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Abstract Background The triglyceride-glucose (TyG) index is linked to both the development and progression of diabetes, while obesity remains a significant risk factor for this disease. However, the relationship between the TyG index and overweight or obese diabetes remains unclear. Methods This study was a cross-sectional analysis of data from 40,633 participants with body mass index (BMI) ≥ 24 kg/m2 who were screened from January 2018 to December 2023 at Henan Provincial People’s Hospital. Participants were divided into groups of overweight or obese individuals with diabetes and those without diabetes according to the diabetes diagnostic criteria. The TyG index, the dependent variable, was determined using the equation ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. We explored the association between TyG index and diabetes in overweight or obese individuals through multivariate logistic regression, subgroup analysis, generalized additive models, smoothed curve fitting, and analysis of threshold effects. Results Patients who were overweight or obese and had diabetes had higher TyG index levels than those without diabetes. After adjusting for confounders, our findings indicated a significant association between the TyG index and the risk of diabetes in overweight or obese individuals [odds ratio (OR) = 7.38, 95% confidence interval (CI): 6.98–7.81]. There was a J-shaped nonlinear association between TyG index and diabetes. When TyG index was > 4.46, the risk of diabetes increased sharply. Notably, a high baseline TyG index (Q4 group) correlated with a notably greater risk of diabetes than did the Q1 group, with an OR of 22.72 (95% CI: 20.52–25.16). Subgroup analysis revealed that the association between TyG and diabetes was stronger in females than in males (OR = 7.57, 95% CI: 6.76–8.48,), more significant in individuals with a BMI of 24–28 kg/m2 than in those with a BMI ≥ 28 kg/m2 (OR = 8.40, 95% CI: 7.83–9.02), and increased with age (OR = 8.15, 95% CI: 7.25–9.17) (all P for interaction 4.46. Furthermore, factors such as sex, age, and BMI significantly influence the risk of diabetes in overweight or obese individuals. Specifically, older women with a BMI of 24–28 kg/m2 are at a greater risk of diabetes under similar TyG index conditions.

Abstract Background The atherogenic index of plasma (AIP) is closely associated with the onset of diabetes, with obesity being a significant risk factor for type 2 diabetes mellitus (T2DM). However, the association between the AIP and T2DM in overweight and obese populations has been infrequently studied. Therefore, this study aimed to explore this association in overweight and obese individuals with T2DM. Methods This cross-sectional analysis utilized data from 40,633 participants with a body mass index (BMI) ≥ 24 kg/m2 who were screened from January 2018 to December 2023 at Henan Provincial People’s Hospital. Participants were categorized into groups of overweight and obese individuals with and without diabetes according to the T2DM criteria. The AIP, our dependent variable, was calculated using the formula log10 [(TG mol/L)/HDL-C (mol/L)]. We investigated the association between the AIP and T2DM in overweight and obese individuals using multivariate logistic regression, subgroup analysis, generalized additive models, smoothed curve fitting, and threshold effect analysis. Additionally, mediation analysis evaluated the role of inflammatory cells in AIP-related T2DM. Results Overweight and obese patients with T2DM exhibited higher AIP levels than those without diabetes. After adjusting for confounders, our results indicated a significant association between the AIP and the risk of T2DM in overweight and obese individuals (odds ratio (OR) = 5.17, 95% confidence interval (CI) 4.69–5.69). Notably, participants with a high baseline AIP (Q4 group) had a significantly greater risk of T2DM than those in the Q1 group, with an OR of 3.18 (95% CI 2.94–3.45). Subgroup analysis revealed that the association between the AIP and T2DM decreased with increasing age (interaction P – 0.07 indicating a significant increase in T2DM risk. Various inflammatory cells, including neutrophils, leukocytes, and monocytes, mediated 4.66%, 4.16%, and 1.93% of the associations, respectively. Conclusion In overweight and obese individuals, the AIP was independently associated with T2DM, exhibiting a nonlinear association. Additionally, the association between the AIP and T2DM decreased with advancing age. Multiple types of inflammatory cells mediate this association.

Along with the battery charge/discharge processes, the migration of the Li ions inevitably leads to an inhomogeneous distribution of the battery internal electrochemical and thermal characteristics, deteriorating the battery capacity and safety. In this work, the internal characteristics of a 20 Ah pouch LiFePO4 lithium-ion battery have been investigated based on a 3D electrochemical-thermal coupled model. It is found that the reduction of the particle size for either the cathode or anode would increase the battery capacity. Moreover, with the modulation of the thickness of cathode (Lpos) and convective heat transfer coefficient (h), the homogeneity of solid lithium concentration and temperature has been optimized. A relatively smaller Lpos and an h higher than the critical value should be adopted. From the aspect of thermal distribution, as the discharge rate increases, the irreversible reaction heat always dominates, while the proportion of ohmic heat among the total heat increases, and the high temperature region always locates on the inner sides of the tabs. The methodology proposed in this work could be applied to pouch batteries with more repeated cell units and larger sizes.

Abstract Background The association between lipid and bone metabolism, particularly the role of high-density lipoprotein cholesterol (HDL-C) in regulating bone mineral density (BMD), is of significant interest. Despite numerous studies, findings on this relationship remain inconclusive, especially since evidence from large, sexually diverse Chinese populations is sparse. This study, therefore, investigates the correlation between HDL-C and lumbar BMD in people of different genders using extensive population-based data from physical examinations conducted in China. Methods Data from a cross-sectional survey involving 20,351 individuals aged > = 20 years drawn from medical records of health check-ups at the Health Management Centre of the Henan Provincial People’s Hospital formed the basis of this study. The primary objective was to determine the correlation between HDL-C levels and lumbar BMD across genders. The analysis methodology included demographic data analysis, one-way ANOVA, subgroup analyses, multifactorial regression equations, smoothed curve fitting, and threshold and saturation effect analyses. Results Multifactorial regression analysis revealed a significant inverse relationship between HDL-C levels and lumbar BMD in both sexes, controlling for potential confounders (Male: β = -8.77, 95% CI -11.65 to -5.88, P 28 kg/m2 and HDL-C > 1.45 mmol/L and in females with a BMI between 24 and 28 kg/m2. Conclusion Elevated HDL-C is associated with decreased bone mass, particularly in obese males. These findings indicate that individuals with high HDL-C levels should receive careful clinical monitoring to mitigate osteoporosis risk. Trial registration The research protocol received ethics approval from the Ethics Committee at Beijing Jishuitan Hospital, in conformity with the Declaration of Helsinki guidelines (No. 2015-12-02). These data are a contribution of the China Health Quantitative CT Big Data Research team, registered at clinicaltrials.gov (code: NCT03699228).

Abstract Background The impact of total cholesterol (TC) on lumbar bone mineral density (BMD) is a topic of interest. However, empirical evidence on this association from demographic surveys conducted in China is lacking. Therefore, this study aimed to examine the relationship between serum TC and lumbar BMD in a sample of 20,544 Chinese adults between the ages of 20 and 80 years over a period of 5 years, from February 2018 to February 2023. Thus, we investigated the effect of serum TC level on lumbar BMD and its relationship with bone reduction in a Chinese adult population. Methods This cross-sectional study used data obtained from the Department of Health Management at Henan Provincial People’s Hospital between February 2018 and February 2023. The aim of this study was to examine the correlation between serum TC and lumbar BMD in individuals of different sexes. The research methodology encompassed population description, analysis of stratification, single-factor and multiple-equation regression analyses, smooth curve fitting, and analysis of threshold and saturation effects. The R and EmpowerStats software packages were used for statistical analysis. Results After adjusting for confounding variables, a multiple linear regression model revealed a significant correlation between TC and lumbar BMD in men. In subgroup analysis, serum TC was found to have a positive association with lumbar BMD in men, specifically those aged 45 years or older, with a body mass index (BMI) ranging from 24 to 28 kg/m2. A U-shaped correlation arose between serum TC and lumbar BMD was detected in women of different ages and BMI, the inflection point was 4.27 mmol/L for women aged ≥ 45 years and 4.35 mmol/L for women with a BMI of ≥ 28 kg/m2. Conclusion In this study, Chinese adults aged 20–80 years displayed different effects of serum TC on lumbar BMD in sex-specific populations. Therefore, monitoring BMI and serum TC levels in women of different ages could prevent osteoporosis and osteopenia. Trial registration The research protocol was approved by the Ethics Committee of Beijing Jishuitan Hospital, in accordance with the Declaration of Helsinki guidelines (No. 2015-12-02). These data are part of the China Health Quantitative CT Big Data Research team, which has been registered at clinicaltrials.gov (code: NCT03699228).

ObjectiveIntermittent energy restriction (IER) is an effective weight loss strategy. However, little is known about the dynamic effects of IER on the brain-gut-microbiome axis.MethodsIn this study, a total of 25 obese individuals successfully lost weight after a 2-month IER intervention. FMRI was used to determine the activity of brain regions. Metagenomic sequencing was performed to identify differentially abundant gut microbes and pathways in from fecal samples.ResultsOur results showed that IER longitudinally reduced the activity of obese-related brain regions at different timepoints, including the inferior frontal orbital gyrus in the cognitive control circuit, the putamen in the emotion and learning circuit, and the anterior cingulate cortex in the sensory circuit. IER longitudinally reduced E. coli abundance across multiple timepoints while elevating the abundance of obesity-related Faecalibacterium prausnitzii, Parabacteroides distasonis, and Bacterokles uniformis. Correlation analysis revealed longitudinally correlations between gut bacteria abundance alterations and brain activity changes.ConclusionsThere was dynamical alteration of BGM axis (the communication of E. coli with specific brain regions) during the weight loss under the IER.

Abstract Purpose ​Develop and validate a nomogram prediction model for hypertension-diabetes comorbidities based on chronic disease management in the community. Patients and methods The nomogram prediction model was developed in a cohort of 7200 hypertensive patients at a community health service center in Hongshan District, Wuhan City. The data were collected from January 2022 to December 2022 and randomly divided into modeling and validation groups at a 7:3 ratio. The Lasso regression model was used for data dimensionality reduction, feature selection, and clinical test feature construction. Multivariate logistic regression analysis was used to build the prediction model. Results The application of the nomogram in the verification group showed good discrimination, with an AUC of 0.9205 (95% CI: 0.8471–0.9527) and a good calibration effect. Decision curve analysis demonstrated that the predictive model was clinically useful. Conclusion This study presents a nomogram prediction model that incorporates age, waist-height ratio and elevated density lipoprotein cholesterol (HDL-CHOLESTEROL), which can be used to predict the risk of codeveloping diabetes in hypertensive patients.

Abstract Background Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology. Methods The subjects' demographic data, baseline examination results, and annual follow‐up low‐dose spiral computerized tomography (LDCT) results were recorded. The findings from annual physical examinations of positive nodules, including highly suspicious nodules and clinically tentative benign nodules, was analyzed. The extreme gradient boosting (XGBoost) model was constructed and the Grid Search CV method was used to select the super parameters. External unit data were used as an external validation set to evaluate the generalization performance of the model. Results A total of 135,503 physical examinees were enrolled. Baseline testing found that 27,636 (20.40%) participants had clinically tentative benign nodules and 611 (0.45%) participants had highly suspicious nodules. The proportion of highly suspicious nodules in participants with negative baseline was about 0.12%–0.46%, which was lower than the baseline level except the follow‐up of >5 years. In the 27,636 participants with clinically tentative benign nodules, only in the first year of LDCT re‐examination was the proportion of highly suspicious nodules (1.40%) significantly greater than that of baseline screening (0.45%) (p 0.05). Furthermore, 322 cases with benign nodules and 196 patients with malignant nodules confirmed by surgery and pathology were compared. A model and the top 15 most important clinical variables were determined by XGBoost algorithm. The area under the curve (AUC) of the model was 0.76 [95% CI: 0.67–0.84], and the accuracy was 0.75. The sensitivity and specificity of the model under this threshold were 0.78 and 0.73, respectively. In the validation of model using external data, the AUC was 0.87 and the accuracy was 0.80. The sensitivity and specificity were 0.83 and 0.77, respectively. Conclusions It is important that pulmonary nodules could be more accurately identified at the first LDCT examination. A model with 15 variables which are routinely measured in the clinic could be helpful to distinguish benign and malignant nodules. It could help the radiological team issue a more accurate report; and it may guide the clinical team regarding LDCT follow‐up.

The exploitation of iron ore could cause heavy metals pollution in the soils, which threatens the ecosystem and human health. In this study, soil, stream sediment, tailings, rock, and atmospheric deposition samples were collected from an iron mine in Baotou City. The concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, Zn, Al2O3, CaO, K2O, MgO, Na2O, SiO2, and Fe2O3, as well as the mineral composition and heavy metal speciation of the samples, were analyzed for pollution assessment and source identification of heavy metals. The results reveal that the concentration of Cu in the soils was significantly higher than the background value, and an unpolluted to moderately polluted state was the main pollution level. By analyzing the relationship between Cu/Al2O3 and CaO in different samples, as well as the characteristics of the chemical index of alteration (CIA), mineral composition, and the chemical speciation of Cu in soils and profiles, the results suggest that tailings were the source of Cu pollution in soils. The distribution characteristics of Cu and CaO in stream sediments indicated that hydraulic transport may be one of the main migration pathways. In addition, wind transport may also be a pathway of migration.

Abstract Background The treatment of insomnia, which is the most common sleep disorder, includes drug and behavioral treatment, but each treatment measure has its limitations. So new treatment method needs to be taken to improve the treatment effect. MN supplementation is a potential promising new method for the treatment of insomnia, resulting in a rising need for methodological research towards verifying its efficacy. Methods/design We describe a proposal for a multicenter, patient-assessor-blinded, randomized controlled trial with two parallel arms. A total of 400 chronic insomnia patients will be allocated 1:1 to the intervention group (treatment with oral NMN 320 mg/day) or control group (treatment with oral placebo). All subjects are clinical chronic insomnia patients who meet all inclusion criteria. All subjects are treated by taking NMN or placebo. The primary outcome is the score on the Pittsburgh Sleep Quality Index (PSQI). Secondary outcomes are the score on the Insomnia Severity Index (ISI) and Epworth Sleeping Scale (ESS), the total sleep time (TST), sleep efficiency (SE), sleep latency, and REM sleep latency to assess sleep quality changes. Subjects are assessed at two time points: baseline and follow-up. The duration of the clinical trial is 60 days. Discussion This study will provide more evidence on the effects of NMN on improving sleep quality among patients with chronic insomnia. If proven effective, NMN supplement can be used as a new treatment for chronic insomnia in the future. Trial registration Chinese Clinical Trial Registry (chictr.org.cn) ChiCTR2200058001. Registered on 26 March 2022.

Abstract With the high-speed development of decentralized applications, account-based blockchain platforms have become a hotbed of various financial scams and hacks due to their anonymity and high financial value. Financial security has become a top priority with the sustainable development of blockchain-based platforms because of an increasing number of cyber attacks, which have resulted in a huge loss of crypto assets in recent years. Therefore, it is imperative to study the real-time detection of cyber attacks to facilitate effective supervision and regulation. To this end, this paper proposes the weighted and extended isolation forest algorithms and designs a novel framework for the real-time detection of cyber-attack transactions by thoroughly studying and summarizing real-world examples. Furthermore, this study develops a new detection approach for locating the compromised address of a cyber attack to resolve the data scarcity of hack addresses and reduce time consumption. Moreover, three experiments are carried out not only to apply on different types of cyber attacks but also to compare the proposed approach with the widely used existing methods. The results demonstrate the high efficiency and generality of the proposed approach. Finally, the lower time consumption and robustness of our method were validated through additional experiments. In conclusion, the proposed blockchain-oriented approach in this study can handle real-time detection of cyber attacks and has significant scope for applications.

For the hybrid multi-infeed HVDC system in which the receiving-end grid is a strong AC grid including LCC-HVDC subsystems and multiple VSC-HVDC subsystems, it has higher voltage support capability. However, for weak AC grid, the voltage support capability of the multi-VSC-HVDC subsystems to the LCC-HVDC subsystem (voltage support capability-mVSCs-LCC) can resist the risk of commutation failure. Based on this consideration, this paper proposes an evaluation index called Dynamic Voltage Support Strength Factor (DVSF) for the hybrid multi-infeed system, and uses this index to qualitatively judge the voltage support capability-mVSCs-LCC in weak AC grid. In addition, the proposed evaluation index can also indirectly judge the ability of the LCC-HVDC subsystem to suppress commutation failure. Firstly, the mathematical model of the power flow of the LCC and VSC networks in the steady-state is analyzed, and the concept of DVSF applied to hybrid multi-infeed system is proposed. Furthermore, the DVSF index is also used to qualitatively judge the voltage support capability-mVSCs-LCC. Secondly, the influence of multiple VSC-HVDC subsystems with different operation strategies on the DVSF is analyzed with reference to the concept of DVSF. Finally, the indicators proposed in this paper are compared with other evaluation indicators through MATLAB simulation software to verify its effectiveness. More importantly, the effects of multi- VSC-HVDC subsystems using different coordinated control strategies on the voltage support capability of the receiving-end LCC-HVDC subsystem are also verified.

The main idea of a session-based recommendation system is to model the user’s historical click sequence and then summarize user preferences and predict the items the user will interact with. The session recommendation model based on graph neural networks has attracted much attention in recent years because it can accurately obtain the local relationship between items. However, the traditional session recommendation model based on graph neural Networks lack the use of user’s higher-order features or fail to address the impact of item position information on the current session, which are both critical to the recommendation system. In addition, some models proposed the position information while neglects the click frequency information. We propose a graph network recommendation model called GPAN based on position attention in response to the abovementioned problems. Specifically, we propose a novel high-low order session perceptron that uses the perceptron to model undirected and directed graphs separately to obtain high and low order item representations in a session. For position information, we designed a position layer to calculate independently. Finally, the user’s short-term preference and long-term preference are aggregated to obtain the recommendation sequence. The results through a large number of experiments on three real datasets show that the performance of the proposed GPAN model is the best.

Triple-negative breast cancer (TNBC) has a shorter survival time and higher mortality rate than other molecular subtypes. RSRC2 is a newly discovered tumor suppressor gene. However, the potential functional mechanism of RSRC2 in TNBC remains unknown so far. Multiple bioinformatics databases were used. A Human Transcriptome Array 2.0 analysis, ChIP-seq analysis, ChIP-qPCR, RT-qPCR, Western blot, cell function assays in vitro and a metastatic mouse model in vivo were performed to demonstrate the role of RSRC2 in TNBC. Through the analysis of various databases, RSRC2 expression was the lowest in TNBC tissues compared to other molecular subtypes. The low expression of RSRC2 was associated with a worse prognosis for patients with breast cancer. The transcriptome array, ChIP-seq and bioinformatics analysis identified that GRHL2 and SCIN might have a close relationship with RSRC2. The functional bioinformatics enrichment analysis and functional cell experiments showed that RSRC2 was involved in cell adhesion, cell proliferation, cell migration and invasion. Furthermore, RSRC2 expression suppressed SCIN expression but not GRHL2 expression. SCIN re-expression in the RSRC2 overexpression cells or SCIN knockdown in the RSRC2 knockdown cells reversed the cellular function caused by RSRC2. Mechanistically, RSRC2 transcriptionally inhibited SCIN expression. In summary, our study reveals that RSRC2 acts as a tumor suppressor in TNBC development and progression through negatively regulating SCIN-mediated cell function, thus providing a potential target for TNBC treatment.

BackgroundIntermittent energy restriction (IER) is an effective weight loss strategy. However, the accompanying changes in spontaneous neural activity are unclear, and the relationship among anthropometric measurements, biochemical indicators, and adipokines remains ambiguous.MethodsThirty-five obese adults were recruited and received a 2-month IER intervention. Data were collected from anthropometric measurements, blood samples, and resting-state functional magnetic resonance imaging at four time points. The regional homogeneity (ReHo) method was used to explore the effects of the IER intervention. The relationships between the ReHo values of altered brain regions and changes in anthropometric measurements, biochemical indicators, and adipokines (leptin and adiponectin) were analyzed.ResultsResults showed that IER significantly improved anthropometric measurements, biochemical indicators, and adipokine levels in the successful weight loss group. The IER intervention for weight loss was associated with a significant increase in ReHo in the bilateral lingual gyrus, left calcarine, and left postcentral gyrus and a significant decrease in the right middle temporal gyrus and right cerebellum (VIII). Follow-up analyses showed that the increase in ReHo values in the right LG had a significant positive correlation with a reduction in Three-factor Eating Questionnaire (TFEQ)-disinhibition and a significant negative correlation with an increase in TFEQ-cognitive control. Furthermore, the increase in ReHo values in the left calcarine had a significant positive correlation with the reduction in TFEQ-disinhibition. However, no significant difference in ReHo was observed in the failed weight loss group.ConclusionOur study provides objective evidence that the IER intervention reshaped the ReHo of some brain regions in obese individuals, accompanied with improved anthropometric measurements, biochemical indicators, and adipokines. These results illustrated that the IER intervention for weight loss may act by decreasing the motivational drive to eat, reducing reward responses to food cues, and repairing damaged food-related self-control processes. These findings enhance our understanding of the neurobiological basis of IER for weight loss in obesity.

Abstract Background and aims Traumatic brain injury (TBI) is the main cause of death and can lead to a variety of physiological complications, including gastrointestinal dysfunction. The present study aimed to confirm the miR‐19a‐mediated suppression of diarrhea after TBI through the regulation of VIP expression. Methods A rat model of TBI induced by controlled cortical injury was used to observe gastrointestinal morphology by opening the abdomen after TBI. After 72 h of injury, the fecal water content of the rats was measured. The end ileal segments were removed, and HE staining was used to observe the histopathological changes in the intestine. The levels of serum miR‐19a and VIP mRNA were detected by qRT‐PCR. ELISA was performed to detect VIP levels in serum. Immunohistochemistry was used to detect the level of VIP in ileal tissues, and immunofluorescence was used to detect c‐kit expression in ileal tissue. CCK‐8 assay was used to detect the cell viability of interstitial cells of Cajal (ICCs), and TUNEL assay was used to detect apoptosis of ICCs. Results miR‐19a and VIP were highly expressed in the serum of TBI rats, and the knockdown of miR‐19a alleviated TBI‐induced diarrhea. In addition, the overexpression of miR‐19a or VIP inhibited the proliferation of ICCs, promoted apoptosis, and suppressed intracellular Ca2+ levels, whereas miR‐19a suppression had the opposite effects. A nonselective nitric oxide synthase inhibitor (L‐NA), PKG inhibitors (KT‐5823 and RP‐8CPT‐cGMPS), and a guanylate cyclase inhibitor (ODQ) restored the inhibitory effects of VIP on ICC proliferation, anti‐apoptosis effects, and Ca2+ concentrations. Conclusion Knockdown of miR‐19a inhibits activation of the VIP–NO–cGMP–PKG pathway through suppression of VIP expression, which in turn inhibits diarrhea after TBI.

Reduction of aeromagnetic noise and extraction of mineralization-related residual anomalies are critical for aeromagnetic data processing in mineral exploration. This study introduced a multifractal singular value decomposition (MSVD) method to remove the noise and improved the bi-dimensional empirical mode decomposition (BEMD) algorithm to extract residual magnetic anomalies. It is shown that MSVD and improved BEMD could effectively reduce the noise and extract residual magnetic anomalies. Then, a wavenumber–domain iterative approach is applied in 3D imaging of magnetic anomalies and gradients with depth constraints, which is a rapid tool for qualitative and quantitative interpretation of magnetic data and is suitable for rapidly imaging large-scale data. The 3D inversion result is verified by four geological sections along the regional tectonic directions and some drilling holes on the deposits. It is revealed that this proposed approach is practical and effective in dealing with aeromagnetic data interpretation and inversion for mineral exploration.

Climate risks are sharpened and adaptation actions are urgent. However, the economic system is a whole composed of numerous interrelated and interactive economic elements and poses a challenge for carbon mitigation. To explore the carbon emission effects of producers in complex economic systems and formulate targeted carbon mitigation policies, this study constructs three indicators to assess the carbon emission effect embodied in the economy through network analysis. More specifically, the total emission effect of each producer is measured by the push-type effect and pull-type effect and tested by comparing with the hypothetical extraction method. Taking China as an example, the results show that the effect of different sectors on carbon emissions varies greatly. The production and distribution of electric power and heat power is a key sector dominated by the push-type effect. This is, its carbon emissions are mainly triggered by the demands of its downstream sectors. The total emission effects of Construction and Smelting and processing of metals are also strong, but their pull-type effect is dominant. According to the dominant effect in the total emission effect, we can draft targeted emission reduction strategies.

Microbial molecular ecology is an interdisciplinary of molecular biology and microbial ecology. It is important to understand the composition and structure, function, the relationship between microorganisms and the relationship between microorganisms and the environment in microbial ecosystems. Microbial molecular ecology research techniques and methods widely used are comprehensively introduced, analyzed and summarized, and the advantages, disadvantages and applications of the techniques and methods are discussed. This study would provide a reference for understanding and regulating the kind and population of microbial communities in ecosystems.

IntroductionPolyetheretherketone (PEEK) is considered to be a new type of orthopedic implant material due to its mechanical properties and biocompatibility. It is becoming a replacement for titanium (Ti) due to its near-human-cortical transmission and modulus of elasticity. However, its clinical application is limited because of its biological inertia and susceptibility to bacterial infection during implantation. To solve this problem, there is an urgent need to improve the antibacterial properties of PEEK implants.MethodsIn this work, we fixed antimicrobial peptide HHC36 on the 3D porous structure of sulfonated PEEK (SPEEK) by a simple solvent evaporation method (HSPEEK), and carried out characterization tests. We evaluated the antibacterial properties and cytocompatibility of the samples in vitro. In addition, we evaluated the anti-infection property and biocompatibility of the samples in vivo by establishing a rat subcutaneous infection model.ResultsThe characterization test results showed that HHC36 was successfully fixed on the surface of SPEEK and released slowly for 10 days. The results of antibacterial experiments in vitro showed that HSPEEK could reduce the survival rate of free bacteria, inhibit the growth of bacteria around the sample, and inhibit the formation of biofilm on the sample surface. The cytocompatibility test in vitro showed that the sample had no significant effect on the proliferation and viability of L929 cells and had no hemolytic activity on rabbit erythrocytes. In vivo experiments, HSPEEK can significantly reduce the bacterial survival rate on the sample surface and the inflammatory reaction in the soft tissue around the sample.DiscussionWe successfully loaded HHC36 onto the surface of SPEEK through a simple solvent evaporation method. The sample has excellent antibacterial properties and good cell compatibility, which can significantly reduce the bacterial survival rate and inflammatory reaction in vivo. The above results indicated that we successfully improved the antibacterial property of PEEK by a simple modification strategy, making it a promising material for anti-infection orthopedic implants.

Li6PS5Cl possesses high ionic conductivity and excellent interfacial stability to electrodes and is known as a promising solid-state electrolyte material for all-solid-state batteries (ASSBs). However, the optimal annealing process of Li6PS5Cl has not been studied systematically. Here, a Box–Behnken design is used to investigate the interactions of the heating rate, annealing temperature, and duration of annealing process for Li6PS5Cl to optimize the ionic conductivity. The response surface methodology with regression analysis is employed for simulating the data obtained, and the optimized parameters are verified in practice. As a consequence, Li6PS5Cl delivers a rather high conductivity of 4.45 mS/cm at 25 °C, and ASSB consisting of a LiNi0.6Co0.2Mn0.2O2 cathode and lithium anode shows a high initial discharge capacity of 151.7 mAh/g as well as excellent cycling performances for more than 350 cycles, highlighting the importance of the design of experiments.

Abstract Human activities widely exhibit a power-law distribution. Considering stock trading as a typical human activity in the financial domain, the first aim of this paper is to validate whether the well-known power-law distribution can be observed in this activity. Interestingly, this paper determines that the number of accumulated lead–lag days between stock pairs meets the power-law distribution in both the U.S. and Chinese stock markets based on 10 years of trading data. Based on this finding this paper adopts the power-law distribution to formally define the lead–lag effect, detect stock pairs with the lead–lag effect, and then design a pure lead–lag investment strategy as well as enhancement investment strategies by integrating the lead–lag strategy into classic alpha-factor strategies. Tests conducted on 20 different alpha-factor strategies demonstrate that both perform better than the selected benchmark strategy and that the lead–lag strategy provides useful signals that significantly improve the performance of basic alpha-factor strategies. Our results therefore indicate that the lead–lag effect may provide effective information for designing more profitable investment strategies.

Introduction: The SARS-CoV-2 pandemic has endangered global health, the world economy, and societal values. Despite intensive measures taken around the world, morbidity and mortality remain high as many countries face new waves of infection and the spread of new variants. Worryingly, more and more variants are now being identified, such as 501Y.V1 (B.1.1.7) in the UK, 501Y.V2 (B.1.351) in South Africa, 501Y.V3 in Manaus, Brazil, and B.1.617/B.1.618 in India, which could lead to a severe epidemic rebound. Moreover, some variants have a stronger immune escape ability. To control the new SARS-CoV-2 variant, we may need to develop and redesign new vaccines repeatedly. So it is important to investigate how our immune system combats and responds to SARS-CoV-2 infection to develop safe and effective medical interventions. Objectives: In this study, we performed a longitudinal and proteome-wide analysis of antibodies in the COVID-19 patients to revealed some immune processes of COVID-19 patients against SARS-CoV-2 and found some dominant epitopes of a potential vaccine. Methods: Microarray assay, Antibody depletion assays, Neutralization assay. Results: We profiled a B-cell linear epitope landscape of SARS-CoV-2 and identified the epitopes specifically recognized by either IgM, IgG, or IgA. We found that epitopes more frequently recognized by IgM are enriched in non-structural proteins. We further identified epitopes with different immune responses in severe and mild patients. Moreover, we identified 12 dominant epitopes eliciting antibodies in most COVID-19 patients and identified five key amino acids of epitopes. Furthermore, we found epitope S-82 and S-15 are perfect immunogenic peptides and should be considered in vaccine design. Conclusion: This data provide useful information and rich resources for improving our understanding of viral infection and developing a novel vaccine/neutralizing antibodies for the treatment of SARS-CoV-2.

IntroductionThe rapid development of fintech has brought opportunities for business operations and economic development. Currently, few researches have focused on how fintech level affects word-of-mouth (WOM) from the perspective of user psychology. Therefore, studying the effect of fintech level on WOM is a worthwhile scientific question.MethodsBased on motivation theory and reinforcement theory, this paper proposes a new psychology-based theoretical framework model to study the relationship between fintech level and WOM and constructs a structural equation model including fintech level, user experience, user trust, user stickiness and WOM through the analysis of 732 questionnaires.ResultsThe results indicate that the improvement of fintech level can enhance WOM. More specifically, fintech level has a significantly positive influence on user stickiness through two mediation variables (user experience and user trust), and further, user stickiness has a significantly positive influence on WOM.DiscussionThis paper analyzes the internal mechanism of fintech level's influence on WOM from the micro psychological perspective, which enriches the psychology theoretical research. And, the conclusions provide specific suggestions for marketing and promotion of financial platforms in the future.

Groundwater provides drinking water to city and rural residents; which is also one of the chief water sources for commercial and agricultural activities in Jieshou City. We collected and analyzed the samples of 18 underground water source wells in Jieshou. We investigated whether the water was of acceptable quality and had characteristics that exceeded the standard. This study was conducted to determine the chemical characteristics of groundwater and abnormally high super-standard ions found in groundwater. The hydrogeological conditions of the study area were analyzed through data collection; through sample collection and sample testing, the characteristics and types of water chemistry were analyzed by means of mathematical statistics analysis and the Piper chart. The genesis of water chemistry was discussed using the Gibbs chart and correlation analysis; the proportional coefficient of ion molar concentration was used to judge the source, origin, and forming process of groundwater chemical composition. The results show that the groundwater is classified as marginally alkaline water, with a composition of Na-HCO3. The cations are mainly Na+, and the anions are mainly HCO3−. According to the Ⅲ water standard of groundwater quality standard and comparing the content of each ion, Na+ and F− are the primary abnormal super-standard ions, and ions and compounds are the main occurrence states. The concentrations of Na+ and F− exceed the standard for class Ⅲ water. There was a positive correlation between the abnormal Na+ and F−, and the concentration of F− increased with the increase in monitoring depth. The causes of abnormal ions were mainly determined by the lithology of the aquifer in the study area, and most of them are fluorine-containing rocks, which are transferred into groundwater through leaching or hydration. The enrichment of Na+ and F− is influenced by the local primary geological setting, hydrochemical type, hydrogeological conditions, pH and artificial activities, and the primary geological setting is the main influencing factor.

Abstract Background The leaf is a determinate organ essential for photosynthesis, whose size and shape determine plant architecture and strongly affect agronomic traits. In soybean, the molecular mechanism of leaf development is not well understood. The flowering repressor gene E1, which encodes a legume-specific B3-like protein, is known to be the gene with the largest influence on soybean flowering and maturity. However, knowledge of its potential other functions remains poor. Results Here, we identified a novel function of E1 protein in leaf development. Unifoliolate leaves of E1-overexpression (E1-OE) lines were smaller and curlier than those of wild type DongNong 50 (DN50) and Williams 82 (W82). Transverse histological sections showed disorganized cells and significantly elevated palisade tissue number, spongy tissue number, and bulliform cell number in E1-OE lines. Our results indicate that E1 binds to the promoters of the leaf- development-related CINCINNATA (CIN)-like TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR (TCP) transcription factor genes to negatively regulate their expression. Conclusions Our findings identify E1 as an important new factor in soybean leaf development.

How soybean, a temperate origin crop, adapted to a tropical environment remains unclear. Here, the authors report Tof16, an ortholog of LHY, and the previously identified J locus, control soybean yield under short-day condition and loss of function of these two genes contributes to the adaptation to tropics.

Flowering time is an important agronomic trait for soybean yield and adaptation. However, the genetic basis of soybean adaptation to diverse latitudes is still not clear. Four NIGHT LIGHT-INDUCIBLE AND CLOCK-REGULATED 2 (LNK2) homeologs of Arabidopsis thaliana LNK2 were identified in soybean. Three single-guide RNAs were designed for editing the four LNK2 genes. A transgene-free homozygous quadruple mutant of the LNK2 genes was developed using the CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 (CRISPR-associated protein 9). Under long-day (LD) conditions, the quadruple mutant flowered significantly earlier than the wild-type (WT). Quantitative real-time PCR (qRT-PCR) revealed that transcript levels of LNK2 were significantly lower in the quadruple mutant than in the WT under LD conditions. LNK2 promoted the expression of the legume-specific E1 gene and repressed the expression of FT2a. Genetic markers were developed to identify LNK2 mutants for soybean breeding. These results indicate that CRISPR/Cas9-mediated targeted mutagenesis of four LNK2 genes shortens flowering time in soybean. Our findings identify novel components in flowering-time control in soybean and may be beneficial for further soybean breeding in high-latitude environments.

Emerging evidence is examining the precise role of intestinal microbiota in the pathogenesis of type 2 diabetes. The aim of this study was to investigate the association of intestinal microbiota and microbiota-generated metabolites with glucose metabolism systematically in a large cross-sectional study in China. 1160 subjects were divided into three groups based on their glucose level: normal glucose group (n=504), prediabetes group (n=394), and diabetes group (n=262). Plasma concentrations of TMAO, choline, betaine, and carnitine were measured. Intestinal microbiota was measured in a subgroup of 161 controls, 144 prediabetes and 56 diabetes by using metagenomics sequencing. We identified that plasma choline [Per SD of log-transformed change: odds ratio 1.36 (95 confidence interval 1.16, 1.58)] was positively, while betaine [0.77 (0.66, 0.89)] was negatively associated with diabetes, independently of TMAO. Individuals with diabetes could be accurately distinguished from controls by integrating data on choline, and certain microbiota species, as well as traditional risk factors (AUC=0.971). KOs associated with the carbohydrate metabolism pathway were enhanced in individuals with high choline level. The functional shift in the carbohydrate metabolism pathway in high choline group was driven by species Ruminococcus lactaris, Coprococcus catus and Prevotella copri. We demonstrated the potential ability for classifying diabetic population by choline and specific species, and provided a novel insight of choline metabolism linking the microbiota to impaired glucose metabolism and diabetes.

BackgroundChronic insomnia disorder (CID) is a highly prevalent sleep disorder, which influences people's daily life and is even life threatening. However, whether the resting-state regional homogeneity (ReHo) of disrupted brain regions in CID can be reshaped to normal after treatment remains unclear.MethodsA novel intervention real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) was used to train 28 CID patients to regulate the activity of the left amygdala for three sessions in 6 weeks. The ReHo methodology was adopted to explore its role on resting-state fMRI data, which were collected before and after training. Moreover, the relationships between changes of clinical variables and ReHo value of altered regions were determined.ResultsResults showed that the bilateral dorsal medial pre-frontal cortex, supplementary motor area (SMA), and left dorsal lateral pre-frontal cortex had decreased ReHo values, whereas the bilateral cerebellum anterior lobe (CAL) had increased ReHo values after training. Some clinical scores markedly decreased, including Pittsburgh Sleep Quality Index, Insomnia Severity Index, Beck Depression Inventory, and Hamilton Anxiety Scale (HAMA). Additionally, the ReHo values of the left CAL were positively correlated with the change in the Hamilton depression scale score, and a remarkable positive correlation was found between the ReHo values of the right SMA and the HAMA score.ConclusionOur study provided an objective evidence that amygdala-based rtfMRI-NF training could reshape abnormal ReHo and improve sleep in patients with CID. The improved ReHo in CID provides insights into the neurobiological mechanism for the effectiveness of this intervention. However, larger double-blinded sham-controlled trials are needed to confirm our results from this initial study.

Graphite tailings were used as the raw materials for the preparation of foamed ceramics via a high-temperature self-foaming method. The residual carbon in the tailings acted as the foaming agent and no additional foaming agents were required. The residual carbon reacted with Fe2O3 and produced gas that was wrapped by the liquid phase to form a porous structure. The effects of sintering temperature, holding time, carbon content, flux on the bulk density, water absorption, and mean pore size of the foamed ceramics were investigated. The bulk density, compressive strength, thermal conductivity, and water absorption of the foamed ceramic prepared using the optimal sample D100 (100 wt% graphite tailings) and sintered at 1200 °C for 20 min were 1.36 g/cm3, 0.35%, 12.03 MPa, and 0.51 W/(m·K), respectively. The raw material contained CaO, Fe2O3, and SiO2, which formed augite with a low fusibility point. In contrast, the molten decomposition products of potassium feldspar and CaSiO3 ions formed anorthite, which has a high fusibility point. Therefore, potassium feldspar is not recommended for use as a flux in tailing systems with high calcium content. Foamed ceramics were prepared using up to 100 wt% graphite tailings. This is a new approach for the economical disposal of graphite tailings.

Multidrug-resistant bacteria are a real threat to human health. Here, the authors investigate a combinatory strategy using blue-laser and thymol against Staphylococcus aureus and Pseudomonas aeruginosa. Blue-laser and thymol succesfully sterilized acute infected or biofilm-associated wounds and prevented systematic dissemination in mice. Compared with mammalian cells, bacteria contain abundant proporphyrin-like compounds that transform harmless thymol into blue-laser sensitizers, thymoquinone and thymohydroquinone. Photo-excitation of thymoquinone and thymohydroquinone augmented reactive oxygen species production in bacteria while completely sparing the host tissue.

PurposeThis study investigates the topological properties of brain functional networks in patients with isolated rapid eye movement sleep behavior disorder (iRBD).Participants and MethodsA total of 21 patients with iRBD (iRBD group) and 22 healthy controls (HCs) were evaluated using resting-state functional MRI (rs-fMRI) and neuropsychological measures in cognitive and motor function. Data from rs-fMRI were analyzed using graph theory, which included small-world properties, network efficiency, network local efficiency, nodal shortest path, node efficiency, and network connectivity, as well as the relationship between behavioral characteristics and altered brain topological features.ResultsRey-Osterrieth complex figure test (ROCFT-copy), symbol digital modalities test (SDMT), auditory verbal learning test (AVLT)-N1, AVLT-N2, AVLT-N3, and AVLT-N1-3 scores were significantly lower in patients with iRBD than in HC (P < 0.05), while trail making test A (TMT-A), TMT-B, and Unified Parkinson’s Disease Rating Scale Part-III (UPDRS-III) scores were higher in patients with iRBD (P < 0.05). Compared with the HCs, patients with iRBD had no difference in the small-world attributes (P > 0.05). However, there was a significant decrease in network global efficiency (P = 0.0052) and network local efficiency (P = 0.0146), while an increase in characteristic path length (P = 0.0071). There was lower nodal efficiency in occipital gyrus and nodal shortest path in frontal, parietal, temporal lobe, and cingulate gyrus. Functional connectivities were decreased between the nodes of occipital with the regions where they had declined nodal shortest path. There was a positive correlation between TMT-A scores and the nodal efficiency of the right middle occipital gyrus (R = 0.602, P = 0.014).ConclusionThese results suggest that abnormal behaviors may be associated with disrupted brain network topology and functional connectivity in patients with iRBD and also provide novel insights to understand pathophysiological mechanisms in iRBD.

Salt stress is a major factor limiting the growth and yield of soybean (Glycine max). Wild soybeans (Glycine soja) contain high allelic diversity and beneficial alleles that can be re-introduced into domesticated soybeans to improve adaption to the environment. However, very few beneficial alleles have been identified from wild soybean. Here, we demonstrate that wild soybean is more salt tolerant than cultivated soybean and examine dehydration responsive element-binding (DREB) family transcription factor genes to look for advantageous alleles that might improve drought tolerance in cultivated soybean. Our genome-wide analysis identified 103 DREB genes from the Glycine max genome. By combined RNA-sequencing and population genetics of wild, landrace, and cultivated soybean accessions, we show that the natural variation in DREB3a and DREB3b is related to differences in salt tolerance in soybean accessions. Interestingly, DREB3b, but not DREB3a, appears to have undergone artificial selection. Soybean plants carrying the wild soybean DREB3b allele (DREB3b39Del) are more salt tolerant than those containing the reference genome allele (DREB3bRef). Together, our results suggest that the loss of the DREB3b39Del allele through domestication of cultivated soybean may be associated with a reduction in salt tolerance. Our findings provide crucial information for improving salt tolerance in soybean through molecular breeding.

Direct discharge of geothermal tail water mixed with pollutants and impurities to surface water bodies will cause water and soil environmental pollution and waste of water resources. In order to effectively reduce pollutants in geothermal tail water and realize the harmless discharge of geothermal tail water, this paper takes the geothermal heating tail water of the Huayuan Town in Lindian County to the south of Daqing as an example to carry out the experimental study of tail water treatment. Laboratory and pilot tests are conducted on heating tail water treatment by coagulation sedimentation, ultrafiltration and nanofiltration processes. The operating parameters of the geothermal tailwater treatment process are obtained through continuous monitoring. During the stable operation of the combined process, TDS is detected from 5824.7 mg/L to 432.40 mg/L, chloride from 3010.13 mg/L to 194.16 mg/L, fluoride from 1.57 mg/L to 0.31 mg/L, and boron from 4.04 mg/L to 1.62mg/L. The best combined process for determining the geothermal tail water treatment in this area is to use the coagulant PAC with an ultrafiltration operating pressure of 0.1 MPa. The filter membrane Dow NF90 is employed, the operating pressure is 0.60 MPa, and the inflow is 0.8 m3/h. In this study, the TDS removal rate in the laboratory experiment is between 90.21% and 92.49%, the chloride removal rate between 91.63% and 93.02%, the fluoride removal rate is 96.81%, and the boron removal rate is 55.20%−55.69%. The TDS removal rate in the pilot test is 92.62%, the product water chloride is 200 mg/L, the chloride removal rate is 92.57%, the product water boron is 1.77 mg/L, and the boron removal rate is 55.7%. The above experimental results have reached the relevant standard. The test results confirm that the process of treating tail water in the Lindian geothermal field in southern Songnen Basin is feasible. The treatment solves the technical problems of resource waste and tail water recycling in tail water treatment, and it provides a new way for geothermal resource utilization.

In view of the fact that the wavelet packet transform (WPT) can only weakly detect the occurrence of fault, this paper applies a fault diagnosis algorithm including wavelet packet transform and principal component analysis (PCA) to the inverter-side fault diagnosis of multi-terminal hybrid highvoltage direct current (HVDC) network, which can significantly improve the speed and accuracy of fault diagnosis. Firstly, current amplitude and current slope are used to sample the data, and the WPT is used to extract the energy spectrum of the signal. Secondly, an energy matrix is constructed, and the PCA method is used to calculate whether the squared prediction error (SPE) statistics of various signals that can reflect the degree of deviation of the measured value from the principal component model at a certain time exceed the limit to judge the occurrence of the fault. Further, its maximum value is compared to determine the fault types. Finally, based on a large number of MATLAB/Simulink simulation results, it is shown that the PCA method using the current slope as the sampled data can detect the occurrence of a ground fault with small transition resistance within 2 ms, and identify the fault types within 10 ms, without being affected by the sampling frequency.

Nanoencapsulated phase change materials (NePCMs) are promising thermal energy storage (TES) and heat transfer materials that show great potential in battery thermal management systems (BTMSs). In this work, nanocapsules with a paraffin core and silica shell were prepared using an optimized sol-gel method. The samples were characterized by different methods regarding chemical composition, thermal properties, etc. Then, the nanocapsules were used as the coolant by mixing with insulation oil in the immersion cooling of a simulative battery. The sample doped with Ag on the shell with a core-to-shell ratio of 1:1 showed the best performance. Compared to the sample without doping material, the thermal conductivity increased by 49%, while the supercooling degree was reduced by 35.6%. The average temperature of the simulative battery cooled by nanocapsule slurries decreased by up to 3.95 °C compared to the test performed with pure insulation oil as the coolant. These novel nanocapsules show great potential in the immersion cooling of a battery.

This study aimed to investigate insomnia-related alterations in gut microbiota and their association with serum metabolites. A total of 24 patients with insomnia disorder and 22 healthy controls were recruited. The fecal and serum samples were collected. The 16s rRNA sequencing and bioinformatics analysis were conducted to explore insomnia-related changes in the diversity, structure, and composition of the gut microbiota. UPLC-MS was performed to identify insomnia-related serum metabolites. Spearman correlation analysis was used to investigate the correlations between insomnia-related gut bacteria and the serum metabolites. Despite the nonsignificant changes in the diversity and structure of gut microbiota, insomnia disorder patients had significantly decreased family Bacteroidaceae, family Ruminococcaceae, and genus Bacteroides, along with significantly increased family Prevotellaceae and genus Prevotella, compared with healthy controls. Genus Gemmiger and genus Fusicatenibacter were dominant in patients with insomnia disorder, whereas genus Coprococcus, genus Oscillibacter, genus Clostridium XI, and family Peptostreptococcaceae were dominant in healthy controls. The UPLC-MS analysis identified 97 significantly decreased metabolites and 74 significantly increased metabolites in the serum samples of patients with insomnia disorder, compared with those of healthy controls. KEGG enrichment analysis revealed 1 significantly upregulated metabolic pathway and 16 downregulated metabolic pathways in patients with insomnia disorder. Furthermore, Spearman correlation analysis unveiled significant correlations among the altered bacteria genus and serum metabolites. Patients with insomnia disorder have differential gut microbiota and serum metabolic profiles compared with healthy controls. The alterations in gut microbiota were correlated with specific serum metabolites, suggesting that some serum metabolites might mediate gut microbiota-brain communication in the pathogenesis of insomnia disorder.

BackgroundChronic insomnia disorder (CID) is considered a major public health problem worldwide. Therefore, innovative and effective technical methods for studying the pathogenesis and clinical comprehensive treatment of CID are urgently needed.MethodsReal-time fMRI neurofeedback (rtfMRI-NF), a new intervention, was used to train 28 patients with CID to regulate their amygdala activity for three sessions in 6 weeks. Resting-state fMRI data were collected before and after training. Then, voxel-based degree centrality (DC) method was used to explore the effect of rtfMRI-NF training. For regions with altered DC, we determined the specific connections to other regions that most strongly contributed to altered functional networks based on DC. Furthermore, the relationships between the DC value of the altered regions and changes in clinical variables were determined.ResultsPatients with CID showed increased DC in the right postcentral gyrus, Rolandic operculum, insula, and superior parietal gyrus and decreased DC in the right supramarginal gyrus, inferior parietal gyrus, angular gyrus, middle occipital gyrus, and middle temporal gyrus. Seed-based functional connectivity analyses based on the altered DC regions showed more details about the altered functional networks. Clinical scores in Pittsburgh sleep quality index, insomnia severity index (ISI), Beck depression inventory, and Hamilton anxiety scale decreased. Furthermore, a remarkable positive correlation was found between the changed ISI score and DC values of the right insula.ConclusionsThis study confirmed that amygdala-based rtfMRI-NF training altered the intrinsic functional hubs, which reshaped the abnormal functional connections caused by insomnia and improved the sleep of patients with CID. These findings contribute to our understanding of the neurobiological mechanism of rtfMRI-NF in insomnia treatment. However, additional double-blinded controlled clinical trials with larger sample sizes need to be conducted to confirm the effect of rtfMRI-NF from this initial study.

Soybean mosaic virus (SMV) is detrimental to soybean (Glycine max) breeding, seed quality, and yield worldwide. Improving the basic resistance of host plants is the most effective and economical method to reduce damage from SMV. Therefore, it is necessary to identify and clone novel SMV resistance genes. Here, we report the characterization of two soybean cultivars, DN50 and XQD, with different levels of resistance to SMV. Compared with XQD, DN50 exhibits enhanced resistance to the SMV strain SC7. By combining bulked-segregant analysis (BSA)-seq and fine-mapping, we identified a novel resistance locus, RSMV-11, spanning an approximately 207-kb region on chromosome 11 and containing 25 annotated genes in the reference Williams 82 genome. Of these genes, we identified eleven with non-synonymous single-nucleotide polymorphisms (SNPs) or insertion-deletion mutations (InDels) in their coding regions between two parents. One gene, GmMATE68 (Glyma.11G028900), harbored a frameshift mutation. GmMATE68 encodes a multidrug and toxic compound extrusion (MATE) transporter that is expressed in all soybean tissues and is induced by SC7. Given that MATE transporter families have been reported to be linked with plant disease resistance, we suggest that GmMATE68 is responsible for SC7 resistance in DN50. Our results reveal a novel SMV-resistance locus, improving understanding of the genetics of soybean disease resistance and providing a potential new tool for marker-assisted selection breeding in soybean.

Seasoned equity offerings (SEO) are an important approach for listed companies to obtain funds after an initial public offering (IPO), which plays a major role in enterprises’ demand for funds. However, due to agency conflicts, information asymmetry, tunneling by major shareholders, and other behaviors, listed companies sometimes fail to make proper use of the refinancing funds. In this study, the impact of SEO on corporate investment efficiency is tested. Furthermore, the impact of board member characteristics on investment efficiency of refinancing companies is tested. Refinancing listed companies in Shenzhen and Shanghai Exchange Stock from 2009 to 2020 form the research samples. The research shows that SEO reduces the corporate investment efficiency. According to the impact analysis of the board member characteristics on the investment efficiency of refinancing companies, age heterogeneity of the board inhibits investment efficiency. The older the mean age of directors, the higher the investment efficiency. Furthermore, sex heterogeneity also inhibits investment efficiency. The more female directors, the lower the investment efficiency. Moreover, overseas backgrounds and experiences of directors also inhibit investment efficiency. These findings not only extend the research on the consequences of SEO in China, but also provide reference points and guidance for listed companies to adopt a scientific and reasonable refinancing method, give full play to the advantages, and overcome disadvantages of board members.

Abstract Background As a leading biomass feedstock, poplar plants provide enormous lignocellulose resource convertible for biofuels and bio-chemicals. However, lignocellulose recalcitrance particularly in wood plants, basically causes a costly bioethanol production unacceptable for commercial marketing with potential secondary pollution to the environment. Therefore, it becomes important to reduce lignocellulose recalcitrance by genetic modification of plant cell walls, and meanwhile to establish advanced biomass process technology in woody plants. Brassinosteroids, plant-specific steroid hormones, are considered to participate in plant growth and development for biomass production, but little has been reported about brassinosteroids roles in plant cell wall assembly and modification. In this study, we generated transgenic poplar plant that overexpressed DEETIOLATED2 gene for brassinosteroids overproduction. We then detected cell wall feature alteration and examined biomass enzymatic saccharification for bioethanol production under various chemical pretreatments. Results Compared with wild type, the PtoDET2 overexpressed transgenic plants contained much higher brassinosteroids levels. The transgenic poplar also exhibited significantly enhanced plant growth rate and biomass yield by increasing xylem development and cell wall polymer deposition. Meanwhile, the transgenic plants showed significantly improved lignocellulose features such as reduced cellulose crystalline index and degree of polymerization values and decreased hemicellulose xylose/arabinose ratio for raised biomass porosity and accessibility, which led to integrated enhancement on biomass enzymatic saccharification and bioethanol yield under various chemical pretreatments. In contrast, the CRISPR/Cas9-generated mutation of PtoDET2 showed significantly lower brassinosteroids level for reduced biomass saccharification and bioethanol yield, compared to the wild type. Notably, the optimal green-like pretreatment could even achieve the highest bioethanol yield by effective lignin extraction in the transgenic plant. Hence, this study proposed a mechanistic model elucidating how brassinosteroid regulates cell wall modification for reduced lignocellulose recalcitrance and increased biomass porosity and accessibility for high bioethanol production. Conclusions This study has demonstrated a powerful strategy to enhance cellulosic bioethanol production by regulating brassinosteroid biosynthesis for reducing lignocellulose recalcitrance in the transgenic poplar plants. It has also provided a green-like process for biomass pretreatment and enzymatic saccharification in poplar and beyond.

Deep network recommendation is a cutting-edge topic in current recommendation system research, which as a combination of recommendation systems and deep learning theory can effectively improve recommendation accuracy. In a real recommendation scenario, all the effective information in a data set should be extracted, both explicit and implicit, because the comprehensive degree of information is proportional to the recommendation performance. This article proposes an enhanced multi-modal recommendation based on alternate training with knowledge graph representation (SI-MKR) based on the MKR deep learning recommendation model. Our framework is an enhanced recommendation system based on knowledge graph representation, using valuable external knowledge as multi-modal information. The SI-MKR model solves the problem of ignoring the diversity of data types in the multi-modal knowledge-based recommendation system, which adds user and item attribute information from a knowledge graph as an enhancement recommendation multi-tasking training. By analysing the content of the item and user attributes, the SI-MKR model classifies the attributes of the items and users, processes the text type attributes and multi-value type attributes separately for feature extraction, and other types of attributes are used as inputs to the knowledge graph embedding unit. In addition, the knowledge graph data form a triplet unit, thus continuing the knowledge graph data training process. The feature extraction unit of the knowledge graph and the recommended unit are connected through the cross-compression unit for alternate training. During the deep learning framework training process, the recommendation system's item has a potential correlation with the head entity in the knowledge graph which embodies the idea of multi-tasking. Through extensive experiments on real-world datasets, we demonstrate that SI-MKR achieves substantial gains in movie recommendation over advanced model baselines. Even user-item interactions are sparse, SI-MKR maintains better performance than the MKR model.

Gold and Bi-bearing parageneses are pivotal to understanding gold concentration and deposition processes. The large-scale Laozuoshan gold deposit is located in the Jiamusi Block, northeastern China, and has experienced complex mineralization processes with abundant gold and Bi-bearing minerals. However, the relationship between Bi-minerals and gold is unclear, preventing our understanding of the gold enrichment and precipitation mechanism in the Laozuoshan gold deposit. Optical microscope and SEM results show three stages of gold mineralization: pyrrhotite (Po-1) + arsenopyrite (Apy-1) + Bi-bearing minerals (Bis-1) + Au-1; arsenopyrite (Apy-2) + chlorite + Bi-bearing minerals (Bis-2) + Au-2; and arsenopyrite (Apy-3) + graphite + Bi-bearing minerals (Bis-3) + Au-3. The abundant amount of gold (Au-1~Au-3) is associated with Bi-bearing minerals (Bis-1~Bis-3), which coexist as inclusions and fill in fractures in these minerals. The mineral assemblages of arsenopyrite, Bi-minerals, and gold exhibit a clear As-Bi-Au mineralogy in the ores, and the ternary diagram of the chemical compositions of the Bi-minerals shows that Bi-minerals all fall in reducing regions, indicating that Bi-minerals are precipitated under reducing conditions. The gold compositions demonstrate a positive correlation (R2 = 0.58) between Au and Bi. Consequently, we propose that the gold experienced the ore-forming fluids concentration and further Bi-melts scavenging for the Laozuoshan gold deposit mineralization. The Bi collector model is essential in interpreting the high-grade gold in the Laozuoshan gold deposit, indicating that the geochemical anomalies observed with bismuth may be a critical potential exploration target for the high-grade gold deposits in the Jiamusi Block.

Staphylococcus aureus can cause a plethora of life-threatening infections. Antibiotics have been extensively used to treat S. aureus infections. However, when antibiotics are used at sub-inhibitory concentrations, especially for β-lactam antibiotics, they may enhance staphylococcal pathogenicity and exacerbate the infection. The combination of antivirulence agents and antibiotics may be a novel approach to controlling antibiotic-induced S. aureus pathogenicity. We have illustrated that under in vitro conditions, antivirulence agent M21, when administered concurrently with ampicillin, suppressed the expression and production of virulence factors induced by ampicillin. In a mouse peritonitis model, M21 reduced bacterial load irrespective of administration of ampicillin. In a bacteremia model, combinatorial treatment consisting of ampicillin or ceftazidime and M21 increased the survival rate of mice and reduced cytokine abundance, suggesting the suppression of antibiotic-induced virulence by M21. Different from traditional antibiotic adjuvants, an antivirulence agent may not synergistically inhibit bacterial growth in vitro, but effectively benefit the host in vivo. Collectively, our findings from this study demonstrated the benefits of antivirulence–antibiotic combinatorial treatment against S. aureus infections and provide a new perspective on the development of antibiotic adjuvants.

In this paper, a novel low voltage ride-through (LVRT) technique for three-phase grid-connected inverters is proposed. The proposed technique consists of two parts: a nonlinear phase locked loop based on complex-coefficient filters (NLCCF-PLL) and an LVRT control scheme. Generally, the synchronization process of three-phase grid-connected inverters is performed via PLL with a relatively low bandwidth, which delays the detection of voltage sag and recovery during the LVRT process. To accelerate the synchronization process, the NLCCF-PLL with adaptive controller gains is proposed to improve both the filtering capability and dynamic performance of PLL at the same time. The stability of the NLCCF-PLL is validated by the second method of Lyapunov in the nonlinear model, and the superiority of its operating performance is verified. The proposed LVRT control scheme consists of a reference current calculation block to effectively suppress the power ripples and an inner loop controller with strong robustness as well as fast dynamic response. By comparing the proposed LVRT technique with the existing LVRT technique on the basis of experimental results, the superiority of the proposed LVRT technique is confirmed.

Since the word2vec model was proposed, many researchers have vectorized the data in the research field based on it. In the field of social network, the Node2Vec model improved on the basis of word2vec can vectorize nodes and edges in social networks, so as to carry out relevant research on social networks, such as link prediction, and community division. However, social network is a network with homogeneous structure. When dealing with heterogeneous networks such as knowledge graph, Node2Vec will lead to inaccurate prediction and unreasonable vector quantization data. Specifically, in the Node2Vec model, the walk strategy for homogeneous networks is not suitable for heterogeneous networks, because the latter has distinguishing features for nodes and edges. In this paper, a Heterogeneous Network vector representation method is proposed based on random walks and Node2Vec, called KG2vec (Heterogeneous Network to Vector) that solves problems related to the inadequate consideration of the full-text semantics and the contextual relations that are encountered by the traditional vector representation of the knowledge graph. First, the knowledge graph is reconstructed and a new random walk strategy is applied. Then, two training models and optimizing strategies are proposed, so that the contextual environment between entities and relations is obtained, semantically providing a full vector representation of the Heterogeneous Network. The experimental results show that the KG2VEC model solves the problem of insufficient context consideration and unsatisfactory results of one-to-many relationship in the vectorization process of the traditional knowledge graph. Our experiments show that KG2vec achieves better performance with higher accuracy than traditional methods.

To alleviate the data sparsity and cold start problems for collaborative filtering in recommendation systems, side information is usually leveraged by researchers to improve the recommendation performance. The utility of knowledge graph regards the side information as part of the graph structure and gives an explanation for recommendation results. In this paper, we propose an enhanced multi-task neighborhood interaction (MNI) model for recommendation on knowledge graphs. MNI explores not only the user-item interaction but also the neighbor-neighbor interactions, capturing a more sophisticated local structure. Besides, the entities and relations are also semantically embedded. And with the cross&compress unit, items in the recommendation system and entities in the knowledge graph can share latent features, and thus high-order interactions can be investigated. Through extensive experiments on real-world datasets, we demonstrate that MNI outperforms some of the state-of-the-art baselines both for CTR prediction and top-N recommendation.

Introducing a knowledge graph into a recommender system as auxiliary information can effectively solve the sparse and cold start problems existing in traditional recommender systems. In recent years, many researchers have performed related work. A recommender system with knowledge graph embedding learning characteristics can be combined with a recommender system of the following three forms: one-by-one learning, joint learning, and alternating learning. For current knowledge graph embedding, a deep learning framework only has one embedding mode, which fails to excavate the potential information from the knowledge graph thoroughly. To solve this problem, this paper proposes the Ripp-MKR model, a multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet, which combines joint learning and alternating learning of knowledge graphs and recommender systems. Ripp-MKR is a deep end-to-end framework that utilizes a knowledge graph embedding task to assist recommendation tasks. Similar to the MKR model, in the Ripp-MKR model, two tasks are associated with cross and compress units, which automatically share latent features and learn the high-order interactions among items in recommender systems and entities in the knowledge graph. Additionally, the model borrows ideas from RippleNet and combines the knowledge graph with the historical interaction record of a user's historically clicked items to represent the user's characteristics. Through extensive experiments on real-world datasets, we demonstrate that Ripp-MKR achieves substantial gains over state-of-the-art baselines in movie, book, and music recommendations.

Capital constraint, immensely existing in practice, became major stressors for manufacturers during the green research and development (R & D) triggered by managers integrating green concept into their business models. Considering the initial capital of a capital-constrained manufacturer, this paper formulates a Stackelberg game model comprising a manufacturer and a retailer, to discuss the optimal operation and financing decisions under the bank financing channel and trade credit financing channel, to detect the relationship between the manufacturer’s initial capital and green R & D investment, and to find which financing channel is better by comparing the two financing channels when the same initial capital is set. According to the above analysis, the results find that the capital-constrained manufacturer prefers financing only when meeting certain conditions. Furthermore, financing might be detrimental to the manufacturer but always beneficial to the retailer. Especially, under trade credit financing channel, the profit improvement of the retailer is higher than the manufacturer in the same financing channel, which suggests that the retailer has strong internal motivation to cooperate with the manufacturer from the perspective of financing.