A P system and a constructive membrane-inspired DNA algorithm for solving the Maximum Clique Problem

We present a P system with replicated rewriting to solve the Maximum Clique Problem for a graph. Strings representing cliques are built gradually. This involves the use of inhibitors that control the space of all generated solutions to the problem. Calculating the maximum clique for a graph is a highly relevant issue not only on purely computational grounds, but also because of its relationship to fundamental problems in genomics. We propose to implement the designed P system by means of a DNA algorithm. This algorithm is then compared with two standard papers that addressed the same problem and its DNA implementation in the past. This comparison is carried out on the basis of a series of computational and physical parameters. Our solution features a significantly lower cost in terms of time, the number and size of strands, as well as the simplicity of the biological implementation.     

Sample size reestimation by Bayesian prediction

We review a Bayesian predictive approach for interim data monitoring and propose its application to interim sample size reestimation for clinical trials. Based on interim data, this approach predicts how the sample size of a clinical trial needs to be adjusted so as to claim a success at the conclusion of the trial with an expected probability. The method is compared with predictive power and conditional power approaches using clinical trial data. Advantages of this approach over the others are discussed.     

白腐真菌生物技术降解氯酚污染物

生物降解是降解氯酚污染物的一条重要的转化途径,白腐真菌是一种高效的生物降解菌种,应用白腐真菌生物技术降解具有毒性和抗降解性的氯酚具有重要意义。本文阐述了白腐菌降解氯酚类污染物的途径,阐述了白腐真菌技术,主要包括酶技术、固定化技术、真菌强化技术、堆肥化和生物反应器等在氯酚污染环境治理中的应用,并概述了近几年白腐菌降解氯酚的研究热点和白腐真菌生物技术的应用趋势。     

Reduced C(beta) statistical potentials can outperform all-atom potentials in decoy identification

We developed a series of statistical potentials to recognize the native protein from decoys, particularly when using only a reduced representation in which each side chain is treated as a single C(beta) atom. Beginning with a highly successful all-atom statistical potential, the Discrete Optimized Protein Energy function (DOPE), we considered the implications of including additional information in the all-atom statistical potential and subsequently reducing to the C(beta) representation. One of the potentials includes interaction energies conditional on backbone geometries. A second potential separates sequence local from sequence nonlocal interactions and introduces a novel reference state for the sequence local interactions. The resultant potentials perform better than the original DOPE statistical potential in decoy identification. Moreover, even upon passing to a reduced C(beta) representation, these statistical potentials outscore the original (all-atom) DOPE potential in identifying native states for sets of decoys. Interestingly, the backbone-dependent statistical potential is shown to retain nearly all of the information content of the all-atom representation in the C(beta) representation. In addition, these new statistical potentials are combined with existing potentials to model hydrogen bonding, torsion energies, and solvation energies to produce even better performing potentials. The ability of the C(beta) statistical potentials to accurately represent protein interactions bodes well for computational efficiency in protein folding calculations using reduced backbone representations, while the extensions to DOPE illustrate general principles for improving knowledge-based potentials.     

Statistical analysis of amplified fragment length polymorphism data: a toolbox for molecular ecologists and evolutionists

Recently, the amplified fragment length polymorphism (AFLP) technique has gained a lot of popularity, and is now frequently applied to a wide variety of organisms. Technical specificities of the AFLP procedure have been well documented over the years, but there is on the contrary little or scattered information about the statistical analysis of AFLPs. In this review, we describe the various methods available to handle AFLP data, focusing on four research topics at the population or individual level of analysis: (i) assessment of genetic diversity; (ii) identification of population structure; (iii) identification of hybrid individuals; and (iv) detection of markers associated with phenotypes. Two kinds of analysis methods can be distinguished, depending on whether they are based on the direct study of band presences or absences in AFLP profiles ('band-based' methods), or on allelic frequencies estimated at each locus from these profiles ('allele frequency-based' methods). We investigate the characteristics and limitations of these statistical tools; finally, we appeal for a wider adoption of methodologies borrowed from other research fields, like for example those especially designed to deal with binary data.     

Establishing a minimum dataset for soil quality assessment based on soil properties and land-use changes

To assess soil quality, a minimum dataset (MDS) of soil properties has to be proposed commonly through calculating the total load of each candidate soil parameter on all of the qualified principal components by use of principal component analysis (PCA) and Norm-value computation. Considering intensive land-use changes, the method introduced in this study on MDS establishment integrates the quantified contributions of land-use type and land-use duration on each soil parameter by using multivariate analysis and mean multiple comparison. In this way, a MDS maximally representing all candidates with minimal loss of the soil quality information contained by those non-MDS soil parameters is established. The MDS proposed can not only well integrate the quantified influence of land-use changes and land-use duration on soil parameters, but is also quite flexible and extendable with the potential to be extrapolated to assess soil quality in other regions. Based on two sets of soil database obtained separately in 1985 and 2004, two MDSs established are compared with each other. It is found that only quite a small change in MDS components occurs during a 20-year period. For a better assessment of soil quality, it seems necessary to examine on what kind of temporal scale and how much MDS will change for a site-specific area with intensive land-use changes.     

Steady-state analysis of metabolic pathways: comparing the double modulation method and the lin-log approach

The increasing interest in studying enzyme kinetics under in vivo conditions requires practical methods to estimate control parameters from experimental data. In contrast to currently established approaches of dynamic modelling, this paper addresses the steady-state analysis of metabolic pathways. Within the framework of metabolic control analysis (MCA), elasticity coefficients are used to describe the control properties of a local enzyme reaction. The double modulation method is one of the first experimental approaches to estimate elasticity coefficients from measurements of steady-state flux rates and metabolite concentrations. We propose a generalized form of the double modulation method and compare it to the recently developed linear-logarithmic approach.     

An unexpected influence of widely used significance thresholds on the distribution of reported P-values

We consider the problematic relationship between publication success and statistical significance in the light of analyses in which we examine the distribution of published probability (P) values across the statistical 'significance' range, below the 5% probability threshold. P-values are often judged according to whether they lie beneath traditionally accepted thresholds (< 0.05, < 0.01, < 0.001, < 0.0001); we examine how these thresholds influence the distribution of reported absolute P-values in published scientific papers, the majority in biological sciences. We collected published P-values from three leading journals, and summarized their distribution using the frequencies falling across and within these four threshold values between 0.05 and 0. These published frequencies were then fitted to three complementary null models which allowed us to predict the expected proportions of P-values in the top and bottom half of each inter-threshold interval (i.e. those lying below, as opposed to above, each P-value threshold). Statistical comparison of these predicted proportions, against those actually observed, provides the first empirical evidence for a remarkable excess of probability values being cited on, or just below, each threshold relative to the smoothed theoretical distributions. The pattern is consistent across thresholds and journals, and for whichever theoretical approach used to generate our expected proportions. We discuss this novel finding and its implications for solving the problems of publication bias and selective reporting in evolutionary biology.     

Systemic Neurochemical Alterations in Schizophrenic Brain: Glutamate Metabolism in Focus

We have used a systemic approach to establish a relationship between enzyme measures of glial glutamate and energy metabolism (glutamine synthetase and glutamine synthetase-like protein, glutamate dehydrogenase isoenzymes, brain isoform creatine phosphokinase) and two major glial proteins (glial fibrillary acidic protein and myelin basic protein) in autopsied brain samples taken from patients with schizophrenia (SCH) and mentally healthy subjects (23 and 22 cases, respectively). These biochemical parameters were measured in tissue extracts in three brain areas (prefrontal cortex, caudate nucleus, and cerebellum). Significant differences in the level of at least one of the glutamate metabolizing enzymes were observed between two studied groups in all studied brain areas. Different patterns of correlative links between the biochemical parameters were found in healthy and schizophrenic brains. These findings give a new perspective to our understanding of the impaired regulation of enzyme levels in the brain in SCH.     

A taxonomic survey of lactic acid bacteria isolated from wheat (Triticum durum) kernels and non-conventional flours

In order to explore the correspondence between raw material- and mature sourdough-lactic acid bacterial (LAB) communities, 59 Italian wheat (Triticum durum) grain samples, one bran and six non-conventional flour samples were analyzed through a culture-dependent approach. The highest cell count by an agar medium specific for LAB was 2.16 log CFU/g. From about 2300 presumptive LAB (Gram-positive and catalase-negative) colonies collected, a total of 356 isolates were subjected to identification by a genetic polyphasic strategy consisting of RAPD-PCR analysis, partial 16S rRNA gene sequencing, species-specific and multiplex PCRs. The isolates were recognized as 137 strains belonging to Aerococcus, Enterococcus, Lactobacillus, Lactococcus and Pediococcus genera and a phylogram based on partial 16S rRNA genes was constructed. The species most frequently found were Enterococcus faecium, Enterococcus mundtii and Lactobacillus graminis, which are not generally reported to be typical in mature sourdoughs.