Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

广州市主城区城市地形类型与风环境评价

对城市地形组合类型及其地形对风场影响的研究,有助于城市风场机理的认识与生态环境的优化。以高层建筑密集的广州市主城区为例,在确定城市宏观地形类型的基础上,基于最小成本路径（LCP）辨识了风道,并对风环境质量进行评价。主要结论有：（1）将城市地形简要概括划分为4个一级地形、11个二级地形类型。（2）以LCP路径与盛行风交角不超过22.5°的标准,筛选确定通风路径作为风道。根据LCP格网的密度与频次,结合不同风向下风速与风频,评价分析了风环境类型与空间分布。（3）根据自然与城市地形的配置,北风风环境质量远高于西北风和东风;东风由于风频与风速最低,因此通风条件较差。（4）珠江航道在三种风向下都是尺度最宽贯穿城市最好的风道;区域性的风道与风环境较好的地段集中在与主风向平行的主干道上,但以近似南北走向的居多;由于广州城市地形高度以珠江新城峰林为中心向外递减,以低地地形为主的主城区外围通风优于中心区域,特别是珠江新城峰林与网络状台地为主的老城区,通风环境较差。基于LCP的评价结果需要同其他方法相互验证,才能使其不断完善优化。     

大比例尺土壤保持服务制图分级方法研究

科学地编制生态系统服务空间分布图,有助于识别生态系统优先保护热点及空缺,能够为国土安全规划提供基础信息。但由于缺乏统一科学的生态系统服务分级方法,生态系统服务分布图的展示效果和制图效率都受到限制。不恰当的分级制图会误导决策者对生态系统服务空间分布特征的判断,增加生态保护决策的不确定性。以构建生态服务制图分级标准为目标,对我国6个典型县区的土壤保持服务数据展开1：25万比例尺制图分级案例研究。采用分级精度、面积均衡性、极差一致性、空间自相关一致性4个分级质量评价指标评价并比较了目前地图编制中常用的自然断点法、几何间隔法、累积比例法、等差法、分位数法5种分级算法的分级制图效果。结果表明,6个县区有相似的土壤保持服务分布特征：低值区覆盖面积大,高值区覆盖面积小。各县区土壤保持服务制图最优分级方法有差异：延庆区、丰满区最优分级方法为自然断点法;永靖县、天山区、安宁市最优分级方法为累积比例法;富阳区最优分级方法为几何间隔法。各分级方法中,累积比例法在6个县区综合制图效果最优或接近最优,适用性最好,能够较好的刻画各县区的土壤保持服务空间分布特征。本文提出的分级评价方法及其结果,可为生态系统土壤保持服务制图规范化和自动化提供科学依据。     

A Fifth of the Protein World: Rossmann-like Proteins as an Evolutionarily Successful Structural unit

The Rossmann-like fold is the most prevalent and diversified doubly-wound superfold of ancient evolutionary origin. Rossmann-like domains are present in a variety of metabolic enzymes and are capable of binding diverse ligands. Discerning evolutionary relationships among these domains is challenging because of their diverse functions and ancient origin. We defined a minimal Rossmann-like structural motif (RLM), identified RLM-containing domains among known 3D structures (20%) and classified them according to their homologous relationships. New classifications were incorporated into our Evolutionary Classification of protein Domains (ECOD) database. We defined 156 homology groups (H-groups), which were further clustered into 123 possible homology groups (X-groups). Our analysis revealed that RLM-containing proteins constitute approximately 15% of the human proteome. We found that disease-causing mutations are more frequent within RLM domains than within non-RLM domains of these proteins, highlighting the importance of RLM-containing proteins for human health.     

甘肃省大豆品种生育期组的划分及地理分布研究

甘肃省地域广大,地形复杂,气候多样,大豆品种资源丰富,但甘肃各地大豆品种生育期生态类型并不明确。本试验以25份MG 0~Ⅴ的北美大豆生育期组标准品种为对照,将从甘肃各地收集到的151个大豆品种在甘肃境内5个生态区进行生育期性状鉴定和生育期组划分。结果表明,甘肃地区种植的大豆品种的生育期组在MG 00和MGⅥ之间。在所鉴定的品种中,属MGⅣ的比例(44.37%)最高,其次为MGⅤ(29.14%)、MGⅢ(11.26%)、MGⅡ(4.64%)、MG 0(3.31%)、MGⅥ(3.31%)、MG 00(2.65%)和MGⅠ(1.32%)。归属于MGⅢ的品种主要分布在河西灌区,占该区域参试品种总数的47.62%;归属于MGⅣ的品种主要分布在中部地区和陇东旱塬区,占中部地区参试品种总数的56.67%,占陇东旱塬区的63.83%;归属于MGⅤ的品种主要分布在陇南地区,占该区域参试品种总数的61.40%。根据甘肃不同地区可成熟的最晚熟大豆品种的生育期组与当地经度、无霜期建立回归方程,推断甘肃省大豆各生育期组的地理分布区间,提出了基于生育期组的甘肃省大豆品种引种方案,以期为甘肃地区大豆育种和品种布局提供科学依据。     

昆虫锌金属蛋白酶研究进展

锌金属蛋白酶是指含有锌离子的蛋白酶,在哺乳动物中参与多项生物学过程的调控.在昆虫中也发现了许多锌金属蛋白酶,能够参与昆虫体内多项生命活动进程如食物的消化与吸收、组织重塑、变态发育、神经系统的调控及免疫防御反应等.本文综述了目前在昆虫中发现的锌金属蛋白酶的结构和底物特异性,聚焦6种锌金属蛋白酶(羧肽酶、基质金属蛋白酶、脑啡肽酶、胰岛素降解酶、血管紧缩素转换酶及氨肽酶)在昆虫中的功能,旨在为昆虫锌金属蛋白酶的深入研究提供参考.     

Objective classification of scapular kinematics in participants with movement faults of the scapula on clinical assessment

The aim of this study was to assess the potential of employing a classification tool to objectively classify participants with clinically assessed movement faults (MFs) of the scapula. Six participants with a history of shoulder pain with MFs of the scapula and 12 healthy participants with no movement faults (NMFs) performed a flexion movement control test of the scapula, while scapular kinematic data were collected. Principal component scores and discrete kinematic variables were used as input into a classifier. Five out of the six participants with a history of pain were successfully classified as having scapular MFs with an accuracy of 72%. Variables related to the upward rotation of the scapula had the most influence on the classification. The results of the study demonstrate the potential of adopting a multivariate approach in objective classification of participants with altered scapular kinematics in pathological groups.