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To uncover the security aspects of the Water-Energy-Food-Nexus (WEFN) and develop innovative methods for evaluating its security, we initially utilize the coefficient of variation-cumulative information contribution rate (COV-CICR) model to select assessment metrics. This approach ensures a well-suited set of indicators for assessing the security of the WEFN. Subsequently, the WEFN security assessment model is developed by utilizing a random forest model that has been fine-tuned with the aquila optimizer. To illustrate this model's application, we use the example of the Hongxinglong branch of China's Beidahuang Farming Group Co., Ltd. The study conducted a detailed analysis of the spatiotemporal variations in the security of the WEFN (Water-Energy-Food Nexus) system across 12 subsidiary farms. Furthermore, it identified key driving factors and elucidated their mechanisms by accounting for data fluctuations. The research included calculating the contribution index of these key driving factors to accurately determine the dominant factors under varying conditions. The results indicate that over time, the WEFN security level in the study trended upward. From 2002–2008, the WEFN security level slowly increased. From 2009–2018, WEFN security remained stable. From 2018–2021, the security level rapidly accelerated. Spatially, the WEFN security in the southern region is lower than that in the northern region, while the central region’s security remained stable. The primary controlling factors for WEFN security also vary at different scales. In comparison to the BP, RF, and PSO-RF models, the AO-RF model demonstrates outstanding advantages in terms of fitting performance, reliability, reasonableness, and stability. This fully supports the applicability of the AO-RF model in WEFN security assessment. The research findings enrich the integrated application of intelligent optimization and machine learning in WEFN studies, introduce a novel analytical model for WEFN security drivers, and expand our understanding of the new mechanisms underlying WEFN security operations.

Molecular oxygen (O2) is an environmentally friendly, cost-effective, and non-toxic oxidant. Activation of O2 generates various highly oxidative reactive oxygen species (ROS), which efficiently degrade pollutants with minimal environmental impact. Despite extensive research on the application of O2 activation in environmental remediation, a comprehensive review addressing this topic is currently lacking. This review provides an informative overview of recent advancements in O2 activation, focusing on three primary strategies: photocatalytic activation, chemical activation, and electrochemical activation of O2. We elucidate the respective mechanisms of these activation methods and discuss their advantages and disadvantages. Additionally, we thoroughly analyze the influence of oxygen supply, reactive temperature, and pH on the O2 activation process. From electron transfer and energy transfer perspectives, we explore the pathways for ROS generation during O2 activation. Finally, we address the challenges faced by researchers in this field and discuss future prospects for utilizing O2 activation in pollution control applications. This detailed analysis enhances our understanding and provides valuable insights for the practical implementation of organic pollutant degradation.

Objective: This study aims to clarify the incidence rate of temporomandibular joint disease in patients with mental disorders.Methods: Data extracted from the Psychiatric Genomics Consortium and FinnGen databases employed the Mendelian Randomization (MR) method to assess the associations of three neurodevelopmental disorders (NDDs)—Attention-Deficit/Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), and Tourette’s Disorder (TD)—as exposure factors with Temporomandibular Disorder (TMD). The analysis used a two-sample MR design, employing the Inverse Variance Weighted (IVW) method to evaluate the relationships between these disorders and Temporomandibular Disorder. Sensitivity analysis and heterogeneity assessments were conducted. Potential confounding factors like low birth weight, childhood obesity, and body mass index were controlled for.Results: The study found that ADHD significantly increased the risks for TMD (OR = 1.2342, 95%CI (1.1448–1.3307), p < 0.00001), TMD (including avohilmo) (OR = 1.1244, 95%CI (1.0643–1.1880), p = 0.00003), TMD-related pain (OR = 1.1590, 95%CI (1.0964–1.2252), p < 0.00001), and TMD-related muscular pain associated with fibromyalgia (OR = 1.1815, 95%CI (1.1133–1.2538), p < 0.00001), while other disorders did not show significant causal relationships.Conclusion: This study reveals the elevated risk of various TMD aspects due to ADHD. Furthermore, we discuss the link between low vitamin D levels ADHD and TMD. Future research should address these limitations and delve further into the complex interactions between ADHD, ASD, TD, and TMD.

As naturally sourced proteins, peanut proteins have garnered significant attention from the food industry, owing to their numerous advantages, such as easy extraction, non-pungency, and high bioavailability. Furthermore, peanut proteins are highly digestible in the gastrointestinal tract and boast a high net protein utilization rate, making them an appealing protein source in food products and a promising alternative to animal protein. In this paper, the recent works on the extraction method, modification method, and application of peanut proteins were reviewed. Both advantages and disadvantages of current extraction and modification were discussed. Recently updated information about peanut protein research was summarized. Based on these, the prospection of peanut proteins research was presented, which may be instructive for future research in this field. Future research is still needed for accessible modification methods to develop the functional properties of peanut proteins.

The deteriorating soil health under continuous monoculture is commonly found across various cropping systems. This study evaluated the effects of different tillage practices (conventional tillage and no till) and species mixtures (legumes and grasses) on arbuscular mycorrhizal fungi (AMF) community properties, soil nutrients, and enzyme activity in a 3-year experiment. Compared with traditional tillage, the number of AMF species under no-till conditions was increased, with the Glomus group being dominant. Under different tillage conditions, TN (total N) and AN (available N) contents under no till were significantly higher than those under conventional tillage, while no significant differences among other nutrients were found. The activities of soil acid phosphatase (S-ACP), soil dehydrogenase (S-DHA), and soil sucrose (S-SC) under conventional tillage were significantly higher than those under no till, and the cover crop mixtures also had an exclusive advantage in yield. Soil organic matter (SOM) indicated a significant negative correlation with glomalin-related soil protein (GRSP). The increase in diversity associated with the AMF species community was strongly correlated with the increase in three enzyme activities, and AN was negatively correlated with all species. Tillage did not significantly change soil chemistry, except for AN, and the high concentration of AN led to a decrease in AMF species. The results of this study showed that no till was an effective measure for enriching soil micro-organism population. Additionally, soil AMF diversity was improved by cover crop mixtures, and microbial diversity was higher than that under monoculture cover crops. Different AMF groups responded differently to tillage and cover crop mixtures. Across all mixtures, the combination of hairy vetch (Vicia villosa R.) and ryegrass (Lolium perenne L.) performed the best.

Combined with the effect of pH on the ζ-potential and contact angle of carboxymethyl nanocellulose (CMCN), low oil phase (20%) Pickering emulsion gels were constructed with CMCN solution as the continuous phase and corn oil as the dispersant phase. The types, microstructure, rheological properties, and stability of CMCN-stabilized Pickering emulsion gels under different conditions were studied, respectively. The results showed that the ζ-potential of CMCN decreased from −2 mV to −67 mV when pH2 to pH9, the absolute value of the ζ-potential was relatively large at pH4 and the contact angle was closest to 90°. Therefore, CMCN at pH4 was more suitable for stabilizing low-oil phase Pickering emulsion gels. Fluorescence microscopy and rheological results showed that the CMCN-stabilized Pickering emulsion gels were O/W type, and the elastic structure (G'>G'') was formed at low oil concentration, which was pseudoplastic fluid. The low oil phase Pickering emulsion gels remained stable after 120 days of storage. The emulsion gels stabilized by CMCN at a concentration above 1.3% at 85 ℃ and above pH4 had no delamination and small droplet size change, which proved that the emulsion gels had excellent pH and thermal stability. The low oil phase Pickering emulsion gels prepared in this study would provide a new idea for the development of new green food, which was expected to solve the problem that high oil emulsion gels were not conducive to health.

Soil heavy metal pollution seriously endangers the soil ecological environment and food safety production. In this study, drip irrigation tests with four irrigation frequencies were conducted by controlling the lower limit of the soil matric potential (D1: −10 kPa; D2: −20 kPa; D3: −30 kPa; D4: −40 kPa). Through comparison with traditional surface irrigation, the effect of drip irrigation on the root zone soil environment under heavy metal pollution and the mechanism through which drip irrigation influences soybean heavy metal enrichment characteristics were explored. The conclusions are as follows. (i) Drip irrigation can improve the root zone soil environment of soybean under combined Cd, Pb and Cr(VI) pollution and is conducive to the recovery of bacterial community structure. (ii) Compared with surface irrigation, drip irrigation reduced the contents of Cd, Pb and Cr(VI) in the root zone soil, with maximum reductions of 34.88% (D1), 31.35% (D2) and 34.20% (D2), respectively. (iii) Drip irrigation increased the accumulation of Cd, Pb and Cr(VI) in soybean. However, compared with surface irrigation, drip irrigation changed the distribution of Cd, Pb and Cr(VI) in soybean organs so that more Cd, Pb and Cr(VI) were stored in roots and significantly less Cd and Cr(VI) were stored in seeds, with maximum reductions of 16.62% (D2) and 19.49% (D2), respectively. These results can be used to develop a new strategy for the prevention and control of soil heavy metal pollution.

The inchworm in-pipe robot has the advantages of stable support, low walking resistance, and high flexibility. However, the gait motion of in-pipe robots relies on the precise coordination of three motors, which greatly increases the complexity of control. To solve this problem, an inchworm in-pipe robot based on a multi-cam combination is proposed. The robot needs only one motor to achieve active support and bidirectional crawling for the pipe wall, mainly used for detecting straight pipelines, such as the main drainage pipeline. In order to obtain the periodic motion law and characteristics of the inchworm in-pipe robot, structure design, constraint analysis, and dynamic simulation were carried out on the robot. Finally, the principle prototype was tested in the transparent pipe, the test results indicate that the robot can achieve bidirectional creep under a single motor drive, which can simplify the control of gait motion for inchworm in-pipe robots. The average displacement errors for horizontal walking and vertical walking are 2.0% and 11.3%, respectively, due to factors such as gravity. Therefore, the robot can achieve a more accurate step distance in the horizontal pipe.

BackgroundAgitation is very common in the intensive care unit (ICU). The causes include pain, delirium, underlying disease, withdrawal syndrome, and some drug treatments. The practical goal of ICU treatment is to find an appropriate sedation regimen to reduce pain, restlessness, and delirium. Previous trials have examined the use of dexmedetomidine, but no trials have evaluated the efficacy and safety of ciprofol, a new sedative drug.MethodsThis study was a multicenter, single-blind, 3-arm parallel randomized controlled trial. ICU patients aged ≥ 18 years with agitation and delirium who met the eligibility criteria were included. The main outcome was the proportion of patients who needed additional study medication or midazolam due to agitation within 4 h after the first intravenous injection of the study medication. The secondary outcomes included the pass rate as indicated by a Richmond Agitation-Sedation Scale (RASS) score < +1, the effectiveness rate of improving delirium symptoms, the number of recurrences of agitation within 24 h, the incidence of rescue treatment, the dose and cost of analgesic and sedative drugs, the length and cost of ICU stay, and the 30-day survival period. The safety evaluation included the incidence of adverse events (hypotension, bradycardia, hypoxia, etc.) and the rate of endotracheal intubation. The subjects were randomly assigned to receive ciprofol, dexmedetomidine, or normal saline at a ratio of 1:1:1. The rates of additional drug administration within 4 h after the first injection of the study drug in the three groups were 40, 50, and 90%, respectively. A total sample size of 81 subjects was required to reach 90% power and an α of 0.05. Considering a 20% loss rate, 102 patients were enrolled and randomly assigned to the three groups in equal proportions.Ethics and communicationThis trial was approved by the Ethics Committee of Dalian Municipal Central Hospital. The communication plan includes presentations at scientific conferences, scientific publications, and presentations to the public through non-professional media.Clinical trial registrationwww.ClinicalTrials.gov, identifier ChiCTR220006 2799.

Resilience is an important indicator for measuring regional sustainable development capacity. The construction of a suitable evaluation indicator system is the premise of evaluating regional sustainable development. In this study, taking the Jiansanjiang Administration of Heilongjiang Province in China as an example, a preliminary selection library of the evaluation indicator system for the resilience of a regional agricultural soil–water resource composite system covering seven subsystems and 59 indicators was established. Selection criteria such as the Dale indicator criteria, subjective and objective combination weighting and principal component analysis were introduced to construct an optimization model for the resilience evaluation indicator system for the ASWRS. First, 14 indicators that were incomplete or incapable were removed. Then, the Dale indicator selection criteria were used to ensure that 14 indicators were selected. The binary fuzzy comparison method and criteria importance through interference correlation method were used to calculate the combination weight. Finally, an indicator system optimization model was established. The indicator system was optimized from 59 to 35 indicators, and the completeness of the indicator system reached 85.75%. The proposed method had obvious advantages in terms of indicator identification and elimination, and it may truly achieve the goal of indicator optimization. HIGHLIGHTS Resilience has become a research hotspot of agricultural soil–water engineering.; According to the Dale criteria construct a suitable evaluation indicator system.; Construction of a suitable indicator system is the key premise for measuring resilience.; Verify the rationality of the indicator system based on the combination weighting.; The model can be used as the basis to select the evaluation indicator resilience.;

To meet the demand of the fast increasing population, enhancing the wheat (Triticum aestivum L.) yield and resource use efficiency by optimizing water and nitrogen (N) management can greatly improve agricultural sustainability and enhance regenerative farming in developing countries such as China. Based on 126 studies conducted in China between 1996 and 2018, using meta-analysis in combination with decision regression tree modeling and life cycle assessment (LCA), this study aimed to (1) quantify the effect of water and N input on wheat yield, water productivity (WPc), and N use efficiency (NUEf), and evaluate the subsequent environmental impact in different regions using LCA; and (2) evaluate, model, and rank the roles of environmental (e.g., soil nutrient status and climatic factors) and agronomic factors (e.g., water and N management practices) affecting wheat yield, WPc, and NUEf. The results showed that irrigation and N addition increased the average yield and WPc by 40 and 15%, respectively, relative to control treatments with no irrigation or fertilizer application. The mean water saving potential (WSP) and N saving potential (NSP) in China were estimated at 11 and 10%, respectively. Soil nutrient status [e.g., initial soil phosphorus (P) and potassium (K)] and soil organic carbon content affected the wheat yield, WPc, and NUEf more significantly than climatic factors [mean annual temperature (MAT)] or water and N management practices. The structural equation-based modeling indicated that initial soil nutrient condition impacted productivity and resource use efficiency more at the below optimal water and N levels than above. The risk-factor-based feature ranking indicated that site-specific environmental and soil condition was highly informative toward model construction but split input of N or water had less impact on yield and input use efficiency. LCA demonstrated that to further mitigate greenhouse gas emissions, water- or N-saving management should be promoted in China. Collectively, our research implies that long-term soil health and nutrient enhancement should be more beneficial for increasing yield and resource use efficiency in wheat production.

Quantifying carbon uptake or gross primary production (GPP) from agroecosystems is important for understanding the spatial and temporal dynamics of carbon fixation by crops. The availability of high-resolution remote sensing data can significantly improve GPP estimation of small-scale agricultural fields. Multispectral satellite data with 3-m spatial resolution and frequent global coverage are available from the PlanetScope network of satellites. However, this data remains largely unexplored for studying the carbon dynamics of agroecosystems. The overarching goal of this study was to develop a simple empirical method for quantifying the GPP of dryland maize (Zea mays L.) using remotely sensed vegetation indices along with in-situ measurements of photosynthetically active radiation and leaf area index by linking it with carbon uptake data from an eddy covariance flux tower. Four vegetation indices were investigated: the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), the weighted difference vegetation index (WDVI), and the two-band enhanced vegetation index (EVI2). This study was conducted over a three-year period from 2017 to 2019 in East-Central Texas. A total of 12 GPP prediction models were developed using individual yearly data and were used for predicting GPP of the other 2 years. Predicted maize GPP values were then compared against tower-based GPP. The NDVI models were the least successful in predicting GPP and had the highest root mean square error (average: 10.1 3 gC m−2; maximum: 26.3 gC m−2). Models based on SAVI performed especially well with error ranging from 0.05 to 0.94 gC m−2. The slope of the regression between SAVI-based estimated GPP and measured GPP was not different from 1.0 in all combinations of years. The success of the SAVI-based GPP models for predicting dryland maize carbon uptake indicates that it was the least affected vegetation index by changing soil background condition in this row cropping system.

Organophosphate esters (OPEs) are still produced and used in large quantities in the world-wide, and the environmental burden and behavior have generated widespread concern, especially in some large-scale waterbodies. This study conducted a comprehensive assessment on the temporal and spatial variabilities and budget of OPEs to trace the source for the Bohai Sea (BS), based on a 5-year seasonal monitoring campaign (June 2014 to May 2019) of 12 atmospheric sites around the BS and our previous studies. The average concentration of Σ10OPEs in atmosphere during the sampling period was 7.65 ± 6.42 ng m−3, and chlorinated OPEs were the major compounds. The Seasonal-Trend decomposition procedure based on Loess (STL) analyzed that during the 5-year sampling period, the atmospheric concentrations of Σ10OPEs had a slightly increasing trend with a rate of + 0.092 ng m−3 yr−1, and the seasonal concentrations had a distinct seasonal distribution. The highest concentration of Σ10OPEs was observed at the sampling site of Dalian, followed by Tianjin, Yantai, and Beihuangcheng. The estimation of the fugacity ratios and air–water gas exchange fluxes established that the concentration levels of two major components of chlorinated OPEs (tris-(2-chloroethyl) phosphate (TCEP) and tris-(1-chloro-2-propyl) phosphate (TCPP)) in the atmosphere were dominated by their volatilization from BS’s seawater (1.24 ± 0.46 t yr−1 for TCEP and 5.15 ± 2.15 t yr−1 for TCPP), with 73% deriving from the coastal seawater. The budget assessment suggested that the volatile fluxes of TCEP and TCPP accounted for 8% and 29% of their storages (15.6 ± 5.32 t for TCEP and 17.6 ± 6.70 t for TCPP) in the BS seawater, which were mainly contributed by continental river input (20% for TCEP and 42% for TCPP). The efforts indicated that river inputs of TCEP and TCPP needed to be paid more attention for the improvement of environmental quality of the BS.

Abstract A major challenge in maize (Zea mays) production is to achieve high grain yield (yield hereafter) by improving resource use efficiency. Using a dataset synthesized from 83 peer-reviewed articles, this study mainly investigated the effects of water and/or nitrogen (N) input on maize yield, water productivity (WP), and N use efficiency (NUE); and evaluated the effects caused by planting density, environmental (temperature, soil texture), and managerial factors (water and/or N input). The input of water increased maize yield, WP, and NUE only when the input was less than 314, 709, and 311 mm, respectively; input of N increased maize yield, WP, and NUE until input was greater than 250, 128, and 196 kg ha−1, respectively. Additionally, results of the mixed-effects model and random forest analysis suggested that mean annual temperature (MAT) was the most critical factor for narrowing gaps (between the actual and attainable variable, which was indicated as response ratio of the treatment relative to the control) of yield (RR Y), WP (RR WP), and NUE (RR NUE), respectively. Specifically, RR Y, RR WP, or RR NUE were negatively correlated to MAT when MAT was higher than 15 °C. Additionally, the structural equation model showed that water input and RR WP with the higher coefficient were more important than N input and RR NUE in improving RR Y. These findings provide new insights into the causes and limitations of global maize production and offer some guidances for water and/or N managements.

The microbes in human bile are closely related to gallbladder health and other potential disorders. Although the bile microbial community has been investigated by recent studies using amplicon or metagenomic sequencing technologies, the genomic information of the microbial species resident in bile is rarely reported. Herein, we isolated 138 bacterial colonies from the fresh bile specimens of four cholecystitis patients using a culturome approach and genomically characterized 35 non-redundant strains using whole-genome shotgun sequencing. The bile bacterial isolates spanned 3 classes, 6 orders, 10 families, and 14 genera, of which the members of Enterococcus, Escherichia–Shigella, Lysinibacillus, and Enterobacter frequently appeared. Genomic analysis identified three species, including Providencia sp. D135, Psychrobacter sp. D093, and Vibrio sp. D074, which are not represented in existing reference genome databases. Based on the genome data, the functional capacity between bile and gut isolates was compared. The bile strains encoded 5,488 KEGG orthologs, of which 4.9% were specific to the gut strains, including the enzymes involved in biofilm formation, two-component systems, and quorum-sensing pathways. A total of 472 antibiotic resistance genes (ARGs) were identified from the bile genomes including multidrug resistance proteins (42.6%), fluoroquinolone resistance proteins (12.3%), aminoglycoside resistance proteins (9.1%), and β-lactamase (7.2%). Moreover, in vitro experiments showed that some bile bacteria have the capabilities for bile salt deconjugation or biotransformation (of primary bile acids into secondary bile acids). Although the physiological or pathological significance of these bacteria needs further exploration, our works expanded knowledge about the genome, diversity, and function of human bile bacteria.

Neonicotinoid insecticides (NNIs) have been intensively used and exploited, resulting in their presence and accumulation in multiple environmental media. We herein investigated the current levels of eight major NNIs in the Harbin section of the Songhua River in northeast China, providing the first systematic report on NNIs in this region. At least four NNIs in water and three in sediment were detected, with total concentrations ranging from 30.8 to 135 ng L-1 and from 0.61 to 14.7 ng g-1 dw, respectively. Larger spatial variations in surface water NNIs concentrations were observed in tributary than mainstream (p

The selection and breeding of deep rooting and drought-tolerant varieties has become a promising approach for improving the yield and adaptability of potato (Solanum tuberosum L.) in arid and semiarid areas. Therefore, the discovery of root-development-related genes and drought tolerance signaling pathways in potato is important. In this study, we used deep-rooting (C119) and shallow-rooting (C16) potato genotypes, with different levels of drought tolerance, to achieve this objective. Both genotypes were treated with 150 mM mannitol for 0 h (T0), 2 h (T2), 6 h (T6), 12 h (T12), and 24 h (T24), and their root tissues were subjected to comparative transcriptome analysis. A total of 531, 1571, 1247, and 3540 differentially expressed genes (DEGs) in C16 and 1531, 1108, 674, and 4850 DEGs in C119 were identified in T2 vs. T0, T6 vs. T2, T12 vs. T6, and T24 vs. T12 comparisons, respectively. Gene expression analysis indicated that a delay in the onset of drought-induced transcriptional changes in C16 compared with C119. Functional enrichment analysis revealed genotype-specific biological processes involved in drought stress tolerance. The metabolic pathways of plant hormone transduction and MAPK signaling were heavily involved in the resistance of C16 and C119 to drought, while abscisic acid (ABA), ethylene, and salicylic acid signal transduction pathways likely played more important roles in C119 stress responses. Furthermore, genes involved in root cell elongation and division showed differential expression between the two genotypes under drought stress. Overall, this study provides important information for the marker-assisted selection and breeding of drought-tolerant potato genotypes.

As the most prevalent epi-transcriptional modification, m6A modifications play essential roles in regulating RNA fate. The molecular functions of YTH521-B homology (YTH) domain proteins, the most known READER proteins of m6A modifications, have been well-studied in animals. Although plants contain more YTH domain proteins than other eukaryotes, little is known about their biological importance. In dicot species Arabidopsis thaliana, the YTHDFA clade members ECT2/3/4 and CPSF30-L are well-studied and important for cell proliferation, plant organogenesis, and nitrate transport. More emphasis is needed on the biological functions of plant YTH proteins, especially monocot YTHs. Here we presented a detailed phylogenetic relationship of eukaryotic YTH proteins and clustered plant YTHDFC clade into three subclades. To determine the importance of monocot YTH proteins, YTH knockout mutants and RNAi-induced knockdown plants were constructed and used for phenotyping, transcriptomic analysis, and stress treatments. Knocking out or knocking down OsYTHs led to the downregulation of multicellular organismal regulation genes and resulted in growth defects. In addition, loss-of-function ythdfa mutants led to better salinity tolerance whereas ythdfc mutants were more sensitive to abiotic stress. Overall, our study establishes the functional relevance of rice YTH genes in plant growth regulation and stress response.

Abstract Recurrent angina (RA) after percutaneous coronary intervention (PCI) has few known risk factors, hampering the identification of high‐risk populations. In this multicenter study, plasma samples are collected from patients with stable angina after PCI, and these patients are followed‐up for 9 months for angina recurrence. Broad‐spectrum metabolomic profiling with LC‐MS/MS followed by multiple machine learning algorithms is conducted to identify the metabolic signatures associated with future risk of angina recurrence in two large cohorts (n = 750 for discovery set, and n = 775 for additional independent discovery cohort). The metabolic predictors are further validated in a third cohort from another center (n = 130) using a clinically‐sound quantitative approach. Compared to angina‐free patients, the remitted patients with future RA demonstrates a unique chemical endophenotype dominated by abnormalities in chemical communication across lipid membranes and mitochondrial function. A novel multi‐metabolite predictive model constructed from these latent signatures can stratify remitted patients at high‐risk for angina recurrence with over 89% accuracy, sensitivity, and specificity across three independent cohorts. Our findings revealed reproducible plasma metabolic signatures to predict patients with a latent future risk of RA during post‐PCI remission, allowing them to be treated in advance before an event.

Methyl siloxanes are identified as emerging persistent toxic compounds and the ecological environment risks of these compounds have been caused of great concern worldwide. In this study, the concentrations of methyl siloxanes were reported in dissolved water and crucian carp around a methyl siloxane production factory located in Liaoning Province, Northeast China. D4, D5, D6, D7, L4, L5 and L6 were detectable both in dissolved water and crucian carp. The total concentrations of 7 methyl siloxanes (Σ7MS) were 14 ± 6.3 ng/L in dissolved water and 43 ± 22 ng/g ww in crucian carp, respectively. D5 has the highest concentration both in dissolved water (5.5 ± 3.5 ng/L) and crucian carp (17 ± 11 ng/g ww). Based on the monitoring values, bioaccumulation factor (BAF) of these compounds were calculated. Significant bioaccumulation potential was observed for D4 (BAF = 5900 ± 3500 L/kg) based on the bioaccumulation criteria suggested by USEPA and EU (BAF > 5000 L/kg). To our understanding, this is the first report of BAF values of methyl siloxane in field study, which will provide important support for further assessment of bioaccumulation of these compounds.

The flexible clamp used in linear ultrasonic motors (LUMs) not only supports the motor but also simplifies the structure and improves the vibration characteristics. However, the flexible clamp causes the mechanical drift phenomenon, leading to a reduction in positional accuracy. To solve this problem, the mechanical drift mechanism and the control methods of V-shaped LUMs are investigated in this work. First, a mechanical model of the stator of a LUM with a flexible clamp is established to analyze the reason for the mechanical drift. Then, based on the mechanical model, the rules of the appearance of mechanical drift in clamping components with different stiffnesses and shapes are studied. The results indicate that mechanical drift is obvious when the stiffnesses of the two flexible clamps are different, whereas it hardly occurs when clamping components with tremendous tangential stiffnesses are used. Finally, two new types of V-shaped LUM stators that have a flexible clamp on one side and an analogous straight-beam clamp on the other side are proposed. Experiments were conducted to validate the mechanical model analysis and the clamp drift findings, and the results demonstrate that the novel motors have little mechanical drift and stable running characteristics and can be used in precision motion platforms.

In China, nitrate (NO3−) becomes the main contributor to fine particles (PM2.5) because the emissions of its precursor, nitrogen oxides (NOx), were not recognized and controlled well in recent years. In this work, sources, conversion, and geographical origin of NOx were interpreted combining the isotopic information (δ15N and δ18O) of NO3− and dual modelling at five Chinese megacities (Beijing, Shanghai, Guangzhou, Wuhan and Chengdu) during 2013–2014. Results showed that the δ15N-NO3− values (n = 512) ranged from −12.3‰ to +22.9‰, and the average δ18O-NO3− value was +83.4‰ ± 17.2‰. The isotopic compositions both had a rising tendency as ambient temperature dropped, attributing largely to the source changes. Bayesian model indicated the percentage for the ·OH pathway of NOx conversion had a clear seasonal variation with a higher value during summer (58.0% ± 9.82%) and a lower value during winter (11.1% ± 3.99%); it was also significantly correlated with latitude (p

Abstract Improving soil physical properties is important to soil conservation. Cover cropping can improve soil physical properties and organic matter content which can reduce soil loss, and thereby improve land productivity and environmental quality. In this article, the benefits of cover crops (CCs) for improving soil physical and hydraulic properties are reviewed as well as some soil conservation benefits that might accrue. The review indicates that CCs reduce soil bulk density by approximately 4%, increase macropores by approximately 33%, and increase water infiltration by as much as 629%, as compared to soil with no CCs. These improvements have been reported to lead to as much as 96% reduction in soil loss. Some current knowledge gaps in understanding how CCs can improve soil physical properties have been identified, including identifying which biomass, aboveground or belowground biomass, plays a greater role in organic C accumulation. Future research should focus on the interconnectedness of soil pores generated by CCs and the influence of CCs on heat transport parameters to further improve soil physical properties and associated benefits.

The rising frequency of extreme weather events and global warming are greatly challenging pastoral ecosystem productivity, particularly in the temperate climate-transition regions. While this could cause greater gross primary production (GPP) mainly contributed by the warm-season vegetation, the consequences for the dynamics of net ecosystem exchange (NEE) and hydrological responses (e.g., evapotranspiration, ET) on an ecosystem level are poorly known. Here, we investigated the evolution of plant phenology, nutritive value, energy balance, and carbon/water budgets of a cool-season dominated pastoral ecosystem in the temperate zone; integrating both eddy covariance (EC) flux measurement and simulation modeling-based uncertainty analysis. Throughout the two-year duration (2017–2018) of this study, the entire pasture ecosystem remained a strong carbon sink (NEE = −1.23 and −1.95 kg C m−2, respectively) with 74% and 62% of available energy loss explained by EC fluxes, respectively. The cumulative ET was 735.8 and 796.8 mm, respectively; and the overall ecosystem water use efficiency (EWUE) were calculated as 6.5 g C kg−1 water across both growing seasons. The above-ground biomass yield agreed with the cumulative GPP and was inversely correlated with grass nutritive value. The uncertainty analysis indicated that accurate EC flux gap-filling models could be constructed using support vector machine trained time-series models (NEE, R2 = 0.77, RMSE = 11.8; ET, R2 = 0.90, RMSE = 73.8). The performance benchmarking tests indicated that REddyProc-based gap-filling performance was very limiting and highly variable (NEE, R2 = 0.21–0.64; ET, R2 = 0.79–0.87), particularly for estimating NEE. Overall, the warm-season vegetation encroachment greatly filled the production gap of cool-season grasses, leading to greater cumulative NEE and EWUE on a system level, compared with those from many other reported field-crop or grassland studies using EC approaches. The complex and dynamic nature of grassland ecosystems greatly challenged the conventional REddyProc-based EC flux gap-filling performance. However, accurate machine learning models could be constructed for error/uncertainty control purposes and, thus, should be encouraged in future studies.

Snow cover is a unique environmental medium in cold regions that can cause potential risks to the ecological environment, due to the release of pollutants that are stored in it. In this study, the Qixing River wetland, located in the Sanjiang Plain of China, was taken as the target research area. Heavy metals in snow cover, including Cu, Ni, Cr, Cd, Pb, and Zn were measured at 19 sampling sites. The results showed that the average concentrations of heavy metals were: Zn (103.46 ± 39.16) > Pb (13.08 ± 4.99) > Cr (11.97 ± 2.82) > Ni (9.55 ± 4.96) > Cu (6.19 ± 1.79) > Cd (0.55 ± 0.25) μg·L−1. Cr and Zn were between Class I and Class II in the “Environmental Quality Standards for Surface Water” of China (GB3838-2002). Pb in snow exceeded the upper limit of Class II, and was significantly higher than concentrations measured in water samples from the Qixing River wetland (p < 0.05), indicating that atmospheric deposition during winter was the major source of Pb. The water pollution index (WPI) indicated that 61.0% of samples could be considered of “clean” status, while the contribution of Zn, Pb, and Cr to WPI were 33.3%, 21.0%, and 19.3%, respectively. A preliminary evaluation of heavy metal inventory in snow cover showed that the residue level of Zn was the highest (2313.57 ± 1194.67 μg·m−2), while Cd was the lowest (13.91 ± 10.45 μg·m−2). The areas with high residues of heavy metals were all located near the buffer zone of the wetland (except for Zn), where snow depth tended to be greatest. Exposure analysis indicated that the risks to winter resident birds from snow ingestion was minimal, but it should be noted that the exposure risk was higher in birds with lower bodyweights. This study provides important information and scientific knowledge on the pollution characteristics and residue inventory of heavy metals in wetland ecosystems, while the results can also provide a monitoring method, reflecting atmospheric environmental quality at a local or regional scale.

Abstract The present study investigated the environmental distribution and fate of low molecular weight (LMW) polychlorinated biphenyls (PCBs) in China using the ChnGIPERM (Chinese Gridded Industrial Pollutants Emission and Residue Model), in which the air-soil exchange, spatial-temporal variations and the heterogeneity of secondary emission and residue in the non-source areas were studied. The model simulated the temporal and spatial variations of the PCB28 concentration in soils and air which agreed well with historical monitoring data across China. The long-range atmospheric transport (LRAT) and temperature was identified as the major factor affecting the distribution patterns of the secondary emissions and residues. Soil residue was considered as important environmental fate of PCB28. However, the intensity of an emissions source and the distance with non-source area strongly affected the spatial and temporal variations of PCB28 residues in soil. Several factors strongly impacted the distribution characteristics and air-soil exchange of PCB28, including emission patterns, atmospheric transport, soil organic carbon (SOC), soil vertical transfer, ambient temperature, and precipitation.

Accurately predicting and testing the types of Pulmonary arterial hypertension (PAH) of each patient using cost-effective microarray-based expression data and machine learning algorithms could greatly help either identifying the most targeting medicine or adopting other therapeutic measures that could correct/restore defective genetic signaling at the early stage. Furthermore, the prediction model construction processes can also help identifying highly informative genes controlling PAH, leading to enhanced understanding of the disease etiology and molecular pathways. In this study, we used several different gene filtering methods based on microarray expression data obtained from a high-quality patient PAH dataset. Following that, we proposed a novel feature selection and refinement algorithm in conjunction with well-known machine learning methods to identify a small set of highly informative genes. Results indicated that clusters of small-expression genes could be extremely informative at predicting and differentiating different forms of PAH. Additionally, our proposed novel feature refinement algorithm could lead to significant enhancement in model performance. To summarize, integrated with state-of-the-art machine learning and novel feature refining algorithms, the most accurate models could provide near-perfect classification accuracies using very few (close to ten) low-expression genes.

The dynamic model is significant for the analysis of the vibrational characteristics of the wheeled tractor system with implement and front axle hydropneumatic suspension. In this work, the nonlinear stiffness and damping equations are derived first. The dynamic coupling relationship among cabin, three-point hitch structure, and implement are figured out, and the dynamic model of the half agricultural wheeled tractor/implement system is presented considering the effects of three-point hitch structure, passive silent blocks of cabin, and front axle hydropneumatic suspension together. To validate the model, the power spectral densities (PSDs) of the driver seat, cabin, chassis, and implement acquired from numerical simulations are compared with those from experiments, respectively. Under different forward speeds, two groups of results match well. Based on the model, the influences of passive cabin suspension, implement, and front axle hydropneumatic suspension on the dynamical characteristics of the tractor system are investigated. Results indicate that the front axle hydropneumatic suspension will deteriorate the ride comfort of the driver but improve the handing stability. The passive cabin suspension reduces the operational stability while improving much more ride comfort than the front axle hydropneumatic suspension does. The driver’s comfortableness will be increased due to implement; meanwhile, handling stability will be compromised. Besides, the impacts of initial nitrogen volume, pressure of accumulators, and orifice diameter of throttle valves on the vibration characteristics of the tractor system are also inspected.

The nonlinear radiation distortions (NRD) among multimodal remote sensing images bring enormous challenges to image registration. The traditional feature-based registration methods commonly use the image intensity or gradient information to detect and describe the features that are sensitive to NRD. However, the nonlinear mapping of the corresponding features of the multimodal images often results in failure of the feature matching, as well as the image registration. In this paper, a modality-free multimodal remote sensing image registration method (SRIFT) is proposed for the registration of multimodal remote sensing images, which is invariant to scale, radiation, and rotation. In SRIFT, the nonlinear diffusion scale (NDS) space is first established to construct a multi-scale space. A local orientation and scale phase congruency (LOSPC) algorithm are then used so that the features of the images with NRD are mapped to establish a one-to-one correspondence, to obtain sufficiently stable key points. In the feature description stage, a rotation-invariant coordinate (RIC) system is adopted to build a descriptor, without requiring estimation of the main direction. The experiments undertaken in this study included one set of simulated data experiments and nine groups of experiments with different types of real multimodal remote sensing images with rotation and scale differences (including synthetic aperture radar (SAR)/optical, digital surface model (DSM)/optical, light detection and ranging (LiDAR) intensity/optical, near-infrared (NIR)/optical, short-wave infrared (SWIR)/optical, classification/optical, and map/optical image pairs), to test the proposed algorithm from both quantitative and qualitative aspects. The experimental results showed that the proposed method has strong robustness to NRD, being invariant to scale, radiation, and rotation, and the achieved registration precision was better than that of the state-of-the-art methods.

Motion-intent-based finger gesture recognition systems are crucial for many applications such as prosthesis control, sign language recognition, wearable rehabilitation system, and human–computer interaction. In this article, a motion-intent-based finger gesture recognition system is designed to correctly identify the tapping of every finger for the first time. Two auto-event annotation algorithms are firstly applied and evaluated for detecting the finger tapping frame. Based on the truncated signals, the Wavelet packet transform (WPT) coefficients are calculated and compressed as the features, followed by a feature selection method that is able to improve the performance by optimizing the feature set. Finally, three popular classifiers including naive Bayes (NBC), K-nearest neighbor (KNN), and support vector machine (SVM) are applied and evaluated. The recognition accuracy can be achieved up to 94%. The design and the architecture of the system are presented with full system characterization results.

As a crop with high water consumption, rice is an important measure of efforts to improve agricultural irrigation efficiency and alleviate the contradiction between the supply and demand of agricultural water resources. This paper takes the Longtouqiao irrigation district, in the hinterland of the Sanjiang Plain, a major rice-producing area in northern China, as an example, and the AquaCrop crop growth model and entropy-cloud model are jointly used to develop a rice irrigation schedule optimization model based on three kinds of typical rainfall years. Different irrigation schemes are established and evaluated by using the model built based on images. The results showed that the yield’s normalized root mean square error (NRMSE) value of the AquaCrop model was 9.949% (< 10%) after calibration, and the our model results showed a good agreement with observed data, which indicated that the calibrated model was suitable for rice growth simulation in the research area. For the same irrigation water amount, rice was irrigated to a great extent at the tillering stage, and a small amount of irrigation water at the regreening stage of rice could improve rice yield. During irrigation, rice production can also be promoted by regulating the irrigation amount according to the rainfall in each growth period, and the optimal irrigation water amount can be controlled between 20 and 60 mm each time. Under the three typical annual scenarios of dry, normal and wet years, the respective optimal quantification results for the field capacity, total irrigation water amount and irrigation times in the rice growth period to attain the optimal irrigation effect were 25%, 425 mm, and 17 times, respectively; 25%, 450 mm, and 14 times; respectively, and 25%, 425 mm, and 17 times, respectively. The research results can provide a decision-making basis for water-saving measures and efficient rice irrigation water management.

The energy detection (ED) Gaussian frequency-shift keying (GFSK) ultra-wideband (UWB) system, transmits bit 0 and 1 using different-order derivatives of the Gaussian pulse. The spectra of the two pulses are separated entirely in the frequency domain, so it generates the orthogonality of these two pulses. This orthogonality in the frequency domain makes the GFSK system more robust in multipath channels and in the presence of synchronisation errors, so it shows a better bit error rate performance than the ED pulse-position modulation UWB system. However, the effect of frequency selectivity and antenna has not been discussed in earlier publications. In this study, the authors will analyse the effect of these two factors and propose a method to compensate the effect.

Irrigation water use efficiency is a primary evaluation index that links economic production development with the efficient use of water resources. Canal water conveyance is an important part of irrigation, and the distribution characteristics of canal systems have an important influence on irrigation water use efficiency. In this paper, 75 irrigated districts in Heilongjiang Province in 2015 were selected as the study objects. The main, branch, lateral, and sublateral canals were graded into first-, second-, third-, and fourth-order classes, respectively. The irrigation districts were divided into three classes, that is, four-order, three-order, and two-order, according to the canal orders that the irrigation districts contained. The canal system framework was described by Horton’s law. The fractal dimension of the canal system was calculated based on the bifurcation ratio and length ratio of the canals. The relationships between fractal dimensions and irrigation water use efficiency were evaluated. The results showed that the irrigation water use efficiency of the four-order and three-order irrigation districts initially increased and then decreased with increases in the fractal dimension (D). In the irrigation districts, an irrigation water use efficiency of more than 10 × 103 hm2 and less than 0.67 × 103 hm2 was proportional to the increase in the fractal dimension, whereas the opposite result was found for districts with (0.67–10) × 103 hm2. The irrigation water use efficiency of the four-order and two-order irrigation districts with less than 3.3 × 103 hm2 had the greatest potential to increase the water use efficiency. Therefore, canal system reconstruction suggestions for different irrigation districts were provided. The results have important theoretical significance and practical value for the improvement of irrigation construction and the promotion of irrigation water efficiency planning.

The potential influence of natural variations in a climate system on global warming can change the hydrological cycle and threaten current strategies of water management. A simulation-based linear fractional programming (SLFP) model, which integrates a runoff simulation model (RSM) into a linear fractional programming (LFP) framework, is developed for optimal water resource planning. The SLFP model has multiple objectives such as benefit maximization and water supply minimization, balancing water conflicts among various water demand sectors, and addressing complexities of water resource allocation system. Lingo and Excel programming solutions were used to solve the model. Water resources in the main stream basin of the Songhua River are allocated for 4 water demand sectors in 8 regions during two planning periods under different scenarios. Results show that the increase or decrease of water supply to the domestic sector is related to the change in population density at different regions in different target years. In 2030, the water allocation in the industrial sector decreased by 1.03–3.52% compared with that in 2020, while the water allocation in the environmental sector increased by 0.12–1.29%. Agricultural water supply accounts for 54.79–77.68% of total water supply in different regions. These changes in water resource allocation for various sectors were affected by different scenarios in 2020; however, water resource allocation for each sector was relatively stable under different scenarios in 2030. These results suggest that the developed SLFP model can help to improve the adjustment of water use structure and water utilization efficiency.

A new energy detection ultra-wideband system based on pulse width modulation is proposed. The bit error rate (BER) performance of this new system is slightly worst than that of a pulse position modulation (PPM) system in additive white Gaussian noise channels. In multipath channels, this system does not suffer from cross-modulation interference as PPM, so it can achieve better BER performance than PPM when cross-modulation interference occurs. In addition, when synchronisation errors occur, this system is more robust than PPM.

Biological prediction of transcription factor binding sites and their corresponding transcription factor target genes (TFTGs) makes great contribution to understanding the gene regulatory networks. However, these approaches are based on laborious and time-consuming biological experiments. Numerous computational approaches have shown great potential to circumvent laborious biological methods. However, the majority of these algorithms provide limited performances and fail to consider the structural property of the datasets. We proposed a refined systematic computational approach for predicting TFTGs. Based on previous work done on identifying auxin response factor target genes from Arabidopsis thaliana co-expression data, we adopted a novel reverse-complementary distance-sensitive n-gram profile algorithm. This algorithm converts each upstream sub-sequence into a high-dimensional vector data point and transforms the prediction task into a classification problem using support vector machine-based classifier. Our approach showed significant improvement compared to other computational methods based on the area under curve value of the receiver operating characteristic curve using 10-fold cross validation. In addition, in the light of the highly skewed structure of the dataset, we also evaluated other metrics and their associated curves, such as precision-recall curves and cost curves, which provided highly satisfactory results.

By imitating the scaffold of lithocholic acid (LCA), a natural steroidal compound displaying Protein Tyrosine Phosphatase 1B (PTP1B) inhibitory activity, a series of stilbene derivatives containing phenyl-substituted isoxazoles were designed and synthesized. The structures of the title compounds were confirmed by 1H-NMR, 13C-NMR and HRMS. Activities of the title compounds were evaluated on PTP1B and the homologous enzyme TCPTP by using a colorimetric assay. Most of the target compounds had good activities against PTP1B. Among them, compound 29 (IC50 = 0.91 ± 0.33 μM), characterized by a 5-(2,3-dichlorophenyl) isoxazole moiety, exhibited an activity about 14-fold higher than the lead compound LCA and a 4.2-fold selectivity over TCPTP. Compound 29 was identified as a competitive inhibitor of PTP1B with a Ki value of 0.78 μM in enzyme kinetic studies.

This study aimed to analyze the relationship between sex and corpus callosum (CC) volume in children with autism spectrum disorders (ASD) aged 2-4 years. This prospective study included 50 children with ASD and 50 typically developing (TD) children aged 2-4 years. Midsagittal slices of the CCs of the participants were divided into five subregions using FreeSurfer software. The PMCC, AMCC and TCC volumes were significantly higher in ASD participants than in TD participants, and results were significant in females with ASD rather than in males with ASD (all P < 0.05). In toddlers with ASD, the CC volumes were increased and more pronounced in females than in males. This could be due to overgrowth of axons or/and axonal pruning disorders., Author(s): Yun Zhang [sup.1] [sup.4] , Bin Qin [sup.1] [sup.4] , Longlun Wang [sup.1] , Ke Zhang [sup.1] , Cui Song [sup.1] [sup.2] , Jie Chen [sup.1] [sup.3] , Jinhua [...]

The sediment-water exchange, spatial variations, and ecological risk of polycyclic aromatic hydrocarbons (PAHs) in the water and sediment of the Songhua River, China, were investigated and assessed in this paper. The fugacity fraction (ff) was used to evaluate the sediment–water exchange of PAHs between the water and sediment. The results suggest that the values of ff decreased with an increasing number of PAH rings. The sediment acts as a secondary emission source for three- and four-ring PAHs, whereas five-ring PAHs were deposited in the sediment from the water. The high ff values of PAHs found in autumn suggest large releases of PAHs after abundant deposition in summer, and the lowest ff values of PAHs occurred in summer. The values were especially low for five- and six-ring PAHs, which exhibited considerable deposition from the water to the sediment. PAHs with low molecular weights showed strong variations, which were potentially caused by their active physical-chemical properties. Additionally, high molecular weight BaP displayed weak variations, increasing the potential risk in the sediment. The simplified qualitative method of Cwater/Csediment is useful for assessing variations in the sediment–water exchange. The relationships between Cwater/Csediment and ff were investigated by determining the Pearson correlation coefficients (R). The results exhibited a significant negative correlation, with R = −1.000 and P = 0.000 for Flu, R = −0.993 and P = 0.007 for Phe, R = −0.998 and P = 0.002 for FlA, and R = −0.971 and P = 0.029 for BaP. The coefficients of variation indicated that five-ring PAHs were more sensitive than three- and four-ring PAHs. Thus, these low-ring PAHs can be easily exchanged between the sediment and the water. Additionally, the ecological risk of PAHs to aquatic organisms in the Songhua River is relatively low.

The accurate and reliable prediction of groundwater depth is the basis of the sustainable utilization of regional groundwater resources. However, the complexity of the prediction has been ignored in previous studies of regional groundwater depth system analysis and prediction, making it difficult to realize the scientific management of groundwater resources. To address this defect, taking complexity diagnosis as the research foundation, this paper proposes a new coupling forecast strategy for evaluating groundwater depth based on empirical mode decomposition (EMD) and a radial basis function neural network (RBFNN). The data used for complexity analysis and modelling are the monthly groundwater depth series monitoring data from 15 long-term monitoring wells from 1997 to 2007, which were collected from the Jiansanjiang Administration of Heilongjiang Agricultural Reclamation in China. The calculation results of the comprehensive complexity index for each groundwater depth series obtained are based on wavelet theory, fractal theory, and the approximate entropy method. The monthly groundwater depth sequence of District 8 of Farm Nongjiang, which has the highest complexity among the five farms in the Jiansanjiang Administration midland, was chosen as the modelling sample series. The groundwater depth series of District 8 of Farm Nongjiang was separated into five intrinsic mode function (IMF) sequences and a remainder sequence by applying the EMD method, which revealed that local groundwater depth has a significant one-year periodic character and an increasing trend. The RBFNN was then used to forecast and stack each EMD separation sequence. The results suggest that the future groundwater depth will remain at approximately 10 m if the past pattern of water use continues, exceeding the ideal depth of groundwater. Thus, local departments should take appropriate countermeasures to conserve groundwater resources effectively.