDDT, is based on a preexisting metric used in the protein structure prediction field. Scores >70 are considered residues predicted with confidence and > 90 are considered as very high confidence prediction (Figure 2). It should be noted that in multi-domain predictions, individual domains may be largely accurate while their relative position is not [22, 23].
Prediction of PEX5 (A) and PEX7 (B) structure by AlphaFold2. Model confidence—Dark blue, very high (pLDDT > 90); light blue, confident (90 > pLLDT > 70); yellow, low (70 > pLDDT > 50); orange, very low (pLDDT < 50). Note that many of the low-confidence structure predictions coincide with intrinsic disorder in the protein.
As of January 2022, AlphaFold database provides free access to >360,000 predicted structures across 21 proteomes. In comparison, the Protein Data Bank (PDB) that was announced 50 years ago contains “only” 180,000 experimentally solved 3D structures, and many of them do not cover the full length of the protein. However, the UniProtKB database includes almost 220 million entries. For many of the PEX proteins (peroxins) involved in peroxisome biogenesis, no experimentally determined structures are available [24]. However, a search for PEX proteins in the current AlphaFold database identified 28 hits from yeast, 26 from Arabidopsis, and 22 from human origin.
For example, the extensively studied peroxisomal targeting signal 1 receptor (PEX5), is represented in the PDB archive (nine entries) that mostly covers the well-folded domain at the C′ terminus. The AlphaFold database, however, includes a prediction of the full-length protein, which includes a very high confidence prediction for the C′ terminal domain, but also revealed some helices that were previously not solved (Figure 2A). It should be noted that the low-confidence regions in AlphaFold predictions largely correspond with known intrinsically disordered regions (IDRs) [25]. For the human peroxisomal targeting signal 2 receptor (PEX7) protein, no experimentally solved structures are available. However, the AlphaFold predicted protein structure is a strikingly beautiful β-propeller structure with seven-fold symmetry, similar to a published structure of its yeast homolog (Figure 2B) [26]. It is now possible to compare structural differences between the yeast and human homologs to shed light on differences in function. We anticipate that many structural and mechanistic questions regarding peroxisomal proteins can now be approached with a wealth of reliable modeled structural data.
The Rosetta software suite was initially developed in the Baker Lab at the University of Washington, Seattle, and was evolved as an active collaborative effort [27]. It includes many functionalities for macromolecular modeling. Its applications and protocols include
Around the same time that the AlphaFold model was published following its winning entry at the CASP14 competition, RoseTTAFold, another extremely successful protein structure prediction model, was published. Like AlphaFold2, it uses deep neural networks to find sequence patterns in databases of similar sequences. When given a new sequence to model, RoseTTAFold proceeds along multiple tracks—one creates an MSA, another predicts pairwise interactions between amino acids within the protein, and the third constructs the 3D model structure. The program bounces among the tracks to refine the model, using the output of each one to update and refine the others [34, 35].
The trRosetta model is also Rosetta-based and incorporates restraints for prediction. The algorithm starts off with MSA for distance and contact prediction to learn probability distributions over distances between residues and determine residue orientation. The predicted distances and orientations are then used to generate 3D structures using constrained energy minimization. The lowest-energy backbone is then subjected to Rosetta full-atom relaxation to add side chains and make the structures physically plausible, and to generate the lowest-energy full-atom model. The trRosetta network is able to identify the most important residues for determining protein folding and can apply this on
The prediction of multi-chain protein complexes is an even greater challenge than the prediction of monomeric protein structures. In the context of peroxisome biology, the functional and organelle mysteries reside in the dynamic interaction between the membranal and matrix proteins [2]. In addition, PEX protein’s subcellular localization is governed by post-translation modifications (PTMs), such as ubiquitination. All these aspects are executed by the dynamic of protein–protein interactions (PPI).
Many docking algorithms are being used for
Another approach is used by pyDock, which uses electrostatics, desolvation energy, and in a limited manner, van der Waals forces, to score rigid-body docking poses. InterEvDock and InterEvDock2 use co-evolutionary information in docking based on rigid-body sampling. In InterEvDock2, protein sequences can be provided as input, not only 3D structures. The algorithm then first performs comparative modeling based on template search. If biological input is available such as a pair of residues known to be in contact, restraints with a tunable distance threshold can be specified for use in the docking procedure. A recent review of multi-molecular modeling approaches can be found in ref. [38].
The latest developments in AI-harnessed modeling approaches will likely become some of our most important tools for the modeling of PPI and protein complexes. AlphaFold-Multimer is an AlphaFold model trained specifically for multimeric inputs of known stoichiometry. For example, an A2B2C2 heteromer was solved with a structural prediction score of 98, virtually identical to the solved structure for this complex [39]. In addition, AlphaFold2 was shown to successfully predict peptide-protein complexes even though it was trained only on monomer chains [40]. Open questions regarding peroxisome complexes can now be approached using this updated model. For example, the human PEX2-PEX10-PEX12 proteins form a protein-ubiquitin ligase complex for which currently no structure is available. The same is true for the AAA+ ATPase heterotrimeric complex PEX1-PEX6, and more.
RoseTTAFold has comparable performance in identifying PPI to that of experimental methods, but the combination of applying the RoseTTAFold model with AlphaFold further increases identification accuracy. A combined protein interaction identification pipeline that incorporates a rapidly computable version of RoseTTAFold with the slower but more accurate AlphaFold, evaluates interactions between the 8.3 million possible pairs of yeast proteins. In total, 106 previously un-identified assemblies and 806 that were structurally uncharacterized, were modeled. These models include higher-order complexes up to pentameric assemblies [41]. This combined approach demonstrates the strength of combining various neural network-based models to maximize modeling accuracy and speed.
The algorithms described above in some cases use experimentally solved structures as templates for the modeling of protein complexes. These are instances of integrative structural modeling, which involves the determination of macromolecular structures by combining experimental and computational modeling approaches [42].
Workflows for experimental methods can be improved with modeling approaches at various steps. In X-ray crystallography, protein modeling can be used to improve the determination of the protein structure, which is hampered by the “phase problem” that prevents the direct determination of the 3D structure from X-ray diffraction data. This approach was used by combining AlphaFold modeling with X-ray diffraction data to the determination of the structure of the ORF8 protein of SARS-CoV-2 [43].
Cryo-EM is more and more often used for structure determination. The highest resolution structures solved with cryo-EM have been solved at less than 2 Å [44]. This improvement in resolution has made cryo-EM a likely candidate to replace X-ray crystallography as the gold standard of experimental structure determination. A variety of software is available for modeling macromolecular assemblies using cryo-EM for
NMR may benefit from systematic back-calculation of expectation spectra across a conformational space should then allow reconstruction of the experimental spectra. This would enable the comparison of the back-calculated and experimental data and provide us with a quantitative quality measure [48]. In addition, complex NMR results can benefit from the strength of a deep neural network-based approach such as DEEP Picker, to aid in the analysis of NMR spectra and correctly characterize overlapping peaks.
In cross-linking MS (CL-MS), an
Importantly, the integration of diverse data sources into unified predictive models is likely to advance the knowledge of the protein complex of peroxisomes. While the structural study of peroxisomal proteins is in its infancy, we demonstrate the strength of recently developed computational software, tools, and algorithms to integrate data using breakthrough AI-based structural prediction approaches and integrative modeling.
Although the studies of individual proteins are important, they mostly ignore the PPI, cellular and physical environment of the studied protein. In recent years, proteomics approaches have become extremely valuable for the systematic analysis and discovery of the involvement of and interaction between proteins at the cellular or organellar level. Depending on the goal of the study, proteome analysis may include the determination of the full cellular or organellar proteome or PPI. The abundance profiles of proteins throughout all fractions of the purification can be compared to the profile of known marker protein to identify proteins that co-fractionate with the organelle of interest. To identify interactions, the abundance of interactors to a tagged protein of interest is compared to the abundance of the same, untagged protein. In addition, proteome dynamics can be followed by analyzing the organellar proteome at various time points following a stimulus [51]. Here, we discuss some of the most advanced methods for the analysis and application of proteomics data, with an emphasis on mass spectrometry (MS)-based proteomics. Importantly, MS and other advanced quantitative proteomics highlight regulation that cannot be explored using nucleic acids sequencing approaches. In the context of peroxisomal biology, the information includes the presence of protein variants, PPI, subcellular localization, and the status of post-translational modifications (PTMs). Analyzing protein levels in an organelle is fundamental to exploring molecular signaling and dynamics in response to varying conditions.
For organelle proteomics, three approaches can be taken to provide input for MS analysis, namely data-dependent acquisition, data-independent acquisition, and targeted proteomics, each of which is briefly discussed below. Following separation of the organelle, the organelle fraction can be run on SDS/PAGE to separate proteins and provide more specific input samples according to the size range of protein bands. The protein bands are then digested by specific proteases (e.g., trypsin) in-gel and analyzed. Alternatively, the organelle can be solubilized as a whole and digested in solution. The digested product is then analyzed. In addition, the organelle fraction can be fed into a workflow of liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) and subsequent data analysis [52]. A number of MS-based proteomics studies were undertaken in recent years to yield a comprehensive list of the mammalian proteome from specific tissues and cell lines [53].
In data-dependent acquisition (DDA), the eluted peptides from LC are detected by the first MS step, usually within a wide range of a mass-to-charge ratio of 400–1,200 m/z. The most abundant peptides are then entered into the second MS step following fractionation to yield a more informative peak spectrum [54]. However, if the number of precursor ions exceeds the number of precursor selection cycles, peptides detected in repeat analysis become irreproducible [55]. An inherent difficulty in quantitative MS proteomics is that only a few peptides are consistently identified in a complex protein mixture. The uncontrolled complexity of biological samples leads to poor reproducibility of MS-identified peptides. Characteristic features of prototypic peptides and their physicochemical properties were the basis for developing successful computational tools for predicting peptides for any organism [56]. To improve the detectability of all peptides in a sample, in data-independent acquisition (DIA), the second MS step receives as input, not individual peaks, but instead all peaks within a defined m/z range, thus using all peptide precursors within a mass range of interest. Usually, most tryptic peptides are within the 400–1200 m/z range, so all the initial peaks within this range are further specified and analyzed [57]. Finally, in targeted proteomics, selected or multiple reaction monitoring (SRM/MRM) is applied, meaning that a set of key peptides from a target list is quantitatively followed in many samples. In this case, the mass spectrometer fragments and analyzes only those peptides, increasing sensitivity and enabling the study of low-copy number proteins [58].
Various proteomics workflow software and pipelines are available, including Proteome Discoverer, MaxQuant, Mascot, OpenMS, CompOmics, and ProteomicsDB, as described in ref. [59]. These tools have developed drastically over the years, including more and more deep learning and advanced statistics capabilities. For example, MaxQuant is an open-source software package that supports DDA data and has an integrated peptide search engine, Andromeda. From the same developers, Perseus provides statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons, and multiple-hypothesis testing. MaxDIA was recently added to the MaxQuant pipeline and provides deep learning-based analysis of DIA data. Similarly, OpenMS is a much-used open-source software framework that enables both analyses of proteomics and metabolomics data. As such, it may be an especially relevant tool for peroxisome research. In addition, Proteome Discoverer includes deep-learning algorithms for the construction of spectral libraries as well as for the improved analysis of low-quality MS/MS spectra.
The retention time of a peptide refers to the time it takes for a peptide to elute from a liquid chromatography (LC) column prior to analysis by MS. As such, a peptide’s retention time is determined by the degree to which it interacts with the column, and as such is highly reproducible under the same LC conditions. The accurate prediction of peptide retention time can be used to improve the sensitivity of peptide identification against a peptide database. Early models such as SSRCalc are based on the retention times of 2000 peptides and base predictions on peptide sequence [60, 61]. More advanced, deep learning-based methods can be used for building libraries of MS spectra to enable data analysis from DIA MS. For example, the ProteomicsDB was recently expanded with Prosit, a neural network model that accurately learns and predicts chromatographic retention time and fragment ion intensity of any peptide, both tryptic (i.e., digested by trypsin) and non-tryptic. Other examples, based on different types of neural network algorithms, include DeepRT and, more recently, DeepDIA. The latter approach generates
The determination of the peroxisome proteome, as well as the interaction networks between them and the modifications they undergo that affect their function, can be done using a variety of computational tools and approaches. The proteome-wide study of peroxisome proteins is especially interesting to understand regulatory and functional aspects. Early experiments identified nearly all peroxisomal proteins, but it was then still challenging to discriminate between genuine peroxisomal proteins and co-isolated non-peroxisomal proteins, mostly derived from interacting organelles (e.g., mitochondria). The increasing sensitivity of MS enabled the detection of rare peroxisomal proteins, using improved experimental procedures such as organellar profiling [63, 64]. A more recent study of the proteome of HeLa cells provides a proteomic workflow for the generation of reproducible organellar maps. In the study, organellar clusters were assigned proteins based on a machine learning approach [65]. Based on this and other studies, an updated list of the mammalian peroxisomal proteome was made available in ref. [53]. However, many proteins are found in multiple organelles and peroxisome composition may vary between cell types.
An alternative approach to the MS-based proteomics approach described above is the prediction of localization to the peroxisome using peroxisomal targeting signals [66]. Matrix proteins are directed to the peroxisome with a type I or type II peroxisomal target sequence (PTS1 and PTS2) and are transported following the binding of PEX5 or PEX5/PEX7, respectively. In most cases, membrane proteins contain either of two types of PTS (mPTS-I/mPTS-II) and are inserted into the membrane by PEX19 and PEX3. Prediction algorithms for mammalian PTS1 motifs were published almost two decades ago and include PTS1-predictor, PTS1Prowler, PeroxiP, and an algorithm included in the PeroxisomeDB database. PTS1-predictor uses a position-specific scoring matrix (PSSM) or position weight matrix (PWM) approach, derived from aligned sequences of proteins known to harbor a PTS1 sequence, but also peptide sequences of proteins bound to various PEX5 homologs. Thus, the tripeptide dataset used for PTS1 prediction includes a larger amount of variations to find less probable proteins as well [67, 68]. The prediction of PTS2 proteins is much more challenging due to the small number of proteins bearing this signal peptide. A PTS2 prediction tool with a limited success rate is included in PeroxisomeDB. Finally, the In-Pero pipeline, based on a machine learning approach, was recently published for the prediction of sub-peroxisomal cellular localization of unclassified peroxisomal proteins.
The local concentration of functionally interconnected proteins yields PPI, the physical contact between two proteins, which may occur in a binary manner or may exist in multimeric complexes. Several proteome-wide PPI maps are available, including HI-II-14, BioPlex 3.0, and CoFrac. The number of protein pairs in each of these maps is well above 10,000, but the overlap between the maps is very limited [69]. The HI-II-14 map includes only binary interactions, generated from yeast-2-hybrid assays, whereas the others are based on affinity purification or co-fractionation followed by MS. An interesting development is the combination of various databases and depositories, such as the recently published MuSIC 1.0 map that integrates immunofluorescence images from the Human Protein Atlas (HPA) with affinity purification data from BioPlex 2.0 [70].
In addition to the interaction between proteins, PTMs include about 300 types of modifications, including phosphorylation, ubiquitination, glycosylation, acetylation but also regulated peptide cleavage. The large number of potential PTMs strongly affects protein function. It is expected to increase the proteome’s complexity by at least an order of magnitude. PTMs can be identified experimentally in high-throughput MS approaches or in small experiments. However, deep learning also enables accurate prediction of whether a given site can be modified (general site prediction) and if a site can be modified by a specific enzyme (enzyme-specific prediction). Multiple PTMs can be predicted using CapsNet or MusiteDeep. In addition, specific modifications can be accurately predicted with dedicated tools like DeepPhos for phosphorylation, DeepAcet for acetylation, DeepUbiquitylation for ubiquitylation, and more [62].
Although not strictly a part of the proteomics field of study, we briefly discuss the understudied metabolomics methods as they are of especial importance in the context of peroxisome metabolism. Metabolomics studies are commonly done using NMR or, more frequently, MS for the analysis of whole-cell or subcellular fractions. As such, much of the metabolomics pipeline is similar to that of the proteomics pipeline—gas chromatography (GC) or liquid chromatography (LC) coupled with MS or tandem MS (MS/MS).
Metabolomics data analysis can be done in an unsupervised or supervised manner. The goal of the unsupervised analysis is the grouping of features (sample, metabolites, and spectral features) according to the measured molecular data, and as such, this approach is suitable when no prior information is available about the system. In contrast, in supervised analysis, a set of features is pre-assigned to a class and is used as a training set for the method of choice to define a classifier that will be used for the classification of an unknown sample [71]. In addition, metabolomics studies can be divided into untargeted and targeted studies. Untargeted, also referred to as discovery-based metabolomics, focuses on global detection and relative quantitation of small molecules in a sample. In contrast, targeted or validation-based metabolomics focuses on measuring well-defined groups of metabolites with opportunities for absolute quantitation [72]. Several metabolomics data analysis tools are available, one of the most popular being MetaboAnalyst. This web-based tool suite covers four analysis categories, including statistical analysis, functional analysis, data integration and systems biology, data processing, and utility functions.
One of the challenges of subcellular or organellar metabolomics is the difficulty to observe metabolic fluxes between compartments. Metabolic flux studies are usually done directly by using isotopically labeled nutrients and measuring isotopically labeled metabolites to infer flux via metabolic flux analysis (MFA) or flux balance analysis (FBA) [73]. In FBA, the flow of metabolites through a metabolic network is analyzed mathematically. Two widely used computational flux inference approaches include isotope tracing coupled with MFA (13C-MFA) and constraint-based reconstruction and analysis (COBRA) [74]. Spatial flux analysis was done for the mitochondria and cytosol using 13C-MFA: isotope tracing in intact cells and subsequent rapid fractionation and metabolism quenching, followed by LC–MS-based metabolomics. Computational deconvolution with metabolic and thermodynamic modeling was used to infer compartment-specific metabolic fluxes [75]. In COBRA, which integrates various experimental and -omics data sources to reconstruct metabolic networks, applying constraints, for example, mass conservation, maximum reaction rates, and regulation, to construct a space of allowed network states [76]. These approaches could be used to model differences in metabolic flux in healthy and mutated peroxisome factors.
The contribution of peroxisomes to cells and organ physiology has been extensively discussed [77]. It was shown to have an indispensable role in the condition of specific metabolic needs, which explains the importance of peroxisomes in human congenital diseases. However, it becomes apparent that the amounts and properties of this organelle with respect to other organelles (e.g., ER, mitochondria) impact other pathologies of cell homeostasis, such as neurodegeneration, obesity, and more [2, 78].
Over the last decade, our knowledge of human diseases has drastically increased due to the breakthrough in sequencing technologies [79, 80, 81]. Projects such as the 1000 Genomes Initiative, ClinVar, OMIM, gnomAD, and others, provide the genetic variation landscape across individuals and populations [82, 83, 84, 85]. Databases from such projects and large biobanks (e.g., UK BioBank [86]) are successfully being used for linking genetics with human diseases. For example, the UK BioBank resource gathered genotyping and exome sequencing data of approximately 500,000 people, combining it with clinical and lifestyle information. The unprecedented quality of these resources, merged with computational solutions, data sharing, and standardization advanced the field of human diseases. We briefly introduce computational-based methodologies used for improving the utility of genetic variations in the case of genetic-based peroxisomal diseases.
Mendelian diseases are caused by pathological mutations in a single gene with high penetrance. Consequently, the manifestation of the disease is determined by the simple rules of dominant or recessive inheritance. On the other hand, complex diseases result from the presence of many variants where each may carry a small effect. To study such diseases, genome-wide association studies (GWAS) have been used to connect human genetics with complex diseases. The ultimate goal of GWAS is to identify causal connections between genetic variants, traits, and phenotypes. GWAS provides a statistical value to a genotyped variant in the genome to assess the contribution of any specific variants to the studied phenotype. GWAS is useful to suggest association in cases where the sample size, the allele frequency, and the effect size of the association reach statistical significance [87]. Unfortunately, GWAS is prone to confounding factors and biases due to unresolved population structure and linkage disequilibrium (LD) [88, 89]. To overcome the drawback of GWAS due to population size, family-based studies are used as an attractive alternative. In such cases, the genetic variations of family members are determined (i.e., genotyping of parents and their inflicted child).
Another subfield in human genetics that is likely to impact modern medicine is polygenic risk scores (PRS). For PRS, genetics and data from health records are used to present a model for individuals’ risk of having the disease of interest [90]. To make a meaningful prediction, the PRS model aggregates an individual’s genotype information. Converting PRS to a machine learning prediction model calls for large-scale individual-level data, often using the summary statistics of GWAS results to build such a PRS model [91]. Currently, PRS is not yet typically incorporated into clinical settings and its utility remains questionable [92]. With the improvement in sequencing quality and amounts, rare variants turned out to carry special interest and importance [93, 94]. They occur at a low frequency, still exerting strong phenotypic effects. It is expected that the contribution of rare variants may be substantial in explaining disease heritability. The release of 500,000 whole-exome sequences (WES) by the UK-Biobank allows us to expand our knowledge including the genetic basis of relatively rare occurring diseases [95]. Burden tests in which genes, or defined chromosomal segments, replace individual variants as the statistical relevant unit improve genetic interpretability and utility [96]. For example, under the PWAS (proteome-based association study) methodology, coding genes are associated with a disease by quantifying the effect of genetic variation on the protein function. To assign a valid association between genetic results and the medical condition, PWAS requests that the impact of the genetic variations in the disease and healthy cohorts will be significantly different [97].
Peroxisomal disease does not always fit a trivial definition of a simple Mendelian disease, nor does it match chronic complex disease (e.g., type 2 diabetes). Peroxisomal diseases occur with a wide range of symptoms, interaction with developmental disorders, and severity. Therefore, studying the genetic basis of peroxisomal diseases covers two molecular etiologies—(i) The genetics of PEX proteins with a cellular understanding of organelle biology (e.g., peroxisome formation); (ii) Compilation of an exhaustive list of genetic variants and genes associated with peroxisomes. In this section, we only address the genetic approach associated with these two research goals.
Peroxisomes play a critical role in a variety of metabolic processes, especially in lipid metabolism. All active cells contain many peroxisomes (100 s to 1000s), therefore displaying varying capacity toward metabolic homeostasis, oxidative stress, and lipid metabolism in general. A failure in producing functional peroxisomes is the cause of numerous genetic diseases [98]. Conversely, one or more genetic defects too may result in failure of functional peroxisome production. Peroxisomal disorders are classified into two groups: (i) Specific peroxisomal enzyme deficiencies; (ii) Peroxisome biogenesis disorders (PBDs). PBDs result from a failure in the post-translational import of proteins to the peroxisome’s matrix. The underlying genetics was used to better understand peroxisome formation while focusing on the failure to regulate multiple processes in peroxisomal cellular dynamics. In this section, we briefly categorized the peroxisomal diseases that are directly attributed to the failure to produce functional organelle. These diseases are connected to defects in the recognition of newly synthesized proteins, defects in the synthesis of peroxisome membranes, and failure in the insertion into the peroxisomal membrane (Figure 3).
Molecular basis of peroxisomal diseases. (a) Molecular view of the peroxisomal membrane with the main PEX proteins. PEX5 and PEX7 are receptors for the PTS1 and PTS2 proteins, respectively. PEX19 binds to proteins for insertion in the peroxisome membrane. Light blue indicates cargo proteins. (b) Frequency distribution of mutations in PEX proteins listed in a. the percentage is based on > 1300 patients diagnosed with peroxisome biogenesis disorder (PBDs). Adapted from [
PBDs display a spectrum of related diseases that differ in their severity, clinical manifestations, and underlying genetics. PBDs with clear molecular causal genes are collectively called Zellweger spectrum syndromes (ZSS). This set includes Zellweger syndrome (ZS), infantile Refsum disease (IRD), and neonatal adrenoleukodystrophy (NALD). The class of peroxisomal diseases that are caused by enzymatic deficiencies includes, for example, rhizomelic chondrodysplasia punctata (RCDP) type 2. Altogether there are 12 known rare diseases whose protein deficiencies are known, simple biomarkers for definitive diagnosis standards are missing. The task of diagnosis of the different peroxisomal diseases was empowered by a machine learning algorithm [100].
In most ZSS diseases, very long-chain fatty acids and branched-chain fatty acids accumulate in the plasma of the affected individuals. The accumulation of these lipids impairs multiple organs, leading to a poor prognosis. Among the ZSS diseases, Zellweger syndrome (ZS) accounts for most cases. ZS is a rare autosomal recessive disorder (1:50,000) that in most cases is caused by mutations in PEX1, a member of the AAA-type ATPase family. Mechanistically, the disease is caused by defective protein import due to the mutations in one of 13 major PEX proteins (Figure 3a). The partition between patients with isolated deficiencies of metabolic enzymes and those with ZSS can be clinically challenging. Thus, a set of biochemical assays were developed to monitor peroxisome functions and facilitate correct diagnosis (e.g., levels of alanine transaminase or alkaline phosphatase). In PBDs, the reported mutations occur in any of the 13 major PEX proteins at different frequencies (Figure 3b). The interpretation of the mutations’ effect on the peroxisome function is based on the accumulated knowledge of the role of PEX genes in the biogenesis of the organelle. Altogether, most peroxisomal human diseases are associated with a failure of the PEX proteins to import peroxisomal matrix proteins. In healthy individuals, newly synthesized matrix proteins reach the peroxisome by interacting with PEX5 or PEX7, cytosolic receptors that recognize either a C′-terminal PTS1 or an N′-terminal PTS2. In humans, two alternatively spliced PEX5 isoforms coexist, but only the longer version also binds PEX7. PEX2, PEX10, and PEX12 are zinc-binding RING finger proteins that are responsible for the ubiquitination of PEX5 which is essential for its recycling. The membranous peroxisomal membrane PEX26 combined with PEX1 and PEX6 is involved in the recycling process by releasing ubiquitinated PEX5 from the membrane. The malfunction of PEX7 results in a clinical phenotype of RCDP type 1 disease.
The identification of the genes underlying PBDs initially relied on detailed studies in yeast using classical genetics complementation groups [101]. With the availability of genotyping and WES data, the field of genetic testing evolved, and currently, searching for pathological mutations in PEX genes is used as a diagnostic service [99]. It is important to note that some of the genetic mutations are not restricted to PEX proteins but are also associated with genes involving the dynamics of peroxisomes and other organelles (e.g., mitochondrial and peroxisomal fission). Remarkably, cells with intact and functional proteins that do not reach the lumen of the peroxisome, result in severe phenotypes, presumably due to the rapid degradation of these misallocated enzymes. This is a general trend among many of the peroxisome matrix proteins.
Mutations in PEX1 are associated with the majority of ZSS cases. PEX1 contains ATP-binding motifs with ATPase activity. All the mutations reported are either defined as loss of function, but others are single missense mutations that affect the ATP binding pocket of the protein interface of PEX1 and PEX6. For a detailed genetic summary of the other PEX proteins, see ref. [99]. In contrast, X-linked adrenoleukodystrophy (ALD), an X-linked disorder, is caused by mutations in the ABCD1 gene that encodes an ABC transporter that act as a channel for the very-long-chain fatty acids entering the peroxisome.
A valuable resource called OpenTargets integrates multiple large-scale omics data including literature, drugs, and pathways [102]. The goal of OpenTargets is to bridge between molecular targets, drugs, and human diseases. Under the term of peroxisomal diseases in OpenTargets, many of the single enzyme defects that specify alteration in the metabolic enzymes are included. The strength of such a resource is in the ability in exposed overlooked processes that are dependent on intact peroxisome function. For example, among the 35 significant gene targets, all major PEX proteins are included (Figure 3). However, the strong support for human diseases that involve PEX11B, indicates that peroxisomal fission is a key process for cell homeostasis as PEX11B acts in recruiting dynamin-related GTPase to the peroxisomal membrane [103].
Based on GWAS results and support from mouse models the list of candidate genes for peroxisomal diseases keeps expanding. Currently, most of the peroxisomal mutated metabolic enzymes are listed, for example, acyl-CoA oxidase 1 (ACOX1), alanine-glyoxylate, serine-pyruvate aminotransferase (AGXT), and many more. Importantly, through an integrative approach that combines medical case studies and model organisms, several candidate genes whose function in peroxisome biology was not established are scored high, suggesting their overlooked roles in peroxisomal function, for example, UniProtKB entries E9PAM4 (Phosphatidylinositol 4-Kinase Type 2) and E9PPB4 (Peroxisomal Biogenesis Factor 19 Isoform 2). In summary, the list of variants associated with peroxisomal diseases was instrumental in shedding light on peroxisome metabolism and dynamics.
The last two decades have seen a fast-growing interest in the understanding of peroxisome biology with works on peroxisome biogenesis, import mechanisms, and the characterization of peroxisome proteins. It also emphasizes the existing gap between the more advanced methodologies of protein research concerning the understudied field of lipidomics. In parallel, the power of computational approaches will become pivotal in answering still-open questions regarding regulation, metabolism, and inter-organelle communication at a high level, and the elucidation of the mechanism and structure of peroxisome proteins and complexes at a more detailed level. We have aimed to provide an overview of some of the most important computational approaches that are likely to serve the research community, both basic and clinical, to expand its research toolbox in the study of peroxisome biology in health and disease.
The study was supported by the Israel Science Foundation (ISF) grant (2753/20) and CIRD (3035000323). We thank Dan Ofer for critical reading and Prof. Maya Schuldiner and Dr. Einat Zalckvar (Weizmann Institute) for sharing their insights on peroxisome biology.
The authors declare no conflict of interest.
Endoplasmic reticulum
Genome-wide association study
Metabolic flux analysis
mass spectrometry
Peroxin
Peroxisomal membrane protein
Peroxisomal targeting signal
protein–protein interactions
post-translational modification
Whole-exome sequencing
Zellweger spectrum syndromes
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Through comparative analysis, the chapter investigates sustainability potential of vernacular architecture in the region to derive core concepts as guidelines of reproducing the characteristics of society and reveal identity of contemporary architecture in the Arab World.",book:{id:"8260",slug:"urban-and-architectural-heritage-conservation-within-sustainability",title:"Urban and Architectural Heritage Conservation within Sustainability",fullTitle:"Urban and Architectural Heritage Conservation within Sustainability"},signatures:"Maha Salman",authors:[{id:"258226",title:"Dr.",name:"Maha",middleName:null,surname:"Salman",slug:"maha-salman",fullName:"Maha Salman"}]},{id:"51000",doi:"10.5772/63726",title:"Towards Sustainable Sanitation in an Urbanising World",slug:"towards-sustainable-sanitation-in-an-urbanising-world",totalDownloads:3202,totalCrossrefCites:11,totalDimensionsCites:17,abstract:"Urban sanitation in low‐ and middle‐income countries is at an inflection point. It is increasingly acknowledged that conventional sewer‐based sanitation cannot be the only solution for expanding urban areas. There are other objective reasons apart from the lack of capital. The lack of stable energy supplies, of spare parts and of human resources for reliable operation, and the increasing water scarcity are factors that seriously limit the expansion of centralised systems. This chapter argues that a new paradigm for urban sanitation is possible, if the heterogeneity within developing cities is reflected in the implementation of different sanitation systems, adapted to each urban context and integrated under one institutional roof. This new paradigm entails: (1) innovative management arrangements; (2) increased participation and the integration of individual, community and private sector initiatives; (3) thinking at scale to open new opportunities; (4) improved analysis of the situation and awareness raising. Moving beyond conventional approaches towards sustainable urbanisation needs to follow both a top‐down and a bottom‐up approach, with proper incentives and a variety of sanitation systems which, in a future perspective, will become part of the ‘urban ecosystem’.",book:{id:"5235",slug:"sustainable-urbanization",title:"Sustainable Urbanization",fullTitle:"Sustainable Urbanization"},signatures:"Philippe Reymond, Samuel Renggli and Christoph Lüthi",authors:[{id:"181079",title:"Dr.",name:"Christoph",middleName:null,surname:"Lüthi",slug:"christoph-luthi",fullName:"Christoph Lüthi"},{id:"182136",title:"Mr.",name:"Philippe",middleName:null,surname:"Reymond",slug:"philippe-reymond",fullName:"Philippe Reymond"},{id:"182137",title:"Mr.",name:"Samuel",middleName:null,surname:"Renggli",slug:"samuel-renggli",fullName:"Samuel Renggli"}]},{id:"42926",doi:"10.5772/55736",title:"Disaster Risk Management and Social Impact Assessment: Understanding Preparedness, Response and Recovery in Community Projects",slug:"disaster-risk-management-and-social-impact-assessment-understanding-preparedness-response-and-recove",totalDownloads:10044,totalCrossrefCites:3,totalDimensionsCites:11,abstract:null,book:{id:"3364",slug:"environmental-change-and-sustainability",title:"Environmental Change and Sustainability",fullTitle:"Environmental Change and Sustainability"},signatures:"Raheem A. Usman, F.B. Olorunfemi, G.P. Awotayo, A.M. Tunde and\nB.A. Usman",authors:[{id:"156875",title:"Dr.",name:"Usman A",middleName:null,surname:"Raheem",slug:"usman-a-raheem",fullName:"Usman A Raheem"},{id:"166449",title:"Dr.",name:"A.M",middleName:null,surname:"Tunde",slug:"a.m-tunde",fullName:"A.M Tunde"},{id:"167886",title:"Dr.",name:"F.B.",middleName:null,surname:"Olorunfemi",slug:"f.b.-olorunfemi",fullName:"F.B. Olorunfemi"},{id:"167887",title:"Dr.",name:"G.P.",middleName:null,surname:"Awotayo",slug:"g.p.-awotayo",fullName:"G.P. Awotayo"}]},{id:"44263",doi:"10.5772/54339",title:"Conservation and Sustainability of Mexican Caribbean Coral Reefs and the Threats of a Human-Induced Phase-Shift",slug:"conservation-and-sustainability-of-mexican-caribbean-coral-reefs-and-the-threats-of-a-human-induced-",totalDownloads:2352,totalCrossrefCites:4,totalDimensionsCites:11,abstract:null,book:{id:"3364",slug:"environmental-change-and-sustainability",title:"Environmental Change and Sustainability",fullTitle:"Environmental Change and Sustainability"},signatures:"José D. Carriquiry, Linda M. Barranco-Servin, Julio A. Villaescusa,\nVictor F. Camacho-Ibar, Hector Reyes-Bonilla and Amílcar L. Cupul-\nMagaña",authors:[{id:"158136",title:"Prof.",name:"Jose D.",middleName:"D.",surname:"Carriquiry",slug:"jose-d.-carriquiry",fullName:"Jose D. Carriquiry"},{id:"160078",title:"Dr.",name:"Julio A.",middleName:null,surname:"Villaescusa",slug:"julio-a.-villaescusa",fullName:"Julio A. Villaescusa"},{id:"160079",title:"MSc.",name:"Linda M.",middleName:null,surname:"Barranco-Servin",slug:"linda-m.-barranco-servin",fullName:"Linda M. Barranco-Servin"},{id:"160082",title:"Prof.",name:"Victor F.",middleName:null,surname:"Camacho-Ibar",slug:"victor-f.-camacho-ibar",fullName:"Victor F. Camacho-Ibar"},{id:"167394",title:"Dr.",name:"Hector",middleName:null,surname:"Reyes-Bonilla",slug:"hector-reyes-bonilla",fullName:"Hector Reyes-Bonilla"},{id:"167395",title:"Dr.",name:"Amilcar L.",middleName:null,surname:"Cupul-Magaña",slug:"amilcar-l.-cupul-magana",fullName:"Amilcar L. Cupul-Magaña"}]}],mostDownloadedChaptersLast30Days:[{id:"64381",title:"Sustainability and Vernacular Architecture: Rethinking What Identity Is",slug:"sustainability-and-vernacular-architecture-rethinking-what-identity-is",totalDownloads:4432,totalCrossrefCites:8,totalDimensionsCites:22,abstract:"Sustainability has often been a fundamental part of the composition of both tangible and intangible cultural resources; sustainability and preservation of cultural identity are complementary. Elements of sustainable design are integral to vernacular architecture that have evolved over time using local materials and technology emerging from ambient natural and cultural environment creating optimum relationships between people and their place. This chapter aims to redefine what identity is as a concept and the impact of globalization on contemporary architecture especially on regions with rich heritage and unique culture as the Arab World. To accomplish this, the chapter examines the emergence of “local identity” as a reaction to the globalization of cultural values, uniform architectural styles, and stereotype patterns through discussing sustainability as a motivation for identity in culture and architecture. The research methodology is based on conducting a qualitative analysis of literature review to the main concepts discussed in this chapter such as: identity, culture, vernacular architecture, and sustainability. Through comparative analysis, the chapter investigates sustainability potential of vernacular architecture in the region to derive core concepts as guidelines of reproducing the characteristics of society and reveal identity of contemporary architecture in the Arab World.",book:{id:"8260",slug:"urban-and-architectural-heritage-conservation-within-sustainability",title:"Urban and Architectural Heritage Conservation within Sustainability",fullTitle:"Urban and Architectural Heritage Conservation within Sustainability"},signatures:"Maha Salman",authors:[{id:"258226",title:"Dr.",name:"Maha",middleName:null,surname:"Salman",slug:"maha-salman",fullName:"Maha Salman"}]},{id:"67342",title:"Introductory Chapter: Heritage Conservation - Rehabilitation of Architectural and Urban Heritage",slug:"introductory-chapter-heritage-conservation-rehabilitation-of-architectural-and-urban-heritage",totalDownloads:2610,totalCrossrefCites:3,totalDimensionsCites:6,abstract:null,book:{id:"8260",slug:"urban-and-architectural-heritage-conservation-within-sustainability",title:"Urban and Architectural Heritage Conservation within Sustainability",fullTitle:"Urban and Architectural Heritage Conservation within Sustainability"},signatures:"Kabila Faris Hmood",authors:[{id:"214741",title:"Prof.",name:"Dr. Kabila",middleName:"Faris",surname:"Hmood",slug:"dr.-kabila-hmood",fullName:"Dr. Kabila Hmood"}]},{id:"76898",title:"The Relationship between Land Use and Climate Change: A Case Study of Nepal",slug:"the-relationship-between-land-use-and-climate-change-a-case-study-of-nepal",totalDownloads:695,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Land Use and Climate change are interrelated to each other. This change influences one another at various temporal and spatial scales; however, improper land uses are the primary causal factor on climate change. It studies relevant literature and Nepal’s case to assess the relationship between land use and climate change. Similarly focuses on how land-use impacts climate change and vice versa. In recent centuries land-use change significant effects on ecological variables and climate change. Likewise, understanding the research on both topics will help decision-makers and conservation planners manage land and climate.",book:{id:"10754",slug:"the-nature-causes-effects-and-mitigation-of-climate-change-on-the-environment",title:"The Nature, Causes, Effects and Mitigation of Climate Change on the Environment",fullTitle:"The Nature, Causes, Effects and Mitigation of Climate Change on the Environment"},signatures:"Pawan Thapa",authors:[{id:"349566",title:"M.Sc.",name:"Pawan",middleName:null,surname:"Thapa",slug:"pawan-thapa",fullName:"Pawan Thapa"}]},{id:"50282",title:"Relation Between Land Use and Transportation Planning in the Scope of Smart Growth Strategies: Case Study of Denizli, Turkey",slug:"relation-between-land-use-and-transportation-planning-in-the-scope-of-smart-growth-strategies-case-s",totalDownloads:4663,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"In the decision-making process of planning residential areas in developing countries, importance of the commercial areas and need for a sustainable urban transportation infrastructure have generally been ignored based on several sociopolitical reasons. Meanwhile, decision-making periods of location choice and determining areal densities are conducted without quantitative spatial/technical analyses. Those urban matters bring along new planning paradigms like smart growth (SG) and new urbanism. SG is a land use planning paradigm which indicates that traffic problems should be minimized by transit alternatives, effective demand management and providing a balance between land use and transportation planning. This study aims to apply SG strategies to the land use planning process and evaluate the accuracy of land use planning decisions in the perspective of sustainable transportation. In order to reveal the effects of land use planning decisions on the available transportation infrastructure, two scenarios are investigated for 2030. In the first scenario “do nothing” option is considered, while the residential area densities and trip generation rates are regulated based on SG strategies in the second scenario. The results showed that the land use and traffic impact analyses should simultaneously be conducted before land use configuration process.",book:{id:"5235",slug:"sustainable-urbanization",title:"Sustainable Urbanization",fullTitle:"Sustainable Urbanization"},signatures:"Gorkem Gulhan and Huseyin Ceylan",authors:[{id:"182126",title:"Dr.",name:"Gorkem",middleName:null,surname:"Gulhan",slug:"gorkem-gulhan",fullName:"Gorkem Gulhan"},{id:"185555",title:"Dr.",name:"Huseyin",middleName:null,surname:"Ceylan",slug:"huseyin-ceylan",fullName:"Huseyin Ceylan"}]},{id:"42926",title:"Disaster Risk Management and Social Impact Assessment: Understanding Preparedness, Response and Recovery in Community Projects",slug:"disaster-risk-management-and-social-impact-assessment-understanding-preparedness-response-and-recove",totalDownloads:10040,totalCrossrefCites:3,totalDimensionsCites:11,abstract:null,book:{id:"3364",slug:"environmental-change-and-sustainability",title:"Environmental Change and Sustainability",fullTitle:"Environmental Change and Sustainability"},signatures:"Raheem A. Usman, F.B. Olorunfemi, G.P. Awotayo, A.M. Tunde and\nB.A. Usman",authors:[{id:"156875",title:"Dr.",name:"Usman A",middleName:null,surname:"Raheem",slug:"usman-a-raheem",fullName:"Usman A Raheem"},{id:"166449",title:"Dr.",name:"A.M",middleName:null,surname:"Tunde",slug:"a.m-tunde",fullName:"A.M Tunde"},{id:"167886",title:"Dr.",name:"F.B.",middleName:null,surname:"Olorunfemi",slug:"f.b.-olorunfemi",fullName:"F.B. Olorunfemi"},{id:"167887",title:"Dr.",name:"G.P.",middleName:null,surname:"Awotayo",slug:"g.p.-awotayo",fullName:"G.P. Awotayo"}]}],onlineFirstChaptersFilter:{topicId:"136",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82644",title:"Climate-Driven Temporary Displacement of Women and Children in Anambra State, Nigeria: The Causes and Consequences",slug:"climate-driven-temporary-displacement-of-women-and-children-in-anambra-state-nigeria-the-causes-and-",totalDownloads:24,totalDimensionsCites:0,doi:"10.5772/intechopen.104817",abstract:"With increasing periods of extreme wet seasons, low lying geographic position, with socioeconomic, and political factors; some communities in Anambra State, Nigeria experience heightened floods annually resulting in loss of shelter, displacement of people with breakdown of livelihoods, particularly in rural communities worsening their risks and vulnerabilities. In 2012, a major flood event in the state temporarily displaced about 2 million people. In this chapter, we used a community-based adaptation approach to investigate the causes and consequences of climate-related temporary displacement on community members in Ogbaru LGA, Anambra State following flood events. We used global positioning system to obtain the community’s ground control points and gathered our data via field observation, transects walks, focus group discussions, photography, and in-depth interviews. Our findings reveal a heightened magnitude of flood related disasters with decreased socio-economic activities, affecting their health and well-being. Also, the community members have a practice of returning to their land, after flood events, as a local mitigating risk management strategy. For multilevel humanitarian responses at the temporary shelter camps, it becomes imperative to meaningfully engage the community members on the challenging risks and vulnerabilities they experience following climate-driven temporary displacement to inform adaptation and resilience research, policy change and advocacy.",book:{id:"7724",title:"Climate Change in Asia and Africa - Examining the Biophysical and Social Consequences, and Society's Responses",coverURL:"https://cdn.intechopen.com/books/images_new/7724.jpg"},signatures:"Akanwa Angela Oyilieze, Ngozi N. Joe-Ikechebelu, Ijeoma N. Okedo-Alex, Kenebechukwu J. Okafor, Fred A. Omoruyi, Jennifer Okeke, Sophia N. Amobi, Angela C. Enweruzor, Chinonye E. Obioma, Princess I. Izunobi, Theresa O. Nwakacha, Chinenye B. Oranu, Nora I. Anazodo, Chiamaka A. Okeke, Uwa-Abasi E. Ugwuoke, Uche M. Umeh, Emmanuel O. Ogbuefi and Sylvia T. Echendu"},{id:"79637",title:"Evaluation of the Spatial Distribution of the Annual Extreme Precipitation Using Kriging and Co-Kriging Methods in Algeria Country",slug:"evaluation-of-the-spatial-distribution-of-the-annual-extreme-precipitation-using-kriging-and-co-krig",totalDownloads:53,totalDimensionsCites:0,doi:"10.5772/intechopen.101563",abstract:"In this chapter, we have conducted a statistical study of the annual extreme precipitation (AMP) for 856 grid cells and during the period of 1979–2012 in Algeria. In the first step, we compared graphically the forecasts of the three parameters of the generalized extreme value (GEV) distribution (location, scale and shape) which are estimated by the Spherical model. We used the Cross validation method to compare the two methods kriging and Co-kriging, based on the based on some statistical indicators such as Mean Errors (ME), Root Mean Square Errors (RMSE) and Squared Deviation Ratio (MSDR). The Kriging forecast error map shows low errors expected near the stations, while co-Kriging gives the lowest errors on average at the national level, which means that the method of co-Kriging is the best. From the results of the return periods, we calculate that after 50 years the estimated of the annual extreme precipitation will exceed the maximum AMP is observed in the 33-year.",book:{id:"7724",title:"Climate Change in Asia and Africa - Examining the Biophysical and Social Consequences, and Society's Responses",coverURL:"https://cdn.intechopen.com/books/images_new/7724.jpg"},signatures:"Hicham Salhi"},{id:"77854",title:"Flooding and Flood Modeling in a Typhoon Belt Environment: The Case of the Philippines",slug:"flooding-and-flood-modeling-in-a-typhoon-belt-environment-the-case-of-the-philippines",totalDownloads:160,totalDimensionsCites:0,doi:"10.5772/intechopen.98738",abstract:"Flooding is a perennial world-wide problem and is a serious hazard in areas where the amount of precipitable water has potential to dump excessive amount of water. The warming of the Earth’s climate due to the increase in greenhouse gases (GHGs) increases the availability of water vapor and hence, of extreme precipitation as observed and forecasted by researchers. With rainfall intensity too high, the torrential rains coupled with weather systems that enhances its effects, flooding not only submerges anything low-lying, it also washes away living and non-living things along the course of the river and the floodplain. The flooding is even worsened by the increase in velocity of flow caused by unsustainable urbanization and denudation of the watershed at the headwaters. Nature’s strength is an order of a magnitude that is way beyond that of the strength of men but human ingenuity enables us to transform our living environment into models that could help us better understand it. Flood modeling provides us decision support tools to deal better with nature. It also enables us to simulate the future especially nowadays that changes in our climate is imminent and even happening already in many parts of the world. Therefore, strategies on how to cope with our ever changing environment is very important particularly to countries that are at more risk to climate change such as the archipelagic Philippines.",book:{id:"7724",title:"Climate Change in Asia and Africa - Examining the Biophysical and Social Consequences, and Society's Responses",coverURL:"https://cdn.intechopen.com/books/images_new/7724.jpg"},signatures:"Fibor J. Tan"},{id:"77797",title:"Adapting to Climatic Extremes through Climate Resilient Industrial Landscapes: Building Capacities in the Southern Indian States of Telangana and Andhra Pradesh",slug:"adapting-to-climatic-extremes-through-climate-resilient-industrial-landscapes-building-capacities-in",totalDownloads:98,totalDimensionsCites:0,doi:"10.5772/intechopen.98732",abstract:"There is now greater confidence and understanding of the consequences of anthropogenic caused climate change. One of the many impacts of climate change, has been the occurrence of extreme climatic events, recent studies indicate that the magnitude, frequency, and intensity of hydro-meteorological events such as heat waves, cyclones, droughts, wildfires, and floods are expected to increase several fold in the coming decades. These climatic extremes are likely to have social, economic, and environmental costs to nations across the globe. There is an urgent need to prepare various stakeholders to these disasters through capacity building and training measures. Here, we present an analysis of the capacity needs assessment of various stakeholders to climate change adaptation in industrial parks in two southern states of India. Adaptation to climate change in industrial areas is an understudied yet highly urgent requirement to build resilience among stakeholders in the Indian subcontinent. The capacity needs assessment was conducted in two stages, participatory rural appraisal (PRA) and focus group discussion (FGD) were conducted among various stakeholders to determine the current capacities for climate change adaptation (CCA) for both, stakeholders and functional groups. Our analysis indicates that in the states of Telangana and Andhra Pradesh, all stakeholder groups require low to high levels of retraining in infrastructure and engineering, planning, and financial aspects related to CCA. Our study broadly supports the need for capacity building and retraining of functionaries at local and state levels in various climate change adaptation measures; likewise industry managers need support to alleviate the impacts of climate change. Specific knowledge, skills, and abilities, with regard to land zoning, storm water management, developing building codes, green financing for CCA, early warning systems for climatic extremes, to name a few are required to enhance and build resilience to climate change in the industrial landscapes of the two states.",book:{id:"7724",title:"Climate Change in Asia and Africa - Examining the Biophysical and Social Consequences, and Society's Responses",coverURL:"https://cdn.intechopen.com/books/images_new/7724.jpg"},signatures:"Narendran Kodandapani"},{id:"77460",title:"Changing Climatic Hazards in the Coast: Risks and Impacts on Satkhira, One of the Most Vulnerable Districts in Bangladesh",slug:"changing-climatic-hazards-in-the-coast-risks-and-impacts-on-satkhira-one-of-the-most-vulnerable-dist",totalDownloads:210,totalDimensionsCites:0,doi:"10.5772/intechopen.98623",abstract:"Changes in the climate due to anthropogenic and natural variation are indicated by parameters including temperature and rainfall. Climate change variability with changing trends of the two have been unpredictable and unprecedented globally leading to changing weather patterns, natural disasters, leading to sectoral impacts on food and water security, livelihood, human health among others. This research analyses the changing patterns of these parameters over the last 35/37 years of Satkhira district of Bangladesh to assess the state and trend across spatial and temporal dimensions. Such, the study validates to rationalize the observed seasonal changes that persist in Satkhira of Bangladesh. Both in terms of intensity and frequency of the occurrences of natural disasters, the series of natural events have been triangulated, with impacts and vulnerability being assessed from temperature variations, erratic rainfall, cyclone, flood and water logging etc. The study’s prime contribution remains in attribution of climate change in relation contextual circumstances in the region including sea level rise, salinity intrusion. Therefore, the risk and climatic hazards and its resulting impacts over time has been assessed to draw deeper connection between theoretical and practical values. The series of analyses also draw conclusion that assets are at risk from changing climatic condition.",book:{id:"7724",title:"Climate Change in Asia and Africa - Examining the Biophysical and Social Consequences, and Society's Responses",coverURL:"https://cdn.intechopen.com/books/images_new/7724.jpg"},signatures:"Md. Golam Rabbani, Md. Nasir Uddin and Sirazoom Munira"},{id:"76915",title:"The Impacts of Climate Change in Lwengo, Uganda",slug:"the-impacts-of-climate-change-in-lwengo-uganda",totalDownloads:100,totalDimensionsCites:0,doi:"10.5772/intechopen.97279",abstract:"Climate Change has become a threat worldwide. Vulnerable communities are at foremost risk of repercussions of climate change. The present study aimed at highlighting a case study of climate change impacts on Lwengo District of Uganda. Out of the total geographical area of the district, 85% hectares are under cultivation and most of its population depends majorly on the rain- fed agriculture sector to meet the food requirement and as a major income source. With the changing climatic conditions, agriculture is the major sector which is being impacted. The region has experienced disasters from some time, usually the second seasons rains used to result in such disasters but since 2016 both seasons have occurred disasters, which majorly include hailstorm, strong wind, long dry spells, pests and diseases. The situation became more severe due to shortage of availability of skilled human resources, quality equipment for disaster management, limited financial resources and weak institutional capacity, which resulted in increasing vulnerability of small farm holders. Some of the adaptation strategies are being taken up by the government but there is a need to understand prospects of decision-making that are site specific and more sustainable for smallholder communities. Climatic changes possess many obstacles to farming communities which require sustainable adaptation to enhance the adaptive capacities of the communities through continued production systems, which are more resilient to the vagaries of weather. Farmers are practising such options which are location specific, governed by policy framework and dependent on dynamism of farmers. 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He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"9",type:"subseries",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11405,editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",slug:"luis-villarreal-gomez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",biography:"Dr. Luis Villarreal is a research professor from the Facultad de Ciencias de la Ingeniería y Tecnología, Universidad Autónoma de Baja California, Tijuana, Baja California, México. Dr. Villarreal is the editor in chief and founder of the Revista de Ciencias Tecnológicas (RECIT) (https://recit.uabc.mx/) and is a member of several editorial and reviewer boards for numerous international journals. He has published more than thirty international papers and reviewed more than ninety-two manuscripts. His research interests include biomaterials, nanomaterials, bioengineering, biosensors, drug delivery systems, and tissue engineering.",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,series:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343"},editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",slug:"cecilia-cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",slug:"gil-goncalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",slug:"johann-f.-osma",fullName:"Johann F. 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