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

Measurement of Aromatic-hydrocarbons With the DOAS Technique

Long-path DOAS (differential optical absorption spectroscopy) in the ultraviolet spectral region has been shown to be applicable for low-concentration measurements of light aromatic hydrocarbons. However, because of spectral interferences among different aromatics as well as with oxygen, ozone, and sulfur dioxide, the application of the DOAS technique for this group of components is not without problems. This project includes a study of the differential absorption characteristics, between 250 and 280 nm, of twelve light aromatic hydrocarbons representing major constituents in technical solvents used in the automobile industry. Spectral overlapping between the different species, including oxygen, ozone, and sulfur dioxide, has been investigated and related to the chemical structure of the different aromatics. Interference effects in the DOAS application due to spectral overlapping have been investigated both in quantitative and in qualitative terms, with data from a field campaign at a major automobile manufacturing plant

Multiagent Rollout with Reshuffling for Warehouse Robots Path Planning

Efficiently solving path planning problems for a large number of robots is critical to the successful operation of modern warehouses. The existing approaches adopt classical shortest path algorithms to plan in environments whose cells are associated with both space and time in order to avoid collision between robots. In this work, we achieve the same goal by means of simulation in a smaller static environment. Built upon the new framework introduced in (Bertsekas, 2021a), we propose multiagent rollout with reshuffling algorithm, and apply it to address the warehouse robots path planning problem. The proposed scheme has a solid theoretical guarantee and exhibits consistent performance in our numerical studies. Moreover, it inherits from the generic rollout methods the ability to adapt to a changing environment by online replanning, which we demonstrate through examples where some robots malfunction

DNAJB6 is a peptide-binding chaperone which can suppress amyloid fibrillation of polyglutamine peptides at substoichiometric molar ratios

Expanded polyglutamine (polyQ) stretches lead to protein aggregation and severe neurodegenerative diseases. A highly efficient suppressor of polyQ aggregation was identified, the DNAJB6, when molecular chaperones from the HSPH, HSPA, and DNAJ families were screened for huntingtin exon 1 aggregation in cells (Hageman et al. in Mol Cell 37(3):355-369, 2010). Furthermore, also aggregation of polyQ peptides expressed in cells was recently found to be efficiently suppressed by co-expression of DNAJB6 (Gillis et al. in J Biol Chem 288:17225-17237, 2013). These suppression effects can be due to an indirect effect of DNAJB6 on other cellular components or to a direct interaction between DNAJB6 and polyQ peptides that may depend on other cellular components. Here, we have purified the DNAJB6 protein to investigate the suppression mechanism. The purified DNAJB6 protein formed large heterogeneous oligomers, in contrast to the more canonical family member DNAJB1 which is dimeric. Purified DNAJB6 protein, at substoichiometric molar ratios, efficiently suppressed fibrillation of polyQ peptides with 45°Q in a thioflavin T fibrillation. No suppression was obtained with DNAJB1, but with the closest homologue to DNAJB6, DNAJB8. The suppression effect was independent of HSPA1 and ATP. These data, based on purified proteins and controlled fibrillation in vitro, strongly suggest that the fibrillation suppression is due to a direct protein-protein interaction between the polyQ peptides and DNAJB6 and that the DNAJB6 has unique fibrillation suppression properties lacking in DNAJB1. Together, the data obtained in cells and in vitro support the view that DNAJB6 is a peptide-binding chaperone that can interact with polyQ peptides that are incompletely degraded by and released from the proteasome.</p

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

FGDB: revisiting the genome annotation of the plant pathogen Fusarium graminearum

The MIPS Fusarium graminearum Genome Database (FGDB) was established as a comprehensive genome database on one of the most devastating fungal plant pathogens of wheat, barley and maize. The current version of FGDB v3.1 provides information on the full manually revised gene set based on the Broad Institute assembly FG3 genome sequence. The results of gene prediction tools were integrated with the help of comparative data on related species to result in a set of 13.718 annotated protein coding genes. This rigorous approach involved adding or modifying gene models and represents a coding sequence gold standard for the genus Fusarium. The gene loci improvements results in 2461 genes which either are new or have different structures compared to the Broad Institute assembly 3 gene set. Moreover the database serves as a convenient entry point to explore expression data results and to obtain information on the Affymetrix GeneChip probe sets. The resource is accessible on http://mips.gsf.de/genre/proj/FGDB/

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

Effects of a dual CCR3 and H1-antagonist on symptoms and eosinophilic inflammation in allergic rhinitis

<p>Abstract</p> <p>Background</p> <p>The CC-chemokine receptor-3 (CCR3) has emerged as a target molecule for pharmacological intervention in allergic inflammation.</p> <p>Objective</p> <p>To examine whether a dual CCR3 and H₁-receptor antagonist (AZD3778) affects allergic inflammation and symptoms in allergic rhinitis.</p> <p>Methods</p> <p>Patients with seasonal allergic rhinitis were subjected to three seven days' allergen challenge series. Treatment with AZD3778 was given in a placebo and antihistamine-controlled design. Symptoms and nasal peak inspiratory flow (PIF) were monitored in the morning, ten minutes post challenge, and in the evening. Nasal lavages were carried out at the end of each challenge series and α₂-macroglobulin, ECP, and tryptase were monitored as indices of allergic inflammation.</p> <p>Results</p> <p>Plasma levels of AZD3778 were stable throughout the treatment series. AZD3778 and the antihistamine (loratadine) reduced rhinitis symptoms recorded ten minutes post challenge during this period. AZD3778, but not the anti-histamine, also improved nasal PIF ten minutes post challenge. Furthermore, scores for morning and evening nasal symptoms from the last five days of the allergen challenge series showed statistically significant reductions for AZD3778, but not for loratadine. ECP was reduced by AZD3778, but not by loratadine.</p> <p>Conclusions</p> <p>AZD3778 exerts anti-eosinophil and symptom-reducing effects in allergic rhinitis and part of this effect can likely be attributed to CCR3-antagonism. The present data are of interest with regard to the potential use of AZD3778 in allergic rhinitis and to the relative importance of eosinophil actions to the symptomatology of allergic rhinitis.</p> <p>Trial registration</p> <p>EudraCT No: 2005-002805-21.</p

Identification and Functional Characterization of Gene Components of Type VI Secretion System in Bacterial Genomes

A new secretion system, called the Type VI Secretion system (T6SS), was recently reported in Vibrio cholerae, Pseudomonas aeruginosa and Burkholderia mallei. A total of 18 genes have been identified to be belonging to this secretion system in V. cholerae. Here we attempt to identify presence of T6SS in other bacterial genomes. This includes identification of orthologous sequences, conserved motifs, domains, families, 3D folds, genomic islands containing T6SS components, phylogenetic profiles and protein-protein association of these components. Our analysis indicates presence of T6SS in 42 bacteria and its absence in most of their non-pathogenic species, suggesting the role of T6SS in imparting pathogenicity to an organism. Analysis of genomic regions containing T6SS components, phylogenetic profiles and protein-protein association of T6SS components indicate few additional genes which could be involved in this secretion system. Based on our studies, functional annotations were assigned to most of the components. Except one of the genes, we could group all the other genes of T6SS into those belonging to the puncturing device, and those located in the outer membrane, transmembrane and inner membrane. Based on our analysis, we have proposed a model of T6SS and have compared the same with the other bacterial secretion systems

ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins

<p>Abstract</p> <p>Background</p> <p>The expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features.</p> <p>Results</p> <p>Here, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM). In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets.</p> <p>Conclusion</p> <p>These results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search based results. The presently developed modules are implemented as web server "ESLpred2" available at <url>http://www.imtech.res.in/raghava/eslpred2/</url>.</p