A potential tradeoff between feeding rate and aversive learning determines intoxication in a Caenorhabditis elegans host-pathogen system

Despite being the first line of defense against infection, little is known about how host-pathogen interactions determine avoidance. Caenorhabditis elegans can become infected by chemoattractant-producing bacteria through ingestion. The worms can learn to associate these chemoattractants with harm through aversive learning. As a result, the worms will avoid the pathogen. Evolutionary constraints have likely shaped the attraction, intoxication and learning dynamics between bacteria and C. elegans, but these have not been explored. Using bacteria engineered to express an acylhomoserine lactone chemoattractant and a nematicidal protein, we explored how manipulating the amount of attractant produced by the bacteria affects learning and intoxication in mixed stage populations of C. elegans. We found that increasing the production rate of the chemoattractant increased the feeding rate in C. elegans, but decreased the time required for C. elegans to learn to avoid the chemoattractant. Learning generally coincided with a decreased feeding rate. We also observed that the percentage of intoxicated worms was maximized at intermediate production rates of the attractant. We propose that interactions between attractant driven feeding rate and aversive learning are likely responsible for this trend. Our results increase our understanding of behavioral avoidance in C. elegans and have implications in understanding host-pathogen dynamics that shape avoidance.     

A method for editing motor unit potential trains obtained by decomposition of surface electromyographic signals

Rather than discarding motor unit potential trains (MUPTs) because they do not meet 100% validity criteria, we describe and evaluate a novel editing routine that preserves valid discharge times, based on decreasing shape variability (variance ratio, VR) within a MUPT. The error filtered estimation (EFE) algorithm is then applied to the remaining ‘high confidence’ discharge times to estimate inter-discharge interval (IDI) statistics. Decomposed surface EMG data from the flexor carpi radialis recorded from 20 participants during 60% MVC wrist flexion was used. There were two levels of denoising criteria (relaxed and strict) criteria for removing MUPs to decrease the VR and increase the signal-to-noise ratio (SNR) of a MUPT. In total, VR decreased 24.88% and SNR increased 6.0% (p’s < 0.05). The MUP template peak-to-peak (P-P) amplitude and P-P duration were dependent on the level of denoising (p’s < 0.05). The standard error of the estimate (SEE) of the mean IDI before and after editing using the relaxed criteria (3.2% versus 3.69%), was very similar (p > 0.05). The same was true for the SEE between denoising criteria, which increased only to 5.14% for the strict criteria (p > 0.05). Editing the MUPTs resulted in a significant decrease in MUP shape variability and in the measures extracted from the MUP templates, with trivial differences between the SEE of the mean IDI between the edited and unedited MUPTs.     

On computation of semiparametric maximum likelihood estimators with shape constraints

Large sample theory of semiparametric models based on maximum likelihood estimation (MLE) with shape constraint on the nonparametric component is well studied. Relatively less attention has been paid to the computational aspect of semiparametric MLE. The computation of semiparametric MLE based on existing approaches such as the expectation‐maximization (EM) algorithm can be computationally prohibitive when the missing rate is high. In this paper, we propose a computational framework for semiparametric MLE based on an inexact block coordinate ascent (BCA) algorithm. We show theoretically that the proposed algorithm converges. This computational framework can be applied to a wide range of data with different structures, such as panel count data, interval‐censored data, and degradation data, among others. Simulation studies demonstrate favorable performance compared with existing algorithms in terms of accuracy and speed. Two data sets are used to illustrate the proposed computational method. We further implement the proposed computational method in R package BCA1SG , available at CRAN.     

原发性醛固酮增多症大鼠自主活动和学习记忆行为的影响

摘要 目的：探讨原发性醛固酮增多症(primary aldosteronism,PA)大鼠其自主活动和对学习记忆行为的影响。方法：8周龄健康雄SD(Sprague-Dawley)大鼠(n=30)随机分为对照组与模型组各15只。两组都皮下埋置微量渗透泵,模型组泵内灌注醛固酮,对照组泵内灌注等量的生理盐水,记录大鼠自主活动和学习记忆行为的变化情况。结果：所有大鼠均存活,模型组都造模成功,切口愈合良好。模型组造模后的收缩压高于对照组(P<0.05),也高于造模前(P<0.05),两组造模前后心率对比差异无统计学意义(P>0.05)。模型组造模后的逃避潜伏期与穿台次数少于对照组(P<0.05),也少于造模前(P<0.05)。模型组造模后的自主活动次数高于对照组(P<0.05),也高于造模前(P<0.05)。造模后模型组的鼠双微基因2(Mouse Double Microgene 2,MDM2)蛋白相对表达水平高于对照组(P<0.05)。造模后模型组的血清醛固酮含量都高对照组(P<0.05),血清钾离子、钠离子、肾素活性低于对照组(P<0.05)。结论：原发性醛固酮增多症大鼠伴随有血清钾离子、钠离子含量降低与MDM2蛋白的高表达,从而导致大鼠出现自主活动和学习记忆行为障碍。     

医学生学习激情现状及影响因素的研究

目的:测量医学生学习激情现状,探索相关人口学变量差异及影响因素作用机制。方法:通过网络问卷对1793名在校医学生进行问卷调查,回收有效问卷1218份,应用SPSS22.0软件对数据进行统计处理。结果:测量对象的学习激情处于中等偏上水平(4.37±0.91);学习激情平均得分在性别(F=9.097,P<0.05)、生源地(F=14.850,P<0.05)、独生子女情况(F=5.470,P<0.05)方面的差异有统计学意义;成绩水平(β=0.094,P<0.01)、发展规划(β=0.105,P<0.01)、生活满意度(β=0.213,P<0.01)和活力(β=0.253,P<0.01)对医学生学习激情的影响有统计学意义。结论:医学生学习激情水平在性别、生源地、独生与否方面存在显著差异;成绩水平、发展规划、生活满意度、活力是影响医学生学习激情的因素。     

黄河首曲玛曲县高寒湿地景观格局演变

高寒湿地是青藏高原地区最重要的生态水源涵养区之一, 也是局部气候的有效调节者, 其动态变化与成因亟待深入研究。该研究基于遥感图像分析、地理信息系统空间分析和景观生态指数分析结合的方法, 以黄河首曲玛曲县高寒湿地为研究对象, 对1995-2018年6期湿地的动态变化进行研究。结果表明, 研究区湿地在1995-2010年间不断退化, 1995-2010年湿地面积总共减少了18 680.31 hm²。在2010-2018年间黄河首曲高寒湿地面积有所增加, 但与20世纪90年代相比, 21世纪初开始湿地的面积普遍呈现下降趋势; 1995-2010年湿地斑块数不断增加, 斑块密度不断增大, 平均斑块面积下降, 景观的破碎度升高; 2010-2015年湿地斑块数和斑块密度减少, 2015-2018年湿地斑块数和斑块密度增加, 平均斑块面积先增大后减小, 景观的破碎度先降低后升高。1995-2010年研究区高寒湿地景观Shannon多样性指数和Shannon均匀度指数均呈现下降的趋势, 湿地的景观结构趋于简单, 景观类型分布更加集中。2010-2018年湿地景观Shannon多样性指数和Shannon均匀度指数均呈现上升趋势, 湿地的景观结构趋于复杂, 景观类型增加且分布更加分散。进一步的驱动力分析表明, 引起黄河首曲高寒湿地景观格局演变的主要因素是蒸发量和降水量, 其次是人口数量和大牲畜数量等人类活动影响。气候因子是影响黄河首曲高寒湿地面积变化的主要原因, 过度的人类经济活动在一定程度上加剧了湿地的变化。     

Ageing Effect Evaluation on HD-sEMG Signals Using CCA Approach

ObjectivesThe objective of the proposed study is to exploit the technology of high-density surface electromyography (HD-sEMG), in order to evaluate the muscle activation in young and elderly subjects during a daily life gesture, namely, Sit To Stand (STS), using wireless connected ambulatory equipment (TMSi©) and Blind Source Separation (BSS) approach with Canonical Correlation Analysis (CCA).Materials and methodsSixteen subjects participated (50% females) divided into two categories (‘H1’: young (30.62 yrs ±5.92, 23.95 kg/m² ±3.08), versus ‘H2’: old (61.87 yrs ±7.98, 23.4 kg/m² ±3.38)), in the recording of HD-sEMG signals, using 32-electrodes square grids (4×8), during Sit To Stand (STS) motion, three times at spontaneous speed. The studied muscle is the Rectus Femoris (RF) muscle. The recorded HD-sEMG signals were analyzed with CCA approach to extract correlation coefficient sets according to two age categories (young versus old), in order to evaluate its discriminating power with ageing. Statistical tests (t-test) were used to evaluate the discrimination for these two categories.ResultsThe calculation of CCA correlation coefficients showed a significant difference between young and old category concerning the mean CCA correlation coefficient (P<0.001***) and also the standard deviation of the CCA correlation coefficients (P<0.0001****).ConclusionThe obtained results are promising and indicate a clear difference between the obtained source variability using CCA method between the young and the old tested subjects during daily life motion. Furthermore, these estimated sources seem to be impacted by both anatomical and functional modifications with ageing.     

Cluster analysis of pressure pain threshold maps from the trapezius muscle

The aim of this study was to investigate and present a new mapping method to describe muscle pain sensitivity based on the assessment of pressure pain threshold (PPT) over the trapezius muscle. PPT data were recorded from 36 points in 20 healthy males using a standardised grid. Points were clustered using the K-means algorithm with a fixed initialisation procedure. The total number of clusters was determined on the basis of (1) R ² evaluation of the clustering outcome compared against a desired 95% reduction in variance criterion and (2) the number of empty clusters. A minimum of three clusters were found which fulfilled the criteria. The proposed method enables the identification of a relation between the muscle subdivisions and pressure pain sensitivity within the trapezius. Further, the cluster analysis will enable the study of differences in pain sensitivity distributions between patients and controls and quantify the effect of intervention (physical or pharmacological treatments).     

PARma: identification of microRNA target sites in AGO-PAR-CLIP data

PARma is a complete data analysis software for AGO-PAR-CLIP experiments to identify target sites of microRNAs as well as the microRNA binding to these sites. It integrates specific characteristics of the experiments into a generative model. The model and a novel pattern discovery tool are iteratively applied to data to estimate seed activity probabilities, cluster confidence scores and to assign the most probable microRNA. Based on differential PAR-CLIP analysis and comparison to RIP-Chip data, we show that PARma is more accurate than existing approaches. PARma is available from http://www.bio.ifi.lmu.de/PARma