Ceased grazing management changes the ecosystem services of semi-natural grasslands

Understanding how drivers of change affect ecosystem services (ES) is of great importance. Indicators of ES can be developed based on biophysical measures and be used to investigate the service flow from ecosystems to socio-ecological systems. However, the ES concept is multivariate and the use of normalized composite indicators reduces complexity and facilitates communication between science and policy. The aim of this study is to analyze how land use change affects ES and species richness and how the effects are modified by environmental factors by using composite indicators based on biophysical indicators. Using multivariate and regression analyses, we analyze the effect of grazing management abandonment in semi-natural grasslands in Norway on six ES: nutrient cycling, pollination, forage quality, aesthetics and global and regional climate regulation in addition to species richness along soil and climate gradients. Nutrient cycling, forage quality, regional climate regulation, aesthetics and species richness are larger in managed compared to abandoned grasslands. There are trade-offs among ES as different management strategies provide various ES and these trade-offs vary along environmental gradients. Management policies that aim to conserve ES need to have conservation goals that are context dependent, should recognize ES trade-offs and be adapted to local conditions

PROlocalizer: integrated web service for protein subcellular localization prediction

Subcellular localization is an important protein property, which is related to function, interactions and other features. As experimental determination of the localization can be tedious, especially for large numbers of proteins, a number of prediction tools have been developed. We developed the PROlocalizer service that integrates 11 individual methods to predict altogether 12 localizations for animal proteins. The method allows the submission of a number of proteins and mutations and generates a detailed informative document of the prediction and obtained results. PROlocalizer is available at http://bioinf.uta.fi/PROlocalizer/

FunSecKB: the Fungal Secretome KnowledgeBase

The Fungal Secretome KnowledgeBase (FunSecKB) provides a resource of secreted fungal proteins, i.e. secretomes, identified from all available fungal protein data in the NCBI RefSeq database. The secreted proteins were identified using a well evaluated computational protocol which includes SignalP, WolfPsort and Phobius for signal peptide or subcellular location prediction, TMHMM for identifying membrane proteins, and PS-Scan for identifying endoplasmic reticulum (ER) target proteins. The entries were mapped to the UniProt database and any annotations of subcellular locations that were either manually curated or computationally predicted were included in FunSecKB. Using a web-based user interface, the database is searchable, browsable and downloadable by using NCBI’s RefSeq accession or gi number, UniProt accession number, keyword or by species. A BLAST utility was integrated to allow users to query the database by sequence similarity. A user submission tool was implemented to support community annotation of subcellular locations of fungal proteins. With the complete fungal data from RefSeq and associated web-based tools, FunSecKB will be a valuable resource for exploring the potential applications of fungal secreted proteins

Evolution of coronary atherosclerosis in patients with mild coronary artery disease studied by serial quantitative coronary angiography at 2 and 4 years follow up

AIMS: Angiographic studies on the natural course of both focal and diffuse coronary atherosclerosis have not been performed before, but can both be assessed by quantitative coronary angiography. The objective of this study was to describe the natural course of focal and diffuse coronary atherosclerosis over time. METHODS AND RESULTS: In 129 patients with mild coronary artery disease, but not on lipid-lowering medication, three coronary angiograms were made each 2 years apart. Nine hundred and sixty five angiographically diseased and non-diseased segments were analysed by quantitative coronary angiography. Mean lumen diameter and minimal lumen diameter were used as measures of diffuse and focal coronary atherosclerosis. Mean lumen diameter and minimum lumen diameter decreased by 0.02 and 0.03 mm per year. The rate of progression was similar in the angiographically non-diseased, as in the mildly and moderately diseased segments. Progression of diffuse coronary atherosclerosis was largest in severely stenosed lesions (percentage diameter stenosis > or = 50%) and in the right coronary artery with a loss of 0.19 mm and 0.16 mm in mean lumen diameter. Progression of focal disease was most prominent in new and mild lesions and the right coronary artery, with a decrease in minimum lumen diameter of 0.34 mm and 0.22 mm. In most subgroups, progression occurred gradually over time. On a per segment level, progression and the occurrence of new lesions occurred in 4.4% and 4.2%. Regression and disappearance of a lesions was found in 2.3% and 1.9%. On a per patient level, 36% were progressors, 12% had a mixed response, 36% were stable, and 16% were regressors. CONCLUSION: Diffuse and focal coronary atherosclerosis progressed at the same rate in the first and second 2 years in stenosed and non-stenosed segments. The rate of coronary atherosclerosis progression was small, but was higher for focal than for diffuse disease. A minority of lesions progressed and spontaneous regression was rare

Myths and Facts About Static Application Security Testing Tools: An Action Research at Telenor Digital

It is claimed that integrating agile and security in practice is challenging. There is the notion that security is a heavy process, requires expertise, and consumes developers’ time. These contrast with the agile vision. Regardless of these challenges, it is important for organizations to address security within their agile processes since critical assets must be protected against attacks. One way is to integrate tools that could help to identify security weaknesses during implementation and suggest methods to refactor them. We used quantitative and qualitative approaches to investigate the efficiency of the tools and what they mean to the actual users (i.e. developers) at Telenor Digital. Our findings, although not surprising, show that several barriers exist both in terms of tool’s performance and developers’ perceptions. We suggest practical ways for improvement.publishedVersio

Realizing a 140\ua0GHz Gap Waveguide–Based Array Antenna by Low-Cost Injection Molding and Micromachining

This paper presents a novel micromachining process to fabricate a 140\ua0GHz planar antenna based on gap waveguide technology to be used in the next-generation backhauling links. The 140\ua0GHz planar array antenna consists of three layers, all of which have been fabricated using polymer-based microfabrication and injection molding. The 140\ua0GHz antenna has the potential to be used as an element in a bigger 3D array in a line-of-sight (LOS) multiple input multiple output (MIMO) configuration to boost the network capacity. In this work, we focus on the fabrication of a single antenna array element based on gap waveguide technology. Depending on the complexity of each antenna layer’s design, three different micromachining techniques, SU8 fabrication, polydimethylsiloxane (PDMS) molding, and injection molding of the polymer (OSTEMER), together with gold (Au) coating, have been utilized to fabricate a single 140\ua0GHz planar array antenna. The input reflection coefficient was measured to be below − 11\ua0dB over a 14% bandwidth from 132 to 152\ua0GHz, and the antenna gain was measured to be 31 dBi at 140\ua0GHz, both of which are in good agreement with the simulations

Flood risk assessment for infrastructure networks

A practical framework for flood risk screening was developed to assess the flood risk to water utility assets within the infrastructure network. The tool is a combination of probability and consequence assessments. The first takes into account how probable it is for a particular asset to flood and cause significant damage. The second estimates the level of consequences a flood will have, considering, for example, the level of loss of service, environmental pollution and cost. The consequence assessment is based on a dependency assessment that identifies knock‐on effects on other assets within the asset network and assesses the level of consequence they will have. The probability and consequence assessments are combined to produce a risk score that can be used to rank assets in a screening process that aims to assist companies in prioritising the investments required for taking action to reduce flood risk to their assets

SIMAP—structuring the network of protein similarities

Protein sequences are the most important source of evolutionary and functional information for new proteins. In order to facilitate the computationally intensive tasks of sequence analysis, the Similarity Matrix of Proteins (SIMAP) database aims to provide a comprehensive and up-to-date dataset of the pre-calculated sequence similarity matrix and sequence-based features like InterPro domains for all proteins contained in the major public sequence databases. As of September 2007, SIMAP covers ∼17 million proteins and more than 6 million non-redundant sequences and provides a complete annotation based on InterPro 16. Novel features of SIMAP include a new, portlet-based web portal providing multiple, structured views on retrieved proteins and integration of protein clusters and a unique search method for similar domain architectures. Access to SIMAP is freely provided for academic use through the web portal for individuals at http://mips.gsf.de/simap/and through Web Services for programmatic access at http://mips.gsf.de/webservices/services/SimapService2.0?wsdl

Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

Background: Subcellular location prediction of proteins is an important and well-studied problem in bioinformatics. This is a problem of predicting which part in a cell a given protein is transported to, where an amino acid sequence of the protein is given as an input. This problem is becoming more important since information on subcellular location is helpful for annotation of proteins and genes and the number of complete genomes is rapidly increasing. Since existing predictors are based on various heuristics, it is important to develop a simple method with high prediction accuracies. Results: In this paper, we propose a novel and general predicting method by combining techniques for sequence alignment and feature vectors based on amino acid composition. We implemented this method with support vector machines on plant data sets extracted from the TargetP database. Through fivefold cross validation tests, the obtained overall accuracies and average MCC were 0.9096 and 0.8655 respectively. We also applied our method to other datasets including that of WoLF PSORT. Conclusion: Although there is a predictor which uses the information of gene ontology and yields higher accuracy than ours, our accuracies are higher than existing predictors which use only sequence information. Since such information as gene ontology can be obtained only for known proteins, our predictor is considered to be useful for subcellular location prediction of newly-discovered proteins. Furthermore, the idea of combination of alignment and amino acid frequency is novel and general so that it may be applied to other problems in bioinformatics. Our method for plant is also implemented as a web-system and available on http://sunflower.kuicr.kyoto-u.ac.jp/~tamura/slpfa.html webcite

A method to improve protein subcellular localization prediction by integrating various biological data sources

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is crucial information to elucidate protein functions. Owing to the need for large-scale genome analysis, computational method for efficiently predicting protein subcellular localization is highly required. Although many previous works have been done for this task, the problem is still challenging due to several reasons: the number of subcellular locations in practice is large; distribution of protein in locations is imbalanced, that is the number of protein in each location remarkably different; and there are many proteins located in multiple locations. Thus it is necessary to explore new features and appropriate classification methods to improve the prediction performance.</p> <p>Results</p> <p>In this paper we propose a new predicting method which combines two key ideas: 1) Information of neighbour proteins in a probabilistic gene network is integrated to enrich the prediction features. 2) Fuzzy k-NN, a classification method based on fuzzy set theory is applied to predict protein locating in multiple sites. Experiment was conducted on a dataset consisting of 22 locations from Budding yeast proteins and significant improvement was observed.</p> <p>Conclusion</p> <p>Our results suggest that the neighbourhood information from functional gene networks is predictive to subcellular localization. The proposed method thus can be integrated and complementary to other available prediction methods.</p