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Beekeeping is an exceptionally weather-sensitive agricultural field. Honey production and pollination services depend on the complex interaction of plants and bees, both of which are impacted by short-term weather changes. In this review, classical and recent research is collected to provide an overview on short-term atmospheric factors influencing honey production, and the optimal and critical weather conditions for bee activity. Bee flight can be directly obstructed by precipitation, wind, extreme temperatures and also air pollution. Bees generally fly within a temperature range of 10-40 °C, with optimal foraging efficiency occurring between 20 and 30 °C. Wind speeds exceeding 1.6-6.7 m/s can reduce foraging efficiency. Additionally, bee activity is significantly correlated with temperature, relative humidity and solar radiation, factors which influence nectar production. Optimal conditions for nectar collection typically occur in the morning and early afternoon hours with mild and moist weather. The diurnal nectar collection habit of bees adjusts to the nectar production of individual plant species. Extreme weather occurring in the sensitive hours is noticeable both in the nectar production of plants and in the activity of bees, thus in the honey yield. Understanding the impact of weather on honey bees is crucial in the management and planning of honey production. This review highlights the importance of studying these interactions to better adapt beekeeping practices to changing environmental conditions.

After recovering from COVID-19, many patients experience "long COVID" symptoms. Existing research has predominantly focused on moderate to severe cases, with limited studies examining mild cases and recurrent infections. The circulating low-molecular-weight (LMW) peptidome, involving lipid metabolism, coagulation, and immune pathways, is crucial for understanding COVID-19's long-term effects. We developed a peptidomics workflow utilizing solid-phase extraction with highly wrinkled GO-Fe₃O₄ composite materials (HWGO-F) and nanoLC-MS/MS detection. By altering the pH, HWGO-F enhances plasma peptide adsorption and purification. Compared to traditional methods, our workflow offers improved detection depth and reproducibility for over 70% of peptide signals with CV < 20%. We investigated plasma peptide profiles in mild COVID-19 patients post-recovery from single or second infections. The findings indicate persistent abnormalities in initial COVID-19 infections' plasma peptide profiles, gradually diminishing over time. Secondary infections prolong recovery. Disrupted functions include lipid metabolism, coagulation and complement cascades, and infection-related pathways. Lipid metabolism may normalize within 3 months, while coagulation and immune abnormalities can last 3-6 months. After secondary infections, lipid metabolism irregularities may last at least 1 month, with extended coagulation and immune imbalances. These results provide a theoretical foundation for understanding the widespread occurrence of long COVID and guide recovery care for mild cases.

This work presents the development and optimisation of an amperometric biosensor for determining aspartate aminotransferase (AST) activity in blood serum, using glutamate oxidase and platinum disc electrodes. AST is a key biomarker for diagnosing cardiovascular and liver diseases. The biosensor's bioselective membrane composition and formation protocol and the working solution (aspartate 8 mM, α-ketoglutarate 2 mM, pyridoxal-5-phosphate 100 µM) were optimised. The sensor demonstrated high selectivity, stability (70% retention over 2 months at - 18 °C), and sensitivity (2.37 nA min⁻¹ per 10 U L⁻¹), with a dynamic range of 0-500 U L⁻¹ and a limit of detection of 1 U L⁻¹. Comparative analysis showed the calibration curve method outperforms the standard addition method for AST measurement in serum samples. Additionally, a reference spectrophotometric technique was adapted for AST level determination, showing a strong correlation (r = 0.989) with the biosensor results. This research offers a fast, affordable, and accurate tool for early check-ups of liver and heart conditions. The biosensor's flexibility and ease of use make it suitable for further development into point-of-care testing and personalised healthcare techniques.

There is a growing interest in weight loss in today's world. Environmental factors are the main contributor behind the rapidly spreading obesity during pandemic. Exercise and diet are two controllable elements that significantly effect on energy balance., The use of cold application such as cold-water immersion, cold abdominal pack, balneotherapy, cold exposure, water drinking, steam, and sauna sessions, has shown a positive impact in weight management. This review explains the mechanism and various types of hydrotherapy applications managing weight through thermogenesis and non-shivering thermogenic pathways, which involve the brown adipose tissue, and dependent on uncoupling protein 1 (UCP1) in the inner mitochondrial membrane. Hence the present literature provides insight into use of hydrotherapy applications for future direction in weight management.

Ischemic stroke (IS) is one of the top risk factors for death and disability. Meteorological conditions have an effect on IS attack. In this study, we try to develop models of medical meteorological forecast for IS attack based on machine learning and deep learning algorithms. The medical meteorological forecast would be beneficial to public health in IS events prevention and treatment. We collected data on IS attacks and climatology in each day from 18th September 2016 to 31th December 2020 in Haikou. Data on IS attacks were from the number of hospital admissions due to IS attack among general population. The random forest (RF) regression and long short-term memory (LSTM) algorithms were respectively used to develop the predictive model based on meteorological data. Performance of the model was assessed by mean squared error (MSE) and root mean squared error (RMSE). A total of 42849 IS attacks was included in this study. IS attacks were significantly decreased in winter. The pattern of climatological data was observed the regularity in seasons. For the performance of RF regression model, the MSE is 243, and the RMSE is 15.6. For LSTM model, the MSE is 36, and the RMSE is 6. In conclusion, LSTM model is more accurate than RF regression model to predict IS attacks in general population based on meteorological data. LSTM model showed acceptable accuracy for the prediction and could be used as medical meteorological forecast to predict IS attack among population according to local climate.

Structural details of oligosaccharides, or glycans, often carry biological relevance, which is why they are typically elucidated using tandem mass spectrometry. Common approaches to distinguish isomers rely on diagnostic glycan fragments for annotating topologies or linkages. Diagnostic fragments are often only known informally among practitioners or stem from individual studies, with unclear validity or generalizability, causing annotation heterogeneity and hampering new analysts. Drawing on a curated set of 237,000 O-glycomics spectra, we here present a rule-based machine learning workflow to uncover quantifiably valid and generalizable diagnostic fragments. This results in fragmentation rules to robustly distinguish common O-glycan isomers for reduced glycans in negative ion mode. We envision this resource to improve glycan annotation accuracy and concomitantly make annotations more transparent and homogeneous across analysts.

Alcoholic liver injury resulting from excessive alcohol consumption is a significant social concern. Alcohol dehydrogenase (ADH) plays a critical role in the conversion of alcohol to acetaldehyde, leading to tissue damage. The management of alcoholic liver injury encompasses nutritional support and, in severe cases liver transplantation, but potential adverse effects exist, and effective medications are currently unavailable. Natural products with their potential benefits and historical use in traditional medicine emerge as promising alternatives. Triphala, a traditional polyherbal formula demonstrates beneficial effects in addressing diverse health concerns, with a notable impact on treating alcoholic liver damage through enhanced liver metabolism. The present study aims to identify potential active phytocompounds in Triphala targeting ADH to prevent alcoholic liver injury. Screening 119 phytocompounds from the Triphala formulation revealed 62 of them showing binding affinity to the active site of the ADH1B protein. Promising lipid-like molecule from Terminalia bellirica, (4aS, 6aR, 6aR, 6bR, 7R, 8aR, 9R, 10R, 11R, 12aR, 14bS)-7, 10, 11-trihydroxy-9-(hydroxymethyl)-2, 2, 6a, 6b, 9, 12a-hexamethyl-1, 3, 4, 5, 6, 6a, 7, 8, 8a, 10, 11, 12, 13, 14b-tetradecahydropicene-4a-carboxylic acid showed high binding efficiency to a competitive ADH inhibitor, 4-Methylpyrazole. Pharmacokinetic analysis further confirmed the drug-likeness and non-hepatotoxicity of the top-ranked compound. Molecular dynamics simulation and MM-PBSA studies revealed the stability of the docked complexes with minimal fluctuation and consistency of the hydrogen bonds throughout the simulation. Together, computational investigations suggest that (4aS, 6aR, 6aR, 6bR, 7R, 8aR, 9R, 10R, 11R, 12aR, 14bS)-7, 10, 11-trihydroxy-9-(hydroxymethyl)-2, 2, 6a, 6b, 9, 12a-hexamethyl-1, 3, 4, 5, 6, 6a, 7, 8, 8a, 10, 11, 12, 13, 14b-tetradecahydropicene-4a-carboxylic acid from the Triphala formulation holds promise as an ADH inhibitor, suggesting an alternative therapy for alcoholic liver injury.

The miRNA binds to AGO's seed region, prompting the exploration of small molecules that can offset miRNA repression of target mRNA. This miRNA-181c-5p was found to be upregulated in the chronic traumatic encephalopathy, a prevalent neurodegenerative disease in contact sports and military personals. The research aimed to identify compounds that disrupt the AGO-assisted loop formation between miRNA-181c-5p and ATM, consequently repressing the translation of ATM. Target genes from commonly three databases (DIANA-microT-CDS, miRDB, RNA22 and TargetScan) were subjected to functional annotation and clustering analysis using DAVID bioinformatics tool. Haddock server were employed to make miRNA-181c-5p:ATM-AGO complex. A total of 2594 small molecules were screened using Glide XP based on their highest binding affinity towards the complex, through a three-phase docking approach. The top 5 compounds (DB00674-Galantamine, DB00371-Meprobamate, DB00694-Daunorubicin, DB00837-Progabide, and DB00851-Dacarbazine) were further analyzed for stability in the miRNA-181c-5p:ATM-AGO-ligand complex interaction using GROMACS (version 2023.2). Hence, these findings suggest that these molecules hold potential for facilitating AGO-assisted repression of ATM gene translation.

Peroxisome proliferator-activated receptor gamma (PPARγ) plays a critical role in adipocyte differentiation and enhances insulin sensitivity. In contemporary drug discovery, in silico design strategies offer significant advantages by revealing essential structural insights for lead optimization. The study is guided by two main objectives: (i) a ligand-based approach to explore the chemical space of PPARγ modulators followed by molecular docking ensembles (MDEs) to investigate ligand-binding interactions, (ii) the development of a supervised ML model for a large dataset of compounds targeting PPARγ. Additionally, the combination of chemical space networks with ML models enables the rapid screening and prediction of PPARγ modulators. These modeling analyses will assist medicinal chemists in designing more potent PPARγ modulators. To further enhance accessibility for the scientific community, we developed an online tool, "PGMP_v1," aimed at prospective screening for PPARγ modulators. The tool "PGMP_v1" is available at the provided link https://github.com/Amincheminfom/PGMP_v1 . The integration of these computational methods has uncovered crucial structural motifs that are essential for PPARγ activity, advancing the development of more effective modulators in the future.

Selective breeding is a powerful tool for improving aquaculture production. A well-managed breeding program is essential, as populations can otherwise lose genetic diversity, leading to reduced selection response and inbreeding excesses. In such cases, genetic diversity in broodstock must be restored by introducing individuals from external populations. However, this can reduce the accumulated genetic gains from selective breeding. However, the selective introduction of individuals with superior phenotypes will allow the restoration of genetic diversity without sacrificing these gains. In this study, we demonstrated this possibility using a selectively bred (SB) and a randomly bred (RB) population of coho salmon (Oncorhynchus kisutch). Forty males with superior growth were selected from the RB population using genomic selection and crossed with 127 randomly collected females from the SB population, producing a newly bred (NB) population. Genetic diversity, assessed from population statistics such as effective number of alleles, allele richness, and observed heterozygosity of 11 microsatellite markers, was higher in NB than in SB and RB. Additionally, fork length and body weight were compared among the three populations after 12 months of growth post-fertilization in common tanks. The least-squares means of fork length and body weight were similar between NB (164.9 mm and 57.9 g) and SB (161.1 mm and 53.7 g), while both were significantly greater than RB (150.4 mm and 43.0 g). Our results highlight the effectiveness of genome-assisted gene flow in restoring the genetic diversity of a population without compromising accumulated genetic gain in growth.