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Thermophilic bacteria play an important role in aroma formation during pile-fermentation process of dark tea. With the aim to reveal the impact of thermophilic bacteria on volatile compounds in dark tea, Bacillus licheniformis (thermophilic bacteria) were inoculated into sun-dried green tea for spontaneous fermentation. In this study, headspace solid phase microextraction combined to gas chromatography mass spectrometry (HS-SPME-GC-MS), odor activity value (OAV), principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA) were employed to investigated the characteristics of volatile compounds during thermophilic bacteria pile-fermentation. According to HS-SPME-GC-MS, a total of 64 volatile compounds were identified. PCA revealed that tea samples could be clearly discriminated from each other. Furthermore, sulcatone, hexanoic acid, linalool, 2-methyl-trans-decalin, myrtenal, and α-ionone were found to play an important role in discrimination of tea samples, based on OPLS-DA. In addition, the OAV could effectively characterize the aroma contribution of volatile compounds during thermophilic bacteria pile-fermentation, and (Z)-linalool oxide (furanoid), α-ionone, (E)-2-nonenal, and linalool were the critical volatile compounds of aroma quality in dark tea. This study provides insight into volatile compounds characteristics during thermophilic bacteria pile-fermentation in dark tea.

Multiview active learning (MVAL) is a technique which can result in a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. This paper made research on MVAL-based scene classification for helping the computer accurately understand diverse and complex environments macroscopically, which has been widely used in many fields such as image retrieval and autonomous driving. The main contribution of this paper is that different high-level image semantics are used for replacing the traditional low-level features to generate more independent and diverse hypotheses in MVAL. First, our algorithm uses different object detectors to achieve local object responses in the scenes. Furthermore, we design a cascaded online LDA model for mining the theme semantic of an image. The experimental results demonstrate that our proposed theme modeling strategy fits the large-scale data learning, and our MVAL algorithm with both high-level semantic views can achieve significant improvement in the scene classification than traditional active learning-based algorithms.

Chaos theory has been proved to be of great significance in a series of critical applications although, until now, its applications in analyzing soil respiration have not been addressed. This study aims to introduce a chaotic component in the control system of soil respiration and explain control complexity of this nonlinear chaotic system. This also presents a theoretical framework for better understanding chaotic components of soil respiration in arid land. A concept model of processes and mechanisms associated with subterranean CO2 evolution are developed, and dynamics of the chaotic system is characterized as an extended Riccati equation. Controls of soil respiration and kinetics of the chaotic system are interpreted and as a first attempt, control complexity of this nonlinear chaotic system is tackled by introducing a period-regulator in partitioning components of soil respiration.

Osteogenesis imperfecta (OI), also known as “brittle bone disease,” is a rare inherited genetic disorder characterized by bone fragility and often associated with short stature. The mutation in WNT1 causes autosomal recessive OI (AR‐OI) due to the key role of WNT/β‐catenin signaling in bone formation. WNT1 mutations cause phenotypes in OI of varying degrees of clinical severity, ranging from moderate to progressively deforming forms. The nucleotide change c.677C > T is one of the recurrent variants in the WNT1 alleles in Chinese AR‐OI patients. To explore the effects of mutation c.677C > T on WNT1 function, we evaluated the activation of WNT/β‐catenin signaling, cell proliferation, osteoblast differentiation, and osteoclast differentiation in WNT1c.677C>T, WNT1c.884C>A, and wild type WNT1 transfected into MC3T3‐E1 preosteoblasts. Plasmids containing wild type WNT1, WNT1c.677C>T, and WNT1c.884C>A cDNAs were constructed. Protein levels of phosphorylation at serine 9 of GSK‐3β (p‐GSK‐3β), GSK‐3β, nonphosphorylated β‐catenin (non‐p‐β‐catenin), and β‐catenin were detected with western blot. Cell proliferation was determined using MTS. BMP‐2 and RANKL mRNA and protein levels were detected by qPCR and western blot. Our results showed that WNT1c.677C>T failed to activate WNT/β‐catenin signaling and impaired the proliferation of preosteoblasts. Moreover, compared to wild type WNT1, WNT1c.677C>T downregulated BMP‐2 protein expression and was exhibited a diminished capacity to suppress the RANKL protein level. In conclusion, mutation c.677C > T hindered the ability of WNT1 to induce the WNT/β‐catenin signaling pathway and it affected the WNT/β‐catenin pathway which might potentially contribute to hampered bone homeostasis.

A strain of thiamethoxam-degrading endophyte, named TMX13, was isolated from roots of mulberry (Morus alba L.) and was identified as Enterobacter sp. Inoculating Chinese cabbage (Brassica chinensis L) with strain TMX13-gfp (gfp-labeled TMX13) could significantly reduce thiamethoxam residues in the aboveground part (edible portion) of the vegetable. The theoretical daily intake (TDI) of thiamethoxam via consumption of TMX13-gfp inoculated Chinese cabbage was 0.17 μg/kg body weight per day, far less than the prescribed acceptable daily intake (ADI) for this pesticide. TMX13-gfp colonization could increase the leaf chlorophyll content and plant biomass and promote the development of plant roots. Compared with the uninoculated treatment, the contents of malondialdehyde (MDA) and hydrogen peroxide (H2O2) and the activity of superoxide dismutase (SOD) in leaves of the TMX13-gfp inoculated plants decreased by 18.4%-60.2%, suggesting that TMX13-gfp colonization could alleviate the oxidative stress induced by thiamethoxam exposure. The total amounts of organic acids and amino acids in root exudates from the TMX13-gfp inoculated Chinese cabbage decreased by 9.2% and 85.2%, respectively. Results of this study lead to the conclusion that the isolated endophyte Enterobacter sp. TMX13 could reduce thiamethoxam residues in edible vegetables, promote plant growth, and alleviate the phytotoxic effects induced by thiamethoxam exposure.

Coal is a particular sedimentary organic ore and has a huge resource worldwide. Coal is a resource primarily used for electric power generation, and currently supplies 41% of global electricity needs. Significant quantities of coal are also employed in metallurgical processes, gasification, cement industries, and as raw materials for activated carbon and many common and industrial chemicals, as well as for heat for the wallboard, aluminum, and cement industries. A number of coals can also be considered as an economic source of some critical metals, and thus are coal-hosted ore deposits. The critical metals in coal and coal-bearing sequences occur at concentrations comparable to or even higher than those in conventional economic deposits. China is, and in the coming decades should continue to be, the largest producer and user of coal in the world. The high volume of coal usage in China has led to attention not only on the toxic elements that may be released from coal combustion but also on the critical metals that may occur in the coal and coal-bearing sequences, as well as coal combustion by-products. Critical elements (e.g., Ge, Ga, U, rare earth elements and Y, Nb, Zr, Se, V, Re, Au, and Ag, as well as the base metal Al) in a number of coals (or coal ashes) and some coal-bearing strata in China occur at concentrations comparable to or even higher than those in conventional economic deposits. This paper reviewed the distribution and ore-forming mechanism of some typical coal-hosted ore deposits in China, including Ge, Ga-Al, rare earth elements (REE), and Nb-Zr-REE-Ga. Additionally, other critical metals such as Be, Sc, Li, Mg, V, and Re in coal and coal bearing sequences have also been briefly reviewed. Several geological factors are responsible for the deposition of coal-hosted critical metals, including injection of hydrothermal fluids during peat accumulation or later epigenetic stages, deposition of syngenetic alkali volcanic ashes in peat swamp, input of terrigenous mineral matter into the coal-forming environment, leaching of partings by groundwater, and mixed processes involving both hydrothermal solutions and volcanic ash. Mixed processes involving both hydrothermal solutions and volcanic ash are also important geological factors that contribute to the deposition of coal-hosted critical metals. The critical metals in Chinese coal and coal-bearing sequences may be associated with either the organic matter or minerals. For example, germanium exclusively occurs in the organic matter; gallium mainly occurs in boehmite and kaolinite, and to a lesser extent, in the organic matter; rare earth elements and Y occur as carbonate minerals (e.g., florencite and parisite), phosphate minerals (e.g., rhabdophane, silico-rhabdophane, and xenotime), and in part are associated with the organic matter. Some critical metals (e.g., Ge, Al, and Ga) have been successfully extracted at an industrial scale from Chinese coals, and others have significant potential for such extraction. Major challenges remaining for coal scientists include the development of economic extraction methods from coal ash, and the control of toxic elements released during the metal extraction process to protect human health and to avoid environmental pollution.

The Bayes classifier is often used because it is simple, and the maximum posterior probability (MPP) criterion it uses is equivalent to the least error rate criterion. However, it has issues in the following circumstances: (i) if information instead of correctness is more important, we should use the maximum likelihood criterion or maximum information criterion, which can reduce the rate of failure to report small probability events. (ii) For unseen instance classifications, the previously optimised classifier cannot be properly used when the probability distribution of true classes is changed. (iii) When classes’ feature distributions instead of transition probability functions (TPFs) are stable, it is improper to train the TPF, such as the logistic function, with parameters. (iv) For multi-label classifications, it is difficult to optimise a group of TPFs with parameters that the Bayes classifier needs. This study addresses these issues by comparing the MPP criterion with the maximum likelihood criterion and maximum mutual information (MMI) criterion. It suggests using the MMI criterion for most unseen instance classifications. It presents a new iterative algorithm, the channel matching (CM) algorithm, for the MMI classification. It uses two examples to show the advantages of the CM algorithm: fast and reliable.

Recent success in the chemical vapor deposition (CVD) growth of atomically thin transition metal dichalcogenide (TMD) crystals opens up prospects for exploiting these materials in nanoelectronic and optoelectronic devices. However, CVD-grown TMDs often suffer from weak crystal quality because of the formation of defects during the growth, which makes a large impact on their electrical and optical properties. Here, we report a facile synthesis of high-quality MoS2 monolayers through a sodium halide-assisted CVD method. Our results show that the addition of sodium halides into MoO3 precursors leads to the rapid growth of highly crystalline MoS2 monolayers. Moreover, the overall photoluminescence (PL) intensity of MoS2 monolayers can be greatly enhanced by up to 2 orders of magnitude. The PL enhancement originates from that the deep trap states induced by sulfur vacancies are passivated by the substitution doping of halogen atoms, which promotes the emission of excitons and trions. Density functional theory calculations indicate that the band gaps of halogen-doped MoS2 monolayers are slightly smaller than those of pristine MoS2 monolayer, which is responsible for the small red shift of PL peaks (∼30 meV). These findings provide a new route toward engineering electronic and optical properties of MoS2 and other TMD monolayers.

Aims Grazing exerts profound effects on grassland ecosystem service and functions by regulating species composition and diversity, and structuring community assembly worldwide. However, adaptions of phylogenetic diversity and phylogenetic community structure to long-term grazing disturbance remain poorly studied, especially for ecosystems distributed in extreme environments. Methods Here, we conducted an experiment with multigrazing intensities to explore the impacts of grazing disturbance on plant phylogenetic diversity and community structure in an alpine grassland of the Tibetan Plateau. Important Findings Grazing disturbance enriched plant species richness (SR), and stimulated species turnover from regional species pool, consequently changing community species composition. Under low intensities, grazing exerted no obvious effects on phylogenetic diversity and community structure, whereas communities changed from overdispersion to clustering under high grazing intensity. High grazing intensity resulted in stronger environmental filtering, which consequently selected those species with high resilience to grazing disturbance. The observed clustering structure was associated with the colonizing species which were closely related to resident species, and locally extinct species, and distantly related to residents. At the plant functional trait level, high grazing intensity increased species colonization largely by altering the effect of root depth on species colonization compared to light grazing. Our results highlight that solely utilization of SR and diversity cannot fully represent grassland communities responses to grazing. The effects of species turnover on community phylogenetic diversity and structure are entailed to be explored in the future grazing studies.

Distinguishing the differences of pore characters between different mixed lithofacies shales is helpful for improving shale gas development efficiencies. In this study, the targeted Longmaxi shale ( L1) of the Southern Sichuan Basin was selected as the research object. Two kinds of mixed lithofacies shale were identified by analyzing total organic carbon and X-ray diffraction results. The forming depositional environment of mixed lithofacies shale was researched by elements analyses. Pores in different mixed lithofacies shale were observed using field emission scanning electronic microscope. Low-field nuclear magnetic resonance and low-temperature nitrogen adsorption were conducted to analyze pore characters of different mixed lithofacies shale. The results showed that L1 were mainly composed of organic rich clay–siliceous mixed shales (OR-M-1) and organic extreme rich calcareous–siliceous mixed shales (OER-M-3). OR-M-1 and OER-M-3 were formed in high paleo-producing dysoxic–oxic seawater and anoxic waterbody, respectively. Micro-pore and meso-pore volumes of OER-M-3 were greater than those of OR-M-1 while macro-pore volume of OER-M-3 was lower than that of OR-M-1. Meso-pore surficial and structural complexities of OER-M-3 were greater than those of OR-M-1. OER-M-3 were greater in oil-wetting micro-pore structural complexities while lower in water-wetting micro-pore structural complexities, compared with OR-M-1. The inherent relationships between lithofacies type and sedimentary environment, total organic carbon, as well as pore characters, respectively, were quite close.

Supplemental Digital Content is available in the text., Background The aim of this study was to evaluate the diagnostic and risk predictive value of emerging proinflammatory and anti-inflammatory adipocytokines on coronary artery disease (CAD). Patients and methods The study involved 259 inpatients suspected acute coronary syndrome who underwent coronary angiography. Demographic, clinical characteristics, and coronary artery stenosis rated by Gensini score were collected by cardiovascular doctors. The levels of serum inflammatory adipocytokines were evaluated by ELISA. The correlations of the cytokines with clinical parameters were assessed. Receiver operating characteristic curves were constructed for the diagnosis of CAD. Results The 259 inpatients were assigned to the CAD (n = 180) and control groups (n = 79). Compared with the control group, the CAD group displayed significantly higher serum levels of retinol-binding protein-4 (RBP4), pentraxin 3 (PTX3), galectin-3 (GAL-3), and plasminogen activator inhibitor (PAI-1), and significantly lower levels of netrin-1 (NTN1), interleukin-37 (IL-37), and adiponectin (ADP) (all P < 0.05). PAI-1 was significantly upregulated, and IL-37 and ADP were significantly downregulated in the three-vessels CAD subgroup compared to the one- and two-vessels CAD subgroups (P < 0.05). The RBP4, PTX3, GAL-3, PAI-1, and IL-37 inflammatory cytokines were significantly positively correlated with Gensini score, and ADP was negatively correlated (all P < 0.001). IL-37 was a more accurate anti-inflammatory biomarker than NTN1 and ADP. Combining cytokines significantly increased the sensitivity and specificity. Conclusion The inflammatory adipocytokines GAL-3, RBP4, PTX3, NTN1, and IL-37 were more effective than the classical biomarkers PAI-1 and ADP in the diagnosis and risk assessment of CAD patients.

Prior research documents large, persistent productivity dispersion even within narrowly defined industries, suggesting the presence of frictions that impede the efficient allocation of resources. In this paper, we consider the role of information frictions. We posit that high-quality financial reporting with respect to firm productivity can mitigate information frictions and hence facilitate the efficient allocation of resources across firms. Using a large sample of firms in the manufacturing sector, we find evidence consistent with our prediction—industries with higher productivity informativeness tend to have smaller within-industry productivity dispersion. We further find that this relation is stronger in industries with greater dependence on external financing, suggesting that reporting quality affects allocation efficiency, at least in part, through the capital market channel. These findings suggest that the extent to which financial reporting is informative about productivity can affect resource allocation efficiency and hence have real effects at the macro level.

Transformation of pore and fracture structure of coals with supercritical CO2 (scCO2) –H2O is a key to CO2 injection and CH4 production efficiencies during the CO2 enhanced coalbed methane process. To study the transformation of pores and fractures in the coals with CO2, two reservoir conditions, which simulate1000m (45 °C, 10 MPa) and 2000 m (80 °C, 20 MPa) depths, are applied to four types of high metamorphic coals from Qinshui Basin to study the influences of temperature and pressure on pore volume and pore sized distribution change. Nuclear magnetic resonance, high pressure mercury intrusion, X-ray CT scanning and permeability experiments are performed and the effects of scCO2 on coal permeability and the influencing factors are discussed. The results show that scCO2-H2O has a positive effect on the improvement on the pore fracture system. It could add or expand pores and fractures, leading to the increase in pore number, porosity, pore volume, pore specific surface area, connected pore volume, and pore throat number. And then, increased the permeability which had a positive correlation with the experimental temperature and pressure. The growth of permeability could be as high as 114.10 times, and it was higher in horizontal to bedding direction than that of the vertical to bedding direction. Coal expansion could lead to the addition and enlargement of micro-fractures and enhance the connectivity between seepage pores and fractures. Mineral dissolution could lead to the formation of a large number of effectively connected and non-effectively connected pores, especially the latter, which was positively correlated with simulated temperature and pressure. In addition, the effectively connected pores tend to develop in vertical original micro-fractures. Moreover, the more complete the reaction is, the more favorable it is to increase the pore volume of fractures.

The Rongyang coals from Guizhou, southwestern China, are significantly enriched with uranium (U) and other toxic elements, such as vanadium (V), chromium (Cr), molybdenum (Mo), cobalt (Co), copper (Cu), and selenium (Se). Therefore, it is necessary to remove these toxic elements from high‐U coals before combustion. Previous studies have shown that U cannot be effectively removed by flotation and gravity separation. In this study, the stepped release flotation and acid leaching tests were conducted for coals of the Longtan Formation from the Rongyang Mine. The results from these analyses are as follows: (a) Stepped release analyses revealed that a multistage cleaning process can help to separate toxic elements from cleaned coals; (b) The removal of toxic elements by stepped release and acid leaching is closely related to their mode of occurrence. Vanadium, Cr, Mo, and U were difficult to be removed by stepped release flotation, which due to a significant proportion of them occur in organic matter and fine‐grained minerals; (c) Compared to flotation, most elements were better to be removed by stepped release flotation. In comparison with gravity separation, U had a higher removability by stepped release flotation, while the removal effect of thiophilic element by gravity separation was greater than by flotation; (d) After stepped release flotation, toxic elements Sc, V, Cu, Se, Mo, Hg, and U are still enriched in cleaned coals, suggesting that these elements cannot be fully removed through stepped release flotation.

Multiunit-flavoenzyme NADPH oxidases (NOXs) play multiple roles in living cells

Machine learning (ML) lifecycle is a cyclic process to build an efficient ML system. Though a lot of commercial and community (non-commercial) frameworks have been proposed to streamline the major stages in the ML lifecycle, they are normally overqualified and insufficient for an ML system in its nascent phase. Driven by real-world experience in building and maintaining ML systems, we find that it is more efficient to initialize the major stages of ML lifecycle first for trial and error, followed by the extension of specific stages to acclimatize towards more complex scenarios. For this, we introduce a simple yet flexible framework, MLife, for fast ML lifecycle initialization. This is built on the fact that data flow in MLife is in a closed loop driven by bad cases, especially those which impact ML model performance the most but also provide the most value for further ML model development—a key factor towards enabling enterprises to fast track their ML capabilities. Better yet, MLife is also flexible enough to be easily extensible to more complex scenarios for future maintenance. For this, we introduce two real-world use cases to demonstrate that MLife is particularly suitable for ML systems in their early phases.

In medical visualization, nursing notes contain rich information about a patient’s pathological condition. However, they are not widely used in the prediction of clinical outcomes. With advances in the processing of natural language, information begins to be extracted from large-scale unstructured data like nursing notes. This study extracted sentiment information in nursing notes and explored its association with in-hospital 28-day mortality in sepsis patients. The data of patients and nursing notes were extracted from the MIMIC-III database. A COX proportional hazard model was used to analyze the relationship between sentiment scores in nursing notes and in-hospital 28-day mortality. Based on the COX model, the individual prognostic index (PI) was calculated, and then, survival was analyzed. Among eligible 1851 sepsis patients, 580 cases suffered from in-hospital 28-day mortality (dead group), while 1271 survived (survived group). Significant differences were shown between two groups in sentiment polarity, Simplified Acute Physiology Score II (SAPS-II) score, age, and intensive care unit (ICU) type (all P < 0.001 ). Multivariate COX analysis exhibited that sentiment polarity (HR: 0.499, 95% CI: 0.409-0.610, P < 0.001 ) and sentiment subjectivity (HR: 0.710, 95% CI: 0.559-0.902, P = 0.005 ) were inversely associated with in-hospital 28-day mortality, while the SAPS-II score (HR: 1.034, 95% CI: 1.029-1.040, P < 0.001 ) was positively correlated with in-hospital 28-day mortality. The median death time of patients with PI ≥ 0.561 was significantly earlier than that of patients with PI < 0.561 (13.5 vs. 49.8 days, P < 0.001 ). In conclusion, sentiments in nursing notes are associated with the in-hospital 28-day mortality and survival of sepsis patients.

With the aim to reveal the microbial community succession at various temperatures in the fermentation of Qingzhuan tea (QZT), the Illumina NovaSeq sequencing was carried out to analyze bacterial and fungal community structure in tea samples collected from the fermentation set at various temperatures, i.e., 25 °C, 30 °C, 37 °C, 45 °C, 55 °C, and room temperature. The results showed that fermentation temperature profoundly affected the microbial community succession in the QZT fermentation. Microbial richness and community diversity decreased along with the increase of fermentation temperature. Despite the differences between microorganisms and their metabolic types among various temperatures, most bacteria and fungi showed positive correlations at the genera level. Klebsiella, Paenibacillus, Cohnella, and Pantoea were confirmed as the main bacterial genera, and Aspergillus and Cyberlindnera were the main fungal genera in QZT fermentation. The microbial genera (i.e. Aspergillus, Rhizomucor, Thermomyces, Ralstonia, Castellaniella, and Vibrio) were positively correlated with fermentation temperature (P 0.05), while Klebsiella, Paenibacillus, and Aspergillus had good adaptability at different temperatures. Conversely, Pantoea and Cyberlindnera were only suitable for low temperature (≤37 °C) growth, and Thermomyces was only suitable for high temperature (37 °C) growth. Aspergillus had a significant positive correlation with tea aroma quality (r = 0.64, p 0.05). This study would help to understand the formation mechanism of QZT from microflora perspective.

Sorption of four perfluoroalkyl carboxylic acids (PFCAs) including perfluoropentanoic acid, perfluoroheptanoic acid, perfluorodecanoic acid, and perfluorododecanoic acid by three soils with cation exchange sites occupied by K

Objective. The study was to develop and externally validate a prognostic nomogram to effectively predict the overall survival of patients with stomach cancer. Methods. Demographic and clinical variables of patients with stomach cancer in the Surveillance, Epidemiology, and End Results (SEER) database from 2007–2016 were retrospectively collected. Patients were then divided into the Training Group (n = 4,456) for model development and the Testing Group (n = 4,541) for external validation. Univariate and multivariate Cox regressions were used to explore prognostic factors. The concordance index (C-index) and the Kolmogorov–Smirnov (KS) value were used to measure the discrimination, and the calibration curve was used to assess the calibration of the nomogram. Results. Prognostic factors including age, race, marital status, TNM stage, surgery, chemotherapy, grade, and the number of regional nodes positive were used to construct a nomogram. The C-index was 0.790 and the KS value was 0.45 for the Training Group, and the C-index was 0.789 for the Testing Group, all suggesting the good performance of the nomogram. Conclusion. We have developed an effective nomogram with ten easily acquired prognostic factors. The nomogram could accurately predict the overall survival of patients with stomach cancer and performed well on external validation, which would help improve the individualized survival prediction and decision-making, thereby improving the outcome and survival of stomach cancer.

In this paper, we mainly adopted 337 patients who had undergone the surgery on lymph node metastasis of papillary thyroid carcinoma (PTC) as the sample population. In order to provide clinical reference for the intelligent decision-making in treatment plan and improvement of prognosis, we utilized ultrasound features and imaging features to construct five early diagnosis models for patients based on the ultrasound features, imaging features, and combined features. The model integrated with broad learning system (BLS) showed the best performance, with the area under the curve (AUC) of 0.857 (95% confidence interval (CI): 0.811–0.902)) and the accuracy of 0.805 (95% CI: 0.759–0.850). For demographic and clinical features, the prediction effect was also good, with the AUC more than 0.700.

Objective. To investigate the effect of the interaction between depression and sleep disorders on the stroke occurrence based on the data from the National Health and Nutritional Examination Survey (NHANES). Methods. Seven cycles of 2-year NHANES data (2005-2018) were analyzed in this study. Univariate analysis was first performed between the stroke and nonstroke patients, and then, multivariate logistic regression models were conducted to analyze the association of depression, sleep disorders, and their interactions with stroke occurrence. Results. A total of 30473 eligible participants were included in this study, including 1138 (3.73%) with stroke and 29335 (96.27%) with nonstroke. Except sex, the differences were all significant between the stroke and nonstroke patients in baseline information (all P < 0.001 ). Depression (odds ratio (OR): 2.494, 95% confidence interval (CI): 2.098-2.964), depression severity (moderate, OR: 2.013, 95% CI: 1.612-2.514; moderately severe, OR: 2.598, 95% CI: 1.930-3.496; severe, OR: 5.588, 95% CI: 3.883-8.043), and sleep disorders (OR: 1.677, 95% CI: 1.472-1.910) were presented to be associated with an increased risk of stroke after correcting all the confounders. The logistic regression analysis showed that there was a synergic, additive interaction between depression and sleep disorders on the stroke occurrence, and the proportion of stroke patients caused by this interaction accounted for 27.1% of all the stroke patients. Conclusion. Depression, depression severity, and sleep disorders are all independently associated with a high risk of stroke. The interaction between depression and sleep disorders can synergistically increase the stroke occurrence.

he present work combines data-driven based logistic function with prediction-area plot for delineating target areas of orogenic gold deposits in eastern margin of Qinling metallogenic belt, central China. Firstly, the values of geological and geochemical information layer were transformed into a series of fuzzy numbers with a range of 0-1 through a data-driven based logistic function on the basis of mineralization theory of the orogenic gold deposits. Secondly, the prediction-area(P-A) plot was performed on the above evidence layers and their corresponding fuzzy overlay layers to pick out a proper prediction scheme for mineral prospectivity mapping(MPM) based on the known gold occurrences. What’s more, to further prove the advantages of this method, we also used a knowledge-driven approach for comparison purpose. Finally, with the concentration-area(C-A) fractal model, the fractal thresholds were determined and a mineral prospecting map was generated. The result, five of the six known gold deposits are located in high and moderate potential areas (accounts for 18.6 % of the study area), one in low potential area (accounts for 38.4 % of the study area) and none in weak potential area (accounts for 43 % of the study area), confirmed the joint application of data-driven based logistic function and prediction-area plot a simple, effective and low-cost method for mineral prospectivity mapping, which can be a guidance for further work in the research area.

Light-modulated lead-free perovskites-based memristors, combining photoresponse and memory, are promising as multifunctional devices. In this work, lead-free double perovskite Cs2AgBiBr6 films with dense surfaces and uniform grains were prepared by the low-temperature sol-gel method on indium tin oxide (ITO) substrates. A memory device based on a lead-free double perovskite Cs2AgBiBr6 film, Pt/Cs2AgBiBr6/ITO/glass, presents obvious bipolar resistive switching behavior. The ROFF/RON ratio under 445 nm wavelength light illumination is ~100 times greater than that in darkness. A long retention capability (&gt, 2400 s) and cycle-to-cycle consistency (&gt, 500 times) were observed in this device under light illumination. The resistive switching behavior is primarily attributed to the trap-controlled space-charge-limited current mechanism caused by bromine vacancies in the Cs2AgBiBr6 medium layer. Light modulates resistive states by regulating the condition of photo-generated carriers and changing the Schottky-like barrier of the Pt/Cs2AgBiBr6 interface under bias voltage sweeping.

Triazole pesticides are widely used for the control of pathogenic fungi in crops, which were frequently detected in edible parts. Its extensive use has caused many environmental pollution and food safety problems. In this study, the uptake, translocation, and subcellular distribution of three triazole pesticides (triadimefon, tebuconazole, and epoxiconazole) in rice were investigated. The results showed that the three triazole pesticides could be taken up by rice roots, but their distribution in plant tissues were different. The accumulation of the three pesticides in rice root followed the order of epoxiconazole (4.26 mg/kg, 24 h) tebuconazole (2.63 mg/kg, 24 h) triadimefon (1.37 mg/kg, 24 h), while a reversed order was observed in rice shoots, triadimefon (0.48 mg/kg, 24 h) tebuconazole (0.40 mg/kg, 24 h) epoxiconazole (0.21 mg/kg, 24 h). The translocation of triazole pesticides within rice tissues involved both symplast and apoplast pathways, with triadimefon preferentially through by the apoplast pathway and epoxiconazole through by the symplast pathway. The proportions of triadimefon, tebuconazole, and epoxiconazole in the symplast and apoplast of rice plants were 15-33%, 6-31%, 7-37%, and 67-85%, 69-94%, 63-93%, respectively. The subcellular distribution revealed that all pesticides have a higher proportion in cell walls than in cell organelles and soluble components. Epoxiconazole has the highest accumulated capacity in the cell wall (45-67%) and triadimefon was more concentrated in the soluble components (24-29%). However, there were no significant differences in the amount of three pesticides in cell organelles. The distribution of the three pesticides in aboveground and underground parts of rice plant, uptake and transportation in symplast and apoplast pathways, and distribution in the subcellular tissue are all related to their hydrophobicity.

In recent years, the number of electric vehicles and charging demand have increased significantly, which puts forward new requirements for power grid planning and operation. Accurate prediction of charging demand in residential areas can effectively guide the planning and construction of charging facilities. In this paper, by mining the relationship between the car ownership per thousand people and economic indicators, a prediction method of car ownership based on correlation coefficient and logistic curve is proposed, and the demand of charging piles under different permeability is calculated. Furthermore, a coordinated charging and discharging strategy for electric vehicles in residential areas is proposed, which takes V2G technology into account, to realize load transfer and avoid the impact of peak load on the safe operation of the power system. Finally, an example is given to verify the effectiveness of the method.

La–Mg–Ni-based superlattice structure compounds are believed to be a promising negative electrode material with high power and superior hydrogen storage capacity for nickel-metal hydride batteries. They are built with [AB5] and [A2B4] subunits, stacked along the c axis, with the following two sequences: one of rhombohedral and the other of hexagonal symmetry, called 3R and 2H, respectively. In this paper, we present the synthesis conditions and detailed structural characterization of rhombohedral La0.63Nd0.16Mg0.21Ni3.53Al0.11 with the AB4-type structure. It is demonstrated that the AB4 phase is formed by a peritectic reaction between Pr5Co19-type (2H) phase and one liquid phase in a range of 1000–1010 °C. The compound exhibits a high discharge capacity of 391.2 mAh g−1 and can reach 286.2 mAh g−1 even at a discharge current of 1800 mA g−1. The superior high rate dischargeability performance could be attributed to its preferable charge transfer rate and hydrogen diffusion speed. Furthermore, the cycling life of the AB4 compound is up to 83.3% after 200 cycles. As a result of these desirable electrochemical properties, the AB4-type La–Mg–Ni-based compound with high power is considered to promote applications for high power battery materials.

High-performance temperature range of the morphotropic phase boundary in the 0.5Ba(Zr0.2Ti0.8)O3-0.5(Ba0.7Ca0.3)TiO3 series of ceramics is broadened by sintering the green ceramic discs formed from the mixture of pre-sintering (Ba0.7Ca0.3)TiO3 gel and Ba(Zr0.2Ti0.8)O3 gel. The morphotropic phase boundary occurring at room temperature with x = 0.5 is found out in the series of the (1-x)Ba(Zr0.2Ti0.8)O3-x(Ba0.7Ca0.3)TiO3 ceramics sintered at 1250 C o by measuring ferroelectric properties and crystal structure. The optimum performance of 0.5Ba(Zr0.2Ti0.8)O3-0.5(Ba0.7Ca0.3)TiO3 is obtained by pre-sintering (Ba0.7Ca0.3)TiO3 gel at 300 C o , and its high-performance temperature range from −77 to 40 C o is about 1.7 times broader than that of the 0.5Ba(Zr0.2Ti0.8)O3-0.5(Ba0.7Ca0.3)TiO3 ceramic sintered directly at 1250 C o . It is found that the contents of phase composition in the optimum sample are 63% for morphotropic phase boundary with x = 0.5, 18% for Rhombohedral phase, and 19% for Tetragonal phase. The pre-sintering approach not only maintains the excellent ferroelectric properties induced by the morphotropic phase boundary, but also broadens the applicable high-performance temperature range of the samples.

In this article, a machine learning method to classify signal with Gaussian noise based on denoising auto encoder (DAE) and convolutional neural network (CNN) is proposed. We combine denoising autoencoder's denoising ability with CNN's feature extraction capability. First, a six-layer neural network is built, including three CNN layers. Then a dataset containing noiseless signal of 11 modulation is generated. In the simulation, we apply this dataset to train neural network and achieve an accuracy of 94%, which is much higher than performance with noisy signal, meaning that noise can greatly influence the accuracy of neural network. Next, we build a denoising autoencoder and train it with signal of 5 dB signal-to-noise ratio (SNR). Compared with neural network without denoising autoencoder, adding a denoising autoencoder can achieve an accuracy of 84% at signal of 18 dB SNR, improved by 58%.

Accurately characterizing meander belts and point bars in fluvial reservoirs is significant for reducing drilling risks and improving the recovery of oil and gas. For this goal, we developed a new seismic inversion method that combines spectral decomposition and genetic inversion for improving vertical and horizontal resolution of seismic interpretation and readjusting tuning effects. This method takes advantage of multiple spectral-decomposition seismic cubes. Seismic traces, integrated traces (90°-phase data), sweetness, and root-mean-square volume attributes were input to consider information from a range of seismic data types. Tests using a sample conceptual model and a real-world dataset demonstrate that the proposed method could achieve satisfactory results, improve the resolution of inversions, and readjust tuning effects. The real dataset is a case study from the Chengdao Oilfield in the Bohai Bay basin in China and the Neogene Guantao formation is the interval of interest. Our study area covers approximately 85 km2, and the dataset used includes mainly 441 wells and a post-stack 3D seismic cube with a dominant frequency of 40 Hz. In the spectral-decomposition genetic inversion results, distributions of inversion values were in relatively good accord with the well logs, and several thin adjacent sandstone bodies that could not be distinguished in the conventional genetic inversion results could be distinguished distinctly. We also proposed a workflow for the characterization of fluvial meander belts and point bars by integrating inversion results and quantitative depositional models. According to the surface attributes and red-green-blue blending maps of multiple stratal slices calculated from the inversion cube, one main meander belt, eight narrow channel fills, and several overbank sandstone bodies were accurately imaged; most of these features were confirmed from well observations. Furthermore, 15 point bars and related abandoned channels were characterized in the main meander belt. The proposed inversion method can improve the resolution of seismic interpretations and the undertaken characterization of meander belts and point bars could provide an example for other fields with a similar dataset.

Biogenic coalbed methane has been attracted much attention due to massive reserves and green performance, but the current research is relatively weak. To further investigate its formation mechanism, the experimental simulation of biogas generation was performed by using lignite, high-volatile bituminous coals and their treated coals as substrates for 90 days. The results show that the lignite has a greater biogas potential than high-volatile bituminous coal in this study. Total gas production in lignite groups (L-BZ-1 and L-LJ-1) is 12.6% more than the bituminous coal groups in the experimental simulation. This is because the lignite has a low degree of thermal evolution and rich in soluble organic matter: chloroform asphalt “A” (CAA), which plays an important role in microbial metabolism to produce biogas. The CAA in coal has a conversion rate of 3.08 mL/g into biogas in this experimental simulation. The saturated hydrocarbons and nonhydrocarbons are more biodegradable components in the CAA. In saturated hydrocarbons, the n-alkanes with shorter chains or an odd carbon number are preferentially degraded. In the experimental simulation, biogas generation results in the consumption of a large number of organic matters with heteroatomic groups such as hydroxyl, carboxyl, pyridine and nitrogen oxides. Aliphatics show a stronger bioactivity than aromatics. The aromatic structure in coal is a few biodegraded in the later stage.

Defining the boundaries, thicknesses and sedimentary facies of fluvial reservoirs (sand bodies) is critical for predicting hydrocarbon volumes, designing schemes for petroleum exploration and development and improving oil recovery. Most reservoirs contain thick and thin sand bodies at the same intervals, while the amplitude values of seismic data usually highlight sand bodies near the 1/4 wavelength for the tuning phenomena. Hence, the application of spectral decomposition to seismic attributes and the combination of multiple frequency-decomposed (spectral-decomposed) seismic attributes have gained increasing attention for the readjustment of tuning thickness to predict sand bodies of various thicknesses. However, the popular method of red-green-blue blending is a simple linear combination of three frequency-decomposed seismic attributes that qualitatively analyzes the sand thickness without well-log interpretation. This research proposes machine learning fusion as a new nonlinear method for fusing high-, middle-, and low-frequency seismic attributes. This method uses machine learning to link well-log interpretation and multiple-frequency seismic attributes for the quantitative prediction of sand thickness, which is important for development work in a mature field. Test results of the conceptual model and the real case indicate that the predicted sand thickness after fusing multiple frequency-decomposed seismic attributes is approximately in line with the actual thickness (correlations between 80 and 90%). Combined with the coherence attribute and the red-green-blue blending results, the distributions and histories of sedimentary facies are analyzed based on the predicted sand thickness and well data. The results suggest that the proposed method can effectively readjust the tuning thickness and improve the resolution of seismic interpretation. This method is a potentially effective technique to characterize the sand thickness and sedimentary facies in other fields using a similar geological setting and dataset.

The point bar is one of the most important reservoirs in a meandering depositional system, and accurately building a 3D architecture model for point bars is crucial to predict hydrocarbon distribution within the reservoir. Unfortunately, we can only obtain a qualitative description about the internal architecture of the point bar due to the limited information or the low resolution of available data (such as reflection seismic data). To build a 3D prototype point bar reservoir model, we analyze the architecture of point bars by integrating high-resolution ground penetrating radar (GPR) data and modern deposition. We found that our GPR data have five main reflection patterns (GPR facies), and GPR facies can be used to relate with architectural elements (the depositional facies and geobodies within depositional facies). The concave-down GPR facies is usually related to the abandoned channel. The continuous, subhorizontal, subparallel GPR facies is commonly related with lateral-accretion sand bodies within the point bar. The multiple stacked small-scale, discontinuous reflections GPR facies is interpreted to be shale drapes within the point bar. We further analyzed the geometry parameters of the identified channels. We found that the nonsymmetric [Formula: see text] of abandoned channel near the channel axis is related to the ratio between the curvature of channel radius [Formula: see text] and channel width [Formula: see text] ([Formula: see text]). Finally, we built two 3D channel reservoir models and our models could provide useful guidance for the architecture analysis of buried meandering fluvial reservoirs.

The prevalence of microplastics in global waters raises the concern about their potential effects on aquatic biota. In aquatic environment, microplastics are almost ubiquitously present in all compartments from surface water to benthic sediment, making them accessible to a wide range of aquatic biota occupying different habitats. Exposure to microplastics may induce detrimental implications to the health of aquatic organisms. This review describes the wide occurrence of microplastics ingestion by aquatic fauna and evaluates the ecotoxicological effects of microplastics as well as the associated chemicals on aquatic biota including phytoplankton and fauna from both freshwater and marine environments. Trophic transfer of microplastics and associated contaminants along the aquatic food chain and potential impacts on human health are also discussed. Finally, this review emphasizes the current knowledge gaps and gives recommendations for the future work.

After flotation cleaning of high-U coal from the Ganhe Mine in China, it was found that levels of V (561 μg/g), Cr (215 μg/g), Mo (159 μg/g), and U (234 μg/g) were still significantly enriched in the clean fraction. However, it was not initially clear whether these elements were associated with organic matter or occurred in fine-grained minerals. In this study, coal samples were analyzed by acid leaching and transmission electron microscopy coupled with energy dispersive spectroscopy (TEM + EDS). Our results showed the following: (1) V and Mo were weakly bonded to organic matter, whereas Cr and U were predominantly associated with organic matter, thereby indicating that Cr and U had a strong organic affinity; (2) the results of high-resolution TEM showed that U occurred in poorly crystalline nanoscale U Ti minerals in flotation-cleaned coals where the mineral was associated with organic matter; and (3) organic-bonded V and Mo were negatively correlated with organic S, but organic-bonded Cr and U were positively related to organic S. Through acid leaching, the contents of V, Cr, Mo, and U in the residue were still much higher than those in world hard coal. Thus, the use of flotation-cleaned Ganhe coals is not recommended to avoid environmental pollution and adverse effects.

This paper mainly studies the threshold dynamics of new multicity HIV (Human Immunodeficiency Virus) epidemic models with switching parameters and pulse control. The model’s parameters are assumed to be time-varying functions and switching functional forms in time due to seasonal changes. And the susceptible population is assumed to become infected via shared injections or sexual contacts with infected individuals and pre-AIDS patients (following infection with HIV but before the full development of acquired immune deficiency syndrome). New threshold conditions are established to ensure the extinction of the disease by using Razumikhin-type approach. Pulse control strategies are then applied to the multicity epidemic model and analyzed to guarantee their success in eradicating the disease. Numerical examples are performed to support the analytical results.

Microplastic pollution in global aquatic environments has aroused increasing concern in recent years. In this study, the occurrence of microplastics in multiple environmental compartments was investigated in Poyang Lake, the largest freshwater lake of China. The abundance of microplastics was respectively 5-34 items/L for surface waters, 54-506 items/kg for sediments, and 0-18 items per individual for wild crucians (Carassius auratus). The distribution of microplastics in Poyang Lake varied heterogeneously in space, with the highest abundance being observed in the middle region of the lake for surface waters and in the northern region for sediments. Anthropogenic and topographic factors were speculated to be the major factors affecting the abundance and distribution of microplastics. The majority of the detected microplastics were found with a size of 0.5 mm, with fibrous and coloured being the predominant characteristics. Polypropylene (PP) and polyethylene (PE) were the major polymer types of the selected plastic particles, indicating that domestic sewage and fishing activities might be the main sources of microplastics in the lake. No significant correlation was observed between microplastic abundance in surface water and sediment samples. Our results demonstrated the wide occurrence of microplastics in water, sediment and biota of the Poyang Lake, which may assist in extending our knowledge regarding microplastics pollution in inland freshwater systems.

Trace elements in coal mine water have pollution potential to environmental water bodies. By process of water-rock interaction in coal seam and coal host rock, trace elements may immigrate from solid matrix to water, including oxidation, dissolution, ion-exchange, adsorption-desorption, etc. Water parameters and occurrence of trace elements in the solid matrix are keys to leaching behavior. In site observation and leaching experiments were designed and implemented so as to model these processes, analyze leaching behavior and mechanisms. The data composes a matrix by two dimensions, samples and trace elements, which were used to analyze trace element occurrence in coal, host rock and leaching behavior in the coal/rock leaching water, by method of multivariate analysis. Analytical results showed major occurrence of trace elements in the coal and host rock and key controlling mechanism of trace elements during water-rock interaction: in coal host rock seam, Se/Cd/Hg/As occurred in sulfide minerals, Be and V occurred in carbonate minerals, Cr and Pb occurred in clay minerals, respectively. Se, As and Hg immigrated through dissolution of sulfide minerals, Cr immigrated through transformation of clay minerals. In coal seam, Se/Cr/Pb occurred in clay minerals, As and Hg occurred in sulfide minerals. Se, Pb and Cr immigrated through transformation of clay minerals, As and Hg immigrated through dissolution of sulfide minerals, respectively.

Rare earth elements and yttrium (REY) in coal, fly ash as well as coal preparation products have attracted much attention due to the increasing demand worldwide. In this paper, the distribution of REY in different size and density fractions, their mode of occurrence, and the insight they provide on the sediment source region of the Reshuihe coal in southern China are studied. The results show that the physical separation method caused light-REY (LREY) to be enriched in the low-density and large-sized coal particles, whereas medium-REY (MREY) and heavy-REY (HREY) are enriched in small-sized and high-density fractions. In addition, LREY are more closely associated with organic matter, while MREY and HREY occur more often in aluminosilicate minerals. Moreover, the sediment source of the Reshuihe coal was mainly derived from the felsic–intermediate rocks at the top of the Emeishan basalt sequence. Further, REY in the feed coal are generally not enriched, though REY are enriched in density fractions of 1.4 kg/L. The combustion products of coal with a density of 1.4 kg/L obtained by gravity separation have a potential economic value as promising raw materials for REY.