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Abstract The bacteria-phage arm race drives the evolution of diverse bacterial defenses. This study identifies and characterizes the defense hotspots in Acinetobacter baumannii using a reference-free approach. Among 4383 high-quality genomes, we found a total of 17,430 phage defense systems and with 54.54% concentrated in 21 hotspots. These hotspots exhibit distinct preferences for different defense systems, and co-occurrence patterns suggest synergistic interactions. Additionally, the mobile genetic elements are abundant around these hotspots, likely facilitating horizontal transfer and evolution of defense systems. The number of hotspots increases in species phylogenetically closer to Acinetobacter baumannii, but the number of defense systems per hotspot varies due to particular selective pressures. These findings provide critical insights into the genetic organization of phage defense systems, contributing to a broader understanding of bacterial immunity and the evolutionary dynamics that shape Acinetobacter genomes. This knowledge lays the foundation for developing targeted interventions to combat antibiotic resistance Acinetobacter baumannii.

This study explored the effect of vacuum heat treatment on the microstructure and mechanical properties of High-Velocity Oxygen Fuel (HVOF) sprayed Cantor alloy (CoCrFeNiMn) coating. The heat treatment improved the microstructure of the coatings by decreasing the size of the pores. Enhanced diffusion distance at the interface was observed with higher heat treatment temperatures, promoting thorough bonding between the coating and substrate. Besides, recrystallization at high temperatures led to a reduction in grain size and improved uniformity. At 900 °C, the density of geometrically necessary dislocations decreased, while microhardness increased, indicating improved coating integrity and durability. The grain boundary strengthening played a dominant role in the coating heat treated at 900 °C compared to the dislocation strengthening. Therefore, optimizing the heat treatment process for HVOF-sprayed Cantor alloy coatings is essential for reduced defects, achieving sufficient diffusion at the interface, and enhanced coating mechanical properties.

In recent years, medium and/or high-entropy alloys (M/HEAs) have attracted attention due to their excellent mechanical properties, positioning them as potential candidates for structural materials in advanced nuclear reactors. This study investigated the effect of He injection amount on the microstructural evolution in Cr0.8FeMnNi MEA and 316L stainless steel. Helium atoms were injected, followed by Fe2+ irradiation. Transmission electron microscopy was used to characterize the irradiation-induced defects. The primary irradiation-induced defects observed across all He injection amounts were black dots; no voids were seen under any irradiation conditions. In He-injected Cr0.8FeMnNi, the number density of black dots was significantly lower than in non-He-injected Cr0.8FeMnNi, while the average size of black dots increased with higher He injection rates. As the He injection amount increased, the binding energy of the self-interstitial atom (SIA) to the He-V complex decreased. This reduction facilitated the nucleation and growth of numerous SIA clusters within the matrix, consequently increasing the number density of black dots. The change in the number density of black dots in Cr0.8FeMnNi was much smaller than that observed in 316L. This could be ascribed to the enhanced self-healing process caused by the diversity of point defects and higher lattice distortion in Cr0.8FeMnNi.

Abstract A novel catalytic system for radical cross‐coupling reactions based on copper and chiral Pyridyl‐bis(imidazole) (PyBim) ligands is described. It overcomes the challenges of chemoselectivity and enantioselectivity, achieving a highly enantioselective vicinal sulfonyl‐esterification reaction of alkenes involving sulfur dioxide. This strategy involves the use of earth‐abundant metal catalyst, mild reaction conditions, a broad range of substrates (84 examples), high yields (up to 97% yield), and exceptional control over enantioselectivity. The reaction system is compatible with different types of radical precursors, including O‐acylhydroxylamines, cycloketone oxime esters, aryldiazonium salts, and drug molecules. Chiral ligand PyBim is identified as particularly effective in achieving the desired high enantioselectivity. Mechanistic studies reveal that copper/PyBim system plays a vital role in C─O coupling, employing an outer‐sphere model. In addition, the side arm effect of ligand is observed.

Globally, age-related macular degeneration (AMD) is the leading cause of irreversible visual impairment. Up to 80% of severe vision loss is caused by AMD, which is characterized by the development of choroidal neovascularization (CNV). Uncertainty exists regarding the precise pathophysiological mechanisms of CNV. It has been suggested that the interleukin (IL) IL-6/IL-6R signaling pathway is crucial in the progression of CNV. Tocilizumab (TCZ), a monoclonal antibody, binds to soluble and membrane-bound IL-6R and competitively inhibits IL-6 downstream signaling. Previous research has demonstrated that TCZ promotes several roles related to inflammation and neovascularization. However, the effects of TCZ on CNV and the underlying mechanism are still unknown. This study found that TCZ administration decreased the area and leakage of CNV lesions in the mice model of laser-induced CNV. Additionally, results demonstrated that TCZ promotes the expression of iNOS, CCL-3, CCL-5, TNF-α and inhibits the expression of Arg-1, IL-10, YM-1 and CD206. Furthermore, TCZ treatment inhibited the signal transducer and activator of transcription (STAT) STAT3/vascular endothelial growth factor (VEGF) pathway, which was activated after CNV formation. Colivelin, a STAT3 agonist, reversed the inhibitory effects of TCZ on CNV formation and macrophage polarization. In a mouse model of laser-induced CNV, our findings demonstrated that TCZ attenuated CNV formation and inhibited the leakage of CNV lesions by regulating macrophage polarization via inhibiting the STAT3/VEGF axis. TCZ is the potential therapeutic strategy for CNV.

Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively describes the principles and steps of four types of path planning algorithms: the Dijkstra algorithm (including its optimized derivatives), the A* algorithm (including its optimized derivatives), the RRT (Rapidly exploring Random Trees) algorithm (including its optimized derivatives), and the BFS (Breadth First Search) algorithm. Secondly, the Dijkstra algorithm, the A* algorithm, the BFS algorithm, and the Dynamic Weighted A* algorithm were utilized to plan the paths required for the process of smart parking. During the analysis, it was found that the Dijkstra algorithm had the drawbacks of planning circuitous paths and taking too much time in the path planning for smart parking. Although the traditional A* algorithm based on the Dijkstra algorithm had greatly reduced the planning time, the effect of path planning was still unsatisfactory. The BFS (Breadth First Search) algorithm had the shortest planning time among the four algorithms, but the paths it plans were unstable and not optimal. The Dynamic Weighted A* algorithm could achieve better path planning results, and with adjustments to the weight values, this algorithm had excellent adaptability. This review provides a reference for further research on path planning algorithms in the process of smart parking.

Abstract This paper applies the mechanics-based approach and five machine learning algorithms to classify the failure mode (leak or rupture) of steel oil and gas pipelines containing longitudinally oriented surface cracks. The mechanics-based approach compares the nominal hoop stress remote from the surface crack at failure and the remote nominal hoop stress to cause unstable longitudinal propagation of the through-wall crack to predict the failure mode. The employed machine learning algorithms consist of three single learning algorithms, namely naïve Bayes, support vector machine and decision tree; and two ensemble learning algorithms, namely random forest and gradient boosting. The classification accuracy of the mechanics-based approach and machine learning algorithms is evaluated based on 250 full-scale burst tests of pipe specimens collected from the open literature. The analysis results reveal that the mechanics-based approach leads to highly biased classifications: many leaks erroneously classified as ruptures. The machine learning algorithms lead to markedly improved accuracy. The random forest and gradient boosting models result in the classification accuracy of over 95% for ruptures and leaks, with the accuracy of the decision tree and support vector machine models somewhat lower. This study demonstrates the value of employing machine learning models to improve the integrity management practice of oil and gas pipelines.

The emerging SARS-CoV-2 variants of concern (VOCs) exhibit enhanced transmission and immune escape, reducing the effectiveness of currently approved mRNA vaccines. To achieve wider coverage of VOCs, we first constructed a cohort of mRNAs harboring a furin cleavage mutation in the spike (S) protein of predominant VOCs, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2). The mutation abolished the cleavage between the S1 and S2 subunits. Systematic evaluation in vaccinated mice discovered that individual VOC mRNAs elicited strong neutralizing activity in a VOC-specific manner. In particular, the neutralizing antibodies (nAb) produced by immunization with Beta-Furin and Washington (WA)-Furin mRNAs showed potent cross-reactivity with other VOCs. However, neither mRNA elicited strong neutralizing activity against the Omicron variant. Hence, we further developed an Omicron-specific mRNA vaccine that restored protection against the original Omicron variant and some sublineages. Finally, to broaden the protection spectrum of the new Omicron mRNA vaccine, we engineered an mRNA-based chimeric immunogen by introducing the receptor-binding domain of Delta variant into the entire S antigen of Omicron. The resultant chimeric mRNA induced potent and broadly nAbs against Omicron and Delta, which paves the way to developing new vaccine candidates to target emerging variants in the future.

Nano-doped hollow fiber is currently receiving extensive attention due to its multifunctionality and booming development. However, the microfluidic fabrication of nano-doped hollow fiber in a simple, smooth, stable, continuous, well-controlled manner without system blockage remains challenging. In this study, we employ a microfluidic method to fabricate nano-doped hollow fiber, which not only makes the preparation process continuous, controllable, and efficient, but also improves the dispersion uniformity of nanoparticles. Hydrogel hollow fiber doped with carbon nanotubes is fabricated and exhibits superior electrical conductivity (15.8 S m−1), strong flexibility (342.9%), and versatility as wearable sensors for monitoring human motions and collecting physiological electrical signals. Furthermore, we incorporate iron tetroxide nanoparticles into fibers to create magnetic-driven micromotors, which provide trajectory-controlled motion and the ability to move through narrow channels due to their small size. In addition, manganese dioxide nanoparticles are embedded into the fiber walls to create self-propelled micromotors. When placed in a hydrogen peroxide environment, the micromotors can reach a top speed of 615 μm s−1 and navigate hard-to-reach areas. Our nano-doped hollow fiber offers a broad range of applications in wearable electronics and self-propelled machines and creates promising opportunities for sensors and actuators.

Soil and water conservation measures (SWCMs) are vital in reducing runoff and ultimately affect water security and regional development. However, previous studies have mainly focused on the impact of a single SWCM, neglecting to distinguish between the effects of different SWCMs on runoff reduction. A Soil and Water Assessment Tool (SWAT) model was established in the Jing River catchment to identify the responses to runoff changes resulting from climate change and human activities. The model was used to quantitatively analyse the impact of different SWCMs on runoff reduction. The results indicated that human activities contributed significantly more to runoff reduction than climate change. The reduction benefits of different unit area changes for each SWCM on discharge, surface runoff and water yield at the outlet were ranked as follows: changing cultivated land to forest land > changing cultivated land to grassland > building terraces on a 5–15° slope > building terraces on a 5–25° slope > building terraces on a 15–25° slope. Regional authorities should comprehensively consider the effects of various SWCMs on water reduction, and optimise the layout of vegetation and terracing measures, to support the efficient utilization of water resources in the Jing River catchment.

In this paper, the ecohydrology measures including water management and interaction of ecological hydrology were considered to take a holistic view on the impact of climate, ecology, environment, and topography on different types of disasters. In the first step, the contents of the grim disaster situation and the information framework of ecohydrology with disasters were evaluated. Nest, the creation of ecohydrology and its work with related disasters were summarized. Three disasters including flood, drought, and geohazard were evaluated and three examples (Dujiangyan Irrigation Project, Karez, and Ecological slope) have been separately discussed for considering the concept of the problem. Depending on the characteristics and potential for an effect in different disasters, there needed to be some challenges and opportunities for ecohydrology facing related disasters in various fields, such as National Space Planning, Sponge City and the ‘One Belt And One Road’ Initiative. Finally, some valuable conclusions were drawn on the application of ecohydrology to disaster prevention and mitigation. Furthermore, the key systemic settings between water management, hydrology, and ecology were described, which is of the main importance for social disaster reduction, emergency management, and protection of accident prevention. HIGHLIGHTS Comprehensive analysis of the convergence of water and environment and economy can be efficiently extended to disaster prevention and mitigation activities.; The creation of ecohydrology in disaster reduction and mitigation activities are closely linked, communicated, and promoted.; Ecohydrology is an application technology that takes a holistic view of ecology, hydrology, topography soil, and biology.;

Persistent Organic Pollutants (POPs) may exert adverse effects on human and ecosystem health. However, as an ecologically fragile zone with strong interaction between river and groundwater, the POPs pollution in the riparian zone has received little attention. The goal of this research is to examine the concentrations, spatial distribution, potential ecological risks, and biological effects of organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in the riparian groundwater of the Beiluo River, China. The results showed that the pollution level and ecological risk of OCPs in riparian groundwater of the Beiluo River were higher than PCBs. The presence of PCBs (Penta-CBs, Hexa-CBs) and CHLs, respectively, may have reduced the richness of bacteria (Firmicutes) and fungi (Ascomycota). Furthermore, the richness and Shannon’s diversity index of algae (Chrysophyceae and Bacillariophyta) decreased, which could be linked to the presence of OCPs (DDTs, CHLs, DRINs), and PCBs (Penta-CBs, Hepta-CBs), while for metazoans (Arthropoda) the tendency was reversed, presumably as a result of SULPHs pollution. In the network analysis, core species belonging to bacteria (Proteobacteria), fungi (Ascomycota), and algae (Bacillariophyta) played essential roles in maintaining community function. Burkholderiaceae and Bradyrhizobium can be considered biological indicators of PCBs pollution in the Beiluo River. Note that the core species of interaction network, playing a fundamental role in community interactions, are strongly affected by POPs pollutants. This work provides insights into the functions of multitrophic biological communities in maintaining the stability of riparian ecosystems through the response of core species to riparian groundwater POPs contamination.

Abstract This paper presents a review of four existing growth models for near-neutral pH stress corrosion cracking (NNpHSCC) defects on buried oil and gas pipelines: Chen et al.’s model, two models developed at the Southwest Research Institute (SwRI) and Xing et al.’s model. All four models consider corrosion fatigue enhanced by hydrogen embrittlement as the main growth mechanism for NNpHSCC. The predictive accuracy of these growth models is investigated based on 39 crack growth rates obtained from full-scale tests conducted at the CanmetMATERIALS of Natural Resources Canada of pipe specimens that are in contact with NNpH soils and subjected to cyclic internal pressures. The comparison of the observed and predicted crack growth rates indicates that the hydrogen-enhanced decohesion (HEDE) component of Xing et al.’s model leads to on average reasonably accurate predictions with the corresponding mean and coefficient of variation (COV) of the observed-to-predicted ratios being 1.06 and 61.2%, respectively. The predictive accuracy of the other three models are markedly poorer. The analysis results suggest that further research is needed to improve existing growth models or develop new growth models to facilitate the pipeline integrity management practice with respect to NNpHSCC.

With population growth and rapid urbanization, agricultural land has become a relatively scarce resource. Given the vast land area in Central Asia, exploring the potential of agricultural land development in Central Asia is a realistic guide to the rational use of land and the alleviation of strained human-land relations and food shortage problems. In this study, Multi-criteria Decision Analysis (MCDA) and Analytic Hierarchy Process (AHP) were used to construct a cropland development potential evaluation system in Central Asia. The results showed that: (1) The development of current cropland basically adapts to local natural conditions in Central Asia. Suitable irrigated cropland is mainly located in areas with low elevation and close to water sources such as rivers and canals. Suitable rain-fed cropland is mainly located in the areas with flat terrain and sufficient rainfall. (2) The land resources of Central Asia still have a large potential for development: 1,008,000 km2 for the irrigated cropland and 217,124 km2 for the rain-fed cropland, mainly concentrated in Kazakhstan and Turkmenistan. The final evaluation results on a province-level scale presented in this study provide a direct and effective reference for future cropland development in Central Asian countries.

Abstract Background The classification of benign and malignant microcalcification clusters (MCs) is an important task for computer-aided diagnosis (CAD) of digital breast tomosynthesis (DBT) images. Influenced by imaging method, DBT has the characteristic of anisotropic resolution, in which the resolution of intra-slice and inter-slice is quite different. In addition, the sharpness of MCs in different slices of DBT is quite different, among which the clearest slice is called focus slice. These characteristics limit the performance of CAD algorithms based on standard 3D convolution neural network (CNN). Methods To make full use of the characteristics of the DBT, we proposed a new ensemble CNN, which consists of the 2D ResNet34 and the anisotropic 3D ResNet to extract the 2D focus slice features and 3D contextual features of MCs, respectively. Moreover, the anisotropic 3D convolution is used to build 3D ResNet to avoid the influence of DBT anisotropy. Results The proposed method was evaluated on 495 MCs in DBT images of 275 patients, which are collected from our collaborative hospital. The area under the curve (AUC) of receiver operating characteristic (ROC) and accuracy of classifying benign and malignant MCs using decision-level ensemble strategy were 0.8837 and 82.00%, which were significantly higher than the experimental results of 2D ResNet34 (AUC: 0.8264, ACC: 76.00%) and anisotropic 3D ResNet (AUC: 0.8455, ACC: 76.00%). Compared with the results of 3D features classification in the radiomics, the AUC of the deep learning method with decision-level ensemble strategy was improved by 0.0435, and the F1 score was improved from 79.37 to 85.71%. More importantly, the sensitivity increased from 78.13 to 84.38%, and the specificity increased from 66.67 to 77.78%, which effectively reduced the false positives of diagnosis Conclusion The results fully prove that the ensemble CNN can effectively integrate 2D features and 3D features, improve the classification performance of benign and malignant MCs in DBT, and reduce the false positives.

The Loess Plateau has been a focus of public discussion and environmental concerns over the past three decades. In this study, in order to investigate the effect of OCP pollution in water of the Beiluo River, concentrations of 25 OCPs at 17 locations in the water were examined. The results showed that the concentration of ∑OCPs in the water ranged from 1.76 to 32.57 ng L−1, with an average concentration of 7.23 ng L−1. Compared with other basins in China and abroad, the OCP content in the Beiluo River was at a medium level. Hexachlorocyclohexane (HCH) pollution in the Beiluo River was mainly from the mixed input of lindane and technical HCHs. Dichlorodiphenyltrichloroethane (DDT) pollution was mainly from the mixed input of technical DDTs and dicofol. Most of the OCP pollution came from historical residues. The risk assessment results showed that hexachlorobenzene (HCB) and endosulfan had high ecological risks in the middle and lower reaches of the Beiluo River. Most residual OCPs were not sufficient to pose carcinogenic and non-carcinogenic health risks to humans. The results of this study can provide a reference for OCP prevention and control and watershed environmental management.

ABSTRACT: The focus of this paper is to investigate the deformation and failure response of a new class of bioresorbable composites subject to flexural loading. The composite consists of a biocompatible polylactic acid (PLA) matrix reinforced by long, continuous, bioresorbable silk fibers, which shows great promise for making load-bearing bone fixation devices. Three-point bend tests were carried out to characterize the material's flexural behavior. Experimental results show that the composite fails due to delamination, which is evident from images processed using the Digital Image Correlation (DIC) technique. To predict crack propagation in the composite, an enhanced fracture energy-based Smeared Crack Approach (SCA) was developed and implemented in a Finite Element Analysis (FEA) framework. The new SCA model incorporates orthogonal cracks that allow for complete energy dissipation under mode-I and mode-II failure types, with the approach being validated through benchmark studies of Double-Cantilever Beam (DCB) and End-Notched Flexure (ENF) simulations. The new fracture model, coupled with a micromechanics-based elastic model, successfully predicts the deformation and progressive damage response of the bioresorbable composite subjected to three-point bending. Effects of the strength and toughness of the binding polymer on the resulting composite response were investigated, providing crucial information for the design of new bioresorbable composites with target properties.

As significant players in material cycling and energy flow, bacteria and eukaryotes play a vital role in the ecosystem. Nevertheless, the community dynamics of bacteria and eukaryotes in rivers and their responses to changes in ecological hydrology have not been studied thoroughly. Based on eDNA technology, this study investigated the bacterial and eukaryotic communities in the upper, middle and lower reaches of the Weihe River in different seasons. The seasonal variation and geographical distribution of bacterial and eukaryotic community structures showed significant heterogeneity. The selective theory well explained the response of microbial community assembly to seasonal changes. Deterministic processes dominate microbial community assembly in the middle and lower reaches. The composition and metabolic potential of key functional genes of nitrogen and phosphorus cycling (nosZ, pqqB, pqqD, and pqqE) exhibited strong seasonal patterns and were significantly correlated with the physical and chemical properties of water. There were significant differences in molecular ecological networks in different periods (p < 0.05), with a gradually increasing trend in the complexity of the network from winter to summer. The keystone species (Hub) of the microbial food web in each season included microorganisms (Malikia), algae (Stephanodiscus), and invertebrates (Polyarthra). Structural equation modeling (SEM) results indicated that invertebrate was an important driving factor affecting the changes in community structures. In micro-food webs, both “bottom-up” (resources) and “top-down” (predation) forces strictly controlled the relationship between taxa. Nitrogen and phosphorus concentrations affected microbial networks, and there was a significant correlation between bacterial and eukaryotic groups and eco-hydrological variables (p < 0.05). Furthermore, we identified the taxon’s change point using threshold indicator taxa analysis (TITAN), quantitatively revealing the response thresholds of taxa to eco-hydrological changes.

Periphytic algae is a useful indicator of aquatic ecological conditions. We investigated the periphytic algae on natural substrate and the environmental variables at 44 sites on three river systems in the Weihe River Basin (WRB). A total of 84 species are identified, representing 37 genera. The most common genera were Navicula, Oscillatoria, Nitzschia, Scenedesmus, Cymbell, and Fragilaria. One-way analysis of variance (ANOVA) indicated significant differences among the three river systems in environmental variables (p < 0.05). Non-metric multidimensional scaling (NMDS) analyses also showed differences in periphytic algae communities in the three river systems (p < 0.05) and identified different dominant species in each river system. Canonical correspondence analysis (CCA) and Monte Carlo permutation tests revealed that nutrient concentration, WT, and altitude were the most important variables affecting the structure and distribution of periphytic algae communities. Chemical variables were the most accounted for environmental variables (12.5%), while physical variable and geographical factors (5.8% in total) play a relevant minor role. Our results demonstrate that Navicula pupula, Navicula radiosq, Nitzschia palea, and Nitzschia denticula, exhibiting wide ecological amplitude, are tolerant of high concentrations of nutrient pollution. Variation of periphytic algae communities in WRB is due to the combination of anthropogenic and natural factors including agricultural and domestic wastes water inputting, land use patterns, geology, climatic changes, and river hydrology. HIGHLIGHTS Structure and distribution of periphytic algae communities were investigated in the Weihe River Basin.; The gradient of organic pollution detected in environmental variables was the main gradient, and the periphytic algae responded to this gradient.; The types of substrates in river impact periphytic algae abundance, causing higher periphytic algae abundance in cobbles substrate.; The variation of periphytic algae communities is attributed to the combination of anthropogenic factors and natural factors.; The results support the periphytic algae as ecological indicators in the Weihe River Basin.;

Central Asia, the pioneering place of the ‘Belt and Road’, is under the threat of prominent water issues. Based on the Gini coefficient model and the matching index, the amount of the total renewable water resources and the cultivated land area were introduced to evaluate the matching pattern between the water and land resources in Central Asia. The water problem of Kazakhstan, being the most prominent, shows low water resources per unit area with the highest reclamation rate. The matching degree for the upstream countries of the Aral Sea (Kyrgyzstan, Tajikistan) was better than those of the downstream countries (Turkmenistan, Uzbekistan, Kazakhstan). The Gini coefficient in Central Asia was 0.32, smaller than that of the global average value (0.59). The overall water available for use and the matching cultivated land resources was reasonable. Large differences exist in the matching degree in water distribution and utilization among Central Asian countries. The matching index of water and land resources in Central Asia was 1.25, similar to the matching degree estimated from the Gini coefficient model. Moreover, rational measures are suggested to alleviate the issue of water and land resources matching in Central Asia. HIGHLIGHTS The Gini coefficient model is to evaluate the overall level of matching degree.; The matching index model is introduced for evaluating the spatial matching pattern.; The matching situation for the upstream countries of the Aral Sea was better than those of the downstream countries.; Two models perform similarly in matching pattern.; The matching index of water and land resources in Central Asia was 1.25.;

A deep understanding of the response characteristics of vegetation to drought is important for ecosystem and water resource management. In this study, we investigate spatiotemporal patterns of vegetation responses to droughts and their causes in the Qinling Mountains (QMs). The study showed that (1) there was a significant positive correlation (p

Limited water resources and rapid socioeconomic development pose new challenges to watershed water resource management. By integrating the perspectives of stakeholders and decision-makers, this study aims to identify cases and approaches to achieve sustainable water resources management. It improves and expands the experience of previous project research. The comparative evaluation provides an analytical basis to verify the importance of stakeholder participation in water policy interactions. The results show that if an effective demand management policy is not implemented, the Guanzhong area will not meet water demand in the future. Through the combination of water-saving policies, water transfer projects and other measures, the available water resources will continue into the future. Optimizing management measures, improving the ecological environment, and encouraging stakeholder participation will help change this situation, although supply-side limitations and future uncertainties likely cause unsustainable water. Therefore, decision-makers should pay attention to the application potential of water-saving and other measures to reduce dependence on external water sources. In addition, the three sustainable development decision-making principles identified in this paper can promote the fairness and stability of water policy.

Subsoiling (SS) is an important technology in conservation tillage, but soil-disturbance characteristics in the SS are rarely described. Research on soil-disturbance characteristics during SS is conducive to the design and optimization of subsoilers, which provides a basis for reducing draft force and energy consumption. This study conducted SS experiments at five different tillage depths in the field with a specific field in situ test-rig facility, and in situ videotaping was made from five positions during SS. The microrelief test, draft force test, disturbance cross-section test and disturbance process analysis were conducted after SS. The results showed that draft force increased with tillage depth as a quadratic function. Soil displacement parameters and soil crack parameters extracted from the video of SS were significantly correlated with tillage depth, which could be used for a quantitative description of the paddy soil-disturbance characteristics. Cross-sectional area showed a trend of “increasing then decreasing” with increasing tillage depth, reaching a maximum at a tillage depth of 20 cm. When the tillage depth was greater than 20 cm, the bottom of the disturbing boundary formed a “mole cavity”. Fallback rate was used to describe the change in disturbed height or width during and after SS, which exceeded 100% at maximum. The surface roughness of microrelief and the size of the average clods reached the maximum at tillage depth of 20 cm. Considering the shallow cultivation layer of paddy soil in rice–wheat rotation, the recommended tillage depth of 20 cm could achieve maximum soil disturbance and minimum energy consumed.

The contradiction between economic growth, social development, and water environment deterioration represent significant challenges for river basin sustainable development. By constructing an indicator system of river basin sustainable development, the entropy method is adopted to conduct a quantitative evaluation of the cities sustainable development level for the Weihe River Basin in Shaanxi Province from 2009 to 2018, and the standard deviational ellipse is used to analyze the evolution of spatial distribution pattern of sustainable development in the study area. Furthermore, the obstacle degree model is applied to analyze the main obstacle factors restricting the improvement of river basin sustainable development. The results show that the sustainable development level of the Weihe River basin in Shaanxi Province improved slowly during the study period and significant regional differences among cities. This study provides a novel approach for future evaluation on sustainable development of the Weihe River basin and even the arid region in Northwest China, to achieve a win-win situation between economic and social development and ecological environment protection.

Occurrence of sulfonamide antibiotics has been reported in surface waters with the exposures ranging from < 1 ng L–1 to approximately 11 μg L–1, which may exert adverse effects on non-target algal species, inhibiting algal growth and further hindering the delivery of several ecosystem services. Yet the molecular mechanisms of sulfonamide in algae remain undetermined. The aims of the present work are: (1) to test the hypothesis whether sulfamethoxazole (SMX) inhibits the folate biosynthesis in a model green alga Raphidocelis subcapitata; and (2) to explore the effects of SMX at an environmentally relevant concentration on algal health. Here, transcriptomic analysis was applied to investigate the changes at the molecular levels in R. subcapitata treated with SMX at the concentrations of 5 and 300 μg L–1. After 7-day exposure, the algal density in the 5 μg L–1 group was not different from that in the controls, whereas a marked reduction of 63% in the high SMX group was identified. Using the adj p < 0.05 and absolute log2 fold change > 1 as a cutoff, we identified 1 (0 up- and 1 downregulated) and 1,103 (696 up- and 407 downregulated) differentially expressed genes (DEGs) in the 5 and 300 μg L–1 treatment groups, respectively. This result suggested that SMX at an environmentally relevant exposure may not damage algal health. In the 300 μg L–1 group, DEGs were primarily enriched in the DNA replication and repair, photosynthesis, and translation pathways. Particularly, the downregulation of base and nucleotide excision repair pathways suggested that SMX may be genotoxic and cause DNA damage in alga. However, the folate biosynthesis pathway was not enriched, suggesting that SMX does not necessarily inhibit the algal growth via its mode of action in bacteria. Taken together, this study revealed the molecular mechanism of action of SMX in algal growth inhibition.

Pluripotency and self-renewal of embryonic stem cells (ESCs) are marked by core transcription regulators such as Oct4, Sox2, and Nanog. Another important marker of pluripotency is the long noncoding RNA (lncRNA). Here, we ind that a novel long noncoding RNA (lncRNA) Lx8-SINE B2 is a marker of pluripotency. LncRNA Lx8-SINE B2 is enriched in ESCs and downregulated during ESC differentiation. By rapid amplification of cDNA ends, we identified the full-length sequence of lncRNA Lx8-SINE B2. We further showed that transposable elements at upstream of lncRNA Lx8-SINE B2 could drive the expression of lncRNA Lx8-SINE B2. Furthermore, ESC-specific expression of lncRNA Lx8-SINE B2 was driven by Oct4 and Sox2. In summary, we identified a novel marker lncRNA of ESCs, which is driven by core pluripotency regulators.

Efficient identification of the voltage sag sources is significant in the power quality studies. This paper presents a novel method for voltage sag source identification which performs automatic feature extraction and shows a superior performance regardless of the insufficient amount of training samples. In the proposed strategy, the input data are preprocessed and fetched into the feature extractor, which is designed based on the convolutional neural network. Then the weighted k-nearest neighbor classifier generates the identification results. In the training period, the few-shot learning technique is harnessed, and the siamese network is constructed such that the proposed model learns efficiently even with a small number of samples. The proposed scheme is implemented in Python and PyTorch framework. Case studies and comparisons with other methods are carried out on 700 samples of voltage sag events in Jiangsu Province, China. Experimental results show the superiority of the proposed method over other identification methods in the tested cases.

Particulate matter contributes much to the haze pollution in China. Meteorological conditions and environmental management significantly influenced the accumulation, deposition, transportation, diffusion, and emission intensity of particulate matter. In this study, temporal and spatial variations of PM10 and PM2.5—and the responses to meteorological factors and environmental regulation intensity—were explored in Xi’an, China. The concentrations of PM10 were higher than those of PM2.5, especially in spring and winter. The mean annual concentrations of PM10 and PM2.5 markedly decreased from 2013 to 2017, but the decreasing trend has plateaued since 2015. The concentrations of PM10 and PM2.5 exhibited seasonal differences, with winter being the highest and summer the lowest. Air quality monitoring stations did not reveal significant spatial variability in PM10 and PM2.5 concentrations. The concentrations of PM10 and PM2.5 were significantly influenced by precipitation, relative humidity, and atmospheric temperature. The impact of wind speed was prominent in autumn and winter, while in spring and summer the impact of wind direction was obvious. Additionally, the emission intensity of SO2, smoke and dust could be effectively decreased with the increasing environmental regulation intensity, but not the concentrations of particulate matter. This study could provide a scientific framework for atmospheric pollution management.

Microorganisms existing in airborne fine particulate matter (PM2.5) have key implications in biogeochemical cycling and human health. In this study, PM2.5 samples, collected in the typical basin cities of Xi’an and Linfen, China, were analyzed through high-throughput sequencing to understand microbial seasonal variation characteristics and ecological functions. For bacteria, the highest richness and diversity were identified in autumn. The bacterial phyla were dominated by Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes. Metabolism was the most abundant pathway, with the highest relative abundance found in autumn. Pathogenic bacteria (Pseudomonas, Acinetobacter, Serratia, and Delftia) were positively correlated with most disease-related pathways. Besides, C cycling dominated in spring and summer, while N cycling dominated in autumn and winter. The relative abundance of S cycling was highest during winter in Linfen. For fungi, the highest richness was found in summer. Basidiomycota and Ascomycota mainly constituted the fungal phyla. Moreover, temperature (T) and sulfur dioxide (SO2) in Xi’an, and T, SO2, and nitrogen dioxide (NO2) in Linfen were the key factors affecting microbial community structures, which were associated with different pollution characteristics in Xi’an and Linfen. Overall, these results provide an important reference for the research into airborne microbial seasonal variations, along with their ecological functions and health impacts.

Ascorbic acid is an organic compound with antioxidant properties that can protect the human body from the threat of free radicals. Therefore, it is important to detect the existence and measure the concentration of ascorbic acid to regulate its content in the human body. In this work, we prepared bitter gourd polysaccharide (BGP)-stabilized platinum nanoclusters (Pt-BGP NCs) by reacting BGP with K2PtCl4. Pt-BGP NCs and catalyzed the decomposition of H2O2 to generate •OH radicals, which could oxidize TMB to form oxidized TMB (oxTMB), indicating their peroxidase-like properties. The kinetics followed the Michaelis–Menten equation. Furthermore, the colorimetric detection of ascorbic acid using Pt-BGP NCs showed high selectivity and a low detection limit of 0.191 μM. The accuracy of real sample detection using Pt-BGP NCs was as high as 98.9%. More importantly, Pt-BGP NCs had high cell biocompatibility and extremely low hemolysis rate due to the component of BGP. In summary, the prepared Pt-BGP NCs with reductive activity and good biocompatibility have good application prospects in colorimetric detection of ascorbic acid.

This study investigates the microstructure and mechanical properties of four different Cr-Fe-Mn-Ni medium entropy alloys (MEAs) for potential nuclear applications. In this work, Cr-Fe-Mn-Ni medium entropy alloys were fabricated and analyzed. Nano hardness measurements showed higher hardness in Cr0.8FeMn1.3Ni, attributed to the increased atomic size difference caused by higher Mn content. The strength of Cr-Fe-Mn-Ni MEAs is mainly affected by solid solution strengthening and grain boundary strengthening. The tensile test revealed that Cr0.8FeMn1.3Ni exhibited the highest strength, which is correlated with its large hardness and small grain size. The increase of Mn content leads to greater atomic size difference, enhancing the hardness and reducing the grain size. Cr0.8FeMn1.3Ni shows superior strength and higher hardness compared to other Cr-Fe-Mn-Ni MEAs, suggesting its potential for nuclear applications. [ABSTRACT FROM AUTHOR]