A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Characteristics and outcomes of out-of-hospital cardiac arrest in Zhejiang Province

China has undergone tremendous economic growth,but there still remains much room for improvement inemergency medical service （EMS） system.[1] The EMSin China comprises of three parts： the pre-hospitalemergency service, the emergency department, andthe intensive care unit. Not much is known about theexact numbers of out-of-hospital cardiac arrest （OHCA）across the whole of China, though there are reports fromspecifi c provinces.

基于re3data的中英科学数据仓储平台对比研究

以re3data为数据获取源,选取中英两国406个科学数据仓储为研究对象,从分布特征、责任类型、仓储许可、技术标准及质量标准等5个方面、11个指标对两国科学数据仓储的建设情况进行对比分析,试图为我国数据仓储的可持续发展提出建议:广泛联结国内外异质机构,推进多学科领域的交流与合作,有效扩充仓储许可权限与类型,优化技术标准的应用现况,提高元数据使用的灵活性。

Application of Out-of-Hospital Extended Nursing in Brace Treatment of Patients with Idiopathic Scoliosis

Objective:To investigate the effect of out-of-hospital extended nursing on the compliance behaviors and therapeutic effect of brace treatment of patients with idiopathic scoliosis.Methods:54 patients with idiopathic scoliosis between February 2015 and December 2017 were randomly divided into control group and observation group.Patients in the control group received pelvic suspension traction,gymnastic exercises,and brace wear at discharge,on the basis of which patients in the observation group were added with extended care outside the hospital.The compliance behaviors and the changes of scoliosis angle(Cobb angle)of patients in the 2 groups were evaluated.Results:Compared with the control group,patients in the observation group had better compliance behaviors in completion status of functional exercise(χ2=5.594,P=0.018),brace wear(χ2=6.171,P=0.013),subsequent visit on time(χ2=9.247,P=0.002).Cobb angle was improved significantly in both groups at the last follow-up compared with that on admission,and the improvement was more significantly in the observation group(P<0.001).Conclusion:Through the implementation of out-of-hospital extended nursing,the compliance behaviors and clinical effect of brace treatment for idiopathic scoliosis patients are obviously improved,and this active nursing model is worth popularizing in clinic.

Data Secure Storage Mechanism for IIoT Based on Blockchain

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of IIoT.Blockchain technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the IIoT.In the traditional blockchain,data is stored in a Merkle tree.As data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based IIoT.Accordingly,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of data.To solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC structure.Secondly,this paper uses PVC instead of the Merkle tree to store big data generated by IIoT.PVC can improve the efficiency of traditional VC in the process of commitment and opening.Finally,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of experiments.This mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

Hadoop-based secure storage solution for big data in cloud computing environment

In order to address the problems of the single encryption algorithm,such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment,we propose a Hadoop based big data secure storage scheme.Firstly,in order to disperse the NameNode service from a single server to multiple servers,we combine HDFS federation and HDFS high-availability mechanisms,and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage.Then,we improve the ECC encryption algorithm for the encryption of ordinary data,and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated.To accelerate the encryption,we adopt the dualthread encryption mode.Finally,the HDFS control module is designed to combine the encryption algorithm with the storage model.Experimental results show that the proposed solution solves the problem of a single point of failure of metadata,performs well in terms of metadata reliability,and can realize the fault tolerance of the server.The improved encryption algorithm integrates the dual-channel storage mode,and the encryption storage efficiency improves by 27.6% on average.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Brugada syndrome associated with out-of-hospital cardiac arrest: A case report

BACKGROUND Brugada syndrome(BrS)is an inherited disease characterized by an electrocardiogram(ECG)with a coved-type ST-segment elevation in the right precordial leads(V1-V3),which predisposes to sudden cardiac death(SCD)due to polymorphic ventricular tachycardia or ventricular fibrillation in the absence of structural heart disease.We report the case of a 29-year-old man with out-ofhospital cardiac arrest.BrS is associated with a high incidence of SCD in adults,and increasing the awareness of BrS and prompt recognition of the Brugada ECG pattern can be lifesaving.CASE SUMMARY A 29-year-old man suffered from out-of-hospital cardiac arrest,and after defibrillation,his ECG demonstrated a coved-type elevated ST segment in V1 and V2.These findings were compatible with type 1 Brugada pattern,and ECG of his brother showed a type 2 Brugada pattern.The diagnosis was BrS,NYHF IV,multiple organ dysfunction syndrome,sepsis,and hypoxic ischemic encephalopathy.The patient had no arrhythmia episodes after discharge throughout a follow-up period of 36 mo.CONCLUSION Increasing awareness of BrS and prompt recognition of the Brugada ECG pattern can be lifesaving.

Association between the Consumption of Carbonated Beverages and Out-of-Hospital Cardiac Arrests of Cardiac Origin in Japan

Background: The consumption of carbonated beverages has been shown to increase the risk of developing metabolic syndrome. The associations between the consumption of carbonated beverages and left arterial dimension or left ventricular mass are believed to be likely related to the greater body weight of carbonated beverage drinkers relative to non-drinkers. Nonetheless, little is known about the association between the consumption of carbonated beverages and out-of-hospital cardiac arrests (OHCAs) in Japan. Methods: We compared the age-adjusted incidence of OHCAs to the expenditures on various beverages per person between 2005 and 2011 in the 47 prefectures of Japan. Patients who suffered from OHCAs of cardiac and non-cardiac origin were enrolled in All-Japan Utstein Registry of the Fire and Disaster Management Agency. The expenditures on various beverages per person in the 47 prefectures in Japan were obtained from data published by the Ministry of Health, Labour and Welfare of Japan. Results: There were 797,422 cases of OHCA in the All-Japan Utstein registry between 2005 and 2011, including 11,831 cases who did not receive resuscitation. Among these 785,591 cases of OHCA, 435,064 (55.4%) were classified as cardiac origin and 350,527 (44.6%) were non-cardiac origin. Non-cardiac origin included cerebrovascular disease, respiratory disease, malignant tumor, and exogenous disease (4.8%, 6.1%, 3.5%, and 18.9%, respectively). The expenditures on carbonated beverages were significantly associated with OHCAs of cardiac origin (r = 0.30, p = 0.04), but not non-cardiac origin (r = -0.03, p = 0.8). Expenditures on other beverages, including green tea, tea, coffee, cocoa, fruit or vegetable juice, fermented milk beverage, milk beverage, and mineral water, were not significantly associated with OHCAs of cardiac origin. Conclusion: Carbonated beverage consumption was significantly and positively associated with OHCAs of cardiac origin in Japan, indicating that beverage habits might play a role in OHCAs of cardiac origin.

Are There Age-Related Differences in the In-Hospital Treatment of Victims from Out-of-Hospital Cardiac Arrest?

Objective: Hardly anything is known about reasons for age-related differences in surviving out-of-hospital cardiac arrest (OHCA) with worse surviving rates in elderly. Methods: 204 victims from OHCA who were admitted in our hospital between January 1st 2008 and December 31st 2013 were identified. According to their mean age (69.1 ± 14.2 years) we classified those patients (pts) who were younger than mean age minus standard deviation (SD) as young, and those victims from OHCA who were older than mean age plus SD as old. Results: Young victims from OHCA (n = 32 pts) presented more often with an initial shockable rhythm than the elderly (n = 38 pts) (50.0% vs. 21.1%;p = 0.014), received more often coronary angiography (71.9% vs. 18.4%;

Enhanced prediction of anisotropic deformation behavior using machine learning with data augmentation

Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition.This characteristic results in a diverse range of flow curves that vary with a deformation condition.This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning(ML)with data augmentation.The developed model combines four key strategies from data science:learning the entire flow curves,generative adversarial networks(GAN),algorithm-driven hyperparameter tuning,and gated recurrent unit(GRU)architecture.The proposed model,namely GAN-aided GRU,was extensively evaluated for various predictive scenarios,such as interpolation,extrapolation,and a limited dataset size.The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions.The GAN-aided GRU results were superior to those of previous ML models and constitutive equations.The superior performance was attributed to hyperparameter optimization,GAN-based data augmentation,and the inherent predictivity of the GRU for extrapolation.As a first attempt to employ ML techniques other than artificial neural networks,this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys.

Effect of Airway Management and Impedance Threshold Device on Circulation, Survival and Neurological Outcome in Adult Out-Of-Hospital Cardiac Arrest

Purpose: This study was designed to study the effect of early use of the King Airway (KA) and impedance threshold device (ITD) in out-of-hospital cardiac arrest on ETCO2 as a surrogate measure of circulation, survival, and cerebral performance category (CPC) scores. After analysis of the first 9 month active period the KA was relegated to rescue airway status. Methods: This was a prospective pre-post study design. Patients >18 years with out-of-hospital cardiac caused arrest were included. Three periods were compared. In the first “non active” period conventional AHA 30/2 compression/ventilation ratio CPR was done with bag mask ventilation (BMV). No ITD was used. After advanced airway placement the compression/ventilation ratio was 10/1. In the second period continuous compressions were done. Primary airway management was a KA with an ITD. After placement of the KA the compression/ventilation ratio was 10/1. In the third period CPR reverted to 30/2 ratio with a two hand seal BMV with ITD. CPR ratio was 10/1 post endotracheal intubation (ETI) or KA. The KA was only recommended for failed BMV and ETI. Results: Survival to hospital discharge was similar in all three study periods. In Period 2 there was a strong trend to CPC scores >2. The study group hypothesized that the KA interfered with cerebral blood flow. For that reason the KA was abandoned as a primary airway. Comparing Period 1 to Period 3 there was a trend to improved survival in the bystander witnessed shockable rhythm (Utstein) subgroup, particularly if a metronome was used. ETCO2 was significantly increased in Period 2 and trended up in Period 3 when compared to Period 1. Advanced airway intervention had a highly significant negative association with survival. Conclusion: The introduction of an ITD into our system did not result in a statistically significant improvement in survival. The study groups were somewhat dissimilar. ETCO2 trended up. When comparing Period 1 to Period 3, the bundle of care was associated with a trend towards increased survival in the Utstein subgroup, particularly with a metronome set at 100. Multiple confounders make a definitive conclusion impossible. Advanced airways showed a significant association with poor survival outcomes. The KA was additionally associated with poor neurologic outcomes.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

Since the impoundment of Three Gorges Reservoir(TGR)in 2003,numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall.One case is the Outang landslide,a large-scale and active landslide,on the south bank of the Yangtze River.The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics.Data mining technology,including the two-step clustering and Apriori algorithm,is then used to identify the dominant triggers of landslide movement.In the data mining process,the two-step clustering method clusters the candidate triggers and displacement rate into several groups,and the Apriori algorithm generates correlation criteria for the cause-and-effect.The analysis considers multiple locations of the landslide and incorporates two types of time scales:longterm deformation on a monthly basis and short-term deformation on a daily basis.This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors.The data mining results reveal different dominant triggering factors depending on the monitoring frequency:the monthly and bi-monthly cumulative rainfall control the monthly deformation,and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide.It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslideprone areas.

Coronary Angiography in Patients with and without STEMI Following Out-of-Hospital Cardiac Arrest

Introduction: Little is known about discrepancies between patients who present with or without STEMI following out-of-hospital cardiac arrest (OHCA). Material and Methods: All patients with OHCA who were admitted to our hospital between January 1st 2008 and December 31st 2013 were classified according to their initial laboratory and electrocardiographic findings into STEMI, NSTEMI or no ACS. Results: Overall, 147 patients [32 STEMI (21.8%), 28 NSTEMI (19.0%) and 87 no ACS (59.2%)] were included with a mean age of 63.7 ± 13.3 years;there were 84 men (57.1%) and 63 (42.9%) women. Of these, 63 patients (51.7%) received coronary angiography [29 STEMI (90.6%), 9 NSTEMI (32.1%) and 38 no ACS (43.7%)] showing a high prevalence of coronary artery disease (CAD) [28 STEMI (96.6%), 9 NSTEMI (100.0%) and 26 no ACS (68.4%)] requiring percutaneous coronary intervention (PCI) in 52 cases [28 STEMI (96.6%), 8 NSTEMI (88.9%) and 16 no ACS (42.1%)]. Discussion: Coronary angiography immediately after hospital admission is feasible if all are prepared for potential further resuscitation efforts during cardiac catheterization. Primary focus on haemodynamic stabilisation may reduce the rates of coronary angiographies in patients following OHCA. Altogether, our data support the call for immediate coronary angiography in all patients following OHCA irrespective of their initial laboratory or electrocardiographic findings.

Benchmark experiment on slab^(238)U with D-T neutrons for validation of evaluated nuclear data

A benchmark experiment on^(238)U slab samples was conducted using a deuterium-tritium neutron source at the China Institute of Atomic Energy.The leakage neutron spectra within energy levels of 0.8-16 MeV at 60°and 120°were measured using the time-of-flight method.The samples were prepared as rectangular slabs with a 30 cm square base and thicknesses of 3,6,and 9 cm.The leakage neutron spectra were also calculated using the MCNP-4C program based on the latest evaluated files of^(238)U evaluated neutron data from CENDL-3.2,ENDF/B-Ⅷ.0,JENDL-5.0,and JEFF-3.3.Based on the comparison,the deficiencies and improvements in^(238)U evaluated nuclear data were analyzed.The results showed the following.(1)The calculated results for CENDL-3.2 significantly overestimated the measurements in the energy interval of elastic scattering at 60°and 120°.(2)The calculated results of CENDL-3.2 overestimated the measurements in the energy interval of inelastic scattering at 120°.(3)The calculated results for CENDL-3.2 significantly overestimated the measurements in the 3-8.5 MeV energy interval at 60°and 120°.(4)The calculated results with JENDL-5.0 were generally consistent with the measurement results.

An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to be in favor of the majority class(usually defined as the negative class),which may do harm to the accuracy of the minority class(usually defined as the positive class),and then lead to poor overall performance of the model.A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article,which is based on a new hybrid resampling approach(MSHR)and a new fine cost-sensitive support vector machine(CS-SVM)classifier(FCSSVM).The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples,based on which,the so-called pseudo-negative samples are screened out to generate new positive samples(over-sampling step)through linear interpolation and are deleted finally(under-sampling step).This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline,without changing the overall scale of the dataset.The FCSSVM is an improved version of the traditional CS-SVM.It considers influences of both the imbalance of sample number and the class distribution on classification simultaneously,and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice(RIME)algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification borderline.To verify the effectiveness of the proposed method,a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced datasets.The experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases,and both the MSHR and the FCSSVM played significant roles.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.