Comparison between SiamMask and the developed method (incorporating low-resolution data during training), under the VOT metric (EAO, Accuracy, Robustness) on VOT2018 (left) and VOT2019 (right), best results are marked in Bold.
\r\n\tIn this book the authors will provide complete introduction of Polymers chemistry. The book is mainly divided into three parts. The readers will learn about the basic introduction of general polymer chemistry in the first part of the book.
\r\n\tThe second part of the book starts with a chapter which includes kinetics of polymerization. Polymer weight determination, molecular weight distribution curve and determination of glass transition temperature. The final part of the book deals polymer degradation which includes types of degradation. The chapters of the present book consist of both tutorial and highly advanced material.
Visual object tracking (VOT), commonly referred to as target tracking, is an open problem in computer vision; this is due to a broad range of possible applications and potential tracking challenges. Thus, it has been divided into sub-challenges according to several factors, which include: the number of targets of interest, the number of cameras, the type of data (i.e., medical, depth, thermal, or RGB images), static or moving camera, offline or online (real-time) processing.
Visual object tracking is the process of estimating and locating a target over time in a video sequence and assigning a consistent label to the tracked object across each video sequence frame. VOT algorithms have been utilized as a building block in more complex applications of computer vision such as traffic flow monitoring [1], human-machine interaction [2], medical systems [3], intelligent cinematography [4], automated surveillance [5], autonomous social navigation [6] and activity recognition [7]. Real-time visual target tracking is the process of locating and associating the target of interest in consecutive video frames while the action is taking place in real-time. Real-time visual target tracking plays an inevitable role in time-sensitive applications such as autonomous mobile robot control to keep track of the target of interest while the viewpoint is changing due to the movement of the target or the robot. In such a scenario, the tracking algorithm must be accurate and fast enough to detect sudden changes in the observed environment and act accordingly to prevent losing track of the quickly moving target of interest.
Since the start of the Visual-Object-Tracking(VOT) Real-time challenge in 2017, Siamese network-based tracking algorithms have achieved top performance and won in the VOT real-time challenge with a considerable margin over the rest of the trackers. Nearly all top ten trackers applied the siamese network, and also the winners. The dominant methodology in real-time tracking, therefore, appears to be associated. A siamese network aims to learn a similarity function. It has a Y-shaped network architecture that takes two input images and returns similarity as an output. Siamese networks are utilized to compare the similarity between the template and the candidate images to determine if the two input images have an identical pattern(similarity). In the past few years, a series of state-of-the-art siamese-based trackers have been proposed, and all of them utilize embedded features by employing CNN to compute similarity and produce various types of output, such as similarity score(probability measure), response map(two-dimensional similarity score map), and bounding box location of the target.
Luca Bertinetto et al. [8] proposed Siamese fully convolutional network (SiameseFC) to addresses the broad similarity learning between a target image and search image, as presented in Figure 1. According to the VOT winner rules, the winning real-time tracker of the VOT2017 [9] was SiamFC. SiamFC applies a fully-convolutional siamese network trained offline to locate an exemplar (template) image inside a larger search image Figure 1. The network is fully convolutional w.r.t search image: dense and efficient sliding window evaluation is achieved with a bilinear layer that computes the cross-correlation of two inputs. The deep convolutional network is trained offline with” ILSVRC VID” dataset [10] to address a general similarity learning problem and maximize target discrimination power. During tracking, SiamFC takes two images and infers a response map using the learned similarity function. The new target position is determined at the maximum value on the response map, where it depicts a maximum similarity Figure 1. As improvement in Siamese based tracking methods, Qiang Wang et al. [11] proposed SiamMask aiming to improve the ability of the SiamFC network to differentiate between the background and the foreground by augmenting their loss with a binary segmentation task. SiamMask is a depth-wise cross-correlation operation performed on a channel-by-channel basis, to keep the number of channels unchanged. The result of the depth-with cross-correlation indicated as RoW (response of candidate window), then distributed into three branches, respectively segmentation, regression, and classification branches Figure 2.
Fully-convolutional Siamese architecture. The output is a scalar-valued score map whose dimension depends on the size of the search image [
An illustration of SiamMask with three branches, respectively segmentation, regression, and classification branches; where
Seven of the top ten realtime trackers (SiamMargin [12], SiamDWST [13], SiamMask [11], SiamRPNpp [14], SPM [15] and SiamCRF-RT) are based on siamese correlation combined with bounding box regression. In contrast, the top performers of the VOT2019 Real-time challenge are from the class of classical siamese correlation trackers, and siamese trackers with region proposals [16]. Although these methods showed a significant improvement, there was small attention on how to carefully update the template of the target as time goes from the start of the tracking. In all top performers, the target template is initialized in the first frame and then kept fixed during the tracking process. However, diverse variations regarding the target usually occur in the process of tracking, i.e., camera orientation, illumination change, self-rotation, self-deformation, scale, and appearance change. Thus, failing to update the target template leads to the early failure of the tracker. In such scenarios, it is crucial to adapt the target template model to the current target appearance. In addition to this, most of the tracking methods fail when motion-blurred frames or frames with low-resolution appear in the video sequence, as depicted in Figures 3 and 4. We believe that this case arguably arises from the complete lack of similar training samples. Therefore one must incorporate a data-augmentation strategy to consider both motion-blur and low-resolution during training to significantly increase the diversity of datasets available for training without actually gathering new data.
An example of SiamMask failure due to motion-blur, green and yellow bounding box indicates ground truth and predicted target respectively.
An example of SiamMask failure due to low resolution, green and yellow bounding box indicates ground truth and predicted target respectively.
The problem of establishing a correspondence between a single target in consecutive frames can be affected by factors such as initializing a track, updating it robustly, and ending the track. The tracking algorithm receives an input frame from the camera module and performs the visual tracking over a frame following a siamese network-based tracking approach. Since developing a new tracking algorithm from scratch is beyond the scope of this chapter, a state-of-the-art siamese-based tracking algorithm called siammask [11], one of the top performers in the VOT2019 real-time challenge, is used as a backbone of our tracking algorithm.
To mitigate the limitations associated with Siamese-based tracking methods. This section presents two improvements on top of the SiamMask implementation.
As mentioned in the introduction, the siamese-based tracker fails when motion-blurred frames or frames with low-resolution appear in the video sequence, as depicted in Figures 3 and 4. Therefore to address the problems, a tracking algorithm should incorporate a data-augmentation strategy to consider both motion-blur and low-resolution during training. Since data augmentation is a strategy that significantly increases the diversity of datasets available for training without actually gathering new data, it will require implementing the data augmentation techniques explained through the following sub-sections.
Kernel filters are a prevalent technique in image processing to blur images. These filters work by sliding an
An example of motion blurred frame (left image) generated from original frame (right image) using the developed data-augmentation for motion-blur technique.
We followed a Zhangyang Wang et al. [18] approach to generate a low-resolution dataset. During training, the original (High Resolution) images are first downscaled by
An illustrates on how the low-resolution data augmentation generation are performed (from (a) to (c)).
The target template update mechanism is an essential step, and its robustness has become a crucial factor influencing the quality of the tracking algorithm. To tackle this problem, more recent Siamese trackers [19, 20, 21] have implemented a simple linear update strategy using a running average with a constant learning rate. However, A simple linear update is often inadequate to cope with the changes needed and to generalize to all potentially encountered circumstances. Lichao Zhang et al. [22] proposes to replace the hand-crafted update function with a method that learns to update, using a convolutional neural network called
One can argue the importance of the original initial and supplementary updatable templates, which incorporate the up-to-date target information. To this end, we have incorporated a template updates strategy that utilizes both the initial template (ground truth template)
Overview on how the target similarity score (red) varies under different occlusion scenario during tracking process. The similarity score is indicated in red color in the top left of each frame, VOT2019 road dataset. Where blue: Ground truth, red: Tracking result.
Overview on how the target similarity score varies under different occlusion scenario during tracking process, VOT2019 girl dataset. Where blue: Ground truth, red: Tracking result.
Target template update strategy: Where T_G is the ground truth template, T_i is an updatable template,
In 2.2 the target template update strategy considers the target appearance only from the previous frame. However, in this section, we introduce an alternative template update strategy that considers both the target appearance from the previous frame and the target appearance in the future frame, which incorporates future information of the target appearance by updating the updatable template T_i described in 2.2. The template updating mechanism is shown in Figure 10. During online tracking, the template updating and the tracking procedure works as follows:
Tracking procedure on the next frame
Updatable template T_i is updated using the predicted target from the next frame to incorporate a piece of future information about the target.
Tracking procedure again on the current frame is applied using both the updated future target template T_i + 1 and the ground truth target template T_G.
Updating with a previous and a future template, where T_G is ground truth target template, T_i is previous target template and T_i + 1 is future target template. Where green: Ground truth template, yellow: Updatable template, blue: Tracking result.
First, a tracking procedure is applied using both the previous target template in T_i and the ground truth template T_G to perform tracking on the next frame. Then the updatable template T_i is updated using the predicted target on the next frame incorporating a piece of future information about the target. Finally, a tracking procedure is again applied to the current frame using both the updated future target template T_i + 1 and the ground truth template T_G.
The SiamMask implementation was trained using 4 Tesla V100 GPUs. In this experiment, only the refinement module of the mask branch is trained. The training process was carried out using COCO3 and Youtube-vos4 Datasets: The training was performed over ten epochs using mini-batches of 32 samples. The data augmentation techniques described in 2.1.1 and 2.1.2 were utilized for generating datasets with motion-blur and low-resolution, respectively.
During tracking, the tracking algorithm is evaluated once per frame. The output mask is selected from the location attaining the maximum score in the classification branch and creating an optimized bounding box. Finally, the highest scoring output of the box branch is used as a reference to crop the next search frame.
As object tracking has gotten significant attention in the last few decades, the number of publications on tracking-related problems has made it difficult to follow the developments in the field. One of the main reasons is that there was a lack of commonly accepted annotated datasets and standardized evaluation protocols that allowed an objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in association with ICCV20135. Researchers from the industry and academia were invited to participate in the first VOT2013 challenge, which was aimed at model-free single-object visual trackers. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual properties such as occlusion, motion change, illumination change, scale, and camera motion, offering a more systematic comparison of the trackers [23]. VOT focused on short-term tracking (no re-detection) until the VOT2017 challenge, where a new”real-time challenge” was introduced. In the Real-time challenge, the tracker constantly receives images at real-time speed. If the tracker does not respond after the new frame becomes available, the last bounding box from the previous frame is reported as the tracking result in the current frame.
The VOT challenges applies a reset-based methodology. Whenever a zero overlap between the predicted bounding box and the ground truth occurs, a failure is detected, and the tracker is re-initialized five frames after the failure. There are three primary metrics used to analyze the tracking performance in visual object tracking challenge benchmark: Accuracy (A), Robustness (R), and Expected Average Overlap (EAO) [9].
Accuracy is calculated as the average overlap between the predicted and ground truth bounding boxes during successful tracking periods [23]. The tracking accuracy at time-step t is defined as the overlap between the tracker predicted bounding box
Robustness measures how often the tracker loses/fails the target, i.e., a zero overlap between the predicted and the ground truth bounding boxes during tracking. The protocol specifies an overlap threshold to determine tracking failure. The number of failed tracked frames are then divided by the total number of frames, as depicted in Eq. (2):
Where
For the purpose of ranking tracking algorithms, it is better to have a single metric. Thus, in 2015 the VOT challenge introduced Expected Average Overlap (EAO), which combines both Accuracy and Robustness. EAO estimates the average overlap that a tracker is expected to achieve on a large collection of short-term sequences with the same visual properties as the given dataset.
The EAO metric can be found by calculating the average of
This first experiment is dedicated to evaluating the impact of the low-resolution data-augmentation technique. The data augmentation technique described in 2.1.2 was applied to generate datasets with low-resolution during the training process of the refinement module of the network.
The performance of the developed method: incorporating low-resolution datasets using data augmentation technique during training has been evaluated using the VOT evaluation metrics on the VOT2018, VOT2019 datasets. The overall Evaluation results are shown in Table 1.
VOT2018 | VOT2019 | ||||
---|---|---|---|---|---|
VOT Metrics | SiamMask | Ours | SiamMask | Ours | |
EAO | 0.380 | 0.280 | |||
Accuracy | 0.589 | 0.586 | |||
Robustness | 0.279 | 0.522 |
Comparison between SiamMask and the developed method (incorporating low-resolution data during training), under the VOT metric (EAO, Accuracy, Robustness) on VOT2018 (left) and VOT2019 (right), best results are marked in Bold.
The term
In Table 1, we compare our approach against the state-of-the-art SiamMask tracker on the VOT2018 and VOT2019 benchmarks, respectively. It can be clearly observed that the data augmentation technique for incorporating low-resolution datasets has contributed to robustness improvements. The tracker’s failure has decreased from 60 to 53 and from 104 to 93 in VOT2018 and VOT2019, respectively. Improvements are clearly shown especially in a video sequence with low-resolution, i.e.
Qualitative comparison between SiamMask and developed data-augmentation technique for incorporating low-resolution datasets during training. Where blue: Ground truth, red: Tracking result.
The results obtained in Table 1 confirm that the developed methodology significantly improved the overall performance of the tracker. This approach outperforms the original SiamMask achieving a relative gain of 2.6% and 0.4% in EAO on VOT2018 and VOT2019, respectively. Most significantly, a gain of around 3
As it is depicted in Figure 11, The data-augmentation for incorporating low-resolution datasets during training has contributed to enhancing the tracker robustness. Thus, the tracker becomes robust against low-resolution frames during inference in relative to the original SiamMask tracker.
In this experiment, the data-augmentation technique for incorporating motion-blurred datasets described in 2.1.1 was applied for generating datasets with motion-blur during the training process of the refinement module of the network.
The performance of the tracking algorithm incorporating the motion-blur data augmentation technique has been evaluated using the VOT evaluation metrics on the VOT2018, VOT2019 datasets. The Overall Evaluation results are shown in Table 2.
VOT2018 | VOT2019 | ||||
---|---|---|---|---|---|
VOT Metrics | SiamMask | Ours | SiamMask | Ours | |
EAO | 0.380 | 0.280 | |||
Accuracy | 0.609 | 0.610 | 0.610 | ||
Robustness | 0.279 | 0.522 |
Comparison between SiamMask and the developed method (incorporating motion-blurred dataset during training), under the VOT metric (EAO, accuracy, robustness) on VOT2018 (left) and VOT2019 (right).
The data augmentation technique for incorporating motion-blurred datasets has contributed to the overall enhancement of the tracker performance. They are clear improvements in terms of Robustness in multiple video sequences relative to SiamMask. From Table 2, it can be concluded that the data augmentation technique for incorporating motion-blurred datasets has contributed to the improvement in Robustness of the tracker, especially in a video sequence with a motion-blur, i.e.,
Figure 12 presents a visual comparison between SiamMask and the developed improvement incorporating motion-blurred datasets during training using data-augmentation. From Figure 12 it can be clearly observed that the data-augmentation for incorporating motion-blurred dataset during training has contributed to enhancing the tracker Robustness. Thus, the tracker has become robust against motion-blurred video frames during inference in relative to the original SiamMask tracker.
Qualitative comparison between SiamMask and developed data-augmentation technique: Incorporating motion-blurred datasets during training. Where blue: Ground truth, red: Tracking result.
When it comes to updating the target template, the question is how and when to update the target. The parameter
This set of experiments compares the effect of the target template updating strategy by varying the score threshold of
VOT-Metrics | |||||
---|---|---|---|---|---|
S | EAO | Accuracy | Robustness | FPS | |
0.65 | 0.377 | 0.602 | 0.267 | 57 | 25 |
0.7 | 0.371 | 0.602 | 0.267 | 57 | 27 |
0.75 | 0.385 | 0.600 | 0.248 | 53 | 28 |
0.8 | 0.387 | 0.603 | 0.258 | 55 | 31 |
0.85 | 0.388 | 0.243 | 52 | 32 | |
0.9 | 0.393 | 0.602 | 0.239 | 51 | 35 |
0.95 | 0.602 |
Determining the optimal score threshold (
VOT-metrics | |||||
---|---|---|---|---|---|
S | EAO | Accuracy | Robustness | FPS | |
0.65 | 0.276 | 0.598 | 0.497 | 99 | 25 |
0.7 | 0.278 | 0.601 | 0.497 | 99 | 26 |
0.75 | 0.278 | 0.601 | 0.497 | 99 | 27 |
0.8 | 0.278 | 0.601 | 0.497 | 99 | 27 |
0.85 | 0.274 | 0.601 | 0.512 | 102 | 32 |
0.9 | 0.278 | 0.600 | 0.512 | 102 | 36 |
0.95 |
Determining the optimal score threshold (
An illustration on the effect of the tracking performance, with a template update strategy by varying the score threshold
Visual illustration on how the target template update strategy decides whether to update the template or not based on the similarity score under different occlusion scenario during tracking process, VOT2019 girl dataset. Where blue: Ground truth, red: Tracking result.
Visual illustration on how the target template update strategy decides whether to update the template or not based on the similarity score under different occlusion scenario during tracking process, VOT2019 girl dataset. Where blue: Ground truth, red: Tracking result.
Figures 14 and 15 are an illustration of how the template update strategy decides when to update the updatable template. For instance in Figure 15a the target is not occluded; as a result the score is high, thus
Table 5 presents a comparison between no-update SiamMask and incorporating the developed template update strategy: it can be observed that a relative gain of 0.7% and 2.0% in Robustness has been achieved by incorporating template update strategy. Thus, the tracker has encountered less failure than the no-update SiamMask, decreasing from 60 to 58 and 104 to 100 in VOT2018 and VOT2019 benchmarks, respectively. The robustness of the tracker is the crucial element for applications such as automatic robotic cameras where there is no human assistance.
VOT2018 | VOT2019 | ||||
---|---|---|---|---|---|
SiamMask | Ours | SiamMask | Ours | ||
EAO | 0.380 | 0.351 | 0.280 | 0.268 | |
Accuracy | 0.609 | 0.593 | 0.610 | 0.593 | |
Robustness | 0.279 | 0.522 | |||
# Lost | 60 | 104 | |||
FPS | 44 | 40 | 44 | 40 |
Comparison between no-update SiamMask and incorporating target template update under VOT2018 (left) and VOT2019 (right) benchmarks.
This experiment dedicated to examine the strength and weakness of the” updating with a previous and future frame” template update strategy described in 2.2.1. As can be seen from Table 6, the method”updating with previous and a future template” has achieved a relative gain of around 0.7% and 2.5% in Robustness value w.r.t SiamMask in both VOT2018 and VOT2019 benchmark, respectively. This indicates that the”Updating with Previous and Future template” strategy has enhanced the tracker’s Robustness, which is the most crucial in automated tracking applications. However, this can not be used for real-time applications as the processing speed is very slow, around 12 FPS on a laptop equipped with NVIDIA GEFORCE GTX1060. The main computational burden on the tracker is related to the target template feature extraction network. Thus, the tracking algorithm processing speed becomes very slow when the target template is updated with the previous and future template, resulting in a poor FPS.
VOT2018 | VOT2019 | ||||
---|---|---|---|---|---|
SiamMask | Ours | SiamMask | Ours | ||
EAO | 0.380 | 0.357 | 0.280 | 0.274 | |
Accuracy | 0.609 | 0.597 | 0.610 | 0.597 | |
Robustness | 0.279 | 0.522 | |||
# Lost | 60 | 104 | |||
FPS | 12 | 12 |
Comparison between no-update SiamMask and incorporating target template updating with previous and future template on VOT2018 (left) and VOT2019 (right) benchmark.
This section compares our tracking framework called VPU_SiamM with other state-of-the-art trackers SiamRPN, SiamMask in the VOT2018 and SiamRPN++, SiamMask in VOT2019.
To take advantage of the incorporated improvements, a tracker named VPU_SiamM has been developed. VPU_SiamM has been trained based on the data augmentation technique incorporating both motion-blur and low-resolution, and during online inference, a target template update strategy is applied.
We have tested our VPU_SiamM tracker on the VOT2018 dataset in comparison with state-of-the-art methods. We compare with the top trackers SiamRPN (winner of the VOT2018 real-time challenge) and SiamMask among the top performer in the VOT2019 challenge. Our tracker obtained a significant relative gain of
VOT2018 | |||
---|---|---|---|
Tracker | EAO | Accuracy | Robustness |
SiamRPN [21] | 0.383 | 0.586 | 0.276 |
SiamMask [11] | 0.38 | 0.609 | 0.279 |
VPU_SiamM | 0.602 |
Comparison of our tracker VPU_SiamM with the state-of-the-art trackers SiamRPN and SiamMask in terms of expected average overlap (EAO), accuracy, and robustness (failure rate) on the VOT2018 benchmark.
Following previous VOT evaluation, we have evaluated our VPU_SiamM tracker on VOT2019 datasets, which contains 60 challenging testing sequences. As shown in Table 8, our VPU_SiamM also achieves the best tracking results on VOT2019 in EAO and Accuracy metrics compared to state-of-the-art trackers SiamMask and SiamRPN++. More specifically, our approach improves the EAO by around 1%.
VOT2019 | |||
---|---|---|---|
Tracker | EAO | Accuracy | Robustness |
SiamRPN++ [14] | 0.282 | 0.598 | 0.482 |
SiamMask [11] | 0.287 | 0.594 | |
VPU_SiamM | 0.482 |
Comparison of our tracker VPU_SiamM with the state-of-the-art trackers SiamRPN++ and SiamMask in terms of expected average overlap (EAO), accuracy, and robustness (failure rate) on the VOT2019 benchmark.
In this chapter, one of the state-of-the-art tracking algorithms based on siamese networks called SiamMask has been used as a backbone, and two improvements have been affixed, each addressing different aspects of the tracking task.
The developed data augmentation technique for incorporating low-resolution and motion-blur has been evaluated separately and jointly, achieving state-of-the-art results in the VOT2018 and VOT2019 benchmarks. From the evaluation results, it is clear to conclude that the data augmentation technique has played an essential role in improving the overall performance of the tracking algorithm. It has outperformed the SiamMask results in both VOT2018 and VOT2019 benchmarks. In contrast, among the three data augmentation techniques, the data augmentation technique for incorporating both motion-blur and low-resolution outperforms the rest in terms of EAO in VOT2018 and VOT2019 benchmarks. Nevertheless, the data-augmentation for incorporating only motion-blur has achieved a top performance according to the Accuracy metric in both VOT2018 and VOT2019 benchmarks. However, the Accuracy is less significant as it only considers the IOU during a successful tracking. According to the VOT ranking method, the EAO value is used to rank tracking methods. Therefore the data augmentation technique for incorporating both motion-blur and low-resolution is ranked top among the others. This indicates that the data-augmentation technique has contributed to the improvement of the overall tracker performance.
Comparable results on VOT2018 and VOT2019 benchmarks confirm that the robust target template update strategy that utilizes both the initial ground truth template and a supplementary updatable template and avoiding template updates during severe occlusion can significantly improve the tracker’s performance with respect to SiamMask results while running at 41 FPS.
A tracker named VPU_SiamM was trained based on the presented approach, and it was ranked
This work has been partially supported by the Spanish Government through its TEC2017-88169-R MobiNetVideo project.
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Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment"},{id:"5",title:"Parasitic Infectious Diseases",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 18th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:287,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",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.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"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",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",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/24396",hash:"",query:{},params:{id:"24396"},fullPath:"/chapters/24396",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()