Drug-target prediction through self supervised learning with dual task ensemble approach.

Drug-Target interaction (DTI) prediction, a transformative approach in pharmaceutical research, seeks novel therapeutic applications for computational method based virtual screening, existing drugs to address untreated diseases and discovery of existing drugs side effects. The proposed model predict DTI through Heterogeneous biological network by combining drug, genes and disease related knowledge. For the purpose of embedding extraction Self-supervised learning (SSL) has been used which, trains models through pretext tasks, eliminating the need for manual annotations.

Ab Initio Study of the Influence of Spin and Orbital Magnetic Moments on the Stability of Magnetic and Charge Distribution in Co:ZnO Monolayer.

The development of ultrathin magnets with tunable magnetic properties is essential for advancing quantum computing technologies. In this study, density functional theory (DFT) calculations were employed to investigate the atomic and electronic structures of a ZnO monolayer embedded with cobalt atoms. The impact of spin dynamics on charge transfer within the Co:ZnO system was thoroughly examined.

Deep learning and content-based filtering techniques for improving plant disease identification and treatment recommendations: A comprehensive review.

The importance of identifying plant diseases has risen recently due to the adverse effect they have on agricultutal production. Plant diseases have been a big concern in agriculture, as they affect crop production, and constitute a major threat to global food security. In the domain of modern agriculture, effective plant disease management is vital to ensure healthy crop yields and sustainable practices.

Forecasting of Indian tourism industry using modeling approach.

Currently, India has become one of the largest economies of the world in which tourism and hospitality have significantly contributed; however, the growth rate of tourism industry has been greatly affected during the COVID-19 pandemic. In this study, we have used the modeling approach to analyze and understand the growth pattern of Indian tourism industry. To achieve this, we consider the data of international tourist arrivals before and after the lockdown.

Tissue specific tumor-gene link prediction through sampling based GNN using a heterogeneous network.

A tissue sample is a valuable resource for understanding a patient's symptoms and health status in relation to tumor growth. Recent research seeks to establish a connection between tissue-specific tumor samples and genetic markers (genes). This breakthrough has paved the way for personalized cancer therapies.

The Role of Bioinformatics and Imaging Models in Tumorigenesis and Treatment Response of Brain and Spinal Cord Neoplasm.

This chapter focuses on the division and location of brain deformities such as tumors in magnetic resonance imaging (MRI) through Chan-Vese active contour segmentation. Brain tumor division and identification is a major test in the area of biomedical picture processing. To detect the size and location of the tumor, various techniques are available, but active contour gives accurate knowledge of the region for segmentation.

COVID-19 outbreak: a predictive mathematical study incorporating shedding effect.

In this paper, a modified SEIR epidemic model incorporating shedding effect is proposed to analyze transmission dynamics of the COVID-19 virus among different individuals' classes. The direct impact of pathogen concentration over susceptible populations through the shedding of COVID-19 virus into the environment is investigated. Moreover, the threshold value of shedding parameters is computed which gives information about their significance in decreasing the impact of the disease.

Towards a super-resolution based approach for improved face recognition in low resolution environment.

The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object in surveillance data is intriguing yet difficult due to the low resolution of captured images or video. The super-resolution approach aims to enhance the resolution of an image to generate a desirable high-resolution one.

Travesty of Life Elders Abuse an Inquiry of Physical and Psychological Abuse.

Elder abuse is evil in human society. The present paper unveils this social issue from two major factors psychological and physical abuse. The study sensitizes the subject matter of study by examining the effects of demographic variables like gender and age on elders.

Principle component analysis for nonlinear optical properties of thiophene-based metal complexes.

The simulation of molecular descriptors of thiophene-based metal complexes has been performed using Gaussian 03 and Atomistic toolkit Virtual Nanolab (ATK-VNL) software. It is found that with respect to the obtained molecular descriptors, the molecules show distinct properties. The dimensions of the data set being large, the principal components (PC1 and PC2) have been obtained using principal component analysis (PCA).

An empirical study of supply chain sustainability with financial performances of Indian firms.

In this research, we examine empirically the impact of sustainable supply chain practices on financial performances, considering the case of Indian firms. Here, we use a sample of 25 Indian firms listed for their sustainability performances in the Thomson Reuters Environmental, Social and Governance (ESG) scores. The sustainability performance data have been accessed from the Bloomberg terminal, where the overall sustainability performance on ESG is measured as a discounted score on ESG considering various controversies on ESG reported for the firm.

Anonymity Preserving IoT-Based COVID-19 and Other Infectious Disease Contact Tracing Model.

Automated digital contact tracing is effective and efficient, and one of the non-pharmaceutical complementary approaches to mitigate and manage epidemics like Coronavirus disease 2019 (COVID-19). Despite the advantages of digital contact tracing, it is not widely used in the western world, including the US and Europe, due to strict privacy regulations and patient rights. We categorized the current approaches for contact tracing, namely: mobile service-provider-application, mobile network operators' call detail, citizen-application, and IoT-based.

A comprehensive evaluation for the prediction of mortality in intensive care units with LSTM networks: patients with cardiovascular disease.

Intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of electronic health records. We aimed to build a mortality prediction model on a Medical Information Mart for Intensive Care (MIMIC-III) database and to assess whether the use of deep learning techniques like long short-term memory (LSTM) can effectively utilize the temporal relations among clinical variables. The models were built on clinical variable dynamics of the first 48 h of ICU admission of 12,550 records from the MIMIC-III database.

A simple, eco-friendly and green approach to synthesis of blue photoluminescent potassium-doped graphene oxide from agriculture waste for bio-imaging applications.

2D carbon nanomaterials such as graphene and its oxide counterpart have sought good research attention for their application as well as fundamental interest. Especially the versatility of graphene oxide establishes its elite candidature in every field because of diverse application potential. Here we are reporting a greener, eco-friendly and cost effective one step hydrothermal route for the synthesis of potassium doped graphene oxide (K-doped GO) from agricultural waste i.

Chemiplasmonics for high-throughput biosensors.

Background: The sensitivity of ELISA for biomarker detection can be significantly increased by integrating fluorescence with plasmonics. In surface-plasmon-coupled emission, the fluorophore emission is generally enhanced through the so-called physical mechanism due to an increase in the local electric field. Despite its fairly high enhancement factors, the use of surface-plasmon-coupled emission for high-throughput and point-of-care applications is still hampered due to the need for expensive focusing optics and spectrometers.

Dynamics of cholera epidemics with impulsive vaccination and disinfection.

Waterborne diseases have a tremendous influence on human life. The contaminated drinking water causes water-borne disease like cholera. Pulse vaccination is an important and effective strategy for the elimination of infectious diseases.

Electronic and optical properties of functionalized zigzag ZnO nanotubes.

The present paper reports the analysis of surface decoration on the structural, electronic, and optical properties of (n,0) ZnO nanotubes, performed by means of a density function theory based ab-initio approach. Fe functionalization induced buckling in ZnO nanotubes affects its electronic and optical properties. Increase in Fe functionalization leads to better stability of ZnO nanotube and shows enhanced metallic character.

Defect-Engineered Graphene for High-Energy- and High-Power-Density Supercapacitor Devices.

Defects are often written off as performance limiters. Contrary to this notion, it is shown that controlling the defect configuration in graphene is critical to overcome a fundamental limitation posed by quantum capacitance and opens new channels for ion diffusion. Defect-engineered graphene flexible pouch capacitors with energy densities of 500% higher than the state-of-the-art supercapacitors are demonstrated.

Steering healthcare service delivery: a regulatory perspective.

Purpose: The purpose of this paper is to explore regulation in India's healthcare sector and makes recommendations needed for enhancing the healthcare service.

Design/methodology/approach: The literature was reviewed to understand healthcare's regulatory context. To understand the current healthcare system, qualitative data were collected from state-level officials, public and private hospital staff.

Electron transport properties of a single-walled carbon nanotube in the presence of hydrogen cyanide: first-principles analysis.

First-principles analysis based on density functional theory was performed to compute the electronic and transport properties of a single-walled carbon nanotube in the presence of hydrogen cyanide. A chiral (4,1) carbon nanotube was found to become less metallic as the number of hydrogen cyanide molecules nearby increased. When there were a sufficient number of hydrogen cyanide molecules close to the nanotube, it became semiconducting.

Quality assessment of modeled protein structure using physicochemical properties.

Physicochemical properties of proteins always guide to determine the quality of the protein structure, therefore it has been rigorously used to distinguish native or native-like structure from other predicted structures. In this work, we explore nine machine learning methods with six physicochemical properties to predict the Root Mean Square Deviation (RMSD), Template Modeling (TM-score), and Global Distance Test (GDT_TS-score) of modeled protein structure in the absence of its true native state. Physicochemical properties namely total surface area, euclidean distance (ED), total empirical energy, secondary structure penalty (SS), sequence length (SL), and pair number (PN) are used.

Feature selection and classification of leukocytes using random forest.

In automatic segmentation of leukocytes from the complex morphological background of tissue section images, a vast number of artifacts/noise are also extracted causing large amount of multivariate data generation. This multivariate data degrades the performance of a classifier to discriminate between leukocytes and artifacts/noise. However, the selection of prominent features plays an important role in reducing the computational complexity and increasing the performance of the classifier as compared to a high-dimensional features space.

Automated microscopic image analysis for leukocytes identification: a survey.

Automatic quantification and classification of leukocytes in microscopic images are of paramount importance in the perspective of disease identification, its progress and drugs development. Extracting numerical values of leukocytes from microscopic images of blood or tissue sections represents a tricky challenge. Research efforts in quantification of these cells include normalization of images, segmentation of its nuclei and cytoplasm followed by their classification.