Hydrogen sulfide(H_(2)S) not only presents significant environmental concerns but also induces severe corrosion in industrial equipment,even at low concentrations.Among various technologies,the selective oxidation of ...Hydrogen sulfide(H_(2)S) not only presents significant environmental concerns but also induces severe corrosion in industrial equipment,even at low concentrations.Among various technologies,the selective oxidation of hydrogen sulfide(SOH_(2)S) to elemental sulfur(S) has emerged as a sustainable and environmentally friendly solution.Due to its unique properties,iron oxide has been extensively investigated as a catalyst for SOH_(2)S;however,rapid deactivation has remained a significant drawback.The causes of iron oxide-based catalysts deactivation mechanisms in SOH_(2)S,including sulfur or sulfate deposition,the transformation of iron species,sintering and excessive oxygen vacancy formation,and active site loss,are thoroughly examined in this review.By focusing on the deactivation mechanisms,this review aims to provide valuable insights into enhancing the stability and efficiency of iron-based catalysts for SOH_(2)S.展开更多
Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many diseases.Employing machine learning(ML)in the prediction of many diseases based on GE data has been a flourishin...Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many diseases.Employing machine learning(ML)in the prediction of many diseases based on GE data has been a flourishing research area.However,some diseases,like Alzheimer’s disease(AD),have not received considerable attention,probably owing to data scarcity obstacles.In this work,we shed light on the prediction of AD from GE data accurately using ML.Our approach consists of four phases:preprocessing,gene selection(GS),classification,and performance validation.In the preprocessing phase,gene columns are preprocessed identically.In the GS phase,a hybrid filtering method and embedded method are used.In the classification phase,three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase.The final phase is to validate the performance of these classifiers using different metrics.The crux of this article is to select the most informative genes from the hybrid method,and the best ML technique to predict AD using this minimal set of genes.Five different datasets are used to achieve our goal.We predict AD with impressive values forMultiLayer Perceptron(MLP)classifier which has the best performance metrics in four datasets,and the Support Vector Machine(SVM)achieves the highest performance values in only one dataset.We assessed the classifiers using sevenmetrics;and received impressive results,allowing for a credible performance rating.The metrics values we obtain in our study lie in the range[.97,.99]for the accuracy(Acc),[.97,.99]for F1-score,[.94,.98]for kappa index,[.97,.99]for area under curve(AUC),[.95,1]for precision,[.98,.99]for sensitivity(recall),and[.98,1]for specificity.With these results,the proposed approach outperforms recent interesting results.With these results,the proposed approach outperforms recent interesting results.展开更多
Parkinson’s disease(PD)is a neurodegenerative disease cause by a deficiency of dopamine.Investigators have identified the voice as the underlying symptom of PD.Advanced vocal disorder studies provide adequate treatment...Parkinson’s disease(PD)is a neurodegenerative disease cause by a deficiency of dopamine.Investigators have identified the voice as the underlying symptom of PD.Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection.Machine learning(ML)models have recently helped to solve problems in the classification of chronic diseases.This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system.It includes PD classification models of Random forest,decision Tree,neural network,logistic regression and support vector machine.The feature selection is made by RF mean-decrease in accuracy and mean-decrease in Gini techniques.Random forest kerb feature selection(RFKFS)selects only 17 features from 754 attributes.The proposed technique uses validation metrics to assess the performance of ML models.The results of the RF model with feature selection performed well among all other models with high accuracy score of 96.56%and a precision of 88.02%,a sensitivity of 98.26%,a specificity of 96.06%.The respective validation score has an Non polynomial vector(NPV)of 99.47%,a Geometric Mean(GM)of 97.15%,a Youden’s index(YI)of 94.32%,and a Matthews’s correlation method(MCC)90.84%.The proposed model is also more robust than other models.It was also realised that using the RFKFS approach in the PD results in an effective and high-performing medical classifier.展开更多
目的探讨NOTCH3基因第5外显子C260S位点突变导致的伴有皮层下梗死和白质脑病的常染色体显性遗传性脑动脉病(cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy,CADASIL)家系的临床和影像学...目的探讨NOTCH3基因第5外显子C260S位点突变导致的伴有皮层下梗死和白质脑病的常染色体显性遗传性脑动脉病(cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy,CADASIL)家系的临床和影像学特征。方法选取2021年12月首都医科大学附属北京同仁医院来自同一家庭的CADASIL患者,对所有患者进行NOTCH3基因测序,回顾性分析患者的临床表现和头颅影像学特征。复习既往文献报道的导致同一位置氨基酸改变的其他突变类型的临床及影像学特征。结果4名家庭成员中,包括先证者(46岁,女)及其两个姐姐(分别为48岁和50岁)和女儿(18岁)。先证者及其父亲、两个姐姐都有偏头痛病史,其中大姐有记忆力减退;先证者患有脑梗死及伴有视觉先兆的偏头痛;先证者女儿体健;先证者父亲因脑梗死去世。4名家庭成员均存在C260S位点的NOTCH3基因突变。既往文献无此位点突变的报道,先证者头颅MRI示右侧脑桥亚急性梗死,颞叶、脑室周围及脑干异常高信号改变,其大姐脑桥可见腔隙性梗死灶。结论NOTCH3基因第5外显子c.778T>A(p.C260S)的罕见突变导致的CADASIL发病时间早,早期会出现认知障碍。合并偏头痛的脑干梗死患者,需警惕CADASIL的可能。展开更多
Parkinson’s disease is a neurodegenerative disease characterized by motor and gastrointestinal dysfunction.Gastrointestinal dysfunction can precede the onset of motor symptoms by several years.Gut microbiota dysbiosi...Parkinson’s disease is a neurodegenerative disease characterized by motor and gastrointestinal dysfunction.Gastrointestinal dysfunction can precede the onset of motor symptoms by several years.Gut microbiota dysbiosis is involved in the pathogenesis of Parkinson’s disease,whether it plays a causal role in motor dysfunction,and the mechanism underlying this potential effect,remain unknown.CCAAT/enhancer binding proteinβ/asparagine endopeptidase(C/EBPβ/AEP)signaling,activated by bacterial endotoxin,can promoteα-synuclein transcription,thereby contributing to Parkinson’s disease pathology.In this study,we aimed to investigate the role of the gut microbiota in C/EBPβ/AEP signaling,α-synuclein-related pathology,and motor symptoms using a rotenone-induced mouse model of Parkinson’s disease combined with antibiotic-induced microbiome depletion and fecal microbiota transplantation.We found that rotenone administration resulted in gut microbiota dysbiosis and perturbation of the intestinal barrier,as well as activation of the C/EBP/AEP pathway,α-synuclein aggregation,and tyrosine hydroxylase-positive neuron loss in the substantia nigra in mice with motor deficits.However,treatment with rotenone did not have any of these adverse effects in mice whose gut microbiota was depleted by pretreatment with antibiotics.Importantly,we found that transplanting gut microbiota derived from mice treated with rotenone induced motor deficits,intestinal inflammation,and endotoxemia.Transplantation of fecal microbiota from healthy control mice alleviated rotenone-induced motor deficits,intestinal inflammation,endotoxemia,and intestinal barrier impairment.These results highlight the vital role that gut microbiota dysbiosis plays in inducing motor deficits,C/EBPβ/AEP signaling activation,andα-synuclein-related pathology in a rotenone-induced mouse model of Parkinson’s disease.Additionally,our findings suggest that supplementing with healthy microbiota may be a safe and effective treatment that could help ameliorate the progression of motor deficits in patients with Parkinson’s disease.展开更多
基金supported by Thailand Science Research and Innovation Fund Chulalongkorn University,Thailand(IND66210014)。
文摘Hydrogen sulfide(H_(2)S) not only presents significant environmental concerns but also induces severe corrosion in industrial equipment,even at low concentrations.Among various technologies,the selective oxidation of hydrogen sulfide(SOH_(2)S) to elemental sulfur(S) has emerged as a sustainable and environmentally friendly solution.Due to its unique properties,iron oxide has been extensively investigated as a catalyst for SOH_(2)S;however,rapid deactivation has remained a significant drawback.The causes of iron oxide-based catalysts deactivation mechanisms in SOH_(2)S,including sulfur or sulfate deposition,the transformation of iron species,sintering and excessive oxygen vacancy formation,and active site loss,are thoroughly examined in this review.By focusing on the deactivation mechanisms,this review aims to provide valuable insights into enhancing the stability and efficiency of iron-based catalysts for SOH_(2)S.
文摘Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many diseases.Employing machine learning(ML)in the prediction of many diseases based on GE data has been a flourishing research area.However,some diseases,like Alzheimer’s disease(AD),have not received considerable attention,probably owing to data scarcity obstacles.In this work,we shed light on the prediction of AD from GE data accurately using ML.Our approach consists of four phases:preprocessing,gene selection(GS),classification,and performance validation.In the preprocessing phase,gene columns are preprocessed identically.In the GS phase,a hybrid filtering method and embedded method are used.In the classification phase,three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase.The final phase is to validate the performance of these classifiers using different metrics.The crux of this article is to select the most informative genes from the hybrid method,and the best ML technique to predict AD using this minimal set of genes.Five different datasets are used to achieve our goal.We predict AD with impressive values forMultiLayer Perceptron(MLP)classifier which has the best performance metrics in four datasets,and the Support Vector Machine(SVM)achieves the highest performance values in only one dataset.We assessed the classifiers using sevenmetrics;and received impressive results,allowing for a credible performance rating.The metrics values we obtain in our study lie in the range[.97,.99]for the accuracy(Acc),[.97,.99]for F1-score,[.94,.98]for kappa index,[.97,.99]for area under curve(AUC),[.95,1]for precision,[.98,.99]for sensitivity(recall),and[.98,1]for specificity.With these results,the proposed approach outperforms recent interesting results.With these results,the proposed approach outperforms recent interesting results.
文摘Parkinson’s disease(PD)is a neurodegenerative disease cause by a deficiency of dopamine.Investigators have identified the voice as the underlying symptom of PD.Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection.Machine learning(ML)models have recently helped to solve problems in the classification of chronic diseases.This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system.It includes PD classification models of Random forest,decision Tree,neural network,logistic regression and support vector machine.The feature selection is made by RF mean-decrease in accuracy and mean-decrease in Gini techniques.Random forest kerb feature selection(RFKFS)selects only 17 features from 754 attributes.The proposed technique uses validation metrics to assess the performance of ML models.The results of the RF model with feature selection performed well among all other models with high accuracy score of 96.56%and a precision of 88.02%,a sensitivity of 98.26%,a specificity of 96.06%.The respective validation score has an Non polynomial vector(NPV)of 99.47%,a Geometric Mean(GM)of 97.15%,a Youden’s index(YI)of 94.32%,and a Matthews’s correlation method(MCC)90.84%.The proposed model is also more robust than other models.It was also realised that using the RFKFS approach in the PD results in an effective and high-performing medical classifier.
文摘目的探讨NOTCH3基因第5外显子C260S位点突变导致的伴有皮层下梗死和白质脑病的常染色体显性遗传性脑动脉病(cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy,CADASIL)家系的临床和影像学特征。方法选取2021年12月首都医科大学附属北京同仁医院来自同一家庭的CADASIL患者,对所有患者进行NOTCH3基因测序,回顾性分析患者的临床表现和头颅影像学特征。复习既往文献报道的导致同一位置氨基酸改变的其他突变类型的临床及影像学特征。结果4名家庭成员中,包括先证者(46岁,女)及其两个姐姐(分别为48岁和50岁)和女儿(18岁)。先证者及其父亲、两个姐姐都有偏头痛病史,其中大姐有记忆力减退;先证者患有脑梗死及伴有视觉先兆的偏头痛;先证者女儿体健;先证者父亲因脑梗死去世。4名家庭成员均存在C260S位点的NOTCH3基因突变。既往文献无此位点突变的报道,先证者头颅MRI示右侧脑桥亚急性梗死,颞叶、脑室周围及脑干异常高信号改变,其大姐脑桥可见腔隙性梗死灶。结论NOTCH3基因第5外显子c.778T>A(p.C260S)的罕见突变导致的CADASIL发病时间早,早期会出现认知障碍。合并偏头痛的脑干梗死患者,需警惕CADASIL的可能。
基金supported by Jiangsu Provincial Medical Key Discipline,No.ZDXK202217(to CFL)Jiangsu Planned Projects for Postdoctoral Research Funds,No.1601056C(to SL).
文摘Parkinson’s disease is a neurodegenerative disease characterized by motor and gastrointestinal dysfunction.Gastrointestinal dysfunction can precede the onset of motor symptoms by several years.Gut microbiota dysbiosis is involved in the pathogenesis of Parkinson’s disease,whether it plays a causal role in motor dysfunction,and the mechanism underlying this potential effect,remain unknown.CCAAT/enhancer binding proteinβ/asparagine endopeptidase(C/EBPβ/AEP)signaling,activated by bacterial endotoxin,can promoteα-synuclein transcription,thereby contributing to Parkinson’s disease pathology.In this study,we aimed to investigate the role of the gut microbiota in C/EBPβ/AEP signaling,α-synuclein-related pathology,and motor symptoms using a rotenone-induced mouse model of Parkinson’s disease combined with antibiotic-induced microbiome depletion and fecal microbiota transplantation.We found that rotenone administration resulted in gut microbiota dysbiosis and perturbation of the intestinal barrier,as well as activation of the C/EBP/AEP pathway,α-synuclein aggregation,and tyrosine hydroxylase-positive neuron loss in the substantia nigra in mice with motor deficits.However,treatment with rotenone did not have any of these adverse effects in mice whose gut microbiota was depleted by pretreatment with antibiotics.Importantly,we found that transplanting gut microbiota derived from mice treated with rotenone induced motor deficits,intestinal inflammation,and endotoxemia.Transplantation of fecal microbiota from healthy control mice alleviated rotenone-induced motor deficits,intestinal inflammation,endotoxemia,and intestinal barrier impairment.These results highlight the vital role that gut microbiota dysbiosis plays in inducing motor deficits,C/EBPβ/AEP signaling activation,andα-synuclein-related pathology in a rotenone-induced mouse model of Parkinson’s disease.Additionally,our findings suggest that supplementing with healthy microbiota may be a safe and effective treatment that could help ameliorate the progression of motor deficits in patients with Parkinson’s disease.
