Negritos, Australian aborigines, and the "proto-sundadont" dental pattern: the basic populations in east Asia, V.

Five evolutionarily significant dental traits were identified from a B-square distance analysis of nine crown characters recorded for several populations of East Asia and Oceania. Intergroup variation in these traits distinguishes three major divisions of the Mongoloid dental complex: sundadonty, sinodonty, and the dental pattern of Australian Aborigines. The Australian crown features may be characterized as having high frequencies of evolutionarily conservative characters. Negritos, one of the probable representatives of indigenous inhabitants of Southeast Asia who may have shared a common ancestor with Australians, possess the more derived sundadont dental pattern. As far as the five crown traits treated here are concerned, Australian dental features may be described as conforming to a "proto-sundadont" dental pattern, applying Turner's terminology. This pattern may represent a microevolutionary step prior to the emergence of the sundadont and sinodont patterns.     

甘肃省生境质量变化的图谱特征

生境质量是影响地区生态系统服务价值和保护地球生态系统中生物多样性的重要因素。以地处三大自然区交汇带的甘肃省为研究对象,在定量估算地区2000—2018年生境质量水平基础上,基于图谱变化分析理论和InVEST模型,探索地区生境质量时空分异格局及其图谱转移状态和变化强度。结果表明: 2000—2018年,甘肃省生境质量总体维持中等水平并略有提升,在空间上自北向南呈逐级递增的阶梯式变化特征,在数量上则高低并存;从图谱转移视角分析,甘肃省生境质量格局较为稳定,未发生状态转移的图谱单元占主导,而在发生了生境质量状态转移的图谱单元中,“较高较低”、“较高高”、“较高低”这6类状态间的互换转移最显著,空间分布也较为集聚; “北剧南和”是甘肃省生境质量变化强度的主要格局,自北向南依次为“强变区”、“复合区”、“温缓区”和“平和区”4类变化强度区。     

Pattern classification of kinematic and kinetic running data to distinguish gender,shod/barefoot and injury groups with feature ranking

The identification of differences between groups is often important in biomechanics. This paper presents group classification tasks using kinetic and kinematic data from a prospective running injury study. Groups composed of gender, of shod/barefoot running and of runners who developed patellofemoral pain syndrome (PFPS) during the study, and asymptotic runners were classified.

The features computed from the biomechanical data were deliberately chosen to be generic. Therefore, they were suited for different biomechanical measurements and classification tasks without adaptation to the input signals. Feature ranking was applied to reveal the relevance of each feature to the classification task.

Data from 80 runners were analysed for gender and shod/barefoot classification, while 12 runners were investigated in the injury classification task. Gender groups could be differentiated with 84.7%, shod/barefoot running with 98.3%, and PFPS with 100% classification rate. For the latter group, one single variable could be identified that alone allowed discrimination.     

Automated analysis of courtship suppression learning and memory in Drosophila melanogaster

Study of the fruit fly, Drosophila melanogaster, has yielded important insights into the underlying molecular mechanisms of learning and memory. Courtship conditioning is a well-established behavioral assay used to study Drosophila learning and memory. Here, we describe the development of software to analyze courtship suppression assay data that correctly identifies normal or abnormal learning and memory traits of individual flies. Development of this automated analysis software will significantly enhance our ability to use this assay in large-scale genetic screens and disease modeling. The software increases the consistency, objectivity, and types of data generated.     

Machine learning techniques for protein function prediction

Proteins play important roles in living organisms, and their function is directly linked with their structure. Due to the growing gap between the number of proteins being discovered and their functional characterization (in particular as a result of experimental limitations), reliable prediction of protein function through computational means has become crucial. This paper reviews the machine learning techniques used in the literature, following their evolution from simple algorithms such as logistic regression to more advanced methods like support vector machines and modern deep neural networks. Hyperparameter optimization methods adopted to boost prediction performance are presented. In parallel, the metamorphosis in the features used by these algorithms from classical physicochemical properties and amino acid composition, up to text-derived features from biomedical literature and learned feature representations using autoencoders, together with feature selection and dimensionality reduction techniques, are also reviewed. The success stories in the application of these techniques to both general and specific protein function prediction are discussed.     

细菌性肝脓肿临床特点的回顾性分析

目的:总结细菌性肝脓肿(pyogenic liver abscesses,PLA)的临床特点、诊断及治疗经验,提高诊疗水平。方法:回顾性分析哈尔滨医科大学第二临床医学院2013年1月-2014年12月收治的75例PLA患者的临床资料。结果:75例患者的平均年龄57岁,最大78岁,最小34岁。其中,男45例,女30例,男:女为1.5:1。75例患者中表现为发热者69例(92.0%)、寒战28例(37.3%)、右上腹痛66例(88.0%),伴恶心呕吐者28例(37.3%),黄疸15例(20.0%),肝肿大8例(10.7%)。有糖尿病病史患者28例(37.3%)。胆系疾病(78.7%)是本组资料主要的易患因素,其中53例患有胆囊炎(70.7%)。外周血白细胞增高67例(89.3%),血红蛋白降低41例(54.7%),白蛋白降低46例(61.3%);75例患者转氨酶升高58例(77.3%)。肝右叶单发脓肿多见(75例患者中36例,占48%)。51例B超确诊,24例行B超+CT或CT检查。48例行穿刺液细菌培养及25例血培养,其中肺炎克雷伯氏菌为主要病原菌。平均住院天数为19天,多数患者经皮下肝脓肿穿刺或置管引流而好转,1例漏诊为肺炎,2例患者反复入院,1例因菌血症死亡。结论:PLA的临床表现多样,以发热最为常见;多为肝右叶单发,部分患者血红蛋白及血清白蛋白降低。糖尿病为PLA最常见基础疾病,胆系疾病是主要的易患因素。肺炎克雷伯杆菌是本组PLA患者的主要致病菌。     

不同性别急性心梗患者的临床特征及院内治疗预后分析

目的:观察首次确诊为急性心肌梗死(AMI)患者的临床特征并分析不同性别人群的在入院治疗及院内短期预后的差异。方法:回顾性分析2010年2月至2012年4月在沈阳军区总医院心内科初次确诊为AMI的患者271例,并按性别分为两组,其中男性组180例,女性组91例,统计其临床特征、入院后的药物和手术治疗情况,以及院内并发症及预后情况,分析总结两组患者各自的特点和差异。结果:女性患者的年龄要大于男性患者(63±14vs71±11,P0.001),且同时更易患有高血压等心血管疾病合并症(73.3%vs 58.2%,P0.05)。女性患者的发病至入院治疗时间长于男性(P0.001),且入院后行PCI治疗的比例要明显低于男性患者(67%vs 79.4%,P0.05)。初步分析结果表明,女性患者的院内死亡率高于男性(11%vs 3.3%,OR:3.11,95%CI:1.53-7.15),但排除不均衡因素的影响后,男女患者的院内死亡率无明显差异(OR:2.11,95%CI:0.68-5.12)。结论:两性AMI患者的临床特征、入院后治疗及院内短期预后均存在一定差异。根据女性患者的临床特征和院内治疗及预后的现状,应进一步加强对女性高危人群的冠心病诊治知识普及和教育,且对入院后的女性患者应采取更为积极的药物和介入手术治疗手段,以改善预后。     

Machine learning approaches in MALDI-MSI: clinical applications

Introduction: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging.

Areas covered: This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing.

Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data.     

沙棘共生固氮根瘤及其内生弗兰克氏菌

用透射电镜研究了中国沙棘（Ｈｉｐｐｏｐｈａｅ ｒｈａｍｎｏｙｄｅｓ Ｌ．）根瘤的超微结构。它的侵梁细胞位于皮层中部,非侵染细胞与之间排列,富含多酚和淀粉粒。在根瘤的发育过程中,具有以下特征：（１）早期侵染细胞中具有核仁联合体;（２）由内生菌丝趋核生长而形成的核膜内陷处,有许多核孔出现;（３）在中期侵染细胞的内生菌丝和泡囊的荚膜附近,有成束的微管存在;（４）在４、５月瘤样的维管束细胞、非侵染细胞及早     

肌切蛋白的结构与功能多样性

肌切蛋白（scinderin）是一种重要的肌动蛋白结合蛋白,在哺乳动物和脊椎动物中广泛表达.肌切蛋白作为凝溶胶蛋白超家族的成员之一,通过肌动蛋白丝切割、肌动蛋白聚集等方式来控制肌动蛋白的结构.肌切蛋白生物活性具有多样性,除影响肌动蛋白丝重组外,肌切蛋白还参与细胞胞吐作用、调节细胞运动、细胞分化等细胞活动.此外,肌切蛋白在慢性炎症、凝血过程、免疫性疾病和肿瘤发生发展中也发挥了重要作用.本文对肌切蛋白的结构特点、参与调节细胞的功能和机制进行概述.