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The fracture (pore) development of coal specimen under axial stress loading and hydraulic permeability affects directly the mechanical indexes and the permeability characteristics. In this paper, using theoretical analysis and mechanical test methods, the relationships between permeability and fractal dimension, ringing count response under coal specimen fracture (pore) were analyzed, and the acoustic emission fractal-permeability model of coal specimens under hydraulic-loading coupling were established. The permeability experiments of coal specimens with hydraulic-loading coupling in different confining pressures (2−5 MPa，Δpw/p'c=0.75) were carried out, and the mechanical behavior, permeability characteristics and acoustic emission rules of coal specimens in different confining pressures (water pressure) were analyzed, and the relationship between the failure morphology of coal samples under different confining pressures and the acoustic emission positioning was discussed. The strength enhancement characteristics and the relationships between failure pattern and acoustic emission location of coal specimens under confining pressures were discussed. The rationality of the fractal-permeability model of coal specimens under hydraulic-loading coupling was verified. The results showed that the fracture (pore) change of coal specimen was consistent with acoustic emission ringing, the fracture (pore) development was closely related to the fractal dimension and the permeability, and the acoustic emission fractal and permeability could be characterized by the volume strain in the permeability experiments. The acoustic emission fractal-permeability model of coal specimen under hydraulic-loading coupling could be analyzed by a two-stage mathematical model. The permeability of the coal specimen showed the process of short-term decrease, long-term slow increase, rapid increase and slight decline, and the ringing count showed the wave-like development of rapid increase - decrease - increase - decrease during the total stress-strain process of coal specimen, the minimum permeability lagged behind the critical point of volume strain compression and expansion, and the minimum permeability varied from 0.124×10−17 m2 to 1.250×10−17 m2. With the increase of confining pressure (2−5 MPa), the peak deviating stress, peak axial strain and peak volume strain of coal specimen all showed an increasing trend, the linear characteristics were significant, the lag of peak permeability behind peak strength showed a decreasing trend, the reduction reached 93.34%, and the loudest ringing count (corresponding to the peak strength) showed an increasing trend. The effective cohesion and the effective internal friction angle of the coal specimen increased to 6.511 6 MPa and 36.56º under hydraulic coupling, respectively. With confining pressure (2−5 MPa) increase, the acoustic emission information of coal specimen changed from single inclined plane to irregular inclined plane, the angle of the main fracture plane from small angle to high angle, and the instability mode from single block shear to multi-block compression, and the specimen fracture could be expressed by acoustic emission positioning ringing count. Three experiment curves of acoustic emission fractal-volume strain, permeability and volume strain, and fractal and permeability of the coal specimens were consistent with the theoretical curves, the correlation during compression stage with confining pressure 2−5 MPa was 0.882−0.999, 0.950−0.998 and 0.849−0.997, respectively. The correlation during the expansion stage was 0.937−0.996, 0.891−0.998 and 0.873−0.966, respectively.

IntroductionWhen a distributed photovoltaic (PV) system has access to a large urban distribution network, the active balance is primarily borne by the main network gas unit; when the scale of the distributed PV system is very large, the main network can only provide limited regulation capacity, and the distribution network must determine the active optimal scheduling strategy.MethodsThis work proposes an active optimization scheduling model for the distribution network by considering the regulation capacity of the main network. In terms of the optimisation objectives, the maximum consumption of the distributed PVs and minimum power fluctuation at the demarcation point of the main distribution network are proposed as the main objectives, while the minimum total exchanged power in a cycle at the main distribution demarcation point and minimum distribution network loss are considered as the secondary objectives. In terms of constraints, it is proposed that the main network’s regulation capacity be characterized by the main network’s gas-fired unit creep constraints. A fast solution method for active optimization of the distribution network is designed herein to formulate the priority control order of the adjustable units according to the dispatch economic performances of various types of adjustable resources in the distribution network; this reduces the number of variables involved in the optimization at each step and improves the optimized solution speed.ResultsFinally, Simulation verification by IEEE 33-node distribution network arithmetic example based on Matlab simulation platform.DiscussionSimulation results show the effectiveness of the method in achieving maximum PV consumption and reflecting the limited regulation capacity of the main grid.

Abstract Background Place of residence plays an influential role in shaping individual development, and studies have established links between Childhood migration experience (CME) and health outcomes through maturity. Over the past three decades, China has undergone one of the largest rural-to-urban migrations, however, little is known about the effect of CME on rural migrants’ adult health in China. Methods Data from 7035 members of the 2016 and 2018 China Labor-force Dynamics Survey were analyzed. CME was measured by whether the place of residence and place of birth changed at the age of 14 years. Three measures of health (self-assessed health, BMI, and mental health scale) were obtained. Causal inferential analysis was performed, using the Probit model, the OLS model and the Propensity Score Matching (PSM) method, to explore the impact of CME on the adult health of rural migrants. Results Overall, compared to individuals who did not migrate in childhood, the probability of reporting “very unhealthy”, “rather unhealthy”, and “fair” in the self-assessed health of the rural migrants with CME decreased by 0.23%, 1.55%, and 5.53%, the probability of reporting “healthy” and “very healthy” increased by 1.94% and 5.38%, the probability of BMI within the normal range was higher by 7.32%, and the mental health test scores were 0.2591 points higher significantly. Furthermore, in comparison with childhood non-migration, both cross-county and cross-city migration promoted the health status of rural migrants, but the positive effect of cross-province migration was not significant; from the gender perspective, CME could more dramatically improve rural women’s adult health than men, especially in mental health. Conclusion CME can significantly improve adult health, including physical and mental health, and the positive effect is more obvious among women, helping to reduce gender differences in health. For the migration distance, attention can be focused on the long-distance migrating individuals, who should get more support.

Abstract Aims This systematic review and meta‐analysis aimed to investigate the association between serum uric acid (SUA) levels and the incidence rate and prognosis of heart failure (HF), as well as the impact of uric acid‐lowering treatment on HF patients. Methods and results PubMed and Embase were searched for original articles reporting on the association between SUA and HF incidence, adverse outcomes, and the effect of uric acid‐lowering treatment in HF patients. Data were pooled using random effects or fixed effects models. Univariable meta‐regression analysis assessed the influence of study characteristics on research outcomes. Statistical analyses were conducted using RevMan software and STATA software version 15.0. Eleven studies on HF incidence and 24 studies on adverse outcomes in HF patients were included. Higher SUA levels were associated with an increased risk of HF (RR: 1.81, 95% CI: 1.53–2.16), all‐cause mortality (RR: 1.44, 95% CI: 1.25–1.66), cardiac death (RR: 1.56, 95% CI: 1.32–1.84), and HF rehospitalization (RR: 2.07, 95% CI: 1.37–3.13) in HF patients. Uric acid‐lowering treatment was found to increase all‐cause mortality in HF patients (RR: 1.15, 95% CI: 1.05–1.25). Conclusions Uric acid is an independent predictor of heart failure occurrence and adverse prognosis. Targeting uric acid lowering as a therapeutic intervention does not improve the prognosis of patients with heart failure. It may not be advisable to use traditional urate‐lowering drugs in young patients with heart failure, and elderly patients should exercise caution when using them.

Large ecological restoration projects have been widely implemented across the world since the 20th century, yielding complex ecological, economic, and social results. Today, balancing ecological restoration with local people’s livelihoods is a key issue. Based on the existing literature, this study proposes a “shock adaptation” mechanism to describe the response of rural residents’ livelihoods to the impact of ecological restoration projects. We hypothesize that adaptability varies across the modes of production. To verify our hypothesis, we used the machine-learning-based local projection (LP) method to analyze China’s Three-North Shelter Forest Program (TNSFP), with data for 596 counties from 2001 to 2020. After the TNSFP started, rural residents’ income dropped, rose, and then exceeded the starting point over 8 years. Moreover, significant heterogeneity exists between agricultural and pastoral areas. Agricultural areas recover faster and improve livelihoods, while pastoral areas take longer to bounce back. The results confirmed the “shock adaptation” mechanism and suggested the importance of the mode of production. Policymakers should add more social–ecological indicators to their evaluation systems, allow local communities more self-management, and offer extra help to those struggling to recover from shocks.

Accurate forecasting of photovoltaic (PV) power is essential for grid scheduling and energy management. In recent years, deep learning technology has made significant progress in time-series forecasting, offering new solutions for PV power forecasting. This study provides a systematic review of deep learning models for PV power forecasting, concentrating on comparisons of the features, advantages, and limitations of different model architectures. First, we analyze the commonly used datasets for PV power forecasting. Additionally, we provide an overview of mainstream deep learning model architectures, including multilayer perceptron (MLP), recurrent neural networks (RNN), convolutional neural networks (CNN), and graph neural networks (GNN), and explain their fundamental principles and technical features. Moreover, we systematically organize the research progress of deep learning models based on different architectures for PV power forecasting. This study indicates that different deep learning model architectures have their own advantages in PV power forecasting. MLP models have strong nonlinear fitting capabilities, RNN models can capture long-term dependencies, CNN models can automatically extract local features, and GNN models have unique advantages for modeling spatiotemporal characteristics. This manuscript provides a comprehensive research survey for PV power forecasting using deep learning models, helping researchers and practitioners to gain a deeper understanding of the current applications, challenges, and opportunities of deep learning technology in this area.

Abstract Background Predicting the risk of malaria in countries certified malaria-free is crucial for the prevention of re-introduction. This review aimed to identify and describe existing prediction models for malaria re-introduction risk in eliminated settings. Methods A systematic literature search following the PRISMA guidelines was carried out. Studies that developed or validated a malaria risk prediction model in eliminated settings were included. At least two authors independently extracted data using a pre-defined checklist developed by experts in the field. The risk of bias was assessed using both the prediction model risk of bias assessment tool (PROBAST) and the adapted Newcastle–Ottawa Scale (aNOS). Results A total 10,075 references were screened and 10 articles describing 11 malaria re-introduction risk prediction models in 6 countries certified malaria free. Three-fifths of the included prediction models were developed for the European region. Identified parameters predicting malaria re-introduction risk included environmental and meteorological, vectorial, population migration, and surveillance and response related factors. Substantial heterogeneity in predictors was observed among the models. All studies were rated at a high risk of bias by PROBAST, mostly because of a lack of internal and external validation of the models. Some studies were rated at a low risk of bias by the aNOS scale. Conclusions Malaria re-introduction risk remains substantial in many countries that have eliminated malaria. Multiple factors were identified which could predict malaria risk in eliminated settings. Although the population movement is well acknowledged as a risk factor associated with the malaria re-introduction risk in eliminated settings, it is not frequently incorporated in the risk prediction models. This review indicated that the proposed models were generally poorly validated. Therefore, future emphasis should be first placed on the validation of existing models.

Abstract Background Epicardial adipose tissue (EAT) accumulation is associated with multiple cardiometabolic risk factors and prognosis of heart failure with preserved ejection fraction (HFpEF). The correlation between EAT density and cardiometabolic risk and the effect of EAT density on clinical outcome in HFpEF remain unclear. We evaluated the relationship between EAT density and cardiometabolic risk factors, also the prognostic value of EAT density in patients with HFpEF. Methods We included 154 HFpEF patients who underwent noncontrast cardiac computed tomography (CT) and all patients received follow-up. EAT density and volume were quantified semi-automatically. The associations of EAT density and volume with cardiometabolic risk factors, metabolic syndrome and the prognostic impact of EAT density were analyzed. Results Lower EAT density was associated with adverse changes in cardiometabolic risk factors. Each 1 HU increase in fat density, BMI was 0.14 kg/m2 lower (95% CI 0.08–0.21), waist circumference was 0.34 cm lower (95% CI 0.12–0.55), non-HDL-cholesterol was 0.02 mmol/L lower (95% CI 0–0.04), triglyceride was 0.03 mmol/L lower (95% CI 0.01–0.04), fasting plasma glucose was 0.05 mmol/L lower (95% CI 0.02–0.08), TyG index was 0.03 lower (95% CI 0.02–0.04), Log2(TG/HDL-C) was 0.03 lower (95% CI 0.02–0.05), METS-IR was 0.36 lower (95% CI 0.23–0.49), MetS Z-score was 0.04 lower (95% CI 0.02–0.06), and Log2(CACS + 1) was 0.09 lower (95% CI 0.02–0.15). After adjusting for BMI and EAT volume, the associations of non-HDL-cholesterol, triglyceride, fasting plasma glucose, insulin resistance indexes, MetS Z-score, and CACS with fat density remained significant. The area under the curve (AUC) for the presence and severity of metabolic syndrome was greater in EAT density than volume (AUC: 0.731 vs 0.694, 0.735 vs 0.662, respectively). Over a median follow-up of 16 months, the cumulative incidence of heart failure readmission and composite endpoint increased with lower level of EAT density (both p

Gallic acid (GA) is a type of polyphenolic compound that can be found in a range of fruits, vegetables, and tea. Although it has been confirmed it improves non-alcoholic fatty liver disease (NAFLD), it is still unknown whether GA can improve the occurrence of NAFLD by increasing the low-density lipoprotein receptor (LDLR) accumulation and alleviating cholesterol metabolism disorders. Therefore, the present study explored the effect of GA on LDLR and its mechanism of action. The findings indicated that the increase in LDLR accumulation in HepG2 cells induced by GA was associated with the stimulation of the epidermal growth factor receptor–extracellular regulated protein kinase (EGFR-ERK1/2) signaling pathway. When the pathway was inhibited by EGFR mab cetuximab, it was observed that the activation of the EGFR-ERK1/2 signaling pathway induced by GA was also blocked. At the same time, the accumulation of LDLR protein and the uptake of LDL were also suppressed. Additionally, GA can also promote the accumulation of forkhead box O3 (FOXO3) and suppress the accumulation of hepatocyte nuclear factor-1α (HNF1α), leading to the inhibition of proprotein convertase subtilisin/kexin 9 (PCSK9) mRNA expression and protein accumulation. This ultimately results in increased LDLR protein accumulation and enhanced uptake of LDL in cells. In summary, the present study revealed the potential mechanism of GA’s role in ameliorating NAFLD, with a view of providing a theoretical basis for the dietary supplementation of GA.

Abstract Background In China diabetes care is gradually shifting from secondary to primary care with great infrastructure investment and GP training. However, most GPs in China lack communication skills training, which is a huge obstacle in communication with their patients in primary care. In this study we seek to identify training priorities that is evidence-based, appropriate for the context of primary care in China, and that meet the real needs of both GPs and people with diabetes. Methods A mixed method approach was used. A conceptual framework was designed based on the MRC framework, action research and adult learning theories. Through a systematic review of the literature and qualitative research with GPs and patients with diabetes, a list of communication skills training components was developed by the research team. A modified nominal group technique (NGT) with GPs was used to evaluate these contents. Purposive sampling was used to recruit a variation of participants (age, work area, practice years and education background) from general practices in Guangzhou city, China. Eight structured nominal groups were facilitated to elicit the views of group members, and participants rated the 9-point Likert scale of importance and feasibility of the training items independently, before and after focus groups. The ranking of each item was calculated, based on the mean Likert score ratings from all participants. Video recordings of four NGT group discussions were thematically analysed using the Framework Method to explore reasons for any differences in rating items. Results 29 males and 29 female GPs from 28 general practices participated in NGT group discussions, with a mean age of 38.5 years and mean 12.3 years of practice experience. Based on the mean scores of importance and feasibility rating scores, the top 3 ranked priorities for communication training were ‘health education’ (importance 8.39, feasibility 7.67), ‘discussing and explaining blood glucose monitoring’ (8.31, 7.46), and ‘diabetes complications and cardiovascular disease risk communication’ (8.36, 7.12). Five main themes were identified from focus group discussions through qualitative analysis: ‘impact on diabetes patients’, ‘GP attitudes towards communication skills’, ‘patient-related factors influencing the application of communication skills by GPs, ‘local contextual factors’, and ‘training implementation’. Conclusions Priorities for communication skills training for Chinese GPs in diabetes care were identified. These are set in the context of GPs’ current experience of communication with patients in China who have diabetes, which is often unsatisfactory. This study describes the baseline from which better primary care for diabetes in China needs to be developed. Based on suggestions from GPs themselves, it identifies an agenda for improvement in communication as a key component of diabetes care in China.

Abstract Background The relationship between ambulatory arterial stiffness index (AASI) and left ventricular diastolic dysfunction (LVDD) in patients with heart failure with preserved ejection fraction (HFpEF) is unknown. We aimed to investigate the association between the AASI and LVDD in HFpEF. Methods We prospective enrolled consecutive patients with HFpEF in Chongqing, China. Twenty-four-hour ambulatory blood pressure monitoring (24 h-ABPM) and echocardiography were performed in each patient. AASI was obtained through individual 24 h-ABPM. The relationship between AASI and LVDD was analyzed. Results A total of 107 patients with HFpEF were included. The mean age was 68.45 ± 14.02 years and 63 (59%) were women. The patients were divided into two groups according to the upper normal border of AASI (0.55). AASI > 0.55 group were more likely to be older, to have higher mean systolic blood pressure and worsen left ventricular diastolic function than AASI group ≤ 0.55. AASI was closely positive related to the diastolic function parameters, including mean E/e′ (r = 0.307, P = 0.001), septal E/e′ (r = 0.290, P = 0.002), lateral E/e′ (r = 0.276, P = 0.004) and E (r = 0.274, P = 0.004). After adjusting for conventional risk factors, AASI was still an independent risk factors of mean E/e′ > 10 in patients with HFpEF (OR: 2.929, 95%CI: 1.214–7.064, P = 0.017), and the association between AASI and mean E/e′ > 14 was reduced (OR: 2.457, 95%CI: 1.030–5.860, P = 0.043). AASI had a partial predictive value for mean E/e′ > 10 (AUC = 0.691, P = 0.002), while the predictive value for mean E/e′ > 14 was attenuated (AUC = 0.624, P = 0.034). Conclusion AASI was positive related to E/e′ in HFpEF and might be an independent risk factor for the increase of mean E/e′.

Abstract Background The rapid emergence and global spread of COVID-19 have caused substantial global disruptions that have impacted malaria programs worldwide. Innovative strategies to enable countries aiming to eliminate malaria as well as those that are already certified as malaria-free, are needed to address malaria importation in the context of the COVID-19 pandemic. China was certified as malaria-free in 2021 and now aims to prevent the malaria re-establishment. Nonpharmaceutical interventions such as entry screening, quarantining, and health education for individuals returning from international travel during the COVID-19 pandemic present both opportunities and challenges to the management of imported malaria. This study aimed to describe and analyze the operational challenges associated with an integrated surveillance and case management program in which malaria re-establishment prevention measures were incorporated into the COVID-19 program in China. Methods After the integration of malaria re-establishment prevention activities into the COVID-19 program for 10 months in Jiangsu Province, China, a focus-group discussion of public health workers working on preventing malaria re-establishment and controlling COVID-19 was held in June 2021, aiming to explore the operational challenges and lessons learned from the integrated approach. Results From 01 August 2020 to 31 May 2021, 8,947 overseas travelers with Yangzhou as the final destination underwent 14-day managed quarantine and 14-day home isolation. Of these travelers, 5,562 were from malaria-endemic regions. A total of 26,026 education booklets and materials were distributed to expand malaria-related knowledge. Twenty-two patients with unknown fever were screened for malaria with rapid diagnostic tests, and one patient was confirmed to have imported malaria. The challenges associated with the implementation of the integrated malaria surveillance and case management program include neglect of malaria due to COVID-19, lack of a standard operating procedure for malaria screening, mobility of public health providers, and difficulties in respecting the timeline of the “1–3-7” surveillance strategy. Conclusions China’s experience highlights the feasibility of integrated case surveillance and management of existing infectious diseases and new emerging infections. It also demonstrates the importance of a sound public health infrastructure with adequate, trained field staff for screening, testing, contact tracing, and providing health education, all of which are crucial for the success of both malaria re-establishment prevention program and the effective control of COVID-19.

Background: Myocardial infarction, a coronary heart disease, is a serious hazard to human health. Cardiomyocyte oxidative stress and apoptosis have been considered as the main causes of myocardial infarction. Here, we aimed to investigate the role of miR-615-3p in oxidative stress and apoptosis of human cardiomyocytes. Methods: Reverse transcription-quantitative polymerase chain reaction was performed to determine miR-615-3p or MEF2A expression in human cardiomyocytes. Apoptosis and viability of human cardiomyocytes were assessed by flow cytometry analysis and CCK-8 assay. In addition, the contents of malondialdehyde, reactive oxygen species, and superoxide dismutase were detected by corresponding commercial kits. The binding of miR-615-3p and MEF2A in human cardiomyocytes was examined by luciferase reporter assay. Results: Hypoxia/reoxygenation treatment downregulated the expression level of miR‐615-3p in human cardiomyocytes. Overexpressing miR-615-3p increased human cardiomyocyte viability and decreased human cardiomyocyte apoptosis. Moreover, miR-615-3p mimics suppressed oxidative stress in hypoxia/reoxygenation-stimulated human cardiomyocytes. MEF2A was confirmed as a target gene of miR-615-3p and was highly expressed in hypoxia/reoxygenation-stimulated human cardiomyocytes, and its upregulation partially reversed the influence of miR-615-3p mimics on oxidative stress and apop-tosis of human cardiomyocytes. Moreover, miR-615-3p inactivated the P13K/Akt pathway by inhibiting MEF2A. Conclusions: Overexpression of miR-615-3p protects human cardiomyocytes from oxida-tive stress injury by targeting MEF2A via the PI3K/Akt signaling.

Background Studies on the associations between maternal complications during pregnancy and childhood asthma are exclusively conducted in Western countries. The findings are mixed and may not be translated to other populations. We aimed to investigate the associations among the Chinese population and to determine whether the associations were mediated through pre-term birth, caesarean delivery, low birthweight and not breastfeeding in the first 6 months. Methods We conducted a retrospective cohort study of 166 772 children in Guangzhou, China. Information on maternal gestational hypertension, gestational diabetes and gestational anaemia during pregnancy was extracted from medical records. Ever-diagnosis of asthma in children aged 6–12 years was obtained by questionnaire. Logistic regression models and mediation analyses were used to estimate the adjusted odds ratios (aORs) and 95% confidence intervals for childhood asthma. Results Gestational hypertension, gestational diabetes and gestational anaemia during pregnancy were associated with an increased risk of ever-diagnosed childhood asthma: aOR 1.48 (95% CI 1.37–1.60), 1.71 (95% CI 1.65–1.78) and 1.34 (95% CI 1.26–1.45), respectively. A stronger association was observed for two or three gestational complications (aOR 2.02 (95% CI 1.93–2.16)) than one gestational complication (aOR 1.64 (95% CI 1.52–1.77)). The aOR for the three gestational complications was 1.35 (95% CI 1.26–1.45), 1.63 (95% CI 1.58–1.70) and 1.32 (95% CI 1.24–1.43), respectively, after controlling for the mediators, including pre-term birth, caesarean delivery, low birthweight and not breastfeeding in the first 6 months. Conclusions Gestational hypertension, gestational diabetes and gestational anaemia were associated with childhood asthma, and the associations were partially explained by the mediation effects.

Background: Electronic stethoscopes are widely used for cardiopulmonary auscultation; their audio recordings are used for the intelligent recognition of cardiopulmonary sounds. However, they generate noise similar to a crackle during use, significantly interfering with clinical diagnosis. This paper will discuss the causes, characteristics, and occurrence rules of the fake crackle and establish a reference for improving the reliability of the electronic stethoscope in lung auscultation.Methods: A total of 56 participants with healthy lungs (no underlying pulmonary disease, no recent respiratory symptoms, and no adventitious lung sound, as confirmed by an acoustic stethoscope) were enrolled in this study. A 30-s audio recording was recorded from each of the nine locations of the larynx and lungs of each participant with a 3M Littmann 3200 electronic stethoscope, and the audio was output in diaphragm mode and auscultated by the clinician. The doctor identified the fake crackles and analyzed their frequency spectrum. High-pass and low-pass filters were used to detect the frequency distribution of the fake crackles. Finally, the fake crackle was artificially regenerated to explore its causes.Results: A total of 500 audio recordings were included in the study, with 61 fake crackle audio recordings. Fake crackles were found predominantly in the lower lung. There were significant differences between lower lung and larynx (p < 0.001), lower lung and upper lung (p = 0.005), lower lung and middle lung (p = 0.005), and lower lung and infrascapular region (p = 0.027). Furthermore, more than 90% of fake crackles appeared in the inspiratory phase, similar to fine crackles, significantly interfering with clinical diagnosis. The spectral analysis revealed that the frequency range of fake crackles was approximately 250–1950 Hz. The fake crackle was generated when the diaphragm of the electronic stethoscope left the skin slightly but not completely.Conclusion: Fake crackles are most likely to be heard when using an electronic stethoscope to auscultate bilateral lower lungs, and the frequency of a fake crackle is close to that of a crackle, likely affecting the clinician’s diagnosis.

Prior to switching operations, the dispatch automation system is required to be able to search for closed-loop paths quickly in real time. In order to improve the efficiency of closed-loop path search, this paper proposes a multi-stage real-time fast search method for closed-loop paths based on the characteristics of power grid hierarchical partitioning. First, we divide the closed-loop judgement process into three phases: initialization, acceptance of the closed-loop judgement command, and post-operation. Then, we define three types of nodes, including root nodes, same-layer contact nodes, and common nodes, and construct path data models for each type of node in accordance with the node liaison relationship in grid hierarchical zoning. In the initialization phase, we design a method for the automatic generation of real-time nodal path data models and study a hierarchical partitioned closed-loop path search method based on a nodal path data model for the phase of accepting closed-loop judgement commands. Next, we design a fast local correction method for nodal path models after the switch closure and disconnection operations in the post-operation idle phase are performed. Finally, a real power grid model is used as an example to test the above closed-loop path search method, which improves the search efficiency by eight times compared with the conventional breadth-first search method. The method is applicable to real large and complex power grids.

Abstract Objective To investigate the relationship between ST-segment resolution (STR) and myocardial scar thickness after percutaneous coronary intervention (PCI) in patients with ST-segment elevation myocardial infarction (STEMI). Methods Forty-two STEMI patients with single-branch coronary artery stenosis or occlusion were enrolled. ST-segment elevations were measured at emergency admission and at 24 h after PCI. Late gadolinium-enhanced cardiac magnetic resonance imaging (CMR-LGE) was performed 7 days after PCI to evaluate myocardial scars. Statistical analyses were performed to assess the utility of STR to predict the development of transmural (> 75%) or non-transmural (

Deep learning has been extensively applied in hyperspectral image (HSI) classification for its excellent representation ability. However, the existing training scheme generally adds a supervised classifier to the last layer of the network, so it is difficult to acquire full-scale fine-grained details and coarse-grained semantic information. Moreover, the robust performance of deep learning is commonly supported by numerous samples, so the effective discriminant features cannot be well learned with small class-imbalanced samples. To solve the above problems, a deeply-supervised pseudo learning framework (DSPL) is proposed, in which a deep supervision global learning network with pair-weighted loss is designed to achieve a stronger prediction on small class-imbalanced datasets, while this architecture of deep supervision can facilitate model generalization. To increase the diversity of samples, a semi-supervised learning method with confidence pseudo labels is proposed, capable of screening for more valid unlabeled samples and synthesizing some new mixed samples. To be more specific, the cost loss function consists of the supervised team (i.e., the labeled loss) and semi-supervised consistency regularization team (i.e., the unlabeled loss and the mixed loss), which can significantly enhance the generalization of the network by all useful samples. As revealed by the experimental results, the DSPL is better than other advanced methods on the Indian Pines (highest OA of 99.54% with 5% samples), the Pavia University (highest OA of 99.79% with 0.5% samples), as well as the Houston University 2013 (highest OA of 99.32% with 5% samples).

With the increasing penetration rate of electric vehicles, the spatiotemporal coupling relationship between the power distribution network and traffic network is stronger than ever before. Under the dynamic wireless charging mode, traffic jam charging is introduced and the dynamic loading process of traffic flow is described using a cellular transmission model. The charging load is related to traffic flow and serves as a bond between the power distribution network and traffic network. The traffic flow achieves balanced allocation under dynamic user equilibrium conditions, and cooperatively optimizes the power flow of the power distribution network in conjunction with charging loads. Numerical analysis shows that this model can accurately depict the congestion situation during peak travel periods, and alleviate traffic congestion and distribution network voltage out of range.

ObjectiveData regarding the influence of arterial stiffness on myocardial work (MW) has been scarce. This study was performed to investigate the association between brachial-ankle pulse wave velocity (baPWV) and MW by non-invasive left ventricular pressure–strain in a population of non-hypertensive and hypertensive individuals.MethodsTwo hundred and eight participants (104 hypertensive and 104 non-hypertensive individuals) were prospectively enrolled into the study. All participants underwent conventional echocardiography, as well as 2D speckle-tracking echocardiography to assess MW by non-invasive left ventricular pressure–strain and global longitudinal strain (GLS). baPWV measurements were made at the same day as the echocardiography. Then, participants were categorized according to baPWV tertiles. Correlation between baPWV and MW were analyzed. Predicting ability of baPWV for abnormal WM was analyzed using receiver operating characteristic (ROC) curve.ResultsThe median baPWV from the low to high tertile groups were 1286.5 (1197.5–1343.5), 1490.0 (1444.5–1544.0), and 1803.8(1708.3–1972.0) cm/s, respectively. In simple linear regression analysis, baPWV had a significant positive association with global work index (GWI), global constructed work (GCW), and global wasted work (GWW), and a negative association with global work efficiency (GWE). The association remained significant after adjusting for major confounding factors in multiple linear regression analysis. The areas under the ROC curve of baPWV for predicting abnormal GWI, GCW, GWW, and GWE were 0.653, 0.666, 0.725, and 0.688, respectively (all p < 0.05).ConclusionsBaPWV is significantly associated with all four components of MW using non-invasive left ventricular pressure-strain method in a mixed population of non-hypertensive and hypertensive individuals.

Quinone organic materials are promising electrodes for the next lithium-ion batteries (LIBs) owing to their versatile molecular designs, high theoretical capacity, flexibility, sustainability, and environmental friendliness. However, quinone organic electrode materials can easily dissolve in organic electrolytes during the cycling process, which leads to the decay of capacity and poor cycling stability. Here, two metal-organic frames (MOFs), one-dimensional (1D) linear structural anthraquinone-2,3-dicarboxylate zinc coordination polymer (ZnAQDC) and two-dimensional (2D) structural anthraquinone-2,3-dicarboxylate manganese coordination polymer (MnAQDC), are synthesized by using anthraquinone 2,3-dicarboxylic acid, zinc acetate, and manganese acetate in a simple hydrothermal reaction. The formed 1D and 2D structures facilitate the insertion and extraction of lithium ions in and from carbonyl groups of anthraquinone. When MnAQDC is used as cathodes for LIBs, MnAQDC electrodes show an initial discharge capacity of ~63 mAh g−1 at 50 mA g−1. After 200 cycles, the MnAQDC electrode still maintains the specific capacity of ~45 mA h g−1, which exhibits good cycle stability. the ZnAQDC electrode displays a initial discharge capacity of ~85 mA h g−1 at 50 mA g−1, and retains the specific capacity of ~40 mA h g−1 after 200 cycles, showing moderate cyclic performance. The lithium-inserted mechanism shows that lithium ions are inserted and extracted in and from the carbonyl groups, and the valences of the Zn and Mn ions in the two MOFs do not change, and coordination metals do not contribute capacities for the two MOFs electrodes. The strategy of designing and synthesizing MOFs with 1D and 2D structures provides guidance for suppressing the dissolution and improving the electrochemical performance of quinone electrode materials.

With the increase in wind power penetration, wind power ramping events have increasingly influenced tie line power control in the power grid. Large power changes during ramping events make it difficult to accurately track the scheduling plans of tie lines and can even lead to overrun. Determining how to evaluate the control performance of automatic generation control (AGC) for wind power ramping has become an urgent problem. In this context, this paper studies the control performance of AGC for wind power ramping based on deep reinforcement learning. First, a tie line power control model of a power system with an AGC module is established. Then, measured data, which include thermal power, wind power, hydropower output and tie line power data, and a deep reinforcement learning method are combined for AGC parameter estimation based on the deep Q-network (DQN) algorithm. Next, the AGC parameter in different scenarios are fit by using measured phasor measurement unit (PMU) data, and on the basis of the fitted model, AGC performance evaluation is performed for wind power ramping events. Finally, the simulation results verify the feasibility and effectiveness of analysing the relationship between wind power ramping and AGC performance based on the AGC parameter fitting model.

Atmospheric conditions vary significantly in terms of the temporal and spatial scales. Therefore, it is critical to obtain atmospheric parameters synchronized with an image for atmospheric correction based on radiative transfer calculation methods. On 3 July 2020, the high resolution and multimode imaging satellite, Gao Fen Duo Mo (GFDM), which was the first civilian high-resolution remote sensing satellite equipped with the Synchronization Monitoring Atmospheric Corrector (SMAC), was launched. The SMAC is a multispectral and polarization detection device that is used to retrieve atmospheric parameters that are time-synchronized with the image sensor of GFDM in the same field-of-view. On the basis of the atmospheric parameters obtained from the SMAC, a synchronization atmospheric correction (Syn-AC) method is proposed to remove the influence of the atmosphere and the adjacency effects to retrieve the surface reflectance. The Syn-AC method was applied in the experiments of synchronous atmospheric correction for GFDM images, where the surface reflectance retrieved via the Syn-AC method was compared with the field-measured values. In addition, the classical correction method, the FLAASH, was applied in the experiments to compare its performance with that of the Syn-AC method. The results indicated that the image possessed better clarity and contrast with the blurring effect removed, and the multispectral reflectance was in agreement with the field-measured spectral reflectance. The deviations between the reflectance retrievals of Syn-AC and the field-measured values of the selected targets were within 0.0625, representing a higher precision than that of the FLAASH method (the max deviation was 0.2063). For the three sites, the mean relative error of Syn-AC was 19.3%, and the mean relative error of FLAASH was 76.6%. Atmospheric correction based on synchronous atmospheric parameters can improve the quantitative accuracy of remote sensing images, and it is meaningful for remote sensing applications.

Background: Cardiac fibrosis is the most important pathogenesis leading to cardiac remodeling and heart failure after myocardial infarction (MI). Tissue nonspecific alkaline phosphatase (TNAP) has recently been recognized as a potential prognostic factor for MI. Nevertheless, the role of TNAP in cardiac fibrosis after MI has not been explicitly delineated. Methods: A systematic review and meta-analysis was conducted to assess the effect of serum TNAP levels on mortality in patients with ischemic heart disease (IHD). A correlation analysis was performed to investigate the relationship between serum levels of TNAP and biomarkers of fibrosis. Heart biopsies from patients with MI and a mouse model of MI were used to detect the expression and distribution of TNAP. Furthermore, we established adenovirus-mediated knockdown and overexpression of TNAP, using a combination of in vivo and in vitro studies in mice, to determine the role and mechanism of TNAP in cardiac fibrosis after MI. In the in vitro studies, cardiac fibroblasts were cultured on soft plates. Findings: After searching the main databases and performing a detailed assessment of the full-text articles, eight studies with 14,816 individuals were included in the quantitative analysis. We found that a high serum TNAP level was associated with an increased risk of mortality in patients with IHD and MI. The correlation analysis revealed a positive correlation between serum TNAP levels and the concentration of fibrosis biomarkers (PICP/PIIINP). The expression of TNAP was upregulated in the myocardium of patients with MI and in a mouse model of MI, accompanied by fibroblast activation and the deposition of collagen fibers. In the in vivo study, TNAP knockdown ameliorated cardiac fibrosis and improved cardiac function in mice. TNAP overexpression aggravated cardiac fibrosis and worsened cardiac function. In the in vitro study, TNAP promoted cardiac fibroblast differentiation, migration and proliferation. Mechanistically, the pro-fibrotic effect of TNAP on cardiac fibroblasts was at least partially achieved by activating the TGF-β1/Smads and ERK1/2 signaling pathways. Interpretation: Based on these findings, TNAP plays an important pro-fibrotic role in cardiac fibrosis after MI by activating TGF-β/Smads and ERK1/2 signaling, indicating that it functions as a potential regulator of and therapeutic target in cardiac fibrosis. Funding: This work was supported by the National Natural Science Foundation of China.

Caffeine is chemically stable and not readily oxidized under normal physiological conditions but also has antioxidant effects, although the underlying molecular mechanism is not well understood. Superoxide dismutase (SOD) 2 is a manganese-containing enzyme located in mitochondria that protects cells against oxidative stress by scavenging reactive oxygen species (ROS). SOD2 activity is inhibited through acetylation under conditions of stress such as exposure to ultraviolet (UV) radiation. Sirtuin 3 (SIRT3) is the major mitochondrial nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase, which deacetylates two critical lysine residues (lysine 68 and lysine 122) on SOD2 and promotes its antioxidative activity. In this study, we investigated whether the antioxidant effect of caffeine involves modulation of SOD2 by SIRT3 using in vitro and in vivo models. The results show that caffeine interacts with SIRT3 and promotes direct binding of SIRT3 with its substrate, thereby enhancing its enzymatic activity. Mechanistically, caffeine bound to SIRT3 with high affinity (KD = 6.858 × 10–7 M); the binding affinity between SIRT3 and its substrate acetylated p53 was also 9.03 (without NAD+) or 6.87 (with NAD+) times higher in the presence of caffeine. Caffeine effectively protected skin cells from UV irradiation-induced oxidative stress. More importantly, caffeine enhanced SIRT3 activity and reduced SOD2 acetylation, thereby leading to increased SOD2 activity, which could be reversed by treatment with the SIRT3 inhibitor 3-(1H-1,2,3-triazol-4-yl) pyridine (3-TYP) in vitro and in vivo. Taken together, our results show that caffeine targets SIRT3 to enhance SOD2 activity and protect skin cells from UV irradiation-induced oxidative stress. Thus, caffeine, as a small-molecule SIRT3 activator, could be a potential agent to protect human skin against UV radiation.

Due to the inconsistent spatiotemporal spectral scales, a remote sensing dataset over a large-scale area and over long-term time series will have large variations and large statistical distribution features, which will lead to a performance drop of the deep learning model that is only trained on the source domain. For building an extraction task, deep learning methods perform weak generalization from the source domain to the other domain. To solve the problem, we propose a Capsule–Encoder–Decoder model. We use a vector named capsule to store the characteristics of the building and its parts. In our work, the encoder extracts capsules from remote sensing images. Capsules contain the information of the buildings’ parts. Additionally, the decoder calculates the relationship between the target building and its parts. The decoder corrects the buildings’ distribution and up-samples them to extract target buildings. Using remote sensing images in the lower Yellow River as the source dataset, building extraction experiments were trained on both our method and the mainstream methods. Compared with the mainstream methods on the source dataset, our method achieves convergence faster, and our method shows higher accuracy. Significantly, without fine tuning, our method can reduce the error rates of building extraction results on an almost unfamiliar dataset. The building parts’ distribution in capsules has high-level semantic information, and capsules can describe the characteristics of buildings more comprehensively, which are more explanatory. The results prove that our method can not only effectively extract buildings but also perform great generalization from the source remote sensing dataset to another.

To assess the efficacy and short-term outcomes of adherence to statin therapy among coronary heart disease (CHD) patients following their hospital discharge, we enrolled 615 CHD patients who were prescribed statins from The First Affiliated Hospital of Chongqing Medical University in China between February 1st and October 31st of 2013. Statin adherence was evaluated by identifying the proportion of patients who remained adherent or became non-adherent to statin therapy over 4–8 months post-discharge from the hospital. The composite outcomes included all-cause mortality and re-hospitalization with cardiovascular disease. We found that 15.9% patients were non-adherent to their statin therapies and that coronary artery stenosis

Objective: This study aims to provide a basis for the curriculum provision, training form and training time of midwifery managers in Beijing and understand the needs of the midwives' participation in specialist certification training. Methods: Ten midwifery managers and midwives in Beijing were selected for a group interview. Based on the interview results, the researchers designed a questionnaire. As the midwives were from different hospital levels in Beijing, stratified random sampling method was adopted. A total of 137 people were surveyed through a web questionnaire. Results: In total, 99.25% of the respondents believe that midwifery specialist training needs to be carried out in Beijing. Moreover, 55% of them believe that half-time training is reasonable. The respondents believe that the proportion of theoretical and practice training should be 1:2. In total, 91.79% and 85.07% of the respondents believe that contents of midwifery research and midwifery management need to be increased, respectively. Conclusion: Midwives in Beijing need midwifery specialist training. Half-time certification training complies with the clinical needs. Midwifery specialist certification enhances the professional identity of midwives. Keywords: Inservice training, Midwives, Specialist certification, Questionaires

Remotely-sensed satellite image fusion is indispensable for the generation of long-term gap-free Earth observation data. While cloud computing (CC) provides the big picture for RS big data (RSBD), the fundamental question of the efficient fusion of RSBD on CC platforms has not yet been settled. To this end, we propose a lightweight cloud-native framework for the elastic processing of RSBD in this study. With the scaling mechanisms provided by both the Infrastructure as a Service (IaaS) and Platform as a Services (PaaS) of CC, the Spark-on-Kubernetes operator model running in the framework can enhance the efficiency of Spark-based algorithms without considering bottlenecks such as task latency caused by an unbalanced workload, and can ease the burden to tune the performance parameters for their parallel algorithms. Internally, we propose a task scheduling mechanism (TSM) to dynamically change the Spark executor pods’ affinities to the computing hosts. The TSM learns the workload of a computing host. Learning from the ratio between the number of completed and failed tasks on a computing host, the TSM dispatches Spark executor pods to newer and less-overwhelmed computing hosts. In order to illustrate the advantage, we implement a parallel enhanced spatial and temporal adaptive reflectance fusion model (PESTARFM) to enable the efficient fusion of big RS images with a Spark aggregation function. We construct an OpenStack cloud computing environment to test the usability of the framework. According to the experiments, TSM can improve the performance of the PESTARFM using only PaaS scaling to about 11.7%. When using both the IaaS and PaaS scaling, the maximum performance gain with the TSM can be even greater than 13.6%. The fusion of such big Sentinel and PlanetScope images requires less than 4 min in the experimental environment.

Mapping rice cropping systems is important for grain yield prediction and food security assessments. Both single- and double-season rice are the dominant rice systems in central China. However, because of increasing labor shortages and high costs, there has been a gradual decline in double-season rice. Ratoon rice (RR) has been proposed as an alternative system that balances the productivity, cost, and labor requirements of rice cultivation. RR has been expanding in central China, encouraged by the improved cultivars, machinery, and favorable policies. However, to our knowledge, the distribution of RR has not been mapped with remote sensing techniques. This study developed a phenology-based algorithm to map RR at a 10 m resolution in Hubei Province, Central China, using dense time stacks of Sentinel-2 images (cloud cover 2, accounting for 10.03% of the total area of paddy rice fields. The overall accuracy of RR, non-RR rice fields, and non-rice land cover types was 0.76. The adjusted overall accuracy for RR and non-RR was 0.91, indicating that the proposed YI and the phenology-based algorithm could accurately identify RR fields from the paddy rice fields.

Atherosclerosis is one of the leading causes of mortality worldwide. Growing evidence suggested that miRNAs contributed to the progression of atherosclerosis. miR-30-5p was found involved in various diseases. However, the role of miR-30-5p in regulation of atherosclerosis is not known. Here, we aim to investigate the effects of miR-30-5p on regulating the progression of atherosclerosis. The expression levels of miR-30-5p in serum collected from atherosclerosis patients and normal healthy people were analyzed by qRT-PCR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway bioinformatics were carried out to reveal the possible signaling pathways involved in the mode of action of miR-30-5p. A potential target gene of miRNA-30-5p was searched and examined by a luciferase reporter assay. ELISA, Western blot, proliferation, and flow cytometry assays were performed to assess the biological functional role of miR-30-5p in vitro. Also, an in vitro monocyte-endothelial cell coculture model was used to study the functional role of miR-30-5p in atherosclerosis. We found that miR-30-5p was significantly decreased in serum samples from atherosclerosis patients compared with control subjects. GO and KEGG analysis results showed that miR-30-5p is highly associated with genetic profile of cardiovascular disease. TCF21 was verified as a target gene of miR-30-5p. Overexpression of miR-30-5p in THP-1 not only protected endothelial cell viability but also inhibited endothelial cell apoptosis, and similar results were observed in cells with that of TCF21 knocked down. Moreover, miR-30-5p decreased the expression levels of lactate dehydrogenase (LDH) and tumor necrosis factor-α (TNF-α) and reduced reactive oxygen species (ROS) accumulation. NF-κB and MAPK/p38 pathways played an indispensable role in the protection ability of miR-30-5p against atherosclerosis. Our results reveal that miR-30-5p suppresses the progression of atherosclerosis through targeting TCF21 in vitro. Therefore, the miR-30-5p-TCF21-MAPK/p38 signaling pathway may be a potential biomarker or therapeutic target in atherosclerosis.

Background The insulin/insulin‐like growth factor/relaxin family represents a group of structurally related but functionally diverse proteins. The family member relaxin‐2 has been evaluated in clinical trials for its efficacy in the treatment of acute heart failure. In this study, we assessed the role of insulin‐like peptide 6 (INSL6), another member of this protein family, in murine heart failure models using genetic loss‐of‐function and protein delivery methods. Methods and Results Insl6‐deficient and wild‐type (C57BL/6N) mice were administered angiotensin II or isoproterenol via continuous infusion with an osmotic pump or via intraperitoneal injection once a day, respectively, for 2 weeks. In both models, Insl6‐knockout mice exhibited greater cardiac systolic dysfunction and left ventricular dilatation. Cardiac dysfunction in the Insl6‐knockout mice was associated with more extensive cardiac fibrosis and greater expression of fibrosis‐associated genes. The continuous infusion of chemically synthesized INSL6 significantly attenuated left ventricular systolic dysfunction and cardiac fibrosis induced by isoproterenol infusion. Gene expression profiling suggests liver X receptor/retinoid X receptor signaling is activated in the isoproterenol‐challenged hearts treated with INSL6 protein. Conclusions Endogenous Insl6 protein inhibits cardiac systolic dysfunction and cardiac fibrosis in angiotensin II– and isoproterenol‐induced cardiac stress models. The administration of recombinant INSL6 protein could have utility for the treatment of heart failure and cardiac fibrosis.

To investigate the effects of a novel synthetic epidermal growth factor receptor inhibitor, COMPOUND7809, on the inhibition of lung cancer growth in vitro and the underlying mechanisms, we treated three lung tumor cell lines (A549, SK-LU-1, and NCI-H23) with COMPOUND7809 and a Food and Drug Administration–approved epidermal growth factor receptor inhibitor gefitinib. Then, we examined cell growth in a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, cell survival in a Cell Counting Kit-8 assay, and cell apoptosis by Annexin V flow cytometry in the presence of fluorouracil. We found that compared to gefitinib, COMPOUND7809 inhibited cell growth more potentially and induced more cell death in the presence of fluorouracil. Thus, our study demonstrates that COMPOUND7809 may be a promising epidermal growth factor receptor inhibitor for human lung cancer therapy.

A contextual effects model, built based on Bayesian spatial modeling strategy, was used to investigate contextual effects on neighborhood burglary risks in Wuhan, China. The contextual effects denote the impact of the upper-level area on the lower-level units of analysis. These effects are often neglected in Bayesian spatial crime analysis. The contextual effects model accounts for the effects of independent variables, overdispersion, spatial autocorrelation, and contextual effects. Both the contextual effects model and the conventional Bayesian spatial model were fitted to our data. Results showed the two models had almost the same deviance information criterion (DIC). Furthermore, they identified the same set of significant independent variables and gave very similar estimates for burglary risks. Nonetheless, the contextual effects model was preferred in the sense that it provides insights into contextual effects on crime risks. Based on the contextual effects model and the map decomposition technique, we identified, worked out, and mapped the relative contribution of the neighborhood characteristics and contextual effects on the overall burglary risks. The research contributes to the increasing literature on modeling crime data by Bayesian spatial approaches.

The grid connection of intermittent energy sources such as wind power and photovoltaic power generation brings new challenges for the economic and safe operation of renewable power systems. To address these challenges, a multi-time-scale active power coordinated operation method, consisting of day-ahead scheduling, hour-level rolling corrective scheduling, and real-time corrective scheduling, is proposed for the combined operation of wind-photovoltaic-thermal-hydro power and battery (WPTHB) to handle renewable power fluctuations. In day-ahead scheduling, the optimal power outputs of thermal power units, hydro-pumped storage units, and batteries are solved with the purpose of minimizing the total power generation cost. In hour-level rolling corrective scheduling, the power output plan of thermal power units and pumped storage units is modified to minimize the correction cost based on the on-off state of thermal power units determined in day-ahead scheduling. In real-time corrective scheduling stage, the feedback correction and rolling optimization-based model predictive control algorithm is adopted to modify the power output of thermal power units, hydro-pumped storage units, and batteries optimized in hour-level rolling correction scheduling, so as to ensure the economy of the correction plan and the static security of system operation. Finally, simulation results demonstrated that the proposed method can accurately track system power fluctuations, and ensure the economic and security operation of a multi-energy-generation system.

Remote sensing big data (RSBD) is generally characterized by huge volumes, diversity, and high dimensionality. Mining hidden information from RSBD for different applications imposes significant computational challenges. Clustering is an important data mining technique widely used in processing and analyzing remote sensing imagery. However, conventional clustering algorithms are designed for relatively small datasets. When applied to problems with RSBD, they are, in general, too slow or inefficient for practical use. In this paper, we proposed a parallel subsampling-based clustering (PARSUC) method for improving the performance of RSBD clustering in terms of both efficiency and accuracy. PARSUC leverages a novel subsampling-based data partitioning (SubDP) method to realize three-step parallel clustering, effectively solving the notable performance bottleneck of the existing parallel clustering algorithms; that is, they must cope with numerous repeated calculations to get a reasonable result. Furthermore, we propose a centroid filtering algorithm (CFA) to eliminate subsampling errors and to guarantee the accuracy of the clustering results. PARSUC was implemented on a Hadoop platform by using the MapReduce parallel model. Experiments conducted on massive remote sensing imageries with different sizes showed that PARSUC (1) provided much better accuracy than conventional remote sensing clustering algorithms in handling larger image data; (2) achieved notable scalability with increased computing nodes added; and (3) spent much less time than the existing parallel clustering algorithm in handling RSBD.

The vegetation supply water index (VSWI = NDVI/LST) is an effective metric estimating soil moisture in areas with moderate to dense vegetation cover. However, the normalized difference vegetation index (NDVI) exhibits a long water stress lag and the land surface temperature (LST), sensitive to water stress, does not contribute considerably to surface soil moisture monitoring due to the constraints of the mathematical characteristics of VSWI: LST influences VSWI less when LST value is sufficiently high. This paper mathematically analyzes the characteristics of VSWI and proposes a new operational dryness index (surface water content temperature index, SWCTI) for the assessment of surface soil moisture status. SWCTI uses the surface water content index (SWCI), which provides a more accurate estimation of surface soil moisture than that of NDVI, as the numerator and the modified surface temperature, which has a greater influence on SWCTI than that of LST, as the denominator. The validation work includes comparison of SWCTI with in situ soil moisture and other remote sensing indices. The results show SWCTI demonstrates the highest correlation with in situ soil moisture; the highest correlation R = 0.801 is found between SWCTI and the 0–5 cm soil moisture in a sandy loam. SWCTI is a functional and effective method that has a great potential in surface soil moisture monitoring.

Abstract.: Sleep deprivation induces several negative effects on behavior, emotion, attention, and learning ability. Sleep appears to be particularly important during adolescent brain development. In the present study, we examined the effects of sleep deprivation on behavior and hypothalamic neurotransmission including histamine and orexin neurons in adolescent rats using the treadmill method. Adolescent male rats were divided into three groups: treadmill sleep-deprived, treadmill control, and cage control groups. Energy expenditure, anxiety-like behavior, and locomotor activity were examined among the three groups. Histamine concentration in the cortex and diencephalon and the number of c-Fos–positive neurons in the hypothalamus were also examined. In addition, histamine and orexin neurons in the hypothalamus were simultaneously identified using rat histidine decarboxylase and orexin-A immunohistochemistry, respectively. Both energy expenditure and anxiety-related behavior significantly increased by the experimental 3-day sleep deprivation, while exploratory locomotor activity significantly decreased. Histamine contents did not change in the cortex, but significantly decreased in the diencephalon of sleep-deprived rats. Increased expression of c-Fos–positive neurons, including subgroup histamine and orexin neurons, was observed in the hypothalamus. These findings indicate that sleep deprivation increases energy expenditure and anxiety in adolescent rats and provide evidence for the pivotal role of hypothalamus subgroup histamine and orexin neurons in the behavioral response to sleep deprivation. Keywords:: sleep deprivation, anxiety, energy expenditure, hypothalamus, histamine

Histamine H3 receptors inhibit the release of not only histamine itself, but also other neurotransmitters including dopamine. Previous papers have reported that histaminergic neurons inhibit psychostimulant-induced behavioral changes. To examine whether deficiency in histamine H3 receptors influences psychostimulant-induced behavioral sensitization and reward, we examined locomotor activity, conditioned place preference (CPP), and c-Fos expression in histamine H3 receptor–gene knockout mice (H3KO) and their wild-type (WT) counterparts before and after treatment with methamphetamine (METH) and 3,4-methylenedioxymethamphetamine (MDMA). The increase in locomotion induced by treatment with METH or MDMA was lower in histamine H3KO mice than in WT mice, while the locomotor sensitization was developed by METH or MDMA in both strains. However, no significant difference in METH- and MDMA-induced preference scores of CPP between histamine H3KO mice and WT mice was observed. Following treatment with METH, the number of c-Fos–positive neurons in the the caudate-putamen of histamine H3KO mice was lower than that in the caudate-putamen of WT mice. In contrast, there was no significant difference in the number of the psychostimulant-induced c-Fos–positive cells in the nucleus accumbens between the two strains of mice. These findings suggest that deficiency in histamine H3 receptors may have inhibitory effects on psychostimulant-induced increase in locomotion, but insignificant effects on the reward. Keywords:: histamine H3 receptor–knockout mouse, methamphetamine, 3,4-methylenedioxymethamphetamine (MDMA), sensitization of locomotion, conditioned place preference

Statins exert pleiotropic effects on endothelial cells in addition to lowering cholesterol. 15-Lipoxygenase-1 (ALOX15) has been implicated in vascular inflammation and disease. The relationship between atorvastatin and ALOX15 was investigated using a rat carotid artery balloon-injury model. Hematoxylin and eosin (HE) staining showed that ALOX15 overexpression increased the thickness of the intima-media (IMT). Immunohistochemistry and western blotting showed that atorvastatin increased the expression of cellular adhesion molecules (CAMs) but decreased the expression of endothelial nitric oxide synthase (eNOS); these effects of atorvastatin were blocked by ALOX15 overexpression. In human umbilical venous endothelial cells (HUVECs), silencing of ALOX15 enhanced the effects of atorvastatin on endothelial function. Expression levels of CAMs and Akt/eNOS/NO under oxidized low-density lipoprotein (ox-LDL) stimulation were modulated by ALOX15 inhibitor and ALOX15 small interfering RNA (siRNA). Atorvastatin abolished the activation of nuclear factor-kappa B (NF-κB) induced by ox-LDL. Exposure to ox-LDL induced upregulation of ALOX15 in HUVECs, but this effect was partially abolished by atorvastatin or the NF-κB inhibitor pyrrolidine dithiocarbamate (PDTC). These results demonstrate that regulation of ALOX15 expression might be involved in the effects of atorvastatin on endothelial dysfunction.

With the growing number of high-resolution satellite images, the traditional image retrieval method has become a bottleneck in the massive application of high-resolution satellite images because of the low degree of automation. However, there are few studies on the automation of satellite image retrieval. This paper presents an automatic high-resolution satellite image accurate retrieval method based on effective coverage (EC) information, which is used to replace the artificial screening stage in traditional satellite image retrieval tasks. In this method, first, we use a convolutional neural network to extract the EC of each satellite image; then, we use an effective coverage grid set (ECGS) to represent the ECs of all satellite images in the library; finally, the satellite image accurate retrieval algorithm is proposed to complete the process of screening images. The performance evaluation of the method is implemented in three regions: Wuhan, Yanling, and Tangjiashan Lake. The large number of experiments shows that our proposed method can automatically retrieve high-resolution satellite images and significantly improve efficiency.

In current upscaling of in situ surface soil moisture practices, commonly used novel statistical or machine learning-based regression models combined with remote sensing data show some advantages in accurately capturing the satellite footprint scale of specific local or regional surface soil moisture. However, the performance of most models is largely determined by the size of the training data and the limited generalization ability to accomplish correlation extraction in regression models, which are unsuitable for larger scale practices. In this paper, a deep learning model was proposed to estimate soil moisture on a national scale. The deep learning model has the advantage of representing nonlinearities and modeling complex relationships from large-scale data. To illustrate the deep learning model for soil moisture estimation, the croplands of China were selected as the study area, and four years of Visible Infrared Imaging Radiometer Suite (VIIRS) raw data records (RDR) were used as input parameters, then the models were trained and soil moisture estimates were obtained. Results demonstrate that the estimated models captured the complex relationship between the remote sensing variables and in situ surface soil moisture with an adjusted coefficient of determination of R ¯ 2 = 0.9875 and a root mean square error (RMSE) of 0.0084 in China. These results were more accurate than the Soil Moisture Active Passive (SMAP) active radar soil moisture products and the Global Land data assimilation system (GLDAS) 0–10 cm depth soil moisture data. Our study suggests that deep learning model have potential for operational applications of upscaling in situ surface soil moisture data at the national scale.

Geospatial big data analysis (GBDA) is extremely significant for time-constraint applications such as disaster response. However, the time-constraint analysis is not yet a trivial task in the cloud computing environment. Spatial query processing (SQP) is typical computation-intensive and indispensable for GBDA, and the spatial range query, join query, and the nearest neighbor query algorithms are not scalable without using MapReduce-liked frameworks. Parallel SQP algorithms (PSQPAs) are trapped in screw-processing, which is a known issue in Geoscience. To satisfy time-constrained GBDA, we propose an elastic SQP approach in this paper. First, Spark is used to implement PSQPAs. Second, Kubernetes-managed Core Operation System (CoreOS) clusters provide self-healing Docker containers for running Spark clusters in the cloud. Spark-based PSQPAs are submitted to Docker containers, where Spark master instances reside. Finally, the horizontal pod auto-scaler (HPA) would scale-out and scale-in Docker containers for supporting on-demand computing resources. Combined with an auto-scaling group of virtual instances, HPA helps to find each of the five nearest neighbors for 46,139,532 query objects from 834,158 spatial data objects in less than 300 s. The experiments conducted on an OpenStack cloud demonstrate that auto-scaling containers can satisfy time-constraint GBDA in clouds.

An in-depth knowledge of the microbiota and metabolites in the solid-state fermentation (SSF) of Post-fermented Pu-erh tea (Pu-erh Shucha, PFPT), a Chinese traditional tea with various health benefits, is essential to develop modern fermentation technology. In this work, the microbial diversity and succession in two laboratory-developed SSF protocols for PFPT were investigated using pyrosequencing analyses of the bacterial 16S rRNA and fungal 18S rRNA genes. The active bacteria in the initial stages of SSF (seven days) were from the raw materials and environment, with a dominance of Proteobacteria in both the raw materials and SSF after seven days. The environmental bacteria were inoculated into the tea mass throughout the fermentation process and multiplied, with a dominance of Firmicutes at day 14 and 21, and then Firmicutes and Actinobacteria at the last stages of fermentation (day 28 and 35). The dominant fungi came from the raw material and were identified at the genus level as Aspergillus throughout the SSF process. The contents of tea polyphenols, free amino acids, gallic acid, theaflavin, thearubigin, and catechins decreased significantly (p < 0.05), while the level of theabrownin increased significantly (p < 0.05). The caffeine content showed no significant change (p > 0.05). In total, 30 bacterial and three fungal genera showed significant correlations to 1–8 and 3–4 identified tea compounds, respectively (p < 0.05). The dynamics of the microbiota and chemical compounds, and correlations between their changes in the SSF of PFPT were revealed, and present a foundation for further studies on the microbial effects on chemical compounds.