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

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

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

The effect of the introduction of Nile tilapia ( Oreochromis niloticus , L.) on small indigenous fish species (mola, Amblypharyngodon mola , Hamilton; chela, Chela cachius , Hamilton; punti, Puntius sophore , Hamilton)

This is the first controlled experiment to quantify the effect of introduced tilapia on indigenous species. This experiment was conducted in small earthen ponds (100 m 2 ) to assess the impact of mixed-sex or all-male Nile tilapia ( Oreochromis niloticus ) on small indigenous species (SIS) commonly found in south Asia, mola ( Amblypharyngodon mola ), chela ( Chela cachius ) and punti ( Puntius sophore ). Ponds were fertilized, then stocked with 0.56 fish m −2 of water surface area in the mixed-sex and all-male tilapia treatments and 0.42 fish m −2 in the treatment without tilapia. No additional nutritional inputs were applied after stocking. Treatments were: mixed-sex tilapia with SIS, mono-sex male tilapia with SIS and SIS without tilapia (control). All treatments were stocked with 14 fish per species. All species reproduced during the 21-month culture duration. The number of recruits varied by species, Tilapia reproduced in greater numbers than SIS. Tilapia numbers at harvest were the highest (451 ± 25/100 m 2 ) in the mixed-sex treatment compared with mola (221 ± 22/100 m 2 ), chela (94 ± 8/100 m 2 ) and punti (100 ± 7/100 m 2 ). The number of mola was higher (399 ± 33/100 m 2 ) in the all-male tilapia treatment. There was reduction in the number of mola and chela in the treatment containing mixed-sex tilapia. Gut content analysis combined with water sampling revealed that all fish species fed selectively. Significant interspecies dietary overlap was found between Nile tilapia and SIS and among SIS. Thus, there is potential for tilapia to compete with indigenous fish species when space and other resources are limiting, but a longer duration study with varying level of management is needed to determine how successfully tilapia competes with locally adapted SIS.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79201/1/j.1365-2109.2009.02372.x.pd