varunr14/newsummary huggingface dataset:
The design process of mechatronic devices which involves experts from different disciplines working together has limited time and resource constraints . These experts normally have their own domain specific designing methods and tools which can lead to incompatibilities when one needs to work together using these those methods and tools . Having a proper framework which integrates different design tools is of interest as such a framework can prevent incompatibilities between parts during the design process . In this paper we propose our co modelling methodology and co simulation tools integration framework which helps to maintain the domain specific properties of the model components during the co design process of various mechatronic devices . To avoid expensive rework later in the design phase and even possible system failure fault modelling and a layered structure with fault tolerance mechanisms for the controller software are introduced . In the end a practical mechatronic device is discussed to illustrate the methods and tools which are presented in this paper in details . 


Summary: A co modelling methodology supported by a co simulation tool frame work is proposed. Fault modelling and a layered structure with fault tolerance mechanisms for the controller software are introduced. The introduced methods and tools are demonstrated on a practical mechatronic device.
This systematic review considers how water quality and aquatic ecology models represent the phosphorus cycle. Although the focus is on phosphorus many of the observations and discussion points here relate to aquatic ecosystem models in general. The review considers how models compare across domains of application the degree to which current models are fit for purpose how to choose between multiple alternative formulations and how models might be improved. Lake and marine models have been gradually increasing in complexity with increasing emphasis on inorganic processes and ecosystems. River models have remained simpler but have been more rigorously assessed. Processes important in less eutrophic systems have often been neglected these include the biogeochemistry of organic phosphorus transformations associated with fluxes through soils and sediments transfer rate limited phosphorus uptake and responses of plants to pulsed nutrient inputs. Arguments for and against increasing model complexity physical and physiological realism are reviewed. 


Summary: The treatment of phosphorus in aquatic models is systematically reviewed. The complexity of lake river and marine models is increasing over time. Catchment river models tend to be simpler than lake and marine models. Performance assessment of lake and marine models is generally inadequate. Processes not included in models are discussed.
Graphical abstract The noise model was implemented in R to call functions from PostgreSQL and GRASS GIS packages and can be obtained from the corresponding author or the following website http www.sahsu.org content data download first available in July 2014 TRANEX requires at least one standard desktop PC . 


Summary: Adaptation of the Calculation of Road Traffic Noise method for exposure assessment. Freely available open source software R with PostgreSQL and GRASS GIS . Model estimates compared well to noise measurements r 0.85 0.95 . Noise level exposures modelled for 8.61 million London residents 2003 2010 . Over 1 million residents exposed to high daytime and night time noise levels.
The purpose of this research is to develop a formal knowledge e discovery methodology using advanced information technology and decision support analysis to define legal case evolution based on Collective Litigation Intelligence . In this research a decade of Australia s retail franchise and trademark litigation cases are used as the corpus to analyze and synthesize the evolution of modern retail franchise law in Australia . The formal processes used in the legal e discovery research include a LexisNexis search strategy to collect legal documents text mining to find key concepts and their representing key phrases in the documents clustering algorithms to associate the legal cases into groups and concept lattice analysis to trace the evolutionary trends of the main groups . The case analysis discovers the fundamental issues for retail modernization advantages and disadvantages of retail franchising systems and the potential litigation hazards to be avoided in the Australian market . Given the growing number of legal documents in global court systems this research provides a systematic and generalized CLI methodology to improve the efficiency and efficacy of research across international legal systems . In the context of the case study the results demonstrate the critical importance of quickly processing and interpreting existing legal knowledge using the CLI approach . For example a brand management company which purchases a successful franchise in one market is under limited time constraints to evaluate the legal environment across global markets of interest . The proposed CLI methodology can be applied to derive market entry strategies to secure growth and brand expansion of a global franchise . 


Summary: E discovery to define legal evolution using Collective Litigation Intelligence. A scientific approach to improve legal practice litigation research. Derives market entry strategies for global franchise brand expansion.
In order to elucidate some basic principles for protein ligand interactions a subset of 87 structures of human proteins with their ligands was obtained from the PDB databank . After a short molecular dynamics simulation a variety of interaction energies and structural parameters were extracted . Linear regression was performed to determine which of these parameters have a potentially significant contribution to the protein ligand interaction . The parameters exhibiting relatively high correlation coefficients were selected . Important factors seem to be the number of ligand atoms the ratio of N O and S atoms to total ligand atoms the hydrophobic polar aminoacid ratio and the ratio of cavity size to the sum of ligand plus water atoms in the cavity . An important factor also seems to be the immobile water molecules in the cavity . Nine of these parameters were used as known inputs to train a neural network in the prediction of seven other . Eight structures were left out of the training to test the quality of the predictions . After optimization of the neural network the predictions were fairly accurate given the relatively small number of structures especially in the prediction of the number of nitrogen and sulfur atoms of the ligand . 


Summary: Molecular dynamics simulations were run on PDB structures containing protein and ligand. Interaction and structural parameters were extracted from the structures. Linear regression was used to check for correlation between these parameters. A neural network NN was used to predict ligand design parameters based on the protein. The NN performance improved when tweaking the protein structural parameters used.
Bacteria are increasingly resistant to existing antibiotics which target a narrow range of pathways . New methods are needed to identify targets including repositioning targets among distantly related species . We developed a novel combination of systems and structural modeling and bioinformatics to reposition known antibiotics and targets to new species . We applied this approach to Mycoplasma genitalium a common cause of urethritis . First we used quantitative metabolic modeling to identify enzymes whose expression affects the cellular growth rate . Second we searched the literature for inhibitors of homologs of the most fragile enzymes . Next we used sequence alignment to assess that the binding site is shared by M. genitalium but not by humans . Lastly we used molecular docking to verify that the reported inhibitors preferentially interact with M. genitalium proteins over their human homologs . Thymidylate kinase was the top predicted target and piperidinylthymines were the top compounds . Further work is needed to experimentally validate piperidinylthymines . In summary combined systems and structural modeling is a powerful tool for drug repositioning . 


Summary: We combined systems and structural modeling to repurpose antibiotics for new hosts. We applied our novel approach to the infectious bacterium Mycoplasma genitalium. Our method suggests that thymidylate kinase is a good potential drug target. Our method suggests that piperidinylthymines are good potential lead compounds. Combined systems and structural modeling is a powerful tool for drug repositioning.
Kansei evaluation plays a vital role in the implementation of Kansei engineering however it is difficult to quantitatively evaluate customer preferences of a product s Kansei attributes as such preferences involve human perceptual interpretation with certain subjectivity uncertainty and imprecision . An effective Kansei evaluation requires justifying the classification of Kansei attributes extracted from a set of collected Kansei words establishing priorities for customer preferences of product alternatives with respect to each attribute and synthesizing the priorities for the evaluated alternatives . Moreover psychometric Kansei evaluation systems essentially require dealing with Kansei words . This paper presents a Kansei evaluation approach based on the technique of computing with words . The aims of this study were to classify collected Kansei words into a set of Kansei attributes by using cluster analysis based on fuzzy relations to model Kansei preferences based on semantic labels for the priority analysis and to synthesize priority information and rank the order of decision alternatives by means of the linguistic aggregation operation . An empirical study is presented to demonstrate the implementation process and applicability of the proposed Kansei evaluation approach . The theoretical and practical implications of the proposed approach are also discussed . 


Summary: This paper presents a Kansei evaluation approach based on the technique of computing with words. Kansei preferences are modeled by positively worded items with 7 levels of semantic labels. Fuzzy relation based clustering is used to extract a set of Kansei attributes from collected Kansei words. A cluster validation index is proposed to assist evaluators in determining the best number of clusters. Linguistic aggregation is used to synthesize Kansei priority information and rank the order of product alternatives.
As a pivotal domain within envelope protein fusion peptide plays a crucial role in pathogenicity and therapeutic intervention . Taken into account the limited FP annotations in NCBI database and absence of FP prediction software it is urgent and desirable to develop a bioinformatics tool to predict new putative FPs in retroviruses . In this work a sequence based FP model was proposed by combining Hidden Markov Method with similarity comparison . The classification accuracies are 91.97 and 92.31 corresponding to 10 fold and leave one out cross validation . After scanning sequences without FP annotations this model discovered 53 946 np FPs . The statistical results on FPs or np FPs reveal that FP is a conserved and hydrophobic domain . The FP software programmed for windows environment is available at https sourceforge.net projects fptool files source navbar . 


Summary: A novel computational model for predicting fusion peptide of retroviruses was proposed. A software tool named FP predict.exe has been developed. A large number of new putative FPs of five typical retroviruses were predicted. Property motif and evolutionary relationship about FP were computed and discussed.
Motivation Protein fold space is a conceptual framework where all possible protein folds exist and ideas about protein structure function and evolution may be analyzed . Classification of protein folds in this space is commonly achieved by using similarity indexes and or machine learning approaches each with different limitations . Results We propose a method for constructing a compact vector space model of protein fold space by representing each protein structure by its residues local contacts . We developed an efficient method to statistically test for the separability of points in a space and showed that our protein fold space representation is learnable by any machine learning algorithm . Availability An API is freely available at https code.google.com p pyrcc . 


Summary: We implemented a vectorial representation of residues contacts We implemented an efficient statistical test for machine learnable data Our vectorial model reproduces protein packing A predictor is trained to effectively reproduce CATH and SCOP classifications Our predictor automatically identified inconsistent classification in CATH and SCOP
The collection and analysis of data on the three dimensional as built status of large scale civil infrastructure whether under construction newly put into service or in operation has been receiving increasing attention on the part of researchers and practitioners in the civil engineering field . Such collection and analysis of data is essential for the active monitoring of production during the construction phase of a project and for the automatic 3D layout of built assets during their service lives . This review outlines recent research efforts in this field and technological developments that aim to facilitate the analysis of 3D data acquired from as built civil infrastructure and applications of such data not only to the construction process per se but also to facility management in particular to production monitoring and automated layout . This review also considers prospects for improvement and addresses challenges that can be expected in future research and development . It is hoped that the suggestions and recommendations made in this review will serve as a basis for future work and as motivation for ongoing research and development . 


Summary: Access to reliable 3D as built data is a critical issue in civil infrastructure. Applications to production monitoring and automated layout are discussed. Research on applications of as built data in the civil engineering field is reviewed. State of the art and other developments in as built data analysis are surveyed. Unsolved problems and challenges for future improvements in this field are discussed.
In this paper the physical and chemical characteristics biological structure and function of a non specific nuclease from Yersinia enterocolitica subsp . palearctica found in our group were studied using multiple bioinformatics approaches . The results showed that Y. NSN had 283 amino acids a weight of 30 692.5ku and a certain hydrophilic property . Y. NSN had a signal peptide no transmembrane domains and disulphide bonds . Cleavage site in Y. NSN was between pos . 23 and 24 . The prediction result of the secondary structure showed Y. NSN was a coil structure based protein . The ratio of helix folded and random coil were 18.73 16.96 and 64.31 respectively . Active sites were pos . 124 125 127 157 165 and 169 . Mg2 binding site was pos . 157 . Substrate binding sites were pos . 124 125 and 169 . The analysis of multisequencing alignment and phylogenetic tree indicated that Y. NSN shared high similarity with the nuclease from Y. enterocolitica subsp . enterocolitica 8081 . The enzyme activity results showed that Y. NSN was a nuclease with good thermostability . 


Summary: It is the first time to study the biological structures and function of Y. NSN using bioinformatics methods. Y. NSN shared high similarity with the nuclease from Yersinia enterocolitica subsp. enterocolitica 8081. Y. NSN showed good thermostability.
Reptin functions in a wide range of biological processes including chromatin remodelling nucleolar organization and transcriptional regulation of WNT signalling . As catenin dependent transcriptional repression and activation events involve binding of Reptin and histone deacetylase 1 to APPL endocytic proteins this complex has become an important target to identify molecules governing endocytic processes and WNT signalling . Here we describe the structural basis of APPL binding to Reptin to explore their mode of binding in context with APPL1 APPL2 dimerization . There is an evidence that both PH and BAR domains of APPL proteins exhibit alternately conserved regions involved in hetero dimerization process and our in silico data also corroborate this fact . Moreover APPL2PH domain binds to the BAR domain region encompassing a nuclear localization signal . We conclude that APPLPH binding to BAR domain and Reptin is mutually exclusive which regulates the nucleocytoplasmic shuttling of Reptin . Furthermore Reptin is unable to bind with membrane associated APPL proteins . These observations were further expanded by experimental approaches where we identified a novel point mutation D316N lying in the APPL1PH domain which resulted in a significantly reduced binding with Reptin . By luciferase assays we observed that overexpression of APPL1D316N and APPL1WT stimulated catenin TCF dependent transcriptional activity in a similar manner which suggested that binding of Reptin to APPL1 is not necessary for catenin dependent target gene expression . Overall our data attempt to highlight a comparative role of APPL proteins in controlling catenin dependent transcription mechanism which may improve our understanding of gene regulation . 


Summary: We study the structural basis of APPL and Reptin binding in context with APPL1 APPL2 dimerization. We perform a detailed in silico analysis using the knowledge of APPL1 2 heterodimerization and infer that binding of APPLPH to BAR domain and Reptin is mutually exclusive which may facilitate Reptin in nucleocytoplasmic shuttling. We report new insights into the distinctive characterization of APPL catenin Reptin HDACs ternary complex in terms of target gene regulation and transcriptional mechanisms.
Background Bacillus anthracis is a gram positive spore forming rod shaped bacteria which is the etiologic agent of anthrax cutaneous pulmonary and gastrointestinal . A recent outbreak of anthrax in a tropical region uncovered natural and in vitro resistance against penicillin ciprofloxacin quinolone due to over exposure of the pathogen to these antibiotics . This fact combined with the ongoing threat of using B. anthracis as a biological weapon proves that the identification of new therapeutic targets is urgently needed . Methods In this computational approach various databases and online based servers were used to detect essential proteins of B. anthracis A0248 . Protein sequences of B. anthracis A0248 strain were retrieved from the NCBI database which was then run in CD hit suite for clustering . NCBI BlastP against the human proteome and similarity search against DEG were done to find out essential human non homologous proteins . Proteins involved in unique pathways were analyzed using KEGG genome database and PSORTb CELLO v.2.5 ngLOC these three tools were used to deduce putative cell surface proteins . Results Successive analysis revealed 116 proteins to be essential human non homologs among which 17 were involved in unique metabolic pathways and 28 were predicted as membrane associated proteins . Both types of proteins can be exploited as they are unlikely to have homologous counterparts in the human host . Conclusion Being human non homologous these proteins can be targeted for potential therapeutic drug development in future . Targets on unique metabolic and membrane bound proteins can block cell wall synthesis bacterial replication and signal transduction respectively . 


Summary: We identified potential drug target for Bacillus anthracis A0248 strain. We selected non human homolog essential proteins involved in metabolic pathways. PSORTb ngLOC and CELLO are used to predict membrane bound proteins. 17 proteins in unique pathways and 28 membrane bound proteins are identified. These findings from this current study will pave the way for further extensive in wet lab experiments and in that way assisting to drug design against anthrax.
Inferring transcriptional regulatory interactions between transcription factors and their targets has utmost importance for understanding the complex regulatory mechanisms in cellular system . In this paper we introduced a computational method to predict regulatory interactions in Arabidopsis based on gene expression data and sequence information . Support vector machine and Jackknife cross validation test were employed to perform our method on a collected dataset including 178 positive samples and 1068 negative samples . Results showed that our method achieved an overall accuracy of 98.39 with the sensitivity of 94.88 and the specificity of 93.82 which suggested that our method can serve as a potential and cost effective tool for predicting regulatory interactions in Arabidopsis . 


Summary: SVM is explored to predict regulatory interactions in Arabidopsis. Experimentally validated regulatory relationships were collected as the positive samples. Negative training samples were randomly selected TF target pairs under some strategies. Each gene pair was represented by incorporating the expression data and sequence information. Through the jackknife test our method reached an overall accuracy of 98.39 with the sensitivity of 94.88 and the specificity of 93.82 .
A short partial sequence of 28 amino acids is all the information we have so far about the putative allergen 2S albumin from almond . The aim of this work was to analyze this information using mainly bioinformatics tools in order to verify its rightness . Based on the results reported in the paper describing this allergen from almond we analyzed the original data of amino acids sequencing through available software . The degree of homology of the almond 12kDa protein with any other known 2S albumin appears to be much lower than the one reported in the paper that firstly described it . In a publicly available cDNA library we discovered an expressed sequence tag which translation generates a protein that perfectly matches both of the sequencing outputs described in the same paper . A further analysis indicated that the latter protein seems to belong to the vicilin superfamily rather than to the prolamin one . The fact that also vicilins are seed storage proteins known to be highly allergenic would explain the IgE reactivity originally observed . Based on our observations we suggest that the IgE reactive 12kDa protein from almond currently known as Pru du 2S albumin is in reality the cleaved N terminal region of a 7S vicilin like protein . 


Summary: The current reported sequence of the almond 2S albumin is a partial 28 aa peptide. The translation into protein of an almond EST sequence matches the partial peptide. The in silico generated aa sequence is a member of the vicilin superfamily. The currently known almond 2S albumin is rather a part of a 7S vicilin like protein.