Enhancing cross-domain robustness in phonocardiogram signal classification using domain-invariant preprocessing and transfer learning.

Background And Objective: Phonocardiogram (PCG) signal analysis is a non-invasive and cost-efficient approach for diagnosing cardiovascular diseases. Existing PCG-based approaches employ signal processing and machine learning (ML) for automatic disease detection. However, machine learning techniques are known to underperform in cross-corpora arrangements.

Demographic-environmental effect on dengue outbreaks in 11 countries.

Background: Dengue outbreaks are common in tropical or temperate countries, and climate change can exacerbate the problem by creating conditions conducive to the spread of mosquitoes and prolonging the transmission season. Warmer temperatures can allow mosquitoes to mature faster and increase their ability to spread disease. Additionally, changes in rainfall patterns can create more standing water, providing a breeding ground for mosquitoes.

Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach.

This article introduces a novel mathematical model analyzing the dynamics of Dengue in the recent past, specifically focusing on the 2023 outbreak of this disease. The model explores the patterns and behaviors of dengue fever in Bangladesh. Incorporating a sinusoidal function reveals significant mid-May to Late October outbreak predictions, aligning with the government's exposed data in our simulation.

Airway stability in sleep apnea: Assessing continuous positive airway pressure efficiency.

Obstructive Sleep Apnea Syndrome (OSAS) disrupts millions of lives with its burden of airway obstruction during sleep. Continuous Positive Airway Pressure (CPAP) therapy has been scrutinized for its biomechanical impact on the respiratory tract. This study leverages computational fluid dynamics to investigate CPAP's effects at 9 cm HO (882.

Effect of microplastics deposition on human lung airways: A review with computational benefits and challenges.

Microplastics have become omnipresent in the environment, including the air we inhale, the water we consume, and the food we eat. Despite limited research, the accumulation of microplastics within the human respiratory system has garnered considerable interest because of its potential implications for health. This review offers a comprehensive examination of the impacts stemming from the accumulation of microplastics on human lung airways and explores the computational benefits and challenges associated with studying this phenomenon.

Autism Detection in Children using Common Spatial Patterns of MEG Signals.

Autism exhibits a wide range of developmental disabilities and is associated with aberrant anatomical and functional neural patterns. To detect autism in young children (4-7 years) in an automatic and non-invasive fashion, we have recorded magnetoencephalogram (MEG) signals from 30 autistic and 30 age-matched typically developing (TD) children. We have used a machine learning classification framework with common spatial pattern (CSP)-based logarithmic band power (LBP) features.

Ensembled Feature based Multi-Label ECG Arrhythmia Classification.

Arrhythmia is one of the major contributors to sudden cardiac death. Compared to single-label arrhythmia classification, multi-label classification provides the advantage of simultaneous detection of different cardiac ailments. Existing multi-label approaches employ deep learning models, which have several limitations, e.

Time-Frequency Fragment Selection for Disease Detection from Imbalanced Phonocardiogram Data.

Cardiovascular disease (CVD) has become the most concerning disease worldwide. A Phonocardiogram (PCG), the graphical representation of heart sound, is a non-invasive method that helps to detect CVD by analyzing its characteristics. Several machine learning (ML) approaches have been proposed in the last decade to assist practitioners in interpreting this disease accurately.

Knowledge, attitude, and practice of Bangladeshi residents during COVID-19 pandemic.

Bangladeshi government has adopted some special steps to control the quick spread of the COVID-19 pandemic situation. However, the residents' knowledge, attitudes, and practices towards the disease directly impact the success of the controlling measures taken by the state. This article explores knowledge (K) about preventions, attitude (A) to the disease, and practices (P) of preventing the COVID-19 infection risks of different age groups residing in Bangladesh.

Functional connectivity based machine learning approach for autism detection in young children using MEG signals.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD.We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4-7 years) and thirty age and gender-matched typically developing (TD) children.

A Fusion-Based Machine Learning Approach for Autism Detection in Young Children Using Magnetoencephalography Signals.

In this study, we aimed to find biomarkers of autism in young children. We recorded magnetoencephalography (MEG) in thirty children (4-7 years) with autism and thirty age, gender-matched controls while they were watching cartoons. We focused on characterizing neural oscillations by amplitude (power spectral density, PSD) and phase (preferred phase angle, PPA).

Macroinvertebrates as engineers for bioturbation in freshwater ecosystem.

Bioturbation is recognized as a deterministic process that sustains the physicochemical properties of the freshwater ecosystem. Irrigation, ventilation, and particle reworking activities made by biotic components on sediment beds influence the flow of nutrients and transport of particles in the sediment-water interface. Thus, the biogenic disturbances in sediment are acknowledged as pivotal mechanism nutrient cycling in the aquatic system.

Influence of habitat complexity on the prey mortality in IGP system involving insect predators (Heteroptera) and prey (Diptera): Implications in biological control.

Intraguild predation (IGP) is common in the freshwater insect communities, involving a top predator, intraguild prey (IG prey) and a shared prey. Influence of the habitat complexity on the prey-predator interactions is well established through several studies. In the present instance, the IGP involving the heteropteran predators and the dipteran prey were assessed in the background of the habitat complexity.

Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age.

Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal.

Ensembled Transfer Learning and Multiple Kernel Learning for Phonocardiogram Based Atherosclerotic Coronary Artery Disease Detection.

Conventional machine learning has paved the way for a simple, affordable, non-invasive approach for Coronary artery disease (CAD) detection using phonocardiogram (PCG). It leaves a scope to explore improvement of performance metrics by fusion of learned representations from deep learning. In this study, we propose a novel, multiple kernel learning (MKL) for their fusion using deep embeddings transferred from pre-trained convolutional neural network (CNN).

Effect of environmental and socio-economic factors on the spreading of COVID-19 at 70 cities/provinces.

The main goal of this article is to demonstrate the impact of environmental and socio-economic factors on the spreading of COVID-19. In this research, data has been collected from 70 cities/provinces of different countries around the world that are affected by COVID-19. In this research, environmental data such as temperatures, humidity, air quality and population density and socio-economic data such as GDP (PPP) per capita, per capita health expenditure, life expectancy and total test in each of these cities/provinces are considered.

Controlling the Effects of External Perturbations on a Gene Regulatory Network Using Proportional-Integral-Derivative Controller.

Gene regulatory networks are biologically robust, which imparts resilience to living systems against most external perturbations affecting them. However, there is a limit to this and disturbances beyond this limit can impart unwanted signalling on one or more master regulators in a network. Certain disturbances may affect the functioning of other constituent genes of the same network.

Association of HLA-DQ and IL13 gene variants with challenge-proven shrimp allergy in West Bengal, India.

Little is known about genetic factors and mechanisms underlying shrimp allergy. Genome-wide association studies identified HLA class-II and IL13 genes as highly plausible candidates for shrimp allergy. The present study was designed to investigate potential associations of HLA-DQ rs9275596, IL13 rs20541, and IL13 rs1800925 polymorphisms with challenge-proven shrimp allergy using the data from 532 people of West Bengal, India; selected on basis of positive skin prick test, elevated specific IgE and medical history.

Switch or stay? Automatic classification of internal mental states in bistable perception.

The human brain goes through numerous cognitive states, most of these being hidden or implicit while performing a task, and understanding them is of great practical importance. However, identifying internal mental states is quite challenging as these states are difficult to label, usually short-lived, and generally, overlap with other tasks. One such problem pertains to bistable perception, which we consider to consist of two internal mental states, namely, transition and maintenance.

Past, Present and Future: Combining habitat suitability and future landcover simulation for long-term conservation management of Indian rhino.

The Indian rhino (Rhinoceros unicornis) is susceptible to habitat change and fragmentation due to illegal logging, rapid urbanization and non-forest use and therefore were confined in to isolated areas throughout its distribution. The present study was conducted in Gorumara landscape which is composed of two protected areas (PAs) viz., Gorumara National Park (GNP) and Chapramari Wildlife Sanctuary.

Changing landscape configuration demands ecological planning: Retrospect and prospect for megaherbivores of North Bengal.

The Gorumara National Park (GNP) is an important conservation area located in the northern region of West Bengal State, India, as it provides habitat for three megaherbivores: Indian One-horned rhinoceros (Rhinoceros unicornis), Asian elephants (Elephas maximus) and Gaurs (Bos gaurus). It harbours one of the last population of the one-horned rhino. In the present study, landscape change and configuration were investigated by comparing three Landsat images, from 1998, 2008 and 2018.