Sustainable economic growth and unemployment nexus of SDG 2030: Bangladesh in Asia.

In this study, we applied the Okun's law to examine the current progress of sustainable economic growth and unemployment goal of the fourteen developed and developing countries of Asia. Our findings suggests in the long-run, only China has achieved the sustainable economic growth and unemployment goal until 2018. However, due to recent downward trend of economic growth and upward trend of unemployment rate, China is currently walking opposite to the said goal.

PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning.

Cancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer research is blooming day by day and new insights of cancer and related treatments are coming into light.

Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets.

Background: Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users' thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users' insomnia and their Big 5 personality traits as derived from social media interactions.

The Disproportionate Impact of COVID-19 among Undocumented Immigrants and Racial Minorities in the US.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), has had an unprecedented effect, especially among under-resourced minority communities. Surveillance of those at high risk is critical for preventing and controlling the pandemic. We must better understand the relationships between COVID-19-related cases or deaths and characteristics in our most vulnerable population that put them at risk to target COVID-19 prevention and management efforts.

ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides.

Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming.

The Stressful Life of the Urban Consumers: The Case of Dhaka City Residents.

The present research examines the metropolitan mental life of consumers of Dhaka, which is one of the most densely populated and least livable cities in the world. Though mental life encompasses a range of factors, the study considered the dynamic interplays of the most pertinent ones, such as perceived stress, the sense of control, materialistic values, and religiosity. These variables were measured and quantified by commonly used measurement tools; a recursive structural equation model was constructed to unearth the causal connections among those variables.

Target specificity of selective bioactive compounds in blocking α-dystroglycan receptor to suppress Lassa virus infection: an approach.

Lassa hemorrhagic fever, caused by Lassa mammarenavirus (LASV) infection, accumulates up to 5000 deaths every year. Currently, there is no vaccine available to combat this disease. In this study, a library of 200 bioactive compounds was virtually screened to study their drug-likeness with the capacity to block the α-dystroglycan (α-DG) receptor and prevent LASV influx.

An Asymmetric Investigation of Remittance and Trade Openness Impact on Inequality: Evidence From Selected South Asian Countries.

This study's motivation is to explore the relationship pattern between remittance, trade openness, and inequality of selected south Asian countries for the 1976-2018 period. The study performed non-linear tests, including unit root tests, non-linearity applying ordinary least squares (OLS) and BDS tests, non-linear autoregressive distributed lagged (NARDL) tests, and asymmetry causality tests to assess their association. Study findings with non-linear unit root tests suggest that the research variables follow the non-linear process of becoming stationary from non-stationary.

Extended Epidemiological Models for Weak Economic Region: Case Studies of the Spreading of COVID-19 in the South Asian Subcontinental Countries.

The ascendancy of coronavirus has become widespread all around the world. For the prevention of viral transmission, the pattern of disease is explored. Epidemiological modeling is a vital component of the research.

CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data.

Background: The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in the brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cutting-edge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications.

Dimensioning of Wide-Area Alternate Wetting and Drying (AWD) System for IoT-Based Automation.

Water, one of the most valuable resources, is underutilized in irrigated rice production. The yield of rice, a staple food across the world, is highly dependent on having proper irrigation systems. Alternate wetting and drying (AWD) is an effective irrigation method mainly used for irrigated rice production.

Female Community Health Workers and Health System Navigation in a Conflict Zone: The Case of Afghanistan.

Afghanistan ranked 171st among 188 countries in the Gender Inequality Index of 2011 and has only 16% of its women participating in the labor force. The country has been mired in violence for decades which has resulted in the destruction of the social infrastructure including the health sector. Recently, Afghanistan has deployed community health workers (CHW) who make up majority of the health workforce in the remote areas of this country.

Dose Remittances Matter for Openness and Financial Stability: Evidence From Least Developed Economies.

The study's motivation is to gauge the effects of remittances on openness: financial and economic openness and financial stability in least developed countries (LDCs) for the period spanning 1975-2018. The study applies Generalized Moment of Methods (GMM) and System-GMM to detect the magnitude of remittances, gross capital formation, and government debt on openness and financial stability, and their directional association is established by performing a Granger causality test with System-GMM specification. The results of cross-sectional dependency ascertain the presence of a common dynamic among the research units; on the other hand, both first, and second-generation unit root tests establish that variables are integrated either at level or after the first difference, neither variables are exposed to order of integration after second difference.

Do financial development, FDI, and globalization intensify environmental degradation through the channel of energy consumption: evidence from belt and road countries.

This study explores the role of foreign direct investment (FDI), financial development (FD), and globalization (GLO) in environmental degradation (ED) through the channel of energy consumption (EC) for the selected panel of belt and road initiative (BRI) countries for 1990-2017. The study applies appropriate panel unit root tests, the Westerlund cointegration test, the dynamic seemingly unrelated regression (DSUR) long-run panel estimation approach, and the Dumitrescu-Hurlin panel causality test. Results of panel unit root test ascertain variables are interred either at a level or after first difference and long-run association documents by implementing conventional and error correction.

Cardiovascular diseases risk prediction in patients with diabetes: Posthoc analysis from a matched case-control study in Bangladesh.

Purpose: This study aimed to investigate the estimated 10-year predicted risk of developing cardiovascular diseases (CVD) among participants with and without diabetes in Bangladesh.

Methods: We performed posthoc analysis from a matched case-control study conducted among 1262 participants. A total of 631 participants with diabetes (case) were recruited from a tertiary hospital, and 631 age, sex and residence matched participants (control) were recruited from the community in Dhaka, Bangladesh.

Economic Policy Uncertainty and Financial Innovation: Is There Any Affiliation?

The impetus of this study is to gauge the nexus between economic policy uncertainty (EPU) and financial innovation in Brazil, Russia, India, China, and South Africa (BRIC) nations for the period from 2004M1 to 2018M12. This study utilizes both the linear and non-linear autoregressive distributed lag (ARDL) models to evaluate the long-run and the short-run association between EPU and financial innovation; furthermore, the causal effects are investigated by following the non-Granger casualty framework. The results of long-run cointegration, i.

Nexus between environmental quality, institutional quality and trade openness through the channel of FDI: an application of common correlated effects estimation (CCEE), NARDL, and asymmetry causality.

Determinants of trade openness are available in the literature; however, no substantial evidence available for the nexus between environmental quality, institutional quality, and trade openness with the presence of FDI as a mediating factor. With this study, we tried to figure out the answer to the question, i.e.

A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome.

DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money. Our study develops a convolutional neural network (CNN) based tool i6mA-CNN capable of identifying 6mA sites in the rice genome.

OriC-ENS: A sequence-based ensemble classifier for predicting origin of replication in S. cerevisiae.

DNA Replication plays the most crucial part in biological inheritance, ensuring an even flow of genetic information from parent to offspring. The beginning site of DNA Replication which is called the Origin of Replication (ORI), plays a significant role in understanding the molecular mechanisms and genomic analysis of DNA. Hence, it is paramount to accurately identify the origin of replication to gain a more accurate understanding of the biochemical and genomic properties of DNA.

SubFeat: Feature subspacing ensemble classifier for function prediction of DNA, RNA and protein sequences.

The information of a cell is primarily contained in deoxyribonucleic acid (DNA). There is a flow of DNA information to protein sequences via ribonucleic acids (RNA) through transcription and translation. These entities are vital for the genetic process.

Convolutional neural networks with image representation of amino acid sequences for protein function prediction.

Proteins are one of the most important molecules that govern the cellular processes in most of the living organisms. Various functions of the proteins are of paramount importance to understand the basics of life. Several supervised learning approaches are applied in this field to predict the functionality of proteins.

Molecular optimization, docking, and dynamic simulation profiling of selective aromatic phytochemical ligands in blocking the SARS-CoV-2 S protein attachment to ACE2 receptor: an approach of targeted drug designing.

Objectives: The comprehensive study aims to figure out the most effective aromatic phytochemical ligands among a number from a library, considering their pharmacokinetic efficacies in blocking "angiotensin-converting enzyme 2 (ACE2) receptor-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) S protein" complex formation as part of a target-specific drug designing.

Materials And Methods: A library of 57 aromatic pharmacophore phytochemical ligands was prepared from where the top five ligands depending on Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) and quantitative structure-activity relationship (QSAR)-based pharmacokinetic properties were considered. The selected ligands were optimized for commencing molecular docking and dynamic simulation as a complex with the ACE2 receptor to compare their blocking efficacy with the control drug.

A Digital Microscreen for the Enhanced Appearance of Ocular Prosthetic Motility (an American Ophthalmological Society Thesis).

Purpose: This study aims to improve the apparent motility of ocular prosthetic devices using technology. Prevailing ocular prostheses are acrylic shells with a static eye image rendered on the convex surface. A limited range of ocular prosthetic movement and lack of natural saccadic movements commonly causes the appearance of eye misalignment that may be disfiguring.

Classification of motor imagery using a time-localised approach.

Brain-computer interface (BCI) is getting increasing attention where classification of motor imagery (MI) using electroencephalography (EEG) signal plays a vital role. In traditional EEG-based BCI setup, after applying pre-processing like band-pass filtering and spatial filtering, features are extracted and are fed to the classifier. However, most of the traditional features are extracted from a single time window, which is usually the full-time frame of a cue-based MI signal.

Progress in Brain Computer Interface: Challenges and Opportunities.

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges.