Fostering employees' innovative behavior: The importance of proactive personality and work-related flow.

Employees' innovative behaviors (EIB) play a crucial role in driving enterprises' innovation and enabling them to adapt to the complex business environment. The purpose of this study is to examine the role of proactive personality in EIB, whereby a mediating effect of work-related flow is proposed in this relationship. The moderating effect of organizational innovation climate is also examined on the indirect of proactive personality on EIB through work-related flow. With purposive sampling, a questionnaire survey was conducted on front-line employees of two digital cultural and creative industrial parks in Southwest China. A total of 279 valid questionnaires were used for hypothesis verification. The findings indicate that employees' proactive personality has a significant positive effect on EIB. Employees with a proactive personality are more likely to experience work-related flow, which in turn promotes their EIB. However, we did not find evidence of the organizational innovation climate moderating the relationship between proactive personality and EIB.

ComplexQA: a deep graph learning approach for protein complex structure assessment.

MOTIVATION: In recent years, the end-to-end deep learning method for single-chain protein structure prediction has achieved high accuracy. For example, the state-of-the-art method AlphaFold, developed by Google, has largely increased the accuracy of protein structure predictions to near experimental accuracy in some of the cases. At the same time, there are few methods that can evaluate the quality of protein complexes at the residue level. In particular, evaluating the quality of residues at the interface of protein complexes can lead to a wide range of applications, such as protein function analysis and drug design. In this paper, we introduce a new deep graph neural network-based method ComplexQA, to evaluate the local quality of interfaces for protein complexes by utilizing the residue-level structural information in 3D space and the sequence-level constraints. RESULTS: We benchmark our method to other state-of-the-art quality assessment approaches on the HAF2 and DBM55-AF2 datasets (high-quality structural models predicted by AlphaFold-Multimer), and the BM5 docking dataset. The experimental results show that our proposed method achieves better or similar performance compared with other state-of-the-art methods, especially on difficult targets which only contain a few acceptable models. Our method is able to suggest a score for each interfac e residue, which demonstrates a powerful assessment tool for the ever-increasing number of protein complexes. AVAILABILITY: https://github.com/Cao-Labs/ComplexQA.git. Contact: caora@plu.edu.

Distribution characteristics and risk assessment of neonicotinoid insecticides in planting soils of mainland China.

Neonicotinoid insecticides (NEOs) are generally used in crop production. Their widespread use on agricultural soil has raised concerns regarding their health and ecological risks. Previous studies have reported the contamination of the farmland soils with NEOs from the coastal provinces of China. Information about NEOs at the national scale as well as the residues of their metabolites are relatively unknown. In this study, 391 soil samples were collected from 31 provinces in nine agricultural regions across mainland China, and the concentrations of ten parent NEOs and three metabolites were determined. At least one NEO was detected in all soil samples, with the sum of the NEOs (ΣNEOs) ranging from 0.04 to 702 µg/kg. The most common parent NEO and metabolite are imidacloprid and imidacloprid-urea, respectively. The concentrations of NEOs in coastal regions at the same latitude were higher than those in inland regions. The NEOs were further compared in the soils of seven types of monocrops and three types of multiple crops (multicrops) (i.e., two types of crops were produced in succession or simultaneously within the decade of this study). The results showed that the highest NEO residues were found in soils planted with vegetables (VE), fruits (FR), and cotton (CO) monocrops and VE & FR multicrops. Differences in NEO concentrations were observed between soils planted with monocrops and multicrops. For example, VE & FR > VE > vegetables and grains (VE & GR) > GR. Moreover, the health risks posed by NEOs in agricultural soils in China are extremely low, and the ecological risks require urgent attention. Particularly, individual NEOs in > 45% of agricultural soils in mainland China may have sublethal effects on two non-target species (HQnon-target > 0.01).

Evaluation of China's provincial digital economy development level and its coupling coordination relationship.

Based on the Office for National Statistics' delineation of the scope of the digital economy industry, this paper selects indicators from five industrial dimensions: digital product manufacturing, digital product service, digital technology application, digital factor drive and digital efficiency improvement, and constructs an evaluation system to measure the development level of China's digital economy at the provincial level. It is found that there is a wide gap in the development of China's provincial digital economy, with the eastern coastal provinces and cities having a high level of digital economy development. The coupling and coordination model was then applied to examine the interrelationships between the five industrial dimensions of the digital economy, and it was found that most of the coupling and coordination relationships of the five industrial dimensions are at the stage of medium-high coupling and low coupling and coordination, and each province and city has different coupling and coordination characteristics. The numerical evaluation results provide an intuitive understanding of the differences and deficiencies in the development of the digital economy in different regions, and serve as a reference for the medium and long-term digital economy development planning of provinces and municipalities as well as the whole country. In the future, the state should invest more in the digital economy in the central and western regions, and each province should cultivate and develop the digital economy in accordance with its own local conditions.

Research progress on source, risk assessment, and management of emerging pollutants in drinking water.

With the extensive production and use of various chemicals, emerging pollutants including environmental endocrine disrupting chemicals, perfluoro chemicals, antibiotics, and microplastics have been continuously entering the environment, and spread to water through multiple pathways. The pollution of these emerging pollutants raised continuous concerns for the safety of drinking water, threating the ecological environment and human health. In combination with international research progress, we discussed in detail about pollution, source, and risk assessment of emerging pollutants in drinking water. We further suggested and prospected the challenge of environmental management of emerging pollutants. This review could promote the public's understanding of emerging pollutants, and provide theoretical support for risk prevention and treatment of emerging pollutants in drinking water.

Assessment of Intestinal Ischemia-Reperfusion Injury Using Diffuse Reflectance VIS-NIR Spectroscopy and Histology.

A porcine model was used to investigate the feasibility of using VIS-NIR spectroscopy to differentiate between degrees of ischemia-reperfusion injury in the small intestine. Ten pigs were used in this study and four segments were created in the small intestine of each pig: (1) control, (2) full arterial and venous mesenteric occlusion for 8 h, (3) arterial and venous mesenteric occlusion for 2 h followed by reperfusion for 6 h, and (4) arterial and venous mesenteric occlusion for 4 h followed by reperfusion for 4 h. Two models were built using partial least square discriminant analysis. The first model was able to differentiate between the control, ischemic, and reperfused intestinal segments with an average accuracy of 99.2% with 10-fold cross-validation, and the second model was able to discriminate between the viable versus non-viable intestinal segments with an average accuracy of 96.0% using 10-fold cross-validation. Moreover, histopathology was used to investigate the borderline between viable and non-viable intestinal segments. The VIS-NIR spectroscopy method together with a PLS-DA model showed promising results and appears to be well-suited as a potentially real-time intraoperative method for assessing intestinal ischemia-reperfusion injury, due to its easy-to-use and non-invasive nature.

Utilization of dielectric properties for assessment of liver ischemia-reperfusion injury in vivo and during machine perfusion.

There is a shortage of donor livers and patients consequently die on waiting lists worldwide. Livers are discarded if they are clinically judged to have a high risk of non-function following transplantation. With the aim of extending the pool of available donor livers, we assessed the condition of porcine livers by monitoring the microwave dielectric properties. A total of 21 livers were divided into three groups: control with no injury (CON), biliary injury by hepatic artery occlusion (AHEP), and overall hepatic injury by static cold storage (SCS). All were monitored for four hours in vivo, followed by ex vivo plurithermic machine perfusion (PMP). Permittivity data was modeled with a two-pole Cole-Cole equation, and dielectric properties from one-hour intervals were analyzed during in vivo and normothermic machine perfusion (NMP). A clear increasing trend in the conductivity was observed in vivo in the AHEP livers compared to the control livers. After four hours of NMP, separations in the conductivity were observed between the three groups. Our results indicate that dielectric relaxation spectroscopy (DRS) can be used to detect and differentiate liver injuries, opening for a standardized and reliable point of evaluation for livers prior to transplantation.

Small intestinal viability assessment using dielectric relaxation spectroscopy and deep learning.

Intestinal ischemia is a serious condition where the surgeon often has to make important but difficult decisions regarding resections and resection margins. Previous studies have shown that 3 h (hours) of warm full ischemia of the small bowel followed by reperfusion appears to be the upper limit for viability in the porcine mesenteric ischemia model. However, the critical transition between 3 to 4 h of ischemic injury can be nearly impossible to distinguish intraoperatively based on standard clinical methods. In this study, permittivity data from porcine intestine was used to analyze the characteristics of various degrees of ischemia/reperfusion injury. Our results show that dielectric relaxation spectroscopy can be used to assess intestinal viability. The dielectric constant and conductivity showed clear differences between healthy, ischemic and reperfused intestinal segments. This indicates that dielectric parameters can be used to characterize different intestinal conditions. In addition, machine learning models were employed to classify viable and non-viable segments based on frequency dependent dielectric properties of the intestinal tissue, providing a method for fast and accurate intraoperative surgical decision-making. An average classification accuracy of 98.7% was obtained using only permittivity data measured during ischemia, and 96.2% was obtained with data measured during reperfusion. The proposed approach allows the surgeon to get accurate evaluation from the trained machine learning model by performing one single measurement on an intestinal segment where the viability state is questionable.

DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures.

Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network-based protein secondary structure method (DNSS1) and successfully advanced the prediction accuracy beyond 80%. In this work, we developed multiple advanced deep learning architectures (DNSS2) to further improve secondary structure prediction. The major improvements over the DNSS1 method include (a) designing and integrating six advanced one-dimensional deep convolutional/recurrent/residual/memory/fractal/inception networks to predict 3-state and 8-state secondary structure, and (b) using more sensitive profile features inferred from Hidden Markov model (HMM) and multiple sequence alignment (MSA). Most of the deep learning architectures are novel for protein secondary structure prediction. DNSS2 was systematically benchmarked on independent test data sets with eight state-of-art tools and consistently ranked as one of the best methods. Particularly, DNSS2 was tested on the protein targets of 2018 CASP13 experiment and achieved the Q3 score of 81.62%, SOV score of 72.19%, and Q8 score of 73.28%. DNSS2 is freely available at: https://github.com/multicom-toolbox/DNSS2.

SAXSDom: Modeling multidomain protein structures using small-angle X-ray scattering data.

Many proteins are composed of several domains that pack together into a complex tertiary structure. Multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for individual domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the orientations between domains. Small-angle X-ray scattering (SAXS) reports the structural properties of entire proteins and has the potential for guiding homology modeling of multidomain proteins. In this article, we describe a novel multidomain protein assembly modeling method, SAXSDom that integrates experimental knowledge from SAXS with probabilistic Input-Output Hidden Markov model to assemble the structures of individual domains together. Four SAXS-based scoring functions were developed and tested, and the method was evaluated on multidomain proteins from two public datasets. Incorporation of SAXS information improved the accuracy of domain assembly for 40 out of 46 critical assessment of protein structure prediction multidomain protein targets and 45 out of 73 multidomain protein targets from the ab initio domain assembly dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain-domain assembly. The source code and tool packages are available at https://github.com/jianlin-cheng/SAXSDom.

Occurrence and distribution of clinical and veterinary antibiotics in the faeces of a Chinese population.

Antibiotics ingested in the human gut may create selective pressure to change the composition of the gut microbiota, which could adversely effect the immune system of the host. However, the occurrence and distribution of antibiotics in the human gut remains unclear. A total population of 180 individuals, across three Chinses regions with different economic development levels, including children, adults, and elders, were sampled in 2017. A total of 19 representative antibiotics, including both clinical and veterinary antibiotics, were investigated in human faeces. While clinical use and prescriptions were the main exposure pathways for children, environmental media were the exposure pathway to adults. In addition, significant differences (P < 0.05) in antibiotic residues in human faeces were observed amongst various economic development levels, where human faeces from underdeveloped areas were mostly associated with higher levels of antibiotics. This study first to investigate the occurrence and distribution of typical antibiotics in the faeces of a Chinese population and thereby provide a reference for the intensive study of the effects and mechanisms of antibiotics on human gut microbiota.

Stock Returns, weather, and air conditioning.

This study investigates the relationship between stock returns and local weather through a new channel-the influence of the air-cooling system installed in the New York Stock Exchange (NYSE). To our knowledge, we are the first to employ the use of air conditioning to examine whether and how weather, especially excessively high temperature, and other factors affect stock returns. Using data for 1885-1914, we show that lower Dow Jones Average (DJA) returns were significantly associated with hotness before the NYSE trading rooms were equipped with the cooling system in 1903, whereas this correlation is largely weakened afterward. We also find that before the introduction of the air-cooling system, the negative effect of high temperatures on stock returns was stronger when the precipitation was lower. We obtain consistent results when controlling for the calendar anomalies such as the May-to-October effect, the Monday effect, and the effect of macroeconomic conditions.

Assessment of the inhibitory effects of pyrethroids against human carboxylesterases.

Pyrethroids are broad-spectrum insecticides that widely used in many countries, while humans may be exposed to these toxins by drinking or eating pesticide-contaminated foods. This study aimed to investigate the inhibitory effects of six commonly used pyrethroids against two major human carboxylesterases (CES) including CES1 and CES2. Three optical probe substrates for CES1 (DME, BMBT and DMCB) and a fluorescent probe substrate for CES2 (DDAB) were used to characterize the inhibitory effects of these pyrethroids. The results demonstrated that most of the tested pyrethroids showed moderate to weak inhibitory effects against both CES1 and CES2, but deltamethrin displayed strong inhibition towards CES1. The IC50 values of deltamethrin against CES1-mediated BMBT, DME, and DMCB hydrolysis were determined as 1.58µM, 2.39µM, and 3.3µM, respectively. Moreover, deltamethrin was cell membrane permeable and capable of inhibition endogenous CES1 in living cells. Further investigation revealed that deltamethrin inhibited CES1-mediated BMBT hydrolysis via competitive manner but noncompetitively inhibited DME or DMCB hydrolysis. The inhibition behaviors of deltamethrin against CES1 were also studied by molecular docking simulation. The results demonstrated that CES1 had at least two different ligand-binding sites, one was the DME site and another was the BMBT site which was identical to the binding site of deltamethrin. In summary, deltamethrin was a strong reversible inhibitor against CES1 and it could tightly bind on CES1 at the same ligand-binding site as BMBT. These findings are helpful for the deep understanding of the interactions between xenobiotics and CES1.

QAcon: single model quality assessment using protein structural and contact information with machine learning techniques.

Motivation: Protein model quality assessment (QA) plays a very important role in protein structure prediction. It can be divided into two groups of methods: single model and consensus QA method. The consensus QA methods may fail when there is a large portion of low quality models in the model pool. Results: In this paper, we develop a novel single-model quality assessment method QAcon utilizing structural features, physicochemical properties, and residue contact predictions. We apply residue-residue contact information predicted by two protein contact prediction methods PSICOV and DNcon to generate a new score as feature for quality assessment. This novel feature and other 11 features are used as input to train a two-layer neural network on CASP9 datasets to predict the quality of a single protein model. We blindly benchmarked our method QAcon on CASP11 dataset as the MULTICOM-CLUSTER server. Based on the evaluation, our method is ranked as one of the top single model QA methods. The good performance of the features based on contact prediction illustrates the value of using contact information in protein quality assessment. Availability and Implementation: The web server and the source code of QAcon are freely available at: http://cactus.rnet.missouri.edu/QAcon. Contact: chengji@missouri.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

ConEVA: a toolbox for comprehensive assessment of protein contacts.

BACKGROUND: In recent years, successful contact prediction methods and contact-guided ab initio protein structure prediction methods have highlighted the importance of incorporating contact information into protein structure prediction methods. It is also observed that for almost all globular proteins, the quality of contact prediction dictates the accuracy of structure prediction. Hence, like many existing evaluation measures for evaluating 3D protein models, various measures are currently used to evaluate predicted contacts, with the most popular ones being precision, coverage and distance distribution score (Xd). RESULTS: We have built a web application and a downloadable tool, ConEVA, for comprehensive assessment and detailed comparison of predicted contacts. Besides implementing existing measures for contact evaluation we have implemented new and useful methods of contact visualization using chord diagrams and comparison using Jaccard similarity computations. For a set (or sets) of predicted contacts, the web application runs even when a native structure is not available, visualizing the contact coverage and similarity between predicted contacts. We applied the tool on various contact prediction data sets and present our findings and insights we obtained from the evaluation of effective contact assessments. ConEVA is publicly available at http://cactus.rnet.missouri.edu/coneva/ . CONCLUSION: ConEVA is useful for a range of contact related analysis and evaluations including predicted contact comparison, investigation of individual protein folding using predicted contacts, and analysis of contacts in a structure of interest.

Reproduction impairment and endocrine disruption in female zebrafish after long-term exposure to MC-LR: A life cycle assessment.

Microcystin-LR (MC-LR) has been found to cause reproductive and developmental impairments as well as to disrupt sex hormone homeostasis of fish during acute and sub-chronic toxic experiments. However, fish in natural environments are continuously exposed to MC-LR throughout their entire life cycle as opposed to short-term exposure. Here, we tested the hypothesis that the mechanism by which MC-LR harms female fish reproduction and development within natural water bodies is through interference of the reproductive endocrine system. In the present study, zebrafish hatchlings (5 d post-fertilization) were exposed to 0, 0.3, 3 and 30 µg/L MC-LR for 90 d until reaching sexual maturity. Female zebrafish were selected, and the changes in growth and developmental indicators, ovarian ultrastructure as well as the levels of gonadal steroid hormones and vitellogenin (VTG) were examined along with the transcription of related genes in the hypothalamic-pituitary-gonadal-liver axis (HPGL-axis). The results showed for the first time, a life cycle exposure to MC-LR caused growth inhibition, decreased ovary weight and ovarian ultra-pathological lesions. Decreased ovarian testosterone levels indicated that MC-LR disrupted sex steroid hormone balance. Significantly up-regulated transcription of brain FSHß and LHß along with ovarian ERα, FSHR and LHR suggested positive feedback regulation in the HPGL-axis was induced as a compensatory mechanism for MC-LR damage. It was also noted that ovarian VTG content and hepatic ERα and VTG1 expression were all down-regulated, which might be responsible for reduced vitellus storage noted in our histological observations. Our findings indicate that a life cycle exposure to MC-LR impairs the development and reproduction of female zebrafish by disrupting the transcription of related HPGL-axis genes, suggesting that MC-LR has potential adverse effects on fish reproduction and thus population dynamics in MCs-contaminated aquatic environment.

Pharmacokinetics of sugammadex 16 mg/kg in healthy Chinese volunteers.

OBJECTIVE: Elimination of sugammadex occurs predominantly via the kidneys, with the majority of the drug excreted unchanged in the urine. To date, most studies with sugammadex have been performed in non-Asian populations. The objectives of this open-label study were to determine the pharmacokinetics (PK) and safety of single-dose sugammadex (16 mg/kg) in healthy Chinese adult volunteers. METHODS: 12 Chinese subjects (6 male; 6 female) received intravenous sugammadex (16 mg/kg) as a 10-second bolus infusion. Blood samples were collected pre-sugammadex and at regular intervals up to 24 hours post-sugammadex for PK assessment. Safety was assessed via AEs, vital signs, electrocardiogram, and laboratory parameters. RESULTS: Following sugammadex 16 mg/kg infusion, peak sugammadex concentration was 197 µg/mL, clearance was 99.7 mL/min, and apparent volume of distribution at equilibrium was 10.5 L. Plasma sugammadex concentrations showed a polyexponential decline over time, with an overall geometric mean (CV%) terminal half-life of 145 minutes (17.9%) (139 minutes (17.7%) for males; 152 minutes (18.6%) for females). No influence of gender on the PK of sugammadex was observed. Three subjects experienced an adverse events (AE) (dysgeusia of mild intensity), which was considered possibly or probably related to sugammadex. There were no clinically significant changes in vital signs, electrocardiography or laboratory parameters. CONCLUSION: PK of sugammadex (16 mg/kg) was characterized in healthy Chinese subjects. Overall between-subject variability on clearance and apparent volume of distribution was ~ 10%. Sugammadex was generally well tolerated.