Experimental results obtained by AlexNet-based FCN.
\r\n\tOver the years, the concept of maintenance became more comprehensive, reducing fault occurrence and increasing industrial system availability. Besides, reliability, safety, and criticality requirements were associated with the system or equipment under analysis. Maintenance strategies or schemes can be classified as corrective (run-to-break), preventive (time-based), and predictive (condition-based maintenance). Corrective maintenance is only performed after an occurrence of a fault. Therefore, it involves unexpected breakdowns, high costs, changes in the production chain, and it could lead to catastrophic events. Preventive maintenance and interventions occur based on a scheduled maintenance plan or the equipment's mean time between failures. Although it is more effective than corrective maintenance, unexpected failure may still occur by preventing most failures. Additionally, the process cost is still high, especially the costs associated with labor, inventory, and unnecessary replacement of equipment or components.
\r\n\tOn the other hand, predictive maintenance analyses the equipment condition so that a possible fault can still be identified at an early stage. Predictive maintenance aims to identify a machine anomaly so that it does not result in a fault. Such maintenance involves advanced monitoring, processing, and signal analysis techniques, which are generally performed non-invasively and, in many cases, in real-time. In the case of machines or processes, these techniques can be developed based on vibration, temperature, acoustic emission, or electrical current signal monitoring. It should be noted that monitoring such signals or parameters to verify the operating condition is called condition monitoring. Condition monitoring aims to observe the machine's current operational condition and predict its future condition, keeping it under a systematic analysis during its remaining life. In this sense, a fault condition can be detected and identified from systematic machine condition monitoring. A diagnosis procedure can be established, whereby properly investigating the fault symptoms and prognosis.
\r\n\t
\r\n\tThis book will aim to merge all these ideas in a single volume, aggregate new maintenance experiences, apply new techniques and approaches, and report field experiences to establish new maintenance processes and management paradigms.
\r\n\t
Robotics, as a commercial technology, started to be widespread some decades ago, but instead of decreasing, it has been growing year by year with new contributions in all the related fields that it integrates. The introduction of new materials, sensors, actuators, software, communications and use scenarios converted Robotics in a pushing area that embraces our everyday life. New robotic morphologies are the most shocking aspect that society perceives (i.e., the first models of each type generally produce the largest impact), but the long-term success of robotics is found in its capability to automate productive processes. Manufacturers and developers know that the market is found not only in large-scale companies (car manufacturers and electronics mainly) but also in the SME that provides solutions to problems that are manually performed so far. Also, robotics has opened the doors to new applications that did not exist some years ago and are also attractive to investors. These facts, together with lower prices for equipment, better programming and communication tools, and new fast-growing user-friendly collaborative robotic frameworks, have pushed robotics technology at the edge in many areas.
It is clear that industrial robotics leads the market worldwide, but social/gaming uses of robots have increased sales. Nevertheless, the most promising scenario for the present time and short term is the use of robots in commercial applications out of the plant floor. Emergency systems, inspection, and maintenance of facilities of any kind, rescues, surveillance, agriculture, fishing, border patrolling, and many other applications (without military use) attract users/clients because their use increases the productivity of the different sectors, low prices and high profitability are the keys.
There exist many robot morphologies and types (surface, underwater, aerial, underground, legged, wheels, caterpillar, etc.) but authors want to draw attention in the unmanned aerial vehicles (UAVs), which have several properties that make them attractive for a set of application that cannot be done with any other type of robot. First, those autonomous robots can fly, and therefore, they can reach areas that humans or other robots cannot. They are light, easy to move from one area to another, and can be adapted to any area, terrain, soil, building, or facility. The drawback is the fragility in front of adverse meteorological events, and their autonomy is quite limited compared with unmanned surface vehicles (USVs).
UAVs have seen the birth of a new era of unthinkable cheap, easy applications up to now. The authors would like to focus its use in the maintenance and inspection of industrial facilities, but specifically in the inspection of pipes in big, complex factories (mainly gas and oil companies) where the manual inspection (and even location and mapping) of pipes becomes an impossible task. Manned helicopters (with thermal engines) cannot fly close to pipes or even among a bunch of pipes. Scaffolds cannot be put up in complex, unstable, and fragile pipes to manually inspect them. Therefore, a complex problem can be solved through the use of UAVs for inspecting pipes of different diameters, colors, textures, and conditions in hazardous factories. This problem is not new and some solutions have been brought to an incipient market. Works as those in [1, 2] propose the creation of a map of the pipe set navigating among it with odometry and inertial units [3]. Obstacle avoidance in a crowded 3D world of pipes becomes of great interest when planning a flight; in [4], some contributions are made in this direction although the accuracy of object is deficient to be a reliable technology. Work in [5] overcomes some of the latter problems with the use of a big range of sensors, cameras, laser, barometer, ultrasound, and a computationally inefficient software scheme made the UAV too heavy and unreliable due to the excessive sensor fusion approach.
Many of the technical developments that have helped robotics grow have had a wider impact, especially those related with increasing computational power and parallelization levels. Faster processors, with tens of cores and additional multiple threat capabilities, and modern GPUs (graphics processing unit) have led to the emergence of GPGPU (general-purpose computing on GPU). These type of computing techniques have led to huge advances in the artificial intelligence (AI) field, producing the emergence of the “deep learning” field. The deep learning (DL) field is focused in using artificial neural networks (ANNs) that present tens or hundreds of layers, exploiting the huge parallelization capabilities of modern GPU. This is used in exploiting computational cores (e.g., CUDA cores), which compared on a one-to-one basis with a processor core, they are less powerful and slower, but can be found in amounts of hundreds or thousands. This has allowed the transition from shallow ANN to the deeper architectures and innovations such as several types of convolutional layers. In this work, the authors present a novel approach to detect pipes in industrial environments based in fully convolutional networks (FCNs). These will be used to extract the apparent contour of the pipes, replacing most of the architecture developed in [6] and discussed in Section 2. To properly train these networks, a custom dataset relevant to the domain is required, so the authors captured a dataset and developed an automatic label generation procedure base in previous works. Two different state-of-the-art semantic segmentation approaches were trained and evaluated with the standard metrics to prove the validity of the whole approach. Thus, in the following section, some generalities about the pipe detection and positioning problem are discussed, and the authors’ previous work [6] on it, as it will be relevant later. The next section discusses the semantic segmentation problem as a way to extract the apparent contour, both surveying classical methods, considered for earlier works, and state of the art deep-learning-based methodologies. The fourth section describes how the automatic label generator using multimodal data was derived and some features to the process. The experimental section starts discussing the metrics employed to validate the results, the particularities of the domain dataset generated and describes how an AlexNet FCN architecture was trained through transfer learning and the results achieved. To conclude, some discussion on the quality of the results and possible enhancements is introduced, discussing which would be the best strategies to follow continuing this research.
As it has been discussed, inspection and surveying are a frequent problem where UAV technologies are applied. The most common scenario found is that of a hard to reach infrastructure that is visually inspected through different sensors onboard a piloted UAV. Some projects have proposed the introduction of higher level perception and automation capacities, depending on the specific problem. In these cases, it is common to join state-of-the-art academic and industrial expertise to reach functional solutions.
In one of these projects, the specific challenge of accurately detecting and positioning a pipe in real time using only the hardware deployable in a small (per industry standards) UAV platform was considered (Figure 1), with several solutions studied and tested (including vision- and LIDAR-based techniques).
One of the UAV used for the development of perception tasks in the AEROARMS project. Several sensors were deployed, processing them with a set of SBCs (single-board computers), including a Velodyne LiDAR, two different cameras, ultrasonic range-finder (height), and optical flow.
In the case of LIDAR-based detection, finding a pipe is generally treated as a segmentation problem in the sensor space (using R3 data collected as “
With these results, discussed in [6], it was apparent that a new solution was needed, as the LiDAR approaches were too slow and the vision-based techniques probed themselves unreliable. The final proposed solution was based on integrating data from the laser and the vision sensors: the RANSAC over LiDAR approach would detect robustly the pipe and provide an initial position, which would then be projected into the image space (accounting for displacements if odometry is available) and used as a seed for the vision-based pipeline described.
In that same work [6], a sensibility analysis studying the effects of the relative pose between the sensor and pipes is provided. Once the pipe is detected in the LiDAR’s space sensor, the cylinder model is projected into the R2 image space using a projection matrix derived from the calibrated camera model (assumed to be a thin lens pinhole model, per classic literature [13]). This provides a region or band of interest where to look for the edges of the pipe in the image and is useful to solve the degenerate conic equation up to scale (i.e., being a function of the radius). An updated architecture version of the process is depicted in Figure 2.
The architecture of the multimodal perception pipeline combining LiDAR and camera vision. An updated version adds to previous works a validation step using odometric measurements.
The detailed architecture of the multimodal approach reveals how the LiDAR-based pipeline minimizes the data dimensionality by filtering non-curved surfaces (i.e., remove walls, floor, etc.) and also by removing entirely regions of the sensed space if priors or relevant data or the expected relative position of the pipe to the sensor is available. This was aimed at minimizing the size of the point cloud to be processed by the RANSAC step. To be able to project the detected pipe from the LiDAR sensor space into the camera image, some additional information was required: the rigid transformation between sensors (i.e., the calibration between LiDAR and camera) and an estimation of the odometry of the UAV. This is due because, even in the best assumption, with a performance slightly over 4 Hz, the delay between the captured point cloud and the produced estimation of the pipe would be over 200 ms. Therefore, the projection of the detected pipe to predict the area of interest to search the apparent contour has to consider the displacement during this period, not only the rigid LiDAR to camera transformation. This predicted region of interest is used in the vision process pipeline, with predictions of the appearance of the pipes into image space used to refine the contour search. This contour search relies on stacking a Hough transform to join line segment detector (LSD) detected segments (to overcome partial obstructions) on the relevant area and allows to choose the nearest correctly aligned lines. Notice that using a visual servoing library [14], an option to use data provided through human interaction was kept as available, though the integration of LiDAR detections as seeds into the visual pipeline made it unnecessary. To avoid degenerate or spurious solutions, a validation step (based on reprojection and “matching” of the Plückerian coordinates [15] for a tracked piped) was later introduced.
This architecture leads to a fast (limited by the performance of the vision-based part) and robust (based on the RANSAC resilience to spurious detections) pipe detector with great accuracy, which was deployed and test in a UAV. The main issue of the approach is the hardware requirements: access to odometry from the avionics systems, LiDAR, and camera sensors, and enough computing power to process them (beyond any other task required from the UAV). All this hardware is focused on solving what can be described as a semantic segmentation problem. This is relevant given the enormous changes produced in the last decade in the computer vision field, and how classic problems like semantic segmentation are currently solved.
In the context of computer vision, the semantic segmentation problem is used to determine which regions of an image present an object of a given category, that is, a class or label is assigned to a given area (be it a pixel, window, or segmented region). The different granularity accepted is produced by how the technique and its solution evolved: for a long time, it was completely unfeasible to produce pixel-wise solutions, so images were split according to different procedures, which added a complexity layer to the problem.
Current off-the-shelf technologies have changed the paradigm, as GPUs present huge capabilities in terms of parallelization, while solid-state disks make fast reliable storage cheap. These technical advancements have increased dramatically the performance, complexity, and memory available for data representation, especially for techniques inherently strong in highly parallelized environments. One of the fields where the impact has been more noticeable has been the artificial intelligence community, where the artificial neural network (ANN) has seen a resurgence thanks to the support this kind of hardware provides to otherwise computationally unfeasible techniques. The most impactful development in recent years has been the convolutional neural networks (CNNs), which have become the most popular computer vision approach for several of the classic problem and the default solution for semantic segmentation.
To understand the impact of deep learning into our proposed solution, we will discuss briefly how the classical segmentation pipeline worked and how the modern CNN-based classifier became the modern semantic segmentation techniques.
The classic semantic segmentation pipeline can be split into two generic blocks, namely image processing for feature extraction and feature level classification. The first block generally includes any image preprocessing done and resizing/resampling, splitting the image into the regions/windows, defining the granularity level of the classification, and finally, extracting the features itself. The features can be of any type and frequently the ones feed to the classification modules will be a composition of several individual features from different detectors. The use of different window/region-based approaches helps build up higher level features, and the classification can be refined at later stages with data from adjacent regions.
Notice that this kind of architecture generally relies on classifiers which required very accurate knowledge or a dataset with the classes to learn specified for each input so it can be trained. Figure 3 shows the detection of pipelines in classic semantic segmentation. Notice that to train the classifier, the image mask or classification result becomes also an input for the training process.
Block diagram of a classical architecture approach for semantic segmentation using computer vision.
So, it can be seen that solving the semantic segmentation problem though classic pattern recognition methods requires acute insight into the specifics of the problem domain, as the features to be detected and extracted are built/designed specifically. This implies (as mentioned earlier) working from low-level features and explicitly deriving the higher level features from them is a very complex problem itself, as they are affected by the input characteristics, what is to be found/discriminated, and which techniques will be used in the classification part of the pipeline.
Modern semantic segmentation techniques have organically evolved with the rise of the deep learning field to its current prominence. This evolution can be seen as a refinement in the scale of the inference produced from very coarse (image level probabilistic detection) to very fine (pixel level classification). The earliest ANN examples made probabilistic predictions about the presence of an object of a given class, that is, detection of objects with a probability assigned. The next step, achieved thanks to increased parallelization and network depth, was starting to tackle the localization problem, providing centroids and/or boxes for the detected classes (the use of
The first big break into the classification problem was done by AlexNet [13] in 2012, when it won the ILSVRC challenge, with a score of 84.6% in the top-5 accuracy test, while the next best score was only 73.8% (based on classic techniques). AlexNet has since then become a known standard and a default network architecture to test problems, as it is actually not very deep or complex (see Figure 4). It presents five convolutional layers, with max-pooling after the first two, three fully connected layers, and a ReLU to deal with non-linearities. This clear victory of the CNN-based approaches was validated next year by Oxford’s VGG16 [16], one of the several architectures presented, winning the ILSVRC challenge with a 92.7% score.
Diagram of the AlexNet architecture, showcasing its pioneering use of convolutional layers.
While several other networks have been presented with deeper architecture, relevant development focused on introducing new types of structures into the networks. GoogLeNet [17], the 2014 ILSVRC winner, achieved victory thanks to the novel contribution of the inception module, which validated the concept that the CNN layers of a network could operate in other orders different from the classic sequential approach. Another relevant contribution produced by technology giants was ResNet [18], which scored a win for Microsoft in 2016. The introduction of residual blocks allowed them to increase the depth to 152 layers while keeping initial data meaningful for training the deeper layers. These residual blocks architecture essentially forwards a copy of the received inputs of a layer; thus, later layers received the results and same inputs of prior layers and can learn from the residuals.
More recently, ReNet [19] architecture was used to extend recurrent neural networks (RNNs) to multidimensional inputs.
The jump from the classification problem with some spatial data to pixel level labeling (refining inference from image/region to pixel level) was presented by Long [20], with the fully convolutional network (FCN). The method they proposed was based on using the full classifier (like the ones just discussed) as layers in a convolutional network architecture. FCN architecture, and its derivatives like U-Net [21] are the best solutions to semantic segmentation for most domains. These derivatives may include classic methods, such as DeepLab’s [22] conditional random fields [23], which reinforces the inference from spatially distant dependencies, usually lost due to CNN spatial invariance. The latest promising contributions to the semantic segmentation problem are based on the encoder-decoder architecture, known as autoenconders, like for example SegNet [24].
For the works discussed in this chapter, a FCN16 model with AlexNet as a semantic segmentation model was used. The main innovation introduced by the general FCN was exploiting the classification power via convolution of the common semantic segmentation DL network, but at the same time, reversing the downsampling effect of the convolution operation itself. Taking AlexNet as an example, as seen in Figure 4, convolutional layers apply a filter like operation while reducing the size of the data forwarded to the next layer. This process allows producing more accurate “deep features” but at the same time also removes high-level information describing the spatial relation between the features found. Thus, in order to exploit the features from the deep layers while the keeping information from spatial relation, data from multiple layers has to be fused (with element-wise summation). In order to be able to produce this fusion, data from the deeper layers are upsampled using deconvolution. Notice that data from shallow layers will be coarser but contain more spatial information. Thus, up to three different levels can be processed through FCN, depending on the quantity of layers deconvoluted and fused, as seen in Figure 5.
Detail of the skip architectures (FCN32, FCN16, and FCN8) used to produce results with data from several layers to recover both deep features and spatial information from shallow layers (courtesy of [
More information on the detailed working of the different FCN models can be found in [25]. It is still worth noting that the more shallow layers are fused, the more accurate the model becomes, but according to the literature, the gain from FCN16 to FCN8 is minimal (below 2%).
Classic methods using trained classifiers would pick designed features (based on several metrics and detectors, as discussed earlier) to parametrize a given sample and assign a label. This would allow creating small specific datasets, which could be used to infer the knowledge to create bigger datasets in a posterior step. The high specificity of the features chosen (generally with expert domain knowledge applied implicitly) with respect to the task generally made them unsuitable to export learning to other domains.
By contrast, deep learning offers several transfer learning options. That is, as it was proven by Yosinski [26], trained with a distant domain dataset are generally useful for different domains and usually better than training from an initial random state. Notice that the transferability of features decreases with the difference between the previously trained task and the target one and implies that the network architecture is the same up to the transferred layers at least.
With this concept in mind, we decided to build a dataset to train an outdoor industrial pipe detector with pixel level annotation to be able to determine the position of the pipe. While the ability of transfer learning allows us to skip building a dataset with several tens of thousands of images, and therefore, the authors will work with a few thousand, which were used to fine-tune the network. These orders of magnitude are required as a “shallow” deep network. For instance, the AlexNet already presents 60 million parameters.
Capturing and labeling a dataset is a cumbersome task, so we also set to automatize this task with minimal human supervision/interaction, exploiting the capabilities of the sensing architecture proposed in earlier works described in Section 2.
This framework, see Figure 6, uses the images captured by the UAV camera sensor, the data processed by the localization approach chosen (see Section 2) to obtain the UAV odometry, and pipe detection seeds from the RANSAC technique treating the LiDAR point cloud data. When a pipe (or generally a cylinder) is detected and segmented in the data sensor provided by the LiDAR, this is used to produce a label for the temporally near images, to identify the region of the image (the set of pixels) containing the pipe or cylinder detected and its pose w.r.t. the camera. Notice that even running the perception part, the camera works at a higher rate than the LiDAR, so the full odometric estimation is used to interpolate between pipe detections, to estimate where the label should be projected into the in-between images (just as it was described for the pipe prediction in Section 2).
The framework proposed to automatically produce labeled datasets with the multimodal perception UAV.
This methodology was used to create an initial labeled dataset with actual data captured in real industrial scenarios during test and development flights, as it will be discussed in the next section.
To evaluate the viability of the proposed automated dataset generation methodology, we apply it to capture a dataset and train several semantic segmentation networks with it. To provide some quantitative quality measurement for the solutions produced, we use modified standard metrics for state-of-the-art deep learning, accounting that in our problem we are dealing with only one semantic class:
Notice that usually, besides the PA the mean pixel accuracy (MPA) is provided, but in our case, it reduces to the same value of PA, thus it will not be provided.
An additional metric usually computed along with the MIU is the frequency weighted MIU, which just weighs the average IoU computed at MIU according to the relative frequency of each class. The MIU, in our case, IoU is the most relevant metric and the most widely used when reporting segmentation results (semantic or otherwise).
The system proposed was implemented over the ROS meta-operating system, just as in previous works [6], where the UAV system used to capture the data is described. A set of real flights in simulated industry environments was performed, where flights around a pipe were done. During these flights, averaging ~240 s, an OTS USB camera was used to capture images (at 640 × 480 resolution), achieving an average frame rate of around 17 fps. This translated in around 20,000 raw images captured, including the parts of flight where no industry-like elements are present, thus of limited use.
Notice that as per the method described, the pipe to be found can be only labeled automatically when the LiDAR sensor can detect it; thus, the number of images was further reduced due to the range limitations of the LiDAR scanner. Other factors, such as vibrations and disruptions in the input or results of required perceptual data, further reduced the number of images with accurate labels.
Around ~2100 images were automatically labeled with a mask assigning a ground truth for the pipe in the image. After an initial human inspection of the assigned label, a further ~320 were rejected, obtaining a final set of 1750. The image rejected produced spurious ground truths/masks. Some of them had inconsistent data and the reprojection of the cylinder detected in through RANSAC in LiDAR scans was not properly aligned (error could be produced by spurious interpolation of poses, faulty synchronization data from the sensors, or due to deformation of the UAV frame, as it is impossible for it to be perfectly rigid). Another group presented partial detections (only one of the edges of the pipe is visible in the image), thus making it useless for the apparent contour optimization. A third type of error found was produced by the vision-based pipeline, where a spurious mask was generated, commonly some shadows/textures displace/retort the edge, or areas not pertaining to the pipe are assigned due similarity of the texture and complexity of delimiting the areas.
A sample of the labeling process can be seen in Figure 7, with the original image, the segmented pipe image, and approximations to centroid and bounding bow.
Left: dataset image. Middle: bounding box and centroid of the region detected. Right: segmentation mask image.
Out of the several options available to test the validity of the dataset produced, the shallow architecture AlexNet was selected, as it could be easily trained and it would provide some insight in the performance that could be realistically expected from a CNN-based approach deployed in the limited hardware of a UAV.
According to previous literature, the dataset was divided into training, validation, and test at the standard ratio of 70, 15, and 15% respectively.
To match the input of AlexNet the images were resized to 256 × 256 resolution. This was mainly done to reduce the computational load, as the input size could be easily fit adjusting some parameters, like the stride. To train and test the network, the Pytorch library was used, which provides full support for its own implementation of AlexNet.
To produce some metrics relevant to the network architecture just trained, a modified version of the technique used to label the dataset was used. Note that this approach, as described in previous sections, uses LiDAR, cameras, and odometry to: acquire an initial robust detection (from LiDAR), track its projection and predict it in the camera image space (using odometric data), and finally determine its edges/contour in the image. The robustness of the LiDAR detection is mainly due to exploiting prior knowledge (in the form of the known radius of the pipe to detect) that cannot be introduced into the AlexNet architecture to produce a meaningful comparison. So, a modified method, referred to as NPMD (no-priors multimodal detector) was employed to estimate the accuracy of earlier work detector without priors. The main difference was modifying the LiDAR pipeline to be able to detect several pipes with different radius (as it should be considered unknown). This led to the appearance of false positives and spurious measurements, which in turn weakened the results produced by the segmentation part of the visual pipeline.
Thus, FCN with AlexNet classification was trained using a pre-trained model for AlexNet, with the standard stochastic gradient descend (SGD) with a momentum of 0.9. A learning rate of 10−3 was used, according to known literature, with image batches of 20. The weight decay and bias learning rate were set to standard values of 5.10−4 and 2, respectively. Without any prior data, and no benefit to obtain by doing otherwise reported in any previous works, the classifier layer was set to 0, and the dropout layer in the AlexNet left unmodified. This trained model produced the results found in Table 1.
AlexNetFCN | UPMD | |
---|---|---|
PA | 73.4 | 56.7 |
IoU | 58.6 | 42.1 |
Experimental results obtained by AlexNet-based FCN.
It can be seen that eliminating the seed/prior data from the multimodal detector made it rather weak, with very low values for IoU, signaling the presence of spurious detections and probably fake positives. The FCN-based solution was around 1.5 times better segmenting the pipe, being a clear winner. This was to be expected as we deliberately removed one of the key factors contributing to the LiDAR-based RANSAC detection robustness, the radius priors, leading to the appearance of spurious detections.
It is worth noting that although the results are not that strong in terms of metrics achieved for a single-class case, there are no other vision-only pipe detectors with better results in the literature, neither other approaches actually tested in real UAV’s platforms, like authors’ previous works [6].
The field of computer vision has been greatly impacted by the advances in deep learning that have emerged in the last decade. This has allowed solving, with purely vision-based approaches, some problems that were considered unsolvable under this restriction. In the case presented, a detection and positioning problem, solved with limited hardware resources (onboard a UAV) in an industry-like uncontrolled scenario through a multimodal approach, has been solved with a vision-only approach. The previous multimodal approach relied in LiDAR, cameras, and odometric measurements (mainly from GPS and IMU) to extract data with complex algorithms like RANSAC and combine them to predict the position of a pipe and produce a measurement. This system was notable thanks to its robustness and performance but presented the huge requirements detailed in [6]. In order to solve the problem in a simpler and more affordable manner, a pure visual solution was chosen as the way to go, exploring the deep learning opportunities.
Although the switch to a pure visual solution meant that during its use, the procedure would only use the camera sensor, the multimodal approach was still used to capture data, and through a series of modifications, turn it into an automatic labeling tool. This allowed building a small but complete dataset with fully labeled images relevant to the problem that we were trying to solve. Finally, to test this dataset, we train a DL architecture able to solve the semantic segmentation problem. Thus, three different contributions have been discussed in this chapter: firstly, a dataset generator exploiting multimodal data captured by the perception system to be replaced has been designed and implemented; secondly, with this dataset generation tool, the data captured has been properly labeled so it can be used for DL applications; and finally, a sample lightweight network model for semantic segmentation, FCN with AlexNet classification, has been trained and evaluated to test the problem.
By the same reasons that there was no dataset available for our challenge and we had to capture and develop one dedicated to our domain, there were no related works to obtain metrics. In order to have some relevant metrics to compare the results of the developed approach, a modified version of [13] was produced and benchmarked without the use of prior knowledge. Under these assumptions, the new CNN-based method was able to clearly surpass the multimodal approach, though it still lacks robustness to be considered ready for industrial standards. Still, these initial tests have proven the viability of the built dataset generator and the utilization of CNN-based semantic segmentation to replace the multimodal approach.
This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness through Project 2016-78957-R.
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This chapter discusses the current knowledge of germ cell specification focusing on the zebrafish as a model system. We also provide a comparative analysis of the mechanism for germ plasm RNP segregation in zebrafish versus other known vertebrate systems of germ cell preformation, such as in amphibian and avian models.",book:{id:"5105",slug:"insights-from-animal-reproduction",title:"Insights from Animal Reproduction",fullTitle:"Insights from Animal Reproduction"},signatures:"Celeste Eno and Francisco Pelegri",authors:[{id:"177209",title:"Prof.",name:"Francisco",middleName:null,surname:"Pelegri",slug:"francisco-pelegri",fullName:"Francisco Pelegri"}]},{id:"62171",doi:"10.5772/intechopen.79106",title:"Intraoviductal Instillation of a Solution as an Effective Route for Manipulating Preimplantation Mammalian Embryos in vivo",slug:"intraoviductal-instillation-of-a-solution-as-an-effective-route-for-manipulating-preimplantation-mam",totalDownloads:1143,totalCrossrefCites:8,totalDimensionsCites:9,abstract:"Preimplantation embryos of mammals are enclosed by a translucent layer called zona pellucida (ZP), which is composed of glycoproteins. ZP is important for protecting against infection by virus and bacteria, and to prevent attachment of embryos to the oviductal epithelia. Due to the presence of ZP, it has been difficult to transfect preimplantation embryos existing within the oviductal lumen, with exogenous nucleic acids, such as DNA and mRNA. However, intraoviductal instillation of nucleic acids, and subsequent in vivo electroporation in pregnant females, enables transfection of these embryos, leading to the production of gene-modified animals. This new method for production of genetically modified animals does not require any ex vivo handling of embryos, which has been essential for traditional transgenesis. In this article, we describe recent advances in the in vivo transfection of preimplantation mammalian embryos, and also the possibility of simple transfection of these embryos through intraoviductal instillation of a solution, alone.",book:{id:"7233",slug:"new-insights-into-theriogenology",title:"New Insights into Theriogenology",fullTitle:"New Insights into Theriogenology"},signatures:"Masahiro Sato, Masato Ohtsuka and Shingo Nakamura",authors:[{id:"177440",title:"Dr.",name:"Masato",middleName:null,surname:"Ohtsuka",slug:"masato-ohtsuka",fullName:"Masato Ohtsuka"},{id:"177444",title:"Dr.",name:"Shingo",middleName:null,surname:"Nakamura",slug:"shingo-nakamura",fullName:"Shingo Nakamura"},{id:"245795",title:"Prof.",name:"Masahiro",middleName:null,surname:"Sato",slug:"masahiro-sato",fullName:"Masahiro Sato"}]},{id:"63404",doi:"10.5772/intechopen.80229",title:"Subclinical Endometritis in Dairy Cattle",slug:"subclinical-endometritis-in-dairy-cattle",totalDownloads:1825,totalCrossrefCites:2,totalDimensionsCites:6,abstract:"Subclinical endometritis is recognized as a cause of poor reproductive performance in dairy cows. Inflammation of the endometrium persisting after postpartum uterine involution has been related with prolonged calving-conception intervals and low fertility in dairy cows. The subclinical nature of this condition makes it necessary in the use of endometrial cytology or biopsy for diagnosing it. There are some controversies among authors in relation to the postpartum period from which a physiological endometrial inflammation should be considered a pathological subclinical endometritis. Therefore, depending on the sampling period after calving, different studies establish a different degree of polymorphonuclear leukocyte infiltration as cutoff point to diagnose subclinical endometritis. Controversies also exist regarding the pathogenesis of the disease and its consequences on the fertility of dairy cattle. The aim of this chapter was to review the current knowledge on this uterine pathology.",book:{id:"7233",slug:"new-insights-into-theriogenology",title:"New Insights into Theriogenology",fullTitle:"New Insights into Theriogenology"},signatures:"Luis Angel Quintela Arias, Marcos Vigo Fernández, Juan José\nBecerra González, Mónica Barrio López, Pedro José García Herradón\nand Ana Isabel Peña Martínez",authors:[{id:"243272",title:"Prof.",name:"Luis Angel",middleName:null,surname:"Quintela Arias",slug:"luis-angel-quintela-arias",fullName:"Luis Angel Quintela Arias"},{id:"243886",title:"Prof.",name:"Ana Isabel",middleName:null,surname:"Peña Martínez",slug:"ana-isabel-pena-martinez",fullName:"Ana Isabel Peña Martínez"},{id:"243887",title:"Prof.",name:"Pedro",middleName:null,surname:"García Herradón",slug:"pedro-garcia-herradon",fullName:"Pedro García Herradón"},{id:"243888",title:"Prof.",name:"Juan José",middleName:null,surname:"Becerra González",slug:"juan-jose-becerra-gonzalez",fullName:"Juan José Becerra González"},{id:"256852",title:"Dr.",name:"Mónica",middleName:null,surname:"Barrio López",slug:"monica-barrio-lopez",fullName:"Mónica Barrio López"},{id:"256854",title:"Dr.",name:"Marcos",middleName:null,surname:"Vigo Fernández",slug:"marcos-vigo-fernandez",fullName:"Marcos Vigo Fernández"}]},{id:"50061",doi:"10.5772/62470",title:"Sperm Motility Regulatory Proteins: A Tool to Enhance Sperm Quality",slug:"sperm-motility-regulatory-proteins-a-tool-to-enhance-sperm-quality",totalDownloads:2143,totalCrossrefCites:2,totalDimensionsCites:6,abstract:"Sperm forward motility is an essential parameter in mammalian fertilization. Studies from our laboratory have identified and characterized a few unique sperm motility regulatory proteins/glycoproteins from the male reproductive fluids and mammalian blood serum. The purified sperm motility-initiating protein (MIP) from caprine epididymal plasma as well as the forward motility-stimulating factor (FMSF) and motility-stimulating protein (MSP) from buffalo and goat serum, respectively, have high efficacy to initiate or increase motility in nonmotile or less motile sperm. Antibody of sperm motility inhibitory factor (MIF-II) has the high potential to enhance sperm vertical velocity and forward motility by increasing intracellular cyclic adenosine monophosphate (cAMP) level. The appearance and disappearance of D-galactose–specific lectin and its receptor along the epididymis has been reported to be involved in motility regulation in spermatozoa. A novel synthetic cryopreservation method and role of lipid to protect membrane damage during cryopreservation have been demonstrated. Motility-promoting proteins may be extremely useful for improving cattle breeding and breeding of endangered species, thereby helping in enhanced production of animal products as well as in the conservation of animals. Isolated proteins and developed cryopreservation technology may also be beneficial in human infertility clinics to increase the chance of fertilization.",book:{id:"5105",slug:"insights-from-animal-reproduction",title:"Insights from Animal Reproduction",fullTitle:"Insights from Animal Reproduction"},signatures:"Sandhya R. Dungdung, Arpita Bhoumik, Sudipta Saha, Prasanta\nGhosh, Kaushik Das, Sandipan Mukherjee, Debjani Nath, Jitamanyu\nChakrabarty, Chanakyanath Kundu, Bijay Shankar Jaiswal, Mahitosh\nMandal, Arunima Maiti, Saswati Banerjee, Madhumita\nRoychowdhury, Debleena Ray, Debdas Bhattacharyya and Gopal C.\nMajumder",authors:[{id:"50052",title:"Dr.",name:"Mahitosh",middleName:null,surname:"Mandal",slug:"mahitosh-mandal",fullName:"Mahitosh Mandal"},{id:"177044",title:"Dr.",name:"Sandhya",middleName:null,surname:"Dungdung",slug:"sandhya-dungdung",fullName:"Sandhya Dungdung"},{id:"177920",title:"Dr.",name:"Arpita",middleName:null,surname:"Bhoumik",slug:"arpita-bhoumik",fullName:"Arpita Bhoumik"},{id:"177921",title:"Dr.",name:"Sudipta",middleName:null,surname:"Saha",slug:"sudipta-saha",fullName:"Sudipta Saha"},{id:"177922",title:"MSc.",name:"Prasanta",middleName:null,surname:"Ghosh",slug:"prasanta-ghosh",fullName:"Prasanta Ghosh"},{id:"177923",title:"Dr.",name:"Kaushik",middleName:null,surname:"Das",slug:"kaushik-das",fullName:"Kaushik Das"},{id:"177924",title:"MSc.",name:"Sandipan",middleName:null,surname:"Mukherjee",slug:"sandipan-mukherjee",fullName:"Sandipan Mukherjee"},{id:"177925",title:"Dr.",name:"Debjani",middleName:null,surname:"Nath",slug:"debjani-nath",fullName:"Debjani Nath"},{id:"177927",title:"Dr.",name:"Jitamanyu",middleName:null,surname:"Chakrabarty",slug:"jitamanyu-chakrabarty",fullName:"Jitamanyu Chakrabarty"},{id:"177928",title:"Dr.",name:"Chanakyanath",middleName:null,surname:"Kundu",slug:"chanakyanath-kundu",fullName:"Chanakyanath Kundu"},{id:"177929",title:"Dr.",name:"Bijay Shankar",middleName:null,surname:"Jaiswal",slug:"bijay-shankar-jaiswal",fullName:"Bijay Shankar Jaiswal"},{id:"177930",title:"Dr.",name:"Arunima",middleName:null,surname:"Maiti",slug:"arunima-maiti",fullName:"Arunima Maiti"},{id:"177931",title:"Dr.",name:"Saswati",middleName:null,surname:"Banerjee",slug:"saswati-banerjee",fullName:"Saswati Banerjee"},{id:"177932",title:"Dr.",name:"Madhumita",middleName:null,surname:"Roychowdhury",slug:"madhumita-roychowdhury",fullName:"Madhumita Roychowdhury"},{id:"177933",title:"MSc.",name:"Debleena",middleName:null,surname:"Ray",slug:"debleena-ray",fullName:"Debleena Ray"},{id:"177934",title:"Dr.",name:"Debdas",middleName:null,surname:"Bhattacharyya",slug:"debdas-bhattacharyya",fullName:"Debdas Bhattacharyya"},{id:"177935",title:"Dr.",name:"Gopal Chandra",middleName:null,surname:"Majumder",slug:"gopal-chandra-majumder",fullName:"Gopal Chandra Majumder"}]}],mostDownloadedChaptersLast30Days:[{id:"49736",title:"Chromosome Abnormalities in Domestic Animals as Causes of Disorders of Sex Development or Impaired Fertility",slug:"chromosome-abnormalities-in-domestic-animals-as-causes-of-disorders-of-sex-development-or-impaired-f",totalDownloads:4164,totalCrossrefCites:8,totalDimensionsCites:20,abstract:"Cytogenetic evaluation is an important step in the diagnosis of infertile or sterile animals. Moreover, the analysis of sex chromosomes is crucial for a proper classification of disorders of sex development (DSD). For many years, chromosome studies mainly addressed the livestock species, while recently, increasing interest in such analysis in companion animals is observed. New molecular and cytogenetic tools and techniques have given opportunities for a precise identification of chromosome mutations. Among them, fluorescence in situ hybridization, besides chromosome banding, has become a gold standard. In this chapter, recent advances in the cytogenetic diagnosis of cattle, pigs, horses, dogs and cats are presented.",book:{id:"5105",slug:"insights-from-animal-reproduction",title:"Insights from Animal Reproduction",fullTitle:"Insights from Animal Reproduction"},signatures:"Izabela Szczerbal and Marek Switonski",authors:[{id:"177030",title:"Prof.",name:"Marek",middleName:null,surname:"Switonski",slug:"marek-switonski",fullName:"Marek Switonski"},{id:"177045",title:"Dr.",name:"Izabela",middleName:null,surname:"Szczerbal",slug:"izabela-szczerbal",fullName:"Izabela Szczerbal"}]},{id:"50144",title:"Proliferative Endometrial Lesions Hidden behind the Feline Pyometra",slug:"proliferative-endometrial-lesions-hidden-behind-the-feline-pyometra",totalDownloads:2499,totalCrossrefCites:0,totalDimensionsCites:4,abstract:"The literature refers to pyometra as the most important pathology in the feline uterus, which is often associated with cystic endometrial disease (cystic endometrial hyperplasia/pyometra complex or CEH-Pyo). The etiology of pyometra is complex and probably multifactorial, but hormonal influences are suggested to play an important role in the pathogenesis. Progestagen-based contraceptives may be risk factors for the CEH-Pyo syndrome, for endometrial adenocarcinoma and also to mammary tumors in this species.",book:{id:"5105",slug:"insights-from-animal-reproduction",title:"Insights from Animal Reproduction",fullTitle:"Insights from Animal Reproduction"},signatures:"Maria dos Anjos Pires, Hugo Vilhena, Sónia Miranda, Miguel\nTavares Pereira, Fernanda Seixas and Ana Laura Saraiva",authors:[{id:"41065",title:"Dr.",name:"Sónia",middleName:null,surname:"Miranda",slug:"sonia-miranda",fullName:"Sónia Miranda"},{id:"161556",title:"Dr.",name:"Maria Dos Anjos",middleName:null,surname:"Pires",slug:"maria-dos-anjos-pires",fullName:"Maria Dos Anjos Pires"},{id:"179547",title:"MSc.",name:"Hugo",middleName:null,surname:"Vilhena",slug:"hugo-vilhena",fullName:"Hugo Vilhena"},{id:"179548",title:"MSc.",name:"Miguel",middleName:null,surname:"Tavares Pereira",slug:"miguel-tavares-pereira",fullName:"Miguel Tavares Pereira"},{id:"179549",title:"Prof.",name:"Fernanda",middleName:null,surname:"Seixas",slug:"fernanda-seixas",fullName:"Fernanda Seixas"},{id:"179550",title:"Prof.",name:"Ana Laura",middleName:null,surname:"Saraiva",slug:"ana-laura-saraiva",fullName:"Ana Laura Saraiva"}]},{id:"49944",title:"The Use of Reproductive Technologies to Produce Transgenic Goats",slug:"the-use-of-reproductive-technologies-to-produce-transgenic-goats",totalDownloads:2443,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"Recombinant DNA technology has revolutionized the production of therapeutic proteins. Thus, genes of a great number of human proteins have already been identified and cloned. The use of farm animals as bioreactors may be the better choice to produce recombinant therapeutic proteins. For this activity, the term “pharming” was created, referring to the use of genetic engineering to obtain a transgenic or genetically modified animal. Considering the advantages and disadvantages of livestock species, goats appear as a very good model. In addition, the first human commercially approved biological drug (antithrombin (AT)) was produced from the milk of transgenic goats. The aim of this chapter is to present various reproductive technologies used to obtain transgenic goats secreting recombinant proteins in milk. Initially, this chapter presents the methods for embryo production (in vivo and in vitro) to realize the DNA microinjection in pronuclear embryos. Thus, the techniques of superovulation of donors (in vivo embryo production) and ovarian stimulation for oocyte recovery (in vitro embryo production) are described. Also, the methods for DNA microinjection and embryo transfer are detailed in this chapter. Finally, this chapter describes the reproductive procedures used for obtaining transgenic goats by cloning.",book:{id:"5105",slug:"insights-from-animal-reproduction",title:"Insights from Animal Reproduction",fullTitle:"Insights from Animal Reproduction"},signatures:"Vicente J. F. Freitas, Luciana M. Melo, Dárcio I.A. Teixeira, Maajid H.\nBhat, Irina A. Serova, Lyudmila E. Andreeva and Oleg L. Serov",authors:[{id:"177122",title:"Dr.",name:"Vicente",middleName:null,surname:"Freitas",slug:"vicente-freitas",fullName:"Vicente Freitas"},{id:"177194",title:"Dr.",name:"Luciana",middleName:null,surname:"Melo",slug:"luciana-melo",fullName:"Luciana Melo"},{id:"177195",title:"Dr.",name:"Dárcio",middleName:null,surname:"Teixeira",slug:"darcio-teixeira",fullName:"Dárcio Teixeira"},{id:"177196",title:"Dr.",name:"Maajid",middleName:null,surname:"Bhat",slug:"maajid-bhat",fullName:"Maajid Bhat"},{id:"185365",title:"Dr.",name:"Irina",middleName:null,surname:"Aleksandrovna SEROVA",slug:"irina-aleksandrovna-serova",fullName:"Irina Aleksandrovna SEROVA"},{id:"185366",title:"Dr.",name:"Lyudmila",middleName:null,surname:"Evgenievna ANDREEVA",slug:"lyudmila-evgenievna-andreeva",fullName:"Lyudmila Evgenievna ANDREEVA"},{id:"185367",title:"Dr.",name:"Oleg",middleName:null,surname:"Leonidovich SEROV",slug:"oleg-leonidovich-serov",fullName:"Oleg Leonidovich SEROV"}]},{id:"63404",title:"Subclinical Endometritis in Dairy Cattle",slug:"subclinical-endometritis-in-dairy-cattle",totalDownloads:1824,totalCrossrefCites:2,totalDimensionsCites:6,abstract:"Subclinical endometritis is recognized as a cause of poor reproductive performance in dairy cows. Inflammation of the endometrium persisting after postpartum uterine involution has been related with prolonged calving-conception intervals and low fertility in dairy cows. The subclinical nature of this condition makes it necessary in the use of endometrial cytology or biopsy for diagnosing it. There are some controversies among authors in relation to the postpartum period from which a physiological endometrial inflammation should be considered a pathological subclinical endometritis. Therefore, depending on the sampling period after calving, different studies establish a different degree of polymorphonuclear leukocyte infiltration as cutoff point to diagnose subclinical endometritis. Controversies also exist regarding the pathogenesis of the disease and its consequences on the fertility of dairy cattle. The aim of this chapter was to review the current knowledge on this uterine pathology.",book:{id:"7233",slug:"new-insights-into-theriogenology",title:"New Insights into Theriogenology",fullTitle:"New Insights into Theriogenology"},signatures:"Luis Angel Quintela Arias, Marcos Vigo Fernández, Juan José\nBecerra González, Mónica Barrio López, Pedro José García Herradón\nand Ana Isabel Peña Martínez",authors:[{id:"243272",title:"Prof.",name:"Luis Angel",middleName:null,surname:"Quintela Arias",slug:"luis-angel-quintela-arias",fullName:"Luis Angel Quintela Arias"},{id:"243886",title:"Prof.",name:"Ana Isabel",middleName:null,surname:"Peña Martínez",slug:"ana-isabel-pena-martinez",fullName:"Ana Isabel Peña Martínez"},{id:"243887",title:"Prof.",name:"Pedro",middleName:null,surname:"García Herradón",slug:"pedro-garcia-herradon",fullName:"Pedro García Herradón"},{id:"243888",title:"Prof.",name:"Juan José",middleName:null,surname:"Becerra González",slug:"juan-jose-becerra-gonzalez",fullName:"Juan José Becerra González"},{id:"256852",title:"Dr.",name:"Mónica",middleName:null,surname:"Barrio López",slug:"monica-barrio-lopez",fullName:"Mónica Barrio López"},{id:"256854",title:"Dr.",name:"Marcos",middleName:null,surname:"Vigo Fernández",slug:"marcos-vigo-fernandez",fullName:"Marcos Vigo Fernández"}]},{id:"79909",title:"Cryopreservation Methods and Frontiers in the Art of Freezing Life in Animal Models",slug:"cryopreservation-methods-and-frontiers-in-the-art-of-freezing-life-in-animal-models",totalDownloads:194,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The development in cryobiology in animal breeding had revolutionized the field of reproductive medicine. The main objective to preserve animal germplasm stems from variety of reasons such as conservation of endangered animal species, animal diversity, and an increased demand of animal models and/or genetically modified animals for research involving animal and human diseases. Cryopreservation has emerged as promising technique for fertility preservation and assisted reproduction techniques (ART) for production of animal breeds and genetically engineered animal species for research. Slow rate freezing and rapid freezing/vitrification are the two main methods of cryopreservation. Slow freezing is characterized by the phase transition (liquid turning into solid) when reducing the temperature below freezing point. Vitrification, on the other hand, is a phenomenon in which liquid solidifies without the formation of ice crystals, thus the process is referred to as a glass transition or ice-free cryopreservation. The vitrification protocol applies high concentrations of cryoprotective agents (CPA) used to avoid cryoinjury. This chapter provides a brief overview of fundamentals of cryopreservation and established methods adopted in cryopreservation. Strategies involved in cryopreserving germ cells (sperm and egg freezing) are included in this chapter. Last section describes the frontiers and advancement of cryopreservation in some of the important animal models like rodents (mouse and rats) and in few large animals (sheep, cow etc).",book:{id:"10664",slug:"animal-reproduction",title:"Animal Reproduction",fullTitle:"Animal Reproduction"},signatures:"Feda S. Aljaser",authors:[{id:"439435",title:"Dr.",name:"Feda S.",middleName:null,surname:"Aljaser",slug:"feda-s.-aljaser",fullName:"Feda S. Aljaser"}]}],onlineFirstChaptersFilter:{topicId:"300",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:320,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:17,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"June 25th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. 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Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. 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She is now a lecturer at the University of Witwatersrand, South Africa, and a principal researcher at the Health Economics and Epidemiology Research Office (HE2RO), South Africa. Dr. Moolla holds a Ph.D. in Psychology with her research being focused on mental health and resilience. In her professional work capacity, her research has further expanded into the fields of early childhood development, mental health, the HIV and TB care cascades, as well as COVID. She is also a UNESCO-trained International Bioethics Facilitator.",institutionString:"University of the Witwatersrand",institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"419588",title:"Ph.D.",name:"Sergio",middleName:"Alexandre",surname:"Gehrke",slug:"sergio-gehrke",fullName:"Sergio Gehrke",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038WgMKQA0/Profile_Picture_2022-06-02T11:44:20.jpg",biography:"Dr. Sergio Alexandre Gehrke is a doctorate holder in two fields. The first is a Ph.D. in Cellular and Molecular Biology from the Pontificia Catholic University, Porto Alegre, Brazil, in 2010 and the other is an International Ph.D. in Bioengineering from the Universidad Miguel Hernandez, Elche/Alicante, Spain, obtained in 2020. In 2018, he completed a postdoctoral fellowship in Materials Engineering in the NUCLEMAT of the Pontificia Catholic University, Porto Alegre, Brazil. He is currently the Director of the Postgraduate Program in Implantology of the Bioface/UCAM/PgO (Montevideo, Uruguay), Director of the Cathedra of Biotechnology of the Catholic University of Murcia (Murcia, Spain), an Extraordinary Full Professor of the Catholic University of Murcia (Murcia, Spain) as well as the Director of the private center of research Biotecnos – Technology and Science (Montevideo, Uruguay). Applied biomaterials, cellular and molecular biology, and dental implants are among his research interests. He has published several original papers in renowned journals. In addition, he is also a Collaborating Professor in several Postgraduate programs at different universities all over the world.",institutionString:null,institution:{name:"Universidad Católica San Antonio de Murcia",country:{name:"Spain"}}},{id:"342152",title:"Dr.",name:"Santo",middleName:null,surname:"Grace Umesh",slug:"santo-grace-umesh",fullName:"Santo Grace Umesh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/342152/images/16311_n.jpg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"333647",title:"Dr.",name:"Shreya",middleName:null,surname:"Kishore",slug:"shreya-kishore",fullName:"Shreya Kishore",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333647/images/14701_n.jpg",biography:"Dr. Shreya Kishore completed her Bachelor in Dental Surgery in Chettinad Dental College and Research Institute, Chennai, and her Master of Dental Surgery (Orthodontics) in Saveetha Dental College, Chennai. She is also Invisalign certified. She’s working as a Senior Lecturer in the Department of Orthodontics, SRM Dental College since November 2019. She is actively involved in teaching orthodontics to the undergraduates and the postgraduates. Her clinical research topics include new orthodontic brackets, fixed appliances and TADs. She’s published 4 articles in well renowned indexed journals and has a published patency of her own. Her private practice is currently limited to orthodontics and works as a consultant in various clinics.",institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"323731",title:"Prof.",name:"Deepak M.",middleName:"Macchindra",surname:"Vikhe",slug:"deepak-m.-vikhe",fullName:"Deepak M. Vikhe",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/323731/images/13613_n.jpg",biography:"Dr Deepak M.Vikhe .\n\n\t\n\tDr Deepak M.Vikhe , completed his Masters & PhD in Prosthodontics from Rural Dental College, Loni securing third rank in the Pravara Institute of Medical Sciences Deemed University. He was awarded Dr.G.C.DAS Memorial Award for Research on Implants at 39th IPS conference Dubai (U A E).He has two patents under his name. He has received Dr.Saraswati medal award for best research for implant study in 2017.He has received Fully funded scholarship to Spain ,university of Santiago de Compostela. He has completed fellowship in Implantlogy from Noble Biocare. \nHe has attended various conferences and CDE programmes and has national publications to his credit. His field of interest is in Implant supported prosthesis. Presently he is working as a associate professor in the Dept of Prosthodontics, Rural Dental College, Loni and maintains a successful private practice specialising in Implantology at Rahata.\n\nEmail: drdeepak_mvikhe@yahoo.com..................",institutionString:null,institution:{name:"Pravara Institute of Medical Sciences",country:{name:"India"}}},{id:"204110",title:"Dr.",name:"Ahmed A.",middleName:null,surname:"Madfa",slug:"ahmed-a.-madfa",fullName:"Ahmed A. Madfa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204110/images/system/204110.jpg",biography:"Dr. Madfa is currently Associate Professor of Endodontics at Thamar University and a visiting lecturer at Sana'a University and University of Sciences and Technology. He has more than 6 years of experience in teaching. His research interests include root canal morphology, functionally graded concept, dental biomaterials, epidemiology and dental education, biomimetic restoration, finite element analysis and endodontic regeneration. Dr. Madfa has numerous international publications, full articles, two patents, a book and a book chapter. Furthermore, he won 14 international scientific awards. Furthermore, he is involved in many academic activities ranging from editorial board member, reviewer for many international journals and postgraduate students' supervisor. Besides, I deliver many courses and training workshops at various scientific events. Dr. Madfa also regularly attends international conferences and holds administrative positions (Deputy Dean of the Faculty for Students’ & Academic Affairs and Deputy Head of Research Unit).",institutionString:"Thamar University",institution:null},{id:"210472",title:"Dr.",name:"Nermin",middleName:"Mohammed Ahmed",surname:"Yussif",slug:"nermin-yussif",fullName:"Nermin Yussif",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210472/images/system/210472.jpg",biography:"Dr. Nermin Mohammed Ahmed Yussif is working at the Faculty of dentistry, University for October university for modern sciences and arts (MSA). Her areas of expertise include: periodontology, dental laserology, oral implantology, periodontal plastic surgeries, oral mesotherapy, nutrition, dental pharmacology. She is an editor and reviewer in numerous international journals.",institutionString:"MSA University",institution:null},{id:"204606",title:"Dr.",name:"Serdar",middleName:null,surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204606/images/system/204606.jpeg",biography:"Dr. Serdar Gözler has completed his undergraduate studies at the Marmara University Faculty of Dentistry in 1978, followed by an assistantship in the Prosthesis Department of Dicle University Faculty of Dentistry. Starting his PhD work on non-resilient overdentures with Assoc. Prof. Hüsnü Yavuzyılmaz, he continued his studies with Prof. Dr. Gürbüz Öztürk of Istanbul University Faculty of Dentistry Department of Prosthodontics, this time on Gnatology. He attended training programs on occlusion, neurology, neurophysiology, EMG, radiology and biostatistics. In 1982, he presented his PhD thesis \\Gerber and Lauritzen Occlusion Analysis Techniques: Diagnosis Values,\\ at Istanbul University School of Dentistry, Department of Prosthodontics. As he was also working with Prof. Senih Çalıkkocaoğlu on The Physiology of Chewing at the same time, Gözler has written a chapter in Çalıkkocaoğlu\\'s book \\Complete Prostheses\\ entitled \\The Place of Neuromuscular Mechanism in Prosthetic Dentistry.\\ The book was published five times since by the Istanbul University Publications. Having presented in various conferences about occlusion analysis until 1998, Dr. Gözler has also decided to use the T-Scan II occlusion analysis method. Having been personally trained by Dr. Robert Kerstein on this method, Dr. Gözler has been lecturing on the T-Scan Occlusion Analysis Method in conferences both in Turkey and abroad. Dr. Gözler has various articles and presentations on Digital Occlusion Analysis methods. He is now Head of the TMD Clinic at Prosthodontic Department of Faculty of Dentistry , Istanbul Aydın University , Turkey.",institutionString:"Istanbul Aydin University",institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"240870",title:"Ph.D.",name:"Alaa Eddin Omar",middleName:null,surname:"Al Ostwani",slug:"alaa-eddin-omar-al-ostwani",fullName:"Alaa Eddin Omar Al Ostwani",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/240870/images/system/240870.jpeg",biography:"Dr. Al Ostwani Alaa Eddin Omar received his Master in dentistry from Damascus University in 2010, and his Ph.D. in Pediatric Dentistry from Damascus University in 2014. Dr. Al Ostwani is an assistant professor and faculty member at IUST University since 2014. \nDuring his academic experience, he has received several awards including the scientific research award from the Union of Arab Universities, the Syrian gold medal and the international gold medal for invention and creativity. Dr. Al Ostwani is a Member of the International Association of Dental Traumatology and the Syrian Society for Research and Preventive Dentistry since 2017. He is also a Member of the Reviewer Board of International Journal of Dental Medicine (IJDM), and the Indian Journal of Conservative and Endodontics since 2016.",institutionString:"International University for Science and Technology.",institution:{name:"Islamic University of Science and Technology",country:{name:"India"}}},{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. After graduating from TED Ankara College in 1994, she attended to Gazi University, Faculty of Dentistry in Ankara. She completed her PhD in orthodontic education at Gazi University between 1999-2005. Dr. Işık Aslan stayed at the Providence Hospital Craniofacial Institude and Reconstructive Surgery in Michigan, USA for three months as an observer. She worked as a specialist doctor at Gazi University, Dentistry Faculty, Department of Orthodontics between 2005-2014. She was appointed as associate professor in January, 2014 and as professor in 2021. Dr. Işık Aslan still works as an instructor at the same faculty. She has published a total of 35 articles, 10 book chapters, 39 conference proceedings both internationally and nationally. Also she was the academic editor of the international book 'Current Advances in Orthodontics'. She is a member of the Turkish Orthodontic Society and Turkish Cleft Lip and Palate Society. She is married and has 2 children. Her knowledge of English is at an advanced level.",institutionString:"Gazi University Dentistry Faculty Department of Orthodontics",institution:null},{id:"178412",title:"Associate Prof.",name:"Guhan",middleName:null,surname:"Dergin",slug:"guhan-dergin",fullName:"Guhan Dergin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178412/images/6954_n.jpg",biography:"Assoc. Prof. Dr. Gühan Dergin was born in 1973 in Izmit. He graduated from Marmara University Faculty of Dentistry in 1999. He completed his specialty of OMFS surgery in Marmara University Faculty of Dentistry and obtained his PhD degree in 2006. In 2005, he was invited as a visiting doctor in the Oral and Maxillofacial Surgery Department of the University of North Carolina, USA, where he went on a scholarship. Dr. Dergin still continues his academic career as an associate professor in Marmara University Faculty of Dentistry. He has many articles in international and national scientific journals and chapters in books.",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"178414",title:"Prof.",name:"Yusuf",middleName:null,surname:"Emes",slug:"yusuf-emes",fullName:"Yusuf Emes",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178414/images/6953_n.jpg",biography:"Born in Istanbul in 1974, Dr. Emes graduated from Istanbul University Faculty of Dentistry in 1997 and completed his PhD degree in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery in 2005. He has papers published in international and national scientific journals, including research articles on implantology, oroantral fistulas, odontogenic cysts, and temporomandibular disorders. Dr. Emes is currently working as a full-time academic staff in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery.",institutionString:null,institution:{name:"Istanbul University",country:{name:"Turkey"}}},{id:"192229",title:"Ph.D.",name:"Ana Luiza",middleName:null,surname:"De Carvalho Felippini",slug:"ana-luiza-de-carvalho-felippini",fullName:"Ana Luiza De Carvalho Felippini",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192229/images/system/192229.jpg",biography:null,institutionString:"University of São Paulo",institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"256851",title:"Prof.",name:"Ayşe",middleName:null,surname:"Gülşen",slug:"ayse-gulsen",fullName:"Ayşe Gülşen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256851/images/9696_n.jpg",biography:"Dr. Ayşe Gülşen graduated in 1990 from Faculty of Dentistry, University of Ankara and did a postgraduate program at University of Gazi. \nShe worked as an observer and research assistant in Craniofacial Surgery Departments in New York, Providence Hospital in Michigan and Chang Gung Memorial Hospital in Taiwan. \nShe works as Craniofacial Orthodontist in Department of Aesthetic, Plastic and Reconstructive Surgery, Faculty of Medicine, University of Gazi, Ankara Turkey since 2004.",institutionString:"Univeristy of Gazi",institution:null},{id:"255366",title:"Prof.",name:"Tosun",middleName:null,surname:"Tosun",slug:"tosun-tosun",fullName:"Tosun Tosun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255366/images/7347_n.jpg",biography:"Graduated at the Faculty of Dentistry, University of Istanbul, Turkey in 1989;\nVisitor Assistant at the University of Padua, Italy and Branemark Osseointegration Center of Treviso, Italy between 1993-94;\nPhD thesis on oral implantology in University of Istanbul and was awarded the academic title “Dr.med.dent.”, 1997;\nHe was awarded the academic title “Doç.Dr.” (Associated Professor) in 2003;\nProficiency in Botulinum Toxin Applications, Reading-UK in 2009;\nMastership, RWTH Certificate in Laser Therapy in Dentistry, AALZ-Aachen University, Germany 2009-11;\nMaster of Science (MSc) in Laser Dentistry, University of Genoa, Italy 2013-14.\n\nDr.Tosun worked as Research Assistant in the Department of Oral Implantology, Faculty of Dentistry, University of Istanbul between 1990-2002. \nHe worked part-time as Consultant surgeon in Harvard Medical International Hospitals and John Hopkins Medicine, Istanbul between years 2007-09.\u2028He was contract Professor in the Department of Surgical and Diagnostic Sciences (DI.S.C.), Medical School, University of Genova, Italy between years 2011-16. \nSince 2015 he is visiting Professor at Medical School, University of Plovdiv, Bulgaria. \nCurrently he is Associated Prof.Dr. at the Dental School, Oral Surgery Dept., Istanbul Aydin University and since 2003 he works in his own private clinic in Istanbul, Turkey.\u2028\nDr.Tosun is reviewer in journal ‘Laser in Medical Sciences’, reviewer in journal ‘Folia Medica\\', a Fellow of the International Team for Implantology, Clinical Lecturer of DGZI German Association of Oral Implantology, Expert Lecturer of Laser&Health Academy, Country Representative of World Federation for Laser Dentistry, member of European Federation of Periodontology, member of Academy of Laser Dentistry. Dr.Tosun presents papers in international and national congresses and has scientific publications in international and national journals. He speaks english, spanish, italian and french.",institutionString:null,institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"260116",title:"Dr.",name:"Mehmet",middleName:null,surname:"Yaltirik",slug:"mehmet-yaltirik",fullName:"Mehmet Yaltirik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/260116/images/7413_n.jpg",biography:"Birth Date 25.09.1965\r\nBirth Place Adana- Turkey\r\nSex Male\r\nMarrial Status Bachelor\r\nDriving License Acquired\r\nMother Tongue Turkish\r\n\r\nAddress:\r\nWork:University of Istanbul,Faculty of Dentistry, Department of Oral Surgery and Oral Medicine 34093 Capa,Istanbul- TURKIYE",institutionString:null,institution:{name:"Istanbul University",country:{name:"Turkey"}}},{id:"171887",title:"Prof.",name:"Zühre",middleName:null,surname:"Akarslan",slug:"zuhre-akarslan",fullName:"Zühre Akarslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/171887/images/system/171887.jpg",biography:"Zühre Akarslan was born in 1977 in Cyprus. She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"256417",title:"Associate Prof.",name:"Sanaz",middleName:null,surname:"Sadry",slug:"sanaz-sadry",fullName:"Sanaz Sadry",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256417/images/8106_n.jpg",biography:null,institutionString:null,institution:null},{id:"272237",title:"Dr.",name:"Pinar",middleName:"Kiymet",surname:"Karataban",slug:"pinar-karataban",fullName:"Pinar Karataban",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/272237/images/8911_n.png",biography:"Assist.Prof.Dr.Pınar Kıymet Karataban, DDS PhD \n\nDr.Pınar Kıymet Karataban was born in Istanbul in 1975. After her graduation from Marmara University Faculty of Dentistry in 1998 she started her PhD in Paediatric Dentistry focused on children with special needs; mainly children with Cerebral Palsy. She finished her pHD thesis entitled \\'Investigation of occlusion via cast analysis and evaluation of dental caries prevalance, periodontal status and muscle dysfunctions in children with cerebral palsy” in 2008. She got her Assist. Proffessor degree in Istanbul Aydın University Paediatric Dentistry Department in 2015-2018. ın 2019 she started her new career in Bahcesehir University, Istanbul as Head of Department of Pediatric Dentistry. In 2020 she was accepted to BAU International University, Batumi as Professor of Pediatric Dentistry. She’s a lecturer in the same university meanwhile working part-time in private practice in Ege Dental Studio (https://www.egedisklinigi.com/) a multidisciplinary dental clinic in Istanbul. Her main interests are paleodontology, ancient and contemporary dentistry, oral microbiology, cerebral palsy and special care dentistry. She has national and international publications, scientific reports and is a member of IAPO (International Association for Paleodontology), IADH (International Association of Disability and Oral Health) and EAPD (European Association of Pediatric Dentistry).",institutionString:null,institution:null},{id:"202198",title:"Dr.",name:"Buket",middleName:null,surname:"Aybar",slug:"buket-aybar",fullName:"Buket Aybar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202198/images/6955_n.jpg",biography:"Buket Aybar, DDS, PhD, was born in 1971. She graduated from Istanbul University, Faculty of Dentistry, in 1992 and completed her PhD degree on Oral and Maxillofacial Surgery in Istanbul University in 1997.\nDr. Aybar is currently a full-time professor in Istanbul University, Faculty of Dentistry Department of Oral and Maxillofacial Surgery. She has teaching responsibilities in graduate and postgraduate programs. Her clinical practice includes mainly dentoalveolar surgery.\nHer topics of interest are biomaterials science and cell culture studies. She has many articles in international and national scientific journals and chapters in books; she also has participated in several scientific projects supported by Istanbul University Research fund.",institutionString:null,institution:null},{id:"172009",title:"Dr.",name:"Fatma Deniz",middleName:null,surname:"Uzuner",slug:"fatma-deniz-uzuner",fullName:"Fatma Deniz Uzuner",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/172009/images/7122_n.jpg",biography:"Dr. Deniz Uzuner was born in 1969 in Kocaeli-TURKEY. After graduating from TED Ankara College in 1986, she attended the Hacettepe University, Faculty of Dentistry in Ankara. \nIn 1993 she attended the Gazi University, Faculty of Dentistry, Department of Orthodontics for her PhD education. After finishing the PhD education, she worked as orthodontist in Ankara Dental Hospital under the Turkish Government, Ministry of Health and in a special Orthodontic Clinic till 2011. Between 2011 and 2016, Dr. Deniz Uzuner worked as a specialist in the Department of Orthodontics, Faculty of Dentistry, Gazi University in Ankara/Turkey. In 2016, she was appointed associate professor. Dr. Deniz Uzuner has authored 23 Journal Papers, 3 Book Chapters and has had 39 oral/poster presentations. She is a member of the Turkish Orthodontic Society. Her knowledge of English is at an advanced level.",institutionString:null,institution:null},{id:"332914",title:"Dr.",name:"Muhammad Saad",middleName:null,surname:"Shaikh",slug:"muhammad-saad-shaikh",fullName:"Muhammad Saad Shaikh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jinnah Sindh Medical University",country:{name:"Pakistan"}}},{id:"315775",title:"Dr.",name:"Feng",middleName:null,surname:"Luo",slug:"feng-luo",fullName:"Feng Luo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sichuan University",country:{name:"China"}}},{id:"423519",title:"Dr.",name:"Sizakele",middleName:null,surname:"Ngwenya",slug:"sizakele-ngwenya",fullName:"Sizakele Ngwenya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"419270",title:"Dr.",name:"Ann",middleName:null,surname:"Chianchitlert",slug:"ann-chianchitlert",fullName:"Ann Chianchitlert",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419271",title:"Dr.",name:"Diane",middleName:null,surname:"Selvido",slug:"diane-selvido",fullName:"Diane Selvido",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419272",title:"Dr.",name:"Irin",middleName:null,surname:"Sirisoontorn",slug:"irin-sirisoontorn",fullName:"Irin Sirisoontorn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"355660",title:"Dr.",name:"Anitha",middleName:null,surname:"Mani",slug:"anitha-mani",fullName:"Anitha Mani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"355612",title:"Dr.",name:"Janani",middleName:null,surname:"Karthikeyan",slug:"janani-karthikeyan",fullName:"Janani Karthikeyan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"334400",title:"Dr.",name:"Suvetha",middleName:null,surname:"Siva",slug:"suvetha-siva",fullName:"Suvetha Siva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}}]}},subseries:{item:{id:"18",type:"subseries",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11414,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,series:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983"},editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",slug:"arli-aditya-parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",slug:"cesar-lopez-camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",slug:"shymaa-enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]},onlineFirstChapters:{paginationCount:17,paginationItems:[{id:"82184",title:"Biological Sensing Using Infrared SPR Devices Based on ZnO",doi:"10.5772/intechopen.104562",signatures:"Hiroaki Matsui",slug:"biological-sensing-using-infrared-spr-devices-based-on-zno",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:[{name:"Hiroaki",surname:"Matsui"}],book:{title:"Biosignal Processing",coverURL:"https://cdn.intechopen.com/books/images_new/11153.jpg",subseries:{id:"7",title:"Bioinformatics and Medical Informatics"}}},{id:"82122",title:"Recent Advances in Biosensing in Tissue Engineering and Regenerative Medicine",doi:"10.5772/intechopen.104922",signatures:"Alma T. 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