Noise-assisted variational quantum thermalization.

Preparing thermal states on a quantum computer can have a variety of applications, from simulating many-body quantum systems to training machine learning models. Variational circuits have been proposed for this task on near-term quantum computers, but several challenges remain, such as finding a scalable cost-function, avoiding the need of purification, and mitigating noise effects. We propose a new algorithm for thermal state preparation that tackles those three challenges by exploiting the noise of quantum circuits.

Genetic risk for subjective reports of insomnia associates only weakly with polygraphic measures of insomnia in 2,770 adults.

Study Objectives: Subjective insomnia complaints and objective sleep changes are mostly studied outside of clinical trial studies. In this study, we tested whether 240 genetic variants associated with subjectively reported insomnia were also associated with objective insomnia parameters extracted from polysomnographic recordings in three studies.

Methods: The study sample (total n = 2,770) was composed of the Wisconsin Sleep Cohort (n = 1,091) and the Osteoporotic Fractures in Men (n = 1,026) study, two population-based studies, and the Stanford Sleep Cohort, a sleep center patient-based sample (n = 653).

Seasonal variation in biopsy-proven giant cell arteritis in Eastern Denmark from 1990-2018.

Purpose: The purpose of this study was to investigate seasonal variation in cases of biopsy-proven GCA in eastern Denmark in a 29-year period.

Methods: Pathology records of all temporal artery biopsies in eastern Denmark between 1990 and 2018 were reviewed. For each patient, data were collected which included age, sex, date of birth and biopsy result.

A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data.

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework.

High body weight gain and reduced bovine mammary growth: physiological basis and implications for milk yield potential.

Available evidence concerning the relationship between growth rate, mammary growth and milk yield in heifers leads to these conclusions: 1) Increased growth rate due to high feeding level before puberty onset can lead to reduced pubertal mammary growth and reduced milk yield potential. 2) Increased growth rate due to high feeding level after puberty and during pregnancy have no effect on mammary growth and milk yield. 3) Higher body weight gain due to higher genetic potential for growth is positively related to milk yield.