Unrelated Databases Research Articles

Tracking gut microbiome and bloodstream infection in critically ill adults.

The gut microbiome is believed to contribute to bloodstream infection (BSI) via translocation of dominant gut bacteria in vulnerable patient populations. However, conclusively linking gut and blood organisms requires stringent approaches to establish strain-level identity. We enrolled a convenience cohort of critically ill patients and investigated 86 bloodstream infection episodes that occurred in 57 patients. Shotgun metagenomic sequencing was used to define constituents of their gut microbiomes, and whole genome sequencing and assembly was done on 23 unique bloodstream isolates that were available from 21 patients. Whole genome sequences were downloaded from public databases and used to establish sequence-identity distribution and define thresholds for unrelated genomes of BSI species. Gut microbiome reads were then aligned to whole genome sequences of the cognate bloodstream isolate and unrelated database isolates to assess identity. Gut microbiome constituents matching the bloodstream infection species were present in half of BSI episodes, and represented >30% relative abundance of gut sequences in 10% of episodes. Among the 23 unique bloodstream organisms that were available for whole genome sequencing, 14 were present in gut at the species level. Sequence alignment applying defined thresholds for identity revealed that 6 met criteria for identical strains in blood and gut, but 8 did not. Sequence identity between BSI isolates and gut microbiome reads was more likely when the species was present at higher relative abundance in gut. In assessing potential gut source for BSI, stringent sequence-based approaches are essential to determine if organisms responsible for BSI are identical to those in gut: of 14 evaluable patients in which the same species was present in both sites, they were identical in 6/14, but were non-identical in 8/14 and thus inconsistent with gut source. This report demonstrates application of sequencing as a key tool to investigate infection tracking within patients.

Physiological Network From Anthropometric and Blood Test Biomarkers.

Currently, research in physiology focuses on molecular mechanisms underlying the functioning of living organisms. Reductionist strategies are used to decompose systems into their components and to measure changes of physiological variables between experimental conditions. However, how these isolated physiological variables translate into the emergence -and collapse- of biological functions of the organism as a whole is often a less tractable question. To generate a useful representation of physiology as a system, known and unknown interactions between heterogeneous physiological components must be taken into account. In this work we use a Complex Inference Networks approach to build physiological networks from biomarkers. We employ two unrelated databases to generate Spearman correlation matrices of 81 and 54 physiological variables, respectively, including endocrine, mechanic, biochemical, anthropometric, physiological, and cellular variables. From these correlation matrices we generated physiological networks by selecting a p-value threshold indicating statistically significant links. We compared the networks from both samples to show which features are robust and representative for physiology in health. We found that although network topology is sensitive to the p-value threshold, an optimal value may be defined by combining criteria of stability of topological features and network connectedness. Unsupervised community detection algorithms allowed to obtain functional clusters that correlate well with current medical knowledge. Finally, we describe the topology of the physiological networks, which lie between random and ordered structural features, and may reflect system robustness and adaptability. Modularity of physiological networks allows to explore functional clusters that are consistent even when considering different physiological variables. Altogether Complex Inference Networks from biomarkers provide an efficient implementation of a systems biology approach that is visually understandable and robust. We hypothesize that physiological networks allow to translate concepts such as homeostasis into quantifiable properties of biological systems useful for determination and quantification of health and disease.

Machine learning for the diagnosis of malaria: a pilot study of transfer learning techniques

Despite being treatable and preventable, malaria continues to affect millions of people worldwide. A major hindrance to the disease’s eradication is difficulty with testing, and attempts have already been made to shift towards more automated testing methods rather than older testing technologies such as light microscopy and rapid diagnostic tests. Machine learning models have shown promise in diagnosing malaria, but prior machine learning algorithms have been contingent upon the availability of clean and relevant data. Furthermore, there are multiple species of malaria that may require different treatments, often resulting in fewer comparative images of individual malarial subtypes on blood slides of a given patient population. In this pilot study, we tested a machine learning technique called transfer learning to reduce the error caused by lesser amounts of available data. By training the algorithm on a known (but not necessarily related) dataset and then re-training on malaria datasets, we were able to significantly increase diagnostic accuracy. Specifically, using transfer learning to prepare the algorithm on a completely unrelated database before applying to malaria slide interpretation, our diagnostic accuracy improved from 65.5% to 80.7% in a small dataset of malaria blood slides, and from 95.5% to 98.0% in a much larger malarial dataset. Moving forward, these types of experiments will help develop a smartphone application to increase the opportunity to diagnose malaria in places where resources are too limited to permit standard diagnostic techniques.

Application of GIS for Urban Traffic Accidents: A Critical Review

The analysis of huge data is a complex task that cannot be executed without a proper system. Geographic information systems (GISs) have been used by many transportation agencies and police departments to analyze and manage urban traffic accident (UTA) data and for decision making aimed at decreasing accident rates and increasing safety. The exact location of accidents and environmental characteristics must be analyzed as UTAs occur in specific locations with specific characteristics. ArcGIS software is the best choice for obtaining meaningful information and analysis results from UTAs in an observational time span. GIS technology is a fundamental element for investigating and evaluating the complex spatial relationship among different components and urban traffic accident is one of them. Micro or macro analysis of UTAs through the spatial prospective within the geographical environment and urban structure can make a deep micro understanding of UTAs patterns in addition to assisting in decision making. UTAs can be considered complex events that occur in two aspects which are spatial and temporal or space and time in other word. A GIS can integrate more than two different and unrelated databases. The evaluation among different spatial objects in a geographical environment and associated factors in urban structure which are included but not limited to land use category, road transportation network qualification, population density, etc., is one of the GIS specification. Traffic safety organizations and UTA researchers use GISs as a key technology to support their research and operational needs. In particular, GIS-T is an often-used GIS application used for planning and decision-making in transportation.

Automated discovery of multi-faceted ontologies for accurate query answering and future semantic reasoning

There has been a surge of interest in the development of probabilistic techniques to discover meaningful data facts across multiple datasets provided by different organizations. The key aim is to approximate the structure and content of the induced data into a concise synopsis in order to extract meaningful data facts. Performing sensible queries across unrelated datasets is a complex task that requires a complete understanding of each contributing database's schema to define the structure of its information. Alternative approaches that use data modeling enterprise tools have been proposed, in order to give users without complex schema knowledge the ability to query databases. Unfortunately, data modeling-based matching is a content-based technique and incurs significant query evaluation costs, due to attribute level pairwise comparisons. We propose a multi-faceted classification technique for performing structural analysis on knowledge domain clusters, using a novel Ontology Guided Data Linkage (OGDL) framework. This framework supports self-organization of contributing databases through the discovery of structural dependencies, by performing multi-level exploitation of ontological domain knowledge relating to tables, attributes and tuples. The framework thus automates the discovery of schema structures across unrelated databases, based on the use of direct and weighted correlations between different ontological concepts, using a h-gram (hash gram) record matching technique for concept clustering and cluster mapping. We demonstrate the feasibility of our OGDL algorithms through a set of accuracy, performance and scalability experimental tests run on real-world datasets, and show that our system runs in polynomial time and performs well in practice. To the best of our knowledge, this is the first attempt initiated to solve data linkage problems using a multi-faceted cluster mapping strategy, and we believe that our approach presents a significant advancement towards accurate query answering and future real-time online semantic reasoning capacity.

DbWFA: a web-based database for functional annotation of Triticum aestivum transcripts

The functional annotation of genes based on sequence homology with genes from model species genomes is time-consuming because it is necessary to mine several unrelated databases. The aim of the present work was to develop a functional annotation database for common wheat Triticum aestivum (L.). The database, named dbWFA, is based on the reference NCBI UniGene set, an expressed gene catalogue built by expressed sequence tag clustering, and on full-length coding sequences retrieved from the TriFLDB database. Information from good-quality heterogeneous sources, including annotations for model plant species Arabidopsis thaliana (L.) Heynh. and Oryza sativa L., was gathered and linked to T. aestivum sequences through BLAST-based homology searches. Even though the complexity of the transcriptome cannot yet be fully appreciated, we developed a tool to easily and promptly obtain information from multiple functional annotation systems (Gene Ontology, MapMan bin codes, MIPS Functional Categories, PlantCyc pathway reactions and TAIR gene families). The use of dbWFA is illustrated here with several query examples. We were able to assign a putative function to 45% of the UniGenes and 81% of the full-length coding sequences from TriFLDB. Moreover, comparison of the annotation of the whole T. aestivum UniGene set along with curated annotations of the two model species assessed the accuracy of the annotation provided by dbWFA. To further illustrate the use of dbWFA, genes specifically expressed during the early cell division or late storage polymer accumulation phases of T. aestivum grain development were identified using a clustering analysis and then annotated using dbWFA. The annotation of these two sets of genes was consistent with previous analyses of T. aestivum grain transcriptomes and proteomes.Database URL: urgi.versailles.inra.fr/dbWFA/

Semantics-aware data integration for heterogeneous data sources

This article presents a novel definition of a declarative mapping language, which is able to map precisely and unambiguously the semantics of a domain conceptualization (defined as an ontology) into queries to a set of data sources, where the data is residing. In this way, a system making use of this mapping language is able to access the data actually stored in the data sources thought a semantically rich representation. The mapping model proposed in this paper is also an ontology and therefore is machine understandable: it can be shared with other users or systems, processed by external tools for consistency checking, or collaboratively created and so on. Besides the contributions of the mapping model itself, this paper introduces the concepts of Semantic Join and Semantic Identifiers: a declarative approach to semantic data fusion and entity resolution over multiple unrelated databases, which allow to define extremely expressive mapping.

Intelligent Data Analysis for Protein Disorder Prediction

Although an ordered 3D structure is generally considered to be a necessary pre-condition for protein functionality, there are disordered counter examples found to have biological activity. The objectives of our data mining project are: (1) to generalize from the limited set of counter examples and then apply this knowledge to large data bases of amino acid sequence in order to estimate commonness of disordered protein regions in nature, and (2) to determine whether there are different types of protein disorder. For general disorder estimation, a neural network based predictor was designed and tested on data built from several public domain data banks through a nontrivial search, statistical analysis and data dimensionality reduction. In addition, predictors for identification of family-specific disorder were developed by extracting knowledge from databases generated through multiple sequence alignments of a known disordered sequence to other highly related proteins. Family-specific predictors were also integrated to test quality of general protein disorder identification from such hybrid prediction systems. Out-of-sample cross validation performance of several predictors was computed first, followed by tests on an unrelated database of proteins with long disordered regions, and the application of few selected predictors to two large protein data banks: Nrll3D, currently containing more than 10,000 protein fragments of known 3D structure, and Swiss Protein, having almost 60,000 protein sequences. The obtained results provide evidence that long disordered regions are common in nature, with an estimate that 11% of all the residues in the Swiss Protein data bank belong to disordered regions of length 40 or greater. The hypothesis that different protein disorder types exist is supported by high specificity/low sensitivity results of two family-specific predictors, by hybrid systems outperforming general models on a two-family test, and by existence of significant gaps in Swiss Protein vs. Nrll3D disorder frequency estimates for both families. These findings prompt the need for a revision in the current understanding of protein structure and function, as well as for the developing of improved disorder predictors that should have important uses in biotechnology applications.