When is star formation episodic? A delay differential equation negative feedback model

We introduce a differential equation for star formation in galaxies that incorporates negative feedback with a delay. When the feedback is instantaneous, solutions approach a self-limiting equilibrium state. When there is a delay, even though the feedback is negative, the solutions can exhibit cyclic and episodic solutions. We find that periodic or episodic star formation only occurs when two conditions are satisfied. Firstly the delay timescale must exceed a cloud consumption timescale. Secondly the feedback must be strong. This statement is quantitatively equivalent to requiring that the timescale to approach equilibrium be greater than approximately twice the cloud consumption timescale. The period of oscillations predicted is approximately 4 times the delay timescale. The amplitude of the oscillations increases with both feedback strength and delay time. We discuss applications of the delay differential equation (DDE) model to star formation in galaxies using the cloud density as a variable. The DDE model is most applicable to systems that recycle gas and only slowly remove gas from the system. We propose likely delay mechanisms based on the requirement that the delay time is related to the observationally estimated time between episodic events. The proposed delay timescale accounting for episodic star formation in galaxy centers on periods similar to P 10 Myrs, irregular galaxies with P 100 Myrs, and the Milky Way disk with P~ 2Gyr, could be that for exciting turbulence following creation of massive stars, that for gas pushed into the halo to return and interact with the disk and that for spiral density wave evolution, respectively.Comment: submitted to MNRA

Towards concolic testing for hybrid systems

Hybrid systems exhibit both continuous and discrete behavior. Analyzing hybrid systems is known to be hard. Inspired by the idea of concolic testing (of programs), we investigate whether we can combine random sampling and symbolic execution in order to effectively verify hybrid systems. We identify a sufficient condition under which such a combination is more effective than random sampling. Furthermore, we analyze different strategies of combining random sampling and symbolic execution and propose an algorithm which allows us to dynamically switch between them so as to reduce the overall cost. Our method has been implemented as a web-based checker named HYCHECKER. HYCHECKER has been evaluated with benchmark hybrid systems and a water treatment system in order to test its effectiveness.CPCI-S(ISTP)[email protected]; [email protected]

High seroprevalence of Toxoplasma gondii infection in a subset of Mexican patients with work accidents and low socioeconomic status

<p>Abstract</p> <p>Background</p> <p><it>Toxoplasma gondii </it>has been associated with reflex impairment and traffic accidents. It is unknown whether <it>Toxoplasma </it>infection might be associated with work accidents. Therefore, using a case-control seroprevalence study design, 133 patients with a recent work accident and 266 control subjects of the general population from the same region were examined with enzyme-linked immunoassays for the presence and levels of anti-<it>Toxoplasma </it>IgG antibodies and anti-<it>Toxoplasma </it>IgM antibodies. Socio-demographic, work, clinical and behavioral characteristics from each worker were obtained.</p> <p>Results</p> <p>Eleven (8.3%) of 133 patients, and 14 (5.3%) of 266 controls had anti-<it>T. gondii </it>IgG antibodies. Anti-<it>T. gondii </it>IgG levels were higher than 150 IU/ml in 8 (6%) patients and 10 (3.8%) controls. Anti-<it>T. gondii </it>IgM antibodies were found in one (0.8%) of the workers, and in 6 (2.3%) of the controls. No statistically significant differences in the IgG seroprevalences, frequencies of high IgG levels, and IgM seroprevalences among patients and controls were found. In contrast, a low socio-economic level in patients with work accidents was associated with <it>Toxoplasma </it>seropositivity (<it>P </it>= 0.01). Patients with work accidents and low socioeconomic status showed a significantly (OR = 3.38; 95% CI: 0.84-16.06; <it>P </it>= 0.04) higher seroprevalence of <it>T. gondii </it>infection than controls of the same socioeconomic status (15.1% vs. 5%, respectively). Multivariate analysis showed a positive association of <it>T. gondii </it>infection with boar meat consumption (OR = 3.04; 95% CI: 1.03-8.94; <it>P </it>= 0.04). In contrast, a negative association between <it>T. gondii </it>infection and national trips (OR = 0.40; 95% CI: 0.17-0.96; <it>P </it>= 0.04), sausage consumption (OR = 0.20; 95% CI: 0.05-0.68; <it>P </it>= 0.01), and ham consumption (OR = 0.16; 95% CI: 0.05-0.51; <it>P </it>= 0.002) was found.</p> <p>Conclusions</p> <p>In the study described here seropositivity to <it>T. gondii </it>was associated to work accidents in a subset of patients with low socioeconomic status. This is the first report of an association of <it>T. gondii </it>infection and work accidents. Further studies to confirm our results are needed. Results may help in designing optimal prevention strategies to avoid <it>T. gondii </it>infection.</p

A Simple Standard for Sharing Ontological Mappings (SSSOM).

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? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple 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 principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

Roflumilast in moderate-to-severe chronic obstructive pulmonary disease treated with longacting bronchodilators: two randomised clinical trials

Background Patients with chronic obstructive pulmonary disease (COPD) have few options for treatment. The efficacy and safety of the phosphodiesterase-4 inhibitor roflumilast have been investigated in studies of patients with moderate-to-severe COPD, but not in those concomitantly treated with longacting inhaled bronchodilators. The effect of roflumilast on lung function in patients with COPD that is moderate to severe who are already being treated with salmeterol or tiotropium was investigated. Methods In two double-blind, multicentre studies done in an outpatient setting, after a 4-week run-in, patients older than 40 years with moderate-to-severe COPD were randomly assigned to oral roflumilast 500 mu g or placebo once a day for 24 weeks, in addition to salmeterol (M2-127 study) or tiotropium (M2-128 study). The primary endpoint was change in prebronchodilator forced expiratory volume in 1s (FEV(1)). Analysis was by intention to treat. The studies are registered with ClinicalTrials.gov, number NCT00313209 for M2-127, and NCT00424268 for M2-128. Findings In the salmeterol plus roflumilast trial, 466 patients were assigned to and treated with roflumilast and 467 with placebo; in the tiotropium plus roflumilast trial, 371 patients were assigned to and treated with roflumilast and 372 with placebo. Compared with placebo, roflumilast consistently improved mean prebronchodilator FEV(1) by 49 mL (p<0.0001) in patients treated with salmeterol, and 80 mL (p<0.0001) in those treated with tiotropium. Similar improvement in postbronchodilator FEV(1) was noted in both groups. Furthermore, roflumilast had beneficial effects on other lung function measurements and on selected patient-reported outcomes in both groups. Nausea, diarrhoea, weight loss, and, to a lesser extent, headache were more frequent in patients in the roflumilast groups. These adverse events were associated with increased patient withdrawal. Interpretation Roflumilast improves lung function in patients with COPD treated with salmeterol or tiotropium, and could become an important treatment for these patients

The Human Phenotype Ontology in 2024: phenotypes around the world

\ua9 The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

A Simple Standard for Sharing Ontological Mappings (SSSOM)

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? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple 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 principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs