Accurate Prediction Methods on Biomolecular Data

With the recent advancements in sequencing technologies, molecular biologists are producing ever-increasing amounts of biomolecular data. Extracting useful information from these massive data sets requires efficient and effective data mining and machine learning methods. In this dissertation, we explore the use of supervised machine learning (ML) to solve some challenging classification problems in molecular biology.First, we devise an ML model for classifying cancer types from very sparse somatic point mutation data. Accumulation of mutation and epigenetic modifications in somatic cells results in various cancer. For this purpose, we propose a method called mClass for efficient feature (gene) ranking that uses clustering, normalized mutual information and logistic regression. We show that somatic mutation data has sufficient discriminative power for cancer type classification.Next, we address the problem of gene essentiality prediction in microbes. Essential genes are significant to identify since their function is vital for the survival of the organism. Our proposed deep learning architecture called DeeplyEssential exclusively uses features extracted from the primary sequence of genes and their corresponding proteins, to maximize the utility and practicality of the tool. DeeplyEssential achieved state-of-the-art performance over previously proposed methods as well as expose and study a hidden performance bias affected previous models.Finally, we consider the problem of predicting the enhancer regions in the human genome from chromatin data. Enhancers contribute to the transcription of target genes. We propose a convolutional neural network framework named Epi2En that takes advantage of epigenetic ChIP-seq data. Epi2En's classification performance is not only very strong on cross-validation experiments, but also when testing across different cell-lines

Eco-friendly Entrepreneurship to Promote Plastic Alternatives

Plastic production has become a global concern, including in Bangladesh. Among all types of plastic products, single-use plastic is considered treacherous and the most responsible for damaging the natural environment. To replace plastics on a daily usage basis, the entrepreneurs of Bangladesh have reiterated some initiatives to manufacture and promote plastic alternatives. This study aims to assess the market potential, existing challenges, and users’/customers’ perceptions of the plastic alternative industry in Bangladesh. Key informant interviews (KIIs) with structured questionnaires were conducted among 15 plastic alternative entrepreneurs and 30 customers through online platforms. The framework analysis method was used to analyse and interpret the collected data. Despite highlighting some challenges, plastic alternative entrepreneurs contribute significantly to replacing plastic products, and the market potential is quite promising for this industry. Moreover, this study portrays an efficacious inclination of consumer behaviours towards using plastic alternatives. By following the concept of the green economy, the entrepreneurs of the plastic alternative industry in Bangladesh are gradually able to replace plastic products and contribute toward building a sustainable city

ThIEF: Finding Genome-wide Trajectories of Epigenetics Marks

We address the problem of comparing multiple genome-wide maps representing nucleosome positions or specific histone marks. These maps can originate from the comparative analysis of ChIP-Seq/MNase-Seq/FAIRE-Seq data for different cell types/tissues or multiple time points. The input to the problem is a set of maps, each of which is a list of genomics locations for nucleosomes or histone marks. The output is an alignment of nucleosomes/histone marks across time points (that we call trajectories), allowing small movements and gaps in some of the maps. We present a tool called ThIEF (TrackIng of Epigenetic Features) that can efficiently compute these trajectories. ThIEF comes into two "flavors": ThIEF:Iterative finds the trajectories progressively using bipartite matching, while ThIEF:LP solves a k-partite matching problem on a hyper graph using linear programming. ThIEF:LP is guaranteed to find the optimal solution, but it is slower than ThIEF:Iterative. We demonstrate the utility of ThIEF by providing an example of applications on the analysis of temporal nucleosome maps for the human malaria parasite. As a surprisingly remarkable result, we show that the output of ThIEF can be used to produce a supervised classifier that can accurately predict the position of stable nucleosomes (i.e., nucleosomes present in all time points) and unstable nucleosomes (i.e., present in at most half of the time points) from the primary DNA sequence. To the best of our knowledge, this is the first result on the prediction of the dynamics of nucleosomes solely based on their DNA binding preference. Software is available at https://github.com/ucrbioinfo/ThIEF

The impact of COVID-19 pandemic on mental health & wellbeing among home-quarantined Bangladeshi students: A cross-sectional pilot study.

BACKGROUND:COVID-19 is imposing threat both on physical and mental health since its outbreak. Bangladesh adopted lockdown strategy with potential consequences on day to day life, mental and physical health and this study aims to explore the impact of COVID-19 on mental health and wellbeing among Bangladeshi students. METHODS:A cross-sectional study was conducted between 9th and 23rd April 2020 among 505 college and university students. Data was collected by using online questionnaire including DASS 21 and IES. Descriptive analysis and bivariate linear regression were performed to examine the association of variables. RESULTS:28.5 % of the respondents had stress, 33.3% anxiety, 46.92% depression from mild to extremely severe, according to DASS 21 and 69.31% had event-specific distress from mild to severe in terms of severity according to IES. Perceiving physical symptoms as COVID-19 was significantly associated with DASS stress subscale (B = 3.71, 95% CI: 1.01 to 6.40), DASS anxiety subscale (B = 3.95, 95% CI: 1.95 to 5.96), DASS depression subscale (B = 3.82, 95% CI: 0.97 to 6.67) and IES scale (B = 7.52, 95% CI: 3.58 to 11.45). Additionally, fear of infection, financial uncertainty, inadequate food supply, absence of physical exercise and limited or no recreational activity had significant association with stress, anxiety, depression and post-traumatic symptoms. CONCLUSION:This COVID-19 outbreak imposes psychological consequences on people to a great extent which requires attention from the concerned authorities to cope with this situation mentally. The perception about the outbreak can also play a big role in psychological impact

Preconditioning of primary human endothelial cells with inflammatory mediators alters the “set point” of the cell

Endothelial cells are highly sensitive to changes in the extracellular milieu. Sepsis results in activation of inflammatory and coagulation pathways. We hypothesized that sepsis-associated mediators may alter the response capacity (so-called “set point”) of endothelial cells. Human umbilical vein endothelial cells (HUVEC) were preincubated in the presence or absence of tumor necrosis factor (TNF)-α, lipopolysaccharide (LPS), hypoxia, hyperthermia, and/or high glucose; treated with or without thrombin for 4 h; and then processed for RNase protection assays of selected activation markers. Priming with TNF-α and LPS significantly inhibited thrombin-mediated induction of vascular cell adhesion molecule-1, intercellular adhesion molecule-1, tissue factor, and E-selectin, but not platelet-derived growth factor-A or CD44. In electrophoretic mobility shift assays, thrombin-treated HUVEC demonstrated inducible binding of p65 NF-κB, an effect that was significantly blunted by pretreatment of cells with TNF-α and LPS. Consistent with these results, TNF-α and LPS attenuated the effect of thrombin on IκB phosphorylation, total cytoplasmic IκB, and nuclear translocation of p65 NF-κB. The inhibitory effect of TNF-α on thrombin signaling persisted for up to 24 h following removal of the cytokine. Taken together, these data suggest that inflammatory mediators prime endothelial cells to modulate subsequent thrombin response

MEDICINAL PLANTS AND FORMULATIONS USED BY THE SOREN CLAN OF THE SANTAL TRIBE IN RAJSHAHI DISTRICT, BANGLADESH FOR TREATMENT OF VARIOUS AILMENTS

The Santals form the largest tribal community in northern Bangladesh reside primarily in Rajshahi and Rangpur Divisions, where they live in the districts of Rajshahi, Rangpur, Thakurgaon, Dinajpur, and Panchagarh. Although they are fast losing their traditional medicinal practices, they still have their own medicinal practitioners who rely mostly on medicinal plants for treatment of a variety of ailments. The traditional medicinal practices vary quite extensively between the twelve clans of the Santals. The objective of the present study was to conduct an ethnomedicinal survey amongst the Soren clan of the Santal community residing in two villages of Tanor Santal Para in Rajshahi district to collect information on their use of medicinal plants. Interviews were conducted of the two existing Santal traditional medicinal practitioners of the Soren clan with the help of a semi-structured questionnaire and using the guided field-walk method. Plant specimens as pointed out by the practitioners were collected and pressed on the field and identification completed at the Bangladesh National Herbarium. Information on 53 medicinal plants distributed into 32 families was obtained in this survey. Ailments treated by these plants included skin disorders, respiratory tract disorders, gastro-intestinal disorders, sexual dysfunctions, sexually transmitted diseases, diabetes, helminthiasis, pain, urinary problems, filariasis, leprosy, tuberculosis, epilepsy, snake bite, enlarged heart, and paralysis. The medicinal plants used by the Santals merit further scientific studies for some of their formulations are used to treat diseases like diabetes, paralysis, enlarged heart, tuberculosis, and filariasis for which modern medicine has no known cure or medicines have developed resistant vectors

The Association Between Bangladeshi Adults’ Demographics, Personal Beliefs, and Nutrition Literacy: Evidence From a Cross-Sectional Survey

Background: Poverty and health illiteracy, combined with inappropriate systems to track disease and infection rates, contribute to children-and-mothers’ poor adherence to nutrient-rich foods intake in Bangladesh. Although risk factors for child and pregnant women malnutrition have been explored, the relationship between Bangladeshi adults’ nutrition literacy and their demographics and personal beliefs remains unknown. The purpose of this study was to examine the association between adults’ nutrition literacy, demographics and personal beliefs in a large sample of Bangladeshi adults. Methods: Four hundred adults from two districts (Dhaka and Chattogram) of Bangladesh participated in a cross-sectional survey. Data were collected by interviews using a structured questionnaire containing the Nutrition Literacy Scale. Multiple linear regression models were employed to analyze associations between nutrition literacy and related factors. Results: The mean nutrition literacy score was 21.6 (SD: 3.7; range: 11–32) on a scale of 32. Multiple linear regression revealed that being a businessman (β = 1.66, p = 0.013) or private employee (β = 1.08, p = 0.030), having a higher family income (β = 1.17, p = 0.009), and a higher educational level were positively associated with higher nutrition literacy scores compared to their counterparts. Participants who had ever completed a nutrition-related course (β = 4.95, p < 0.001), and who perceived themselves as having a need for accessing nutrition-related information were positively associated with the higher nutrition literacy compared to their counterparts. Conclusion: Findings from this study suggest the need for an integrated response plan involving educational interventions and accessible dietary plans targeting adult populations to enhance their nutritional literacy