Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces

Inexpensive machine learning potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting and applying interatomic forces. In these settings, it is crucial to detect when predictions are unreliable to avoid wrong or misleading results. Here, we present a complete framework for training and recalibrating graph neural network ensemble models to produce accurate predictions of energy and forces with calibrated uncertainty estimates. The proposed method considers both epistemic and aleatoric uncertainty and the total uncertainties are recalibrated post hoc using a nonlinear scaling function to achieve good calibration on previously unseen data, without loss of predictive accuracy. The method is demonstrated and evaluated on two challenging, publicly available datasets, ANI-1x (Smith et al.) and Transition1x (Schreiner et al.), both containing diverse conformations far from equilibrium. A detailed analysis of the predictive performance and uncertainty calibration is provided. In all experiments, the proposed method achieved low prediction error and good uncertainty calibration, with predicted uncertainty correlating with expected error, on energy and forces. To the best of our knowledge, the method presented in this paper is the first to consider a complete framework for obtaining calibrated epistemic and aleatoric uncertainty predictions on both energy and forces in ML potentials

Reducing the rate and duration of Re-ADMISsions among patients with unipolar disorder and bipolar disorder using smartphone-based monitoring and treatment -- the RADMIS trials: study protocol for two randomized controlled trials

Abstract Background Unipolar and bipolar disorder combined account for nearly half of all morbidity and mortality due to mental and substance use disorders, and burden society with the highest health care costs of all psychiatric and neurological disorders. Among these, costs due to psychiatric hospitalization are a major burden. Smartphones comprise an innovative and unique platform for the monitoring and treatment of depression and mania. No prior trial has investigated whether the use of a smartphone-based system can prevent re-admission among patients discharged from hospital. The present RADMIS trials aim to investigate whether using a smartphone-based monitoring and treatment system, including an integrated clinical feedback loop, reduces the rate and duration of re-admissions more than standard treatment in unipolar disorder and bipolar disorder. Methods The RADMIS trials use a randomized controlled, single-blind, parallel-group design. Patients with unipolar disorder and patients with bipolar disorder are invited to participate in each trial when discharged from psychiatric hospitals in The Capital Region of Denmark following an affective episode and randomized to either (1) a smartphone-based monitoring system including (a) an integrated feedback loop between patients and clinicians and (b) context-aware cognitive behavioral therapy (CBT) modules (intervention group) or (2) standard treatment (control group) for a 6-month trial period. The trial started in May 2017. The outcomes are (1) number and duration of re-admissions (primary), (2) severity of depressive and manic (only for patients with bipolar disorder) symptoms; psychosocial functioning; number of affective episodes (secondary), and (3) perceived stress, quality of life, self-rated depressive symptoms, self-rated manic symptoms (only for patients with bipolar disorder), recovery, empowerment, adherence to medication, wellbeing, ruminations, worrying, and satisfaction (tertiary). A total of 400 patients (200 patients with unipolar disorder and 200 patients with bipolar disorder) will be included in the RADMIS trials. Discussion If the smartphone-based monitoring system proves effective in reducing the rate and duration of re-admissions, there will be basis for using a system of this kind in the treatment of unipolar and bipolar disorder in general and on a larger scale. Trial registration ClinicalTrials.gov, ID: NCT03033420 . Registered 13 January 2017. Ethical approval has been obtained

An integrated expression atlas of miRNAs and their promoters in human and mouse

MicroRNAs (miRNAs) are short non-coding RNAs with key roles in cellular regulation. As part of the fifth edition of the Functional Annotation of Mammalian Genome (FANTOM5) project, we created an integrated expression atlas of miRNAs and their promoters by deep-sequencing 492 short RNA (sRNA) libraries, with matching Cap Analysis Gene Expression (CAGE) data, from 396 human and 47 mouse RNA samples. Promoters were identified for 1,357 human and 804 mouse miRNAs and showed strong sequence conservation between species. We also found that primary and mature miRNA expression levels were correlated, allowing us to use the primary miRNA measurements as a proxy for mature miRNA levels in a total of 1,829 human and 1,029 mouse CAGE libraries. We thus provide a broad atlas of miRNA expression and promoters in primary mammalian cells, establishing a foundation for detailed analysis of miRNA expression patterns and transcriptional control regions

Local biomarkers involved in the interplay between obesity and breast cancer

Obesity is associated with an increased risk of breast cancer, which is the most common cancer in women worldwide (excluding non‐melanoma skin cancer). Furthermore, breast cancer patients with obesity have an impaired prognosis. Adipose tissue is abundant in the breast. There-fore, breast cancer develops in an adipose‐rich environment. During obesity, changes in the local environment in the breast occur which are associated with breast cancer. A shift towards a pro-inflammatory state is seen, resulting in altered levels of cytokines and immune cells. Levels of adi-pokines, such as leptin, adiponectin, and resistin, are changed. Aromatase activity rises, resulting in higher levels of potent estrogen in the breast. Lastly, remodeling of the extracellular matrix takes place. In this review, we address the current knowledge on the changes in the breast adipose tissue in obesity associated with breast cancer initiation and progression. We aim to identify obesity‐asso-ciated biomarkers in the breast involved in the interplay between obesity and breast cancer. Hereby, we can improve identification of women with obesity with an increased risk of breast cancer and an impaired prognosis. Studies investigating mammary adipocytes and breast adipose tissue in women with obesity versus women without obesity are, however, sparse and further research is needed