In silico screening of phytoconstituents of Cissus quadrangularis and Chromolaena odorata against proteins of antimicrobial resistance and wound healing

In silico screening is a methodological approach, which is invaluable for rational drug design and the identification of potential therapeutic agents. In the context of antibiotic-resistant infectious wounds, molecular docking can provide a deeper understanding of how phytocompounds might interfere with bacterial virulence and antibiotic resistance. In this study, proteins involved in antimicrobial resistance and wound healing were docked against major phytoconstituents of ethyl acetate extract of Cissus quadrangularis (EACQ) and ethanol extract of Chromolaena odorata (EECO), two medicinal plants that have been traditionally used. Receptor structures for interleukin 6 (PDB id: 1n26) IL6, of human and mice, IL6 (Uniprot id p 20607) of rat, vascular endothelial growth factor (VEGFR, PDB id: 2ctw) for human, mice, rat and penicillin binding protein 2a (PBP2a, PDB id: 1vqq) of S. aureus were downloaded from the database of the RCSB protein data bank. The ligand structures were downloaded from PubChem compound database in structure data file (.SDF) format. The docking studies were conducted using Autodock4. and the results of the docking analysis were visualised using Discovery Studio Visualizer. The docking log (dlg) file, featuring an RMSD table, provides binding energy values in Kcal/mol for each molecule at its optimal docked postures, offering insights into structural accuracy and ligand-receptor interaction strength in molecular docking simulations. In silico analysis of ligands showed that squalene of EACQ and epilupeol of EECO had the least binding energy towards proteins of antimicrobial resistance and wound healing. Thus, these compounds could emerge as promising lead molecules against infectious wounds

Satellite chlorophyll concentration as an aid to understanding the dynamics of Indian oil sardine in the southeastern Arabian Sea

Coastal waters of Kerala, which form an integral part of the Malabar upwelling zone off the southwest coast of India, constitute an important fishing region for small pelagics. Satellite remote sensing data from 1998−2014 were used to test the hypothesis that fluctuations in the landings of Sardinella longiceps, the major pelagic fish landed in the area designated as the South Eastern Arabian Sea (SEAS), are influenced by seasonal variability in phytoplankton biomass (measured as chlorophyll a [chl a] concentration), under the changing strength of physical para - meters such as sea surface temperature (SST), alongshore wind stress, Ekman mass transport, sea level anomaly (SLA) and Kerala rainfall. Multiple linear regression analysis (MLRA) was used to assess the influence of physical forcing mechanisms on chl a concentration on monthly and seasonal scales. We found that SLA, alongshore wind stress, SST and rainfall were ranked 1 to 4, respectively, and the first 3 factors significantly influenced the chl a concentration of SEAS. Pearson’s correlation analysis between monthly chl a and sardine landing (with chl a leading) showed a maximum positive correlation (+0.26) at 2 and 3 mo lags, emphasizing that the influence of chl a on the fishery of S. longiceps is seasonal (r = 0.35 for seasonal lead−lag correlation) in the coastal waters of SEAS. Variation in phytoplankton biomass, as evidenced by chl a fluctuations, seems to have a decisive role in regulating the physiological condition of larvae spawned during the southwest monsoon season, their juveniles and finally the adults that are recruited into the fishery in the next season. Using the quantity of phytoplankton as a predictive tool will exploit the presumptive trophic link to aid understanding of sardine fishery dynamics in upwelling zones

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development

Sensitivity of the simulated Oxygen Minimum Zone to biogeochemical processes at an oligotrophic site in the Arabian Sea

Oxygen minimum zones (OMZs) are large, low-oxygen areas in the global oceans. Although OMZs represent a serious threat to ecosystem functioning and services, our capability of modelling the main biogeochemical processes driving OMZ dynamic are still limited. Here we performed a full sensitivity analysis of a complex ecosystem model to rank the most important biogeochemical parameters influencing the simulation of the OMZ at an oligotrophic site in the open Arabian Sea. We applied a one-dimensional configuration of the European Regional Seas Ecosystem Model (ERSEM) - here advanced by including denitrification - coupled with the General Ocean Turbulence Model (GOTM). The coupled model was skilled in simulating the vertical gradients of climatological data of oxygen and nutrients. The sensitivity analysis of the model was carried out in two steps: i) a preliminary Morris screening analysis of 207 ERSEM parameters, which selected the three most influential groups of parameters; and ii) a subsequent Monte Carlo sampling-based analysis for ranking the importance of the 38 parameters within the three selected groups. Overall, the four most important parameters for the simulation of the minimum oxygen concentration were found to be: 1) the cubic half saturation constant for oxygenic control of denitrification; 2) the parameter regulating the fraction of ingested matter excreted by heterotrophic nanoflagellates; 3) the bacterial efficiency at low oxygen levels; and 4) the specific rate of bacterial release of capsular material. Based on these findings, and assuming that the ranking of the model parameters reflects the relevance of the process they characterize, we present a conceptual model describing the most important biogeochemical processes affecting the OMZ at the study site. Our results suggest that including bacteria explicitly in ecosystem models is useful to simulate and predict OMZs, provided that efforts are invested in estimating parameters characterizing the microbial loop in marine ecosystems

Revolutionary physics-based design tools for quiet helicopters

This paper describes a revolutionary, fully-integrated approach for modeling the noise characteristics of maneuvering rotorcraft. The primary objective of this effort is the development of a physics-based software tool that enables the design of quiet rotors without performance penalties. This tool shall accurately predict the rotorcraft flight state and rotor trim, the unsteady aerodynamic loading, the time-dependent flow field around the rotor blades, and the radiated noise, in all flight conditions including maneuver. This objective is achieved through the use of advanced computational fluid dynamics (CFD), computational structural dynamics (CSD), and computational aeroacoustics (CAA). The predictions are validated and verified against benchmark test cases. The advanced CFD methods include innovations in Large Eddy Simulation, novel techniques for flexible deforming blades, high-order methods for accuracy, and adaptive grids to accurately capture important flow features. CSD methods are coupled with the CFD and acoustics codes using generic interfaces. The aeroacoustic predictions build on an advanced method with enhancements for maneuvering flight

Monitoring of Harmful Algal Bloom (HAB) of Noctiluca scintillans (Macartney) along the Gulf of Mannar, India using in-situ and satellite observations and its impact on wild and maricultured finfishes

In the Gulf of Mannar, Noctiluca scintillans blooms have been observed three times in September 2019, September and October 2020, and October 2021. It was determined and measured how the bloom period affects ichthyo-diversity. Noctiluca cell density varied slightly from year to year, ranging from1.8433 × 103 cells/L to 1.3824 x 106cells/L. In surface and sea bottom waters, high ammonia levels and low dissolved oxygen levels were noted. During the bloom period a significant increase in chlorophyll concentration was found. The amount of chlorophyll in GOM was extremely high, according to remote sensing photos made using MODIS-Aqua 4 km data. Acute hypoxia caused the death of wild fish near coral reefs and also in fish reared in sea cages. The decay of the bloom resulted in significant ammonia production, a dramatic drop in the amount of dissolved oxygen in the water, and ultimately stress, shock, and mass mortality of fishes