Tools used per step of data science.
\r\n\tWe need such information of the environmental indicators day and night, from the crowded cities and the most remote locations. Therefore the study, development, and application of automated sensing systems have been booming during the last decades and the progress in this field is really fast.
\r\n\r\n\tThe current book intends to provide the reader with the most recent trends in the development of sensing technologies for environmental control and monitoring, application of these novel technologies for the detection and monitoring of different environmental indicators, but also identification of hazardous chemical compounds and pathogens, and to introduce various aspects of using the online sensing data for decision-making in different fields of social life.
\r\n\t
Beer is a popular alcoholic drink around the world. Beer is mainly made from sprouted cereals (mostly barley malt) that are fermented with
Although studies have shown that beer contains several functional components, amounts of these functional compounds in beer are diluted since high water content of beer, and the substantial calorie value and alcoholic content also limit health effects of beer. Conversely, brewing byproducts may contain more functional components during separation procedure. Their further recycling not only achieves waste reduction but also increases their commercial value. This review is aimed at providing detailed information on beer brewing process and the related byproduct generated, as well as the content of functional components in beer brewing byproducts and their health potential.
The common beer brewing process contains four major stages: malting, mashing, wort boiling, and fermentation [1]. Related processes and the corresponding byproducts are shown in Figure 1 [2].
Schematic representation of the brewing process and points where the main byproducts are generated Mussatto [
After barley grain has been sieved to remove thinner barley, dust, and impurities, it is steeped in water for germination. During this malting stage, enzymes decompose the complex protein–carbohydrate structure into smaller molecules and expose the starch granules inside the endosperm. Barley grain is allowed to grow only partially because fully grown consumes excessive amounts of starch, and this affects the subsequent fermentation process. Therefore, after the barley grain has germinated, it is dried to halt growth [3].
The malted barley from the malting stage is milled to crush the endosperm completely. Next, the crushed barley is mixed with water and the temperature of the mixture is slowly increased from 37 to 78°C to enhance the hydrolysis capacity of α-amylase and β-amylase. This process converts starch into fermentable sugars (mainly maltose and maltotriose) and unfermentable sugars (maltodextrin), whereas protein is degraded into peptide and amino acids. The process of starch conversion with enzymes is called “mashing”. Subsequently, the mixture is filtered; the resultant liquid part is called “wort” and the solid residue is called “spent grains” [4]. During processing, hot water is sometimes added to spent grain to collect second wort. Most beer brews are fermented with a mixture of first and second wort.
During wort boiling step, hops (
The wort is cooled to between 12 and 18°C for yeast inoculation. Commonly employed yeasts include
The beer brewing process produces three byproducts (i.e., spent grains, spent hops, and surplus yeasts. These byproducts remain limited in terms of application and are often used in animal feeds in animal husbandry [2]. The following subsection briefly introduces spent grains, spent hops, and surplus yeasts.
Spent grains, the most abundant byproduct of beer brewing, accounting for approximately 85% of a brew’s total byproduct yield. Spent grains are mainly composed of barley shells, remaining endosperm starch granules, and other cereal additives (e.g., wheat, rice, and corn) added to give beer its unique flavor [1]. The main chemical compositions of spent grains include cellulose, hemicellulose, and lignin. Cellulose and hemicellulose account for 50% of all spent grains. Moreover, spent grains are rich in protein, essential amino acids, minerals, and single sugars (glucose, xylose, and arabinose) [8, 9].
Spent grains are also abundant in phenolics such as ferulic acid,
Because spent grains are rich in protein, cellulose, and minerals, they are often used to feed ruminants [11]. Studies have investigated adding spent grains to bread and snacks to increase their fiber content [12]. Spent grains contain many functional groups such as hydroxyl, amine, and carboxyl, all of which can be used as biosorbents. For examples, spent grains can effectively remove volatile substances in exhaust gas and remove contaminants in waste water such as heavy metals (lead, chromium, zinc, copper, and cadmium) and dyes [1, 9, 13]. Moreover, spent grains have proven effective in antioxidation and for antibacterial and anti-inflammatory uses, and can even serve as a precursor for food flavors [14], a medium for producing microbes and enzymes [15], and a raw material for bioethanol [11]. Compared with spent hops and surplus yeasts, spent grains can more easily be reused. In addition, because of the advantages of being high-yielding and containing complex chemical components, spent grain can be used in a variety of areas [4].
Hops (
Of the added hops, only 15% remain in the final product of beer; the other 85% are residue in spent hops. Therefore, spent hops are abundant in nitrogen-free extract, fiber, and protein [4] and rich in essential amino acids; the composition of spent hops is similar to that of hops [18].
Although spent hops are rich in nitrogen, carbon, and protein, their bitterness limits their application in food. The bitterness of spent hops can be removed with
Surplus yeasts are the second most abundant byproduct of beer brewing, accounting for approximately 10% of the total amount of byproducts [1]. After being separated from beer, surplus yeasts are first heated to halt its activity [22]. The composition of
The protein compositions in surplus yeasts, spent grains, and spent hops are similar in that they all contain several essential amino acids, vitamins, and minerals [2]. The total mineral content in yeast is 5–10% of their dry weight; yeast is especially high in potassium and phosphorus [2].
Chae et al. [23] stated that compared with spent hops, surplus yeast contains more proteins, vitamins, and amino acids, and thus is more often used in animal feeds and nutritional supplements. However, because yeast contains ribonucleic acid (RNA), which is produced by metabolizing uric acid, eating excessive amounts of surplus yeasts can cause gout. Consequently, yeast is limited in the extent to which it can be used in foods [22]. Surplus yeast is often used as a source of carbon and nitrogen when cultivating microorganisms and in health foods in different food seasons [24]. β-glucan, a type of hydrocolloid, can be extracted from surplus yeasts for its ability to improve food characteristics as a thickener, water retaining agent, oil-retaining agent, emulsifier, or foam stabilizer [25]. Parvathi et al. [26] discovered that surplus yeast has the ability to absorb lead.
Study revealed that spent grains contained 60–68% of insoluble dietary fibers (IDF) [27]. Another study revealed that dried hops contained 40–50% IDF [16], and beer contained 0.4–6.2 g/L dietary fibers [28]. Lin [29] reported that spent grains contained the most IDFs, followed by hops, spent hops, and surplus yeasts. Spent hops had the highest soluble dietary fibers, followed by spent grains, spent hops, and surplus yeasts. The aforementioned results indicated that during the beer brewing process, dietary fibers in malt and hops were left in spent grains, spent hops, and surplus yeasts, whereas few of them are retained in beer.
Lewis and Young [30] stated surplus yeasts contained vitamins B1, B2, B3, B5, B6, and B9 in proportions of 15, 7, 50, 10, 3, and 4 mg/100 g, respectively. Bamforth [31] discovered that beer contained vitamin B1 (0.003–0.08 mg/L), B2 (0.02–0.8 mg/L), B3 (3–8 mg/L), B6 (0.07–1.7 mg/L), and B12 (0.003–0.03 mg/L), whereas vitamin E was not detected. However, Lin [29] reported that neither vitamins B, C, nor E were detected in beer brewing byproducts, possibly because the boiling stage in the beer brewing process causes splitting decomposition in vitamins due to high heat and oxidation. Moreover, spent hops and surplus yeast are considered food waste, and thus if brewers do not store them properly, vitamins can be lost.
Studies have revealed that the sources of phenolic compounds in beer were barley and hops, with barley providing 70–80% and hops providing 20–30% [2]. During the boiling, filtering, and ageing processes, the amount of phenolic compounds changed because phenolic compounds were used to scavenge free radicals and stabilize the flavoring materials and foam in beer [32]. During the cooling process after hops were added, some insoluble materials and polyphenols in the hops formed complices with protein in the wort, precipitated, and were then filtered out together in the next stage, resulting in phenolic compounds being present in beer brewing byproducts.
Spent grains contains ferulic acid,
Prenylflavonoids are a subclass of flavonoids with a prenyl group of aromatic rings. Based on whether they have an open ring structure, they are divided into prenylchalcones and prenylflavanones (Figure 2) [34]. If prenylchalcones have a methoxy group attached to C6 in the aromatic ring, they are xanthohumol, whereas if they have a hydroxyl group attached, they are desmethylxanthohumol. If prenylflavanones have a methoxy group attached to C5 in the aromatic ring, they are isoxanthohumol, whereas if they have a hydroxyl attached, they are 8-prenylnaringenin.
Prenylated chalcones and flavanones from hops Stevens and Page [
To date, related studies on prenylflavonoids have mainly focused on prenylflavonoids in relation to beer and hops. Most prenylchalcones in hops and beer were xanthohumol and dexmethylxanthohumol, whereas most prenylflavanones were isoxanthohumol, 6-prenylnaringenin, and 8-prenylnaringenin [35].
Regarding byproducts, Kao and Wu [36] indicated that beer lees contained isoxanthohumol (36.22 μg/g), xanthohumol (7.84 μg/g), 8-prenylnaringenin (19.17 μg/g), and 6-prenylnaringenin (29.56 μg/g). Lin [29] reported that surplus yeasts were most abundant in isoxanthohumol, followed by xanthohumol, 6-prenylnaringenin, and 8-prenylnaringenin. Spent hops contained mainly xanthohumol, followed by isoxanthohumol and 6-prenylnaringenin. Surplus yeasts contained most types of prenylflavonoids but spent hops had higher prenylflavonoid content.
The secondary metabolites of hops include hop bitter acids, volatile oil, and polyphenols. Hop bitter acids are divided into α-acids, or “humulone,” and β-acids, or “lupulone,” both of which are derivatives of prenylated phloroglucinol [37] (Figure 3). Based on the side chains on the acyl group, hop bitter acids can be divided into five types; those that start with “n,” “co,” “ad,” “pre,” and “post” are isovaleroyl-, isobutyroyl-, 2-methylbutyrol-, isohexanoyl-, and propanoyl-, respectively. The compositions and contents of α-acids and β-acids differ according to the type of hops and their growth conditions.
Structures of hop α-acids and β-acids Van Cleemput et al [
The dominant bitter acids in hops are α-acids, which are categorized as cohumulone, humulone, adhumulone, prehumulone, and posthumulone. Among these, the contents of cohumulone, humulone, and adhumulone were higher, accounting for 35–70%, 20–65%, and 10–15% of α-acids, respectively [38]. Because α-acids are less water soluble and because during wort boiling, α-acids isomerize into iso-α-acids that are more water soluble, only approximately 25% of α-acids are retained in beer [6].
β-acids mainly exists in hops in the forms of lupulone and colupulone, both of which accounted for20–55% of the total β-acid content; adlupulone accounted for 10–15% and the contents of prelupulone and postlupulone were very low [37].
Because the tertiary alcohol on the C6 in β-acids is replaced by a prenyl side chain, its acidity is lower than that in α-acids. β-acids are highly sensitive to oxygen; they can easily autoxidize into stable hulupones, which include cohulupone, hulupone, and adhulupone [39]. Hulupones exist in matured hops and beer at degrees of condensation of 2–10 ppm, and provide beer with a bitter taste. Hulupones degrade into hulupinic acid, which is not bitter. The content of β-acids in beer is lower than that of α-acids, and thus β-acids do not considerably affect beer quality [40].
During the wort boiling process, α-acids isomerize through the acyloin-type ring into iso-α-acids. The condensation of iso-α-acids in beer is a low 15–100 ppm; however, these acids are the source of 80% of the bitter taste in beer [41].
When heating α-acids in an alkaline solution containing divalent cations such as magnesium ion or calcium ion as a catalyst for isomerization, α-acids become
Kao and Wu [36] discovered cohumulone, humulone, adhumulone, colupulone, lupulone], and adlupulone in beer lees. Among these components, humulone was the most dominant. Lin [29] reported findings of 8017 and 1130 μg/g of hop bitter acids in spent hops and surplus yeast, respectively. The hop bitter acids detected in spent hops were cohulupone, hulupone, adhulupone,
To date, insufficient research has been devoted to the physiological functions of byproducts from beer brewing. However, these byproducts are rich in prenylflavonoids and hop bitter acids, which, as proven by many studies, have several health benefits that are detailed in this section. Therefore, beer brewing byproducts may have specific physiological activities. This section organizes physiological activities observed in previous studies, however, further research is required to clarify the physiological functions of the byproducts of beer brewing.
Surplus yeast hinders the survival ratios of preadipocytes and adipocytes and can reduce cell apoptosis. The surplus yeast extract process arrests preadipocytes at G2/M, and cyclin B1 (a protein related to the cell cycle) substantially increases in number. Adding surplus yeast extract during the process of preadipocyte differentiation into adipocytes effectively reduces the number of adipocytes and hinders the formation of triglycerides [43].
Animal models have proven that feeding of brewer’s yeast biomass with ethanol extract to male Sprague-Dawley rats could reduce the weight of fat around the kidneys and paraplegia, reduced triglyceride content in serum and the liver, and increased antioxidant capacity in the liver [44].
When xanthohumol and isoxanthohumol treated preadipocyte 3T3-L1, the expressions of transcription factors for lipid metabolism reduced, hence preventing fat tissue from forming and inducing apoptosis of adipocytes [45]. Xanthohumol could suppress the weight of rats with high-fat diets, reduce weight gain around the liver, and reduce the triglyceride content in their serum and liver [46].
Surplus yeasts extract in non-small cell lung carcinoma A549 increased the protein expression from those of the mitogen-activated protein kinase family, including p-ERK1/2, p-JNK, and p-p38, and suppressed the expression of cyclin E1 and arrested the cell cycle at G0/G1, thereby reducing the survival of A549 cells. In H460 lung cancer cells, surplus yeast extract significantly increased the expressions of proteins p-ERK1/2 and p-p38 and decreased the protein content of cyclin D1 and cyclin E1, thereby arresting cell cycle at G0/G1. The main suppressants in these two experiments were xanthohumol and hop bitter acids [47].
Studies have shown that xanthohumol, β-acids and hexahydro-β-acids could suppress the generation of chemokine and cytokine, as well as their gene expression in inflammatory cells [48, 49]. In animal models, xanthohumol was considered effective in reducing liver inflammation, thereby minimizing liver fibrosis [50].
Hop bitter acids could suppress the activation of nuclear factor-κB (NF-κB) in liver astrocytes, and could also suppress the generation of monocyte chemoattractant protein-1 (MCP-1) and α-smooth muscle actin (α-SMA) to achieve anti-inflammatory and antifibrotic outcomes [51]. In an animal test, feeding 1.25 g of hop extract to LPS-induced mice for 10 days reduced the PGE2 content in their blood [52].
Feeding xanthohumol to rats with chemical hepatitis could increase the glutathione content and the activities of antioxidant enzymes in liver [53]. Dorn et al. [54] reported that after xanthohumol treatment, the active oxygen content due to ischemia–reperfusion injury had reduced. In addition, isoxanthohumol was able to remove active oxygen [55].
Iso-α-acids, α-acids, and β-acids all have the abilities to clear the peroxynitrite free radical and reduce lipid peroxidation [56]. Namikoshi et al. [57] reported that the rat kidneys on a high-salt diet produced excessive amounts of active oxygen, resulting in kidney tissue damage due to oxidation. Iso-α-acids were able to suppress the formation of active oxygen to achieve the goal of antioxidation.
8-prenylnaringenin exhibited a structure similar to that of estrogen and could be bound with estrogen acceptors, indicating that 8-prenylnaringenin also exhibited activity similar to that of estrogen [58]. Christoffel et al. [59] reflected that feeding Sprague Dawley rats 8-prenylnaringenin for 3 months could reduce the content of follicle-stimulating hormone and luteinizing hormone in serum, increase the weight of the uterus, and stimulate the secretion of insulin-like growth factor I and prolactin.
Xanthohumol could suppress the formation of vascular endothelial growth factor (VEGF) and interleukin-8 in pancreatic cancer cells, and the antiangiogenesis effect was achieved through suppressing NF-κB [60].
Shimamura et al. [61] proved that 100 μM of humulone was effective in suppressing the formation of the VEGF and could prevent angiogenesis from Co26s cells. An amount of 2.5 to 50 μg/mL of lupulone could suppress the proliferation of human umbilical vein endothelial cells and the secretion of fibronectin.
Xanthohumol, isoxanthohumol, 8-prenylnaringenin, and 6-prenylnaringnin were all effective in suppressing the proliferation of human prostate cancer cells PC-3 and DU145 [62]. In addition, xanthohumol was able to reduce gene expressions related to the NOTCH1 pathway, thereby arresting the cell cycle of human ovarian carcinoma cells SKOV3 and OVCAR3 in the G2/M period and further causing apoptosis [63].
Festa et al. [64] revealed that xanthohumol activated caspase-3 and caspase-9, through mitochondrial depolarization, releasing cytochrome C, reducing Bcl-2 protein expression, and increasing the oxidation pressure, causing apoptosis of human glioblastoma multiforme tumor cell (T98G). Xanthohumol could prevent carcinogen metabolites from being processed by phase I enzymes (e.g. cytochrome P450) or activated by cytochrome isomers (e.g., cytochrome 1A1 and cytochrome 1A2). Xanthohumol was also able to promote carcinogens excreted from human body by increase their water-solubility that effected by phase II enzymes (glutathione S-transferase and UDP-glucuronyl transferase) [34].
Apoptosis of HL-60 (human promyelocytic leukemia cells) and SW 620 (human colorectal adenocarcinoma cells) can be induced by β-acids and α-acids [65]. Yasukawa et al. [66] maintained that humulone can suppress tumor formation in mice treated by 7,12-dimethylbenz[a]anthracene.
In contrast to β-acids, which are sedatives, α-acids are the major anxiolytic substances in hops [67]. Therefore, through
Isohumulones could activate peroxisome proliferator activated receptors α (PPARα) to increase sensitivity to insulin and help protect against type II diabetes [69]. Miura et al. [70] revealed that isohumulone could increase the mRNA expressions of acyl-CoA oxidase, acyl-CoA synthetase, the fatty acid transport protein, and lipoprotein lipase and reduce the mRNA activity of Apo CIII to achieve regulation of liver lipid synthesis.
Tobe et al. [71] discovered that α-acids were able to suppress dentin bone loss but that cohumulone was unable to exert the suppression effect. The researchers also discovered that β-acids were the most effective acids in suppressing dentin bone loss. Ding et al. [72] have evaluated cytokines such as interleukin-6 and TNF-α in serum and have proven that the cytokine content had a negative correlation with bone mass. Therefore, the effect of hop bitter acids on inflammation could be applied for osteoporosis prevention and treatments.
Prenylflavonoids and hop bitter acids are the most important functional components from hop, and they may remain in the byproducts especially spent hops and surplus yeasts during beer brewing. Both prenylflavonoids and hop bitter acids have been confirmed to possess many physiological activities in current research, but studies for establishing the proper health benefit of beer brewing byproducts remained uncertain. As mentioned in the preceding section, the future considerations and latent problems have to be emphasized. First, beer brewing technology should be optimized in order to minimize the amounts of waste arising. Second, methods for complete recovery of by-products during beer brewing on a large scale and at an affordable level should be developed. Third, specific analytical methods for the characterization and quantification of organic micronutrients and other functional compounds need to be built. Fourth, the bioactivity, bioavailability and toxicology of the functional components in beer brewing byproducts need to be carefully assessed by
This review was supported by a grant (104-2221-E-030-018-MY2, 106-2221-E-030-015) from Taiwan National Science Council, and Office Research and Development from Fu Jen University.
Private corporate networks, as well as the Internet, generate and share data at ever increasing rates. This unconstrained growth can easily lead disorganization and, as a consequence, missed opportunities to analyze and extract knowledge for these data. There is an essential difference between the concepts of data and information. Data cannot express something outside a particular field of expertise. In turn, information enables the coherent transmission of knowledge. Data science aims to close the gap between data and knowledge through the use of computational tools. More specifically, data science is a tool for converting raw data into knowledge [1]. The field of data science leverages many methods originating from computer science and statistics [2]. Figure 1 illustrates a Venn’s diagram that correlates the research areas with major influence in data science.
\nVenn’s diagram for correlating the influence of other research areas on data science.
Although data science receives significant influence from expert knowledge, it is plausible to say that a data scientist knows more about computer science than a statistician and more about statistics than a computer scientist [3]. Besides, it also encompasses the intersection of data analytics and machine learning. Therefore, data science encompasses a heterogeneous group of studies and methodologies such as big data, machine learning, data analytics, and statistics, which challenge the implementation of a single platform capable of incorporating all the available techniques.
\nThere are a variety of widely adopted platforms available for data analysis and knowledge extraction, for example, Tableau,1 Dataiko,2 Microsoft Azure Machine Learning Studio,3 Orange BioLab,4 each one suitable for a specific step of a data science process. A workflow can be formulated based on the coordinated application of different tools to extract knowledge from massive datasets. In this context, the use of cloud platforms for data science steadily grows because they offer scalability and distributed execution of individual tasks.
\nIn data science, a large dataset allows the generation of a more in-depth model, which provides more robust insights because there are more instances to compose the statistical analysis of data. One of the most relevant aspects regarding a dataset is the quality of available data. Thus, before the use of any statistical method, the dataset must go through a cleaning process that ensures the uniformity of values and the elimination of duplicated data. On one hand, a large dataset with high-quality data enables an insightful model. On the other hand, the computational power required to process data is directly proportional to the size of the available dataset. In this scenario, high-performance computing (HPC) provides the infrastructure (clusters, grids, and cloud platforms) required to optimize the processing time of data science workflows. In particular, data science demands are transformed in a collection of tasks, with or without the notion of dependency among them, which must be efficiently scheduled along the computational resources [memory, processors, cores, cluster nodes, graphical processing unit (GPU) cores, grid nodes, and virtual machines, for example] to provide the results in an acceptable time interval. To map such tasks to resources, a scheduling policy takes place where load balancing algorithms are important to provide a better execution equilibrium among the tasks and a fast response time, mainly when considering either dynamic or heterogeneous environments. While some articles explore the use of HPC for data science tasks [4, 5, 6], in the best of our knowledge, there are no studies that conduct an in-depth analysis of how the aspects of scheduling and load balancing affect data science workflows.
\nHence, the present book chapter proposes an analysis of scheduling and load balancing from the perspective of data science scenarios. Furthermore, it presents concepts, environments, and tools for data science to summarize the theoretical background required to understand the definition and execution of data science workflows. Even though its focus lies on presenting concepts, the chapter also illustrates new trends concerning the intersection of data science, scheduling, and load balance.
\nThe remainder of this chapter is organized as follows: Section 2 presents an in-depth explanation of concepts, workflow, problem classes, and tools used by data science. Section 3 explores scheduling and load balancing as tools to leverage the computational power required by data science applications. Section 4 points to open challenges and trends in the use of HPC applied to data science problems. Finally, Section 5 concludes the chapter with closing remarks and directions for future work.
\nThis section presents the fundamental concepts related to data science. These are key to understand the concept of HPC, more specifically scheduling and load balancing, impact data science processes, as discussed later in the chapter. The remainder of the section discusses the fundamental components of a data science pipeline, as observed in real-world scenarios.
\nData science is highly dependent on its application domain and employs complex methods. Nevertheless, it has a very organized pipeline, which varies in the number of steps required to extract knowledge. Current work explores a pipeline that varies between five and seven steps, but in all cases, the process yields similar outputs. This section aims at presenting the most complete process, composed of seven steps, widely used by both companies and researchers. Figure 2 depicts the flow of information step by step. Moreover, the seven proposed steps can be enumerated as:
business understanding;
data extraction;
data preparation;
data exploring;
data model;
results evaluation; and
implementation.
Flow diagram of data science steps.
Step 1 refers to the process of understanding in which context the data are inserted on, and what is the expected output. This is a high time-consuming process in a project. However, data scientist must have a deep understanding about the application domain to validate the model’s structure as well as its outputs. After understanding the scope of the project, on Step 2, exploring the data that correlate with the problem understood in the last step. These data can be hosted at the client or not. If the client does not have useful data available, the data scientist must look for a synthetic or publicly available dataset to extract the knowledge. Furthermore, on Step 3, techniques are employed to clean data because there is a high chance that it is unorganized or unreadable, so it is necessary to preprocess and standardize it. An example of this step is a dataset that has a column with country names, but in some registers, the value of this column is “Brasil” and in others, it is “Brazil,” both values symbolize the same information but are encoded in different languages. Regarding Step 4, the data are organized, and it is indispensable to execute a detailed analysis in order to figure out patterns or insights that would be valuable to the client. In this stage, the data scientist usually uses plotting techniques to make the data more readable and figure out information.
\nOn Step 5, previously identified insights serve as input. But at this step, it is vital to fully understand the data since, without formal knowledge, it is very hard to fit a model that correctly represents it. At this stage, it is required that the data scientist uses computer science expertise to choose the better approach to plan and validate the model. In Step 6, outputs generated by the model are evaluated in order to analyze how useful they actually are. Usually, this evaluation is conducted by the client and the data scientist together, to examine graphs, numbers, and tables and define if the model generated acceptable results. Finally, on Step 7, the results are validated, and the model is ready to be implemented and deployed in production; thus, it is applied to prediction tasks, having real data as input. The architecture used to implement the model is very important, that is, it is necessary to understand what will be used for the client application since if the client needs a real-time response, the structure will be very different than a nonreal-time scenario.
\nData science is not exclusively employed in business scenarios, and it can be generalized to a plethora of applications, such as in Obama’s campaign for US presidential elections in 2012. In the context of this election, technology was applied to identify who were the voters that should receive more attention and marketing influence. Some analysts highlighted the use of data science as fundamental to Obama’s victory. However, data science is not limited to the analysis of scenarios, such as in the above example. Many other challenges can benefit from solutions based on data science methodologies. For instance, some problem classes in data science are pattern detection, anomaly detection, cleaning, alignment, classification, regression, knowledge base construction, and density estimation [7]. These classes of problems are explored next.
\nThe patterns existing in a dataset are not always easily identifiable due to the organization of the data. It is the method employed to discover information standards in the dataset. Figuring patterns hidden into data is a relevant task in several scenarios, for example, to join the clients with similar characteristics, such as those with the same taste or opinion.
\nThe distribution of a dataset regards the positions of data points among each other in the dataset. Usually, the representation of this distribution employs a Cartesian plane, in which each point is an instance of the dataset. Within this representation, regions with a defined concentration of data points become clusters. Therefore, outliers are the data points that are too far from these clusters. Anomaly detection aims to classify each data into a dataset as an outlier or not. For example, the banks employ this approach in the scenario of fraud detection. In this scenario, transactions of a client become clusters, and a new transaction is considered as unclassified data.
\nDataset contents can present a broad variety of formats, for example, dates, numeric values, and text data. In many cases, values may also be differently formatted across dataset entries, even for the same field, due to human error or lack of standardization. This situation incurs in errors in the dataset, resulting in possible information loss when processing it. The best solution to this problem is to employ methods that manage the dataset contents and standardize the data. For example, in a dataset containing clients birthdays, it is possible that a user fills the information with an invalid date. This case results in information loss because the analysis cannot extract useful information related to the user’s birthday.
\nAn organized and standardized dataset is fundamental for the generation of trustworthy outputs from data science processes. The process of data alignment is an essential step in dataset standardization. It involves updating the dataset to avoid the use of multiple values to represent the same information. For example, in a dataset containing a gender field, users may use both “M” and “Male” values to represent the same gender. In this context, it is fundamental to unify both entries in one because the information in both is the same.
\nClassification regards assigning specific labels to entries in a dataset. A label is any information that presents a limited scope of possibilities, for example, a dozen options present in a specific dataset field. Examples of labels include sentiments, states, and the scale of integer numbers. This dataset field can then be used to group multiple dataset entries according to the unique values that the label may take. For example, in a dataset about client’s purchases, each product may be associated with a set of keywords. These keywords can then be used to classify the types of purchases a particular client makes, enabling targeted recommendations for other products.
\nDatasets do not always contain fields with labels that enable the classification of data. Nevertheless, in some problems, it is necessary to label values without a restricted group of options, for example, using a field that contains real numbers. This scenario requires a regression approach to estimate which classes an entry should receive, without considering the limited options available in labels. For example, in a dataset with prices and sizes of houses in New York, it is possible to use regression to estimate the price of a new house according to its size. Although the regression problem has an output similar to classification, its output does not have a limited set of values, as occurs in labeling.
\nDatasets are essential for data science, and the problem of knowledge base construction refers to the process of compiling information to create them. Frequently, this process requires the use of cleaning and alignment methods to standardize data. There are a broad group of knowledge bases on the Internet, for example, Kaggle,5 UCI Repository,6 Quandl,7 and MSCOCO.8\n
\nDensity estimation focuses on identifying the clusters that group sets of data points that represent the entries in a dataset. This process is a fundamental step to generate the clusters required by anomaly detection methodologies, as described above. Clustering is another suitable technique to identify groups of entries that may contain related knowledge within a dataset.
\nIt is difficult to find a tool that fits all data science processes because, as previously mentioned, there are multiple steps with a variety of methods available for use. Hence, there are specific tools for each step, which will provide the most appropriate result. Table 1 summarizes the most commonly cited tools for each one of the data science workflow steps. The table has a row that is not considered a step of the process, but it is fundamental to results that are Storage Data, which refer to all technologies used for persisting the data in an environment. In the section of data model, some programming languages are cited, but it is hard for a data scientist to employ a language without libraries. For example, using Python is very usual to use libraries such as pandas, sci-kit learn, numpy, and ggplot.
\nStage | \nTools | \n
---|---|
Store data | \nMySql, Mongo DB, Cassandra, PLSql, Redis, HBase | \n
Data preparation | \nApache Hive | \n
Data exploring | \nKnime, Elasticsearch | \n
Data model | \nPython, R, Julia, Clojure, SPSS, SAS, Apache Manhout | \n
Results evaluation | \nTableau, Cognos, ggplot, QlikView, Power BI | \n
Development | \nApache Hadoop, Java, Scala, C, Apache Spark, Haskell | \n
Tools used per step of data science.
The scheduling problem, in a general view, comprises both a set of resources and a set of consumers [8]. Its focus is to find an appropriate policy to manage the use of resources by several consumers in order to optimize a particular performance metric chosen as a parameter. The evaluation of a scheduling proposal commonly considers two features: (1) performance and (2) efficiency [9]. More specifically, the evaluation comprises the obtained scheduling as well as the time spent to execute the scheduler policies. For example, if the parameter to analyze the achieved scheduling is the application execution time, the lower this value, the better the scheduler performance. In turn, efficiency refers to the policies adopted by the scheduler and can be evaluated using computational complexity functions [10].
\nThe general scheduling problem is the unification of two terms in everyday use in the literature. There is often an implicit distinction between the terms scheduling and allocation. Nevertheless, it can be argued that these are merely alternative formulations of the same problem, with allocation posed in terms of resource allocation (from the resources point of view), and scheduling viewed from the consumers’ point of view. In this sense, allocation and scheduling are merely two terms describing the same general mechanism but described from different viewpoints. One important issue when treating scheduling is the grain of the consumers [11]. For example, we can have a graph of tasks, a set of processes, and jobs that need resources to execute. In this context, scheduling schemes for multiprogrammed parallel systems can be viewed in two levels. In the first level, processors are allocated to a specific job. In the second level, processes from a job are scheduled using this pool of processors.
\nWe define static scheduling considering the scheduling grain as a task [8]. If data such as information about the processors, the execution time of the tasks, the size of the data, the communication pattern, and the dependency relation among the tasks are known in advance, we can affirm that we have a static or deterministic scheduling model. In this approach, each executable image in the system has a static assignment to a particular set of processors. Scheduling decisions are made deterministically or probabilistically at compile time and remain constant during runtime. The static approach is simple to be implemented. However, it is pointed out that it has two significant disadvantages [11]. First, the workload distribution and the behavior of many applications cannot be predicted before program execution. Second, static scheduling assumes that the characteristics of the computing resources and communication network are known in advance and remain constant. Such an assumption may not be applied to grid environments, for instance.
\nIn the general form of a static task scheduling problem, an application is represented by a directed acyclic graph (DAG) in which nodes represent application tasks, and edges represent intertask data dependencies [12].
\nEach node label shows computation cost (expected computation time) of the task, and each edge label shows intertask communication cost (expected communication time) between tasks. The objective function of this problem is to map tasks onto processors and order their executions, so that task-precedence requirements are satisfied, and the minimum overall completion time is obtained.
\nIn the case that all information regarding the state of the system as well as the resource needs of a process is known, an optimal assignment can be proposed [11]. Even with all information required for the scheduling, the static method is often computationally expensive getting to the point of being infeasible. Thus, this fact results in suboptimal solutions. We have two general categories within the realm of suboptimal solutions for the scheduling problem: (1) approximate and (2) heuristic. Approximate scheduling uses the same methods used in the optimal one, but instead exploring all possible ideal solutions, it stops when a good one is achieved. Heuristic scheduling uses standard parameters and ideas that affect the behavior of the parallel system. For example, we can group processes with higher communication rate to the same local network or sort works and processors in lists following some predefined criteria in order to perform an efficient mapping among them (list scheduling).
\nDynamic scheduling works with the idea that a little (or none) a priori knowledge about the needs and the behavior of the application is available [9]. It is also unknown in what environment the process will execute during its lifetime. The arrival of new tasks, the relation among them, and data about the target architecture are unpredictable, and the runtime environment takes the decision of the consumer-resource mapping. The responsibility of global scheduling can be assigned either to a single processor (physically nondistributed) or practiced by a set of processors (physically distributed). Within the realm of this last classification, the taxonomy may also distinguish between those mechanisms that involve cooperation between the distributed components (cooperative) and those in which the individual processors make decisions independent of the actions of the other processors (noncooperative). In the cooperative case, each processor has the responsibility to carry out its portion of the scheduling, but all processors are working toward a common system-wide goal.
\nData science comprises the manipulation of a large set of data to extract knowledge [13, 14]. To accomplish this, we have input that is passed through processing engines to generate valuable outputs. In particular, this second step is usually processed as sequential programs that implement both artificial intelligence and statistical-based computational methods. We can take profit from the several processing cores that exist in today’s processors to map this sequential demand to be executed in a multithreading program. To accomplish this, Pthreads library and OpenMP are the most common approaches to write multithread parallel programs, where each thread can be mapped to a different core, so exploiting the full power of a multiprocessor HPC architecture.
\nIn addition to multiprocessor architectures, it is possible to transform a sequential code in message passing interface (MPI)-based parallel one, so targeting distributed architectures such as clusters and grids [15]. In this way, contrary to the prior alternative that encompasses the use of standard multiprocessor systems, the efficient use of MPI needs a parallel machine that generally has higher financial costs. Also, a distributed program is more error prone, since problems in the nodes or the network can put all application down. Repairing these future problems sometimes is not trivial, requiring graphical tools to observe processes’ interactions. Finally, in addition to multicore and multicomputer architectures, we also have the use of GPU, where graphic cards present a set of nongeneral purpose processors to execute vector calculus much faster than the conventional general-purpose processors [14]. The challenge consists in transforming a sequential code in a parallel one in a transparent way at the user viewpoint, in such a way the data science demand can run faster in parallel deployments. Moreover, the combination of these three aforesaid parallel techniques is also a challenge since optimizations commonly vary from one application to another.
\nCloud computing environments today also represent a viable solution to run data science demands [16]. Providers such as Amazon EC2, Microsoft Azure, and Google Cloud have HPC-driven architectures to exploit multiprocessor, multicomputer, and GPU parallelism. In particular, different from standard distributed systems, cloud computing presents the resource elasticity feature where an initial deployment can be on-the-fly changed following the input demand. Thus, it is possible to scale resources in or out (through the addition or removal of containers/virtual machines) or to scale down or up (by performing resource resizing in virtual units) in a transparent way to the user. Logically, the own data science application must be written in such a way to take profit of newly available resources as the current set of working resources.
\nThe CPU load is the most common metric to drive resource elasticity data science demands since most of them execute CPU-bound artificial intelligence-based algorithms. Any network data manipulation through the TCP protocol uses CPU cycles since this is a software protocol executed in the kernel of the operating system and executes software routines to provide data transfer reliability.
\nLoad balancing and resource scheduling are sometimes seen as having the same functionality. However, there is a slight difference: one of the members of resource scheduling is the scheduling, and this policy can employ or not load balancing algorithms [14]. The basic idea of load balancing is to attempt to balance the load on all processors in such a way to allow all processes on all nodes to proceed at approximately with the same rate. The most significant reason to launch the load balancing is the fact that exists an amount of processing power that is not used efficiently, mainly in dynamic and heterogeneous environments, including grids. In this context, schedulers’ policies can use load balancing mechanisms for several purposes, such as: (1) to choose the amount of work to be sent to a process; (2) to move work from an overloaded processor to another that presents a light load; (3) to choose a place (node) to launch a new process from a parallel application; and (4) to decide about process migration. Load balancing is especially essential for some parallel applications that present synchronization points, in which the processes must execute together with the next step.
\nThe most fundamental topic in load balancing consists of determining the measure of the load [13, 15]. There are many different possible measures of load including: (1) number of tasks in a queue; (2) CPU load average; (3) CPU utilization at specific moment; (4) I/O utilization; (5) amount of free CPU; (6) amount of free memory; (7) amount of communication among the processes; and so on. Besides this, we can have any combinations of the above indicators. Considering the scope of processes from the operating system, such measures will influence in deciding about when to trigger the load balancing, which processes will be involved, and where are the destination places in which these processes will execute. Especially on the last topic, other factors to consider when selecting where to put a process include the nearness to resources, some processor and operating system capabilities, and specialized hardware/software features. We must first determine when to balance the load to turn the mechanism useful. Doing so is composed of two phases: (1) detecting that a load unbalancing exists and (2) determining if the cost of load balancing exceeds its possible benefits.
\nThe use of load balancing in data science demands can vary depending on the structure of the parallel applications: Master-Slave, Bag of Tasks, Divide-and-Conquer, Pipeline, or Bulk-Synchronous Parallel [15, 16]. In the first two, we usually have a centralized environment where it is easy to know data about the whole set of resources, to dispatching tasks to them following their load and theoretical capacity. A traditional example of a combination of these parallel applications is the MapReduce framework. In the divide-and-conquer applications, we have a recursive nature to execute the parallel application where new levels of child nodes are created with the upper one cannot execute the tasks in an acceptable time interval. The challenge consists of dividing the tasks rightly following the capacity of the resources. Pipeline-based applications, in their turn, have a set of stages where each incoming task must cross. In order to maintain the cadence between the stages, they must execute in the same time interval, so an outcoming task from the stage
This section aims at compiling the previous two sections, so detailing open opportunities and trends when joining resource scheduling and load balancing and the area of data science. In this way, we compile these aspects as follows:
Automatic transformation of a sequential data science demand to a parallel one—today data science executes locally to query databases and to build knowledge graphs. Sometimes these tasks are time consuming, then it is pertinent to transform a sequential demand in a parallel one to execute faster on multicore, multinode, and GPU architectures.
Use of GPU cards as an accelerator for data science algorithms—write of data science demands that combine R and Python together with OpenCL or CUDA programming languages, so combining CPU and GPU codes with running fast and in parallel to address a particular data science demand.
Combination of multimetric load balancing engine to handle data science efficiently—data science typically encompasses excellent access to IO (including main memory and hard disk) and a high volume of CPU cycles to process CPU-bound algorithms. In this way, the idea is to execute data science demands and learn their behavior, so proposing an adaptable load balancing metrics that take into account different parameters as input.
Task migration heuristics—when developing long-running data science parallel codes, it is essential to develop task migration alternatives to reschedule demands from one resource to another. This is particularly pertinent on dynamic environments, either at the application or infrastructure level.
Cloud elasticity to address data science demand—cloud elasticity comes to adapt the number of the resource following the current demand. Thus, we propose a combination of vertical and horizontal elasticity, together with reactive and proactive approaches to detect abnormal situations. We can use both consolidation and inclusion of resources, aiming to always accommodate the most appropriate number of resources for a particular and momentaneous data science demand.
Definition of a standard API to deal with data science—frequently enterprises present several departments, each one with its data science demands. In this way, we envisage an opportunity on developing a standard framework (with a standard API too) to support the data science demands of the whole enterprise. The idea is to provide a dashboard with a collection of data science functions, also expressing the expected input and the output for each one.
Smart correlation of events—enterprises regularly have timed data in several databases. We present an opportunity, at each time a problem is found, to take this particular timestamp and compare in the data sources looking for eventual data correlations. Thus, we can perceive relations such as: (1) if this happens, these other things will also happen and (2) this event happened because a set of prior events happened beforehand.
Benchmark to evaluate a mapping of data science tasks to HPC resources—how we know if particular scheduling outperforms another one for executing a particular data science demand? We see as an opportunity for the exploration of benchmarks to evaluate scheduling and load balancing techniques that manipulate data science tasks. Thus, such benchmarks must define what they expect as input and provide a set of metrics as output. Yet, the output can be a single value, a collection of values (as a data vector), or a collection of elements of a data structure (e.g., timestamp and data are useful to develop user profiles and tracking of assets).
Simulation environment to execute data science demands on distributed resources, but doing all of this a local program—simulation environments, like Simgrid or GridSim, are useful to use a sequential program to test and simulate complex parallel demands on a set of virtual resources. Thus, we can save time on testing different parameters and algorithms when developing scheduling and load balancing algorithms for data science.
Definition of metrics to evaluate the scheduling and/or load balancing of data science tasks—CPU load, memory footprint, disk space, network throughput, and cache hit rate are examples of metrics that are commonly employed on distributed systems. Data science is a new area of knowledge, where we encourage the definition of new metrics to compare the execution of data science demands.
The continuous generation of data by different industry segments presents a valuable opportunity for analysis and knowledge extraction through data science methods. There is a high interest in studies that explore the application of data science to a variety of scenarios, each one with distinct characteristics that reflect on the composition of available datasets. Furthermore, there is not a single data science methodology that is applied to all possible data science problems. Consequently, the most common approach to data science problems is to define a sequence of methods that depend on the characteristics of the dataset and the intended results.
\nThe constant growth in dataset sizes and the complexity of specific data science methods also impose a considerable challenge to provide the computational power required to process data and extract meaningful knowledge. In this context, cloud, fog, and grid computing architectures present themselves as ideal solutions to apply data science processes to massively sized datasets.
\nThe distributed nature of such environments raises a series of new challenges, some of which widely studied in the literature. Nevertheless, the unique characteristics of data science workloads bring new aspects to these challenges, which require renewed attention from the scientific community.
\nThis chapter focused on the specific challenge of scheduling and load balancing in the context of computational environments applied to data science. We presented an overview of data science processes, in addition to how scheduling and load balancing methodologies impact these processes and what aspects to consider when using distributed environments applied to data science. In particular, the challenge of enabling the automatic transformation of sequential data science demands into parallel ones is of particular interest because it abstracts part of the complexity involved in parallelizing data science tasks. As a result, such an automatic transformation promotes wider adoption of distributed environments as standard tools for large-scale data science processes.
\nAnother notable challenge is to develop cloud elasticity techniques tailored to data science tasks. Such techniques must consider the specific requirements of data science processes to guarantee the proper reservation of resources and migration of tasks in order to guarantee a high throughput for such scenarios. These and the other investigated challenges represent prime research opportunities to increase the performance of data science processes.
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\n\nBook Chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen maintains a very flexible Copyright Policy that ensures that there is no copyright transfer to the publisher. Therefore, Authors retain exclusive copyright to their work. All Monographs are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) and journal articles are distributed under a Creative Commons 4.0 International Licence.
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\n\nBased on your preferences and the stage of your scientific projects, you have multiple options for publishing your scientific research with IntechOpen:
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\n\nThe Open Access publishing model followed by IntechOpen eliminates subscription charges and pay-per-view fees, thus enabling readers to access research at no cost to themselves. In order to sustain these operations, and keep our publications freely accessible, we levy an Open Access Publishing Fee on all manuscripts accepted for publication to help cover the costs of editorial work and the production of books.
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His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. 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She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNVJQA4/Profile_Picture_2022-03-07T13:23:04.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Associate Prof.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/15648_n.jpg",biography:"Dr. Mohd Aftab Siddiqui is currently working as Assistant Professor in the Faculty of Pharmacy, Integral University, Lucknow for the last 6 years. He has completed his Doctor in Philosophy (Pharmacology) in 2020 from Integral University, Lucknow. He completed his Bachelor in Pharmacy in 2013 and Master in Pharmacy (Pharmacology) in 2015 from Integral University, Lucknow. He is the gold medalist in Bachelor and Master degree. He qualified GPAT -2013, GPAT -2014, and GPAT 2015. His area of research is Pharmacological screening of herbal drugs/ natural products in liver and cardiac diseases. He has guided many M. Pharm. research projects. He has many national and international publications.",institutionString:"Integral University",institution:null},{id:"255360",title:"Dr.",name:"Usama",middleName:null,surname:"Ahmad",slug:"usama-ahmad",fullName:"Usama Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255360/images/system/255360.png",biography:"Dr. Usama Ahmad holds a specialization in Pharmaceutics from Amity University, Lucknow, India. He received his Ph.D. degree from Integral University. Currently, he’s working as an Assistant Professor of Pharmaceutics in the Faculty of Pharmacy, Integral University. From 2013 to 2014 he worked on a research project funded by SERB-DST, Government of India. He has a rich publication record with more than 32 original articles published in reputed journals, 3 edited books, 5 book chapters, and a number of scientific articles published in ‘Ingredients South Asia Magazine’ and ‘QualPharma Magazine’. He is a member of the American Association for Cancer Research, International Association for the Study of Lung Cancer, and the British Society for Nanomedicine. Dr. Ahmad’s research focus is on the development of nanoformulations to facilitate the delivery of drugs that aim to provide practical solutions to current healthcare problems.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"30568",title:"Prof.",name:"Madhu",middleName:null,surname:"Khullar",slug:"madhu-khullar",fullName:"Madhu Khullar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/30568/images/system/30568.jpg",biography:"Dr. Madhu Khullar is a Professor of Experimental Medicine and Biotechnology at the Post Graduate Institute of Medical Education and Research, Chandigarh, India. She completed her Post Doctorate in hypertension research at the Henry Ford Hospital, Detroit, USA in 1985. She is an editor and reviewer of several international journals, and a fellow and member of several cardiovascular research societies. Dr. Khullar has a keen research interest in genetics of hypertension, and is currently studying pharmacogenetics of hypertension.",institutionString:"Post Graduate Institute of Medical Education and Research",institution:{name:"Post Graduate Institute of Medical Education and Research",country:{name:"India"}}},{id:"223233",title:"Prof.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/223233/images/system/223233.png",biography:"Xianquan Zhan received his MD and Ph.D. in Preventive Medicine at West China University of Medical Sciences. He received his post-doctoral training in oncology and cancer proteomics at the Central South University, China, and the University of Tennessee Health Science Center (UTHSC), USA. He worked at UTHSC and the Cleveland Clinic in 2001–2012 and achieved the rank of associate professor at UTHSC. Currently, he is a full professor at Central South University and Shandong First Medical University, and an advisor to MS/PhD students and postdoctoral fellows. He is also a fellow of the Royal Society of Medicine and European Association for Predictive Preventive Personalized Medicine (EPMA), a national representative of EPMA, and a member of the American Society of Clinical Oncology (ASCO) and the American Association for the Advancement of Sciences (AAAS). He is also the editor in chief of International Journal of Chronic Diseases & Therapy, an associate editor of EPMA Journal, Frontiers in Endocrinology, and BMC Medical Genomics, and a guest editor of Mass Spectrometry Reviews, Frontiers in Endocrinology, EPMA Journal, and Oxidative Medicine and Cellular Longevity. He has published more than 148 articles, 28 book chapters, 6 books, and 2 US patents in the field of clinical proteomics and biomarkers.",institutionString:"Shandong First Medical University",institution:{name:"Affiliated Hospital of Shandong Academy of Medical Sciences",country:{name:"China"}}},{id:"297507",title:"Dr.",name:"Charles",middleName:"Elias",surname:"Assmann",slug:"charles-assmann",fullName:"Charles Assmann",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/297507/images/system/297507.jpg",biography:"Charles Elias Assmann is a biologist from Federal University of Santa Maria (UFSM, Brazil), who spent some time abroad at the Ludwig-Maximilians-Universität München (LMU, Germany). He has Masters Degree in Biochemistry (UFSM), and is currently a PhD student at Biochemistry at the Department of Biochemistry and Molecular Biology of the UFSM. His areas of expertise include: Biochemistry, Molecular Biology, Enzymology, Genetics and Toxicology. He is currently working on the following subjects: Aluminium toxicity, Neuroinflammation, Oxidative stress and Purinergic system. Since 2011 he has presented more than 80 abstracts in scientific proceedings of national and international meetings. Since 2014, he has published more than 20 peer reviewed papers (including 4 reviews, 3 in Portuguese) and 2 book chapters. He has also been a reviewer of international journals and ad hoc reviewer of scientific committees from Brazilian Universities.",institutionString:"Universidade Federal de Santa Maria",institution:{name:"Universidade Federal de Santa Maria",country:{name:"Brazil"}}},{id:"217850",title:"Dr.",name:"Margarete Dulce",middleName:null,surname:"Bagatini",slug:"margarete-dulce-bagatini",fullName:"Margarete Dulce Bagatini",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/217850/images/system/217850.jpeg",biography:"Dr. Margarete Dulce Bagatini is an associate professor at the Federal University of Fronteira Sul/Brazil. She has a degree in Pharmacy and a PhD in Biological Sciences: Toxicological Biochemistry. She is a member of the UFFS Research Advisory Committee\nand a member of the Biovitta Research Institute. She is currently:\nthe leader of the research group: Biological and Clinical Studies\nin Human Pathologies, professor of postgraduate program in\nBiochemistry at UFSC and postgraduate program in Science and Food Technology at\nUFFS. She has experience in the area of pharmacy and clinical analysis, acting mainly\non the following topics: oxidative stress, the purinergic system and human pathologies, being a reviewer of several international journals and books.",institutionString:"Universidade Federal da Fronteira Sul",institution:{name:"Universidade Federal da Fronteira Sul",country:{name:"Brazil"}}},{id:"226275",title:"Ph.D.",name:"Metin",middleName:null,surname:"Budak",slug:"metin-budak",fullName:"Metin Budak",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226275/images/system/226275.jfif",biography:"Metin Budak, MSc, PhD is an Assistant Professor at Trakya University, Faculty of Medicine. He has been Head of the Molecular Research Lab at Prof. Mirko Tos Ear and Hearing Research Center since 2018. His specializations are biophysics, epigenetics, genetics, and methylation mechanisms. He has published around 25 peer-reviewed papers, 2 book chapters, and 28 abstracts. He is a member of the Clinical Research Ethics Committee and Quantification and Consideration Committee of Medicine Faculty. His research area is the role of methylation during gene transcription, chromatin packages DNA within the cell and DNA repair, replication, recombination, and gene transcription. His research focuses on how the cell overcomes chromatin structure and methylation to allow access to the underlying DNA and enable normal cellular function.",institutionString:"Trakya University",institution:{name:"Trakya University",country:{name:"Turkey"}}},{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",slug:"anca-pantea-stoian",fullName:"Anca Pantea Stoian",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",biography:"Anca Pantea Stoian is a specialist in diabetes, nutrition, and metabolic diseases as well as health food hygiene. She also has competency in general ultrasonography.\n\nShe is an associate professor in the Diabetes, Nutrition and Metabolic Diseases Department, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania. She has been chief of the Hygiene Department, Faculty of Dentistry, at the same university since 2019. Her interests include micro and macrovascular complications in diabetes and new therapies. Her research activities focus on nutritional intervention in chronic pathology, as well as cardio-renal-metabolic risk assessment, and diabetes in cancer. She is currently engaged in developing new therapies and technological tools for screening, prevention, and patient education in diabetes. \n\nShe is a member of the European Association for the Study of Diabetes, Cardiometabolic Academy, CEDA, Romanian Society of Diabetes, Nutrition and Metabolic Diseases, Romanian Diabetes Federation, and Association for Renal Metabolic and Nutrition studies. She has authored or co-authored 160 papers in national and international peer-reviewed journals.",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",country:{name:"Romania"}}},{id:"279792",title:"Dr.",name:"João",middleName:null,surname:"Cotas",slug:"joao-cotas",fullName:"João Cotas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/279792/images/system/279792.jpg",biography:"Graduate and master in Biology from the University of Coimbra.\n\nI am a research fellow at the Macroalgae Laboratory Unit, in the MARE-UC – Marine and Environmental Sciences Centre of the University of Coimbra. My principal function is the collection, extraction and purification of macroalgae compounds, chemical and bioactive characterization of the compounds and algae extracts and development of new methodologies in marine biotechnology area. \nI am associated in two projects: one consists on discovery of natural compounds for oncobiology. The other project is the about the natural compounds/products for agricultural area.\n\nPublications:\nCotas, J.; Figueirinha, A.; Pereira, L.; Batista, T. 2018. An analysis of the effects of salinity on Fucus ceranoides (Ochrophyta, Phaeophyceae), in the Mondego River (Portugal). Journal of Oceanology and Limnology. in press. DOI: 10.1007/s00343-019-8111-3",institutionString:"Faculty of Sciences and Technology of University of Coimbra",institution:null},{id:"279788",title:"Dr.",name:"Leonel",middleName:null,surname:"Pereira",slug:"leonel-pereira",fullName:"Leonel Pereira",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/279788/images/system/279788.jpg",biography:"Leonel Pereira has an undergraduate degree in Biology, a Ph.D. in Biology (specialty in Cell Biology), and a Habilitation degree in Biosciences (specialization in Biotechnology) from the Faculty of Science and Technology, University of Coimbra, Portugal, where he is currently a professor. In addition to teaching at this university, he is an integrated researcher at the Marine and Environmental Sciences Center (MARE), Portugal. His interests include marine biodiversity (algae), marine biotechnology (algae bioactive compounds), and marine ecology (environmental assessment). Since 2008, he has been the author and editor of the electronic publication MACOI – Portuguese Seaweeds Website (www.seaweeds.uc.pt). He is also a member of the editorial boards of several scientific journals. Dr. Pereira has edited or authored more than 20 books, 100 journal articles, and 45 book chapters. He has given more than 100 lectures and oral communications at various national and international scientific events. He is the coordinator of several national and international research projects. In 1998, he received the Francisco de Holanda Award (Honorable Mention) and, more recently, the Mar Rei D. Carlos award (18th edition). He is also a winner of the 2016 CHOICE Award for an outstanding academic title for his book Edible Seaweeds of the World. In 2020, Dr. Pereira received an Honorable Mention for the Impact of International Publications from the Web of Science",institutionString:"University of Coimbra",institution:{name:"University of Coimbra",country:{name:"Portugal"}}},{id:"61946",title:"Dr.",name:"Carol",middleName:null,surname:"Bernstein",slug:"carol-bernstein",fullName:"Carol Bernstein",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/61946/images/system/61946.jpg",biography:"Carol Bernstein received her PhD in Genetics from the University of California (Davis). She was a faculty member at the University of Arizona College of Medicine for 43 years, retiring in 2011. Her research interests focus on DNA damage and its underlying role in sex, aging and in the early steps of initiation and progression to cancer. In her research, she had used organisms including bacteriophage T4, Neurospora crassa, Schizosaccharomyces pombe and mice, as well as human cells and tissues. She authored or co-authored more than 140 scientific publications, including articles in major peer reviewed journals, book chapters, invited reviews and one book.",institutionString:"University of Arizona",institution:{name:"University of Arizona",country:{name:"United States of America"}}},{id:"182258",title:"Dr.",name:"Ademar",middleName:"Pereira",surname:"Serra",slug:"ademar-serra",fullName:"Ademar Serra",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/182258/images/system/182258.jpeg",biography:"Dr. Serra studied Agronomy on Universidade Federal de Mato Grosso do Sul (UFMS) (2005). He received master degree in Agronomy, Crop Science (Soil fertility and plant nutrition) (2007) by Universidade Federal da Grande Dourados (UFGD), and PhD in agronomy (Soil fertility and plant nutrition) (2011) from Universidade Federal da Grande Dourados / Escola Superior de Agricultura Luiz de Queiroz (UFGD/ESALQ-USP). Dr. Serra is currently working at Brazilian Agricultural Research Corporation (EMBRAPA). His research focus is on mineral nutrition of plants, crop science and soil science. Dr. Serra\\'s current projects are soil organic matter, soil phosphorus fractions, compositional nutrient diagnosis (CND) and isometric log ratio (ilr) transformation in compositional data analysis.",institutionString:"Brazilian Agricultural Research Corporation",institution:{name:"Brazilian Agricultural Research Corporation",country:{name:"Brazil"}}}]}},subseries:{item:{id:"28",type:"subseries",title:"Animal Reproductive Biology and Technology",keywords:"Animal Reproduction, Artificial Insemination, Embryos, Cryopreservation, Conservation, Breeding, Epigenetics",scope:"The advances of knowledge on animal reproductive biology and technologies revolutionized livestock production. Artificial insemination, for example, was the first technology applied on a large scale, initially in dairy cattle and afterward applied to other species. Nowadays, embryo production and transfer are used commercially along with other technologies to modulate epigenetic regulation. Gene editing is also emerging as an innovative tool. This topic will discuss the potential use of these techniques, novel strategies, and lines of research in progress in the fields mentioned above.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/28.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11417,editor:{id:"177225",title:"Prof.",name:"Rosa Maria Lino Neto",middleName:null,surname:"Pereira",slug:"rosa-maria-lino-neto-pereira",fullName:"Rosa Maria Lino Neto Pereira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9wkQAC/Profile_Picture_1624519982291",biography:"Rosa Maria Lino Neto Pereira (DVM, MsC, PhD and) is currently a researcher at the Genetic Resources and Biotechnology Unit of the National Institute of Agrarian and Veterinarian Research (INIAV, Portugal). She is the head of the Reproduction and Embryology Laboratories and was lecturer of Reproduction and Reproductive Biotechnologies at Veterinary Medicine Faculty. She has over 25 years of experience working in reproductive biology and biotechnology areas with a special emphasis on embryo and gamete cryopreservation, for research and animal genetic resources conservation, leading research projects with several peer-reviewed papers. Rosa Pereira is member of the ERFP-FAO Ex situ Working Group and of the Management Commission of the Portuguese Animal Germplasm Bank.",institutionString:"The National Institute for Agricultural and Veterinary Research. Portugal",institution:null},editorTwo:null,editorThree:null,series:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517"},editorialBoard:[{id:"90066",title:"Dr.",name:"Alexandre",middleName:"Rodrigues",surname:"Silva",slug:"alexandre-silva",fullName:"Alexandre Silva",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRt8pQAC/Profile_Picture_1622531020756",institutionString:null,institution:{name:"Universidade Federal Rural do Semi-Árido",institutionURL:null,country:{name:"Brazil"}}},{id:"176987",title:"Ph.D.",name:"María-José",middleName:"Carrascosa",surname:"Argente",slug:"maria-jose-argente",fullName:"María-José Argente",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9vOQAS/Profile_Picture_1630330499537",institutionString:null,institution:{name:"Miguel Hernandez University",institutionURL:null,country:{name:"Spain"}}},{id:"321396",title:"Prof.",name:"Muhammad 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