Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

Correlation between key indicators of continuous glucose monitoring and the risk of diabetic foot

BACKGROUND Continuous glucose monitoring(CGM)metrics,such as time in range(TIR)and glycemic risk index(GRI),have been linked to various diabetes-related complications,including diabetic foot(DF).AIM To investigate the association between CGM-derived indicators and the risk of DF in individuals with type 2 diabetes mellitus(T2DM).METHODS A total of 591 individuals with T2DM(297 with DF and 294 without DF)were enrolled.Relevant clinical data,complications,comorbidities,hematological parameters,and 72-hour CGM data were collected.Logistic regression analysis was employed to examine the relationship between these measurements and the risk of DF.RESULTS Individuals with DF exhibited higher mean blood glucose(MBG)levels and increased proportions of time above range(TAR),TAR level 1,and TAR level 2,but lower TIR(all P<0.001).Patients with DF had significantly lower rates of achieving target ranges for TIR,TAR,and TAR level 2 than those without DF(all P<0.05).Logistic regression analysis revealed that GRI,MBG,and TAR level 1 were positively associated with DF risk,while TIR was inversely correlated(all P<0.05).Achieving TIR and TAR was inversely correlated with white blood cell count and glycated hemoglobin A1c levels(P<0.05).Additionally,achieving TAR was influenced by fasting plasma glucose,body mass index,diabetes duration,and antidiabetic medication use.CONCLUSION CGM metrics,particularly TIR and GRI,are significantly associated with the risk of DF in T2DM,emphasizing the importance of improved glucose control.

Continuous-flow columns packed with zero-valent iron and iron sulfide as a feasible strategy to remediate the persistent contaminant nitroguanidine

The insensitive munitions compound nitroguanidine(NQ)is used by the U.S.Army to avoid unintended explosions.However,NQ also represents an emerging contaminant whose environmental emissions can cause toxicity toward aquatic organisms,indicating the need for effective remediation strategies.Thus,we investigated the feasibility of treating water contaminated with NQ in continuous-flow columns packed with zero-valent iron(ZVI)or iron sulfide(FeS).Initially,the impact of pH on NQ transformation by ZVI or FeS was evaluated in batch experiments.The pseudo first-order rate constant for NQ transformation(k_(1,NQ))by ZVI was 8-10 times higher at pH 3.0 compared to pH 5.5 and 7.0,whereas similar k_(1,NQ)values were obtained for FeS at pH 5.5-10.0.Based on these findings,the influent p H fed to the ZVIand Fe S-packed columns was adjusted to 3.0 and 5.5,respectively.Both reactors transformed NQ into nitrosoguanidine(Nso Q).Further transformation of Nso Q by ZVI produced aminoguanidine,guanidine,and cyanamide,whereas Nso Q transformation by Fe S produced guanidine,ammonium,and traces of urea.ZVI outperformed Fe S as a reactive material to remove NQ.The ZVI-packed column effectively removed NQ below detection even after 45 d of operation(490 pore volumes,PV).In contrast,NQ breakthrough(removal efficiency<85%)was observed after 18 d(180 PV)in the Fe S-packed column.The high NQ removal efficiency and long service life of the ZVI-packed column(>490 PV)suggest that the technology is a promising approach for NQ treatment in packed-bed reactors and in situ remediation.

Genomic selection for meat quality traits based on VIS/NIR spectral information

The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects' associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4breeding strategies applied to different scenarios: Ⅰ, only spectral and genotypic data exist for the target population;Ⅱ, only spectral data exist for the target population;Ⅲ, only spectral and genotypic data but with different prediction processes exist for the target population;and Ⅳ, only spectral and phenotypic data exist for the target population.The 4 scenarios were used to evaluate the genomic estimated breeding value(GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies Ⅱ, Ⅲ, and Ⅳ was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy Ⅱ for fat(%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy Ⅰ was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy Ⅳ being the lowest as it did not require genotyping.Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.

Early and accurate diagnosis and selection of appropriate treatment plans are crucial for patients with gastrointestinal hemangiomas

Gastrointestinal hemangioma(GIH)is clinically rare,accounting for 7%-10%of benign gastrointestinal tumors and 0.5%of systemic hemangiomas.GIH can occur as either solitary or multiple lesions,with gastrointestinal bleeding as a significant clinical manifestation.Understanding the clinical and endoscopic features of GIH is essential for improving diagnostic accuracy,particularly through endoscopy and selective arteriography,which are highly effective in diagnosing GIH and preventing misdiagnosis and inappropriate treatment.Upon confirmed diagnosis,it is essential to thoroughly evaluate the patient's condition to determine the most suitable treatment modality—whether surgical,endoscopic,or minimally invasive intervention.The minimally invasive interventional partial embolization therapy using polyvinyl alcohol particles,proposed and implemented by Pospisilova et al,has achieved excellent clinical outcomes.This approach reduces surgical trauma and the inherent risks of traditional surgical treatments.

CONTINUOUS SELECTION THEOREMS FOR FAN-BROWDER MAPPINGS IN TOPOLOGICAL SPACES AND THEIR APPLICATIONS

The concept of Fan-Browder mappings was first introduced in topological spaces without any convex structure. Then a new continuous selection theorem was obtained for the Fan-Browder mapping with range in a topological space without any convex structure and noncompact domain. As applications, some fixed point theorems, coincidence theorems and a nonempty intersection theorem were given. Both the new concepts and results unify and extend many known results in recent literature.

CONTINUOUS SELECTIONS OF THE SET-VALUED METRIC GENERALIZED INVERSE IN 2-STRICTLY CONVEX BANACH SPACES

In this paper,we prove that if X is an almost convex and 2-strictly convex space,linear operator T:X→Y is bounded,N(T)is an approximative compact Chebyshev subspace of X and R(T)is a 3-Chebyshev hyperplane,then there exists a homogeneous selection T^(σ)of T^(■)such that continuous points of T^(σ)and T^(■)are dense on Y.

Continuous Selections and Fixed Points forφ-maps with Their Applications to Section Problems

The concept of finitely continuous topological space is introduced and the basic properties of the space are given. Several continuous selection theorems and fixed point theorems for Ф-maps are established, and as applications of the above fixed point theorems, some section problems are discussed. The results generalize and improve many corresponding conclusions.

Diversity and selection of the continuous-flowering gene, RoKSN, in rose

Blooming seasonality is an important trait in ornamental plants and was selected by humans.Wild roses flower only in spring whereas most cultivated modern roses can flower continuously.This trait is explained by a mutation of a floral repressor gene,RoKSN,a TFL1 homologue.In this work,we studied the origin,the diversity and the selection of the RoKSN gene.We analyzed 270 accessions,including wild and old cultivated Asian and European roses as well as modern roses.By sequencing the RoKSN gene,we proposed that the allele responsible for continuous-flowering,RoKSN copia,originated from Chinese wild roses(Indicae section),with a recent insertion of the copia element.Old cultivated Asian roses with the RoKSN copia allele were introduced in Europe,and the RoKSN copia allele was progressively selected during the 19th and 20th centuries,leading to continuous-flowering modern roses.Furthermore,we detected a new allele,RoKSN A181,leading to a weak reblooming.This allele encodes a functional floral repressor and is responsible for a moderate accumulation of RoKSN transcripts.A transient selection of this RoKSN A181 allele was observed during the 19th century.Our work highlights the selection of different alleles at the RoKSN locus for recurrent blooming in rose.

Regulatable Orthotropic 3D Hybrid Continuous Carbon Networks for Efficient Bi-Directional Thermal Conduction

Vertically oriented carbon structures constructed from low-dimen-sional carbon materials are ideal frameworks for high-performance thermal inter-face materials(TIMs).However,improving the interfacial heat-transfer efficiency of vertically oriented carbon structures is a challenging task.Herein,an orthotropic three-dimensional(3D)hybrid carbon network(VSCG)is fabricated by depositing vertically aligned carbon nanotubes(VACNTs)on the surface of a horizontally oriented graphene film(HOGF).The interfacial interaction between the VACNTs and HOGF is then optimized through an annealing strategy.After regulating the orientation structure of the VACNTs and filling the VSCG with polydimethylsi-loxane(PDMS),VSCG/PDMS composites with excellent 3D thermal conductive properties are obtained.The highest in-plane and through-plane thermal conduc-tivities of the composites are 113.61 and 24.37 W m^(-1)K^(-1),respectively.The high contact area of HOGF and good compressibility of VACNTs imbue the VSCG/PDMS composite with low thermal resistance.In addition,the interfacial heat-transfer efficiency of VSCG/PDMS composite in the TIM performance was improved by 71.3%compared to that of a state-of-the-art thermal pad.This new structural design can potentially realize high-performance TIMs that meet the need for high thermal conductivity and low contact thermal resistance in interfacial heat-transfer processes.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Selection of dialysis methods for end-stage kidney disease patients with diabetes

The increasing prevalence of diabetes has led to a growing population of endstage kidney disease(ESKD)patients with diabetes.Currently,kidney transplantation is the best treatment option for ESKD patients;however,it is limited by the lack of donors.Therefore,dialysis has become the standard treatment for ESKD patients.However,the optimal dialysis method for diabetic ESKD patients remains controversial.ESKD patients with diabetes often present with complex conditions and numerous complications.Furthermore,these patients face a high risk of infection and technical failure,are more susceptible to malnutrition,have difficulty establishing vascular access,and experience more frequent blood sugar fluctuations than the general population.Therefore,this article reviews nine critical aspects:Survival rate,glucose metabolism disorder,infectious complications,cardiovascular events,residual renal function,quality of life,economic benefits,malnutrition,and volume load.This study aims to assist clinicians in selecting individualized treatment methods by comparing the advantages and disadvantages of hemodialysis and peritoneal dialysis,thereby improving patients’quality of life and survival rates.

Application and management of continuous glucose monitoring in diabetic kidney disease

Diabetic kidney disease(DKD)is a common complication of diabetes mellitus that contributes to the risk of end-stage kidney disease(ESKD).Wide glycemic var-iations,such as hypoglycemia and hyperglycemia,are broadly found in diabetic patients with DKD and especially ESKD,as a result of impaired renal metabolism.It is essential to monitor glycemia for effective management of DKD.Hemoglobin A1c(HbA1c)has long been considered as the gold standard for monitoring glycemia for>3 months.However,assessment of HbA1c has some bias as it is susceptible to factors such as anemia and liver or kidney dysfunction.Continuous glucose monitoring(CGM)has provided new insights on glycemic assessment and management.CGM directly measures glucose level in interstitial fluid,reports real-time or retrospective glucose concentration,and provides multiple glycemic metrics.It avoids the pitfalls of HbA1c in some contexts,and may serve as a precise alternative to estimation of mean glucose and glycemic variability.Emerging studies have demonstrated the merits of CGM for precise monitoring,which allows fine-tuning of glycemic management in diabetic patients.Therefore,CGM technology has the potential for better glycemic monitoring in DKD patients.More research is needed to explore its application and management in different stages of DKD,including hemodialysis,peritoneal dialysis and kidney transplantation.

Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

Effect of continuous nursing on treatment compliance and side effect management of coronary heart disease

BACKGROUND There are relatively few studies on continuing care of coronary heart disease(CHD),and its research value needs to be further clarified.AIM To investigate the effect of continuous nursing on treatment compliance and side effect management in patients with CHD.METHODS This is a retrospective study with patients from January 2021 to 2023.The study was divided into two groups with 30 participants in each group.Self-rating anxiety scale(SAS)and Self-rating depression scale(SDS)were used to assess patients'anxiety and depression,and medical coping questionnaire was used to assess patients'coping styles.The pelvic floor dysfunction questionnaire(PFDI-20)was used to assess the status of pelvic floor function,including bladder symptoms,intestinal symptoms,and pelvic symptoms.RESULTS SAS score decreased from 57.33±3.01before treatment to 41.33±3.42 after treatment,SDS score decreased from 50.40±1.45 to 39.47±1.57.The decrease of these two indexes was statistically significant(P<0.05).PFDI-20 scores decreased from the mean 16.83±1.72 before treatment to 10.47±1.3the mean after treatment,which was statistically significant(P<0.05).CONCLUSION The results of this study indicate that pioneering research in continuous care of CHD has a positive impact on improving patients'treatment compliance,reducing anxiety and depression levels,and improving coping styles and pelvic floor functional status.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Assessment of molecular markers and marker-assisted selection for drought tolerance in barley(Hordeum vulgare L.)

This review updates the present status of the field of molecular markers and marker-assisted selection(MAS),using the example of drought tolerance in barley.The accuracy of selected quantitative trait loci(QTLs),candidate genes and suggested markers was assessed in the barley genome cv.Morex.Six common strategies are described for molecular marker development,candidate gene identification and verification,and their possible applications in MAS to improve the grain yield and yield components in barley under drought stress.These strategies are based on the following five principles:(1)Molecular markers are designated as genomic‘tags’,and their‘prediction’is strongly dependent on their distance from a candidate gene on genetic or physical maps;(2)plants react differently under favourable and stressful conditions or depending on their stage of development;(3)each candidate gene must be verified by confirming its expression in the relevant conditions,e.g.,drought;(4)the molecular marker identified must be validated for MAS for tolerance to drought stress and improved grain yield;and(5)the small number of molecular markers realized for MAS in breeding,from among the many studies targeting candidate genes,can be explained by the complex nature of drought stress,and multiple stress-responsive genes in each barley genotype that are expressed differentially depending on many other factors.

Exploring the interplay of T cell receptor-V gene copy numbers and major histocompatibility complex selection pressure in avian species:Insights into immune system evolution and reproductive investment

Birds,a fascinating and diverse group occupying various habitats worldwide,exhibit a wide range of life-history traits,reproductive methods,and migratory behaviors,all of which influence their immune systems.The association between major histocompatibility complex(MHC)genes and certain ecological factors in response to pathogen selection has been extensively studied;however,the role of the co-working molecule T cell receptor(TCR)remains poorly understood.This study aimed to analyze the copy numbers of TCR-V genes,the selection pressure(ωvalue)on MHC genes using available genomic data,and their potential ecological correlates across 93 species from 13 orders.The study was conducted using the publicly available genome data of birds.Our findings suggested that phylogeny influences the variability in TCR-V gene copy numbers and MHC selection pressure.The phylogenetic generalized least squares regression model revealed that TCR-Vαδcopy number and MHC-I selection pressure were positively associated with body mass.Clutch size was correlated with MHC selection pressure,and Migration was correlated with TCR-Vβcopy number.Further analyses revealed that the TCR-Vβcopy number was positively correlated with MHC-IIB selection pressure,while the TCR-Vγcopy number was negatively correlated with MHC-I peptide-binding region selection pressure.Our findings suggest that TCR-V diversity is significant in adaptive evolution and is related to species’life-history strategies and immunological defenses and provide valuable insights into the mechanisms underlying TCR-V gene duplication and MHC selection in avian species.