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Salicylate preservatives are widely used in consumer products and pharmaceuticals. This study investigates their potential endocrine-disrupting effects on neurosteroidogenesis, focusing on 5α-reductase type 1 (SRD5A1). We evaluated the effects of 13 salicylates on human SRD5A1 using SF126 glioblastoma cell microsomes and rat brain microsomes, examining dihydrotestosterone production in SF126 cells. Results revealed a hierarchy of inhibitory potency against human SRD5A1, with methyl salicylate (IC50, 71.93 μM) to menthyl salicylate (2.41 μM), indicating increasing potency. Kinetic analysis indicates their mixed/noncompetitive inhibitions. In SF126 cells, all salicylates at 100 μM significantly reduced dihydrotestosterone production. Rat SRD5A1 showed reduced sensitivity, with menthyl salicylate as the most potent inhibitor (IC50, 17.12 μM). Docking analysis suggests salicylates bind to the reduced nicotinamide adenine dinucleotide phosphate site of both human and rat SRD5A1. Bivariate correlation analysis highlights the influence of LogP, molecular weight, carbon number in the alcohol moiety, and pKaon inhibitory potency. 3D-QSAR revealed the importance of hydrophobic aromatic regions in SRD5A1 binding. This study delineates the inhibitory effects of salicylates and binding mechanisms on human and rat SRD5A1, providing insights into their impact on neurosteroid production.

Data transformation of the reference soil organic matter (SOM) decomposition rates (kref), often derived as turnover times or in alternative formats, is commonly used to develop ecological models for projecting the persistence of SOM. However, the effects of reciprocal or logarithmic transformation of krefon model performance and edaphic‐climatic patterns remain uncertain. Here, we convert published krefvalues into reciprocal or logarithmic formats and establish machine learning models between the transformed krefand edaphic‐climatic predictors. We show that models trained with the transformed krefexhibit a 11.6%−68.4% reduction in performance upon re‐conversion to krefcompared to those trained with the original kref. The variable importance analysis identifies distinct key predictors governing the original krefand its transformed counterparts. This suggests that data transformation alters the relative significance of predictors without necessarily improving krefprediction performance. Consequently, our study underscores the importance of directly focusing on the original values rather than alternative representations when dissecting a given variable's patterns and mechanisms in ecological modeling. Data transformation caused reduced predictive performance upon re‐conversion to krefcompared to the original kref.Models trained with data transformation exhibited divergent edaphic‐climatic controls for different formatted kref.This study underscores the importance of directly focusing on original values rather than alternative representations. The first‐order model is commonly used to simulate the dynamics of soil organic matter decomposition. The reference decomposition rates (kref) in the first‐order model are usually transformed into reciprocal or logarithmic formats, which may affect model performance as well as the regulatory patterns of edaphic‐climatic factors. In this study, we investigated the effects of data transformation on predictive performance and variable importance. We found that models trained with transformed krefexhibited reduced performance upon re‐conversion to kref, compared to those trained with the original kref. In addition, data transformation altered the distribution and range of the dependent variable, leading to divergent controlling roles of explanatory variables pertaining to models trained with different formatted kref. Our study underscores the need to apply models directly to the original variables rather than their transformed representations.

Western equine encephalitis virus (WEEV) is an arthropod-borne virus (arbovirus) that frequently caused major outbreaks of encephalitis in humans and horses in the early twentieth century, but the frequency of outbreaks has since decreased markedly, and strains of this alphavirus isolated in the past two decades are less virulent in mammals than strains isolated in the 1930s and 1940s1–3. The basis for this phenotypic change in WEEV strains and coincident decrease in epizootic activity (known as viral submergence3) is unclear, as is the possibility of re-emergence of highly virulent strains. Here we identify protocadherin 10 (PCDH10) as a cellular receptor for WEEV. We show that multiple highly virulent ancestral WEEV strains isolated in the 1930s and 1940s, in addition to binding human PCDH10, could also bind very low-density lipoprotein receptor (VLDLR) and apolipoprotein E receptor 2 (ApoER2), which are recognized by another encephalitic alphavirus as receptors4. However, whereas most of the WEEV strains that we examined bind to PCDH10, a contemporary strain has lost the ability to recognize mammalian PCDH10 while retaining the ability to bind avian receptors, suggesting WEEV adaptation to a main reservoir host during enzootic circulation. PCDH10 supports WEEV E2–E1 glycoprotein-mediated infection of primary mouse cortical neurons, and administration of a soluble form of PCDH10 protects mice from lethal WEEV challenge. Our results have implications for the development of medical countermeasures and for risk assessment for re-emerging WEEV strains.

Real-time semantic segmentation of LiDAR measurements is crucial for high-level perception in unmanned systems, such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The limited computation and memory capacity of onboard devices, however, restricts most existing methods to offline analyses. To solve this problem, this article proposes an attention-based 3-D semantic segmentation approach, 3D-ARSS, which is able to classify measurements from a Velodyne HDL-64E sensor at the speed of 5 FPS on AGX Xavier. In our approach, we present two plug-and-play attention modules, a spatial attention module and a channel attention module. The former is to learn local-global context by reweighting features from different regions; the latter is to model the importance of different-scale features for semantic information fusion. To effectively process large-scale point clouds, a sparse-tensor (ST)-based implementation is introduced. Two kinds of sparse convolution operations are used to reduce unnecessary computation and memory costs on free spaces. Experimental results on the SemanticKITTI and NuScenes datasets demonstrate that our method outperforms state-of-the-art real-time methods by +3.1% and +2.2% mean intersection over union (mIoU), respectively. The floating-point operations of our method are reduced to 2/5 of SPVNAS and 1/4 of RPVNet. Our source code is available at https://github.com/wuzhaoo/3D-ARSS.

River algal blooms have become a global environmental problem due to their large impact range and environmental hazards. However, the complex mechanisms underlying these blooms make prediction and prevention challenging. Here, we employed empirical dynamic modeling (EDM) and machine learning to reveal the causes and predict diatom blooms from 2003 to 2017 in the Han River of China. The diatom cell density ranged from 0.1 to 5.1 × 107cells L−1, whereas algal blooms often lasted for 10 days with density exceeding 107cells L−1. The EDM results elucidated that, under consistent high nutrient concentrations, algal blooms were primarily regulated by eight environmental factors: water temperature in the Han River; water levels, flow velocities, and streamflow discharges in the Han River and the Yangtze River; and water level variation in the Han River. The poor performance (coefficient of determination R2< 0) of the multiple linear regression, EDM, and random forest models indicated the challenge of predicting algae density. Therefore, we used machine learning classification models to predict algal blooms occurrences. With the resampling techniques to account for imbalanced data, machine learning models achieved perfect classification prediction (Kappa value = 1) of the 13 algae‐bloom events with a 10‐day lead time during a 15‐year period, providing an important reference for preemptive warning of riverine algal blooms. River algal blooms have become a global environmental problem due to their large impact range and environmental hazards. The Han River is one of the most frequent areas of river algal blooms in China, but there is no clear conclusion on the causes of algal blooms formation. In this study, we combined two methods to reveal the causes and predict the occurrence of algal blooms in the Han River. Our results show that the prediction of algal blooms in the Han River is feasible. These blooms are primarily affected by the water temperature and seven other hydrological variables in the Han and Yangtze River. This study provides a starting point for the preemptive warning of riverine algal blooms. Empirical dynamic modeling detects causality for river algal bloomsMachine learning accurately predicts the occurrence of algal blooms in the Han RiverResampling techniques improve robustness and accuracy of machine learning Empirical dynamic modeling detects causality for river algal blooms Machine learning accurately predicts the occurrence of algal blooms in the Han River Resampling techniques improve robustness and accuracy of machine learning

Microbial ecological models become increasingly complex owing to the incorporation of many parameters and multiple biotic and abiotic processes. However, little attention has been paid to the variations in the parameter sensitivity during long‐term versus short‐term simulations. Here, we developed a Multi‐Objective Parameter Sensitivity Analysis (MOPSA) method to efficiently identify the important parameters in complex microbial ecological models with multiple response variables of interest in terms of short‐ and long‐term model simulations. We found that MOPSA was more computationally efficient for complex microbial ecological models than Sobol's method because of MOPSA's reliability and low computational sample size. In addition, we address the increased significance of microbial physiology in mediating long‐term than short‐term soil C‐N cycling, indicating that experiment‐model integration practices should examine model behaviors beyond the conventional short‐term experimental period. The outcomes of this study provide an efficient global sensitivity analysis method for parameterization and the scientific foundation for microbial physiology in mediating long‐term microbial ecological processes. Biogeochemical cycles can be effectively characterized by process‐based modeling. Microbial ecological models are more intricate and include more parameters in comparison to traditional ecological models. This complexity demands substantial computational resources when using current methods to estimate model sensitivity to different parameters. To address this challenge, we developed an efficient global sensitivity analysis method, known as Multi‐Objective Parameter Sensitivity Analysis (MOPSA). Using MOPSA, we successfully conducted sensitivity analyses over both short and long timeframes for the complex microbial model. Our study reveals that microbial physiology becomes more significant in mediating long‐term carbon and nitrogen cycling, compared to short‐term processes. This highlights the need to pay close attention to the long‐term processes when we conduct experiment‐model integration practices. A new, computationally efficient parameter sensitivity analysis method was applied to a microbially explicit soil carbon modelThe importance of microbial physiology is higher in long‐term than in short‐term simulationsThe outcomes have implications for other microbially explicit biogeochemical models A new, computationally efficient parameter sensitivity analysis method was applied to a microbially explicit soil carbon model The importance of microbial physiology is higher in long‐term than in short‐term simulations The outcomes have implications for other microbially explicit biogeochemical models

In this study, loop-mediated isothermal amplification (LAMP) was used to establish a rapid, specific, and visual detection method for duck hepatitis B virus (DHBV). The design and synthesis of 4 specific LAMP primers were based on the conserved gene region of the DHBV genome, and the optimum temperature and time of the LAMP reaction were 63°C and 50 min, respectively. The LAMP assay was confirmed to be specific for DHBV detection and had the same sensitivity as the quantitative PCR assay. A visual detection method for rapid determination of results was developed using a color indicator containing phenol red and cresol red. A color change was produced based on a pH change in the reaction system, indicating a positive reaction. For the detection of samples from ducks and geese, the LAMP method has the advantages of simplicity, high sensitivity and specificity, good visibility, and low cost. Moreover, it is more practical and convenient than PCR-related assays for the clinical detection of DHBV.

The contribution of spike photosynthesis to wheat yield grain yield (GY) has been overlooked in crop models. Our objectives were to (i) construct a yield-related phenotypic trait, spike–leaf composite indicator (SLI), accounting for the contribution of the spike to photosynthesis, (ii) develop an enhanced triangle vegetation index 3 (ETVI3) based on the SLI response to canopy spectra, and (iii) establish and evaluate SLI estimation models by integrating spectral indices and eXtreme Gradient Boosting with feature selection (XGB-FS) algorithm and four regression models, using field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years. The results showed that spike photosynthesis accounted for 28 % of GY. SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than leaf area index. ETVI3 displayed SLI differences through wheat canopy tissue abundance, chlorophyll content, and plant water content, was strongly correlated with SLI, whereas other spectral indices were more poorly correlated. Spectral features selected from XGB-FS algorithm enhanced the interpretability of the regression models and improved the estimation accuracy of SLI, wherein XGB-FS-LASSO was validated with higher accuracy for SLI estimation at the post-heading stage with coefficient of determination, root mean square error (RMSE), and relative RMSE values of 0.71 %, 0.047 %, and 26.93 %, respectively. These results provide new insights into the role of fruiting organs for yield prediction. This high-throughput SLI estimation approach can be applied for accurate spectral prediction of GY at whole growth stages and may be assisted with agronomical practices and variety selection.

Objective/Background:Cancer is one of the leading causes of death in the world. Therefore, there is an urgent need to develop new drugs for fighting this disease. Recent studies have reported that antimicrobial peptides (AMPs) can be used as anticancer drugs. Methods:We discovered brevinin-1EG, a novel antimicrobial peptide from Pelophylax nigromaculatus, using gene cloning techniques. Brevinin-1EG was then investigated for its structure and biological activity. Results:Brevinin-1EG, with an alpha-helical structure, showed broad-spectrum anti-proliferative activity against several cancer cell lines, including U-2OS, HepG2, HT-29, A375, HeLa, and A549 cells, with IC50values ranging from 15 to 22 μg/mL. These same concentrations were insufficient to induce hemolysis in erythrocytes, indicating the safety of this molecule. Brevinin-1EG also exhibited antioxidant activity, scavenging ABTS·+and DPPH free radicals. Conclusion:Brevinin-1EG has good safety and potential use as an anticancer therapeutic agent. It provides support for the research of new peptide drugs and templates for structural modification.

Emerging evidence has linked the gut microbiome to human obesity. We performed a metagenome-wide association study and serum metabolomics profiling in a cohort of lean and obese, young, Chinese individuals. We identified obesity-associated gut microbial species linked to changes in circulating metabolites. The abundance of Bacteroides thetaiotaomicron, a glutamate-fermenting commensal, was markedly decreased in obese individuals and was inversely correlated with serum glutamate concentration. Consistently, gavage with B. thetaiotaomicron reduced plasma glutamate concentration and alleviated diet-induced body-weight gain and adiposity in mice. Furthermore, weight-loss intervention by bariatric surgery partially reversed obesity-associated microbial and metabolic alterations in obese individuals, including the decreased abundance of B. thetaiotaomicron and the elevated serum glutamate concentration. Our findings identify previously unknown links between intestinal microbiota alterations, circulating amino acids and obesity, suggesting that it may be possible to intervene in obesity by targeting the gut microbiota.

Owing to its high similarity to human hepatitis B virus (HBV), duck HBV (DHBV) is often used as an essential model for HBV research. Although intergenotypic recombination of HBV is common, it remains unclear whether the intergenotypic recombination of human HBV is exactly the same as that of DHBV. In this study, 119 serum samples of duck and goose were collected from 51 farms (29 duck and 22 goose farms) in the central and eastern regions of China. A total of 22 strains isolated from the 22 DHBV positive flock were sequenced. Genome sequence alignment revealed that the duck- and goose-origin strains shared the highest and lowest similarities (99.7 and 90.52%, respectively). The complete genomes of these DHBV and 31 reference strains were analyzed using phylogenetic methods and classified into 3 clusters, which corresponded to the previously identified DHBV-I, DHBV-II, and DHBV-III branches. Recombination analyses of the 53 DHBV genomes indicated 2 major intergenotypic recombination events with high confidence values. These recombination events occurred between the genotypes of the Chinese isolates Y180813HB (Chinese branch [DHBV-Ⅰ]) and E170101AH (Chinese branch [DHBV-Ⅱ]) and the Western isolate DHBV-XY (Western branch [DHBV-Ⅲ]), resulting in the emergence of 2 Chinese recombinant isolates Y190303HN and Y170101HB. In addition, 40% (2/5) goose-origin and 58.8% (10/17) duck-origin DHBV in this study harbored the mutation site of G133E in preS, which promote the pathogenicity of DHBV. This is the first study to report on the genome analysis and recombination characterization of DHBV isolated from Chinese geese. Further, continuous investigation and molecular identification of DHBV should be conducted to attract researchers’ attention.

A liposomal delivery system with a high efficiency of accumulation in tumor tissue and then transportation of the cargo into tumor cells was developed here and evaluated via systemic administration. 1,2-Distearoyl-sn-glycero-3-phosphoethanolamine-poly(ethylene glycol)2000(DSPE-PEG2000)-TAT and protective DSPE-PEG2000modified liposomes possessing good stability in 50% FBS (fetal bovine serum) and good uptake efficiency were used as the basic formulation (TAT-SL; SL = stealth liposome), and then longer cysteine (Cys)-cleavable PEG5000was incorporated to modulate the function of TAT. All of the formulations to be used in vivohad sizes in a range of 80–100 nm and were stable in the presence of 50% FBS. Optical imaging showed that the incorporation of cleavable PEG5000into TAT-SL (i.e., C-TAT-SL) led to much more tumor accumulation and much less liver distribution compared with TAT-SL. The in vivodelivery profiles of C-TAT-SL were investigated using DiD as a fluorescent probe. Confocal laser scanning microscopy and flow cytometry showed that C-TAT-SL had a 48% higher (p< 0.001) delivery efficiency in the absence of Cys and a 130% higher (p< 0.001) delivery efficiency in the presence of Cys than the control (SL), indicating the successful targeted delivery of cargo was achieved by C-TAT-SL via systemic administration especially with a subsequent administration of Cys.

Primary biliary cirrhosis (PBC)–autoimmune hepatitis (AIH) overlap syndrome is frequently associated with extrahepatic autoimmune disorders. Paroxysmal nocturnal hemoglobinuria (PNH) is an acquired disease that is characterized by complement-mediated hemolysis due to erythrocyte membrane defects. However, autoimmune liver disease was not previously reported to be associated with PNH. A 37-year-old female patient was referred to our hospital with elevated liver enzymes and hematuria. On the basis of the symptoms and results of laboratory tests, radiographic studies, and pathologic results, she was diagnosed with PBC–AIH overlap syndrome and PNH. She was treated with a combination of ursodeoxycholic acid and prednisolone. The patient was symptom-free, with laboratory findings within near-normal range. The patient had recovered well at the 24-month follow-up evaluation. While we acknowledge that this was a single case, these findings expand our knowledge of immunological diseases that are associated with PNH and suggest an immune-mediated pathogenic pathway between PNH and PBC–AIH overlap syndrome. The combination of ursodeoxycholic acid and prednisolone can achieve therapeutic success. Routine follow-up of these patients is necessary to document disease progression.

We perform a CRISPR-Cas9 genome-wide screen in glioblastoma stem cells and identify integrin αvβ5 as an internalization factor for Zika virus (ZIKV). Expression of αvβ5 is correlated with ZIKV susceptibility in various cells and tropism in developing human cerebral cortex. A blocking antibody against integrin αvβ5, but not αvβ3, efficiently inhibits ZIKV infection. ZIKV binds to cells but fails to internalize when treated with integrin αvβ5-blocking antibody. αvβ5 directly binds to ZIKV virions and activates focal adhesion kinase, which is required for ZIKV infection. Finally, αvβ5 blocking antibody or two inhibitors, SB273005 and cilengitide, reduces ZIKV infection and alleviates ZIKV-induced pathology in human neural stem cells and in mouse brain. Altogether, our findings identify integrin αvβ5 as an internalization factor for ZIKV, providing a promising therapeutic target, as well as two drug candidates for prophylactic use or treatments for ZIKV infections.