A local landscape mapping method for protein structure prediction in the HP model

The hydrophobic-polar (HP) model has been widely studied in the field of protein structure prediction both for theoretical purposes and as a benchmark for new optimization strategies. In this work we present results of the recently proposed Hybrid Monte Carlo Ant Colony Optimization heuristic in the HP model using a fragment assembly-like strategy. Moreover we extend that method introducing a general framework for optimization in the HP model, called Local Landscape Mapping, and we test it using the pull moves set to generate solutions. We describe the heuristic and compare results obtained on well known HP instances in the 3-dimensional cubic lattice to those obtained with standard Ant Colony optimization and Simulated Annealing. Fragment assembly-like tests were performed using a modified objective function to prevent the creation of overlapping walks. Results show that our method performs better than the other heuristics in all benchmark instances when the fragment assembly-like strategy is used while in the case of pull moves-based neighborhood its performance is comparable to that of simulated annealing.     

An ensemble evolutionary constraint-based approach to understand the emergence of metabolic phenotypes

Constraint-based modeling is largely used in computational studies of metabolism. We propose here a novel approach that aims to identify ensembles of flux distributions that comply with one or more target phenotype(s). The methodology has been tested on a small-scale model of yeast energy metabolism. The target phenotypes describe the differential pattern of ethanol production and O₂ consumption observed in “Crabtree-positive” and “Crabtree-negative” yeasts in changing environment (i.e., when the upper limit of glucose uptake is varied). The ensembles were obtained either by selection among sampled flux distributions or by means of a search heuristic (genetic algorithm). The former approach provided indication about the probability to observe a given phenotype, but the resulting ensembles could not be unambiguously partitioned into “Crabtree-positive” and “Crabtree-negative” clusters. On the contrary well-separated clusters were obtained with the latter method. The cluster analysis further allowed identification of distinct groups within each target phenotype. The method may thus prove useful in characterizing the design principles underlying metabolic plasticity arising from evolving physio-pathological or developmental constraints.     

A bio-inspired predictive sensory-motor coordination scheme for robot reaching and preshaping

This paper presents a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach an object while pre-shaping the fingers to the required grasp configuration and while predicting the tactile image that will be perceived after grasping. A model for sensory-motor coordination derived from studies in humans inspired the development of this scheme. A peculiar feature of this model is the prediction of the tactile image. The implementation of the proposed scheme is based on a neuro-fuzzy module that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for reaching, selects the best-suited hand configuration, and predicts the tactile feedback. The implementation of the scheme on a humanoid robot allowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control.     

Non-confluence in divisionless P systems with active membranes

We describe a solution to the SAT problem via non-confluent P systems with active membranes, without using membrane division rules. Furthermore, we provide an algorithm for simulating such devices on a nondeterministic Turing machine with a polynomial slowdown. Together, these results prove that the complexity class of problems solvable non-confluently and in polynomial time by this kind of P system is exactly the class .     

Path dependent stochastic models to detect planned and actual technology use: A case study of OpenOffice

Context

Adopting IT innovation in organizations is a complex decision process driven by technical, social and economic issues. Thus, those organizations that decide to adopt innovation take a decision of uncertain success of implementation, as the actual use of a new technology might not be the one expected. The misalignment between planned and effective use of innovation is called assimilation gap.

Objective

This research aims at defining a quantitative instrument for measuring the assimilation gap and applying it to the case of the adoption of OSS.

Method

In this paper, we use the theory of path dependence and increasing returns of Arthur. In particular, we model the use of software applications (planned or actual) by stochastic processes defined by the daily amounts of files created with the applications. We quantify the assimilation gap by comparing the resulting models by measures of proximity.

Results

We apply and validate our method to a real case study of introduction of OpenOffice. We have found a gap between the planned and the effective use despite well-defined directives to use the new OS technology. These findings suggest a need of strategy re-calibration that takes into account environmental factors and individual attitudes.

Conclusions

The theory of path dependence is a valid instrument to model the assimilation gap provided information on strategy toward innovation and quantitative data on actual use are available.     

Hierarchies of parallel rewriting P systems — A survey

The paper is about some families of rewriting P systems, where the application of evolution rules is extended from the classical sequential rewriting to the parallel one (as, for instance, in Lindenmayer systems). As a result, consistency problems for the communication of strings may arise. Three variants of parallel rewriting P systems (already present in the literature) are considered here, together with the strategies they use to face the communication problem, and some parallelism methods for string rewriting are defined. We give a survey of all known results about each variant and we state some relations among the three variants, thus establishing hierarchies of parallel rewriting P systems. Various open problems related to the subject are also presented. Danicla Besozzi: She is assistant professor at the University of Milano. She received her M.S. in Mathematics (2000) from the University of Como and Ph.D. in Computer Science (2004) from the University of Milano. Her research interests cover topics in Formal Language Theory, Molecular Computing, Systems Biology. She is member of EATCS (European Association for Theoretical Computer Science) and EMCC (European Molecular Computing Consortium). Giancarlo Mauri: He is full professor of Computer Science at the University of Milano-Bicocca. His research interests are mainly in the area of theoretical computer science, and include: formal languages and automata, computational complexity, computational learning theory, soft computing techniques, cellular automata, bioinformatics and molecular computing. On these subjects, he published more than 150 scientific papers in international journals, contributed volumes and conference proceedings. Claudio Zandron: He received Ph.D. in Computer Science at the University of Milan, Italy, in 2001. Since 2002 he is assistant professor at the University of Milano-Bicocca, Italy. He is member of the EATCS (European Association for Theoretical Computer Science) and of EMCC (European Molecular Computing Consortium). His research interests are Molecular Computing (DNA and Membrane Computing) and Formal Languages.     

Distribution of nitric oxide synthase immunoreactivity in the mouse brain

Nitric oxide (NO) is a gaseous intercellular messenger with a wide range of neural functions. NO is synthesized by activation of different isoforms of nitric oxide synthases (NOS). At present NOS immunoreactivity has been described in mouse brain in restricted and definite areas and no detailed mapping studies have yet been reported for NOS immunoreactivity. We have studied the distribution of neuronal NOS-containing neurons in the brain of three months male mice, using a specific commercial polyclonal antibody against the neuronal isoform of nitric oxide synthase (nNOS). Neuronal cell bodies exhibiting nNOS immunoreactivity were found in several distinct nuclei throughout the brain. The neurons that were positively stained exhibited different intensities of reaction. In some brain areas (i.e., cortex, striatum, tegmental nuclei) neurons were intensely stained in a Golgi-like fashion. In other regions, immunoreactive cells are moderately stained (i.e., magnocellular nucleus of the posterior commissure, amygdaloid nucleus, interpeduncular nucleus, lateral periaqueductal gray) or weakly stained (i.e., vascular organ of the lamina terminalis, hippocampus, inferior colliculus, reticular nucleus). In the mouse, the NO-producing system appears well developed and widely diffused. In particular, nNOS immunoreactive neurons seem chiefly present in several sensory pathways like all the nuclei of the olfactory system, as well as in many regions of the lymbic system. These data suggest a widespread role for the NO system in the mouse nervous system.     

Mass spectrometry for detection of 4-hydroxy-trans-2-nonenal (HNE) adducts with peptides and proteins

Despite the great technical advancement of mass spectrometry, this technique has contributed in a limited way to the discovery and quantitation of specific/precocious markers linked to free radical-mediated diseases. Unsaturated aldehydes generated by free radical-induced lipid peroxidation of polyunsaturated fatty acids, and in particular 4-hydroxy-trans-2 nonenal (HNE), are involved in the onset and progression of many pathologies such as cardiovascular (atherosclerosis, long-term complications of diabetes) and neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, and cerebral ischemia). Most of the biological effects of HNE are attributed to the capacity of HNE to react with the nucleophilic sites of proteins and peptides (other than nucleic acids), to form covalently modified biomolecules that can disrupt important cellular functions and induce mutations. By considering the emerging role of HNE in several human diseases, an unequivocal analytical approach as mass spectrometry to detect/elucidate the structure of protein-HNE adducts in biological matrices is strictly needed not only to understand the reaction mechanism of HNE, but also to gain a deeper insight into the pathological role of HNE. This with the aim to provide intermediate diagnostic biomarkers for human diseases. This review sheds focus on the "state-of-the-art" of mass spectrometric applications in the field of HNE-protein adducts characterization, starting from the fundamental early studies and discussing the different MS-based approaches that can provide detailed information on the mechanistic aspects of HNE-protein interaction. In the last decade, the increases in the accessible mass ranges of modern instruments and advances in ionization methods have made possible a fundamental improvement in the analysis of protein-HNE adducts by mass spectrometry, and in particular by matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI) tandem mass spectrometry. The recent developments and uses of combined analytical approaches to detect and characterize the type/site of interaction have been highlighted, and several other aspects, including sample preparation methodologies, structure elucidation, and data analysis have also been considered.     

A Food-Grade Method for Enhancing the Levels of Low Molecular Weight Proanthocyanidins with Potentially High Intestinal Bioavailability

Proanthocyanidins (PACs) are a group of bioactive molecules found in a variety of plants and foods. Their bioavailability depends on their molecular size, with monomers and dimers being more bioavailable than those that have a higher polymerization degree. This study aimed to develop a method to convert high-molecular-weight PACs to low-molecular-weight ones in a grape seed extract (GSE) from Vitis vinifera L. Therefore, GSE was subjected to alkaline treatment (ATGSE), and its difference in chemical composition, compared to GSE, was evaluated using a molecular networking (MN) approach based on results obtained from HPLC-ESI HRMS/MS characterization analysis. The network analysis mainly noted the PAC cluster with about 142 PAC compounds identified. In particular, the obtained results showed a higher content of monomeric and dimeric PACs in ATGSE compared to GSE, with 58% and 49% monomers and 31% and 24% dimers, respectively. Conversely, trimeric (9%), polymeric (4%), and galloylated PACs (14%) were more abundant in GSE than in ATGSE (6%, 1%, and 4%, respectively). Moreover, in vitro antioxidant and anti-inflammatory activities were investigated, showing the high beneficial potential of both extracts. In conclusion, ATGSE could represent an innovative natural matrix rich in bioavailable and bioaccessible PACs for nutraceutical applications with potential beneficial properties.     

From first principles to the Burrows and Wheeler transform and beyond,via combinatorial optimization

We introduce a combinatorial optimization framework that naturally induces a class of optimal word permutations with respect to a suitably defined cost function taking into account various measures of relatedness between words. The Burrows and Wheeler transform (bwt) (cf. [M. Burrows, D. Wheeler, A block sorting lossless data compression algorithm, Technical Report 124, Digital Equipment Corporation, 1994]), and its analog for labelled trees (cf. [P. Ferragina, F. Luccio, G. Manzini, S. Muthukrishnan, Structuring labeled trees for optimal succinctness, and beyond, in: Proc. of the 45th Annual IEEE Symposium on Foundations of Computer Science, 2005, pp. 198–207]), are special cases in the class. We also show that the class of optimal word permutations defined here is identical to the one identified by Ferragina et al. for compression boosting [P. Ferragina, R. Giancarlo, G. Manzini, M. Sciortino, Boosting textual compression in optimal linear time, Journal of the ACM 52 (2005) 688–713]. Therefore, they are all highly compressible. We also provide, by using techniques from Combinatorics on Words, a fast method to compute bwt without using any end-of-string symbol. We also investigate more general classes of optimal word permutations, where relatedness of symbols may be measured by functions more complex than context length. For this general problem we provide an instance that is MAX-SNP hard, and therefore unlikely to be solved or approximated efficiently. The results presented here indicate that a key feature of the Burrows and Wheeler transform seems to be, besides compressibility, the existence of efficient algorithms for its computation and inversion.