Circulating microRNAs as potential biomarkers for smoking-related interstitial fibrosis

Numerous efforts have been made to indentify reliable and predictive biomarkers to detect the early signs of smoking-induced lung disease. Using 6-month cigarette smoking in mice, we have established smoking-related interstitial fibrosis (SRIF). Microarray analyses and cytokine/chemokine biomarker measurements were made to select circulating microRNAs (miRNAs) biomarkers. We have demonstrated that specific miRNAs species (miR-125b-5p, miR-128, miR-30e, and miR-20b) were significantly changed, both in the lung tissue and in plasma, and exhibited mainstream (MS) exposure duration-dependent pathological changes in the lung. These findings suggested a potential use of specific circulating miRNAs as sensitive and informative biomarkers for smoking-induced lung disease.     

一株分离自绿化废弃物堆肥枯草芽孢杆菌的常压室温等离子诱变及其发酵条件

【背景】为了提高堆肥降解有机废弃物的效率,高效堆肥菌剂成为了研究热点,其中以真菌应用的研究为多,但真菌也有对氧气和底物敏感等缺点,细菌对堆肥的作用开始被研究。本实验室以羧甲基纤维素钠(CMC-Na)为底物,从绿化废弃物堆肥中筛选得到枯草芽孢杆菌(Bacillussubtilis,B.subtilis) BL03,它具有较好的纤维素分解能力,能提高绿化废弃物堆肥中纤维素降解和腐殖质合成的速度。【目的】进一步提高B.subtilisBL03的纤维素酶生产能力。【方法】利用常压室温等离子(Atmospheric and room temperature plasma,ARTP)诱变BL03菌,通过CMC-刚果红固体培养基观察水解透明圈,以及液体发酵后检测酶活力的方法进行3轮筛选;通过连续多代培养观察突变株的遗传稳定性;通过梯度温度、p H培养研究突变株发酵的最适生长温度、培养基初始pH;利用正交设计方法研究适合突变株发酵培养的工业级原料配方。【结果】筛选到2株正突变株,酶活力分别提高了69%和72%;连续10代培养稳定,验证了突变株的遗传稳定性;其中酶活力最高的突变株BLA3890最适培养温度为37°C、培养基初始pH为5.0-6.5,研究得到较经济的发酵培养基配方。【结论】ARTP诱变B. subtilis BL03后得到的突变株BLA1973和BLA3890在绿化废弃物堆肥或其他纤维素降解行业具有进一步研究和应用的价值。     

基于常压室温等离子体技术的金藻诱变筛选方法

以高生长速率或高油脂含量藻株为目标,以湛江等鞭金藻为例,报道了一种基于常压室温等离子体技术的微藻诱变及快速分级筛选方法。即以叶绿素荧光动力学参数F_v/F_m大于0.68的金藻为出发藻株、以致死率90%为阈值确定最适诱变电流为1.4~1.5A,诱变时间为24~30s。分别在室温常光及胁迫条件(高温常光和高光室温)下,按照孔板至摇瓶至反应器三级培养进行筛选。在孔板培养过程中以比生长速度结合尼罗红荧光强度变化实现高通量初筛,最终在反应器培养中进行提取验证,并以候选藻株为出发进行二次诱变筛选。结果表明,室温常光条件诱变株筛出率为0.7%,胁迫条件筛出率为0.9%;一次诱变诱变株筛出率为0.6%,二次诱变筛出率为1.2%;二次诱变和胁迫条件筛出率更高,更易得到性状变化的诱变株。     

常压室温等离子体快速诱变绿色糖单孢菌筛选 木聚糖酶高产菌株及其酶学性质研究

[目的]利用常压室温等离子体快速诱变绿色糖单孢菌,筛选耐热耐碱木聚糖酶高产菌株,并对其进行酶学性质分析,确保其适用于生物制浆漂白工艺.[方法]采用刚果红平板水解圈法结合摇瓶发酵胞外酶测定法进行菌株筛选,并通过DNS木聚糖酶活性测定等方法对来源于不同突变株的木聚糖酶进行酶学性质分析对比.[结果]筛选出遗传稳定性良好的两株木聚糖酶高产菌株AT24和AT22-2,以麦草浆为诱导底物的粗酶液中,突变株AT24及AT22-2所产的木聚糖酶活性分别为512.74、552.70U/mL,分别为原始菌株S.v的16和17倍的.来源于突变株AT22-2的木聚糖酶的最适反应pH为9.5,最适反应温度为90℃,在50℃-90℃温度范围内具有良好的热稳定性,在100℃条件下处理30 min剩余酶活仍为68％;突变株AT24所产木聚糖酶的最适反应温度为60℃,最适pH为10.0,在60℃-80℃的高温环境下,突变株AT24所产的木聚糖酶具有良好的热稳定性.[结论]突变株AT22-2所产具有耐碱耐高温性质的木聚糖酶,在应用领域尤其在纸浆造纸行业具有较大的潜在应用价值.     

Cytotoxicity and cellular accumulation of palladium(II) complexes of tetracyclines

We studied the cytotoxic effect and the uptake of Pd(II) complexes of doxycycline (Dox), [Pd(Dox)Cl2], and tetracycline (Tc), [Pd(Tc)Cl2], in chronic myelogenous leukemia cells. The effect of the compounds on macrophage viability was also investigated. Compound 1 is more effective than compound 2 in inhibiting the growth of K562 cells with the IC(50) values of 14.44 and 34.54 microM, respectively. There is a good correlation between cell-growth inhibition and intracellular metal concentrations, determined by inductively coupled plasma atomic emission spectroscopy (ICP-AES). Incubation of the cells with equitoxic concentrations of both compounds yields approximately the same intracellular Pd concentration. At the IC(50) doses, intracellular concentration is ca. 33 x 10(-16) mol/cell for both compounds 1 and 2. This suggests that more [Pd(Tc)Cl2] is needed to produce a cytotoxic effect, because it enters cells more slowly. Both compounds up to 16 microM did not affect the viability of mouse peritoneal macrophages after a 48-h incubation. After 72 h of incubation, the IC(50) values are 22 for [Pd(Dox)Cl2] and 40 microM for [Pd(Tc)Cl(2)]. Therefore, the cytotoxic effect in cancer cells exhibited by both compounds is higher than their effect in macrophages.     

耐碱性木聚糖酶

造纸工业是我国国民经济中具有可持续发展特点的重要产业,也是我国重要的工业污染源之一.近年来,世界范围内的造纸工业都受到资源、环境和效益等多方面的约束,都力求寻找能够达到节能减耗、保护生态环境、提高生产效率和经济效益及产品质量的先进生产技术.木聚糖酶及其潜在的工业应用前景引起人们关注,低(无)纤维素酶活性的木聚糖酶尤其有着诱人的应用前景,它可以应用于生物制浆、纸浆漂白、废纸二次纤维回收、废纸脱墨处理、纸浆纤维改性剂和纺织工业等,特别是其在纸浆漂白工艺中的巨大应用潜力,已成为各界同行的研究热点.应用于生物漂白过程中的木聚糖酶必须满足没有或者只有少量纤维素酶伴随产生,具有良好的耐碱性及耐高温性,在纸浆中具有稳定的酶活性等条件.自然界中微生物产生的木聚糖降解酶系比较复杂,许多会伴随纤维素酶的产生,这些酶的性质相近,不易分离纯化.此外,除了一些生长在极端环境中的微生物如嗜热菌、嗜碱菌等分泌的木聚糖酶能够满足生物漂白的要求之外,大多数来源于真菌、细菌或放线菌的木聚糖酶都需要在应用过程中调节反应条件.以上这些限制条件严重阻碍了木聚糖酶规模化生产及应用工艺的建立.     

常压室温等离子体(ARTP)诱变及高通量筛选那西肽高产菌株

采用新型常压室温等离子体(ARTP)诱变活跃链霉菌(Streptomyces actuosu),并应用抑菌圈和48孔板培养方法高通量筛选高产那西肽菌株。研究表明抑菌圈径的大小与48孔板效价之间以及48孔板效价与摇瓶效价之间均有较好的相关性,系数R分别达到0.534和0.896。通过多轮ARTP诱变及高通量筛选最终获得了3株相对效价提高50%以上的遗传性能稳定的突变株。ARTP诱变技术作为获得那西肽高产菌株的有效途径,与传统摇瓶发酵筛选相比,48孔板及抑菌圈法能显著提高那西肽高产菌株的筛选效率。     

A Data Characterization Framework for Material Flow Analysis

The validity of material flow analyses (MFAs) depends on the available information base, that is, the quality and quantity of available data. MFA data are cross‐disciplinary, can have varying formats and qualities, and originate from heterogeneous sources, such as official statistics, scientific models, or expert estimations. Statistical methods for data evaluation are most often inadequate, because MFA data are typically isolated values rather than extensive data sets. In consideration of the properties of MFA data, a data characterization framework for MFA is presented. It consists of an MFA data terminology, a data characterization matrix, and a procedure for database analysis. The framework facilitates systematic data characterization by cell‐level tagging of data with data attributes. Data attributes represent data characteristics and metainformation regarding statistical properties, meaning, origination, and application of the data. The data characterization framework is illustrated in a case study of a national phosphorus budget. This work furthers understanding of the information basis of material flow systems, promotes the transparent documentation and precise communication of MFA input data, and can be the foundation for better data interpretation and comprehensive data quality evaluation.     

常见寿命数据类型及生命表的编制方法

生命表是描述种群死亡过程的有用工具,介绍了4种常见的寿命数据类型;寿终数据,右删失数据,左删失数据和区间型数据特征及其相应的数据分析处理方法即生命表法,乘积限估计和Turbull估计法,对生命表法和乘积限估计法应用上的特点进行了比较,同时还对特殊的寿命数据类型－－截断数据做了简要介绍。     

Web Resources for Mass Spectrometry-based Proteomics

With the development of high-resolution and high-throughput mass spectrometry(MS)technology, a large quantum of proteomic data is continually being generated. Collecting and sharing these data are a challenge that requires immense and sustained human effort. In this report, we provide a classification of important web resources for MS-based proteomics and present rating of these web resources, based on whether raw data are stored, whether data submission is supported,and whether data analysis pipelines are provided. These web resources are important for biologists involved in proteomics research.     

Mixed-effects nonlinear regression for unbalanced repeated measures.

Repeated measures data, such as clinical pharmacokinetic data, growth data, and dose-response data, are often inherently nonlinear with respect to a given response function and are frequently incomplete and/or unbalanced. Nonlinear random-effects models together with a variety of estimation procedures have been proposed for the analysis of such data. This paper is concerned with a straightforward procedure for estimating and comparing the parameters of a generalized mixed-effects nonlinear regression model. The asymptotic properties of the proposed estimators are given and large-sample tests of hypothesis provided. The results are applied to in vitro data on the water transport kinetics of hemodialyzers used in the treatment of patients with chronic renal failure.     

Modeling baseline conditions of ecological indicators: Marine renewable energy environmental monitoring

Ecological indicators are often collected to detect and monitor environmental change. Statistical models are used to estimate natural variability, pre-existing trends, and environmental predictors of baseline indicator conditions. Establishing standard models for baseline characterization is critical to the effective design and implementation of environmental monitoring programs. An anthropogenic activity that requires monitoring is the development of Marine Renewable Energy sites. Currently, there are no standards for the analysis of environmental monitoring data for these development sites. Marine Renewable Energy monitoring data are used as a case study to develop and apply a model evaluation to establish best practices for characterizing baseline ecological indicator data. We examined a range of models, including six generalized regression models, four time series models, and three nonparametric models. Because monitoring data are not always normally distributed, we evaluated model ability to characterize normal and non-normal data using hydroacoustic metrics that serve as proxies for ecological indicator data. The nonparametric support vector regression and random forest models, and parametric state-space time series models generally were the most accurate in interpolating the normal metric data. Support vector regression and state-space models best interpolated the non-normally distributed data. If parametric results are preferred, then state-space models are the most robust for baseline characterization. Evaluation of a wide range of models provides a comprehensive characterization of the case study data, and highlights advantages of models rarely used in Marine Renewable Energy environmental monitoring. Our model findings are relevant for any ecological indicator data with similar properties, and the evaluation approach is applicable to any monitoring program.     

The PlaNet Consortium: a network of European plant databases connecting plant genome data in an integrated biological knowledge resource

The completion of the Arabidopsis genome and the large collections of other plant sequences generated in recent years have sparked extensive functional genomics efforts. However, the utilization of this data is inefficient, as data sources are distributed and heterogeneous and efforts at data integration are lagging behind. PlaNet aims to overcome the limitations of individual efforts as well as the limitations of heterogeneous, independent data collections. PlaNet is a distributed effort among European bioinformatics groups and plant molecular biologists to establish a comprehensive integrated database in a collaborative network. Objectives are the implementation of infrastructure and data sources to capture plant genomic information into a comprehensive, integrated platform. This will facilitate the systematic exploration of Arabidopsis and other plants. New methods for data exchange, database integration and access are being developed to create a highly integrated, federated data resource for research. The connection between the individual resources is realized with BioMOBY. BioMOBY provides an architecture for the discovery and distribution of biological data through web services. While knowledge is centralized, data is maintained at its primary source without a need for warehousing. To standardize nomenclature and data representation, ontologies and generic data models are defined in interaction with the relevant communities.Minimal data models should make it simple to allow broad integration, while inheritance allows detail and depth to be added to more complex data objects without losing integration. To allow expert annotation and keep databases curated, local and remote annotation interfaces are provided. Easy and direct access to all data is key to the project.     

Neural networks for clustered and longitudinal data using mixed effects models

Although most statistical methods for the analysis of longitudinal data have focused on retrospective models of association, new advances in mobile health data have presented opportunities for predicting future health status by leveraging an individual's behavioral history alongside data from similar patients. Methods that incorporate both individual-level and sample-level effects are critical to using these data to its full predictive capacity. Neural networks are powerful tools for prediction, but many assume input observations are independent even when they are clustered or correlated in some way, such as in longitudinal data. Generalized linear mixed models (GLMM) provide a flexible framework for modeling longitudinal data but have poor predictive power particularly when the data are highly nonlinear. We propose a generalized neural network mixed model that replaces the linear fixed effect in a GLMM with the output of a feed-forward neural network. The model simultaneously accounts for the correlation structure and complex nonlinear relationship between input variables and outcomes, and it utilizes the predictive power of neural networks. We apply this approach to predict depression and anxiety levels of schizophrenic patients using longitudinal data collected from passive smartphone sensor data.     

Joint Modelling of Repeated Measures and Survival Time Data

In many clinical trials both repeated measures data and event history data are simultaneously observed from the same subject. These two types of responses are usually correlated, because they are from the same subject. In this article, we propose a joint model for the combined analysis of repeated measures data and event history data in the framework of hierarchical generalized linear models. The correlation between repeated measures and event time is modelled by introducing a shared random effect. The model parameters are estimated using the hierarchical‐likelihood approach. The proposed model is illustrated using a real data set for the renal transplant patients.     

基因表达谱芯片及核酸测序技术在癌症研究中的应用现状

基因表达谱芯片和核酸序列数据在癌症研究中占有很重要的地位。基因表达谱芯片被广泛的应用在医学研究中,它的主要优势在于灵敏快速成本低,缺点只能对现有基因进行研究,无法进行新基因发现以及变异等方面的研究;而核酸序列数据在这方面则具有很大优势。总体来说,二者在癌症研究中都发挥着巨大的作用。随着精准医学的不断发展,对这些高通量数据的深入研究可以有助于人们进一步了解癌症的分子机制,从而加速个体化治疗的进程。