OTSUCNV: an adaptive segmentation and OTSU-based anomaly classification method for CNV detection using NGS data.

Copy-number variations (CNVs), which refer to deletions and duplications of chromosomal segments, represent a significant source of variation among individuals, contributing to human evolution and being implicated in various diseases ranging from mental illness and developmental disorders to cancer. Despite the development of several methods for detecting copy number variations based on next-generation sequencing (NGS) data, achieving robust detection performance for CNVs with arbitrary coverage and amplitude remains challenging due to the inherent complexity of sequencing samples. In this paper, we propose an alternative method called OTSUCNV for CNV detection on whole genome sequencing (WGS) data. This method utilizes a newly designed adaptive sequence segmentation algorithm and an OTSU-based CNV prediction algorithm, which does not rely on any distribution assumptions or involve complex outlier factor calculations. As a result, the effective detection of CNVs is achieved with lower computational complexity. The experimental results indicate that the proposed method demonstrates outstanding performance, and hence it may be used as an effective tool for CNV detection.

Assessing sustainable waste management practices in Rajshahi City Corporation: an analysis for local government enhancement using IoT, AI, and Android technology.

This study addresses the pressing challenge of inefficient waste management practices within the Rajshahi City Corporation (RCC), Bangladesh. Despite rapid urbanization and escalating waste generation rates, RCC struggles with diverse waste disposal practices, limited supervision, irregular waste collection schedules, and inadequate disposal infrastructure. In this context, this study examines the possible improvements that could be made by combining the Internet of Things (IoT), artificial intelligence (AI), and Android application to improve waste management methods in the RCC. The study's foundation is a vast amount of information gathered from residents, with particular attention paid to waste disposal methods, the role of the local government, the frequency of waste collection, and public attitudes toward waste management. The results point to a complicated waste management environment with a range of waste disposal practices, little supervision, irregular waste collection, and insufficient disposal methods. The importance of RCC in waste management is emphasized, highlighting the need for proactive measures including effective monitoring, constant waste collection, and routine drain cleaning. Additionally, it is suggested that combining IoT, AI, and Android technology is a possible way to improve waste management procedures. These technologies have the potential to increase productivity, lessen their negative effects on the environment, and produce cleaner, more sustainable urban environments.

Detection of Campylobacter spp. in farmed sheep in Mymensingh division of Bangladesh: Prevalence, risk factors, and antimicrobial susceptibility status.

Background and Aim: Campylobacter infections in sheep may be asymptomatic or cause enteritis, ileitis, infertility, and abortion. Thus, this study aimed to estimate the prevalence of Campylobacter spp. in farming sheep and to detect risk factors, molecular patterns, and antimicrobial susceptibility status of these pathogens. Materials and Methods: Four hundred and eight fecal samples were collected from 12 flocks in the Mymensingh and Sherpur districts. Samples were tested by both basic (culture and biochemical tests) and molecular (initially 16S rRNA and later hipO gene-based polymerase chain reaction). Furthermore, the antimicrobial susceptibility status of Campylobacter jejuni was confirmed using disk diffusion. Flock- and animal-level data were captured using semi-structured interviews with farm owners under bivariate and multivariate logistic regression analyses to confirm the risk factors for Campylobacter-positive status. Results: The prevalence of C. jejuni staining at the animal and flock levels was 8.82% (36/408) and 66.70% (8/12), respectively. The age of sheep was identified as an important risk factor. Up to 1 year of age, sheep were 3.78 times more likely to be infected with C. jejuni (95% confidence interval: 1.0736-13.3146, p = 0.038). Of the 36 isolates of C. jejuni, all were found to be fully susceptible (100%) to gentamicin and ciprofloxacin. In this study, three antimicrobial agents, oxytetracycline, azithromycin, and ceftriaxone, were fully resistant (100%). The majority of isolates were resistant to a combination of 4-6 antimicrobial agents. Conclusion: The present study highlights the predominant maintenance of zoonotic Campylobacter species in sheep, and their burden on human health is enormous. Therefore, environmental, animal, and human health needs to be focused under a One Health lens to mitigate the occurrence of Campylobacter in farm settings and to prevent further introduction to animals and humans.

Enhancing agricultural automation through weather invariant soil parameter prediction using machine learning.

Soil parameters are crucial aspects in increasing agricultural production. Even though Bangladesh is heavily dependent on agriculture, little research has been done regarding its automation. And a vital aspect of agricultural automation is predicting soil parameters. Generally, sensors relating to soil parameters are quite expensive and are often done in a controlled environment such as a greenhouse. However, a large scale implementation of such expensive sensors is not very feasible. This work tries to find an inexpensive solution towards predicting soil parameters such as soil moisture and temperature, both of which are crucial to the growth of crops. We focus on finding a robust relation between the above mentioned soil parameters with the nearby weather parameters such as humidity and temperature, irrespective of the weather. We apply different machine learning models like multilayer perceptron (MLP), random forest, etc. to predict the soil parameters, given the humidity and temperature of the surrounding environment. For all the experiments we have used a custom made dataset, which contains around 9000 datapoints of soil moisture & temperature, ambient humidity & temperature. The data has been collected in an uncontrolled agriculture bed via inexpensive sensors. Our results show that XGBoost regressor achieves the best results with an R2 score of 0.93 and 0.99 for soil moisture and soil temperature data respectively. This suggests very high correlation between the weather parameters and soil parameters. The model also portrayed a very low root mean squared error and mean absolute error of 0.037 & 0.015 for soil moisture and 0.001 & 0.0008 for soil temperature. Our results show that it is indeed possible to find the soil parameters from the corresponding weather, which will have great impact on mass agricultural automation. The dataset has been made publicly available at https://github.com/Nadimulhaque0403/Soil_parameter_prediction_dataset.

Clinical Manifestation and Multidisciplinary Treatment Approaches for Primary Tracheal Carcinoma in Bangladesh: A Clinical and Therapeutic Review of 13 Patients.

BACKGROUND: Primary tracheal carcinoma is an exceptionally rare and life-threatening disease that presents significant diagnostic and therapeutic challenges. Delayed diagnosis due to misinterpretation of airway obstruction symptoms often leads to poorer prognoses for patients. This study aimed to explore the clinical manifestations and multidisciplinary treatment approaches for primary tracheal carcinoma in Bangladesh, with a focus on recent advancements in diagnosis and treatment. METHODS: A retrospective observational study was conducted at Bangladesh Specialized Hospital Limited, involving patients aged over 30 who were diagnosed with tracheal carcinoma and underwent multidisiplinary treatment from July 2018 to June 2019. Data were collected through patient interviews and medical record reviews. Descriptive and inferential statistical analyses were performed to examine demographic characteristics, histological variations, tumor locations, clinical signs and symptoms, treatment approaches, and outcomes. RESULTS: The study illuminated varied clinical presentations and the successful application of multidisciplinary approaches among the 13 patients. Invasive squamous cell carcinoma and adenoid cystic carcinoma were the predominant histological subtypes. Symptomatology, including dyspnea, cough, and hemoptysis, highlighted the challenge of early detection. Despite the rarity and intricacies associated with primary tracheal carcinoma, the multidisciplinary strategy yielded generally positive outcomes, as evidenced by a 1-year survival rate of 92.31% and a 5-year survival rate of 76.92%. Kaplan-Meier survival curves underscored the superior efficacy of surgical interventions over non-surgical approaches. CONCLUSION: Despite some limitations, this study contributes crucial insights into the nuanced management of primary tracheal carcinoma in the Bangladeshi context. The demonstrated success of the multidisciplinary strategy, especially surgical interventions, accentuates the importance of definitive resection. The lone case of local recurrence emphasizes the necessity for vigilant follow-up.