Predicting Lifetime Suicide Attempts in a Community Sample of Adolescents Using Machine Learning Algorithms

Gefördert im Rahmen eines Open-Access-Transformationsvertrags mit dem Verla

The Road to Hell Is Paved With Good Intentions: How Common Practices in Scale Construction Hurt Validity

Gefördert im Rahmen des Projekts DEA

Actin-dependent activation of serum response factor in T cells by the viral oncoprotein tip

Serum response factor (SRF) acts as a multifunctional transcription factor regulated by mutually exclusive interactions with ternary complex factors (TCFs) or myocardin-related transcription factors (MRTFs). Binding of Rho- and actin-regulated MRTF:SRF complexes to target gene promoters requires an SRF-binding site only, whereas MAPK-regulated TCF:SRF complexes in addition rely on flanking sequences present in the serum response element (SRE). Here, we report on the activation of an SRE luciferase reporter by Tip, the viral oncoprotein essentially contributing to human T-cell transformation by Herpesvirus saimiri. SRE activation in Tip-expressing Jurkat T cells could not be attributed to triggering of the MAPK pathway. Therefore, we further analyzed the contribution of MRTF complexes. Indeed, Tip also activated a reporter construct responsive to MRTF:SRF. Activation of this reporter was abrogated by overexpression of a dominant negative mutant of the MRTF-family member MAL. Moreover, enrichment of monomeric actin suppressed the Tip-induced reporter activity. Further upstream, the Rho-family GTPase Rac, was found to be required for MRTF:SRF reporter activation by Tip. Initiation of this pathway was strictly dependent on Tip's ability to interact with Lck and on the activity of this Src-family kinase. Independent of Tip, T-cell stimulation orchestrates Src-family kinase, MAPK and actin pathways to induce SRF. These findings establish actin-regulated transcription in human T cells and suggest its role in viral oncogenesis

Anwendung von Allgemeintoleranzen im ISO GPSNormensystem – Aspekte der Normung

Bei der Erstellung der Produktdokumentation nach dem ISO GPS-Normensystem sind die Normen ISO 22081 und DIN 2769 bezüglich der Angabe von Allgemeintoleranzen anzuwenden. Beide Normen erreichen in der Anwendung noch nicht die Akzeptanz der Vorläufernormen ISO 2768, Teile 1 und 2. Dies gilt insbesondere, wenn auf die Erstellung einer Zeichnung verzichtet wird und die geometrische Spezifikation ausschließlich im 3DCAD-Modell und dem zugeordneten Datensatz erstellt wird. Gründe sind neben ungenügendem Wissen in der Anwendung der GPS-Normen auch Limitierungen der in den Normen angegebenen Regeln für die Anwendbarkeit von Allgemeintoleranzen. Darüber hinaus bestehen begriffliche Unklarheiten innerhalb des GPS-Normensystems sowie zwischen dem GPS-Normensystem und den Normen zur Technischen Produktdokumentation. Im Interesse einer aufwandsarmen Erstellung der Produktdefinition ist die weitreichende Anwendung von Allgemeintoleranzen erstrebenswert, da sie die Anzahl der erforderlichen individuellen Toleranzspezifikationen teils deutlich reduziert. Der Beitrag identifiziert einige normative Festlegungen, die das einschränken bzw. dem Entwickler derzeit wenig Hilfe bei der Festlegung der Allgemeintoleranzen geben. Ansätze für die Verbesserung werden vorgeschlagen

Positive psychology in a pandemic: buffering, bolstering, and building mental health

As the COVID-19 global health disaster continues to unfold across the world, calls have been made to address the associated mental illness public crisis. The current paper seeks to broaden these calls by considering the role that positive psychology factors can play in buffering against mental illness, bolstering mental health during COVID-19 and building positive processes and capacities that may help to strengthen future mental health. The paper explores evidence and applications from nine topics in positive psychology that support people through a pandemic: meaning, coping, self-compassion, courage, gratitude, character strengths, positive emotions, positive interpersonal processes and high-quality connections. In times of intense crisis, such as COVID-19, it is understandable that research is heavily directed towards addressing the ways in which people are wounded and weakened. However, this need not come at the expense of also investigating the ways in which people are sustained and strengthened

Anthropogenic Impact on Tropical Perennial River in South India: Snapshot of Carbon Dynamics and Bacterial Community Composition

Riverine systems play an important role in the global carbon cycle, and they are considered hotspots for bacterial activities such as organic matter decomposition. However, our knowledge about these processes in tropical or subtropical regions is limited. The aim of this study was to investigate anthropogenically induced changes of water quality, the distribution of selected pharmaceuticals, and the effects of pollution on greenhouse gas concentrations and bacterial community composition along the 800 km long Cauvery river, the main river serving as a potable and irrigation water supply in Southern India. We found that in situ measured pCO₂ and pCH₄ concentrations were supersaturated relative to the atmosphere and ranged from 7.9 to 168.7 µmol L⁻¹ , and from 0.01 to 2.76 µmol L⁻¹ , respectively. Pharmaceuticals like triclosan, carbamazepine, ibuprofen, naproxen, propylparaben, and diclofenac exceeded warning limits along the Cauvery. Proteobacteria was the major phylum in all samples, ranging between 26.1% and 82.2% relative abundance, and it coincided with the accumulation of nutrients in the flowing water. Results emphasized the impact of industrialization and increased population density on changes in water quality, riverine carbon fluxes, and bacterial community structure

Composition of microbial communities in composts

The potential of microbial community fingerprinting methods to provide information about compost maturity in composting facilities was investigated. Studies in a pilot-scale reactor equipped with independent control of oxygen content and temperature showed that low oxygen contents or low temperature had no dramatic effects on overall development of microbial community structure, determined with the PLFA (phospholipid fatty acid) method, although the process was generally delayed. Analyses of PLFAs typical for Actinobacteria, however, revealed that these bacteria were favoured when the maximum temperature was 40ºC. Actinobacteria populations constituted almost 50% of the microbial community during later stages of the reactor experiments, when only relatively complex, recalcitrant compounds remained, suggesting that some Actinobacteria may be suitable as indicator organisms for mature compost. Studies in a full-scale composting system showed that Actinobacteria constituted a relatively low, but constant proportion at roughly 10% of the microbial community. Analyses of Actinobacteria species composition using PCR-DGGE (denaturing gradient gel electrophoresis) targeting 16S rRNA genes demonstrated that members of Corynebacterium were present at early stages and that thermo-tolerant Actinobacteria, e.g. Thermobifida, Streptosporangium, Saccharomonospora and Saccharopolyspora, were found throughout the long thermophilic phase. During the stage of decreasing temperature, the community included both thermo-tolerant and mesophilic Actinobacteria. The ester-linked fatty acid (EL) method for describing microbial community structure was shown to provide information related to aspects of maturity, and is potentially a relatively simple and fast method of assessing compost maturity. Combination of signature lipid and nucleic acid-based analyses greatly expanded the specificity and scope for assessing microbial community composition in composts

Predicting Lifetime Suicide Attempts in a Community Sample of Adolescents Using Machine Learning Algorithms

ObjectiveSuicide is a major global health concern and a prominent cause of death in adolescents. Previous research on suicide prediction has mainly focused on clinical or adult samples. To prevent suicides at an early stage, however, it is important to screen for risk factors in a community sample of adolescents. MethodWe compared the accuracy of logistic regressions, elastic net regressions and gradient boosting machines in predicting suicide attempts by 17-year-olds in the Millennium Cohort Study (N = 7,347 of which 48.6% were male), combining a large set of self- and other-reported variables from different categories (e.g., demographics, health, drug use, personality, sexuality). ResultsBoth machine learning algorithms outperformed logistic regressions and achieved quite similar predictive accuracies: Balanced accuracy was .76 for predicting suicide attempts three years in advance and even higher when using data from the same measurement wave (.85). The most important predictor of future suicide attempts was previous self-harm, followed by variables of mental health, emotion and motivation, drug use, sexuality, demography, and victimization. ConclusionSuicide attempts in adolescence can be accurately predicted using panel data of community samples. However, suicide prevention should be tailored to specific phases in the development of adolescents. Our results additionally show that more complex models that allow for nonlinear and interaction effects do not lead to better performance in comparison to linear regularized models.</p

Predicting Suicide Attempts in a Community Sample of Adolescents Using Machine Learning Algorithms

Suicide is a major global health concern and a prominent cause of death in adolescents. Previous research on suicide prediction has mainly focused on clinical or adult samples. To prevent suicides at an early stage, however, it is important to screen for risk factors in a community sample of adolescents. We compared the accuracy of logistic regressions, elastic net regressions and gradient boosting machines in predicting suicide attempts by 17-year-olds in the Millennium Cohort Study (N = 7,347), combining a large set of self- and other-reported variables from different categories. Both machine learning algorithms outperformed logistic regressions and achieved similar balanced accuracies (.76 when using data 3 years before the self-reported lifetime suicide attempts and .85 when using data from the same measurement wave). We identified essential variables that should be considered when screening for suicidal behavior. Finally, we discuss the usefulness of complex machine learning models in suicide prediction