Left ventricular motion estimation is very important for diagnosing cardiac abnormality. One of the popular techniques, optical flow technique, promises useful results for motion quantification. However, optical flow technique often failed to provide smooth vector field due to the complexity of cardiac motion and the presence of speckle noise. This chapter proposed a new filtering technique, called quasi-Gaussian discrete cosine transform (QGDCT)-based filter, to enhance the optical flow field for myocardial motion estimation. Even though Gaussian filter and DCT concept have been implemented in other previous researches, this filter introduces a different approach of Gaussian filter model based on high frequency properties of cosine function. The QGDCT is a customized quasi discrete Gaussian filter in which its coefficients are derived from a selected two-dimensional DCT. This filter was implemented before and after the computation of optical flow to reduce the speckle noise and to improve the flow field smoothness, respectively. The algorithm was first validated on synthetic echocardiography image that simulates a contracting myocardium motion. Subsequently, this method was also implemented on clinical echocardiography images. To evaluate the performance of the technique, several quantitative measurements such as magnitude error, angular error, and standard error of measurement are computed and analyzed. The final motion estimation results were in good agreement with the physician manual interpretation.
In this chapter, the computational biology of cardiac cavity images is proposed. The method uses collinear and triangle equation algorithms to detect and reconstruct the boundary of the cardiac cavity. The first step involves high boost filter to enhance the high frequency component without affecting the low frequency component. Second, the morphological and thresholding operators are applied to the image to eliminate noise and convert the image into a binary image. Next, the edge detection is performed using the negative Laplacian filter and followed by region filtering. Finally, the collinear and triangle equations are used to detect and reconstruct the more precise cavity boundary. Results obtained have proved that this technique is able to perform better segmentation and detection of the boundary of cardiac cavity from echocardiographic images.
The central dogma of molecular biology (DNA, RNA, protein and metabolite) has engraved our understanding of genetics in all living organisms. While the concept has been embraced for many decades, the development of high-throughput technologies particularly omics (genomics, transcriptomics, proteomics and metabolomics) has revolutionised the field to incorporate big data analysis including bioinformatics and systems biology as well as synthetic biology area. These omics approaches as well as systems and synthetic biology areas are now increasingly popular as seen by the growing numbers of publication throughout the years. Several journals which have published most of these related fields are also listed in this chapter to overview their impact and target journals.
A brain machine interface (BMI) design for controlling the navigation of a power wheelchair is proposed. Real-time experiments with four able bodied subjects are carried out using the BMI-controlled wheelchair. The BMI is based on only two electrodes and operated by motor imagery of four states. A recurrent neural classifier is proposed for the classification of the four mental states. The real-time experiment results of four subjects are reported and problems emerging from asynchronous control are discussed.
Colorectal carcinoma (CRC) is a malignancy of epithelial origin in the large bowel. The elucidation of the biological functions of programmed cell death ligand-1 (PD-L1), thymidylate synthase (TYMS), and deleted in colorectal cancer (DCC) biomarkers including their roles in the pathophysiology of CRC - has led to their applications in diagnostic and chemo-pharmaceutics. We investigated whether PD-L1, TYMS, and DCC protein expression in CRC tumors are predictive biomarkers of treatment outcome for CRC patients. The expressions of PD-L1, TYMS, and DCC were evaluated by immunohistochemistry (IHC) in 91 paraffin-embedded samples from patients who underwent colectomy procedure in Hospital Serdang, Selangor, Malaysia. There was high expression of DCC in most cases: 84.6% (77/91). PD-L1 showed low expression in 93.4% (86/91) of cases and high expression in 6.6% (5/91) of cases. Low and high expressions of TYMS were detected in 53.8% (49/91) and 46.2% (42/91) of the CRC cases, respectively. There was a significant association between the TYMS expression and gender (P
Since antiquity, ginger or Zingiber officinale, has been used by humans for medicinal purposes and as spice condiments to enhance flavor in cooking. Ginger contains many phenolic compounds such as gingerol, shogaol and paradol that exhibit antioxidant, anti-tumor and anti-inflammatory properties. The role of ginger and its constituents in ameliorating diseases has been the focus of study in the past two decades by many researchers who provide strong scientific evidence of its health benefit. This review discusses research findings and works devoted to gingerols, the major pungent constituent of ginger, in modulating and targeting signaling pathways with subsequent changes that ameliorate, reverse or prevent chronic diseases in human studies and animal models. The physical, chemical and biological properties of gingerols are also described. The use of ginger and especially gingerols as medicinal food derivative appears to be safe in treating or preventing chronic diseases which will benefit the common population, clinicians, patients, researchers, students and industrialists.
In this paper, application of Artificial Neural Network (ANN) for electrocardiogram (ECG) signal noise removal has been investigated. First, 100 number of ECG signals are selected from Physikalisch-Technische Bundesanstalt (PTB) database and Kalman filter is applied to remove their low pass noise. Then a suitable dataset based on denoised ECG signal is configured and used to a Multilayer Perceptron (MLP) neural network to be trained. Finally, results and experiences are discussed and the effect of changing different parameters for MLP training is shown.
Activated sludge system is generally used in wastewater treatment plants for processing domestic influent. Conventionally the activated sludge wastewater treatment is monitored by measuring physico-chemical parameters like total suspended solids (TSSol), sludge volume index (SVI) and chemical oxygen demand (COD) etc. For the measurement, tests are conducted in the laboratory, which take many hours to give the final measurement. Digital image processing and analysis offers a better alternative not only to monitor and characterize the current state of activated sludge but also to predict the future state. The characterization by image processing and analysis is done by correlating the time evolution of parameters extracted by image analysis of floc and filaments with the physico-chemical parameters. This chapter briefly reviews the activated sludge wastewater treatment; and, procedures of image acquisition, preprocessing, segmentation and analysis in the specific context of activated sludge wastewater treatment. In the latter part additional procedures like z-stacking, image stitching are introduced for wastewater image preprocessing, which are not previously used in the context of activated sludge. Different preprocessing and segmentation techniques are proposed, along with the survey of imaging procedures reported in the literature. Finally the image analysis based morphological parameters and correlation of the parameters with regard to monitoring and prediction of activated sludge are discussed. Hence it is observed that image analysis can play a very useful role in the monitoring of activated sludge wastewater treatment plants.
Hypertension is a silent killer worldwide, caused by both genetic and environmental factors. Until now, genetic and genomic association studies of hypertension are reporting different degree of association on hypertension. Hence, it is essential to gather all the available information on the reported genetic loci and to determine if any biomarker(s) is/are significantly associated with hypertension. Current review concluded the potential biomarkers for hypertension, with regards to electrolyte and fluid transports, as well as sodium/potassium ions homeostasis, which are supported by the results of case-controls and meta-analyses.
Functional genomics encompasses diverse disciplines in molecular biology and bioinformatics to comprehend the blueprint, regulation, and expression of genetic elements that define the physiology of an organism. The deluge of sequencing data in the postgenomics era has demanded the involvement of computer scientists and mathematicians to create algorithms, analytical software, and databases for the storage, curation, and analysis of biological big data. In this chapter, we discuss on the concept of functional genomics in the context of systems biology and provide examples of its application in human genetic disease studies, molecular crop improvement, and metagenomics for antibiotic discovery. An overview of transcriptomics workflow and experimental considerations is also introduced. Lastly, we present an in-house case study of transcriptomics analysis of an aromatic herbal plant to understand the effect of elicitation on the biosynthesis of volatile organic compounds.
Conditioned medium from cultured fibroblast cells is recognized to promote wound healing and growth through the secretion of enzymes, extracellular matrix proteins, and various growth factors and cytokines. The objective of this study was to profile the secreted proteins present in nasal fibroblast conditioned medium (NFCM). Nasal fibroblasts isolated from human nasal turbinates were cultured for 72 h in Defined Keratinocytes Serum Free Medium (DKSFM) or serum-free F12: Dulbecco's Modified Eagle's Medium (DMEM) to collect conditioned medium, denoted as NFCM_DKSFM and NFCM_FD, respectively. SDS-PAGE was performed to detect the presence of protein bands, followed by MALDI-TOF and mass spectrometry analysis. SignalP, SecretomeP, and TMHMM were used to identify the secreted proteins in conditioned media. PANTHER Classification System was performed to categorize the protein according to protein class, whereas STRING 10 was carried out to evaluate the predicted proteins interactions. SDS-PAGE results showed the presence of various protein with molecular weight ranging from ~10 kDa to ~260 kDa. Four protein bands were identified using MALDI-TOF. The analyses identified 104, 83, and 7 secreted proteins in NFCM_FD, NFCM_DKSFM, and DKSFM, respectively. Four protein classes involved in wound healing were identified, namely calcium-binding proteins, cell adhesion molecules, extracellular matrix proteins, and signaling molecules. STRING10 protein prediction successfully identified various pathways regulated by secretory proteins in NFCM. In conclusion, this study successfully profiled the secreted proteins of nasal fibroblasts and these proteins are predicted to play important roles in RECs wound healing through various pathways.
The use of monoclonal antibody as the next generation protein therapeutics with remarkable success has surged the development of antibody engineering to design molecules for optimizing affinity, better efficacy, greater safety and therapeutic function. Therefore, computational methods have become increasingly important to generate hypotheses, interpret and guide experimental works. In this chapter, we discussed the overall antibody design by computational approches.
The incident of two children in Europe who died of diphtheria due to a shortage of anti-toxin drugs has highlighted the need for alternative anti-toxins. Historically, antiserum produced from immunised horses have been used to treat diphtheria. Despite the potential of antiserum, the economical and medial concerns associated with the use of animal antiserum has led to its slow market demise. Over the years, new and emerging infectious diseases have grown to be a major global health threat. The emergence of drug-resistant superbugs has also pushed the boundaries of available therapeutics to deal with new infectious diseases. Antibodies have emerged as a possible alternative to combat the continuous onslaught of various infectious agents. The isolation of antibodies against pathogens of infectious diseases isolated from immune libraries utilising phage display has yielded promising results in terms of affinities and neutralizing activities. This chapter focuses on the concept of immune antibody libraries and highlights the application of immune antibody libraries to generate antibodies for various infectious diseases.
Many countries are facing an uphill battle in combating the spread of infectious diseases. The constant evolution of microorganisms magnifies the problem as it facilitates the re-emergence of old infectious diseases as well as promote the introduction of new and more deadly variants. Evidently, infectious diseases have contributed to an alarming rate of mortality worldwide making it a growing concern. Historically, antibodies have been used successfully to prevent and treat infectious diseases since the nineteenth century using antisera collected from immunized animals. The inherent ability of antibodies to trigger effector mechanisms aids the immune system to fight off pathogens that invades the host. Immune libraries have always been an important source of antibodies for infectious diseases due to the skewed repertoire generated post infection. Even so, the role and ability of naïve antibody libraries should not be underestimated. The naïve repertoire has its own unique advantages in generating antibodies against target antigens. This chapter will highlight the concept, advantages and application of human naïve libraries as a source to isolate antibodies against infectious disease target antigens.
Arsenic (As) is one of the most potent natural as well as anthropogenic metalloid toxicants that have various implications in the everyday life of humans. It is found in several chemical forms such as inorganic salt, organic salt, and arsine (gaseous form). Although it is mostly released via natural causes, there are many ways through which humans come in contact with As. Drinking water contamination by As is one of the major health concerns in various parts of the world. Arsenic exposure has the ability to induce adverse health effects including reproductive problems. Globally, around 15% of the couples are affected with infertility, of which about 20-30% are attributed to the male factor. Arsenic affects the normal development and function of sperm cells, tissue organization of the gonads, and also the sex hormone parameters. Stress induction is one of the implications of As exposure. Excessive stress leads to the release of glucocorticoids, which impact the oxidative balance in the body leading to overproduction of reactive oxygen species (ROS). This may in turn result in oxidative stress (OS) ultimately interfering with normal sperm and hormonal parameters. This study deals with As-induced OS and its association with sex hormone disruption as well as its effect on sperm and semen quality.
circRNAs have emerged as one of the key regulators in many cellular mechanisms and pathogenesis of diseases. However, with the limited knowledge and current technologies for circRNA investigations, there are several challenges that need to be addressed for. These include challenges in understanding the regulation of circRNA biogenesis, experimental designs, and sample preparations to characterize the circRNAs in diseases as well as the bioinformatics pipelines and algorithms. In this chapter, we discussed the above challenges and possible strategies to overcome those limitations. We also addressed the differences between the existing applications and technologies to study the circRNAs in diseases. By addressing these challenges, further understanding of circRNAs roles and regulations as well as the discovery of novel circRNAs could be achieved.
Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of various central nervous system disorders like seizures, epilepsy, and brain damage and for categorizing sleep stages in patients. The artifacts caused by various factors such as Electrooculogram (EOG), eye blink, and Electromyogram (EMG) in EEG signal increases the difficulty in analyzing them. Discrete wavelet transform has been applied in this research for removing noise from the EEG signal. The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Difference. This paper reports on the effectiveness of wavelet transform applied to the EEG signal as a means of removing noise to retrieve important information related to both healthy and epileptic patients. Wavelet-based noise removal on the EEG signal of both healthy and epileptic subjects was performed using four discrete wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze EEG significantly. Result of this study shows that WF Daubechies 8 (db8) provides the best noise removal from the raw EEG signal of healthy patients, while WF orthogonal Meyer does the same for epileptic patients. This algorithm is intended for FPGA implementation of portable biomedical equipments to detect different brain state in different circumstances.
During low-temperature exposure, temperate plant species increase their freezing tolerance in a process termed cold acclimation. The molecular mechanisms involved in cold acclimation have been mostly investigated in Arabidopsis thaliana. In addition, other Brassicaceae species related to A. thaliana have been employed in recent years to study plant stress responses on a phylogenetically broader basis and in some cases with extremophile species with a much higher stress tolerance. In this paper, we briefly summarize cold acclimation responses in A. thaliana and current knowledge about cold acclimation in A. thaliana relatives with special emphasis on Eutrema salsugineum and two closely related Thellungiella species. We then present a transcriptomic and metabolomic analysis of cold acclimation in five A. thaliana and two E. salsugineum accessions that differ widely in their freezing tolerance. Differences in the cold responses of the two species are discussed.
Proteomics is the study of proteins, the workhorses of cells. Proteins can be subjected to various post-translational modifications, making them dynamic to external perturbation. Proteomics can be divided into four areas: sequence, structural, functional and interaction and expression proteomics. These different areas used different instrumentations and have different focuses. For example, sequence and structural proteomics mainly focus on elucidating a particular protein sequence and structure, respectively. Meanwhile, functional and interaction proteomics concentrate on protein function and interaction partners, whereas expression proteomics allows the cataloguing of total proteins in any given samples, hence providing a holistic overview of various proteins in a cell. The application of expression proteomics in cancer and crop research is detailed in this chapter. The general workflow of expression proteomics consisting the use of mass spectrometry instrumentation has also been described, and some examples of proteomics studies are also presented.