A study of the Haor areas of Sylhet-Mymensing districts with ERTS imageries (winter crop estimation)

There are no author-identified significant results in this report

Recombination and Excision: DNA Repair Proteins in Prokaryotic Host-Virus Conflicts

Bacteriophages, or simply phages, are viruses that infect bacteria. They are the most abundant biological entity on our planet and outnumber bacteria 10:1 in the ocean. In response to this threat, bacteria have evolved a diverse battery of immune systems that prevent infection, which in turn has resulted in the development of numerous counter-defense mechanisms by phages. This evolutionary arms race drives molecular innovations and presents exciting avenues for the discovery of new molecular biology and new biotechnology tools, such as restriction enzymes andCRISPR-Cas9. My thesis investigates how mechanisms of DNA repair,specifically recombination and base excision,have been co-opted by phages and bacteria to execute non-canonical immune and counter-immune functions in prokaryotic host-virus genetic conflicts

Exploring knowledge and practices regarding menstrual hygiene management among Bihari women in the Geneva Camp in Bangladesh

Background: Research into menstrual hygiene management (MHM) has been mainly based on menstruation-related knowledge and practices of women and girls in the mainstream Bangladeshi society; socially disadvantaged groups, such as the Bihari refugee women, have largely been ignored. Purpose: This study aims to assess knowledge and practices about MHM among Bihari women in the Mohammadpur Geneva Camp in Dhaka, Bangladesh. Methods: In 2017, a cross-sectional survey was conducted among Bihari women and girls by the trained interviewers using a structured questionnaire. The purposive sampling was applied to select 160 Bihari women aged between 15 and 49. Data were entered, cleaned, and analysed using SPSS software. Both univariate and bivariate analyses were undertaken to examine knowledge and MHM-related practices with a significance level of p<0.01. Results: Overall, most women (59.4%) had low knowledge about menstruation. More than one-quarter (27.0%) used disposable sanitary napkins. The Bihari women who did not use sanitary pads (73%) reported that they used old disposable clothes (59.83%), reusable cloths (25.64%), cotton (9.40%), or toilet tissue paper (4.27%). Around two-thirds of the women (68.0%) performed special baths and 36.9% followed socio-cultural taboos during menstruation. The bivariate analyses revealed that higher menstruation knowledge was associated with higher use of disposable sanitary napkins (low knowledge: 18.9%, high knowledge: 38.5%; p<0.01). Conclusions: The findings suggest that it is imperative for Bihari women to have adequate and appropriate menstruation knowledge so that they can maintain good menstrual hygiene practices. The findings highlight challenges experienced by the refugee women in maintaining MHM and can be used to improve women’s reproductive health and well-being and reduce the risk of reproductive tract infections (RTI) among socially disadvantaged women

AutoML Systems For Medical Imaging

The integration of machine learning in medical image analysis can greatly enhance the quality of healthcare provided by physicians. The combination of human expertise and computerized systems can result in improved diagnostic accuracy. An automated machine learning approach simplifies the creation of custom image recognition models by utilizing neural architecture search and transfer learning techniques. Medical imaging techniques are used to non-invasively create images of internal organs and body parts for diagnostic and procedural purposes. This article aims to highlight the potential applications, strategies, and techniques of AutoML in medical imaging through theoretical and empirical evidence.Comment: 11 pages, 4 figures; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

Introduction of Medical Imaging Modalities

The diagnosis and treatment of various diseases had been expedited with the help of medical imaging. Different medical imaging modalities, including X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Imaging, Ultrasound, Electrical Impedance Tomography (EIT), and Emerging Technologies for in vivo imaging modalities is presented in this chapter, in addition to these modalities, some advanced techniques such as contrast-enhanced MRI, MR approaches for osteoarthritis, Cardiovascular Imaging, and Medical Imaging data mining and search. Despite its important role and potential effectiveness as a diagnostic tool, reading and interpreting medical images by radiologists is often tedious and difficult due to the large heterogeneity of diseases and the limitation of image quality or resolution. Besides the introduction and discussion of the basic principles, typical clinical applications, advantages, and limitations of each modality used in current clinical practice, this chapter also highlights the importance of emerging technologies in medical imaging and the role of data mining and search aiming to support translational clinical research, improve patient care, and increase the efficiency of the healthcare system.Comment: 19 pages, 7 figures, 1 table; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

Active Learning on Medical Image

The development of medical science greatly depends on the increased utilization of machine learning algorithms. By incorporating machine learning, the medical imaging field can significantly improve in terms of the speed and accuracy of the diagnostic process. Computed tomography (CT), magnetic resonance imaging (MRI), X-ray imaging, ultrasound imaging, and positron emission tomography (PET) are the most commonly used types of imaging data in the diagnosis process, and machine learning can aid in detecting diseases at an early stage. However, training machine learning models with limited annotated medical image data poses a challenge. The majority of medical image datasets have limited data, which can impede the pattern-learning process of machine-learning algorithms. Additionally, the lack of labeled data is another critical issue for machine learning. In this context, active learning techniques can be employed to address the challenge of limited annotated medical image data. Active learning involves iteratively selecting the most informative samples from a large pool of unlabeled data for annotation by experts. By actively selecting the most relevant and informative samples, active learning reduces the reliance on large amounts of labeled data and maximizes the model's learning capacity with minimal human labeling effort. By incorporating active learning into the training process, medical imaging machine learning models can make more efficient use of the available labeled data, improving their accuracy and performance. This approach allows medical professionals to focus their efforts on annotating the most critical cases, while the machine learning model actively learns from these annotated samples to improve its diagnostic capabilities.Comment: 12 pages, 8 figures; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

HLA Class II Defects in Burkitt Lymphoma: Bryostatin-1-Induced 17 kDa Protein Restores CD4+ T-Cell Recognition

While the defects in HLA class I-mediated Ag presentation by Burkitt lymphoma (BL) have been well documented, CD4+ T-cells are also poorly stimulated by HLA class II Ag presentation, and the reasons underlying this defect(s) have not yet been fully resolved. Here, we show that BL cells are deficient in their ability to optimally stimulate CD4+ T cells via the HLA class II pathway. The observed defect was not associated with low levels of BL-expressed costimulatory molecules, as addition of external co-stimulation failed to result in BL-mediated CD4+ T-cell activation. We further demonstrate that BL cells express the components of the class II pathway, and the defect was not caused by faulty Ag/class II interaction, because antigenic peptides bound with measurable affinity to BL-associated class II molecules. Treatment of BL with broystatin-1, a potent modulator of protein kinase C, led to significant improvement of functional class II Ag presentation in BL. The restoration of immune recognition appeared to be linked with an increased expression of a 17 kDa peptidylprolyl-like protein. These results demonstrate the presence of a specific defect in HLA class II-mediated Ag presentation in BL and reveal that treatment with bryostatin-1 could lead to enhanced immunogenicity

Redistribution of potentially toxic elements in the hydrosphere after the relocation of a group of tanneries

Simultaneous relocation of a group of pollutant sources in a heavily polluted area is a rare event. Such a relocation has been implemented in Hazaribagh, a tannery built-up area with heavy pollution, in Bangladesh. This provides a valuable opportunity to compare the changes in environmental conditions associated with the relocation of multiple putative sources. Our environmental monitoring for a period of 6 years at the stationary areas centered on Hazaribagh geographically revealed trivalent [Cr(III)], hexavalent [Cr(VI)] chromium, lead, iron, and manganese as tannery-related elements after the legal deadline for tannery relocation. The median Cr(III) level in canal water, into which wastewater from tanneries was directly discharged, after the relocation was 97% lower of that before the relocation, indicating a beneficial effect of the relocation. In contrast, the median Cr(VI) level in water samples just after the relocation and 2 years after the relocation were approximately 5-fold and 30-fold higher, respectively, than those before the relocation. These results indicate not only a harmful effect of the relocation but also the possibility of conversion from Cr(III) to Cr(VI) in nature. Although the health hazard indexes considering all of the tannery-related elements in all of the canal water samples before the relocation exceeded the safety thresholds, the percentages of samples in which the indexes exceeded their safety thresholds after the relocation decreased by 32.5%–45.0%. Treatment with our patented hydrotalcite-like compound consisting of magnesium and iron (MF-HT) resulted in decreases in the health hazard indexes in all of the water samples in which the indexes exceeded their safety thresholds to levels lower than their thresholds. Thus, this study shows the double-edged effects associated with the relocation and a potential solution.journal articl