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Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications.The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

Endosome-localized Toll-like receptors (TLRs) 3 and 9 are expressed and functionally active in adipocytes. The functionality and role of TLR7 in adipocyte biology and innate immunity of adipose tissue (AT) is poorly characterized. We analyzed TLR7 mRNA and protein expression in murine 3T3-L1 and primary adipocytes, in co-cultures of 3T3-L1 adipocytes with murine J774A.1 monocytes and in human AT. The effects of TLR7 agonists imiquimod (IMQ) and cell-free nucleic acids (cfDNA) on adipokine concentration in cell-culture supernatants and gene expression profile were investigated. We found that TLR7 expression is strongly induced during adipocyte differentiation. TLR7 gene expression in adipocytes and AT stroma-vascular cells (SVC) seems to be independent of TLR9. IMQ downregulates resistin concentration in adipocyte cell-culture supernatants and modulates gene expression of glucose transporter Glut4. Adipocyte-derived cfDNA reduces adiponectin and resistin in cell-culture supernatants and potentially inhibits Glut4 gene expression. The responsiveness of 3T3-L1 adipocytes to imiquimod is preserved in co-culture with J774A.1 monocytes. Obesity-related, adipocyte-derived cfDNA engages adipocytic pattern recognition receptors (PRRs), modulating AT immune and metabolic homeostasis during adipose inflammation.

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Are they associated in some other way? Such relationships between the mapped terms are often not documented, leading to incorrect assumptions and making them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Also, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. The Simple Standard for Sharing Ontological Mappings (SSSOM) addresses these problems by: 1. Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. 2. Defining an easy to use table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data standards. 3. Implementing open and community-driven collaborative workflows designed to evolve the standard continuously to address changing requirements and mapping practices. 4. Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases, and survey some existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable, and Reusable (FAIR). The SSSOM specification is at http://w3id.org/sssom/spec., Comment: Corresponding author: Christopher J. Mungall

Despite great strides in the development and wide acceptance of standards for exchanging structured information about genomic variants, there is no corresponding standard for exchanging phenotypic data, and this has impeded the sharing of phenotypic information for computational analysis. Here, we introduce the Global Alliance for Genomics and Health (GA4GH) Phenopacket schema, which supports exchange of computable longitudinal case-level phenotypic information for diagnosis and research of all types of disease including Mendelian and complex genetic diseases, cancer, and infectious diseases. To support translational research, diagnostics, and personalized healthcare, phenopackets are designed to be used across a comprehensive landscape of applications including biobanks, databases and registries, clinical information systems such as Electronic Health Records, genomic matchmaking, diagnostic laboratories, and computational tools. The Phenopacket schema is a freely available, community-driven standard that streamlines exchange and systematic use of phenotypic data and will facilitate sophisticated computational analysis of both clinical and genomic information to help improve our understanding of diseases and our ability to manage them.

Contains fulltext : 242557.pdf (Publisher’s version ) (Open Access) 2 p.

Controlled vocabularies and ontologies have become a valuable resource for knowledge representation, data integration, and downstream analyses in the biomedical domain. In precision medicine, especially in clinical genetics, the Human Phenotype Ontology (HPO) as well as disease ontologies like the Orphanet Rare Disease Ontology (ORDO) or Medical Subject Headings (MeSH) are often used for deep phenotyping of patients and coding of clinical diagnoses. However, the process of assigning ontology classes (annotating) to patient descriptions is often disconnected from the process of writing patient reports or manuscripts in word processing software such as Microsoft Word or LibreOffice. This additional workload and the requirement to install dedicated software may discourage usage of ontologies for parts of the target audience.To improve this situation, we present Phenotero, a freely available and simple solution to annotate patient phenotypes and diseases at the time of writing clinical reports or manuscripts. We adopt Zotero, a well-established, actively developed citation management software to generate a tool which allows to reference classes from ontologies within clinical reports or manuscripts at the time of writing. We expect this approach to decrease the additional workload to a minimum while ensuring high quality associations with ontology classes. Standardised collection of phenotypic information at the time of describing the patient allows for streamlining of clinic workflow, efficient data entry, and will subsequently promote clinical and molecular diagnosis, remove ambiguousness from manuscripts, and allow sharing of anonymised patient phenotype data with ultimate goal of a better understanding of the disease. Thus, we hope that our integrated approach will further promote the usage of ontologies and controlled vocabularies in the clinical setting and in the biomedical domain.

In this chapter, I combine the insights from the literatures on lobbying and bargaining into a formal model of interest group activities. I distinguish public and private communication as classes of structurally equivalent lobbying activities. I show that the bargaining environment constrains lobbying activities. I demonstrate theoretically that the decision to send private messages depends on the ideological distance to the constraining chamber, while the decision to engage in public communication rests on the distance to the expected policy outcome.

In this chapter, I discuss approaches to understand political bargaining. I show that bargaining implies a conflict of interest which creates uncertainty over the expected policy outcome. I discuss how the political process in Germany is structured and show that process uncertainty depends on the type of political process. This uncertainty is consequential for interest group activities.

In this chapter, I discuss the data and the main variables. These include the measurement of preferences of interest groups and political decision-makers. Based on those, distance measures are developed. Lobbying costs and several control variables are operationalized.

In this chapter, I discuss the approaches to understand interest groups strategies. I show that both theoretically and empirically one of the major factors for lobbying is the demand for information. It is created by the fact that decision-makers decide in uncertain situations. Empirical evidence demonstrates that the provision of information is one of the main activities of interest groups.

In this chapter, I test the formal model empirically. I analyze the decision to mobilize and the decision to send public or private messages. I can demonstrate that the decision to send public messages depends on the distance to the expected policy outcome, while the decision to send private messages depends on the distance to the constraining actor. This is based on an identification strategy which uses the fact that the German political system functions as if the relevant decision-makers are exogenously assigned to issues based on the constitution.

BackgroundGlycosylphosphatidylinositol Biosynthesis Defects (GPIBDs) cause a group of phenotypically overlapping recessive syndromes with intellectual disability, for which pathogenic mutations have been described in 16 genes of the corresponding molecular pathway. An elevated serum activity of alkaline phosphatase (AP), a GPI-linked enzyme, has been used to assign GPIBDs to the phenotypic series of Hyperphosphatasia with Mental Retardation Syndrome (HPMRS) and to distinguish them from another subset of GPIBDs, termed Multiple Congenital Anomalies Hypotonia Seizures syndrome (MCAHS). However, the increasing number of individuals with a GPIBD shows that hyperphosphatasia is a variable feature that is not ideal for a clinical classification.MethodsWe studied the discriminatory power of multiple GPI-linked substrates that were assessed by flow cytometry in blood cells and fibroblasts of 39 and 14 individuals with a GPIBD, respectively. On the phenotypic level, we evaluated the frequency of occurrence of clinical symptoms and analyzed the performance of computer-assisted image analysis of the facial gestalt in 91 individuals.ResultsWe found that certain malformations such as Morbus Hirschsprung and Diaphragmatic defects are more likely to be associated with particular gene defects (PIGV, PGAP3, PIGN). However, especially at the severe end of the clinical spectrum of HPMRS, there is a high phenotypic overlap with MCAHS. Elevation of AP has also been documented in some of the individuals with MCAHS, namely those with PIGA mutations. Although the impairment of GPI-linked substrates is supposed to play the key role in the pathophysiology of GPIBDs, we could not observe gene-specific profiles for flow cytometric markers or a correlation between their cell surface levels and the severity of the phenotype. In contrast, it was facial recognition software that achieved the highest accuracy in predicting the disease-causing gene in a GPIBD.ConclusionsDue to the overlapping clinical spectrum of both, HPMRS and MCAHS, in the majority of affected individuals, the elevation of AP and the reduced surface levels of GPI-linked markers in both groups, a common classification as GPIBDs is recommended. The effectiveness of computer-assisted gestalt analysis for the correct gene inference in a GPIBD and probably beyond is remarkable and illustrates how the information contained in human faces is pivotal in the delineation of genetic entities.

A typical use case of ontologies is the calculation of similarity scores between items that are annotated with classes of the ontology. For example, in differential diagnostics and disease gene prioritisation, the Human Phenotype Ontology (HPO) is often used to compare a query phenotype profile against gold-standard phenotype profiles of diseases or genes. The latter have long been constructed as flat lists of ontology classes, which, as we show in this work, can be improved by exploiting existing structure and information in annotation datasets or full text disease descriptions.We derive a study-wise annotation model of diseases and genes and show that this can improve the performance of semantic similarity measures. Inferred weights of individual annotations are one reason for this improvement, but more importantly using the study-wise structure further boosts the results of the algorithms according to precision-recall analyses. We test the study-wise annotation model for diseases annotated with classes from the HPO and for genes annotated with Gene Ontology (GO) classes. We incorporate this annotation model into similarity algorithms and show how this leads to improved performance.This work adds weight to the need for enhancing simple list-based representations of disease or gene annotations. We show how study-wise annotations can be automatically derived from full text summaries of disease descriptions and from the annotation data provided by the GO Consortium and how semantic similarity measure can utilise this extended annotation model.

Background:Differences in dementia risk across the gradient of socioeconomic status exist, but its determinants are not under- stood. Therefore, the aim of this study was to investigate whether differences in socioeconomic status are associated with incident dementia and whether this association is ex- plained by health- and lifestyle-related factors. Methods: 6,346 participants (mean age 1⁄4 64.9, SD 1⁄4 8.6; 55.7% fe- male) were followed up for a maximum of 7 years. Dementia was based on self-reported physician-diagnosed dementia or Alzheimer’s disease at each wave and a score of 3.38 or higher on the shortened version of the IQ-CODE. Socioeco- nomic status was based on wealth defined as household net of debt and included cash, investments, savings and physical wealth. Health- and lifestyle-related factors were captured by a poly-environmental risk score, called the ‘Lifestyle for BRAin Health’ (LIBRA) index. LIBRA consists of a weighted sum score of twelve modifiable risk and protective factors for cognitive decline or dementia. Higher scores indicate higher risk (range -5.9 to +10.3). Cox regression adjusted for age, gender and education and clustering at the household level examined the association between socioeconomic status and time to dementia in a structural equation model including po- tential mediation or effect modification by LIBRA. Results: LIBRA scores decreased with increasing wealth. During a me- dian follow-up of 6.0 years, 192 individuals (3.0%) developed dementia. High socioeconomic status was associated with a decreased dementia risk (highest versus lowest tertile: HR 1⁄4 0.54, 95%CI 1⁄4 0.36-0.79), but this was substantially attenuated after adjusting for differences in LIBRA scores (highest versus lowest tertile: HR 1⁄4 0.77, 95%CI 1⁄4 0.51-1.16). Mediation anal- ysis showed that 58% of the risk difference between the highest and the lowest tertile of wealth was mediated by differences in LIBRA. A one point increase in LIBRA was associated with a 13% increase in dementia risk (HR 1⁄4 1.13, 95%CI 1⁄4 1.07- 1.19) on average (no interaction). Conclusions: Differences in dementia risk between socioeconomic groups can be partly attributed to differences in treatable health- and lifestyle related factors. Future public health campaigns for dementia risk reduction should try hard to reach out to this vulnerable group.

The European sovereign debt crisis continues to hold Europe and the world captive. Will the euro and the fiscal mechanism of the eurozone survive? And how effective is the Stability and Growth Pact (SGP)? Do the euro countries generally fail to comply with the rules of fiscal governance, or does the eurozone need a more member-specific fiscal mechanism? This article examines whether and how the SGP influenced the development of government debt making in the euro countries after the introduction of the common currency. While the SGP could not prevent euro countries from exceeding their deficits, this study’s synthetic control analysis reveals that the mechanism has effectively reduced the overall government debt of euro countries since 1999. In particular, donor countries were able to control governmental spending, while many recipient countries—including Greece, Portugal and Italy—have increased government debt ever since, resulting in the European sovereign debt crisis. This suggests that while the SGP effectively constrained overall government debt making, a more sophisticated mechanism is required for safeguarding compliance in large recipient countries.

This paper focuses on stat ionary detect ion of the pedestrian's intention to enter the traffic lane at intersections. We use an Interacting Multiple Model Extended Kalman Filter (IMM-EKF) based tracking approach as basic method to recognize this. In addition, we propose a novel Motion Contour image based HOG-like descriptor (MCHOG) in combination with Support Vector Machine (SVM) classification that reaches the decision at an accuracy of 99 % within the initial step at the curb of smart infrastructure. MCHOG implicitly comprises the body language of gait initiation, especially the body bending and the spread of legs. As a result of a case study at laboratory conditions we present Receiver Operating Characteristic (ROC) performance data and an evaluation of the span of time necessary for recognition. While MCHOG in special cases indicates detection of the intention before the whole body moves, on average it allows for detection of gait initiation within 6 frames at a frame rate of 50 Hz and an accuracy of 80%. In addition to a comprehensive evaluation under laboratory conditions we demonstrate feasibility of the methods in a real world intersection scenario and we show the gain which we achieve with MCHOG compared to the IMM-EKF approach.

Contains fulltext : 167669.pdf (Publisher’s version ) (Closed access) OBJECTIVES: Understanding which persons most likely use particular combinations of service types is important as this could lead to a better understanding of care pathways. The aim of this study is to identify combinations of service use within a sample of community-dwelling people with mild cognitive impairment (MCI) and dementia and identify factors related to these service use combinations. METHODS: A latent class analysis performed at baseline on a merged dataset (n = 530) was used to classify care recipients based on following service use types: general practitioner visits, physiotherapist visits, hospital outpatient specialist visits, emergency room visits, hospital inpatient visits with stay over, day care visits, use of domestic homecare, use of personal homecare, and informal care on (instrumental) activities of daily living. Multinomial logistic regression was performed to identify factors associated with service use combinations using clinical characteristics of the care recipient and demographic characteristics of the care recipient and caregiver. RESULTS: Three service use classes were identified; a formal homecare class (10% of participants), an informal care class (46% of participants), and a low user class (44% of participants). Factors increasing the likelihood of being in the formal homecare class compared with the low service use class included a diagnosis of MCI or dementia, activities of daily living impairment, older age of the care recipient, and care recipient not living together with the caregiver. CONCLUSIONS: Besides a diagnosis of MCI or dementia, other factors (activities of daily living impairment, age, and living situation) were associated with service use. We recommend using these factors alongside the diagnostic label for care indication.

One strategy for building ontologies covering domains such as disease or anatomy is to weave together existing knowledge sources (databases, vocabularies and ontologies) into single cohesive whole. A first step in this process is to generate mappings between the elements of these different sources. There are a number of well-known techniques for generating mappings (also known as ontology alignemnt), both manual and automatic[7]. Sometimes mappings are seen as an end in themselves, with the sources remaining in a loosely connected state. However, if we want to take the next step and use the mappings to integrate the different sources into a cohesive reference ontology, then we need to translate the mappings into precise logical relationships. This will allow us to safely merge equivalent concepts, creating a unified ontology. This translation is a non-trivial step, as each mapping can be interpreted as multiple different logical relationships, with each interpretation affecting the likelihood of the others. There is a lack of automated methods to assist with this last step; this resolution is typically performed by expert ontologists.Here we describe an ontology construction technique that takes two or more ontologies linked by hypothetical axioms, and estimates the most likely unified logical ontology. Hypothetical axioms can themselves be derived from semantically loose mappings. The method combines deductive reasoning and probabilistic inference and is called Bayesian OWL Ontology Merging (BOOM). We describe a special form k-BOOM that works by factorizing the probabilistic ontology into k submodules. We also briefly describe a supplemental lexical and knowledge-based technique for generating a set of hypothetical axioms from loose mappings.We are currently using this technique to build a merged disease ontology (Monarch Disease Ontology; MonDO) that unifies a broad range of vocabularies into a consistent and coherent whole.

This paper analyzes pedestrian gait termination at a public urban intersection. Head-tracks of 183 uninstructed, randomly selected pedestrians are acquired by a wide angle stereo camera setup. Analyses of characteristic velocity patterns, deceleration and step parameters are conducted separately for children, adults and elderly people. Results show that stopping patterns at intersections comprise fast stopping within 2 - 3 steps, patterns of slightly slower stopping and increasing deceleration, and, most common, early intended stopping performed over a deceleration time of about 3 s, i.e. over about 6 steps, on average. Step analysis shows that stopping is a combination of step length decrease and step duration increase, especially for the last step. The authors measured significant lower steady state velocity and step length of the elderly, longer minimum deceleration times for the majorities of the children and of the elderly, and the authors observed that stopping behavior during the last 2 - 3 steps is very similar for all three classes.