Unveiling the interspecies correlation and sensitivity factor analysis of rat and mouse acute oral toxicity of antimicrobial agents: first QSTR and QTTR Modeling report.

This study aims to identify toxic potential and environmental hazardousness of antimicrobials. In this regard, the available experimental toxicity data with rat and mouse acute oral toxicity have been gathered from ChemID Plus database ( = 202) and subjected to data curation. Upon the data curation 51 and 68 compounds were left for the rat and mouse respectively for the modeling.

First report on exploration of structural features of natural compounds (NPACT database) for anti-breast cancer activity (MCF-7): QSAR-based virtual screening, molecular docking, ADMET, MD simulation, and DFT studies.

Unlabelled: Due to the high toxicity, poor efficacy and resistance associated with current anti-breast cancer drugs, there's growing interest in natural products (NPs) for their potential anti-cancer properties. Computational modelling of NPs to identify key structural features can aid in developing novel natural inhibitors. In this study, we developed statistically significant QSAR models based on NPs from the NPACT database, which have shown potential anticancer activity against the MCF-7 cancer cell lines.

Chlorophyll to zeaxanthin energy transfer in nonphotochemical quenching: An exciton annihilation-free transient absorption study.

Zeaxanthin (Zea) is a key component in the energy-dependent, rapidly reversible, nonphotochemical quenching process (qE) that regulates photosynthetic light harvesting. Previous transient absorption (TA) studies suggested that Zea can participate in direct quenching via chlorophyll (Chl) to Zea energy transfer. However, the contamination of intrinsic exciton-exciton annihilation (EEA) makes the assignment of TA signal ambiguous.

Cognitive workload estimation using physiological measures: a review.

Estimating cognitive workload levels is an emerging research topic in the cognitive neuroscience domain, as participants' performance is highly influenced by cognitive overload or underload results. Different physiological measures such as Electroencephalography (EEG), Functional Magnetic Resonance Imaging, Functional near-infrared spectroscopy, respiratory activity, and eye activity are efficiently used to estimate workload levels with the help of machine learning or deep learning techniques. Some reviews focus only on EEG-based workload estimation using machine learning classifiers or multimodal fusion of different physiological measures for workload estimation.

soil degradation and ecotoxicity analysis of veterinary pharmaceuticals on terrestrial species: first report.

With the aim of persistence property analysis and ecotoxicological impact of veterinary pharmaceuticals on different terrestrial species, different classes of veterinary pharmaceuticals (n = 37) with soil degradation property (DT) were gathered and subjected to QSAR and q-RASAR model development. The models were developed from 2D descriptors under organization for economic cooperation and development guidelines with the application of multiple linear regressions along with genetic algorithm. All developed QSAR and q-RASAR were statistically significant (Internal = R: 0.

Integrated predictive QSAR, Read Across, and q-RASAR analysis for diverse agrochemical phytotoxicity in oat and corn: A consensus-based approach for risk assessment and prioritization.

In the modern fast-paced lifestyle, time-efficient and nutritionally rich foods like corn and oat have gained popularity for their amino acids and antioxidant contents. The increasing demand for these cereals necessitates higher production which leads to dependency on agrochemicals, which can pose health risks through residual present in the plant products. To first report the phytotoxicity for corn and oat, our study employs QSAR, quantitative Read-Across and quantitative RASAR (q-RASAR).

Network pharmacology and bioinformatics based investigation of : herb-drug interaction study.

(PF), a plant from the Euphorbiaceae family, is used extensively in ayurvedic formulations for its significant medicinal properties. When PF is administered alongside conventional drugs, there could be potential herb-drug interactions between the active compounds and the genes involved in drug transport and metabolism. Hence, this study was designed to investigate potential herb-drug interactions, focusing on elucidating their functional and pharmacological mechanisms, using an integrated approach of metabolite profiling and network pharmacology.

Strategy: A Promising Implement in the Development of Multitarget Drugs against Neurodegenerative Diseases.

Multi-target drug development (MTDD) is the demand of the recent era, especially in the case of multi-factorial conditions such as cancer, depression, neurodegenerative diseases (NDs), etc. The MTDD approaches have many advantages; avoidance of drug-drug interactions, predictable pharmacokinetic profile, and less drug resistance. The wet lab practice in MTDD is very challenging for the researchers, and the chances of late-stage failure are obvious.

1,3,4-Oxadiazole: An Emerging Scaffold to Inhibit the Thymidine Phosphorylase as an Anticancer Agent.

Thymidine phosphorylase (TP), also referred to as "platelet-derived endothelial cell growth factor" is crucial to the pyrimidine salvage pathway. TP reversibly transforms thymidine into thymine and 2-deoxy-D-ribose-1-phosphate (dRib-1-P), which further degraded to 2-Deoxy-D-ribose (2DDR), which has both angiogenic and chemotactic activity. In several types of human cancer such as breast and colorectal malignancies, TP is abundantly expressed in response to biological disturbances like hypoxia, acidosis, chemotherapy, and radiation therapy.

Infrared Signatures of Phycobilins within the Phycocyanin 645 Complex.

Aquatic photosynthetic organisms evolved to use a variety of light frequencies to perform photosynthesis. Phycobiliprotein phycocyanin 645 (PC645) is a light-harvesting complex in cryptophyte algae able to transfer the absorbed green solar light to other antennas with over 99% efficiency. The infrared signatures of the phycobilin pigments embedded in PC645 are difficult to access and could provide useful information to understand the mechanism behind the high efficiency of energy transfer in PC645.

Comparing resting state and task-based EEG using machine learning to predict vulnerability to depression in a non-clinical population.

Major Depressive Disorder (MDD) affects a large portion of the population and levies a huge societal burden. It has serious consequences like decreased productivity and reduced quality of life, hence there is considerable interest in understanding and predicting it. As it is a mental disorder, neural measures like EEG are used to study and understand its underlying mechanisms.

Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning.

With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy.

Discovery of novel VEGFR2-TK inhibitors by phthalimide pharmacophore based virtual screening, molecular docking, MD simulation and DFT.

Currently, numerous potent chemotherapeutic agents are available in the market but most of them show poor pharmacokinetics, lethal effects and drug resistance during their enduring use. The increased cancer cases, deaths and need of better treatment stimulates us to give newer lifesaving anticancer drugs. The phthalimide derivatives are structurally diverse and exert potential anticancer activity.

Decoding the cognitive states of attention and distraction in a real-life setting using EEG.

Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study.

Pharmacophore based virtual screening of cholinesterase inhibitors: search of new potential drug candidates as antialzheimer agents.

Unlabelled: Alzheimer's disease (AD) is a distinctive medical condition characterized by loss of memory, orientation, and cognitive impairments, which is an exceptionally universal form of neurodegenerative disease. The statistical data suggested that it is the 3rd major cause of death in older persons. Butyrylcholinesterase (BChE) and acetylcholinesterase (AChE) inhibitors play a vital role in the treatment of AD.

Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach.

With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q = 0.

Interference between Franck-Condon and Herzberg-Teller Terms in the Condensed-Phase Molecular Spectra of Metal-Based Tetrapyrrole Derivatives.

The commonly used Franck-Condon (FC) approximation is inadequate for explaining the electronic spectra of compounds that possess vibrations with substantial Herzberg-Teller (HT) couplings. Metal-based tetrapyrrole derivatives, which are ubiquitous natural pigments, often exhibit prominent HT activity. In this paper, we compare the condensed phase spectra of zinc-tetraphenylporphyrin (ZnTPP) and zinc-phthalocyanine (ZnPc), which exhibit vastly different spectral features in spite of sharing a common tetrapyrrole backbone.

Enhancing object detection in aerial images.

Unmanned Aerial Vehicles have proven to be helpful in domains like defence and agriculture and will play a vital role in implementing smart cities in the upcoming years. Object detection is an essential feature in any such application. This work addresses the challenges of object detection in aerial images like improving the accuracy of small and dense object detection, handling the class-imbalance problem, and using contextual information to boost the performance.

In silico selectivity modeling of pyridine and pyrimidine based CYP11B1 and CYP11B2 inhibitors: A case study.

of selective drug candidates for highly structural similar targets is a challenging task for researchers. The main objective of this study was to explore the selectivity modeling of pyridine and pyrimidine scaffold towards the highly homologous targets CYP11B1 and CYP11B2 enzymes by in silico (Molecular docking and QSAR) approaches. In this regard, a big dataset (n = 228) of CYP11B1 and CYP11B2 inhibitors were gathered and classified based on heterocyclic ring and the exhaustive analysis was carried out for pyridine and pyrimidinescaffolds.

Franck-Condon and Herzberg-Teller Signatures in Molecular Absorption and Emission Spectra.

Some molecules of chemical and biological significance possess vibrations with significant Herzberg-Teller (HT) couplings, which render the Franck-Condon (FC) approximation inadequate and cause the breakdown of the well-known mirror-image symmetry between linear absorption and emission spectra. Using a model two-state system with displaced harmonic potential surfaces, we show analytically that the FC-HT interference gives rise to asymmetric intensity modification, which has the same sign for all transitions on one side of the 0-0 absorption line and the opposite sign in the equivalent fluorescence transitions, while the trend is exactly reversed for all transitions on the other side the 0-0 line. We examine the dependence of the absorption-emission asymmetry on the mode frequency, Huang-Rhys factor, and dipole moment parameters to show the recovery of symmetry with particular combinations of parameters and a crossover from fluorescence to absorption dominance.

Synthetic Control of Exciton Dynamics in Bioinspired Cofacial Porphyrin Dimers.

Understanding how the complex interplay among excitonic interactions, vibronic couplings, and reorganization energy determines coherence-enabled transport mechanisms is a grand challenge with both foundational implications and potential payoffs for energy science. We use a combined experimental and theoretical approach to show how a modest change in structure may be used to modify the exciton delocalization, tune electronic and vibrational coherences, and alter the mechanism of exciton transfer in covalently linked cofacial Zn-porphyrin dimers ( linked and linked ). While both and feature zinc porphyrins linked by a 1,2-phenylene bridge, differences in the interporphyrin connectivity set the lateral shift between macrocycles, reducing electronic coupling in and resulting in a localized exciton.

Crowd Characterization in Surveillance Videos Using Deep-Graph Convolutional Neural Network.

Crowd behavior is a natural phenomenon that can provide valuable insight into the crowd characterization process. Modeling the visual appearance of a large crowd gathering can reveal meaningful information about its dynamics. Parametric modeling can be used to develop efficient and robust crowd monitoring systems.

Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning.

Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual's workload using EEG is of paramount importance.

The modulatory role of prime identified compounds in Geophila repens in mitigating scopolamine-induced neurotoxicity in experimental rats of Alzheimer's disease via attenuation of cholinesterase, β-secretase, MAPt levels and inhibition of oxidative stress imparts inflammation.

Ethnopharmacological Relevance: Geophila repens (L.) I.M.

In silico local QSAR modeling of bioconcentration factor of organophosphate pesticides.

The persistent and accumulative nature of the pesticide of indiscriminate use emerged as ecotoxicological hazards. The bioconcentration factor (BCF) is one of the key elements for environmental assessments of the aquatic compartment. Limitations of prediction accuracy of global model facilitate the use of local predictive models in toxicity modeling of emerging compounds.