BackgroundThe LIFE-Adult-Study is a population-based cohort study, which has recently completed the baseline examination of 10,000 randomly selected participants from Leipzig, a major city with 550,000 inhabitants in the east of Germany. It is the first study of this kind and size in an urban population in the eastern part of Germany. The study is conducted by the Leipzig Research Centre for Civilization Diseases (LIFE). Our objective is to investigate prevalences, early onset markers, genetic predispositions, and the role of lifestyle factors of major civilization diseases, with primary focus on metabolic and vascular diseases, heart function, cognitive impairment, brain function, depression, sleep disorders and vigilance dysregulation, retinal and optic nerve degeneration, and allergies.Methods/designThe study covers a main age range from 40-79 years with particular deep phenotyping in elderly participants above the age of 60. The baseline examination was conducted from August 2011 to November 2014. All participants underwent an extensive core assessment programme (5-6 h) including structured interviews, questionnaires, physical examinations, and biospecimen collection. Participants over 60 underwent two additional assessment programmes (3-4 h each) on two separate visits including deeper cognitive testing, brain magnetic resonance imaging, diagnostic interviews for depression, and electroencephalography.DiscussionThe participation rate was 33 %. The assessment programme was accepted well and completely passed by almost all participants. Biomarker analyses have already been performed in all participants. Genotype, transcriptome and metabolome analyses have been conducted in subgroups. The first follow-up examination will commence in 2016.

The LIFE Child study is a large population-based longitudinal childhood cohort study conducted in the city of Leipzig, Germany. As a part of LIFE, a research project conducted at the Leipzig Research Center for Civilization Diseases, it aims to monitor healthy child development from birth to adulthood and to understand the development of lifestyle diseases such as obesity. The study consists of three interrelated cohorts; the birth cohort, the health cohort, and the obesity cohort. Depending on age and cohort, the comprehensive study program comprises different medical, psychological, and sociodemographic assessments as well as the collection of biological samples. Optimal data acquisition, process management, and data analysis are guaranteed by a professional team of physicians, certified study assistants, quality managers, scientists and statisticians. Due to the high popularity of the study, more than 3000 children have already participated until the end of 2015, and two-thirds of them participate continuously. The large quantity of acquired data allows LIFE Child to gain profound knowledge on the development of children growing up in the twenty-first century. This article reports the number of available and analyzable data and demonstrates the high relevance and potential of the study.

The optimization of both probe design and analysis algorithms for microarray experiments requires improved understanding and predictability of oligonucleotide hybridization behavior. Our physicochemical theory of GeneChip probe sensitivities divides the probe intensity into an averaged intensity value which serves as a relative measure of the RNA target concentration and the sensitivity of each probe. The sensitivity decomposes into additive terms because of specific and nonspecific hybridization, saturation, the heterogeneous distribution of labels, and intramolecular folding of target and probe. The observed heterogeneity of probe sensitivities is mainly caused by variations of the probe affinity for target binding owing to sequence differences between the probes. The sensitivity values are therefore analyzed in terms of simple molecular characteristics, which consider the base composition and sequence of the probes. We found that the mean sensitivity, averaged over all probes of a chip containing a certain number of bases of one type, strongly increases with an increasing number of C nucleotides per oligomer, whereas A nucleotides show the opposite tendency. These trends are asymmetrical with respect to the number of G and T nucleotides, which have a much weaker, and perhaps a somewhat opposite, effect in probes of intermediate and high sensitivity. The middle base systematically affects the relationship between the sensitivities of perfect match (PM) and mismatch (MM) probes. MM probes are, on the average, more sensitive than the respective PM probes if the middle base is a purine in the PM probe of the respective probe pair. For pyrimidines, this relationship reverses. This purine-pyrimidine asymmetry is partly related to the effect of labeling.

While studies of the evolutionary histories of protein families are common place, little is known on noncoding RNAs beyond microRNAs and some snoRNAs. Here we investigate in detail the evolutionary history of the 9 spliceosomal snRNA families (U1, U2, U4, U5, U6, U11, U12, U4atac, and U6atac) across the completely or partially sequenced genomes of metazoan animals. Representatives of the five major spliceosomal snRNAs were found in all genomes. None of the minor splicesomal snRNAs was detected in Nematodes and in the shotgun traces of Oikopleura dioica, while in all other animal genomes at most one of them is missing. Although snRNAs are present in multiple copies in most genomes, distinguishable paralog groups are not stable over long evolutionary times, although they appear independently in several clades. In general, animal snRNA secondary structures are highly conserved, albeit in particular U11 and U12 in insects exhibit dramatic variations. An analysis of genomic context of snRNAs reveals that they behave like mobile elements, exhibiting very little syntenic conservation.

BackgroundSurprisingly little is known about the organization and distribution of tRNA genes and tRNA-related sequences on a genome-wide scale. While tRNA gene complements are usually reported in passing as part of genome annotation efforts, and peculiar features such as the tandem arrangements of tRNA gene in Entamoeba histolytica have been described in some detail, systematic comparative studies are rare and mostly restricted to bacteria. We therefore set out to survey the genomic arrangement of tRNA genes and pseudogenes in a wide range of eukaryotes to identify common patterns and taxon-specific peculiarities.ResultsIn line with previous reports, we find that tRNA complements evolve rapidly and tRNA gene and pseudogene locations are subject to rapid turnover. At phylum level, the distributions of the number of tRNA genes and pseudogenes numbers are very broad, with standard deviations on the order of the mean. Even among closely related species we observe dramatic changes in local organization. For instance, 65% and 87% of the tRNA genes and pseudogenes are located in genomic clusters in zebrafish and stickleback, resp., while such arrangements are relatively rare in the other three sequenced teleost fish genomes. Among basal metazoa, Trichoplax adhaerens has hardly any duplicated tRNA gene, while the sea anemone Nematostella vectensis boasts more than 17000 tRNA genes and pseudogenes. Dramatic variations are observed even within the eutherian mammals. Higher primates, for instance, have 616 ± 120 tRNA genes and pseudogenes of which 17% to 36% are arranged in clusters, while the genome of the bushbaby Otolemur garnetti has 45225 tRNA genes and pseudogenes of which only 5.6% appear in clusters. In contrast, the distribution is surprisingly uniform across plant genomes. Consistent with this variability, syntenic conservation of tRNA genes and pseudogenes is also poor in general, with turn-over rates comparable to those of unconstrained sequence elements. Despite this large variation in abundance in Eukarya we observe a significant correlation between the number of tRNA genes, tRNA pseudogenes, and genome size.ConclusionsThe genomic organization of tRNA genes and pseudogenes shows complex lineage-specific patterns characterized by an extensive variability that is in striking contrast to the extreme levels of sequence-conservation of the tRNAs themselves. The comprehensive analysis of the genomic organization of tRNA genes and pseudogenes in Eukarya provides a basis for further studies into the interplay of tRNA gene arrangements and genome organization in general.

The microarray technology enables to estimate the expression degree of thousands of genes at once by the measurement of the abundance of the respective messenger RNA. This method is based on the sequence specific binding of RNA to DNA probes and its detection using fluorescent labels. The raw intensity data are affected by the sequence-specific affinity of probe and RNA for duplex formation, by the background intensity due to non-specific hybridization at small transcript concentrations and by the saturation of the probes at high transcript concentration owing to surface adsorption. We address these issues using a binding model which describes specific and non-specific hybridization in terms of a competitive two-species Langmuir isotherm and DNA/RNA duplex formation in terms of sequencespecific, single-base related interactions. The GeneChip microarrays technology uses pairs of so-called perfect match (PM) and mismatch (MM) oligonucleotide probes to estimate the amount of nonspecific hybridization. The mean affinity of the probes decrease according to PM(specific)>MM(specific)>>PM(non-specific)≈MM(non-specific). The stability of specific and nonspecific DNA/RNA duplexes is mainly determined by Watson Crick (WC) pairings. Mismatched self complementary pairings in the middle of the MM sequence only weakly contribute to the duplex stability. The asymmetry of base pair interaction in the DNA/RNA hybrid duplexes gives rise to a duplet-like symmetry of the PM-MM intensity difference at dominating non-specific hybridization and a triplet-like symmetry at specific hybridization. The signal intensities of the PM and MM probes and their difference are assessed in terms of sensitivity and specificity. The presented results imply the refinement of existing algorithms of probe level analysis to correct microarray data for non-specific background intensities and saturation on the basis of the probe sequence.

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.forest ecosystem change through effective global coordination [3] good observations, indicators and scenarios of biodiversity and ecosystem services change [4].There is still a large discrepancy between the information required by forest managers and scientists and the information that is available for understanding and assessing the complexity and multidimensionality of forest health drivers, stressors, disturbances, and effects. Decision makers require information on forest health in high spatial and temporal accuracy, from the local to the global, for short and long-term periods that can be recorded ...

Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. “Smart Medical Information Technology for Healthcare (SMITH)” is one of four consortia funded by the German Medical Informatics Initiative (MI-I) to create an alliance of universities, university hospitals, research institutions and IT companies. SMITH’s goals are to establish Data Integration Centers (DICs) at each SMITH partner hospital and to implement use cases which demonstrate the usefulness of the approach. Objectives: To give insight into architectural design issues underlying SMITH data integration and to introduce the use cases to be implemented. Governance and Policies: SMITH implements a federated approach as well for its governance structure as for its information system architecture. SMITH has designed a generic concept for its data integration centers. They share identical services and functionalities to take best advantage of the interoperability architectures and of the data use and access process planned. The DICs provide access to the local hospitals’ Electronic Medical Records (EMR). This is based on data trustee and privacy management services. DIC staff will curate and amend EMR data in the Health Data Storage. Methodology and Architectural Framework: To share medical and research data, SMITH’s information system is based on communication and storage standards. We use the Reference Model of the Open Archival Information System and will consistently implement profiles of Integrating the Health Care Enterprise (IHE) and Health Level Seven (HL7) standards. Standard terminologies will be applied. The SMITH Market Place will be used for devising agreements on data access and distribution. 3LGM 2 for enterprise architecture modeling supports a consistent development process. The DIC reference architecture determines the services, applications and the standards-based communication links needed for efficiently supporting the ingesting, data nourishing, trustee, privacy management and data transfer tasks of the SMITH DICs. The reference architecture is adopted at the local sites. Data sharing services and the market place enable interoperability. Use Cases: The methodological use case “Phenotype Pipeline” (PheP) constructs algorithms for annotations and analyses of patient-related phenotypes according to classification rules or statistical models based on structured data. Unstructured textual data will be subject to natural language processing to permit integration into the phenotyping algorithms. The clinical use case “Algorithmic Surveillance of ICU Patients” (ASIC) focusses on patients in Intensive Care Units (ICU) with the acute respiratory distress syndrome (ARDS). A model-based decision-support system will give advice for mechanical ventilation. The clinical use case HELP develops a “hospital-wide electronic medical record-based computerized decision support system to improve outcomes of patients with blood-stream infections” (HELP). ASIC and HELP ...