6,091 research outputs found

    Machine Learning-Guided Discovery of AcrB and MexB Efflux Pump Inhibitors

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    Multidrug efflux pump is one of the reasons behind the antimicrobial inactivity related to infection caused by Gram-negative pathogens. The inner membrane resistance–nodulation–cell division transporter proteins, AcrB and MexB, in association with outer membrane proteins, TolC and OprM, are responsible for the extrusion of a broad range of substrates, followed by recognizing them. Although various inhibitors were proposed to stop the efflux activity of the transporter protein, none of them had been approved clinically. Our study aims to identify potent inhibitor-like molecules employing supervised classification models trained upon the molecular descriptors of previously known inhibitors. Based on the intrinsic minimum inhibitory concentration (MIC) values of the reported inhibitors, they were classified into highly potent and less potent categories. A total of 10 different classification models were built using various molecular descriptors; among them, support vector machine, Random Forest, AdaBoost, and LightGBM models appeared to deliver promising results with >80% accuracy. These top four models were implemented on a library of 5043 to obtain 8 hit molecules after the multistep filtering process. To assess their activity toward AcrB and MexB, several molecular dynamics simulations of their ligand-bound structures were performed. We also calculated the binding free-energy values and analyzed other structural properties. Mol.3488 of the unknown molecules showed higher binding affinities for both AcrB and MexB. Also, the presence of “pyridopyrimidone” and “benzothiazole” moieties in the molecules and “V”-shaped orientation of ligands inside the deep binding pocket increase the binding affinity, thereby higher inhibitory properties

    Artificial neural networks for the wavelet analysis of Lane-Emden equations: exploration of astrophysical enigma

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    The equations of Lane-Emden (LE) can be visualized in various phenomena of astrophysics, fluid mechanics, polymer science and material science, thus the main concern of the present study is to put a novel effort to resolve these equations by utilizing the artificial neural networking approach incorporation with Vieta-Lucas wavelets called as VLW-ANN method. This unique combination of neural networking and Vieta-Lucas wavelets has been prepared to reduce the computational challenges as well as to overcome the obstacles while dealing with singularity. Many examples of the LE variety are solved by this approach. The effectiveness, accuracy and simplicity of the VLW-ANN scheme are demonstrated by a comparative study between the VLW-ANN results and existing results. Additionally, the results are shown in tables and figures, which give a more favorable impression of the scheme’s dependability. VLW-ANN scheme will provide interesting results for other non-linear models.</p

    Table2_Development of early maturing salt-tolerant rice variety KKL(R) 3 using a combination of conventional and molecular breeding approaches.DOCX

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    Introduction: Soil salinity poses a severe threat to rice production, resulting in stunted growth, leaf damage, and substantial yield losses. This study focuses on developing an early maturing seedling stage salinity tolerant rice variety by integrating conventional breeding methods with marker assisted breeding (MAB) approaches.Methods: Seedling-stage salinity tolerance Quantitative Trait Locus (QTL) “Saltol” from the salt-tolerant parent FL478 was introduced into the high-yielding but salt-sensitive rice variety ADT 45. This was achieved through a combination of conventional breeding and MAB. The breeding process involved rigorous selection, screening, and physiological parameter assessments.Results: KKL(R) 3 (KR 15066) identified as the top performing Recombinant Inbred Line (RIL), consistently demonstrating maximum mean grain yields under both salinity (3435.6 kg/ha) and normal (6421.8 kg/ha) conditions. In comparison to the early maturing, salt-tolerant national check variety CSR 10, KKL(R) 3 exhibited a substantial yield increase over 50%.Discussion: The notable improvement observed in KKL(R) 3 positions it as a promising variety for release, offering a reliable solution to maximize yields, ensure food security, and promote agricultural sustainability in both saline and non-saline environments. The study highlights the effectiveness of MAB in developing salt-tolerant rice varieties and emphasizes the significance of the Saltol QTL in enhancing seedling stage salinity tolerance. The potential release of KKL(R) 3 has the capacity to revolutionize rice production in salt affected regions, providing farmers with a reliable solution to maximize yields and contribute to food security while ensuring agricultural sustainability.</p

    Multivariate associations between cognition and neighborhood geospatial characteristics in schizophrenia

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    Cognitive impairment contributes to functional impairment in schizophrenia. Yet, little is known about how environmental characteristics are related to cognition in schizophrenia. By examining how cognition and the environment are intertwined, it may be possible to identify modifiable risk and protective factors that can improve cognitive outcomes in schizophrenia. We aimed to identify multivariate associations between cognition and three geospatial characteristics (built-space density, habitable green spaces, and public spaces for social interaction) within one's immediate neighborhood among individuals with schizophrenia. We recruited participants with schizophrenia from three sites – an urban metropolitan and two towns in southern India. We administered standard cognitive assessments and performed a principal axis factoring to identify episodic memory, cognitive control, and social inference-making factors for use in further analyses. We estimated geospatial characteristics of an individual's neighborhood, i.e., up to 1 km2 around the residence, by sourcing data from Google Earth. We performed unconditional and conditional (to examine the effect of clinical covariates) canonical correlation analyses to understand the multivariate relationship between cognition and geospatial characteristics. We analyzed data from 208 participants; the first canonical cognitive variate (higher social inference-making and poorer cognitive control) shared 24% of the variance (r = 0.49; P &lt; 0.001) with the first geospatial variate (lower built density and poorer access to public spaces). Years of education, age at onset, and place of residence significantly modulated this relationship. We observe differential associations of the built environment with social and non-social cognition in schizophrenia, and highlight the clinical and demographic characteristics that shape these associations.</p

    A Review on Phytochemical and Pharmacological Aspects of Ochradenus baccatus

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    Ochradenus baccatus, also known as "Taily Weed," is a perennial shrub that belongs to the Resedaceae family and is often used as a folk medicine for the treatment of headache, fever or sexual disorders in the Middle Eastern countries. It is also used as an antibiotic, cooling agent and deodorant. Due to its widespread use in traditional medicine in the Middle Eastern countries and lack of scientific evidence, it is being investigated for its phytotherapeutic potential in different disease conditions

    Early prognosis prediction in acute myeloid and acute lymphoid leukemia patients using cell-free DNA concentration ratios

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    Background: Cell-free DNA (cfDNA) is a promising biomarker for disease prediction in many cancers, including acute leukemia (acute myeloid leukemia [AML] and acute lymphoblastic leukemia [ALL]). This study investigated the role of cfDNA in predicting relapse or unfavorable outcomes in acute leukemia patients upon initial diagnosis.Methods: Paired peripheral blood samples of 25 patients with ALL and AML were compared at baseline and induction/follow-up and clinically correlated with clinicopathological and outcome variables according to the risk category. cfDNA was isolated using commercial cfDNA extraction kits. The probability of poor outcomes in high-risk groups and a cut-off value for risk stratification minimal residual disease (MRD) positivity and outcome prediction were derived.Results: Twenty-five patients diagnosed with AML and ALL were risk-stratified based on NCI risk stratification, and of these 25 patients, 4 patients were of standard risk (SR) and 1 patient was of intermediate risk (IR), while a majority of patients (80%) were of high risk (HR). Of these, four HR patients passed away. The ratio of cfDNA reduction at baseline and the end of induction was a strong predictor of poor outcomes in high-risk patients, regardless of the MRD status. A cfDNA ratio score of 2.6 or higher at diagnosis/remission predicted poor outcomes, with higher accuracy than conventional MRD detection by flow cytometry.Conclusion: A higher cfDNA ratio at diagnosis/remission or at baseline predicts poor outcomes in acute leukemia patients. This pilot study suggests that cfDNA ratio scoring may be a useful tool for predicting prognosis in acute leukemia patients, regardless of the MRD status

    Parametric investigation of rectangular CFRP-confined concrete columns reinforced by inner elliptical steel tubes using finite element and machine learning models

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    Nowadays, due to the structural advantages gained by combining three different materials’ properties, columns made of carbon-fiber reinforced polymer (CFRP)-confined concrete with inner steel tube have received researchers’ interest. This article presents the nonlinear finite element analysis and multiple machine learning (ML) model-based study on the behavior of round corner rectangular CFRP-confined concrete short columns reinforced by the inner high-strength elliptical steel tube under the axial load. The reliability of the proposed nonlinear finite element model was verified against the existing experimental investigations. The effects of the parameters such as the concrete grade, thickness of reinforcing steel tube, cross-sectional size of inner steel tube, and thickness of CFRP on the behavior of the columns are comprehended in this study. Furthermore, multiple ML models were proposed to predict the ultimate axial load, ultimate axial strain, and lateral strain of the test specimens. The reliability of the proposed ML models was evaluated by six distinct performance metrics. From the parametric investigation, it was found that concrete with lower compressive strength gained more strength enhancement because of confinement between CFRP and the inner steel tube than high-strength concrete relative to its unconfined compressive strength. The proposed ML models of extreme gradient boosting and random forest provided the best-fit results than the artificial neural network and Gaussian process regression models in predicting the axial load and axial and lateral strains of the columns

    Design and implementation of optical tweezers arrays using diffractive optical elements

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    Although optical tweezers have been a valuable research tool since their invention in the 1980s, they have remained limited for many years to trapping only one particle per laser beam. One of the most exciting developments in optical tweezers in recent years has been the creation of two- and three-dimensional arrays of optical traps by using diffractive optical elements (DOEs). We have developed our own algorithms and codes to design phase DOEs that can transform a single laser beam into an array of independent traps, each with individually specified characteristics, arranged in various geometrical configurations. The DOEs were fabricated by means of e-beam lithography in PMMA and recently were implemented in computer addressed liquid crystal spatial modulators. This allows us to control the configuration of the optical tweezers almost in real time. Experimental results presented in this paper show trapping and manipulation of multiple silica micro-spheres immersed in water. The trapped particles are moved independently along the x-y-z directions and rotated along circular trajectories with different angular velocities.</p

    Data_Sheet_1_Evaluation of lablab bean [Lablab purpureus (L.) sweet] genotypes: unveiling superior pod yield, nutritional quality, and collar rot resistance.docx

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    IntroductionMalnutrition continues to be a significant concern at unacceptably high levels globally. There is significant potential for addressing malnutrition of human population through the biofortification of climate-resilient vegetables using strategic breeding strategies. Lablab bean [Lablab purpureus (L.) Sweet], a underutilized nutrient-dense crop holds great potential in this aspect. Despite its advantageous nutritional profile, the production, research, and consumption of lablab bean are currently limited. Addressing these limitations and unlock the nutritional benefits of lablab beans needs to prioritized for fighting malnutrition in local inhabitants on a global scale.Materials and methodsTwenty five genotypes of lablab bean collected through exploration survey in Eastern India and were evaluated in 2020–2021. Among them, the nine highly diverse well adapted genotypes were again evaluated at the experimental farm of ICAR-Research Complex for Eastern Region, Patna, Bihar, India in 2021–2022. Horticultural important traits of lablab bean were recorded by using the minimum descriptors developed by ICAR-NBPGR in New Delhi and biochemical analysis was done by using standard protocols. Genotypic and phenotypic correlation and path coefficient analysis was done used understand relationships, interdependencies, and causal pathways between different traits. The outcome was revalidated by using principal component analysis (PCA).ResultsDescriptive statistics revealed substantial heterogeneity across the traits of lablab bean evaluated. Vitamin A content showed nearly a five-fold variation, Fe ranged from 5.97 to 10.5 mg/100 g, and Vitamin C varied from 4.61 to 9.45 mg/100 g. Earliness and dwarf growth was observed in RCPD-1 (60 cm) and early flowering (41 days). RCPD-3 and RCPD-12 had high pod yield due to their high number of pods and pod weight. Pod yield was significantly correlated with number of pod per plant (NPP) (rg = 0.995) and with average pod weight (APW) (rg = 0.882). A significant positive correlation was also found between protein and Zn content (rg = 0.769). Path coefficient analysis revealed that average pod weight had the most direct positive effect on pod yield, followed by NPP and protein content. The reaction of lablab bean genotypes to collar rot disease was also evaluated and significant differences in disease intensity were observed among the genotypes, with the resistant check RCPD-15 exhibiting the lowest disease intensity.DiscussionThe study highlights the substantial heterogeneity in lablab bean traits, particularly in nutritional components such as vitamin A, iron, and vitamin C concentrations. Early flowering and dwarf growth habit are desirable qualities for lablab bean, and certain genotypes were found to exhibit these traits. Positive correlations, both phenotypic and genotypic, existed among different traits, suggesting the potential for simultaneous improvement. Path coefficient and PCA revealed genotypes with high yield and nutritional traits. Finally, resistant and moderately resistant lablab bean genotypes to collar rot disease were identified. These findings contribute to the selection and breeding strategies for improving lablab bean production and nutritional value.</p
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