Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

BackgroundSuicide prevention is a public health priority, but risk factors for suicide after medical hospitalization remain understudied. This problem is critical for women, for whom suicide rates in the United States are disproportionately increasing.ObjectiveTo differentiate the risk of suicide attempt and self-harm following general medical hospitalization among women with depression, bipolar disorder, and chronic psychosis.MethodsWe developed a machine learning algorithm that identified risk factors of suicide attempt and self-harm after general hospitalization using electronic health record data from 1628 women in the University of California Los Angeles Integrated Clinical and Research Data Repository. To assess replicability, we applied the algorithm to a larger sample of 140,848 women in the New York City Clinical Data Research Network.ResultsThe classification tree algorithm identified risk groups in University of California Los Angeles Integrated Clinical and Research Data Repository (area under the curve 0.73, sensitivity 73.4, specificity 84.1, accuracy 0.84), and predictor combinations characterizing key risk groups were replicated in New York City Clinical Data Research Network (area under the curve 0.71, sensitivity 83.3, specificity 82.2, and accuracy 0.84). Predictors included medical comorbidity, history of pregnancy-related mental illness, age, and history of suicide-related behavior. Women with antecedent medical illness and history of pregnancy-related mental illness were at high risk (6.9%-17.2% readmitted for suicide-related behavior), as were women below 55 years old without antecedent medical illness (4.0%-7.5% readmitted).ConclusionsPrevention of suicide attempt and self-harm among women following acute medical illness may be improved by screening for sex-specific predictors including perinatal mental health history

Six year follow-up of students who participated in a school-based physical activity intervention: a longitudinal cohort study

Background: The purpose of this paper was to evaluate the long-term impact of a childhood motor skill intervention on adolescent motor skills and physical activity. Methods: In 2006, we undertook a follow-up of motor skill proficiency (catch, kick, throw, vertical jump, side gallop) and physical activity in adolescents who had participated in a one year primary school intervention Move It Groove It (MIGI) in 2000. Logistic regression models were analysed for each skill to determine whether the probability of children in the intervention group achieving mastery or near mastery was either maintained or had increased in subsequent years, relative to controls. In these models the main predictor variable was intervention status, with adjustment for gender, grade, and skill level in 2000. A general linear model, controlling for gender and grade, examined whether former intervention students spent more time in moderate-to-vigorous physical activity at follow-up than control students. Results: Half (52%, n = 481) of the 928 MIGI participants were located in 28 schools, with 276 (57%) assessed. 52% were female, 58% in Grade 10, 40% in Grade 11 and 54% were former intervention students. At follow-up, intervention students had improved their catch ability relative to controls and were five times more likely to be able to catch: OR catch = 5.51, CI (1.95 - 15.55), but had lost their advantage in the throw and kick: OR throw = .43, CI (.23 - .82), OR kick = .39, CI (.20 - .78). For the other skills, intervention students appeared to maintain their advantage: OR jump = 1.14, CI (.56 - 2.34), OR gallop = 1.24, CI (.55 - 2.79). Intervention students were no more active at follow-up. Conclusion: Six years after the 12-month MIGI intervention, whilst intervention students had increased their advantage relative to controls in one skill, and appeared to maintain their advantage in two, they lost their advantage in two skills and were no more active than controls at follow up. More longitudinal research is needed to explore whether gains in motor skill proficiency in children can be sustained and to determine the intervention characteristics that translate to subsequent physical activity

Ligand selectivity in stabilising tandem parallel folded G-quadruplex motifs in human telomeric DNA sequences

Biophysical studies of ligand interactions with three human telomeric repeat sequences (d(AGGG(TTAGGG)n, n = 3, 7 and 11)) show that an oxazole-based ‘click’ ligand, which induces parallel folded quadruplexes, preferentially stabilises longer telomeric repeats providing evidence for selectivity in binding at the interface between tandem quadruplex motifs

Crowdsourced earthquake early warning

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an M_w (moment magnitude) 7 earthquake on California’s Hayward fault, and real data from the M_w 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing

Force and power output of fast and slow skeletal muscles from mdx mice 6-28 months old

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66165/1/j.1469-7793.2001.00591.x.pd

Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System

By synthesising remote-sensing measurements made in the central Arctic into a model-gridded Cloudnet cloud product, we evaluate how well the Met Office Unified Model (UM) and the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) capture Arctic clouds and their associated interactions with the surface energy balance and the thermodynamic structure of the lower troposphere. This evaluation was conducted using a 4-week observation period from the Arctic Ocean 2018 expedition, where the transition from sea ice melting to freezing conditions was measured. Three different cloud schemes were tested within a nested limited-area model (LAM) configuration of the UM – two regionally operational single-moment schemes (UM_RA2M and UM_RA2T) and one novel double-moment scheme (UM_CASIM-100) – while one global simulation was conducted with the IFS, utilising its default cloud scheme (ECMWF_IFS). Consistent weaknesses were identified across both models, with both the UM and IFS overestimating cloud occurrence below 3 km. This overestimation was also consistent across the three cloud configurations used within the UM framework, with >90 % mean cloud occurrence simulated between 0.15 and 1 km in all the model simulations. However, the cloud microphysical structure, on average, was modelled reasonably well in each simulation, with the cloud liquid water content (LWC) and ice water content (IWC) comparing well with observations over much of the vertical profile. The key microphysical discrepancy between the models and observations was in the LWC between 1 and 3 km, where most simulations (all except UM_RA2T) overestimated the observed LWC. Despite this reasonable performance in cloud physical structure, both models failed to adequately capture cloud-free episodes: this consistency in cloud cover likely contributes to the ever-present near-surface temperature bias in every simulation. Both models also consistently exhibited temperature and moisture biases below 3 km, with particularly strong cold biases coinciding with the overabundant modelled cloud layers. These biases are likely due to too much cloud-top radiative cooling from these persistent modelled cloud layers and were consistent across the three UM configurations tested, despite differences in their parameterisations of cloud on a sub-grid scale. Alarmingly, our findings suggest that these biases in the regional model were inherited from the global model, driving a cause–effect relationship between the excessive low-altitude cloudiness and the coincident cold bias. Using representative cloud condensation nuclei concentrations in our double-moment UM configuration while improving cloud microphysical structure does little to alleviate these biases; therefore, no matter how comprehensive we make the cloud physics in the nested LAM configuration used here, its cloud and thermodynamic structure will continue to be overwhelmingly biased by the meteorological conditions of its driving model

A systematic review of biomarkers for disease progression in Parkinson's disease

Peer reviewedPublisher PD

Phase Ib study of unesbulin (PTC596) plus dacarbazine for the treatment of locally recurrent, unresectable or metastatic, relapsed or refractory leiomyosarcoma

PURPOSE: This multicenter, single-arm, open-label, phase Ib study was designed to determine the recommended phase II dose (RP2D) and to evaluate the safety and preliminary efficacy of unesbulin plus dacarbazine (DTIC) in patients with advanced leiomyosarcoma (LMS). PATIENTS AND METHODS: Adult subjects with locally advanced, unresectable or metastatic, relapsed or refractory LMS were treated with escalating doses of unesbulin orally twice per week in combination with DTIC 1,000 mg/m RESULTS: Unesbulin 300 mg administered orally twice per week in combination with DTIC 1,000 mg/m CONCLUSION: Unesbulin 300 mg twice per week plus DTIC 1,000 mg/