Spatially Optimized Compact Deep Metric Learning Model for Similarity Search

Spatial optimization is often overlooked in many computer vision tasks. Filters should be able to recognize the features of an object regardless of where it is in the image. Similarity search is a crucial task where spatial features decide an important output. The capacity of convolution to capture visual patterns across various locations is limited. In contrast to convolution, the involution kernel is dynamically created at each pixel based on the pixel value and parameters that have been learned. This study demonstrates that utilizing a single layer of involution feature extractor alongside a compact convolution model significantly enhances the performance of similarity search. Additionally, we improve predictions by using the GELU activation function rather than the ReLU. The negligible amount of weight parameters in involution with a compact model with better performance makes the model very useful in real-world implementations. Our proposed model is below 1 megabyte in size. We have experimented with our proposed methodology and other models on CIFAR-10, FashionMNIST, and MNIST datasets. Our proposed method outperforms across all three datasets.Comment: 5 pages, 3 figures

Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our code and dataset will be made public when publishing the paper.Comment: 18 pages, 7 figures, submitted to Nature Scientific Report

Novel therapeutics against breast cancer stem cells by targeting surface markers and signaling pathways

Background: Breast cancer remains to be one of the deadliest forms of cancers, owing to the drug resistance and tumor relapse caused by breast cancer stem cells (BCSCs) despite notable advancements in radio-chemotherapies. Objectives: To find out novel therapeutics against breast cancer stem cells by aiming surface markers and signaling pathways. Methods: A systematic literature search was conducted through various electronic databases including, Pubmed, Scopus, Google scholar using the keywords "BCSCs, surface markers, signaling pathways and therapeutic options against breast cancer stem cell. Articles selected for the purpose of this review were reviewed and extensively analyzed. Results: Novel therapeutic strategies include targeting BCSCs surface markers and aberrantly activated signaling pathways or targeting their components, which play critical roles in self-renewal and defense, have been shown to be significantly effective against breast cancer. In this review, we represent a number of ways against BCSCs surface markers and hyper-activated signaling pathways to target this highly malicious entity of breast cancer more effectively in order to make a feasible and useful strategy for successful breast cancer treatment. In addition, we discuss some characteristics of BCSCs in disease progression and therapy resistance. Conclusion: BCSCs involved in cancer pathogenesis, therapy resistance and cancer recurrence. Thus, it is suggested that a multi-dimensional therapeutic approach by targeting surface markers and aberrantly activated signaling pathways of BCSCs alone or in combination with each other could really be worthwhile in the treatment of breast cancer

Determinants of food safety knowledge and practices among food handlers in Bangladesh: An institution-based cross-sectional study

The engagement of a large number of people in big-scale cooking raises the danger of food contamination due to incorrect handling, whether deliberate or unintentional. Contamination during large-scale production poses a serious hazard to consumer health and has significant financial implications for a nation. This study aimed to investigate the food safety knowledge and practices of institutional food handlers in Bangladesh, considering the growing concern surrounding this issue and the lack of available information on foodborne illnesses related to institutions. In addition, the study aimed to determine the factors influencing both knowledge and practices. A cross-sectional study was conducted from June to September 2022, involving 408 institutional food handlers. The sample size was determined using Cochran's formula, and data was collected through purposive sampling. The participants were interviewed in person and completed a pilot-tested questionnaire. A multiple linear regression analysis was conducted to determine the factors related to food safety knowledge and practices. The majority of participants were female (71.3%) and aged between 26 and 35 (mean age 34.53 ± 9.06 years). They were most knowledgeable about hand hygiene and food separation but lacked knowledge about foodborne pathogens and food storage. Thawing food at room temperature was the most inappropriate practice (86%). The mean scores for knowledge and practice were found to be 16.11 ± 2.76 on a 26-point scale (61%), and 9.59 ± 2.07 on a 15-point scale (64%), respectively. Rural food handlers, those with higher education, working more than 10 h per day, and being familiar with HACCP, had higher knowledge. Food handlers aged 18 to 25, with higher income, working in private institutions, having food safety authority knowledge, actively engaging in food safety training, working more than 10 h per day, and having a positive health perception, had better food safety practices.The results of this study reinforce the notion that institutional food handlers would benefit from enhanced exposure to food safety interventions, active participation in training sessions, and strict adherence to food hygiene regulations in their food handling knowledge and practices

Electrochemical Detection of FAM134B Mutations in Oesophageal Cancer Based on DNA-Gold Affinity Interactions

nexpensive, simple and rapid DNA sensors capable of accurate and sensitive detection of cancer specific point mutations in DNA biomarkers are crucial for the routine screening of genetic mutations in cancer. Conventional approaches based on sequencing, mass spectroscopy, and fluorescence are highly effective, but they are tedious, slow and require labels and expensive equipment. Recent electrochemistry based approaches mostly rely on conventional DNA biosensing using recognition and transduction layers, and hence limited by the complicated steps of sensor fabrication associated with surface cleaning, self-assembled monolayer formation, and target hybridization. Herein we report a relatively simple and inexpensive method for detecting point mutation in cancer by using the direct adsorption of purified DNA sequences onto an unmodified gold surface. The method relies on the base dependent affinity interaction of DNA with gold. Since the affinity interaction (adsorption) trend of DNA bases follows as adenine (A) > cytosine (C) > guanine (G)> thymine (T), two DNA sequences with different DNA base compositions (i. e., amplified mutated sequences will be distinctly different than its original sequence) will have different adsorption affinity towards gold. The amount of mutation sites on a DNA sequence is quantified by monitoring the electrochemical current as a function of the relative adsorption level of DNA samples onto a bare gold electrode. This method can successfully distinguish single point mutation in DNA from oesophageal cancer. We demonstrated the clinical utility of this approach by detecting different levels of mutations in tissue samples (n=9) taken from oesophageal cancer patients. Finally, the method was validated with High Resolution Melt (HRM) curve analysis and Sanger Sequencing.Office of the Snr Dep Vice Chancellor, Institute for GlycomicsNo Full Tex

Fenugreek seed powder protects mice against arsenic-induced neurobehavioral changes

The current study was designed to evaluate the protective effect of fenugreek seed powder against As-induced neurobehavioral and biochemical perturbations using a mouse model. Mice exposed to arsenic at 10 mg/kg body weight showed development of anxiety-like behavior and memory impairment compared to control mice in elevated plus maze and Morris water maze tests, respectively. A significantly decreased acetyl and butyrylcholinesterase, superoxide dismutase and glutathione reductase activities and brain-derived neurotrophic factor levels were found in the brain of arsenic-exposed mice compared to control mice. Interestingly, supplementation of fenugreek seed powder to arsenic-treated mice significantly restored the activity of cholinesterase and antioxidant enzymes (e.g. superoxide dismutase, glutathione reductase) as well as brain-derived neurotrophic factor levels in the brain tissue of arsenic-exposed mice. Consequently, reduced anxiety-like behavior, improved learning and memory were observed in fenugreek supplemented arsenic treated mice compared to only arsenic-exposed mice group. Thus, this study suggests that fenugreek seed powder reduces arsenic-induced neurotoxicity in mice

An electrochemical method for sensitive and rapid detection of FAM134B protein in colon cancer samples.

Despite the excellent diagnostic applications of the current conventional immunoassay methods such as ELISA, immunostaining and Western blot for FAM134B detection, they are laborious, expensive and required a long turnaround time. Here, we report an electrochemical approach for rapid, sensitive, and specific detection of FAM134B protein in biological (colon cancer cell extracts) and clinical (serum) samples. The approach utilises a differential pulse voltammetry (DPV) in the presence of the [Fe(CN)6]3−/4− redox system to quantify the FAM134B protein in a two-step strategy that involves (i) initial attachment of FAM134B antibody on the surface of extravidin-modified screen-printed carbon electrode, and (ii) subsequent detection of FAM134B protein present in the biological/clinical samples. The assay system was able to detect FAM134B protein at a concentration down to 10 pg μL−1 in phosphate buffered saline (pH 7.4) with a good inter-assay reproducibility (% RSD = <8.64, n = 3). We found excellent sensitivity and specificity for the analysis of FAM134B protein in a panel of colon cancer cell lines and serum samples. Finally, the assay was further validated with ELISA method. We believe that our assay could potentially lead a low-cost alternative to conventional immunological assays for target antigens analysis in point-of-care applications

Catastrophic Health Expenditure, Distress Financing and Impoverishment due to Out-of-Pocket Expenses for Healthcare among Patients with Chronic Liver Disease: A Cross-sectional Study among Hospitalized Patients in Bangladesh

Out-of-pocket (OOP) expenses for hospitalized patients with chronic liver disease (CLD) poses an economic challenge on affected household in the form of catastrophic health expenditure (CHE), distress financing and impoverishment. OOP Expenses data for hospitalized CLD patients from Bangladesh is scarce. This study aimed to estimate the OOP expenses and resulting CHE, distress financing and impoverishment among hospitalized patients with CLD. This cross-sectional study was conducted among conveniently selected 107 diagnosed CLD patients admitted at Bangabandhu Sheikh Mujib Medical University (BSMMU) and Dhaka Medical College Hospital (DMCH) aged 18 years and above. Data were collected from the respondents using a semi-structured questionnaire through face to face interview during discharge from hospital. Out of pocket expenditure for chronic liver disease in selected hospitals was Bangladeshi Taka (BDT) 19,262. Direct medical, direct non-medical and indirect cost was BDT 16,240; 2,165 and 1,510, respectively. Investigation cost and medicine cost contributed to 48.48% and 31.81% of the total OOP expenses, respectively. At 10% threshold level, 29% of the respondents were affected by CHE. 64.5% of the respondents were facing distress financing due to OOP expenses. Among the respondents, 1.9% slipped below the international poverty line of $1.90 (BDT 161.10, in 2019).There was statistically significant (p < 0.05) difference among the mean OOP expenses for different etiological types of chronic liver disease. The study concluded that it requires establishing a more accessible and affordable decentralized health care system for CLD treatment along with the implementation of financial risk protection

Natural compounds targeting cancer stem cells: a promising resource for chemotherapy

Background: Cancer Stem Cells (CSCs) are the subpopulation of cancer cells which are directly involved in drug resistance, metastases to distant organ and cancer recurrence.Methods: A systematic literature search was conducted through various electronic databases including, Pubmed, Scopus, Google scholar using the keywords "cancer stem cells" and "natural compounds" in the present study. Articles published between 1999 and 2019 were reviewed. All the expositions concerning CSCs associated cancer pathogenesis and therapy resistance, as well as targeting these properties of CSCs by natural compounds were selected for the current study.Results: Natural compounds have always been thought as a rich source of biologically active principles, which target aberrantly activated signaling pathways and other modalities of CSCs, while tethering painful side effects commonly involved in the first-line and second-line chemo-radiotherapies. In this review, we have described the key signaling pathways activated in CSCs to maintain their survival and highlighted how natural compounds interrupt these signaling pathways to minimize therapy resistance, pathogenesis and cancer recurrence properties of CSCs, thereby providing useful strategies to treat cancer or aid in cancer therapy improvement. Like normal stem cells, CSCs rely on different signaling pathways and other properties for their maintenance. Therefore, the success of cancer treatment depends on the development of proper anti-neoplastic drugs capable of intercepting those signaling pathways as well as other properties of CSCs in order to eradicate this evasive subpopulation of cancer cells.Conclusion: Compounds of natural origin might act as an outstanding source to design novel therapies against cancer stem cells

Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

<p>Short Description:</p> <p>In this research, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos captured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos captured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model's ability to act as a Picture-based Predictor of PM2.5 Concentration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone.</p> <p>Code and More Details: https://github.com/lepotatoguy/aqi</p&gt