The in vivo assessment of mechanical loadings on pectoral pacemaker implants

Reduced sizes of implantable cardiac pacemakers and clinical advances have led to a higher feasibility of using such devices in younger patients including children. Increased structural demands deriving from reduced device size and more active recipients require detailed knowledge of in vivo mechanical conditions to ensure device reliability. Objective of this study was the proof of feasibility of a system for the measurement of in vivo mechanical loadings on pacemaker implants. The system comprised the following: implantable instrumented pacemaker (IPM) with six force sensors, accelerometer and radio-frequency (RF) transceiver; RF data logging system and video capture system. Three Chacma baboons (20.6±1.15 kg) received one pectoral sub-muscular IPM implant. After wound healing, forces were measured during physical activities. Forces during range of motion of the arm were assessed on the anaesthetized animals prior to device explantation. Mass, volume and dimensions of the excised Pectoralis major muscles were determined after device explantation. Remote IPM activation and data acquisition were reliable in the indoor cage environment with transceiver distances of up to 3 m. Sampling rates of up to 1000 Hz proved sufficient to capture dynamic in vivo loadings. Compressive forces on the IPM in conscious animals reached a maximum of 77.2±54.6 N during physical activity and were 22.2±7.3 N at rest, compared with 34.6±15.7 N maximum during range of motion and 13.4±3.3 N at rest in anaesthetized animals. The study demonstrated the feasibility of the developed system for the assessment of in vivo mechanical loading conditions of implantable pacemakers with potential for use for other implantable therapeutic devices.     

High resolution mechanical function in the intact porcine heart: mechanical effects of pacemaker location

The necessity to quantify the mechanical function with high spatial resolution stemmed from the advancement of myocardial salvaging techniques. Since these therapies are localized interventions, a whole field technique with high spatial resolution was needed to differentiate the normal, diseased, and treated myocardium. We developed a phase correlation algorithm for measuring myocardial displacement at high spatial resolution and to determine the regional mechanical function in the intact heart. Porcine hearts were exposed and high contrast microparticles were placed on the myocardium. A pressure transducer, inserted into the left ventricle, synchronized the pressure (LVP) with image acquisition using a charge-coupled device camera. The deformation of the myocardium was measured with a resolution of 0.58+/-0.04 mm. Within the region of interest (ROI), regional stroke work (RSW), defined as the integral of LVP with respect to regional area, was determined on average at 21 locations with a resolution of 27.1+/-2.7 mm2. To alter regional mechanical function, the heart was paced at three different locations around the ROI. Independent of the pacemaker location, RSW decreased in the ROI. In addition, a gradient of increasing RSW in the outward direction radiating from the pacemaker was observed in all pacing protocols. These data demonstrated the ability to determine regional whole field mechanical function with high spatial resolution, and the significant alterations induced by electrical pacing.     

基于种间竞争模型的毒杂草入侵空间分布模拟研究

以天然草场中毒杂草与可食牧草为研究对象,在种间竞争的生态数学模型的基础上引入入侵扩散因素建立毒杂草入侵扩散模型。采用元胞自动机理论将竞争模型扩展到空间网络进行模拟研究,分析毒杂草属的空间分布类型,为毒杂草的控制提供数据支持。研究表明:(1)在入侵扩散作用下,毒杂草与可食牧草的共存平衡点由一个增加为两个,增加了共存的可能性;(2)入侵扩散作用影响了毒杂草种群的空间分布特征,减少了种群空间分布的聚集程度。     

基于迭代自学习的操纵子结构预测

原核生物操纵子结构的准确注释对基因功能和基因调控网络的研究具有重要意义,通过生物信息学方法计算预测是当前基因组操纵子结构注释的最主要来源.当前的预测算法大都需要实验确认的操纵子作为训练集,但实验确认的操纵子数据的缺乏一直成为发展算法的瓶颈.基于对操纵子结构的认识,从基因间距离、转录翻译相关的调控信号以及COG功能注释等特征出发,建立了描述操纵子复杂结构的概率模型,并提出了不依赖于特定物种操纵子数据作为训练集的迭代自学习算法.通过对实验验证的操纵子数据集的测试比较,结果表明算法对于预测操纵子结构非常有效.在不依赖于任何已知操纵子信息的情况下,算法在总体预测水平上超过了目前最好的操纵子预测方法,而且这种自学习的预测算法要优于依赖特定物种进行训练的算法.这些特点使得该算法能够适用于新测序的物种,有别于当前常用的操纵子预测方法.对细菌和古细菌的基因组进行大规模比较分析,进一步提高了对基因组操纵子结构的普遍特征和物种特异性的认识.     

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash.     

基于分组重量编码的蛋白质功能预测

从蛋白质序列出发,采用分组重量编码(Encoding Based on Grouped Weight,简记EBGW),并结合最近邻居算法对蛋白质功能进行预测。对酵母(Saccharomyces cerevisiae)蛋白质的1826条序列进行预测,整体预测准确率与其他基于序列信息的蛋白质功能预测方法相当。实验结果表明基于EBGW编码方案的新方法可有效地应用于蛋白质功能预测。     

Patient-specific bone modelling and remodelling simulation of hypoparathyroidism based on human iliac crest biopsies

We previously developed a load-adaptive bone modelling and remodelling simulation model that can predict changes in the bone micro-architecture as a result of changes in mechanical loading or cell activity. In combination with a novel algorithm to estimate loading conditions, this offers the possibility for patient-specific predictions of bone modelling and remodelling. Based on such models, the underlying mechanisms of bone diseases and/or the effects of certain drugs and their influence on the bone micro-architecture can be investigated. In the present study we test the ability of this approach to predict changes in bone micro-architecture during hypoparathyroidism (HypoPT), as an illustrative example. We hypothesize that, apart from reducing bone turnover, HypoPT must also lead to increased osteocyte mechanosensitivity in order to explain the changes in bone mass seen in patients. Healthy human iliac crest biopsies were used as the starting point for the simulations that mimic HypoPT conditions and the resultant micro-architectures were compared to age-matched clinical HypoPT biopsies. Simulation results were in good agreement with the clinical data when osteocyte mechanosensitivity was increased by 40%. In conclusion, the results confirm our hypothesis, and also demonstrate that patient-specific bone modelling and remodelling simulations are feasible.     

Structure based function prediction of proteins using fragment library frequency vectors

The function of the protein is primarily dictated by its structure. Therefore it is far more logical to find the functional clues of the protein in its overall 3-dimensional fold or its global structure. In this paper, we have developed a novel Support Vector Machines (SVM) based prediction model for functional classification and prediction of proteins using features extracted from its global structure based on fragment libraries. Fragment libraries have been previously used for abintio modelling of proteins and protein structure comparisons. The query protein structure is broken down into a collection of short contiguous backbone fragments and this collection is discretized using a library of fragments. The input feature vector is frequency vector that counts the number of each library fragment in the collection of fragments by all-to-all fragment comparisons. SVM models were trained and optimised for obtaining the best 10-fold Cross validation accuracy for classification. As an example, this method was applied for prediction and classification of Cell Adhesion molecules (CAMs). Thirty-four different fragment libraries with sizes ranging from 4 to 400 and fragment lengths ranging from 4 to 12 were used for obtaining the best prediction model. The best 10-fold CV accuracy of 95.25% was obtained for library of 400 fragments of length 10. An accuracy of 87.5% was obtained on an unseen test dataset consisting of 20 CAMs and 20 NonCAMs. This shows that protein structure can be accurately and uniquely described using 400 representative fragments of length 10.     

基于生物信息学的胃癌早期诊断预测模型研究

利用The Cancer Genome Atlas和Genotype-Tissue Expression公共数据检索收集胃癌(Gastric cancer, GC)基因表达数据集,筛选与早期胃癌密切相关的基因并构建胃癌早期诊断预测模型。运用Deseq2软件包筛选早期胃癌差异基因,并对差异基因进行GO和KEGG富集分析。通过STRING数据库建立其蛋白质相互作用网络并利用Cytoscape软件提取关键子网得到候选关键基因,进一步利用MedCalc软件确认胃癌早期诊断关键基因。根据筛选得到的10个关键基因构建基于支持向量机、随机森林、朴素贝叶斯、K-近邻、极限梯度提升和自适应提升等六种算法的胃癌早期诊断预测模型,依据ROC曲线和准确率等评价指标对各个分类器模型进行评估,通过独立测试集验证得到极致梯度提升诊断预测模型为最优模型。本研究成果为提高结胃癌早期诊断的研究提供了新的思路和方法。     

Improved prediction of critical residues for protein function based on network and phylogenetic analyses

Background  

Phylogenetic approaches are commonly used to predict which amino acid residues are critical to the function of a given protein. However, such approaches display inherent limitations, such as the requirement for identification of multiple homologues of the protein under consideration. Therefore, complementary or alternative approaches for the prediction of critical residues would be desirable. Network analyses have been used in the modelling of many complex biological systems, but only very recently have they been used to predict critical residues from a protein's three-dimensional structure. Here we compare a couple of phylogenetic approaches to several different network-based methods for the prediction of critical residues, and show that a combination of one phylogenetic method and one network-based method is superior to other methods previously employed.     

Protein secondary structure prediction based on an improved support vector machines approach

The prediction of protein secondary structure is an important step in the prediction of protein tertiary structure. A new protein secondary structure prediction method, SVMpsi, was developed to improve the current level of prediction by incorporating new tertiary classifiers and their jury decision system, and the PSI-BLAST PSSM profiles. Additionally, efficient methods to handle unbalanced data and a new optimization strategy for maximizing the Q(3) measure were developed. The SVMpsi produces the highest published Q(3) and SOV94 scores on both the RS126 and CB513 data sets to date. For a new KP480 set, the prediction accuracy of SVMpsi was Q(3) = 78.5% and SOV94 = 82.8%. Moreover, the blind test results for 136 non-redundant protein sequences which do not contain homologues of training data sets were Q(3) = 77.2% and SOV94 = 81.8%. The SVMpsi results in CASP5 illustrate that it is another competitive method to predict protein secondary structure.     

Development of an open sandwich fluoroimmunoassay based on fluorescence resonance energy transfer

We have developed a sensitive, one-step, homogeneous open sandwich fluoroimmunoassay (OsFIA) based on fluorescence resonance energy transfer (FRET) and luminescent semiconductor quantum dots (QDs). In this FRET assay, estrogen receptor beta (ER-beta) antigen was incubated with QD-labeled anti-ER-beta monoclonal antibody and Alexa Fluor (AF)-labeled anti-ER polyclonal antibody for 30 min, followed by FRET measurement. The dye separation distance was estimated between 80 and 90 A. The current method is rapid, simple, and highly sensitive, and it did not require the bound/free reagent separation steps and solid-phase carriers. A concentration as low as 0.05 nM (2.65 ng/ml) receptor was detected with linearity. In addition, the assay was performed with commercial antibodies. This assay provides a convenient alternative to conventional, laborious sandwich immunoassays.     

RAPID: Fast and accurate sequence-based prediction of intrinsic disorder content on proteomic scale

Recent research in the protein intrinsic disorder was stimulated by the availability of accurate computational predictors. However, most of these methods are relatively slow, especially considering proteome-scale applications, and were shown to produce relatively large errors when estimating disorder at the protein- (in contrast to residue-) level, which is defined by the fraction/content of disordered residues. To this end, we propose a novel support vector Regression-based Accurate Predictor of Intrinsic Disorder (RAPID). Key advantages of RAPID are speed (prediction of an average-size eukaryotic proteome takes < 1 h on a modern desktop computer); sophisticated design (multiple, complementary information sources that are aggregated over an input chain are combined using feature selection); and high-quality and robust predictive performance. Empirical tests on two diverse benchmark datasets reveal that RAPID's predictive performance compares favorably to a comprehensive set of state-of-the-art disorder and disorder content predictors. Drawing on high speed and good predictive quality, RAPID was used to perform large-scale characterization of disorder in 200 + fully sequenced eukaryotic proteomes. Our analysis reveals interesting relations of disorder with structural coverage and chain length, and unusual distribution of fully disordered chains. We also performed a comprehensive (using 56000+ annotated chains, which doubles the scope of previous studies) investigation of cellular functions and localizations that are enriched in the disorder in the human proteome. RAPID, which allows for batch (proteome-wide) predictions, is available as a web server at http://biomine.ece.ualberta.ca/RAPID/.     

Genome wide prediction of protein function via a generic knowledge discovery approach based on evidence integration

Background  

The automation of many common molecular biology techniques has resulted in the accumulation of vast quantities of experimental data. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system (e.g. knowledge of genes and their products, and the biological roles of proteins, their molecular functions, localizations and interaction networks). We present a technique called Global Mapping of Unknown Proteins (GMUP) which uses the Gene Ontology Index to relate diverse sources of experimental data by creation of an abstraction layer of evidence data. This abstraction layer is used as input to a neural network which, once trained, can be used to predict function from the evidence data of unannotated proteins. The method allows us to include almost any experimental data set related to protein function, which incorporates the Gene Ontology, to our evidence data in order to seek relationships between the different sets.     

基于转移方程的蔬菜镉累积预测和土壤风险阈值推导

重金属镉(Cd)在土壤-蔬菜系统中转移方程的建立是农田Cd污染控制和风险评估的关键.本研究通过调查湖南省攸县745个土壤-蔬菜样品Cd含量,应用转移方程、敏感性分布曲线(SSD)和多元回归方法分析不同类别蔬菜Cd累积特征和影响因素,预测不同土壤条件下蔬菜Cd含量并推导相应土壤Cd风险阈值.结果表明: 叶菜对Cd胁迫较根菜敏感;土壤pH、土壤总Cd和土壤有机质(SOM)是影响蔬菜Cd富集的3个主要因子;转移方程对叶菜和根菜的解释程度分别为54.2%和69.1%.土壤Cd风险阈值随土壤pH和SOM的增加而增加,根菜在严重酸化土壤区Cd累积风险较高.当前国家土壤环境质量标准对于严重酸化、有机质含量较低的土壤过于宽泛.     

Inference of intrinsic spiking irregularity based on the Kullback-Leibler information

We have recently established an empirical Bayes method that extracts both the intrinsic irregularity and the time-dependent rate from a spike sequence [Koyama, S., Shinomoto, S., 2005. Empirical Bayes interpretations of random point events. J. Phys. A: Math. Gen. 38, L531-L537]. In the present paper, we examine an alternative method based on the more fundamental principle of minimizing the Kullback-Leibler information from the original distribution of spike sequences to a model distribution. Not only the empirical Bayes method but also the Kullback-Leibler information method exhibits a switch of the most plausible interpretation of the spikes between (I) being derived irregularly from a nearly constant rate, and (II) being derived rather regularly from a significantly fluctuating rate.The model distributions selected by both methods are similar for the same spike sequences derived from a given rate-fluctuating gamma process.