Association of the Asp312Asn and Lys751 Gln polymorphisms in the XPD gene with the risk of non-Hodgkin's lymphoma:evidence from a meta-analysis

Polymorphisms in DNA repair genes may alter DNA repair capacity and,consequently,lead to genetic instability and carcinogenesis.Several studies have investigated the association of the Asp312 Asn and Lys751 Gln polymorphisms in the xeroderma pigmentosum complementation group D {XPD) gene with the risk of non-Hodgkin's lymphoma(NHL),but the conclusions have been inconsistent.Therefore,we performed this meta-analysis to more precisely estimate these relationships.A systematic literature search was performed using the PubMed,Embase,and Chinese Biomedical(CBM) databases.Ultimately,6 studies of Asp312 Asn,comprising 3,095 cases and 3,306 controls,and 7studies of Lys751 Gln,consisting of 3,249 cases and 3,676 controls,were included.Pooled odds ratios(ORs) and 95%confidence intervals(CIs) were calculated to assess the strength of each association.Overall,no association was observed between the Asp312 Asn polymorphism and NHL risk(homozygous:OR = 1.11,95%CI = 0.94-1.32;heterozygous:OR = 1.00,95%CI = 0.89-1.11;recessive:OR = 1.12,95%CI = 0.95-1.31;dominant:OR = 1.02,95%CI = 0.92-1.13;and allele comparison:OR = 1.04,95%CI = 0.96-1.12) or between the Lys751 Gln polymorphism and NHL risk(homozygous:OR = 0.97,95%CI = 0.83-1.15;heterozygous:OR = 0.96,95%CI = 0.86-1.06;recessive:OR = 1.00,95%CI = 0.86-1.16;dominant:OR = 0.96,95%CI = 0.87-1.06;and allele comparison:OR = 0.98,95%CI = 0.91-1.05).Furthermore,subgroup analyses did not reveal any association between these polymorphisms and ethnicity,the source of the controls,or the NHL subtype.These results indicated that neither the Asp312 Asn nor Lys751 Gln XPD polymorphism was related to NHL risk.Large and well-designed prospective studies are required to confirm this finding.

One-Year Outcomes of Women Started on Antiretroviral Therapy during Pregnancy before and after the Implementation of Option B+ in Malawi: A Retrospective Chart Review from Three Facilities

Objective: To compare one-year outcomes of women started on antiretroviral therapy (ART) during?pregnancy in the pre-Option B+ era to those in the Option B+ era. Methods: A retrospective chart review was performed at three sites in Malawi. Women were included in the “pre-Option B+” cohort if they started ART during pregnancy for a CD4 count 3?or WHO 3/4 condition and in the “Option B+” cohort if they started ART during pregnancy regardless of CD4 count or clinical stage. One-year outcomes were compared using Fisher’s exact and ANOVA F-tests. Results: A higher proportion of women in the pre-Option B+ cohort started ART at WHO stage 3/4 (11.9% versus 1.1%, P < 0.001), switched ART regimens (5.9% versus 0%, P = 0.002), or died in the first year after starting treatment (3.9% versus 0.5%, P = 0.05). While more women in the Option B+ cohort had poor adherence or defaulted, these differences were not significant. Conclusions: At our study sites, the transition to Option B+ has been associated with ART initiation in women with less advanced HIV infection, improved medication tolerability, and lower mortality. Further research is needed to better understand outcomes of Option B+.

Modeling the Browsing Behavior of World Wide Web Users

The World Wide Web is essential to general public nowadays. From a data analysis viewpoint, it provides rich opportunities to gather observational data on a large-scale. This paper focuses on modeling the behavior of visitors to an academic website. Although the conventional probability models, which were used by other literature for fitting in a commercial web site, capture the power law behavior in our data, they fail to capture other important features like the long tail. We propose a new model based on the identities of the users. Qualitative and quantitative tests, which are used for comparing the model fitting to our data, show that the new model outperforms other two conventional probability models.

On the Use of Local Assessments for Monitoring Centrally Reviewed Endpoints with Missing Data in Clinical Trials

Due to ethical and logistical concerns it is common for data monitoring committees to periodically monitor accruing clinical trial data to assess the safety, and possibly efficacy, of a new experimental treatment. When formalized, monitoring is typically implemented using group sequential methods. In some cases regulatory agencies have required that primary trial analyses should be based solely on the judgment of an independent review committee (IRC). The IRC assessments can produce difficulties for trial monitoring given the time lag typically associated with receiving assessments from the IRC. This results in a missing data problem wherein a surrogate measure of response may provide useful information for interim decisions and future monitoring strategies. In this paper, we present statistical tools that are helpful for monitoring a group sequential clinical trial with missing IRC data. We illustrate the proposed methodology in the case of binary endpoints under various missingness mechanisms including missing completely at random assessments and when missingness depends on the IRC’s measurement.

一种基于二代测序辨识短串联重复序列长度变异的新方法及其在人类遗传疾病研究中的应用（英文）

二代测序技术的涌现推动了基因组学研究,特别是在疾病相关的遗传变异研究中发挥了重要作用.虽然大多数遗传变异类型都可以借助于各种二代测序分析工具进行检测,但是仍然存在局限性,比如短串联重复序列的长度变异.许多遗传疾病是由短串联重复序列的长度扩张导致的,尤其是亨廷顿病等多种神经系统疾病.然而,现在几乎没有工具能够利用二代测序检测长度大于测序读长的短串联重复序列变异.为了突破这一限制,我们开发了一个全新的方法,该方法基于双末端二代测序辨识短串联重复序列长度变异,并可估计其扩张长度,将其应用于一项基于全外显子组测序的运动神经元疾病临床研究中,成功地鉴定出致病的短串联重复序列长度扩张.该方法首次原创性地利用测序读长覆盖深度特征来解决短串联重复序列变异检测问题,在人类遗传疾病研究中具有广泛的应用价值,并且对于其他二代测序分析方法的开发具有启发性意义.

Effect of planting date and nitrogen fertilization on photosynthesis and soluble carbohydrate contents of cotton in relation to silverleaf whitefly （Bemisia tabaci biotype “B”） populations

The impacts of planting date and nitrogen fertilization on cotton （Gossypium hirsutum L.） photosynthesis and soluble carbohydrate contents in relation to silverleaf whitefly, Bemisia tabaci （Gennadius） biotype “B”, populations were examined in field experiments. Cotton planted in late April and early June was treated with 0, 112, 168 and 224 kg/N hectare in soil using urea fertilizer. The mean photosynthetic rate of April-planted cotton was 4%-20% higher than that of June-planted cotton early in the season, but 10%- 18% lower than that of June-planted cotton late in the season. The photosynthetic rates for both planting dates were positively correlated with levels of added nitrogen. While levels of glucose for both planting dates were positively correlated with nitrogen levels, fructose and sucrose levels were not. The mean levels of fructose were up to 40% lower, while that of sucrose were up to 59% higher, in April-planted cotton than in June-planted cotton. Levels of photosynthetic rate or stomatal conductance were not correlated with adult whitefly densities for either planting date. Levels of glucose and fructose were positively correlated with whitefly densities only for June-planted cotton late in the season.

Modeling and analysis of RNA-seq data： a review from a statistical perspective

Background： Since the invention of next-generation RNA sequencing （RNA-seq） technologies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The analysis of RNA-seq data at four different levels （samples, genes, transcripts, and exons） involve multiple statistical and computational questions, some of which remain challenging up to date. Results： We review RNA-seq analysis tools at the sample, gene, transcript, and exon levels from a statistical perspective. We also highlight the biological and statistical questions of most practical considerations. Conclusions： The development of statistical and computational methods for analyzing RNA-seq data has made significant advances in the past decade. However, methods developed to answer the same biological question often rely on diverse statistical models and exhibit different performance under different scenarios. This review discusses and compares multiple commonly used statistical models regarding their assumptions, in the hope of helping users select appropriate methods as needed, as well as assisting developers for future method development.

Development of growth rate, body lipid,moisture,and energy models for white sturgeon (Acipenser transmontanus) fed at various feeding rates

The objectives were to develop and evaluate: 1) growth rate models, 2) body lipid, moisture, and energy models for white sturgeon fed at various feeding rates(FR; % body weight [BW] per day) and then evaluate responses at proportions of optimum feeding rate(OFR) across increasing BW(g). For objective1,19 datasets from the literature containing initial BW, FR and specific growth rate(SGR; % BW increase per day) were used. For objective 2.12 datasets from the literature(11 from objective 1) containing SGR,FR,final BW,body lipid(%),protein(%),ash(%),moisture(%),and energy(kJ/g) were used. The average rearing temperatures was 19.2 ± 1.5 ℃(mean ± SD). The average nutrient compositions and gross energy of the diets were 45.7 ± 4.3% protein,14.8 ± 3.2% lipid, and 20.4 ± 1.3 kJ/g,respectively. The logistic model was used for objectives 1 and 2 to develop a statistical relationship between SGR and FR, then an iterative technique was used to estimate OFR for each dataset. For objective 2, the statistical relationship between body lipid, energy, and moisture and FR was established. Using the OFR estimate, SGR, body lipid, energy and moisture were computed at various FR as a proportion of OFR, Finally, a nonparametric fitting procedure was used to establish relationships between SGR, body lipid, energy and moisture(responses)compared with BW(predictor) at various proportions of OFR. This allows visualization of the effect of under-or over-feeding on the various responses. When examining the differences between OFR at 100%and various proportions of OFR, SGR differences decrease and moisture differences increase as BW increases. Lipid and energy differences decrease as BW increases. To our knowledge, these are the first description of changes in nutrient compositions when white sturgeon are fed at various FR. Because physiological and behavioral properties that are unique to sturgeon, results from this study are specific to sturgeon under the conditions of this study and cannot be compared directly with salmonids even if some of the results are similar. This research provides insight to designing future nutritional studies in sturgeon.

二分类结果组间比较的样本量计算（英文）

概述:样本大小是临床研究的一个重要参数。然而,把握度和样本量分析对许多生物医学和社会心理调查者来说似乎是一个统计学家的魔术。在本文中,我们继续讨论二分类结果的把握度和样本量的计算。我们再次强调了在建立假设和进行把握度分析中调查者和生物统计学家之间密切联系的重要性。

Effects of biscuit-type feeding supplementation on the neurocognitive outcomes of HIV-affected school-age children:a randomized,double-blind,controlled intervention trial in Kenya

Objective:To determine if meat or soy protein dietary supplementation will enhance the neuro-cognitive performance of HIV-affected children at-risk of malnutrition and food insecurity.Methods:A randomized,double-blind,controlled intervention trial evaluated the effect of nu-tritional supplementation on the neurocognitive outcomes of 49 HIV-affected school-age children in western Kenya.The intervention consisted in providing the mother,target child,and siblings with one of three isocaloric biscuit-type supplements-soy,wheat,or beef-on 5 days per week for 18 months.Neurocognitive outcomes of the target children were assessed by a battery of eight measures and followed up longitudinally for up to 24 months.Results:Mixed effects modeling demonstrated significant differences in the rates of increase over time among all three groups(F test degrees of freedom of 2,P<0.05)for Raven’s progressive matrices performance,but not for verbal meaning,arithmetic,digit span backward,forward,and total,embedded figure test,and Beery visual-motor integration scores.Conclusion:HIV-affected school-age children provided with soy protein supplementation showed greater improvement in nonverbal cognitive(fluid intelligence)performance compared with peers who received isocaloric beef or wheat biscuits.Soy nutrients may have an enhancing effect on neurocognitive skills in HIV-affected school-age children.

白内障摘除术后的急性眼内炎文献的系统回顾

目的：确定白内障摘除术后随时间推移报道的急性眼内炎的发生率并探讨可能引起的因素，如白内障手术切口类型。 方法：以白内障摘除术、眼内炎及术后并发症为关键词，对1963—2003年PubMed上的英文文献进行广泛检索。并进行系统性回顾。附加的研究确认为与其相切题的文献和已发表的会议记录。在可能提供的情况下。记录手术方式。对用不同手术切口技术导致眼内炎发展的累计发生率和相关危险度进行评估。用不同切口的技术评估累计眼内炎的发生率与发展眼内炎的相对危险因素。 结果：从4916篇特定且具潜在相关性的引文中对其中215篇涉及眼内炎且符合入选标准的研究进行分析。总共3140650例白内障手术后，累计0．128％发生眼内炎。然而，急性眼内炎的发生率随着时代不同而改变。与过去lO年相比。2000年以来发生率明显升高（相对危险度，2．44；95％可信区间，2．27—2．61）。2000—2003年眼内炎发生率为0．265％。20世纪90年代为0．087％，80年代为0．158％，70年代为0．327％。并且，与1991年及以前相比，1992年以来发生率有上升趋势。手术切口类型似乎是产生显著影响的危险因素，因为1992—2003年中，经透明角膜切口白内障手术后眼内炎发生率为0．189％。与经巩膜切口0．074％（相对危险度，2．55；95％可信区间，1．75—3．71）及经角巩膜缘切口0．062％（相对危险度，3．06；95％可信区间，2．48—3．76）的发生率相比较，手术切口类型似乎是产生显著影响的危险因素。 结论：这一系统回顾显示，与白内障摘除术相关眼内炎的发生率在过去10年中有所上升。其发生频率的上升趋势与当前无缝线透明角膜白内障摘除术的进展相一致。

Survey on person re-identification based on deep learning

Person re-identification(Re-ID)is a fundamental subject in the field of the computer vision technologies.The traditional methods of person Re-ID have difficulty in solving the problems of person illumination,occlusion and attitude change under complex background.Meanwhile,the introduction of deep learning opens a new way of person Re-ID research and becomes a hot spot in this field.This study reviews the traditional methods of person Re-ID,then the authors focus on the related papers about different person Re-ID frameworks on the basis of deep learning,and discusses their advantages and disadvantages.Finally,they propose the direction of further research,especially the prospect of person Re-ID methods based on deep learning.

An analysis of Chinese Super League partial results

Some of the history of soccer/world football in China is presented. Then consideration turns to the 2008 Chinese Super League. It has 16 teams. The results from the first half of the season, i.e. 15 rounds, are studied. The response of interest for a specific game is whether the home team won, tied or lost, who the home team was, and who the opponent was. The response is ordinal-valued. A generalized linear model is fit and then, given the remaining fixtures, used to predict the final standings of the season. Other explanatories, such as round number, are considered for inclusion in the model. Simulation is employed to estimate probabilities of interest.

Local generalized empirical estimation of regression

Letf(x) be the density of a design variableX andm(x) = E[Y∣X = x] the regression function. Thenm(x) = G(x)/f(x), whereG(x) = rn(x)f(x). The Dirac δ-function is used to define a generalized empirical functionG n(x) forG(x) whose expectation equalsG(x). This generalized empirical function exists only in the space of Schwartz distributions, so we introduce a local polynomial of orderp approximation toG n(.) which provides estimators of the functionG(x) and its derivatives. The densityf(x) can be estimated in a similar manner. The resulting local generalized empirical estimator (LGE ) ofm(x) is exactly the Nadaraya-Watson estimator at interior points whenp = 1, but on the boundary the estimator automatically corrects the boundary effect. Asymptotic normality of the estimator is established. Asymptotic expressions for the mean squared errors are obtained and used in bandwidth selection. Boundary behavior of the estimators is investigated in details. We use Monte Carlo simulations to show that the proposed estimator withp = 1 compares favorably with the Nadaraya-Watson and the popular local linear regression smoother.

Perception of Static and Dynamic Forces with a Bio-inspired Tactile Fingertip

With the aid of different types of mechanoreceptors,human is capable of perceiving stimuli from surrounding environments and manipulating various objects dexterously.In this paper,a bio-inspired tactile fingertip is designed mimicking human fingertip in both structures and functionalities.Two pairs of strain gages and(Polyvinylidene Fluoride)PVDF films are perpendicularly arranged to simulate the Fast-Adapting(FA)and Slowly Adapting(SA)type mechanoreceptors in human hands,while silicones,Polymethyl Methacrylate(PMMA),and electronic wires are applied to mimic the skin,bone and nerve fibers.Both static and dynamic forces can be perceived sensitively.A preprocessing electric circuit is further designed to transform the resistor changes into voltages,and then filter and amplify the four-channel signals.In addition to strong robustness due to the embedded structure,the developed fingertip is found sensitive to deformations via a force test experiment.Finally,two robotic experiments explore its recognition ability of contact status and object surface.Excellent performance is found with high accuracy of 99.72%achieved in discriminating six surfaces that are ubiquitous in daily life,which demonstrates the effectiveness of our designed tactile sensor.

Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap

We propose a novel conditional graphical model -- spaceMap -- to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations （CNAs） on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNAs as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNAs perturb the protein network, spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular profiles, especially those exhibiting hub structures. Simulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applies spaceMap to the CNAs, gene expression and proteomics data sets from CPTAC-TCGA breast （n = 77） and ovarian （n = 174） cancer studies. Each cancer exhibits disruption of＇ion transmembrane transport＇ and ＇regulation from RNA polymerase lI promoter＇ by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers.

Modeling and inferring 2.1D sketch with mixed Markov random field

This paper presents a method of computing a 2.1D sketch (i.e., layered image representation) from a single image with mixed Markov random field (MRF) under the Bayesian framework. Our model consists of three layers: the input image layer, the graphical representation layer of the computed 2D atomic regions and 3-degree junctions (such as T or arrow junctions), and the 2.1D sketch layer. There are two types of vertices in the graphical representation of the 2D entities: (i) regions, which act as the vertices found in traditional MRF, and (ii) address variables assigned to the terminators decomposed from the 3-degree junctions, which are a new type of vertices for the mixed MRF. We formulate the inference problem as computing the 2.1D sketch from the 2D graphical representation under the Bayesian framework, which consists of two components: (i) region layering/coloring based on the Swendsen-Wang cuts algorithm, which infers partial occluding order of regions, and (ii) address variable assignments based on Gibbs sampling, which completes the open bonds of the terminators of the 3-degree junctions. The proposed method is tested on the D-Order dataset, the Berkeley segmentation dataset and the Stanford 3D dataset. The experimental results show the efficiency and robustness of our approach. © 2017 Beijing Institute of Aerospace Information.

Improve Survival Prediction Using Principal Components of Gene Expression Data

The purpose of many microarray studies is to find the association between gene expression and sample characteristics such as treatment type or sample phenotype. There has been a surge of efforts developing different methods for delineating the association. Aside from the high dimensionality of microarray data, one well recognized challenge is the fact that genes could be complicatedly inter-related, thus making many statistical methods inappropriate to use directly on the expression data. Multivariate methods such as principal component analysis （PCA） and clustering are often used as a part of the effort to capture the gene correlation, and the derived components or clusters are used to describe the association between gene expression and sample phenotype. We propose a method for patient population dichotomization using maximally selected test statistics in combination with the PCA method, which shows favorable results. The proposed method is compared with a currently well-recognized method.

A discussion of prior-based Bayesian information criterion (PBIC)

Professor Bayarri and coauthors’ paper (hereafter,PBIC) offers a stimulating and welcomed addition tothe already extensive and yet still rapid expanding literature on model selection and related topics. In a 2013review on model selection in linear mixed models byMüller, Scealy, and Welsh (2013), the authors classified main approaches in mixed model selection intothree categories, the information criteria, the shrinkagemethods and the fence methods. The current paper isnot specifically regarding mixed model selection problems;however, as one shall see, there is a connection invarious ways.

Probabilistic models of vision and max-margin methods

It is attractive to formulate problems in computer vision and related fields in term of probabilis- tic estimation where the probability models are defined over graphs, such as grammars. The graphical struc- tures, and the state variables defined over them, give a rich knowledge representation which can describe the complex structures of objects and images. The proba- bility distributions defined over the graphs capture the statistical variability of these structures. These proba- bility models can be learnt from training data with lim- ited amounts of supervision. But learning these models suffers from the difficulty of evaluating the normaliza- tion constant, or partition function, of the probability distributions which can be extremely computationally demanding. This paper shows that by placing bounds on the normalization constant we can obtain compu- rationally tractable approximations. Surprisingly, for certain choices of loss functions, we obtain many of the standard max-margin criteria used in support vector machines （SVMs） and hence we reduce the learning to standard machine learning methods. We show that many machine learning methods can be obtained in this way as approximations to probabilistic methods including multi-class max-margin, ordinal regression, max-margin Markov networks and parsers, multiple- instance learning, and latent SVM. We illustrate this work by computer vision applications including image labeling, object detection and localization, and motion estimation. We speculate that rained by using better bounds better results can be ob- and approximations.