Explore the vast range of titles available.

Human gut microbiota is believed to be directly or indirectly involved in cardiovascular diseases and hypertension. However, the identification and functional status of the hypertension-related gut microbe(s) have not yet been surveyed in a comprehensive manner. Here we characterized the gut microbiome in hypertension status by comparing fecal samples of 60 patients with primary hypertension and 60 gender-, age-, and body weight-matched healthy controls based on whole-metagenome shotgun sequencing. Hypertension implicated a remarkable gut dysbiosis with significant reduction in within-sample diversity and shift in microbial composition. Metagenome-wide association study (MGWAS) revealed 53,953 microbial genes that differ in distribution between the patients and healthy controls (false discovery rate, 0.05) and can be grouped into 68 clusters representing bacterial species. Opportunistic pathogenic taxa, such as, spp., spp., and were frequently distributed in hypertensive gut microbiome, whereas the short-chain fatty acid producer, such as, spp. and , were higher in controls. The number of hypertension-associated species also showed stronger correlation to the severity of disease. Functionally, the hypertensive gut microbiome exhibited higher membrane transport, lipopolysaccharide biosynthesis and steroid degradation, while in controls the metabolism of amino acid, cofactors and vitamins was found to be higher. We further provided the microbial markers for disease discrimination and achieved an area under the receiver operator characteristic curve (AUC) of 0.78, demonstrating the potential of gut microbiota in prediction of hypertension. These findings represent specific alterations in microbial diversity, genes, species and functions of the hypertensive gut microbiome. Further studies on the causality relationship between hypertension and gut microbiota will offer new prospects for treating and preventing the hypertension and its associated diseases.

bifunctional enzyme GlmU is a novel target for anti-TB drugs and is involved in glycosyl donor UDP-N-acetylglucosamine biosynthesis. Here, we found that TPSA (2-[5-(2-{[4-(2-thienyl)-2-pyrimidinyl]sulfanyl}acetyl)-2-thienyl]acetic acid) was a novel inhibitor for GlmU acetyltransferase activity (IC : 5.3 μM). The interaction sites of GlmU and TPSA by molecular docking were confirmed by site-directed mutagenesis. TPSA showed an inhibitory effect on Mtb H37Ra growth and intracellular H37Ra in macrophage cells (MIC: 66.5 μM). To investigate why TPSA at a higher concentration (66.5 μM) was able to inhibit H37Ra growth, proteome and transcriptome of H37Ra treated with TPSA were analyzed. The expression of two methyltransferases MRA_0565 (Rv0558) and MRA_0567 (Rv0560c) were markedly increased. TPSA was pre-incubated with purified Rv0558 and Rv0560c in the presence of S-adenosylmethionine (methyl donor) respectively, resulting in its decreased inhibitory effect of GlmU on acetyltransferase activity. The inhibition of TPSA on growth of H37Ra with overexpressed Rv0558 and Rv0560c was reduced. These implied that methyltransferases could modify TPSA. The methylation of TPSA catalyzed by Rv0560c was subsequently confirmed by LC-MS. Therefore, TPSA as a GlmU acetyltransferase activity inhibitor may offer a structural basis for new anti-tuberculosis drugs. TPSA needs to be modified further by some groups to prevent its methylation by methyltransferases.

Cushion plants (as nurse species) can ameliorate soil conditions under their canopy in alpine environments. However, this amelioration (or the intensity of soil fertility islands) related with the size of plants is rarely studied. To assess size effects of Thylacospermum caespitosum on soil fertility islands, soil properties (pH; electric conductivity, EC; soil organic carbon, SOC; available nitrogen, AN; available phosphorus, AP; available potassium, AK) and microbial biomass (soil microbial biomass carbon, SMBC; soil microbial biomass nitrogen, SMBN) were investigated at three different elevations in the Qilian Mountains. Plant size significantly influenced (P < 0.001) soil properties (pH, EC, SOC, AN, AP, AK) and microbial biomass (SMBC and SMBN) at all three elevations. A size-dependent fertile island effect occurred beneath T. caespitosum, where the relative interaction index (RII) of soil properties and SMBC was affected (P < 0.001) by plant size at the three elevations. Moreover, most parameters of soil nutrition and microbial biomass under T. caespitosum were reduced (P < 0.001) with increases in elevation, but the RII was increased (P < 0.05). In short, soil amelioration by T. caespitosum was clearly dependent on plant size at all elevations, and this effect on soil increased with elevation. Thus, the existence of size-dependent fertility islands together with elevation should be regarded as a central mechanism of the nurse effect of T. caespitosum in harsh alpine ecosystems, where many ecological processes rely on the nurse effect of cushion plants.

Lateral handling stability control is crucial for ensuring vehicle driving safety. To address this issue, this paper proposes a lateral handling stability control method that considers time-varying full-state constraints. By constructing a time-varying symmetric Barrier Lyapunov Function (TS-BLF), this method imposes time-varying nonlinear constraints on both the sideslip angle and yaw rate, thereby ensuring full-state constrained stability control of vehicles under complex operating conditions. Additionally, a second-order command filtering technique with an error compensation mechanism is introduced to reduce the computational complexity of control laws while mitigating filter-induced errors that may degrade system performance. To validate the effectiveness and robustness of the proposed method, the vehicle’s dynamic response is analyzed under different speeds on both dry asphalt pavement and dry gravel surfaces. The simulation results demonstrate that the proposed method effectively suppresses understeer and oversteer, enhances the dynamic stability margin under extreme operating conditions, and improves vehicle adaptability in complex environments.

Relationships between biodiversity and multiple ecosystem functions (that is, ecosystem multifunctionality) are context-dependent. Both plant and soil microbial diversity have been reported to regulate ecosystem multifunctionality, but how their relative importance varies along environmental gradients remains poorly understood. Here, we relate plant and microbial diversity to soil multifunctionality across 130 dryland sites along a 4,000 km aridity gradient in northern China. Our results show a strong positive association between plant species richness and soil multifunctionality in less arid regions, whereas microbial diversity, in particular of fungi, is positively associated with multifunctionality in more arid regions. This shift in the relationships between plant or microbial diversity and soil multifunctionality occur at an aridity level of ∼0.8, the boundary between semiarid and arid climates, which is predicted to advance geographically ∼28% by the end of the current century. Our study highlights that biodiversity loss of plants and soil microorganisms may have especially strong consequences under low and high aridity conditions, respectively, which calls for climate-specific biodiversity conservation strategies to mitigate the effects of aridification. Biodiversity-ecosystem functioning relationships may vary with climate. Here, the authors study relationships of plant and soil microbial diversity with soil nutrient multifunctionality in 130 dryland sites in China, finding a shift towards greater importance of soil microbial diversity in arid conditions.

To explore the contributions of ω-3 fatty acid desaturases (FADs) to cold stress response in a special cryophyte, Chorispora bungeana , two plastidial ω-3 desaturase genes ( CbFAD7 , CbFAD8 ) were cloned and verified in an Arabidopsis fad7fad8 mutant, before being compared with the microsomal ω-3 desaturase gene ( CbFAD3 ). Though these genes were expressed in all tested tissues of C. bungeana , CbFAD7 and CbFAD8 have the highest expression in leaves, while CbFAD3 was mostly expressed in suspension-cultured cells. Low temperatures resulted in significant increases in trienoic fatty acids (TAs), corresponding to the cooperation of CbFAD3 and CbFAD8 in cultured cells, and the coordination of CbFAD7 and CbFAD8 in leaves. Furthermore, the cold induction of CbFAD8 in the two systems were increased with decreasing temperature and independently contributed to TAs accumulation at subfreezing temperature. A series of experiments revealed that jasmonie acid and brassinosteroids participated in the cold-responsive expression of ω-3 CbFAD genes in both C. bungeana cells and leaves, while the phytohormone regulation in leaves was complex with the participation of abscisic acid and gibberellin. These results point to the hormone-regulated non-redundant contributions of ω-3 CbFADs to maintain appropriate level of TAs under low temperatures, which help C. bungeana survive in cold environments .

Background The AP2/ERFs belong to a large family of transcription factors in plants. The AP2/ERF gene family has been identified as a key player involved in both biotic and abiotic stress responses in plants, however, no comprehensive study has yet been carried out on the AP2/ERF gene family in rose ( Rosa sp.), the most important ornamental crop worldwide. Results The present study comprises a genome-wide analysis of the AP2/ERF family genes ( RcERFs ) in the rose, involving their identification, gene structure, phylogenetic relationship, chromosome localization, collinearity analysis, as well as their expression patterns. Throughout the phylogenetic analysis, a total of 131 AP2/ERF genes in the rose genome were divided into 5 subgroups. The RcERFs are distributed over all the seven chromosomes of the rose, and genome duplication may have played a key role in their duplication. Furthermore, Ka/Ks analysis indicated that the duplicated RcERF genes often undergo purification selection with limited functional differentiation. Gene expression analysis revealed that 23 RcERFs were induced by infection of the necrotrophic fungal pathogen Botrytis cinerea . Presumably, these RcERFs are candidate genes which can react to the rose’s resistance against Botrytis cinerea infection. By using virus-induced gene silencing, we confirmed that RcERF099 is an important regulator involved in the B.cinerea resistance in the rose petal. Conclusion Overall, our results conclude the necessity for further study of the AP2/ERF gene family in rose, and promote their potential application in improving the rose when subjected to biological stress.

Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples, significantly hindering the development of deep learning technologies in high-security domains. A key challenge is that current defense methods often lack universality, as they are effective only against certain types of adversarial attacks. This study addresses this challenge by focusing on analyzing adversarial examples through changes in model attention, and classifying attack algorithms into attention-shifting and attention-attenuation categories. Our main novelty lies in proposing two defense modules: the Feature Pyramid-based Attention Space-guided (FPAS) module to counter attention-shifting attacks, and the Attention-based Non-Local (ANL) module to mitigate attention-attenuation attacks. These modules enhance the model’s defense capability with minimal intrusion into the original model. By integrating FPAS and ANL into the Wide-ResNet model within a boosting framework, we demonstrate their synergistic defense capability. Even when adversarial examples are embedded with patches, our models showed significant improvements over the baseline, enhancing the average defense rate by 5.47% and 7.74%, respectively. Extensive experiments confirm that this universal defense strategy offers comprehensive protection against adversarial attacks at a lower implementation cost compared to current mainstream defense methods, and is also adaptable for integration with existing defense strategies to further enhance adversarial robustness.

Salinization is an important and increasingly prevalent issue which has broad and profound effects on plant survival and distribution pattern. To understand the patterns and potential drivers of leaf traits in saline environments, we determined the soil properties, leaf morphological traits (specific leaf area, SLA, and leaf dry matter content, LDMC), leaf chemical traits (leaf carbon, C, nitrogen, N, and phosphorus, P, stoichiometry) based on 142 observations collected from 23 sites in an arid saline environment, which is a vulnerable ecosystem in northwest China. We also explored the relationships among leaf traits, the responses of leaf traits, and plant functional groups (herb, woody, and succulent woody) to various saline environments. The arid desert halophytes were characterized by lower leaf C and SLA levels, higher N, but stable P and N:P. The leaf morphological traits were correlated significantly with the C, N, and P contents across all observations, but they differed within each functional group. Succulent woody plants had the lowest leaf C and highest leaf N levels among the three functional groups. The growth of halophytes might be more limited by N rather than P in the study area. GLM analysis demonstrated that the soil available nutrients and plant functional groups, but not salinity, were potential drivers of leaf C:N:P stoichiometry in halophytes, whereas species differences accounted for the largest contributions to leaf morphological variations. Our study provides baseline information to facilitate the management and restoration of arid saline desert ecosystem.

Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for regional or local studies. Therefore, in this paper, to derive finer-resolution estimates of SSW, we present a novel statistical downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as a typical example. The dual learning scheme performs a primal task to reconstruct high resolution SSW, and a dual task to estimate the degradation kernels, which form a closed loop and are simultaneously learned, thus introducing an additional constraint to reduce the solution space. The integration of a dual learning scheme as the generator into the generative adversarial network structure further yield better downscaling performance by fine-tuning the generated SSW closer to high-resolution SSW. Besides, a model adaptation strategy was exploited to enhance the capacity for downscaling from low-resolution SSW without high-resolution ground truth. Comprehensive experiments were conducted on both the synthetic paired and unpaired SSW data. In the study areas of the East Coast of North America and the North Indian Ocean, in this work, the downscaling results to 0.25° (high resolution on the synthetic dataset), 0.03125° (8× downscaling), and 0.015625° (16× downscaling) of the proposed approach achieve the highest accuracy in terms of root mean square error and R-Square. The downscaling resolution can be enhanced by increasing the basic blocks in the generator. The highest downscaling reconstruction quality in terms of peak signal-to-noise ratio and structural similarity index was also achieved on the synthetic dataset with high-resolution ground truth. The experimental results demonstrate the effectiveness of the proposed downscaling network and the superior performance compared with the other typical advanced downscaling methods, including bicubic interpolation, DeepSD, dual regression networks, and adversarial DeepSD.