Influence of probiotic strains on growth performance, carcass characteristics and seasonal changes of growing New Zealand white rabbits.

This experiment was designed to explore how different types of probiotics affect the growth, carcass traits, and seasonal variations in growing New Zealand White rabbits (NZW). Two parallel experiments using the same strain of NZW during winter and summer, each alone from 5 to 13 weeks of age. Each experiment uses a total of 125 unsexed rabbits. These rabbits are separated into 5 groups of 25 rabbits each. Each group has five replicates, with five rabbits in every replicate. In each experiment, 1st group acting as the control group did not receive any probiotics. The 2nd was given a dose of 1 ml of Bifidobacterium bifidum, the 3rd received a dose of 1 ml of Lactobacillus acidophilus, and the 4th was treated with a 1 ml blend of both Bifidobacterium bifidum and Lactobacillus acidophilus, and 5th group was treated with 1 ml of Saccharomyces cerevisiae. Results indicated that the Bifidobacterium bifidum group had the best live body weight (LBW) values and daily weight gain (DWG). Meanwhile, during summer, the Lactobacillus acidophilus group had the best feed conversion ratio (FCR) and performance index (PI) values. Also, growing rabbits fed Lactobacillus acidophilus and Bifidobacterium bifiduim had significantly increased carcass traits during the summer and winter seasons. Furthermore, seasonal changes indicated that the Bifidobacterium bifiduim group improved LBW, DWG, and PI during summer than winter. So, it could be concluded that using Bifidobacterium bifidum can enhance rabbit growth by improving feed utilization and carcass traits, making it an effective addition to hot weather diets.

Analyzing hope speech from psycholinguistic and emotional perspectives.

Hope is a vital coping mechanism, enabling individuals to effectively confront life's challenges. This study proposes a technique employing Natural Language Processing (NLP) tools like Linguistic Inquiry and Word Count (LIWC), NRC-emotion-lexicon, and vaderSentiment to analyze social media posts, extracting psycholinguistic, emotional, and sentimental features from a hope speech dataset. The findings of this study reveal distinct cognitive, emotional, and communicative characteristics and psycholinguistic dimensions, emotions, and sentiments associated with different types of hope shared in social media. Furthermore, the study investigates the potential of leveraging this data to classify different types of hope using machine learning algorithms. Notably, models such as LightGBM and CatBoost demonstrate impressive performance, surpassing traditional methods and competing effectively with deep learning techniques. We employed hyperparameter tuning to optimize the models' parameters and compared their performance using both default and tuned settings. The results highlight the enhanced efficiency achieved through hyperparameter tuning for these models.

In vivo evaluation of medicinal effects of Myristica fragrans, Cinnamomum zeylanicum, Mucuna pruriens on male murine fertility.

Herbal remedies are used for managing different ailments including male sexual abnormalities. Mucuna pruriens, Cinnamomum zeylanicum and Myristica fragrans, are some of the important herbs of these remedies for male sexual disorders. This study has been conducted to evaluate the effects of these drugs, individually and in combination on fertility parameters in mice. The study was carried out on male and female albino mice of BALB/c strain bearing weight of 20-25 g and age 12 to 13 weeks. Animals were divided into control and test batches (n=10). Drugs were given to the male mice test groups daily for 52 days by oral route and on 53rd day the fertility parameters were measured. Afterwards, histopathological analysis was also done. One-way analysis of variance (ANOVA) followed by post hoc was applied for statistical analysis. Important contrast was found in fertility parameters, including pregnancy outcome, serum testosterone, luteinizing hormone, follicle stimulating hormone and histological examination of tested batches as compared to control. The fertility enhancing effect of the drugs were found in the tested doses used in this study in male albino mice of BALB/c strain. However further preclinical and clinical studies are necessary to determine the safety of these drugs.

Molecular characterization, genetic divergence, expression of encapsidiation and synergism by a bipartite begomovirus; Tomato leaf curl Palampur virus (ToLCPMV) infecting bitter gourd (Momordica charantia).

The Tomato leaf curl Palampur virus (ToLCPMV) is a bipartite begomovirus that poses a substantial risk to agriculture by infecting a variety of crops, including cucurbitaceous group. This study examines the manifestation of encapsidation and synergism by ToLCPMV in bitter gourd (Momordica charantia) and focuses on its epidemiological approaches and implications of managing this virus in tomatoes growing areas. Through the utilization of molecular and biological techniques, we have successfully ascertained the epidemiology of this highly destructive virus, highlighting the vital roles played by its two genetic components. An analysis was conducted to identify the mechanism by which the virus clusters its DNA into virions, known as the encapsidation process. Additionally, the impact of synergism with other viral or environmental factors over the degree of infection was examined. The evolutionary rate differences among sites were modeled deploying a discrete Gamma distribution with 5 categories and a [+G] parameter. The results of this study provide important and unique information about synergism, encapsidiation and host-virus interactions. Sequencing study revealed that the bipartite ToLCPMV is linked to the occurrence of leaf curl disease in bitter gourd. The DNA-A and DNA-B of the ToLCPMV isolates infecting bitter gourd (SP1-4) showed 89 %, 93 %, 95 %, and 98 % similarity respectively. Mean evolutionary rates in these categories were 0.19, 0.47, 0.79, 1.24, 2.31 substitutions per site. Unexpectedly, the DNA-A sequences of ToLCPMV that infect this particular host seemed to be an amalgamation of sequences that are closely associated with tomato leaf curl New Delhi virus (ToLCNDV). Additionally, reiterate cropping of tomatoes with vegetables expanded the virus's host geographic region. This understanding will create some specific ways to regulate the dissemination of ToLCPMV and minimize its adverse impacts in tomato growing regions. Through the implementation of these strategies, the ability of crops to withstand and recover from adverse conditions can be enhanced, so encouraging the adoption of sustainable farming practices in affected regions.

The proximate composition of vegetables enriched by incorporation of municipal solid waste into fertilizers and its impacts on environment and human health.

The recent over production of municipal solid waste (MSW) poses a significant threat to both the ecosystem and human health. Utilizing MSW for agricultural purposes has emerged as a promising strategy to reduce solid waste disposal while simultaneously increasing soil fertility. To explore this potential solution further, an experiment was designed to assess the impact of varying concentrations of MSW (25%, 50%, and 75%) on the proximate composition of 15 different vegetable species. The experiment, conducted between 2018 and 2019, involved treating soil with different levels of solid waste and analyzing the proximate components, such as crude protein, dry matter, crude fiber, crude fat, and moisture content, in the 15 selected crops. The results indicate that the application of 25% MSW significantly increased the levels of crude protein, crude fiber, dry matter, and fat in Spinacia oleracea, Solanum tuberosum, Solanum melongena, and Abelmoschus esculentus. Conversely, the addition of 75% MSW notably elevated the moisture and ash content in Cucumis sativus. Correlation and scatter matrix analyses were conducted to elucidate the relationships between the protein, fiber, dry matter, ash, and fat contents. Principal component analysis and clustering confirmed the substantial impact of Treatment_1 (25% MSW) and Treatment_3 (75% MSW) on the proximate composition of the aforementioned vegetables, leading to their categorization into distinct groups. Our study highlights the efficacy of using 25% MSW to enhance the proximate composition and nutritional value of vegetables. Nonetheless, further research is warranted to investigate the mineral, antioxidant, vitamin, and heavy metal contents in the soil over an extended period of MSW application.

Response and disease resistance evaluation of sorghum seedlings under anthracnose stress.

Sorghum is the world's fifth-largest cereal crop, and anthracnose (Colletotrichum sublineola) is the main disease affecting sorghum. However, systematic research on the cellular structure, physiological and biochemical, and genes related to anthracnose resistance and disease resistance evaluation in sorghum is lacking in the field. Upon inoculation with anthracnose (C. sublineola) spores, disease-resistant sorghum (gz93) developed a relative lesion area (RLA) that was significantly smaller than that of the disease-susceptible sorghum (gz234). The leaf thickness, length and profile area of leaf mesophyll cells, upper and lower epidermal cells decreased in the lesion area, with a greater reduction observed in gz234 than in gz93. The damage caused by C. sublineola resulted in a greater decrease in the net photosynthetic rate (Pn) in gz234 than in gz93, with early-stage reduction due to stomatal limitation and late-stage reduction caused by lesions. Overall, the activities of superoxide dismutase (SOD) and catalase (CAT), the content of proline (Pro), abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA), and gibberellic acid (GA3), are higher in gz93 than in gz234 and may be positively correlated with disease resistance. While malondialdehyde (MDA) may be negatively correlated with disease resistance. Disease-resistant genes are significantly overexpressed in gz93, with significant expression changes in gz234, which is related to disease resistance in sorghum. Correlation analysis indicates that GA3, MDA, peroxidase (POD), and disease-resistance genes can serve as reference indicators for disease severity. The regression equation RLA = 0.029 + 8.02 × 10-6 JA-0.016 GA3 can predict and explain RLA. Principal component analysis (PCA), with the top 5 principal components for physiological and biochemical indicators and the top 2 principal components for disease-resistant genes, can explain 82.37% and 89.11% of their total variance, reducing the number of evaluation indicators. This study provides a basis for research on the mechanisms and breeding of sorghum with resistance to anthracnose.

A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications.

Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces (BCI). Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.

Chemometric Modeling Revealed Oleic and Linoleic Acids as Varietal Biomarkers for Six Sesame Varieties-In Vitro and UHPLC Analyses.

Pakistan once considered self-sufficient for edible oil production now became the major importer with 88.6% imports and producing only the minor portion. Scientific negligence in oil seed crops led to a dramatic decrease in edible oil production depending mainly on only the imports. Sesamum indicum L., "Queen of Oil seeds" with 50-55% oil, is cultivated in various geographical regions of Pakistan, but farmers are not considering this crop because of insufficient knowledge, poor crop management practices, and low yielding varieties. This study was conducted to check the nutritional, biochemical, antioxidant, and yield potentials of six major varieties, i.e., TS-5, TH-6, Til-18, NIAB-Mil, NIAB-Pearl, and NS-16, and to compare the nutritionals, oil quality, and oil yield potential of these varieties. Field experiment was conducted, and various crop growth biomarkers were analyzed. Chlorophyll content and superoxide dismutase activity were found to be highest in NIAB-Mil followed by NIAB-Pearl and comparable to those of Til-18, while APX, Cat, and GPX activity was found to be highest in Til-18 with 25.6 and 5.9 and 6.02 mg/g, respectively. Seed antioxidant parameters showed a mixed response, but NIAB-Mil, NIAB pearl, and Til-18 were found to be highest in all antioxidant parameters. UHPLC analysis of seed oil resulted in a total of 14 triacylglycerols (TAGs), and principal component analysis and OPLS-Da analysis showed seven TAG biomarkers responsible for the separation of sesame varieties. Til-18 was found to be highest in oil content (53.3%) more abundant with oleic acid, while NIAB-Pearl, NIAB-Mil, and NS-16 were found to be abundant with linoleic acid, both considered as potential TAG biomarkers for sesame oil separation. This study concluded that, in general, Til-18 variety is more resistant with high nutritional status, high antioxidant activity, and oil yielding variety, followed by NIAB-Mil and NIAB-Pearl.

A novel deep learning identifier for promoters and their strength using heterogeneous features.

Promoters, which are short (50-1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by genetic variations in promoters. Consequently, the correct identification and characterization of promoters are significant for the discovery of drugs. However, experimental approaches to recognizing promoters and their strengths are challenging in terms of cost, time, and resources. Therefore, computational techniques are highly desirable for the correct characterization of promoters from unannotated genomic data. Here, we designed a powerful bi-layer deep-learning based predictor named "PROCABLES", which discriminates DNA samples as promoters in the first-phase and strong or weak promoters in the second-phase respectively. The proposed method utilizes five distinct features, such as word2vec, k-spaced nucleotide pairs, trinucleotide propensity-based features, trinucleotide composition, and electron-ion interaction pseudopotentials, to extract the hidden patterns from the DNA sequence. Afterwards, a stacked framework is formed by integrating a convolutional neural network (CNN) with bidirectional long-short-term memory (LSTM) using multi-view attributes to train the proposed model. The PROCABLES model achieved an accuracy of 0.971 and 0.920 and the MCC 0.940 and 0.840 for the first and second-layer using the ten-fold cross-validation test, respectively. The predicted results anticipate that the proposed PROCABLES protocol outperformed the advanced computational predictors targeting promoters and their types. In summary, this research will provide useful hints for the recognition of large-scale promoters in particular and other DNA problems in general.

StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features.

Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform vital role in various biological process. Conventional Experiments to identify defensin peptides (DPs) are time consuming and expensive. Thus, the shortcomings of wet lab experiments are leveraged by computational methods to accurately predict the functional types of DPs. In this paper, we aim to propose a novel multi-class ensemble-based prediction model called StackDPPred for identifying the properties of DPs. The peptide sequences are encoded using split amino acid composition (SAAC), segmented position specific scoring matrix (SegPSSM), histogram of oriented gradients-based PSSM (HOGPSSM) and feature extraction based graphical and statistical (FEGS) descriptors. Next, principal component analysis (PCA) is used to select the best subset of attributes. After that, the optimized features are fed into single machine learning and stacking-based ensemble classifiers. Furthermore, the ablation study demonstrates the robustness and efficacy of the stacking approach using reduced features for predicting DPs and their families. The proposed StackDPPred method improves the overall accuracy by 13.41% and 7.62% compared to existing DPs predictors iDPF-PseRAAC and iDEF-PseRAAC, respectively on validation test. Additionally, we applied the local interpretable model-agnostic explanations (LIME) algorithm to understand the contribution of selected features to the overall prediction. We believe, StackDPPred could serve as a valuable tool accelerating the screening of large-scale DPs and peptide-based drug discovery process.

Classification, synthesis, characterization, and applications of metal nanoparticle-containing hybrid microgels: a comprehensive review.

In the last ten years, there has been significant interest in the integration of metal nanoparticles (MNPs) in smart microgels (SMGs). These combined structures of metal nanoparticles and smart microgels possess unique behaviors that make them suitable for a wide range of applications in catalysis, environmental and biological fields. The intrinsic responsiveness of microgels within these hybrid systems shows significant potential for application across multiple fields. Extensive literature provides diverse insights into the morphologies and compositions of metal nanoparticles in microgels. The design of these hybrid microgels plays a crucial role in determining their applicability, leading to tailored solutions for specific purposes under specific conditions. This review aims to summarize the latest advancements in the classification, synthesis, responsiveness, characterizations, and applications of hybrid microgel systems. Additionally, it explores the recent advancements in the applications of metal nanoparticle-decorated microgels in catalysis, adsorption, sensing, biomedical and environmental fields.

Protein-peptide binding residue prediction based on protein language models and cross-attention mechanism.

Accurate identifications of protein-peptide binding residues are essential for protein-peptide interactions and advancing drug discovery. To address this problem, extensive research efforts have been made to design more discriminative feature representations. However, extracting these explicit features usually depend on third-party tools, resulting in low computational efficacy and suffering from low predictive performance. In this study, we design an end-to-end deep learning-based method, E2EPep, for protein-peptide binding residue prediction using protein sequence only. E2EPep first employs and fine-tunes two state-of-the-art pre-trained protein language models that can extract two different high-latent feature representations from protein sequences relevant for protein structures and functions. A novel feature fusion module is then designed in E2EPep to fuse and optimize the above two feature representations of binding residues. In addition, we have also design E2EPep+, which integrates E2EPep and PepBCL models, to improve the prediction performance. Experimental results on two independent testing data sets demonstrate that E2EPep and E2EPep + could achieve the average AUC values of 0.846 and 0.842 while achieving an average Matthew's correlation coefficient value that is significantly higher than that of existing most of sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis shows that the primary strength of E2EPep lies in the effectiveness of feature representation using cross-attention mechanism to fuse the embeddings generated by two fine-tuned protein language models. The standalone package of E2EPep and E2EPep + can be obtained at https://github.com/ckx259/E2EPep.git for academic use only.

The adult ADHD assessment quality assurance standard.

Background: Attention Deficit Hyperactivity Disorder (ADHD) frequently persists into adulthood. There are practice guidelines that outline the requirements for the assessment and treatment of adults. Nevertheless, guidelines specifying what constitutes a good quality diagnostic assessment and report and the competencies required to be a specialist assessor are lacking. This can lead to variation in the quality and reliability of adult ADHD assessments. Poor quality assessments may not be accepted as valid indicators of the presence of ADHD by other clinicians or services, resulting in wasteful re-assessments and delays in providing treatment. To address this issue the UK Adult ADHD Network (UKAAN) proposes a quality framework for adult ADHD assessments - the Adult ADHD Assessment Quality Assurance Standard (AQAS). Methods: The co-authors agreed on five questions or themes that then guided the development of a set of consensus statements. An initial draft was reviewed and amended in an iterative process to reach a final consensus. Results: What constitutes a high-quality diagnostic assessment and report was agreed by consensus of the co-authors. The resulting guideline emphasises the need to evaluate impairment, describes core competencies required by the assessor and highlights the importance of linking the diagnosis to an appropriate post-diagnostic discussion. Assessments should be completed in the context of a full psychiatric and neurodevelopmental review, and need good interview skills, using a semi-structured interview with open questioning and probing to elicit real life examples of symptoms and impairments. It is recommended that 2 hours or more is required for an adequate assessment including both the diagnostic assessment and initial post-assessment discussions. Conclusion: The AQAS has been developed as a practical resource to support reliable and valid diagnostic assessments of adult ADHD. It is intended to complement formal training. A secondary objective is to empower patients by providing them with evidence-based information on what to expect from an assessment and assessment report.

Can urea-coated fertilizers be an effective means of reducing greenhouse gas emissions and improving crop productivity?

Given the significance of nitrogen (N) as the most constraining nutrient in agro-ecosystems, it is crucial to develop an updated model for N fertilizers management to achieve higher crop yields while minimizing the negative impacts on the environment. Coated urea is touted as one of the most important controlled-release N fertilizers used in agriculture to reduce cropland emissions and improve nitrogen use efficiency (NUE) for optimal crop yields. The sustainability of coated urea depends on the trade-offs between crop productivity, NUE and greenhouse gas emissions (CO2, CH4 and N2O); however, role of various agro-edaphic factors in influencing these trade-offs remains unclear. To determine the effects of soil properties, climatic conditions, experimental conditions, and type of coated urea on greenhouse gas emissions, NH3 losses, crop productivity, and NUE, we conducted a meta-analysis using data from 76 peer-reviewed studies. Our results showed that the application of coated urea under field conditions contributed to a greater reduction in N2O emissions (-48.67%) and higher NUE (58.72%), but crop yields were not significant. Across different climate regions, subtropical monsoon climate showed a perceptible mitigation for CO2, CH4 and NH3 (-78.38%; -83.33%; -27.46%), while temperate climate reduced N2O emissions by -70.36%. For different crops, only rice demonstrated reduction in CO2, CH4, N2O and NH3 losses. On the other hand, our findings revealed a mitigating trade-off between CO2 and CH4 emissions on medium-textured soils and N2O emissions on fine-textured soils. A significant reduction in N2O and NH3 losses was evident when coated urea was applied to soils with a pH > 5.5. Interestingly, application of coated urea to soils with higher C/N ratios increased NH3 losses but showed a noticeable N2O reduction. We found that polymer-coated urea reduced CH4 and N2O emissions and NH3 losses at the expense of higher CO2 emissions. Moreover, application of a lower dose of coated urea (0-100 kg N ha-1) enhanced CO2 and CH4 mitigation, while N2O mitigation increased linearly with increasing dose of coated urea. Most importantly, our results showed that the application of coated urea leads to a large mismatch between NUE, crop yields and greenhouse gas mitigation. By and large, the application of coated urea did not correspond with higher crop yields despite significant reduction in the emissions and improved NUE. Overall, these results suggest that site-specific agro-edaphic conditions should be considered when applying coated urea to reduce these emissions and N volatilization losses for increasing NUE and crop yields.

Discovery and prospects of new heterocyclic Isatin-hydrazide derivative with a novel role as estrogen receptor α degrader in breast cancer cells.

Introduction: Isatin, a heterocycle scaffold, is the backbone of many anticancer drugs and has previously been reported to engage multiple cellular targets and mechanisms, including angiogenesis, cell cycle, checkpoint pathways and multiple kinases. Here, we report that a novel isatin derivative, 5i, degrades estrogen receptor alpha (ERα) in estrogen-dependent breast cancer cells. This effect of the isatin nucleus has not been previously reported. Tamoxifen and fulvestrant represent standard therapy options in estrogen-mediated disease but have their own limitations. Isatin-based triple angiokinase inhibitor BIBF1120 (Nintedanib) and multikinase inhibitor Sunitinib (Sutent) have been approved by the FDA. Methods: Keeping this in view, we synthesized a series of N'-(1-benzyl-2-oxo-1, 2-dihydro-3H-indol-3-ylidene) hydrazide derivatives and evaluated them in vitro for antiproliferative activities in MCF-7 (ER+) cell line. We further investigated the effect of the most potent compound (5i) on the Erα through Western Blot Analysis. We used in silico pharmacokinetics prediction tools, particularly pkCSM tool, to assess the activity profiles of the compounds. Results and discussion: Compound 5i showed the best antiproliferative activity (IC50 value; 9.29 ± 0.97 µM) in these cells. Furthermore, 5i downregulated ERα protein levels in a dose-dependent manner in MCF-7. A multifaceted analysis of physicochemical properties through Data Warrior software revealed some prominent drug-like features of the synthesized compounds. The docking studies predicted the binding of ligands (compounds) with the target protein (ERα). Finally, molecular dynamics (MD) simulations indicated stable behavior of the protein-ligand complex between ERα and its ligand 5i. Overall, these results suggest that the new isatin derivative 5i holds promise as a new ERα degrader.

PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation.

Anticancer peptides (ACPs) perform a promising role in discovering anti-cancer drugs. The growing research on ACPs as therapeutic agent is increasing due to its minimal side effects. However, identifying novel ACPs using wet-lab experiments are generally time-consuming, labor-intensive, and expensive. Leveraging computational methods for fast and accurate prediction of ACPs would harness the drug discovery process. Herein, a machine learning-based predictor, called PLMACPred, is developed for identifying ACPs from peptide sequence only. PLMACPred adopted a set of encoding schemes representing evolutionary-property, composition-property, and protein language model (PLM), i.e., evolutionary scale modeling (ESM-2)- and ProtT5-based embedding to encode peptides. Then, two-dimensional (2D) wavelet denoising (WD) was employed to remove the noise from extracted features. Finally, ensemble-based cascade deep forest (CDF) model was developed to identify ACP. PLMACPred model attained superior performance on all three benchmark datasets, namely, ACPmain, ACPAlter, and ACP740 over tenfold cross validation and independent dataset. PLMACPred outperformed the existing models and improved the prediction accuracy by 18.53%, 2.4%, 7.59% on ACPmain, ACPalter, ACP740 dataset, respectively. We showed that embedding from ProtT5 and ESM-2 was capable of capturing better contextual information from the entire sequence than the other encoding schemes for ACP prediction. For the explainability of proposed model, SHAP (SHapley Additive exPlanations) method was used to analyze the feature effect on the ACP prediction. A list of novel sequence motifs was proposed from the ACP sequence using MEME suites. We believe, PLMACPred will support in accelerating the discovery of novel ACPs as well as other activities of microbial peptides.

Catalytic reduction of nitroarenes by palladium nanoparticles decorated silica@poly(chitosan-N-isopropylacrylamide-methacrylic acid) hybrid microgels.

Conversion of toxic nitroarenes into less toxic aryl amines, which are the most suitable precursors for different types of compounds, is done with various materials which are costly or take more time for this conversion. In this regards, a silica@poly(chitosan-N-isopropylacrylamide-methacrylic acid) Si@P(CS-NIPAM-MAA) Si@P(CNM) core-shell microgel system was synthesized through free radical precipitation polymerization (FRPP) and then fabricated with palladium nanoparticles (Pd NPs) by in situ-reduction method to form Si@Pd-P(CNM) and characterized with XRD, TEM, FTIR, SEM, and EDX. The catalytic efficiency of Si@Pd-P(CNM) hybrid microgels was studied for reduction of 4-nitroaniline (4NiA) under diverse conditions. Different nitroarenes were successfully transformed into their corresponding aryl amines with high yields using the Si@Pd-P(CNM) system as catalyst and NaBH4 as reductant. The Si@Pd-P(CNM) catalyst exhibited remarkable catalytic efficiency and recyclability as well as maintaining its catalytic effectiveness over multiple cycles.

Chronic Alcohol Intake Compromises Lung Immunity by Altering Immunometabolism in Humans and Mouse Models.

Chronic alcohol consumption disrupts lung immunity and host defense mechanisms, rendering individuals with alcohol use disorder more susceptible to developing inflammatory lung conditions with poor prognoses. Here, we focused on investigating the molecular and cellular effects of alcohol ingestion on lung immunity in male and female subjects using population-based human lung transcriptomics analysis and an experimental mouse model of chronic alcohol drinking using the NIAAA alcohol feeding model. Flow cytometry and transcriptomics analyses in lungs revealed a sexually dimorphic effect of chronic alcohol drinking on lung immunity of both human and mouse. The male lungs were more sensitive to chronic alcohol drinking-induced dysregulation of lung immunity compared to the females. Furthermore, comparative transcriptomics analysis using lungs and liver samples from matched human and mouse subjects exhibited that lungs were more sensitive than the liver to the effects of alcohol in down-regulating immune-related genes and pathways. Furthermore, the transcriptomics analysis provided evidence that immunometabolic change is a central driver in lung alteration by downregulating the immune pathways and upregulating metabolic pathways. Chronic alcohol consumption resulted in reduced mTOR signaling and decreased immune cell populations. mTOR signaling axis may serve as an upstream regulator of alcohol-induced dysregulation in lung immunity.

A comprehensive review on crosslinked network systems of zinc oxide-organic polymer composites.

In recent years, the synergistic crosslinked networks formed by zinc oxide (ZnO) particles and organic polymers have gained significant attention. This importance is ascribed due to the valuable combination of low band gap containing ZnO particles with responsive behavior containing organic polymers. These properties of both ZnO and organic polymers make a suitable system of crosslinked ZnO-organic polymer composite (CZOPC) for various applications in the fields of biomedicine, catalysis, and environmental perspectives. The literature extensively provided the diverse morphologies and structures of CZOPC, and these architectural structures play a crucial role in determining their efficiency across various applications. Consequently, the careful design of CZOPC shapes tailored to specific purposes has become a focal point. This comprehensive review provides insights into the classifications, synthetic approaches, characterizations, and applications of ZnO particles decorated in organic polymers with crosslinked network. The exploration extends to the adsorption, environmental, catalytic, and biomedical applications of ZnO-organic polymer composites. Adopting a tutorial approach, the review systematically investigates and elucidates the applications of CZOPC with a comprehensive understanding of their diverse capabilities and uses.

A holistic overview of the in-situ and ex-situ carbon mineralization: Methods, mechanisms, and technical challenges.

To mitigate anthropogenic CO2 emissions and address the climate change effects, carbon capture and storage by mineralization (CCSM) and industrial mineral carbonation are gaining attraction. Specifically, in-situ carbon mineralization in the subsurface geological formations occurs due to the transformation of silicate minerals into carbonates (e.g., CaCO3, MgCO3) while ex-situ carbon mineralization at the surface undergoes chemical reactions with metal cations - thus leading to permanent storage. However, both processes are complex and require a rigorous investigation to enable large-scale mineralization. This paper, therefore, aims to provide an overreaching review of the in-situ and ex-situ methods for carbon mineralization for different rock types, various engineered processes, and associated mechanisms pertinent to mineralization. Furthermore, the factors influencing in-situ and ex-situ processes, e.g., suitable minerals, optimal operating conditions, and technical challenges, have also been inclusively reviewed. Our findings suggest that in-situ carbon mineralization, i.e., subsurface permanent storage of CO2 by mineralization, arguably is more promising than ex-situ mineralization due to energy efficiency and large-scale storage potential. Furthermore, the effect of rock type can be ranked as igneous (basalt) > carbonates (sedimentary) > sandstone (sedimentary) to consider for rapid and large-scale CCSM. The findings of this review will, therefore, help towards a better understanding of carbon mineralization, which contributes towards large-scale CO2 storage to meet the global net-zero targets.