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In this work, SLM TA31 alloys were fabricated by varying the laser scanning speed while maintaining a constant laser power. Two well-formed SLM TA31 alloys were selected for detailed investigation of their corrosion resistance and hardness, with rolled TA31 alloys used as a benchmark for comparison. The results showed that the SLM TA31 alloy fabricated at a scanning speed of 900 mm/s exhibited the best corrosion resistance and improved hardness, while that fabricated at 1000 mm/s showed the poorest corrosion resistance but still had enhanced hardness compared to the rolled alloy. The observed variations in corrosion resistance among the SLM TA31 alloys were attributed to the combined influence of the metastable martensite α′ phase content and the variations in grain boundary density within their microstructures. The beneficial effect of grain refinement leads to an increase in the number of grain boundaries and facilitates the nucleation of passive films, thereby overcoming the detrimental effects of the metastable α′ phase. This results in SLM TA31−900 alloy having excellent corrosion resistance compared to rolled TA31 alloy. The improved hardness was primarily due to grain refinement and the formation of harder martensite α'/α phase.

Abstract The locomotive action involves diverse coordination, necessitating the integration of multiple motor neural circuits. However, the precise circuitry mechanism governing emotion-driven accelerated locomotion remains predominantly elusive. Here we dissect projections from the tuberomammillary nucleus (TMN) to subiculum (SUB) which promote alert-driven accelerated locomotion. We find that TMN histaminergic neurons respond to high-speed locomotion in both natural and alert acceleration. The TMN-SUB circuit is sufficient but not essential for amplifying accelerated locomotion from low to high-speed movement in basal condition, but it is both sufficient and necessary in alert condition for modulating accelerated locomotion during high-speed escape behavior. TMN histaminergic neuron activates SUB glutamatergic “fast locomotor cell” that projects to retrosplenial granular cortex (RSG) mainly through histamine H2 receptor (H2R). This study reveals the critical role of the histamine-tuned SUB circuit in alert-driven accelerated locomotion in mice, providing a theoretical foundation for comprehending neural circuit mechanisms of instinctive behaviors under alert.

Background Acute kidney injury (AKI) and acute kidney disease (AKD) are prevalent among pediatric patients, both linked to increased mortality and extended hospital stays. Early detection of kidney injury is crucial for improving outcomes. This study presents a machine learning-based risk prediction model for AKI and AKD in pediatric patients, enabling personalized risk predictions.Methods Data from 2,346 hospitalized pediatric patients, collected between January 2020 and January 2023, were divided into an 85% training set and a 15% test set. Predictive models were constructed using eight machine learning algorithms and two ensemble algorithms, with the optimal model identified through AUROC. SHAP was used to interpret the model, and an online prediction tool was developed with Streamlit to predict AKI and AKD.Results The incidence of AKI and AKD were 14.90% and 16.26%, respectively. Patients with AKD combined with AKI had the highest mortality rate, at 6.94%, when analyzed by renal function trajectories. The LightGBM algorithm showed superior predictive performance for both AKI and AKD (AUROC: 0.813, 0.744). SHAP identified top predictors for AKI as serum creatinine, white blood cell count, neutrophil count, and lactate dehydrogenase, while key predictors for AKD included proton pump inhibitor, blood glucose, hemoglobin, and AKI grade.Conclusion The high incidence of AKI and AKD among hospitalized children warrants attention. Renal function trajectories are strongly associated with prognosis. Supported by a web-based tool, machine learning models can effectively predict AKI and AKD, facilitating early identification of high-risk pediatric patients and potentially improving outcomes.

We characterized the evolution and molecular characteristics of avian influenza A(H7N9) viruses isolated in China during 2021–2023. We systematically analyzed the 10-year evolution of the hemagglutinin gene to determine the evolutionary branch. Our results showed recent antigenic drift, providing crucial clues for updating the H7N9 vaccine and disease prevention and control.

Abstract Background Research into the acute kidney disease (AKD) after acute ischemic stroke (AIS) is rare, and how clinical features influence its prognosis remain unknown. We aim to employ interpretable machine learning (ML) models to study AIS and clarify its decision-making process in identifying the risk of mortality. Methods We conducted a retrospective cohort study involving AIS patients from January 2020 to June 2021. Patient data were randomly divided into training and test sets. Eight ML algorithms were employed to construct predictive models for mortality. The performance of the best model was evaluated using various metrics. Furthermore, we created an artificial intelligence (AI)-driven web application that leveraged the top ten most crucial features for mortality prediction. Results The study cohort consisted of 1633 AIS patients, among whom 257 (15.74%) developed subacute AKD, 173 (10.59%) experienced AKI recovery, and 65 (3.98%) met criteria for both AKI and AKD. The mortality rate stood at 4.84%. The LightGBM model displayed superior performance, boasting an AUROC of 0.96 for mortality prediction. The top five features linked to mortality were ACEI/ARE, renal function trajectories, neutrophil count, diuretics, and serum creatinine. Moreover, we designed a web application using the LightGBM model to estimate mortality risk. Conclusions Complete renal function trajectories, including AKI and AKD, are vital for fitting mortality in AIS patients. An interpretable ML model effectively clarified its decision-making process for identifying AIS patients at risk of mortality. The AI-driven web application has the potential to contribute to the development of personalized early mortality prevention.

Hemoperfusion is one of the most important therapies for progressive chronic kidney disease (CKD) and is effective at removing toxins from the blood. Increasing the efficiency of adsorbents applied in hemoperfusion is crucial. In the present study, shell of areca nut, one of the most common waste Chinese herb medicine residue with a porous structure was carbonized and activated at different temperatures to obtain two kinds of porous materials. The biocompatibility of the as-prepared porous materials was estimated via a hemolytic test, and the removal efficiency of the materials toward toxins was tested via an adsorption experiment in solution and blood samples from CKD patients, simulated hemoperfusion and in vivo hemoperfusion. After 4 h of adsorption, free and protein-bound toxins in solution were efficiently removed by the prepared porous materials, and the removal efficiency was better than that of commonly used hemoperfutor adsorbents. Most of the tested toxins can be removed from CKD blood samples and simulated hemoperfusion samples. Blood uremic toxins from CKD mice were also efficiently and safely removed after in vivo hemoperfusion using the as-prepared adsorbent. This work highlights promising adsorbents for hemoperfusion that could increase the therapeutic efficacy in patients with progressive CKD.

BackgroundAcute kidney injury (AKI) is a common adverse outcome following nephrectomy. The progression from AKI to acute kidney disease (AKD) and subsequently to chronic kidney disease (CKD) remains a concern; yet, the predictive mechanisms for these transitions are not fully understood. Interpretable machine learning (ML) models offer insights into how clinical features influence long-term renal function outcomes after nephrectomy, providing a more precise framework for identifying patients at risk and supporting improved clinical decision-making processes. ObjectiveThis study aimed to (1) evaluate postnephrectomy rates of AKI, AKD, and CKD, analyzing long-term renal outcomes along different trajectories; (2) interpret AKD and CKD models using Shapley Additive Explanations values and Local Interpretable Model-Agnostic Explanations algorithm; and (3) develop a web-based tool for estimating AKD or CKD risk after nephrectomy. MethodsWe conducted a retrospective cohort study involving patients who underwent nephrectomy between July 2012 and June 2019. Patient data were randomly split into training, validation, and test sets, maintaining a ratio of 76.5:8.5:15. Eight ML algorithms were used to construct predictive models for postoperative AKD and CKD. The performance of the best-performing models was assessed using various metrics. We used various Shapley Additive Explanations plots and Local Interpretable Model-Agnostic Explanations bar plots to interpret the model and generated directed acyclic graphs to explore the potential causal relationships between features. Additionally, we developed a web-based prediction tool using the top 10 features for AKD prediction and the top 5 features for CKD prediction. ResultsThe study cohort comprised 1559 patients. Incidence rates for AKI, AKD, and CKD were 21.7% (n=330), 15.3% (n=238), and 10.6% (n=165), respectively. Among the evaluated ML models, the Light Gradient-Boosting Machine (LightGBM) model demonstrated superior performance, with an area under the receiver operating characteristic curve of 0.97 for AKD prediction and 0.96 for CKD prediction. Performance metrics and plots highlighted the model’s competence in discrimination, calibration, and clinical applicability. Operative duration, hemoglobin, blood loss, urine protein, and hematocrit were identified as the top 5 features associated with predicted AKD. Baseline estimated glomerular filtration rate, pathology, trajectories of renal function, age, and total bilirubin were the top 5 features associated with predicted CKD. Additionally, we developed a web application using the LightGBM model to estimate AKD and CKD risks. ConclusionsAn interpretable ML model effectively elucidated its decision-making process in identifying patients at risk of AKD and CKD following nephrectomy by enumerating critical features. The web-based calculator, found on the LightGBM model, can assist in formulating more personalized and evidence-based clinical strategies.

IntroductionAcute kidney injury (AKI) is a prevalent complication in older people, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models for predicting the occurrence of AKD, AKI and mortality in older patients.MethodsWe retrospectively reviewed the medical records of older patients (aged 65 years and above). To explore the trajectory of kidney dysfunction, patients were categorized into four groups: no kidney disease, AKI recovery, AKD without AKI, or AKD with AKI. We developed eight machine learning models to predict AKD, AKI, and mortality. The best-performing model was identified based on the area under the receiver operating characteristic curve (AUC) and interpreted using the Shapley additive explanations (SHAP) method.ResultsA total of 22,005 patients were finally included in our study. Among them, 4,434 patients (20.15%) developed AKD, 4,000 (18.18%) occurred AKI, and 866 (3.94%) patients deceased. Light gradient boosting machine (LGBM) outperformed in predicting AKD, AKI, and mortality, and the final lite models with 15 features had AUC values of 0.760, 0.767, and 0.927, respectively. The SHAP method revealed that AKI stage, albumin, lactate dehydrogenase, aspirin and coronary heart disease were the top 5 predictors of AKD. An online prediction website for AKD and mortality was developed based on the final models.DiscussionThe LGBM models provide a valuable tool for early prediction of AKD, AKI, and mortality in older patients, facilitating timely interventions. This study highlights the potential of machine learning in improving older adult care, with the developed online tool offering practical utility for healthcare professionals. Further research should aim at external validation and integration of these models into clinical practice.

Abstract Purpose Furosemide, a frequently prescribed diuretic for managing congestive heart failure and edema, remains a topic of debate regarding its potential risk of inducing acute kidney injury (AKI) in patients. Consequently, this study aims to examine the occurrence of hospital-acquired AKI (HA-AKI) in hospitalized patients who are administered furosemide and to investigate potential risk factors associated with this outcome. Methods This study encompassed a cohort of 22374 hospitalized patients who either received furosemide treatment or not from June 1, 2012, to December 31, 2017. Propensity score matching was employed to establish comparability between the two groups regarding covariates. Subsequently, a nomogram was constructed to predict the probability of AKI occurrence among patients who underwent furosemide treatment. Results The regression analysis identified the single-day total dose of furosemide as the most significant factor for AKI, followed by ICU administration, estimated glomerular filtration rate, antibiotic, statin, NSAIDs, β-blockers, proton pump inhibitor, chronic kidney disease, and 7 other indicators. Subgroup analysis revealed a synergistic effect of furosemide with surgical operation, previous treatment with β-blockers, ACEI/ARB and antibiotics, leading to an increased risk of AKI when used in combination. Subsequently, a visually represented prognostic nomogram was developed to predict AKI occurrence in furosemide users. The predictive accuracy of the nomogram was assessed through calibration analyses, demonstrating an excellent agreement between the nomogram predictions and the actual likelihood of AKI, with a probability of 77.40%. Conclusions Careful consideration of factors such as dosage, concurrent medication use, and renal function of the patient is necessary for clinical practice when using furosemide. Our practical prognostic model for HA-AKI associated with furosemide use can be utilized to assist clinicians in making informed decisions about patient care and treatment.

Abstract Introduction Hexarelin exhibits significant protection against organ injury in models of ischemia/reperfusion (I/R)-induced injury (IRI). Nevertheless, the impact of Hexarelin on acute kidney injury (AKI) and its underlying mechanism remains unclear. In this study, we investigated the therapeutic potential of Hexarelin in I/R-induced AKI and elucidated its molecular mechanisms. Methods We assessed the protective effects of Hexarelin through both in vivo and in vitro experiments. In the I/R-induced AKI model, rats were pretreated with Hexarelin at 100 μg/kg/d for 7 days before being sacrificed 24 h post-IRI. Subsequently, kidney function, histology, and apoptosis were assessed. In vitro, hypoxia/reoxygenation (H/R)-induced HK-2 cell model was used to investigate the impact of Hexarelin on apoptosis in HK-2 cells. Then, we employed molecular docking using a pharmmapper server and autodock software to identify potential target proteins of Hexarelin. Results In this study, rats subjected to I/R developed severe kidney injury characterized by tubular necrosis, tubular dilatation, increased serum creatinine levels, and cell apoptosis. However, pretreatment with Hexarelin exhibited a protective effect by mitigating post-ischemic kidney pathological changes, improving renal function, and inhibiting apoptosis. This was achieved through the downregulation of conventional apoptosis-related genes, such as Caspase-3, Bax and Bad, and the upregulation of the anti-apoptotic protein Bcl-2. Consistent with the in vivo results, Hexarelin also reduced cell apoptosis in post-H/R HK-2 cells. Furthermore, our analysis using GSEA confirmed the essential role of the apoptosis pathway in I/R-induced AKI. Molecular docking revealed a strong binding affinity between Hexarelin and MDM2, suggesting the potential mechanism of Hexarelin’s anti-apoptosis effect at least partially through its interaction with MDM2, a well-known negative regulator of apoptosis-related protein that of p53. To validate these findings, we evaluated the relative expression of MDM2 and p53 in I/R-induced AKI with or without Hexarelin pre-administration and observed a significant suppression of MDM2 and p53 by Hexarelin in both in vivo and in vitro experiments. Conclusion Collectively, Hexarelin was identified as a promising medication in protecting apoptosis against I/R-induced AKI.

BackgroundUnderstanding the diverse factors influencing physical activity-related injuries is crucial for developing effective interventions that enable individuals to participate in physical activity (PA) while minimizing injury risk. Currently, research evidence on the multiple factors associated with PA-related injuries is inadequate. This study aimed to examine the associations between PA-related injuries and various biological, psychological, and social factors among first-year university students in China.MethodsWe recruited first-year university students from Shantou University in Guangdong Province, China, to participate in our study. Data collection employed a structured self-administered questionnaire, gathering information on PA-related injuries, as well as relevant biological, psychological, and social factors. Binary logistic regression, using a stepwise modeling approach, was employed for the data analysis.ResultsAmong 1,051 first-year university students, 28.16% reported having experienced PA-related injuries in the past year. Most of the injuries reported were minor, with the knee or lower leg being the most frequently injured part of the body. Improper posture, environmental conditions, and excessive physical load were the leading causes of PA-related injuries. Multiple logistic regression analysis revealed that female students (OR = 0.67, 95% CI: 0.47–0.94, p = 0.022) had reduced odds of PA-related injuries. Conversely, high neuroticism (OR = 1.61, 95% CI: 1.07–2.41, p = 0.022), being a member of a sports team (OR = 2.09, 95% CI: 1.34–3.27, p

Abstract Background and aims Acute hyperglycemia has been identified as a risk factor for acute kidney injury occurrence and mortality in various diseases. The aim of the current study was to investigate the relationship between stress-induced hyperglycemia and adverse outcomes in critically ill patients with AKI. Methods We extracted clinical data from Multiparameter Intelligent Monitoring in Intensive Care III version 1.4. Blood glucose and glycosylated hemoglobin during the first 24 h of ICU admission were used to calculate glycemic gap and stress hyperglycemia ratio (SHR). The outcomes included ICU mortality and need for renal replacement therapy. The association of the glycemic gap and SHR with outcomes were determined via logistic regression model and receiver-operating curves. The subgroup analysis of patients with and without diabetes was performed separately. Results Higher glycemic gap and SHR were observed in patients who had increased need of RRT, higher mortality rates and longer ICU stay. Multivariate analysis demonstrated that higher glycemic gap (OR 1.01, 95%CI 1.00-1.02, P = 0.015), as well as SHR (OR 1.32; 95%CI 1.07–1.64, P = 0.009), were independently associated with ICU mortality after adjusting for potential covariates. In subgroup analysis, the association of glycemic gap and SHR were only significant in the non-diabetic population as for the outcome of ICU mortality (OR 2.25, 95%CI 1.64–3.08, P

Background Patients with end-stage renal disease (ESRD) especially those undergoing dialysis have a high prevalence of hyperkalemia, which must be detected and treated immediately. But the initial symptoms of hyperkalemia are insidious, and traditional laboratory serum potassium concentration testing takes time. Therefore, rapid and real-time measurement of serum potassium is urgently needed. In this study, different machine learning methods were used to make rapid predictions of different degrees of hyperkalemia by analyzing the ECG.Methods A total of 1024 datasets of ECG and serum potassium concentrations were analyzed from December 2020 to December 2021. The data were scaled into training and test sets. Different machine learning models (LR, SVM, CNN, XGB, Adaboost) were built for dichotomous prediction of hyperkalemia by analyzing 48 features of chest leads V2-V5. The performance of the models was also evaluated and compared using sensitivity, specificity, accuracy, accuracy, F1 score and AUC.Results We constructed different machine models to predict hyperkalemia using LR and four other common machine-learning methods. The AUCs of the different models ranged from 0.740 (0.661, 0.810) to 0.931 (0.912,0.953) when different serum potassium concentrations were used as the diagnostic threshold for hyperkalemia, respectively. As the diagnostic threshold of hyperkalemia was raised, the sensitivity, specificity, accuracy and precision of the model decreased to various degrees. And AUC also performed less well than when predicting mild hyperkalemia.Conclusion Noninvasive and rapid prediction of hyperkalemia can be achieved by analyzing specific waveforms on the ECG by machine learning methods. But overall, XGB had a higher AUC in mild hyperkalemia, but SVM performed better in predicting more severe hyperkalemia.

Cloud computing system has the characteristics of large-scale servers and a wide range of users.However, it also consumes a huge number of energy, resulting in high operating costs of cloud providers and high carbon emissions issue.Cloud computing is highly virtualized.How to allocate and manage the virtual resources to ensure efficient physical resource utilization and energy consumption control is a multi-parameter game problem, and it is also a research hotspot in this field.A virtual machine scheduling model and the corresponding SV-GA were proposed, which could calculate the contribution value of the physical machine participating in the work through the Shapley value, and modify the probability parameter of the mutation step in the genetic algorithm through the contribution value, so as to complete the task of virtual machine scheduling.The experimental results show that during the comparison with Max-Min, LrMmt and DE, the SV-GA shows its excellent performance in the multi-parameter game including migration time, times, SLA violation rate and energy consumption in the virtual machine scheduling process.

Abstract Background This study investigated accuracy and consistency of epicardial adipose tissue (EAT) quantification in non-ECG-gated chest computed tomography (CT) scans. Methods EAT volume was semi-automatically quantified using a standard Hounsfield unit threshold (− 190, − 30) in three independent cohorts: (1) Cohort 1 (N = 49): paired 120 kVp ECG-gated cardiac non-contrast CT (NCCT) and 120 kVp non-ECG-gated chest NCCT; (2) Cohort 2 (N = 34): paired 120 kVp cardiac NCCT and 100 kVp non-ECG-gated chest NCCT; (3) Cohort 3 (N = 32): paired non-ECG-gated chest NCCT and chest contrast-enhanced CT (CECT) datasets (including arterial phase and venous phase). Images were reconstructed with the slice thicknesses of 1.25 mm and 5 mm in the chest CT datasets, and 3 mm in the cardiac NCCT datasets. Results In Cohort 1, the chest NCCT-1.25 mm EAT volume was similar to the cardiac NCCT EAT volume, while chest NCCT-5 mm underestimated the EAT volume by 7.5%. In Cohort 2, 100 kVp chest NCCT-1.25 mm were 13.2% larger than 120 kVp cardiac NCCT EAT volumes. In Cohort 3, the chest arterial CECT and venous CECT dataset underestimated EAT volumes by ~ 28% and ~ 18%, relative to chest NCCT datasets. All chest CT-derived EAT volumes were similarly associated with significant coronary atherosclerosis with cardiac CT counterparts. Conclusion The 120 kVp non-ECG-gated chest NCCT-1.25 mm images produced EAT volumes comparable to cardiac NCCT. Chest CT EAT volumes derived from consistent imaging settings are excellent alternatives to the cardiac NCCT to investigate their association with coronary artery disease.

Abstract Red tide data are typical multivariate time series (MTS) and complete data help analyze red tide more conveniently. However, missing values due to artificial or accidental events hinder further analysis of red tide phenomenon. Generative adversarial network (GAN) is effective in capturing distribution of MTS while the imputation performance is far from satisfactory, especially in conditions of high missing rate. One of the remaining open challenges is that common GAN-based imputation methods usually lack the ability to excavate implicit correlations between different attributions and downstream tasks, from which advanced latent information about missing values can be mined to improve imputation performance. To deal with the problem, a novel multi-task learning-based generative adversarial imputation network (MTGAIN) is proposed by introducing the prediction task into GAN to unearth more detailed information about missing values to better model distribution of red tide MTS. Furthermore, the homoscedastic uncertainty of multiple tasks is exploited to balance the weights of losses between generation and prediction tasks. The experiments conducted on a real-world dataset demonstrate that MTGAIN outperforms existing methods in terms of imputation and post-imputation performances, especially in conditions of high missing rate.

BackgroundMany patients with breast cancer receive therapies with the potential to cause cardiotoxicity. Echocardiography and multiple-gated acquisition (MUGA) scans are the most used modalities to assess cardiac function during treatment in high-risk patients; however, the optimal imaging strategy and the impact on outcome are unknown.MethodsConsecutive patients with stage 0-3 breast cancer undergoing pre-treatment echocardiography or MUGA were identified from a tertiary care cancer center from 2010-2019. Demographics, medical history, imaging data and clinical events were collected from hospital charts and administrative databases. The primary outcome is a composite of all-cause death or heart failure event. Clinical and imaging predictors of outcome were evaluated on univariable and multivariable analyses.Results1028 patients underwent pre-treatment MUGA and 1032 underwent echocardiography. The groups were well matched for most clinical characteristics except patients undergoing MUGA were younger, had more stage 3 breast cancer and more HER2 over-expressing and triple negative cases. Routine follow-up cardiac imaging scan was obtained in 39.3% of patients with MUGA and 38.0% with echocardiography. During a median follow-up of 2448 (1489, 3160) days, there were 194 deaths, including 7 cardiovascular deaths, and 28 heart failure events with no difference in events between the MUGA and echocardiography groups. There were no imaging predictors of the primary composite outcome or cardiac events. Patients without follow-up imaging had similar adjusted risk for the composite outcome compared to those with imaging follow-up, hazard ratio 0.8 (95% confidence interval 0.5,1.3), p=0.457.ConclusionThe selection of pretreatment echocardiography or MUGA did not influence the risk of death or heart failure in patients with early breast cancer. Many patients did not have any follow-up cardiac imaging and did not suffer worse outcomes. Cardiovascular deaths and heart failure event rates were low and the value of long-term cardiac imaging surveillance should be further evaluated.

Although reports of human infection with influenza A(H5N6) increased in 2021, reports of similar H5N6 virus infection in poultry are few. We detected 10 avian influenza A(H5N6) clade 2.3.4.4b viruses in poultry from 4 provinces in China. The viruses showed strong immune-escape capacity and complex genetic reassortment, suggesting further transmission risk.

The accurate diagnosis of autism spectrum disorder (ASD) is of great practical significance in clinical practice. The spontaneous hemodynamic fluctuations were collected by functional near-infrared spectroscopy (fNIRS) from the bilateral frontal and temporal cortices of typically developing (TD) children and children with ASD. Since traditional machine learning and deep learning methods cannot make full use of the potential spatial dependence between variable pairs, and require a long time series to diagnose ASD. Therefore, we use adaptive spatiotemporal graph convolution network (ASGCN) and short time series to classify ASD and TD. To capture spatial and temporal features of fNIRS multivariable time series without the pre-defined graph, we combined the improved adaptive graph convolution network (GCN) and gated recurrent units (GRU). We conducted a series of experiments on the fNIRS dataset, and found that only using 2.1 s short time series could achieve high precision classification, with an accuracy of 95.4%. This suggests that our approach may have the potential to detect pathological signals in autism patients within 2.1 s. In different brain regions, the left frontal lobe has the best classification effect, and the abnormalities occur more frequently in left hemisphere and frontal lobe region. Moreover, we also found that there were correlations between multiple channels, which had different degrees of influence on the classification of ASD. From this, we can also generalize to a wider range, there may be potential correlations between different brain regions. This may help to better understand the cortical mechanism of ASD.

Abstract Aims Surveillance imaging is often used to detect remodelling, a change in cardiac geometry, and/or function; however, there are limited data in patients with chronic heart failure (HF). We sought to characterize cardiac remodelling in patients with chronic HF and evaluate its association with outcome. Methods and results A prospective cohort of patients at risk for HF or with chronic HF underwent cardiac magnetic resonance (CMR) at baseline and 1 year. Ventricular function, volumes, mass, left atrial volume, global longitudinal strain, and myocardial scar were measured. The primary outcome was a composite of death or cardiovascular hospitalization up to 5 years from the 1 year scan. Cox regression was used to identify 1 year CMR predictors of outcome after adjusting for baseline risk. A total of 262 patients (median age 68 years, 57% males) including 96 at risk for HF, 97 with HF and preserved ejection fraction, and 69 with HF and reduced ejection fraction were included. In the patients with HF, 55 events were identified during follow‐up. After adjustment for baseline clinical risk, Cox proportion hazard regressions only identified 1 year change in left ventricular (LV) mass index as a CMR predictor of outcome, adjusted hazard ratio 1.21 (1.02, 1.44) per 10% increase, P = 0.031. Cardiac remodelling defined as a 1 year change in LV mass index ≥15% was observed in 35% of patients with HF. Patients with adverse remodelling of LV mass index had more events on Kaplan–Meier analyses compared to those with no remodelling, log‐rank P = 0.004 for overall cohort, P = 0.035 for heart failure with preserved ejection fraction and P = 0.035 for heart failure and reduced ejection fraction. Conclusions Cardiac remodelling is common during serial CMR assessment of patients with chronic HF. Change in LV mass predicted long‐term outcomes whereas change in left ventricular ejection fraction did not.

H9N2 avian influenza viruses are endemic and persistent in China, but those that are prevalent in different provinces are also causes of wide epidemics, related to the spread of wild birds and the cross-regional trade in live poultry. For the past 4 years, beginning in 2018, we have sampled a live-poultry market in Foshan, Guangdong, in this ongoing study. In addition to the prevalence of H9N2 avian influenza viruses in China during this period, we identified isolates from the same market belonging to clade A and clade B, which diverged in 2012–2013, and clade C, which diverged in 2014–2016, respectively. An analysis of population dynamics revealed that, after a critical divergence period from 2014 to 2016, the genetic diversity of H9N2 viruses peaked in 2017. Our spatiotemporal dynamics analysis found that clade A, B, and C, which maintain high rates of evolution, have different prevalence ranges and transmission paths. Clades A and B were mainly prevalent in East China in the early stage, and then spread to Southern China, becoming epidemic with clade C. Strains from different regions converge at the same live-poultry market to communicate, which may be one reasons the H9N2 viruses are difficult to eradicate and increasingly dominant throughout China. Selection pressure and molecular analysis have demonstrated that single amino acid polymorphisms at key receptor binding sites 156, 160, and 190 under positive selection pressure, suggesting that H9N2 viruses are undergoing mutations to adapt to new hosts. Live-poultry markets are important because people who visit them have frequent contact with poultry, H9N2 viruses from different regions converge at these markets and spread through contact between live birds and humans, generating increased risks of human exposure to these viruses and threatening public health safety. Thus, it is important to reducing the cross-regional trade of live poultry and strengthening the monitoring of avian influenza viruses in live-poultry markets to reduce the spread of avian influenza viruses.

BackgroundSchool bullying among adolescents has been a worldwide public health issue. It has been observed that adolescents who are exposed to violent video games (VVGs) are often more aggressive. However, research on the association between violent video game exposure (VVGE) and different types of school bullying is limited in the Chinese context.ObjectiveThe purpose of this study was to explore whether VVGE is linked to school bullying behaviors among Chinese adolescents and to examine the relationship between different levels of violent game exposure and four sub-types (physical, verbal, relational, and cyber) of school bullying involvement.MethodsThis was a cross-sectional study of 1,992 Chinese students (55.02% boys and 44.98% girls) with the average age of 15.84 ± 1.62 years. Sub-types of school bullying victimization and perpetration, Internet addiction, and VVGE were measured by using a self-administrated questionnaire. The association was examined by multiple logistic regression analysis, adjusting for covariates.ResultsPhysical, verbal, relational, and cyber school bullying victimization were reported by 18.12, 60.34, 11.75, and 12.05% of the adolescents, and physical, verbal, relational, and cyber school bullying perpetration were reported by 16.62, 54.62, 21.49, and 8.23% of them. Of the students, 1,398 (70.18%) were normal Internet users, 514 (25.80%) showed moderate Internet addictive behaviors, and 31 (1.56%) of the students showed severe Internet addictive behaviors. The prevalence of no VVGE, low-level VVGE, medium-level VVGE, and high-level of VVGE were 27.70, 24.10, 24.20, and 24.00%, respectively. The risk of physical victimization and physical perpetration significantly increased with the increasing degree of violent video game exposure (P for trend < 0.001), with the highest adjusted odds ratios (ORs) of 2.251 (95% CI 1.501–3.375) and 2.554 (95% CI 1.685–3.870), when comparing high-level VVGE with no VVGE.ConclusionThese findings highlight the specific association between different sub-types of school bullying involvement and violent video game exposure. Physical school bullying prevention and intervention programs should be conducted after adolescents are exposed to violent video games.

Renal ischemia reperfusion injury (IRI) is a leading and common cause of acute kidney injury (AKI), and inflammation is a critical factor in ischemic AKI progression. Calycosin (CAL), a major active component of Radix astragali, has been reported to have anti-inflammatory effect in multiple organs. However, whether CAL can alleviate renal IRI and its mechanism remain uncertain. In the present study, a renal IRI model is established by bilateral renal pedicles occlusion for 35 min in male C57BL/6 mice, and the effect of CAL on renal IRI is measured by serum creatinine and pathohistological assay. Hypoxia/reoxygenation (H/R) stimulated human renal tubular epithelial cells HK-2 were applied to explore the regulatory mechanisms of CAL. Luciferase reporter assay and molecular docking were applied to identify the CAL’s target protein and pathway. In the mice with renal IRI, CAL dose dependently alleviated the renal injury and decreased nuclear factor kappa B (NF-κB) mediated inflammatory response. Bioinformatics analysis and experiments showed that early growth response 1 (EGR1) increased in mice with renal IRI and promoted NF-κB mediated inflammatory processes, and CAL dose-dependably reduced EGR1. Through JASPAR database and luciferase reporter assay, peroxisome proliferator-activated receptor γ (PPARγ) was predicted to be a transcription factor of EGR1 and repressed the expression of EGR1 in renal tubular epithelial cells. CAL could increase PPARγ in a dose dependent manner in mice with renal IRI and molecular docking predicted CAL could bind stably to PPARγ. In HK-2 cells after H/R, CAL increased PPARγ, decreased EGR1, and inhibited NF-κB mediated inflammatory response. However, PPARγ knockdown by siRNA transfection abrogated the anti-inflammation therapeutic effect of CAL. CAL produced a protective effect on renal IRI by attenuating NF-κB mediated inflammatory response via PPARγ/EGR1 pathway.

In the context of carbon neutralization and carbon peak, carbon reduction in key industries has become a central topic in our country. As an important part of technological progress, it is necessary to study the effect of technology import on carbon emission reduction in key industries. Based on the panel data of 30 provinces. from 2011 to 2020, this paper used the fixed-effect model to analyze the emission reduction effect in key industries on the development status of technology import. The spatial econometric model was used to analyze the spatial characteristics of carbon emissions of technology import and key industries. Then, the mediating effect model was used to bring industrial technological innovations into the research category to analyze the mediating role of technology imports on the carbon emissions of key industries. Finally, a robustness test proved the reliability of the model. The findings were as follows: (1) Technology import significantly promoted carbon emission reduction in key industries; (2) In terms of the spatial relationship, technology import and carbon dioxide emissions had significant spillover effects, and there were trends of high and high aggregation and low and low aggregation, with the impact of technology import on carbon dioxide emissions having a siphon effect; (3) Industrial technological innovation played an intermediary role in this path, but it was a negative role, which was not, in general, conducive to the reduction of carbon emissions of key industries. On this basis, the paper puts forward several policy suggestions.

BackgroundZinc is an essential trace element involved in multiple metabolic processes. Acute kidney injury (AKI) is associated with low plasma zinc, but outcomes with zinc supplementation in critically ill patients with AKI remain unknown. Our objective was to investigate the effectiveness of zinc supplementation in this patient population.MethodsCritically ill patients with AKI were identified from the Medical Informative Mart for Intensive Care IV database. Prosperity score matching (PSM) was applied to match patients receiving zinc treatment to those without zinc treatment. The association between zinc sulfate use and in-hospital mortality and 30-day mortality, need for renal replacement therapy (RRT), and length of stay was determined by logistic regression and Cox proportional hazards modeling.ResultsA total of 9,811 AKI patients were included in the study. PSM yielded 222 pairs of patients who received zinc treatment and those who did not. Zinc supplementation was associated with reduced in-hospital mortality (HR = 0.48 (95% CI: 0.28, 0.83) P = 0.009) and 30-day mortality (HR = 0.51 (95% CI, 0.30, 0.86) P = 0.012). In the subgroup analysis, zinc use was associated with reduced in-hospital mortality in patients with stage 1 AKI and those with sepsis.ConclusionsZinc supplementation was associated with improved survival in critically ill patients with AKI. The supplementation was especially effective in those with stage 1 AKI and sepsis. These results need to be verified in randomized controlled trials.

Background: Acute kidney injury (AKI) is a common syndrome impacting about 13.3 million patients per year. Tilianin has been reported to alleviate myocardial ischemia/reperfusion (I/R) injury, while its effect on AKI is unknown; thus, this study aimed to explore if tilianin protects I/R-induced AKI and the underlying mechanisms.Methods: The microarray dataset GSE52004 was downloaded from GEO DataSets (Gene Expression Omnibus). Differential expression analysis and gene-set enrichment analysis (GSEA) were performed by R software to identify apoptosis pathway-related genes. Then, RcisTarget was applied to identify the transcription factor (TF) related to apoptosis. The STRING database was used to construct a protein–protein interaction (PPI) network. Cytoscape software visualized PPI networks, and hub TFs were selected via cytoHubba. AutoDock was used for molecular docking of tilianin and hub gene-encoded proteins. The expression levels of hub genes were assayed and visualized by quantitative real-time PCR, Western blotting, and immunohistochemistry by establishing I/R-induced AKI mouse models.Results: Bioinformatics analysis showed that 34 genes, including FOS, ATF4, and Gadd45g, were involved in the apoptosis pathway. In total, seven hub TFs might play important roles in tilianin-regulating apoptosis pathways. In in vivo, tilianin improved kidney function and reduced the number of TUNEL-positive renal tubular epithelial cells (RTECs) after I/R-induced AKI. Tilianin reduced the activation of the ERK pathway and then downregulated the expression of EGR1. This further ameliorated the expression of anti-apoptotic genes such as BCL2L1 and BCL2, reduced pro-apoptotic genes such as BAD, BAX, and caspase-3, and reduced the release of cytochrome c.Conclusion: Tilianin reduced apoptosis after I/R-induced AKI by the ERK/EGR1/BCL2L1 pathway. Our findings provided novel insights for the first time into the protective effect and underlying molecular mechanisms of tilianin on I/R-induced AKI.

Abstract Background Global longitudinal strain (GLS), most commonly measured at the endocardium, has been shown to be superior to left ventricular (LV) ejection fraction (LVEF) for the identification of systolic dysfunction and prediction of outcomes in heart failure (HF). We hypothesized that strains measured at different myocardial layers (endocardium = ENDO, epicardium = EPI, average = AVE) will have distinct diagnostic and predictive performance for patients with HF. Methods Layer-specific GLS, layer-specific global circumferential strain (GCS) and global radial strain (GRS) were evaluated by cardiovascular magnetic resonance imaging (CMR) feature tracking in the Alberta HEART study. A total of 453 subjects consisted of healthy controls (controls, n = 77), at-risk for HF (at-risk, n = 143), HF with preserved ejection fraction (HFpEF, n = 87), HF with mid-range ejection fraction (HFmrEF, n = 88) and HF with reduced ejection fraction (HFrEF, n = 58). For outcomes analysis, CMR-derived imaging parameters were adjusted with a base model that included age and N-terminal prohormone of b-type natriuretic peptide (NT-proBNP) to test their independent association with 5-year all-cause mortality. Results GLS_EPI distinguished all groups with preserved LVEF (controls − 16.5 ± 2.4% vs. at-risk − 15.5 ± 2.7% vs. HFpEF − 14.1 ± 3.0%, p 55%), in whom lower strains were associated with increased concentricity.

Abstract Background We aimed to develop a nomogram based on preprocedural features for early prediction of acute kidney injury (AKI) and to assess the prognosis in patients after radical and partial nephrectomy. Methods The study included a development cohort of 1111 patients who were treated between June 2012 and June 2017 and an additional validation cohort of 356 patients who were treated between July 2017 and June 2018. Stepwise regression and logistic regression analyses were used to evaluate the association between predictors and AKI. Incorporating all independent predictors, a nomogram for postoperative AKI was developed and externally validated. Patients were followed up for 5 years to assess renal function, acute kidney disease (AKD), chronic kidney disease (CKD), hospital readmission and mortality were key prognosis we focused on. Results After multivariate logistic regression, radical nephrectomy (odds ratio (OR) = 3.57, p

Predatory hunting is an innate appetite-driven and evolutionarily conserved behavior essential for animal survival, integrating sequential behaviors including searching, pursuit, attack, retrieval, and ultimately consumption. Nevertheless, neural circuits underlying hunting behavior with different features remain largely unexplored. Here, we deciphered a novel function of lateral hypothalamus (LH) calcium/calmodulin-dependent protein kinase II α (CaMKIIα+) neurons in hunting behavior and uncovered upstream/downstream circuit basis. LH CaMKIIα+ neurons bidirectionally modulate novelty-seeking behavior, predatory attack, and eating in hunting behavior. LH CaMKIIα+ neurons integrate hunting-related novelty-seeking information from the medial preoptic area (MPOA) and project to the ventral periaqueductal gray (vPAG) to promote predatory eating. Our results demonstrate that LH CaMKIIα+ neurons are the key hub that integrate MPOA-conveyed novelty-seeking signals and encode predatory eating in hunting behavior, which enriched the neuronal substrate of hunting behavior.

Urban expressways take on rapid and external transport in the city due to their fast, safe, and large capacity. Implementing intelligent and active traffic control can effectively improve the performance of urban traffic and mitigate the urban traffic congestion problem. Real-time traffic guidance is one critical way of intelligent active traffic control, and travel time is the most important input for real-time traffic guidance. We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. The ePL model has a promising nonlinear mapping potential, which is well suitable for the traffic prediction. We used generalized recursive least square (GRLS) to improve the estimation accuracy of the model’s parameters. This model is a fuzzy control model. Its output is the forecasting result which is also the fuzzy reasoning result. We tested this model by comparing it to other travel time prediction approaches, with the freeway data from the Caltrans Performance Measurement System. The results from the improved ePL model showed mean absolute error of 5.941 seconds, mean absolute percentage error of 1.316%, and root mean square error of 10.923 s. The performances are better than those of the baseline models including ARIMA and BPN. This model can be used to predict the travel time in the field to be used for active traffic control and traffic guidance.

The multi-model ensemble (MME) forecast for meteorological elements has been proved many times to be more skillful than the single model. It improves the forecast quality by integrating multiple sets of numerical forecast results with different spatial-temporal characteristics. Currently, the main numerical forecast results present a grid structure formed by longitude and latitude lines in space and a special two-dimensional time structure in time, namely the initial time and the lead time, compared with the traditional one-dimensional time. These characteristics mean that many MME methods have limitations in further improving forecast quality. Focusing on this problem, we propose a deep MME forecast method that suits the special structure. At spatial level, our model uses window self-attention and shifted window attention to aggregate information. At temporal level, we propose a recurrent like neural network with rolling structure (Roll-RLNN) which is more suitable for two-dimensional time structure that widely exists in the institutions of numerical weather prediction (NWP) with running service. In this paper, we test the MME forecast for sea level pressure as the forecast characteristics of the essential meteorological element vary clearly across institutions, and the results show that our model structure is effective and can make significant forecast improvements.

The neural circuits through which the substantia nigra pars reticulata (SNr) exerts its role in epilepsy control are not known. Here the authors reveal that a long-range SNr-parafascicular nucleus disinhibitory circuit participates in regulating seizures in temporal lobe epilepsy and inhibition of this circuit can alleviate severity of epileptic seizures.

Seasonal H3N2 influenza virus has always been a potential threat to public health. The reassortment of the human and avian H3N2 influenza viruses has resulted in major influenza outbreaks, which have seriously damaged human life and health. To assess the possible threat of the H3N2 avian influenza virus to human health, we performed whole-genome sequencing and genetic evolution analyses on 10 H3N2 field strains isolated from different hosts and regions in 2019–2020 and selected representative strains for pathogenicity tests on mice. According to the results, the internal gene cassettes of nine strains had not only undergone reassortment with the H1, H2, H4, H6, and H7 subtypes, which circulate in poultry and mammals, but also with H10N8, which circulates in wild birds in the natural environment. Three reassorted strains were found to be pathogenic to mice, of these one strain harboring MP from H10N8 showed a stronger virulence in mice. This study indicates that reassorted H3N2 AIVs may cross the host barrier to infect mammals and humans, thereby, necessitating persistent surveillance of H3N2 AIVs.

Considerable evidences have indicated that elevated uric acid (UA) was involved in renal tubular injury leading to hyperuricemic nephropathy (HN). Scutellarin is a biologically active flavonoid derived from the Chinese traditional herb Erigeron breviscapus Hand-Mazz, which has been widely used in the treatment of cardiovascular and cerebrovascular diseases. In the present study, we analyzed the effect of scutellarin on HN, by using C57BL/6 mice and human renal tubular epithelial cell line HK-2 which was subjected to adenine/potassium oxonate and UA to mimic a HN injury. The HN mice showed a significant decrease in renal function with the increased SCr and blood urea nitrogen (BUN) (p < 0.05). Hematoxylin–eosin staining results showed a histological injury in HN mice kidney tissues with severe tubular damage. Scutellarin dose dependently alleviated the renal injury of the HN model (p < 0.05), and a dose of 20 mg/kg/day remarkably reduced the Scr level (26.10 ± 3.23 μmol/ml vs. 48.39 ± 7.51 μmol/ml, p < 0.05) and BUN (151.12 ± 30.24 mmol/L vs. 210.43 ± 45.67 mmol/L, p < 0.05) compared with the HN model group. Similarly, scutellarin decreased NGAL, Kim-1, cystatin C, and IL-18 protein expression levels in HN mouse (p < 0.05). Overexpressed CCN1 could not induce NLRP3 inflammasome activation, with no change of mRNA and protein expression levels of NLRP3, ASC, and pro-caspase-1 compared with the control HK-2. However, HK-2 showed a significant NLRP3 inflammasome activation and apoptosis. Importantly, knockdown of CCN1 not only aggravated NLRP3 inflammasome activation and apoptosis but also abrogated the protective effect of scutellarin in UA-induced HK-2 injury. Thus, scutellarin might alleviate HN progression via a mechanism involved in CCN1 regulation on NLRP3 inflammasome activation.

The accurate prediction of sea surface temperature (SST) is the basis for our understanding of local and global climate characteristics. At present, the existing sea temperature prediction methods fail to take full advantage of the potential spatial dependence between variables. Among them, graph neural networks (GNNs) modeled on the relationships between variables can better deal with space–time dependency issues. However, most of the current graph neural networks are applied to data that already have a good graph structure, while in SST data, the dependency relationship between spatial points needs to be excavated rather than existing as prior knowledge. In order to predict SST more accurately and break through the bottleneck of existing SST prediction methods, we urgently need to develop an adaptive SST prediction method that is independent of predefined graph structures and can take full advantage of the real temporal and spatial correlations hidden indata sets. Therefore, this paper presents a graph neural network model designed specifically for space–time sequence prediction that can automatically learn the relationships between variables and model them. The model automatically extracts the dependencies between sea temperature multi-variates by embedding the nodes of the adaptive graph learning module, so that the fine-grained spatial correlations hidden in the sequence data can be accurately captured. Figure learning modules, graph convolution modules, and time convolution modules are integrated into a unified end-to-end framework for learning. Experiments were carried out on the Bohai Sea surface temperature data set and the South China Sea surface temperature data set, and the results show that the model presented in this paper is significantly better than other sea temperature model predictions in two remote-sensing sea temperature data sets and the surface temperature of the South China Sea is easier to predict than the surface temperature of the Bohai Sea.

Ocean current (OC) prediction plays an important role for carrying out ocean-related activities. There are plenty of studies for OC prediction with deep learning to pursue better prediction performance, and the attention mechanism was widely used for these studies. However, the attention mechanism was usually combined with deep learning models rather than purely used to predict OC, or, if it was purely used, did not further optimize the attention weight. Therefore, a deep learning model based on weighted pure attention mechanism is proposed in this paper. This model uses the pure attention mechanism, introduces a weight parameter for the generated attention weight, and moves more attentions from other elements to the key elements based on weight parameter setting. To our knowledge, it is the first attempt to use the weighted pure attention mechanism to improve the OC prediction performance, and it is an innovation for OC prediction. The experiment results indicate that the proposed model can fully take advantage of the strengths from the pure attention mechanism; it can further optimize the pure attention mechanism and significantly improve the prediction performance, and is reliable for OC prediction with high performance for a wide time range and large spatial scope.

The prediction of marine elements has become increasingly important in the field of marine research. However, time series data in a complex environment vary significantly because they are composed of dynamic changes with multiple mechanisms, causes, and laws. For example, sea surface temperature (SST) can be influenced by ocean currents. Conventional models often focus on capturing the impact of historical data but ignore the spatio–temporal relationships in sea areas, and they cannot predict such widely varying data effectively. In this work, we propose a cyclic evolutionary network model (CENS), an error-driven network group, which is composed of multiple network node units. Different regions of data can be automatically matched to a suitable network node unit for prediction so that the model can cluster the data based on their characteristics and, therefore, be more practical. Experiments were performed on the Bohai Sea and the South China Sea. Firstly, we performed an ablation experiment to verify the effectiveness of the framework of the model. Secondly, we tested the model to predict sea surface temperature, and the results verified the accuracy of CENS. Lastly, there was a meaningful finding that the clustering results of the model in the South China Sea matched the actual characteristics of the continental shelf of the South China Sea, and the cluster had spatial continuity.

This study aims to explore the possibility of using a multilayer artificial neural network for the classification between children with autism spectrum disorder (ASD) and typically developing (TD) children based on short-time spontaneous hemodynamic fluctuations. Spontaneous hemodynamic fluctuations were collected by a functional near-infrared spectroscopy setup from bilateral inferior frontal gyrus and temporal cortex in 25 children with ASD and 22 TD children. To perform feature extraction and classification, a multilayer neural network called CGRNN was used which combined a convolution neural network (CNN) and a gate recurrent unit (GRU), since CGRNN has a strong ability in finding characteristic features and acquiring intrinsic relationship in time series. For the training and predicting, short-time (7 s) time-series raw functional near-infrared spectroscopy (fNIRS) signals were used as the input of the network. To avoid the over-fitting problem and effectively extract useful differentiation features from a sample with a very limited size (e.g., 25 ASDs and 22 TDs), a sliding window approach was utilized in which the initially recorded long-time (e.g., 480 s) time-series was divided into many partially overlapped short-time (7 s) sequences. By using this combined deep-learning network, a high accurate classification between ASD and TD could be achieved even with a single optical channel, e.g., 92.2% accuracy, 85.0% sensitivity, and 99.4% specificity. This result implies that the multilayer neural network CGRNN can identify characteristic features associated with ASD even in a short-time spontaneous hemodynamic fluctuation from a single optical channel, and second, the CGRNN can provide highly accurate prediction in ASD.

Wave is a common environmental load that often causes serious damages to offshore structures. In addition, the stability for the submarine artificial slope is also affected by the wave loading. Although the landslide of submarine slopes induced by the waves received wide attention, the research on the influence of solitary wave is rare. In this study, a 2-D integrated numerical model was developed to investigate the stability of the foundation trench under the solitary wave loading. The Reynolds-averaged Stokes (RANS) equations were used to simulate the propagation of a solitary wave, while the current was realized by setting boundary inlet/outlet velocity. The pore pressure induced by the solitary wave was calculated by Darcy’s law, and the seabed was characterized by Mohr–Coulomb constitutive model. Firstly, the wave model was validated through the comparison between analytical solution and experimental data. The initial consolidation state of slope under hydrostatic pressure was achieved as the initial state. Then, the factor of stability (FOS) for the slope corresponding to different distances between wave crest and slope top was calculated with the strength reduction method. The minimum of FOS was defined as the stability index for the slope with specific slope ratio during the process of dynamic wave loading. The parametric study was conducted to examine the effects of soil strength parameters, slope ratio, and current direction. At last, the influence of upper slope ratio in a two-stage slope was also discussed.

Nowadays, mobile devices have been considered as a new platform for information services, and have been widely used in many fields. In mobile application services, the processing and representation of data is a key issue which has a great impact on the service quality. Knowledge map is regarded as an effective method and has been widely utilized in mobile devices. However, traditional knowledge maps employed in mobile devices are subject to a lack of cognition characteristics, which results in corresponding information services' being unable to match the users' cognition level, thus affecting the quality of services. In this paper, we propose a hierarchical cognitive academic map (HCAM) for the specific academic domain application background. HCAM can meet the needs of three basic levels of Bloom's cognition taxonomy model by distinguishing the academic attributes of nodes and relations between nodes. First, academic concepts are the basic units in HCAM and are classified into research object concepts and method/technique concepts, which meet the human's remembering cognition levels. Second, HCAM provides the implementation and collaboration relation between concepts, which satisfies the human's applying and understanding cognition levels. Third, technique/method concepts are organized in the form of hierarchical structure from the top down of which concepts' specificity for the domain get higher and higher. In addition, Bayesian rose tree clustering is adopted in the construction of this hierarchical structure and acquiring the cognition depth for each concept. Furthermore, experiments on information retrieval field and data mining field are performed to demonstrate the effectiveness and cognition characteristics of HCAM.

Harmful algal blooms (HABs) often cause great harm to fishery production and the safety of human lives. Therefore, the detection and prediction of HABs has become an important issue. Machine learning has been increasingly used to predict HABs at home and abroad. However, few of them can capture the sudden change of Chl-a in advance and handle the long-term dependencies appropriately. In order to address these challenges, the Long Short-Term Memory (LSTM) based spatial-temporal attentions model for Chlorophyll-a (Chl-a) concentration prediction is proposed, a model which can capture the correlation between various factors and Chl-a adaptively and catch dynamic temporal information from previous time intervals for making predictions. The model can also capture the stage of Chl-a when values soar as red tide breaks out in advance. Due to the instability of the current Chl-a concentration prediction model, the model is also applied to make a prediction about the forecast reliability, to have a basic understanding of the range and fluctuation of model errors and provide a reference to describe the range of marine disasters. The data used in the experiment is retrieved from Fujian Marine Forecasts Station from 2009 to 2011 and is combined into 8-dimension data. Results show that the proposed approach performs better than other Chl-a prediction algorithms (such as Attention LSTM and Seq2seq and back propagation). The result of error prediction also reveals that the error forecast method possesses established advantages for red tides prevention and control.

Previous research on financial time-series data mainly focused on the analysis of market evolution and trends, ignoring its characteristics in different resolutions and stages. This paper discusses the evolution characteristics of the financial market in different resolutions, and presents a method of complex network analysis based on wavelet transform. The analysis method has proven the linkage effects of the plate sector in China’s stock market and has that found plate drift phenomenon occurred before and after the stock market crash. In addition, we also find two different evolutionary trends, namely the W-type and M-type trends. The discovery of linkage plate and drift phenomena are important and referential for enterprise investors to build portfolio investment strategy, and play an important role for policy makers in analyzing evolution characteristics of the stock market.

Multivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks generate a significant amount of multivariate time series data that can be used to build networks that reflect market behavior. However, traditional commercial complex networks cannot fully utilize the multiple attributes of stocks and redundant filter relationships and reveal a more authentic financial stock market. We propose a fusion similarity of multiple time series and construct a threshold network with similarity. Furthermore, we define the connectivity efficiency to choose the best threshold, establishing a high connectivity efficiency network with the optimal network threshold. By searching the central node in the threshold network, we have found that the network center nodes constructed by our proposed method have a more comprehensive industry coverage than the traditional techniques to build the systems, and this also proves the superiority of this method.