Navigating the pathways:TAR-DNA-binding-protein-43 aggregation,axonal transport,and local synthesis in amyotrophic lateral sclerosis pathology

Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and metabolic events,which need to be carried out at the right place,time,and intensity.Such mechanisms include axonal transport,local synthesis,and liquid-liquid phase separations.Alterations and malfunctions in these processes are correlated to neurodegenerative diseases such as amyotrophic lateral sclerosis(ALS).

Steel Surface Defect Recognition in Smart Manufacturing Using Deep Ensemble Transfer Learning-Based Techniques

Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,reduce costs,and ensure product quality.In light of the recent advancement of Industry 4.0,identifying defects has become important for ensuring the quality of products during the manufacturing process.In this research,we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network(CNN)architectures:VGG16,VGG19,Xception,and Mobile-Net V2,compensating for their individual weaknesses.We evaluated our methodology on the Xsteel surface defect dataset(XSDD),which comprises seven different classes.The ensemble methodology integrated the predictions of individual models through two methods:model averaging and weighted averaging.Our evaluation showed that the model averaging ensemble achieved an accuracy of 98.89%,a recall of 98.92%,a precision of 99.05%,and an F1-score of 98.97%,while the weighted averaging ensemble reached an accuracy of 99.72%,a recall of 99.74%,a precision of 99.67%,and an F1-score of 99.70%.The proposed weighted averaging ensemble model outperformed the model averaging method and the individual models in detecting defects in terms of accuracy,recall,precision,and F1-score.Comparative analysis with recent studies also showed the superior performance of our methodology.

Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models

This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.

Medical Diagnosis Based on Multi-Attribute Group Decision-Making Using Extension Fuzzy Sets,Aggregation Operators and Basic Uncertainty Information Granule

Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.

Age-dependent alpha-synuclein aggregation and Lewy body formation in Parkinson's disease

Parkinson's disease (PD) is a common degenerative disorder that is becoming increasingly prevalent because of the global aging population.The exact cause of the disorder is unknown;however,recent studies have suggested that multiple factors may contribute to its pathogenesis.PD is characterized by a movement disorder that primarily affects motor control;pathologically,the disease is marked by the presence of Lewy bodies (LBs) in the brain.

基于改进SMOTE算法和Ensemble模型的学习结果预测方法

为解决不同领域的数据分类和预测任务中单个机器学习算法适用性较差的问题,以及缓解数据集严重不平衡对预测性能的影响,提出了基于合成少数类过采样(SMOTE)和Ensemble集成模型的数据分类方法。传统SMOTE算法通过对少数类样本进行插值来生成新的合成样本,合成样本中存在噪声和样本间相似性较高的问题。为此,提出了改进的SMOTE算法,通过距离计算移除噪声样本和易混淆样本,得到高区分度的纯净合成样本。然后,利用Ensemble方法调整样本和分类器权重,并组成分类效果更好的强分类器。在公开在线学习数据集Kalboard360上的实验结果表明,使用极限随机树(ERT)分类器时,结合改进SMOTE和Ensemble模型后实现了97.9%的预测准确度,比单个ERT分类器提升了5.5%,证明所提改进SMOTE算法能够生成高质量的均衡化数据,且集成学习模型的性能显著优于单个机器学习算法。

Straw return increases crop production by improving soil organic carbon sequestration and soil aggregation in a long-term wheat-cotton cropping system

Straw return is a promising strategy for managing soil organic carbon(SOC)and improving yield stability.However,the optimal straw return strategy for sustainable crop production in the wheat(Triticum aestivum L.)-cotton(Gossypium hirsutum L.)cropping system remains uncertain.The objective of this study was to quantify the long-term(10 years)impact of carbon(C)input on SOC sequestration,soil aggregation and crop yields in a wheat-cotton cropping system in the Yangtze River Valley,China.Five treatments were arranged with a single-factor randomized design as follows:no straw return(Control),return of wheat straw only(Wt),return of cotton straw only(Ct),return of 50%wheat and 50%cotton straw(Wh-Ch)and return of 100%wheat and 100%cotton straw(Wt-Ct).In comparison to the Control,the SOC content increased by 8.4 to 20.2%under straw return.A significant linear positive correlation between SOC sequestration and C input(1.42-7.19 Mg ha^(−1)yr^(−1))(P<0.05)was detected.The percentages of aggregates of sizes>2 and 1-2 mm at the 0-20 cm soil depth were also significantly elevated under straw return,with the greatest increase of the aggregate stability in the Wt-Ct treatment(28.1%).The average wheat yields increased by 12.4-36.0%and cotton yields increased by 29.4-73.7%,and significantly linear positive correlations were also detected between C input and the yields of wheat and cotton.The average sustainable yield index(SYI)reached a maximum value of 0.69 when the C input was 7.08 Mg ha^(−1)yr^(−1),which was close to the maximum value(SYI of 0.69,C input of 7.19 Mg ha^(−1)yr^(-1))in the Wt-Ct treatment.Overall,the return of both wheat and cotton straw was the best strategy for improving SOC sequestration,soil aggregation,yields and their sustainability in the wheat-cotton rotation system.

A novel approach to Parkinson's disease treatment with a potentially dual-acting therapeutic agent that targetsα-synuclein aggregation and neuron death

Parkinson's disease(PD),a prevalent neurodegenerative disorder,is chara cterized by the loss of dopaminergic neurons and the aggregation ofα-synuclein protein into Lewy bodies.While the current standards of therapy have been successful in providing some symptom relief,they fail to address the underlying pathophysiology of PD and as a result,they have no effect on disease progression.

EDSUCh:A robust ensemble data summarization method for effective medical diagnosis

Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data.Data summarization can create a concise version of the original data that can be used for effective diagnosis.In this paper,we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns.To the best of our knowledge,there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis.The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used.Therefore,the medical diagnosis becomes more effective,and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.

RBC aggregation,deformation and adhesion to endothelium:Role of nitric oxide derived from L-Arginine and sodium nitroprusside

Red blood cells(RBCs)are the most abundant human blood cells.RBC aggregation and deformation strongly determine blood viscosity which impacts hemorheology and microcirculation.In turn,RBC properties depend on di®erent endogenous and exogenous factors.One such factor is nitric oxide(NO),which is mainly produced by endothelial cells(EC)from L-arginine amino acid in the circulatory system.Since the mechanisms of the RBC-endothelium interplay are not clear up to date and considering its possible clinical importance,the aims of this study are to investigate in vitro:(1)The effect of L-arginine induced NO on RBC aggregation and adhesion to endothelium;(2)the NO e®ect on RBC aggregation and deformation induced by L-arginine and sodium nitroprusside without the presence of endothelium in the samples.The RBC aggregation and adhesion to a monolayer of EC were studied using optical tweezers(OT).The RBC deformability and aggregation without endothelium in the samples were studied using the flow chamber method and Myrenne aggregometer.We confirmed that NO increases deformability and decreases aggregation of RBCs.We showed that the soluble guanylate cyclase pathway appears to be the only NO signaling pathway involved.In the samples with the endothelium,the "bell-shaped"dependence of RBC aggregation force on L-arginine concentration was observed,which improves our knowledge about the process of NO production by endothelium.Additionally,data related to L-arginine accumulation by endothelium were obtained:Necessity of the presence of extracellular L-arginine stated by other authors was put under question.In our study,NO decreased the RBC-endothelium adhesion,however,the tendency appeared to be weak and was not confirmed in another set of experiments.To our knowledge,this is the first attempt to measure the forces of RBC adhesion to endothelium monolayer with OT.

Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts

Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.

Detection and defending the XSS attack using novel hybrid stacking ensemble learning-based DNN approach

Existing web-based security applications have failed in many situations due to the great intelligence of attackers.Among web applications,Cross-Site Scripting(XSS)is one of the dangerous assaults experienced while modifying an organization's or user's information.To avoid these security challenges,this article proposes a novel,all-encompassing combination of machine learning(NB,SVM,k-NN)and deep learning(RNN,CNN,LSTM)frameworks for detecting and defending against XSS attacks with high accuracy and efficiency.Based on the representation,a novel idea for merging stacking ensemble with web applications,termed“hybrid stacking”,is proposed.In order to implement the aforementioned methods,four distinct datasets,each of which contains both safe and unsafe content,are considered.The hybrid detection method can adaptively identify the attacks from the URL,and the defense mechanism inherits the advantages of URL encoding with dictionary-based mapping to improve prediction accuracy,accelerate the training process,and effectively remove the unsafe JScript/JavaScript keywords from the URL.The simulation results show that the proposed hybrid model is more efficient than the existing detection methods.It produces more than 99.5%accurate XSS attack classification results(accuracy,precision,recall,f1_score,and Receiver Operating Characteristic(ROC))and is highly resistant to XSS attacks.In order to ensure the security of the server's information,the proposed hybrid approach is demonstrated in a real-time environment.

A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation

Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.

Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

Machine learning ensemble model prediction of northward shift in potato cyst nematodes(Globodera rostochiensis and G.pallida)distribution under climate change conditions

Potato cyst nematodes(PCNs)are a significant threat to potato production,having caused substantial damage in many countries.Predicting the future distribution of PCN species is crucial to implementing effective biosecurity strategies,especially given the impact of climate change on pest species invasion and distribution.Machine learning(ML),specifically ensemble models,has emerged as a powerful tool in predicting species distributions due to its ability to learn and make predictions based on complex data sets.Thus,this research utilised advanced machine learning techniques to predict the distribution of PCN species under climate change conditions,providing the initial element for invasion risk assessment.We first used Global Climate Models to generate homogeneous climate predictors to mitigate the variation among predictors.Then,five machine learning models were employed to build two groups of ensembles,single-algorithm ensembles(ESA)and multi-algorithm ensembles(EMA),and compared their performances.In this research,the EMA did not always perform better than the ESA,and the ESA of Artificial Neural Network gave the highest performance while being cost-effective.Prediction results indicated that the distribution range of PCNs would shift northward with a decrease in tropical zones and an increase in northern latitudes.However,the total area of suitable regions will not change significantly,occupying 16-20%of the total land surface(18%under current conditions).This research alerts policymakers and practitioners to the risk of PCNs’incursion into new regions.Additionally,this ML process offers the capability to track changes in the distribution of various species and provides scientifically grounded evidence for formulating long-term biosecurity plans for their control.

Impacts of Aggregation Methods and Trophospecies Number on the Structure and Function of Marine Food Webs

Aggregation of species with similar ecological properties is one of the effective methods to simplify food web researches.However,species aggregation will affect not only the complexity of modeling process but also the accuracy of models’outputs.Selection of aggregation methods and the number of trophospecies are the keys to study the simplification of food web.In this study,three aggregation methods,including taxonomic aggregation(TA),structural equivalence aggregation(SEA),and self-organizing maps(SOM),were analyzed and compared with the linear inverse model–Markov Chain Monte Carlo(LIM-MCMC)model.Impacts of aggregation methods and trophospecies number on food webs were evaluated based on the robustness and unitless of ecological net-work indices.Results showed that aggregation method of SEA performed better than the other two methods in estimating food web structure and function indices.The effects of aggregation methods were driven by the differences in species aggregation principles,which will alter food web structure and function through the redistribution of energy flow.According to the results of mean absolute percentage error(MAPE)which can be applied to evaluate the accuracy of the model,we found that MAPE in food web indices will increase with the reducing trophospecies number,and MAPE in food web function indices were smaller and more stable than those in food web structure indices.Therefore,trade-off between simplifying food webs and reflecting the status of ecosystem should be con-sidered in food web studies.These findings highlight the importance of aggregation methods and trophospecies number in the analy-sis of food web simplification.This study provided a framework to explore the extent to which food web models are affected by dif-ferent species aggregation,and will provide scientific basis for the construction of food webs.

Statistical Process Monitoring Based on Ensemble Structure Analysis

Dear Editor,This letter presents a novel process monitoring model based on ensemble structure analysis(ESA).The ESA model takes advantage of principal component analysis(PCA),locality preserving projections(LPP),and multi-manifold projections(MMP)models,and then combines the multiple solutions within an ensemble result through Bayesian inference.In the developed ESA model,different structure features of the given dataset are taken into account simultaneously,the suitability and reliability of the ESA-based monitoring model are then illustrated through comparison.Introduction:The requirement for ensuring safe operation and improving process efficiency has led to increased research activity in the field of process monitoring.

Small molecules to target tau amyloid aggregation

Protein aggregation has been linked with many neurodegenerative diseases,such as Alzheimer’s disease(AD)or Parkinson’s disease.AD belongs to a group of heterogeneous and incurable neurodegenerative disorders collectively known as tauopathies.They comprise frontotemporal dementia,Pick’s disease,or corticobasal degeneration,among others.The symptomatology varies with the specific tau protein variant involved and the affected brain region or cell type.However,they share a common neuropathological hallmark-the formation of proteinaceous deposits named neurofibrillary tangles.Neurofibrillary tangles,primarily composed of aggregated tau(Zhang et al.,2022),disrupt normal neuronal functions,leading to cell death and cognitive decline.

基于Local Cascade Ensemble方法的胎儿健康自动分类

为更好地自动评估胎儿宫内状态,提出一种基于local cascade ensemble(LCE)方法的胎儿健康状态分类模型。选用UCI数据集,使用ADASYN方法对不平衡数据集进行填充平衡,接着结合随机森林算法对数据特征进行选择,最后使用LCE方法对胎儿状态进行自动分类。实验结果表明,所提出模型使用的方法平均准确率、精确率、召回率和F1分数分别达到了0.9554、0.9054、0.9557和0.9290,对比传统的机器学习算法能得到更好的分类效果,有效降低了误判率。

Aggregation-regulated bioreduction process of graphene oxide by Shewanella bacteria

The bioreduction of graphene oxide(GO)using environmentally functional bacteria such as Shewanella represents a green approach to produce reduced graphene oxide(rGO).This process differs from the chemical reduction that involves instantaneous molecular reactions.In bioreduction,the contact of bacterial cells and GO is considered the rate-limiting step.To reveal how the bacteria-GO integration regulates rGO production,the comparative experiments of GO and three Shewanella strains were carried out.Fourier-transform infrared spectroscopy,X-ray photoelectron spectroscopy,Raman spectroscopy,and atomic force microscopy were used to characterize the reduction degree and the aggregation degree.The results showed that a spontaneous aggregation of GO and Shewanella into the condensed entity occurred within 36 h.A positive linear correlation was established,linking three indexes of the aggregation potential,the bacterial reduction ability,and the reduction degree(ID/IG)comprehensively.