一株产木质素降解酶真菌的分离与鉴定

从自然界中分离到一株产木质素降解酶真菌.平板显色反应显示,该菌株具有产多种木质素降解酶的能力,并通过酶活力测定得到证实.为确定该菌株的分类地位,对其ITS序列进行了扩增并测序,并利用MEGA4.0生物学软件计算其与同属其它菌株的遗传距离并构建系统发育树,在分子水平上确定它们之间的亲缘关系.试验结果表明,该菌株的ITS序列与Irpex lacteus乳白耙菌序列相似度达99%,且与Irpex lacteus XSD-2亲缘关系最近.     

Purification and Characterization of a Bacterial Phytase Whose Properties Make it Exceptionally Useful as a Feed Supplement

A periplasmatic phytase from a bacterium isolated from Malaysian waste water was purified about 173-fold to apparent homogeneity with a recovery of 10% referred to the phytase activity in the crude extract. It behaved as a monomeric protein with a molecular mass of about 42 kDa. The purified enzyme exhibited a single pH optimum at 4.5. Optimum temperature for the degradation of phytate was 65°C. The kinetic parameters for the hydrolysis of sodium phytate were determined to be K _M = 0.15 mmol/l and k _cat = 1164 s⁻¹ at pH 4.5 and 37°C. The purified enzyme was shown to be highly specific. Among the phosphorylated compounds tested, phytate was the only one which was significantly hydrolysed. Some properties such as considerable activity below pH 3.0, thermal stability and resistance to pepsin make the enzyme attractive for an application as a feed supplement.     

Desorption Chemical Ionization Mass Spectra of Underivatized Abscisic Acid-conjugated Metabolites,a Useful Aid for Structural Characterization

The use of desorption chemical ionization (D/CI) mass spectrometry for the structural characterization of the abscisic acid (ABA)-conjugated metabolites, abscisic acid-γ-d-glucosyl ester (ABAGE), 4′-O-dihydrophaseic acid-γ-d-glucoside (DPAGS) and γ-hydroxy-γ-methylglutaryl-hydroxyabscisic acid (HMG-HOABA), was studied. The effects of the D/CI source temperature on the spectral features are described. Enhanced quasi-molecular ion intensities (e.g., [M·NH₄]⁺) were observed in the D/CI(NH₃) spectrum at a low source temperature (170°C). When a higher source temperature (250°C) was used, more extensive fragmentation occurred, and structurally diagnostic fragment ions appeared in the spectra. The spectra obtained at two degrees of source temperature were complementary to each other for the structural characterization.     

Heat-Passing Framework for Robust Interpretation of Data in Networks

Researchers are regularly interested in interpreting the multipartite structure of data entities according to their functional relationships. Data is often heterogeneous with intricately hidden inner structure. With limited prior knowledge, researchers are likely to confront the problem of transforming this data into knowledge. We develop a new framework, called heat-passing, which exploits intrinsic similarity relationships within noisy and incomplete raw data, and constructs a meaningful map of the data. The proposed framework is able to rank, cluster, and visualize the data all at once. The novelty of this framework is derived from an analogy between the process of data interpretation and that of heat transfer, in which all data points contribute simultaneously and globally to reveal intrinsic similarities between regions of data, meaningful coordinates for embedding the data, and exemplar data points that lie at optimal positions for heat transfer. We demonstrate the effectiveness of the heat-passing framework for robustly partitioning the complex networks, analyzing the globin family of proteins and determining conformational states of macromolecules in the presence of high levels of noise. The results indicate that the methodology is able to reveal functionally consistent relationships in a robust fashion with no reference to prior knowledge. The heat-passing framework is very general and has the potential for applications to a broad range of research fields, for example, biological networks, social networks and semantic analysis of documents.     

Evaluation of an Integrated Framework for Biodiversity with a New Metric for Functional Dispersion

Growing interest in understanding ecological patterns from phylogenetic and functional perspectives has driven the development of metrics that capture variation in evolutionary histories or ecological functions of species. Recently, an integrated framework based on Hill numbers was developed that measures three dimensions of biodiversity based on abundance, phylogeny and function of species. This framework is highly flexible, allowing comparison of those diversity dimensions, including different aspects of a single dimension and their integration into a single measure. The behavior of those metrics with regard to variation in data structure has not been explored in detail, yet is critical for ensuring an appropriate match between the concept and its measurement. We evaluated how each metric responds to particular data structures and developed a new metric for functional biodiversity. The phylogenetic metric is sensitive to variation in the topology of phylogenetic trees, including variation in the relative lengths of basal, internal and terminal branches. In contrast, the functional metric exhibited multiple shortcomings: (1) species that are functionally redundant contribute nothing to functional diversity and (2) a single highly distinct species causes functional diversity to approach the minimum possible value. We introduced an alternative, improved metric based on functional dispersion that solves both of these problems. In addition, the new metric exhibited more desirable behavior when based on multiple traits.     

ToPS: A Framework to Manipulate Probabilistic Models of Sequence Data

Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling. We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i) independent and identically distributed process; (ii) variable-length Markov chain; (iii) inhomogeneous Markov chain; (iv) hidden Markov model; (v) profile hidden Markov model; (vi) pair hidden Markov model; (vii) generalized hidden Markov model; and (viii) similarity based sequence weighting. The framework includes functionality for training, simulation and decoding of the models. Additionally, it provides two methods to help parameter setting: Akaike and Bayesian information criteria (AIC and BIC). The models can be used stand-alone, combined in Bayesian classifiers, or included in more complex, multi-model, probabilistic architectures using GHMMs. In particular the framework provides a novel, flexible, implementation of decoding in GHMMs that detects when the architecture can be traversed efficiently.

This is a PLOS Computational Biology Software Article.

     

Application of Kinetic and Dynamic Data for Antihistamines to Risk Characterization in Sensitive Populations

A major goal of risk assessment is to protect the health of individuals who may be more sensitive than the general population. This study compared human phar-macokinetic and pharmacodynamic data in sensitive groups (i.e., children, the elderly, diseased states, and poor metabolizers) versus young, healthy adults for the antihistamines cetirizine, fexofenadine, loratadine, azelastine, ebastine, chlorpheniramine, and diphenhydramine. The default components (3.16 each for kinetic and dynamic aspects) of the intraspecies uncertainty factor were adjusted with compound specific data for the antihistamines. The majority (16 of 18) of the composite factors (kinetics X dynamics) for the sensitive groups were less than 10. Children had the lowest composite factors for antihistamines, ranging from 1.1 to 6.3. Application of kinetic and dynamic data for antihistamines to the Renwick/International Programme on Chemical Safety (IPCS) scheme can aid in characterizing the extent of variability in sensitive populations, thereby reducing the uncertainty associated with the risk assessment of sensitive populations.     

A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.     

Sequence to Medical Phenotypes: A Framework for Interpretation of Human Whole Genome DNA Sequence Data

High throughput sequencing has facilitated a precipitous drop in the cost of genomic sequencing, prompting predictions of a revolution in medicine via genetic personalization of diagnostic and therapeutic strategies. There are significant barriers to realizing this goal that are related to the difficult task of interpreting personal genetic variation. A comprehensive, widely accessible application for interpretation of whole genome sequence data is needed. Here, we present a series of methods for identification of genetic variants and genotypes with clinical associations, phasing genetic data and using Mendelian inheritance for quality control, and providing predictive genetic information about risk for rare disease phenotypes and response to pharmacological therapy in single individuals and father-mother-child trios. We demonstrate application of these methods for disease and drug response prognostication in whole genome sequence data from twelve unrelated adults, and for disease gene discovery in one father-mother-child trio with apparently simplex congenital ventricular arrhythmia. In doing so we identify clinically actionable inherited disease risk and drug response genotypes in pre-symptomatic individuals. We also nominate a new candidate gene in congenital arrhythmia, ATP2B4, and provide experimental evidence of a regulatory role for variants discovered using this framework.     

Leaf Disk Inoculation — a Useful Tool for Selecting Infections of Sunflower Downy Mildew at Low Inoculum Concentration, but Inappropriate to Pathotype Characterization

A modified technique for leaf disk inoculation of sunflower with zoosporangia of the downy mildew pathogen Plasmopara halstedii was developed. Infection with low concentrations of inoculum was obtained down to the level of single sporangia. No significant difference in the infection rate was seen between disks from cotyledons and true leaves. This makes leaf disk inoculation particularly suitable for infections at low sporangium density and for the investigation on plant tissue which cannot be infected with whole seedling inoculation. The technique seems, however, to be inappropriate for pathotype characterization using differential host lines.     

A Framework for the Economic Analysis of Data Collection Methods for Vital Statistics

Background

Over recent years there has been a strong movement towards the improvement of vital statistics and other types of health data that inform evidence-based policies. Collecting such data is not cost free. To date there is no systematic framework to guide investment decisions on methods of data collection for vital statistics or health information in general. We developed a framework to systematically assess the comparative costs and outcomes/benefits of the various data methods for collecting vital statistics.

Methodology

The proposed framework is four-pronged and utilises two major economic approaches to systematically assess the available data collection methods: cost-effectiveness analysis and efficiency analysis. We built a stylised example of a hypothetical low-income country to perform a simulation exercise in order to illustrate an application of the framework.

Findings

Using simulated data, the results from the stylised example show that the rankings of the data collection methods are not affected by the use of either cost-effectiveness or efficiency analysis. However, the rankings are affected by how quantities are measured.

Conclusion

There have been several calls for global improvements in collecting useable data, including vital statistics, from health information systems to inform public health policies. Ours is the first study that proposes a systematic framework to assist countries undertake an economic evaluation of DCMs. Despite numerous challenges, we demonstrate that a systematic assessment of outputs and costs of DCMs is not only necessary, but also feasible. The proposed framework is general enough to be easily extended to other areas of health information.     

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Measuring fruit morphology and color traits of vegetable and fruit crops in an objective and reproducible way is important for detailed phenotypic analyses of these traits. Tomato Analyzer (TA) is a software program that measures 37 attributes related to two-dimensional shape in a semi-automatic and reproducible manner^1,2. Many of these attributes, such as angles at the distal and proximal ends of the fruit and areas of indentation, are difficult to quantify manually. The attributes are organized in ten categories within the software: Basic Measurement, Fruit Shape Index, Blockiness, Homogeneity, Proximal Fruit End Shape, Distal Fruit End Shape, Asymmetry, Internal Eccentricity, Latitudinal Section and Morphometrics. The last category requires neither prior knowledge nor predetermined notions of the shape attributes, so morphometric analysis offers an unbiased option that may be better adapted to high-throughput analyses than attribute analysis. TA also offers the Color Test application that was designed to collect color measurements from scanned images and allow scanning devices to be calibrated using color standards³. TA provides several options to export and analyze shape attribute, morphometric, and color data. The data may be exported to an excel file in batch mode (more than 100 images at one time) or exported as individual images. The user can choose between output that displays the average for each attribute for the objects in each image (including standard deviation), or an output that displays the attribute values for each object on the image. TA has been a valuable and effective tool for indentifying and confirming tomato fruit shape Quantitative Trait Loci (QTL), as well as performing in-depth analyses of the effect of key fruit shape genes on plant morphology. Also, TA can be used to objectively classify fruit into various shape categories. Lastly, fruit shape and color traits in other plant species as well as other plant organs such as leaves and seeds can be evaluated with TA.     

Community Dialogue to Shift Social Norms and Enable Family Planning: An Evaluation of the Family Planning Results Initiative in Kenya

IntroductionUse of family planning (FP) is powerfully shaped by social and gender norms, including the perceived acceptability of FP and gender roles that limit women’s autonomy and restrict communication and decision-making between men and women. This study evaluated an intervention that catalyzed ongoing community dialogues about gender and FP in Siaya county, Nyanza Province, Kenya. Specifically, we explored the changes in perceived acceptability of FP, gender norms and use of FP.MethodsWe used a mixed-method approach. Information on married men and women’s socio-demographic characteristics, pregnancy intentions, gender-related beliefs, FP knowledge, attitudes, and use were collected during county-representative, cross-sectional household surveys at baseline (2009; n₁₁ = 650 women; n₁₂ = 305 men) and endline (2012; n₂₁ = 617 women; n₂₂ = 317 men); exposure to the intervention was measured at endline. We assessed changes in FP use at endline vs. baseline, and fitted multivariate logistic regression models for FP use to examine its association with intervention exposure and explore other predictors of use at endline. In-depth, qualitative interviews with 10 couples at endline further explored enablers and barriers to FP use.ResultsAt baseline, 34.0% of women and 27.9% of men used a modern FP method compared to 51.2% and 52.2%, respectively, at endline (p<0.05). Exposure to FP dialogues was associated with 1.78 (95% CI: 1.20–2.63) times higher odds of using a modern FP method at endline for women, but this association was not significant for men. Women’s use of modern FP was significantly associated with higher spousal communication, control over own cash earnings, and FP self-efficacy. Men who reported high approval of FP were significantly more likely to use modern FP if reporting high approval of FP and more equitable gender beliefs. FP dialogues addressed persistent myths and misconceptions, normalized FP discussions, and increased its acceptability. Public examples of couples making joint FP decisions legitimized communication and decision-making with spouses about FP especially for men; women described partner support as key enabler of FP use.ConclusionsOur evaluation demonstrates that an intervention that catalyzes open dialogue about gender and FP can shift social norms, enable more equitable couple communication and decision-making and, ultimately, increase use of FP.     

Design and Evaluation of Useful Bacterium-Specific PCR Primers That Amplify Genes Coding for Bacterial 16S rRNA

We report the design and evaluation of PCR primers 63f and 1387r for amplification of 16S rRNA genes from bacteria. Their specificity and efficacy were tested systematically with a variety of bacterial species and environmental samples. They were found to be more useful for 16S rRNA gene amplification in ecological and systematic studies than PCR amplimers that are currently more generally used.     

A Framework for Regularized Non-Negative Matrix Factorization,with Application to the Analysis of Gene Expression Data

Non-negative matrix factorization (NMF) condenses high-dimensional data into lower-dimensional models subject to the requirement that data can only be added, never subtracted. However, the NMF problem does not have a unique solution, creating a need for additional constraints (regularization constraints) to promote informative solutions. Regularized NMF problems are more complicated than conventional NMF problems, creating a need for computational methods that incorporate the extra constraints in a reliable way. We developed novel methods for regularized NMF based on block-coordinate descent with proximal point modification and a fast optimization procedure over the alpha simplex. Our framework has important advantages in that it (a) accommodates for a wide range of regularization terms, including sparsity-inducing terms like the penalty, (b) guarantees that the solutions satisfy necessary conditions for optimality, ensuring that the results have well-defined numerical meaning, (c) allows the scale of the solution to be controlled exactly, and (d) is computationally efficient. We illustrate the use of our approach on in the context of gene expression microarray data analysis. The improvements described remedy key limitations of previous proposals, strengthen the theoretical basis of regularized NMF, and facilitate the use of regularized NMF in applications.     

Synthesis and Characterization of Time-resolved Fluorescence Probes for Evaluation of Competitive Binding to Melanocortin Receptors

Probes for use in time-resolved fluorescence competitive binding assays at melanocortin receptors based on the parental ligands MSH(4), MSH(7), and NDP-α-MSH were prepared by solid phase synthesis methods, purified, and characterized. The saturation binding of these probes was studied using HEK-293 cells engineered to overexpress the human melanocortin 4 receptor (hMC4R) as well as the human cholecystokinin 2 receptor (hCCK2R). The ratios of non-specific binding to total binding approached unity at high concentrations for each probe. At low probe concentrations, receptor-mediated binding and uptake was discernable, and so probe concentrations were kept as low as possible in determining K_d values. The Eu-DTPA-PEGO-MSH(4) probe exhibited low specific binding relative to non-specific binding, even at low nanomolar concentrations, and was deemed unsuitable for use in competition binding assays. The Eu-DTPA-PEGO probes based on MSH(7) and NDP-α-MSH exhibited K_d values of 27 ± 3.9 nM and 4.2 ± 0.48 nM, respectively, for binding with hMC4R. These probes were employed in competitive binding assays to characterize the interactions of hMC4R with monovalent and divalent MSH(4), MSH(7), and NDP-α-MSH constructs derived from squalene. Results from assays with both probes reflected only statistical enhancements, suggesting improper ligand spacing on the squalene scaffold for the divalent constructs. The K_i values from competitive binding assays that employed the MSH(7)-based probe were generally lower than the K_i values obtained when the probe based on NDP-α-MSH was employed, which is consistent with the greater potency of the latter probe. The probe based on MSH(7) was also competed with monovalent, divalent, and trivalent MSH(4) constructs that previously demonstrated multivalent binding in competitive binding assays against a variant of the probe based on NDP-α-MSH. Results from these assays confirm multivalent binding, but suggest a more modest increase in avidity for these MSH(4) constructs than was previously reported.     

Topographical and Physicochemical Modification of Material Surface to Enable Patterning of Living Cells

ABSTRACT:?

Precise control of the architecture of multiple cells in culture and in vivo via precise engineering of the material surface properties is described as cell patterning. Substrate patterning by control of the surface physicochemical and topographic features enables selective localization and phenotypic and genotypic control of living cells. In culture, control over spatial and temporal dynamics of cells and heterotypic interactions draws inspiration from in vivo embryogenesis and haptotaxis. Patterned arrays of single or multiple cell types in culture serve as model systems for exploration of cell-cell and cell-matrix interactions. More recently, the patterned arrays and assemblies of tissues have found practical applications in the fields of Biosensors and cell-based assays for Drug Discovery. Although the field of cell patterning has its origins early in this century, an improved understanding of cell-substrate interactions and the use of microfabrication techniques borrowed from the microelectronics industry have enabled significant recent progress. This review presents the important early discoveries and emphasizes results of recent state-of-the-art cell patterning methods. The review concludes by illustrating the growing impact of cell patterning in the areas of bioelectronic devices and cell-based assays for drug discovery.