Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

35例新型冠状病毒肺炎患者中医临床特征分析

目的分析广东省新型冠状病毒肺炎患者临床资料及中医证候,为该病的防治提供科学依据。方法将2020年1月23日至2020年2月14日本院收治的35例新型冠状病毒肺炎患者纳为研究对象,收集患者的基本情况、流行病学史及疾病史,通过血液生化分析及胸部CT检查患者临床情况,由5名高级职称中医师对患者舌象进行诊断,并根据《广东省新型冠状病毒感染的肺炎中医药治疗方案(试行第一版)》进行辨证。结果新型冠状病毒肺炎患者年龄分布较广,平均年龄为(44.00±15.17)岁,以男性居多,57.14%的患者有武汉及周边地区旅居史,42.86%的患者有确诊患者接触史;50%以上的患者淋巴细胞绝对值降低,CD3、CD4和、CD8降低以及C反应蛋白增加;胸部CT显示,80%以上的患者出现肺部病变,其中双肺病变占60%,病变类型以斑块状阴影居多;多数患者伴有发热(85.71%)、咳嗽(80.00%)、肌肉酸痛(42.86%)、乏力(40.00%)等临床症状;患者的证型主要是湿邪郁肺(45.71%),其次为邪热壅肺(37.14%),病机特点以湿为主,其次为热;舌苔以腻苔为主,舌质以红和淡红为主。结论新型冠状病毒肺炎患者的中医证型以湿邪郁肺为主,致病特点以"湿"为主,可针对性地采取中医治疗。     

Quantitative Parameters of High-Frame-Rate Strain in Patients with Echocardiographically Normal Function

Recently, we developed a high-frame-rate echocardiographic imaging system capable of acquiring images at rates up to 2500 per second. High imaging rates were used to quantify longitudinal strain parameters in patients with echocardiographically normal function. These data can serve as a baseline for comparing strain parameters in disease states. The derived timing data also reveal the propagation of mechanical events in the left ventricle throughout the cardiac cycle. High-frame-rate echocardiographic images were acquired from 17 patients in the apical four-chamber view using Duke University's phased array ultrasound system, T5. B-Mode images were acquired at 500–1000 images per second by employing 16:1 or 32:1 parallel processing in receive, a scan depth ≤14 cm and an 80° field of view with a 3.5-MegaHertZ (MHz), 96-element linear array. The images were analyzed using a speckle tracking algorithm tailored for high-frame-rate echocardiographic images developed at Aalborg and Duke University. Four specific mechanical events were defined using strain curves from six regions along the myocardial contour of the left ventricle. The strain curves measure the local deformation events of the myocardium and are independent of the overall cardiac motion. We observed statistically significant differences in the temporal sequence among different myocardial segments for the first mechanical event described, myocardial tissue shortening onset (p < 0.01). We found that the spatial origin of tissue shortening was located near the middle of the interventricular septum in patients with echocardiographically normal function. The quantitative parameters defined here, based on high-speed strain measurements in patients with echocardiographically normal function, can serve as a means of assessing degree of contractile abnormality in the myocardium and enable the identification of contraction propagation. The relative timing pattern among specific events with respect to the Q wave may become an important new metric in assessing cardiac function and may, in turn, improve diagnosis and prognosis.     

MRI Radiomic Analysis of IMRT-Induced Bladder Wall Changes in Prostate Cancer Patients: A Relationship with Radiation Dose and Toxicity

BackgroundThe main purpose of this study was to assess the structural changes in the bladder wall of prostate cancer patients treated with intensity-modulated radiation therapy using magnetic resonance imaging texture features analysis and to correlate image texture changes with radiation dose and urinary toxicity.MethodsEthical clearance was granted to enroll 33 patients into this study who were treated with intensity-modulated radiation therapy for prostate cancer. All patients underwent two magnetic resonance imagings before and after radiation therapy (RT). A total of 274 radiomic features were extracted from MR-T2W–weighted images. Wilcoxon singed rank-test was performed to assess significance of the change in mean radiomic features post-RT relative to pre-RT values. The relationship between radiation dose and feature changes was assessed and depicted. Cystitis was recorded as urinary toxicity. Area under receiver operating characteristic curve of a logistic regression–based classifier was used to find correlation between radiomic features with significant changes and radiation toxicity.ResultsThirty-three bladder walls were analyzed, with 11 patients developing grade ≥2 urinary toxicity. We showed that radiomic features may predict radiation toxicity and features including S5.0SumVarnc, S2.2SumVarnc, S1.0AngScMom, S0.4SumAverg, and S5. _5InvDfMom with area under receiver operating characteristic curve 0.75, 0.69, 0.65, 0.63, and 0.62 had highest correlation with toxicity, respectively. The results showed that most of the radiomic features were changed with radiation dose.ConclusionFeature changes have a good correlation with radiation dose and radiation-induced urinary toxicity. These radiomic features can be identified as being potentially important imaging biomarkers and also assessing mechanisms of radiation-induced bladder injuries.     

“中国科技期刊卓越行动计划”资助期刊特征分析

【目的】 对“中国科技期刊卓越行动计划”资助期刊的特征进行分析,可以了解这些具有较高国际影响力的国内科技期刊的特点,为其他有潜力的科技期刊发展提供参考。【方法】 利用文献调研、网站调研、统计分析等方法对入选科技期刊的主办及主管单位、学科类别、国内外学术影响力等方面进行系统剖析,同时通过SCI收录情况与JCR分区指标对顶层的领军期刊和重点期刊进行重点分析。【结果】 领军期刊和重点期刊项目重点支持英文期刊;以域选刊,重点关注优先建设领域;所有期刊均被SCI收录,主要引证指标表现优秀。梯队期刊兼顾中、英文期刊;资助数量最多,覆盖学科领域较广;整体国际学术影响力不高,但其中的中文期刊具有较高的国内影响力。高起点新刊项目主要支持英文期刊,关注优先建设领域。【结论】 “中国科技期刊卓越行动计划”资助期刊在国际影响力、主要引证指标、出版体系等方面呈现出尖兵引领、梯次跟进的态势,同层次期刊可以逐步探索良性的竞争与合作模式,冲击更优、更强的国际一流期刊;未受到资助的期刊可参照相应的指标不断追跑,从而促进我国科技期刊体系的持续健康发展。     

A Scientific Software Product Line for the Bioinformatics domain

ContextMost specialized users (scientists) that use bioinformatics applications do not have suitable training on software development. Software Product Line (SPL) employs the concept of reuse considering that it is defined as a set of systems that are developed from a common set of base artifacts. In some contexts, such as in bioinformatics applications, it is advantageous to develop a collection of related software products, using SPL approach. If software products are similar enough, there is the possibility of predicting their commonalities, differences and then reuse these common features to support the development of new applications in the bioinformatics area.ObjectivesThis paper presents the PL-Science approach which considers the context of SPL and ontology in order to assist scientists to define a scientific experiment, and to specify a workflow that encompasses bioinformatics applications of a given experiment. This paper also focuses on the use of ontologies to enable the use of Software Product Line in biological domains.MethodIn the context of this paper, Scientific Software Product Line (SSPL) differs from the Software Product Line due to the fact that SSPL uses an abstract scientific workflow model. This workflow is defined according to a scientific domain and using this abstract workflow model the products (scientific applications/algorithms) are instantiated.ResultsThrough the use of ontology as a knowledge representation model, we can provide domain restrictions as well as add semantic aspects in order to facilitate the selection and organization of bioinformatics workflows in a Scientific Software Product Line. The use of ontologies enables not only the expression of formal restrictions but also the inferences on these restrictions, considering that a scientific domain needs a formal specification.ConclusionsThis paper presents the development of the PL-Science approach, encompassing a methodology and an infrastructure, and also presents an approach evaluation. This evaluation presents case studies in bioinformatics, which were conducted in two renowned research institutions in Brazil.     

瓜蒌道地性的性状特征分析＊

目的 对不同产区种植的栝楼加工获得的瓜蒌进行性状的测定和比较，指出传统道地产区瓜蒌的性状特征。方法 同时采收6个不同产区种植的栝楼果实各20个，在北京的实验室内统一阴干后，测定纵径、横径、纵横比、单果重、果皮重量占比、水溶性浸出物等性状指标，取平均值进行比较和分析。结果 传统道地产区的山东长清庄科产栝楼果实阴干获得的瓜蒌，单果重最重，为95.39 g；纵径最长，为10.22 mm；横径最短，为7.19 mm；纵横比最大，为1.43；果皮重量占比最轻，为23.07%；水溶性浸出物含量较高，为50.44%。结论 山东长清在悠久的瓜蒌生产历史中，形成了外观为长圆形，单果重，水溶性浸出物含量高，成熟度高的性状，具有其他产区种植的栝楼果实所没有的特征，当为道地产区瓜蒌的性状特征。     

Similarity preserving low-rank representation for enhanced data representation and effective subspace learning

Latent Low-Rank Representation (LatLRR) delivers robust and promising results for subspace recovery and feature extraction through mining the so-called hidden effects, but the locality of both similar principal and salient features cannot be preserved in the optimizations. To solve this issue for achieving enhanced performance, a boosted version of LatLRR, referred to as Regularized Low-Rank Representation (rLRR), is proposed through explicitly including an appropriate Laplacian regularization that can maximally preserve the similarity among local features. Resembling LatLRR, rLRR decomposes given data matrix from two directions by seeking a pair of low-rank matrices. But the similarities of principal and salient features can be effectively preserved by rLRR. As a result, the correlated features are well grouped and the robustness of representations is also enhanced. Based on the outputted bi-directional low-rank codes by rLRR, an unsupervised subspace learning framework termed Low-rank Similarity Preserving Projections (LSPP) is also derived for feature learning. The supervised extension of LSPP is also discussed for discriminant subspace learning. The validity of rLRR is examined by robust representation and decomposition of real images. Results demonstrated the superiority of our rLRR and LSPP in comparison to other related state-of-the-art algorithms.     

Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification

Objective

Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new approach for feature subset selection.

Methods

Feature selection process using ant colony optimization (ACO) for 6 channel pre-treatment electroencephalogram (EEG) data from theta and delta frequency bands is combined with back propagation neural network (BPNN) classification method for 147 major depressive disorder (MDD) subjects.

Results

BPNN classified R subjects with 91.83% overall accuracy and 95.55% subjects detection sensitivity. Area under ROC curve (AUC) value after feature selection increased from 0.8531 to 0.911. The features selected by the optimization algorithm were Fp1, Fp2, F7, F8, F3 for theta frequency band and eliminated 7 features from 12 to 5 feature subset.

Conclusion

ACO feature selection algorithm improves the classification accuracy of BPNN. Using other feature selection algorithms or classifiers to compare the performance for each approach is important to underline the validity and versatility of the designed combination.     

抗肿瘤多肽类药物的来源及其机制的研究进展

恶性肿瘤已成为严重影响人类健康的一种疾病,其治疗手段一直是人们所探索的内容,药物治疗就是其中一种治疗方法。与传统化疗药物相比,抗肿瘤多肽类药物以其分子量小、特异性强、毒性低等特点作为新的治疗肿瘤药物一直广受人们关注。多肽的来源广泛,有存在于天然动植物及微生物体内,也可以通过蛋白质酶解或人工合成得到。近年来,越来越多的多肽类药物被发现除抗菌外还具有抗肿瘤的作用,其抗肿瘤的机制多种多样但尚未完全清楚,是许多研究者研究的重点。本文从多肽类药物的来源与特点及它们的抗肿瘤机制等方面对多肽类药物的研究进展进行综述。