scholarly journals Identification of a Distinct Metabolomic Subtype of Sporadic ALS Patients

2018 ◽  
Author(s):  
Qiuying Chen ◽  
Davinder Sandhu ◽  
Csaba Konrad ◽  
Dipa Roychoudhury ◽  
Benjamin I. Schwartz ◽  
...  
Keyword(s):  
Metabolite Profiling ◽  
Dermal Fibroblast ◽  
Tca Cycle ◽  
Sporadic Amyotrophic Lateral Sclerosis ◽  
Support Vector ◽  
Machine Model ◽  
Disease Etiology ◽  
Glucose Flux ◽  
Sporadic Als ◽  
Death Genes

AbstractSporadic amyotrophic lateral sclerosis (sALS) is a progressive motor neuron disease resulting in paralysis and death. Genes responsible for familial ALS have been identified, however the molecular basis for sALS is unknown. To discover metabotypic biomarkers that inform on disease etiology, untargeted metabolite profiling was performed on 77 patient-derived dermal fibroblast lines and 45 age/sex-matched controls. Surprisingly, 25% of sALS lines showed upregulated methionine-derived homocysteine, channeled to cysteine and glutathione (GSH). Stable isotope tracing of [U-13C]-glucose showed activation of the trans-sulfuration pathway, associated with accelerated glucose flux into the TCA cycle, glutamate, GSH, alanine, aspartate, acylcarnitines and nucleotide phosphates. A four-molecule support vector machine model distinguished the sALS subtype from controls with 97.5% accuracy. Plasma metabolite profiling identified increased taurine as a hallmark metabolite for this sALS subset, suggesting systemic perturbation of cysteine metabolism. Furthermore, integrated multiomics (mRNAs/microRNAs/metabolites) identified the super-trans-sulfuration pathway as a top hit for the sALS subtype. We conclude that sALS can be stratified into distinct metabotypes, providing for future development of personalized therapies that offer new hope to sufferers.

scholarly journals Accelerated trans-sulfuration metabolically defines a discrete subclass of ALS patients

2019 ◽  
Author(s):  
Qiuying Chen ◽  
Davinder Sandhu ◽  
Csaba Konrad ◽  
Dipa Roychoudhury ◽  
Benjamin I. Schwartz ◽  
...  
Keyword(s):  
Metabolite Profiling ◽  
Dermal Fibroblast ◽  
Metabolic Stress ◽  
Tca Cycle ◽  
Growth Defect ◽  
Support Vector ◽  
Machine Model ◽  
Formidable Challenge ◽  
Glucose Flux ◽  
Metabolic Biomarkers

AbstractAmyotrophic lateral sclerosis (ALS) is a disease characterized by progressive paralysis and death. Most ALS cases are sporadic (sALS) and patient heterogeneity poses a formidable challenge for the development of viable biomarkers and effective therapies. Applying untargeted metabolite profiling on 77 sALS patient-derived primary dermal fibroblast lines and 45 sex/age matched controls, we found that ∼25% of cell lines (termed sALS-1) are characterized by upregulated trans-sulfuration, where methionine-derived homocysteine is channeled into cysteine and glutathione synthesis. sALS-1 fibroblasts exhibit a growth defect when grown under oxidative conditions, that can be fully-rescued by N-acetylcysteine. [U-13C]-glucose tracing shows that activation of the trans-sulfuration pathway is associated with accelerated glucose flux into the TCA cycle. Based on four metabolites, we developed a support vector machine model capable of distinguishing sALS-1 with 97.5% accuracy. Importantly, plasma metabolite profiling identifies a systemic perturbation of cysteine metabolism as a hallmark of sALS-1. These results indicate that sALS patients can be stratified into distinct metabotypes, differently sensitive to metabolic stress, and provides new insights into metabolic biomarkers for personalized sALS therapy.

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

scholarly journals Glutathione S-transferase Omega 2 DD genotype is associated with an increased risk of sporadic amyotrophic lateral sclerosis in Chinese men

2021 ◽  
Vol 49 (7) ◽  
pp. 030006052110332
Author(s):  
Zhiliang Fan ◽  
Hong Jiang ◽  
Xueqin Song ◽  
Yansu Guo ◽  
Xinying Tian
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Healthy Subjects ◽  
Chinese Population ◽  
Sporadic Amyotrophic Lateral Sclerosis ◽  
Glutathione S Transferase ◽  
Genotype Frequencies ◽  
Sporadic Als ◽  
Increased Risk ◽  
Chi Squared ◽  
Lateral Sclerosis

Objective To investigate whether GSTA1, GSTO2, and GSTZ1 are relevant to an increased risk of amyotrophic lateral sclerosis (ALS) in a Chinese population. Methods In this study, 143 sporadic ALS (sALS) patients (83 men, 60 women) and 210 age- and sex-matched healthy subjects were enrolled. Blood samples were collected by venipuncture. Genomic DNA was isolated by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) according to the manufacturer’s instructions. The potential associations between ALS and GSTA1, GSTO2, and GSTZ1 polymorphisms were estimated using chi-squared analysis and unconditional logistic regression. Results The D allele and genotype frequencies of GSTO2 were increased in sALS patients compared with healthy subjects, indicating that the GSTO2 DD genotype was associated with an increased risk of sALS (odds ratio [OR] = 3.294, 95% confidence interval [CI] = 1.039–10.448). However, a significant association between the DD genotype and the risk of sALS was evident in men only (OR = 7.167, 95% CI = 1.381–37.202). Conclusion This study revealed that the D allele and genotype frequencies of GSTO2 were increased in sALS patients. The GSTO2 DD genotype was associated with an increased risk of sALS in men in a Chinese population.

Research on SVM Line Loss Rate Prediction Based on Heuristic Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2164-2168 ◽  
Author(s):  
Li Tian ◽  
Qiang Qiang Wang ◽  
An Zhao Cao
Keyword(s):  
Support Vector Machine ◽  
Heuristic Algorithm ◽  
Support Vector Machine Model ◽  
Loss Rate ◽  
Heuristic Algorithms ◽  
Support Vector ◽  
Machine Model ◽  
Rate Prediction ◽  
Line Loss

With the characteristic of line loss volatility, a research of line loss rate prediction was imperatively carried out. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a heuristic algorithm-support vector machine model is constructed. Case study shows that, compared with other heuristic algorithms’, the search efficiency and speed of genetic algorithm are good, and the prediction model is with high accuracy.

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