Abstract Wind speed changes impact society and have important implications for climate change studies. Thus, high-resolution and high-quality wind speed datasets are necessary for environmental monitoring and ecosystem research. However, there is no complete set of high spatial and temporal resolution wind speed datasets for China. Additionally, it is extremely challenging to produce wind speed data at high spatial and temporal resolution for large-scale regions with diverse climate types and complex topographies, such as China. In this study, we used multisource remote sensing images, obtained data on various environmental factors through the Google Earth Engine and Evapotranspiration (ET) Watch Cloud platforms, and combined machine learning algorithms to downscale the ERA5 reanalysis wind speed data, and finally obtained the daily wind speed datasets with 1 km spatial resolution for China in 2015. To verify the accuracy of the model and data products, we selected several metrics to evaluate in conjunction with the actual site observed data. The results show that the multifactor combination model of artificial neural network combining land surface temperature, sunshine durations and roughness factors outperforms a single-factor combination model, and the results were in good agreement with the original data (R2 of 0.95 and RMSE of 0.40 m·s−1). The final wind speed data products were also in good agreement with the observed meteorological data (R2 range of 0.86–0.95 and RMSE range of 0.33–0.44 m·s−1); moreover, the accuracy and precision were greatly improved over the original data. This study provided a dataset that has potential applications in future climate change and ecosystem studies.

The aim of this study was to evaluate the effect of dry cow therapy (DCT) on the antimicrobial resistance (AMR) profile of mastitis pathogens post-calving. A repository of isolates based on a DCT trial was utilized for the current study. A stratified random survey sample of cows from the trial were identified within the strata of season, herd, and trial treatment resulting in 382 cows. All isolates from the 382 cows were selected for the current study, which identified 566 isolates from milk samples collected at dry off (S1), post-calving (S2), and at the first clinical mastitis event up to 150 days in milk (S3). The AMR profiles were determined using broth microdilution method. Less than 10% of the coagulase-negative species (CNS) isolates ( = 421) were resistant to tetracycline, ceftiofur, penicillin/novobiocin or erythromycin, while higher proportions of resistance to sulfadimethoxine (72%) and penicillin (28%) were observed. All ( isolates ( = 4) were susceptible to all tested AMD except sulfadimethoxine, to which all isolates were resistant. Similarly, all spp. ( = 37) were susceptible to penicillin, penicillin/novobiocin, and ampicillin while resistant to tetracycline (17%). All coliforms ( = 21) were susceptible to ceftiofur, but resistance was recorded for sulfadimethoxine (70%), cephalothin (56%), and tetracycline (43%). The increased resistance percent from S1 to S2 was observed in CNS isolates from AMD-treated cows, with the highest increase recorded for penicillin (12.2%). Parametric survival interval regression models were used to explore the association between antimicrobial drug (AMD) therapy at dry off and the AMR phenotype post-calving. The accelerated failure-time metric was adopted to minimum inhibitory concentration measurements to permit interpretation of model exponentiated coefficients. Models for cows with CNS isolated at both S1 and S2 showed increased resistance against cephalothin, oxacillin, and ceftiofur in cows that received DCT from the same drug class, or a class with a shared resistance mechanism. In contrast, resistance of CNS isolates to tetracycline were associated with any AMD therapy at dry off. Resistance of CNS isolates to Penicillin decreased in CNS isolates in cows that received any AMD therapy at dry off compared to those that didn't. The study provided evidence that dry-cow IMM AMD was associated with AMR post-calving.

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Excisional wounds are considered one of the most common physical injuries. This study aims to test the effect of a nanophytosomal formulation loaded with a dried hydroalcoholic extract of on promoting excisional wound healing. The nanophytosomal formulation (SPNP) containing 100 mg PC and 50 mg CH exhibited optimum physicochemical characteristics regarding particle size (598.40 ± 9.68 nm), zeta potential (-19.8 ± 0.49 mV), entrapment efficiency (62.76 ± 1.75%), and Q6h (74.00 ± 1.90%). It was selected to prepare an HPMC gel (SPNP-gel). Through metabolomic profiling of the algal extract, thirteen compounds were identified. Molecular docking of the identified compounds on the active site of the HMGB-1 protein revealed that 12,13-DiHome had the highest docking score of -7.130 kcal/mol. SPNP-gel showed higher wound closure potential and enhanced histopathological alterations as compared to standard (MEBO ointment) and gel in wounded Sprague-Dawley rats. Collectively, NPS promoted the wound healing process by enhancing the autophagy process (LC3B/Beclin-1) and the NRF-2/HO-1antioxidant pathway and halting the inflammatory (TNF-, NF-κB, TlR-4 and VEGF), apoptotic processes (AIF, Caspase-3), and the downregulation of HGMB-1 protein expression. The present study's findings suggest that the topical application of SPNP-gel possesses a potential therapeutic effect in excisional wound healing, chiefly by downregulating HGMB-1 protein expression.