Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.

Deep learning approaches to recover the plasma current density profile from the safety factor based on Grad–Shafranov solutions across multiple tokamaks

Many magnetohydrodynamic stability analyses require generation of a set of equilibria with a fixed safety factor q-profile while varying other plasma parameters.A neural network(NN)-based approach is investigated that facilitates such a process.Both multilayer perceptron(MLP)-based NN and convolutional neural network(CNN)models are trained to map the q-profile to the plasma current density J-profile,and vice versa,while satisfying the Grad–Shafranov radial force balance constraint.When the initial target models are trained,using a database of semianalytically constructed numerical equilibria,an initial CNN with one convolutional layer is found to perform better than an initial MLP model.In particular,a trained initial CNN model can also predict the q-or J-profile for experimental tokamak equilibria.The performance of both initial target models is further improved by fine-tuning the training database,i.e.by adding realistic experimental equilibria with Gaussian noise.The fine-tuned target models,referred to as fine-tuned MLP and fine-tuned CNN,well reproduce the target q-or J-profile across multiple tokamak devices.As an important application,these NN-based equilibrium profile convertors can be utilized to provide a good initial guess for iterative equilibrium solvers,where the desired input quantity is the safety factor instead of the plasma current density.

Intensive care unit-acquired weakness:Unveiling significant risk factors and preemptive strategies through machine learning

This editorial discusses an article recently published in the World Journal of Clinical Cases,focusing on risk factors associated with intensive care unit-acquired weak-ness(ICU-AW).ICU-AW is a serious neuromuscular complication seen in criti-cally ill patients,characterized by muscle dysfunction,weakness,and sensory impairments.Post-discharge,patients may encounter various obstacles impacting their quality of life.The pathogenesis involves intricate changes in muscle and nerve function,potentially leading to significant disabilities.Given its global significance,ICU-AW has become a key research area.The study identified critical risk factors using a multilayer perceptron neural network model,highlighting the impact of intensive care unit stay duration and mechanical ventilation duration on ICU-AW.Recommendations were provided for preventing ICU-AW,empha-sizing comprehensive interventions and risk factor mitigation.This editorial stresses the importance of external validation,cross-validation,and model tran-sparency to enhance model reliability.Moreover,the application of machine learning in clinical medicine has demonstrated clear benefits in improving disease understanding and treatment decisions.While machine learning presents oppor-tunities,challenges such as model reliability and data management necessitate thorough validation and ethical considerations.In conclusion,integrating ma-chine learning into healthcare offers significant potential and challenges.Enhan-cing data management,validating models,and upholding ethical standards are crucial for maximizing the benefits of machine learning in clinical practice.

Influencing Factors of Chinese Proficiency Test Learning Application Under the Framework of Seamless Learning

With the development of modern information technology and network society,the era of digital transformation has arrived,many educational resources have been developed and utilized,and the problem of space-time separation of global Chinese learners is gradually being solved.The use of mobile application learning can achieve convenient and fast learning,reduce learning costs,and flexibly adjust learning progress.After experiencing COVID-19,the demand for human-computer interaction in mobile learning is even more necessary.Therefore,to make applications achieve seamless docking with the needs of learners in the design of learning,the research investigates and analyzes the five dimensions of time,space,mode,subject,and object.The research tests the reliability and validity of the questionnaire,removes the relevant items and conducts non-parametric tests,then puts forward corresponding strategies according to the results of the questionnaire.It aims to enhance the efficiency of Chinese learners in HSK(Hanyu Shuiping Kaoshi,Chinese Proficiency Test)learning and promote Chinese learning and the spread of the Chinese language and culture.

Identifying influencing factors and characterizing key issues in urban sustainable development capacity through machine learning

In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of China’s Innovation Demonstration Zones for Sustainable Development Agenda(IDZSDAs),combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity(USDC).After obtaining USDC assessment results through the assessment system,an approach combining Least Absolute Shrinkage and Selection Operator(LASSO)regression and Random Forest(RF)based on machine learning is proposed for identifying influencing factors and characterizing key issues.Combining Coupling Coordination Degree(CCD)analysis,the study further summarizes the systemic patterns and future directions of urban sustainable development.A case study on the IDZSDAs from 2015 to 2022 reveals that:(1)the combined identification method based on machine learning and CCD models effectively quantifies influencing factors and key issues in the urban sustainable development process;(2)the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities;and(3)the machine learning-based combined recognition method is scalable and dynamic.It enables decision-makers to accurately identify influencing factors and characterize key issues based on actual urban development needs.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

Adolescent suicide risk factors and the integration of socialemotional skills in school-based prevention programs

Adolescents are considered one of the most vulnerable groups affected by suicide.Rapid changes in adolescents’physical and mental states,as well as in their lives,significantly and undeniably increase the risk of suicide.Psychological,social,family,individual,and environmental factors are important risk factors for suicidal behavior among teenagers and may contribute to suicide risk through various direct,indirect,or combined pathways.Social-emotional learning is considered a powerful intervention measure for addressing the crisis of adolescent suicide.When deliberately cultivated,fostered,and enhanced,selfawareness,self-management,social awareness,interpersonal skills,and responsible decision-making,as the five core competencies of social-emotional learning,can be used to effectively target various risk factors for adolescent suicide and provide necessary mental and interpersonal support.Among numerous suicide intervention methods,school-based interventions based on social-emotional competence have shown great potential in preventing and addressing suicide risk factors in adolescents.The characteristics of school-based interventions based on social-emotional competence,including their appropriateness,necessity,cost-effectiveness,comprehensiveness,and effectiveness,make these interventions an important means of addressing the crisis of adolescent suicide.To further determine the potential of school-based interventions based on social-emotional competence and better address the issue of adolescent suicide,additional financial support should be provided,the combination of socialemotional learning and other suicide prevention programs within schools should be fully leveraged,and cooperation between schools and families,society,and other environments should be maximized.These efforts should be considered future research directions.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Parameterization Method of Wind Drift Factor Based on Deep Learning in the Oil Spill Model

Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to be constant or parameterized by linear regression and other methods in existing studies,which may limit the accuracy of the oil spill simulation.A parameterization method for wind drift factor(PMOWDF)based on deep learning,which can effectively extract the time-varying characteristics on a regional scale,is proposed in this paper.The method was adopted to forecast the oil spill in the East China Sea.The discrepancies between predicted positions and actual measurement locations of the drifters are obtained using seasonal statistical analysis.Results reveal that PMOWDF can improve the accuracy of oil spill simulation compared with the traditional method.Furthermore,the parameteriza-tion method is validated with satellite observations of the Sanchi oil spill in 2018.

Research on the Influencing Factors of College Students’Learning Experience in Livestreaming Courses

The learning experience of online courses has always been a hot topic.As livestreaming courses are online courses with real-time interaction between instructors and students,its learning experience directly affects the learning behavior and effect.The evaluation indicators related to online course learning experience were combined with the attitudes of scholars,enterprise practitioners,and learners in livestreaming courses.When the questionnaire was designed,the exploratory and confirmatory factors were analyzed,the Questionnaire on Learning Experience of College Students in Livestreaming Courses was developed to evaluate the learning experience of college students attending livestreaming courses.Last but not the least,based on the survey data,important factors affecting college students’learning experience in livestreaming courses,including course content,learning environment,course interaction,and learning incentives were discussed and analyzed;strategies to optimize the learning experience in livestreaming courses were proposed.

Efficient Clustering Network Based on Matrix Factorization

Contrastive learning is a significant research direction in the field of deep learning.However,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing methods.To address these challenges,we propose the Efficient Clustering Network based on Matrix Factorization(ECN-MF).Specifically,we design a batched low-rank Singular Value Decomposition(SVD)algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the data.Additionally,we design a Mutual Information-Enhanced Clustering Module(MI-ECM)to accelerate the training process by leveraging a simple architecture to bring samples from the same cluster closer while pushing samples from other clusters apart.Extensive experiments on six datasets demonstrate that ECN-MF exhibits more effective performance compared to state-of-the-art algorithms.

Gene expression,transcription factor binding and histone modification predict leaf adaxial-abaxial polarity related genes

Leaf adaxial-abaxial(ad-abaxial)polarity is crucial for leaf morphology and function,but the genetic machinery governing this process remains unclear.To uncover critical genes involved in leaf ad-abaxial patterning,we applied a combination of in silico prediction using machine learning(ML)and experimental analysis.A Random Forest model was trained using genes known to influence ad-abaxial polarity as ground truth.Gene expression data from various tissues and conditions as well as promoter regulation data derived from transcription factor chromatin immunoprecipitation sequencing(ChIP-seq)was used as input,enabling the prediction of novel ad-abaxial polarity-related genes and additional transcription factors.Parallel to this,available and newly-obtained transcriptome data enabled us to identify genes differentially expressed across leaf ad-abaxial sides.Based on these analyses,we obtained a set of 111 novel genes which are involved in leaf ad-abaxial specialization.To explore implications for vegetable crop breeding,we examined the conservation of expression patterns between Arabidopsis and Brassica rapa using single-cell transcriptomics.The results demonstrated the utility of our computational approach for predicting candidate genes in crop species.Our findings expand the understanding of the genetic networks governing leaf ad-abaxial differentiation in agriculturally important vegetables,enhancing comprehension of natural variation impacting leaf morphology and development,with demonstrable breeding applications.

The Effect of Inquiry-Based Learning Strategy on EFL Tenth-Grade Students’ Reading Comprehension

This study examined the potential effect of Inquiry-Based Learning Strategy(IBL)on the tenth-grade students’reading comprehension.Two groups and a quasi-experimental design were used.Two complete sections of grade 10 students from a public Secondary School for Girls in Irbid was randomly assigned by the researcher.The experimental group of 30 students was chosen first,and then the control group of 30 students.A pre-post reading comprehension test was designed before and after the study in order to fulfill its goals.Additionally,the experimental group was taught using the IBL strategy,whereas the control group was taught using the traditional teaching methods recommended in the tenth-grade Teacher’s Book.According to the findings,there were significant statistical differences favoring the experimental group over the control group.In light of the findings,the researcher recommended employing the IBL strategy to students with various levels and EFL skills.

Prediction and driving factors of forest fire occurrence in Jilin Province,China

Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.

Influencing factors and solution strategies of chimeric antigen receptor T-cell therapy(CAR–T)cell immunotherapy

Chimeric antigen receptor T-cesll therapy(CAR–T)has achieved groundbreaking advancements in clinical application,ushering in a new era for innovative cancer treatment.However,the challenges associated with implementing this novel targeted cell therapy are increasingly significant.Particularly in the clinical management of solid tumors,obstacles such as the immunosuppressive effects of the tumor microenvironment,limited local tumor infiltration capability of CAR–T cells,heterogeneity of tumor targeting antigens,uncertainties surrounding CAR–T quality,control,and clinical adverse reactions have contributed to increased drug resistance and decreased compliance in tumor therapy.These factors have significantly impeded the widespread adoption and utilization of this therapeutic approach.In this paper,we comprehensively analyze recent preclinical and clinical reports on CAR–T therapy while summarizing crucial factors influencing its efficacy.Furthermore,we aim to identify existing solution strategies and explore their current research status.Through this review article,our objective is to broaden perspectives for further exploration into CAR–T therapy strategies and their clinical applications.

Unveiling significant risk factors for intensive care unit-acquired weakness:Advancing preventive care

In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World Journal of Clinical Cases.Intensive care unit-acquired weakness(ICU-AW)is a debilitating condition that affects critically ill patients,with significant implications for patient outcomes and their quality of life.This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors.Data from a cohort of 1063 adult intensive care unit(ICU)patients were analyzed,with a particular emphasis on variables such as duration of ICU stay,duration of mechanical ventilation,doses of sedatives and vasopressors,and underlying comorbidities.A multilayer perceptron neural network model was developed,which exhibited a remarkable impressive prediction accuracy of 86.2%on the training set and 85.5%on the test set.The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.

Concomitant epidermal growth factor receptor mutation/c-ros oncogene 1 rearrangement in non-small cell lung cancer: A case report

BACKGROUND Epidermal growth factor receptor(EGFR)mutation and c-ros oncogene 1(ROS1)rearrangement are key genetic alterations and predictive tumor markers for non-small cell lung cancer(NSCLC)and are typically considered to be mutually exc-lusive.EGFR/ROS1 co-mutation is a rare event,and the standard treatment appr-oach for such cases is still equivocal.CASE SUMMARY Herein,we report the case of a 64-year-old woman diagnosed with lung adenocar-cinoma,with concomitant EGFR L858R mutation and ROS1 rearrangement.The patient received two cycles of chemotherapy after surgery,but the disease prog-ressed.Following 1-month treatment with gefitinib,the disease progressed again.However,after switching to crizotinib,the lesion became stable.Currently,crizotinib has been administered for over 53 months with a remarkable treatment effect.CONCLUSION The efficacy of EGFR tyrosine kinase inhibitors and crizotinib was vastly different in this NSCLC patient with EGFR/ROS1 co-mutation.This report will aid future treatment of such patients.

Study on the Influencing Factors of L2 Reading Ability and Its Regulation Strategies

This paper takes college students as research subjects to investigate the factors affecting L2 Reading ability and their regulation strategies through scale surveys.The findings are as follows:(1)Linguistic factors have significant impacts on L2 Reading ability,and the influence of non-linguistic factors,such as non-intellectual factors and cultural background knowledge,is also important;and(2)flexible use of reading regulation strategies according to the learning conditions(such as the information extraction strategy,the metacognitive reading regulation strategy,and the interactive reading strategy)can effectively improve learners’L2 Reading ability.The findings of this study have important theoretical and practical value for improving L2 learners’Reading ability.

Using Cooperative Learning Strategy to Teach Speaking among Chinese College EFL Students

Chinese English speaking class faces lots of problems,such as lack of motivation,the big class size,the gap between stu⁃dents from urban and rural areas,etc.In order to solve these problems,the cooperative learning strategy is introduced and analyzed in this article.This article defines the cooperative learning strategy and then introduces the benefits of it and at last suggests how to using it in English teaching,especially in speaking.

Accelerating Factor Xa inhibitor discovery with a de novo drug design pipeline

Small-molecule drugs are essential for maintaining human health. The objective of this study is to identify a molecule that can inhibit the Factor Xa protein and be easily procured. An optimization-based de novo drug design framework, Drug CAMD, that integrates a deep learning model with a mixed-integer nonlinear programming model is used for designing drug candidates. Within this framework, a virtual chemical library is specifically tailored to inhibit Factor Xa. To further filter and narrow down the lead compounds from the designed compounds, comprehensive approaches involving molecular docking,binding pose metadynamics(BPMD), binding free energy calculations, and enzyme activity inhibition analysis are utilized. To maximize efficiency in terms of time and resources, molecules for in vitro activity testing are initially selected from commercially available portions of customized virtual chemical libraries. In vitro studies assessing inhibitor activities have confirmed that the compound EN300-331859shows potential Factor Xa inhibition, with an IC_(50)value of 34.57 μmol·L^(-1). Through in silico molecular docking and BPMD, the most plausible binding pose for the EN300-331859-Factor Xa complex are identified. The estimated binding free energy values correlate well with the results obtained from biological assays. Consequently, EN300-331859 is identified as a novel and effective sub-micromolar inhibitor of Factor Xa.