A unified neurocomputational model of prospective and retrospective timing.

Time is a central dimension against which perception, action, and cognition play out. From anticipating when future events will happen to recalling how long ago previous events occurred, humans and animals are exquisitely sensitive to temporal structure. Empirical evidence seems to suggest that estimating time prospectively (i.

Enterocyte-like differentiation defines metabolic gene signatures of CMS3 colorectal cancers and provides therapeutic vulnerability.

Colorectal cancer (CRC) is stratified into four consensus molecular subtypes (CMS1-4). CMS3 represents the metabolic subtype, but its wiring remains largely undefined. To identify the underlying tumorigenesis of CMS3, organoids derived from 16 genetically engineered mouse models are analyzed.

'Let it stay in the heart': cultural and gendered experiences of distress among Syrian refugees in Jordan.

Objective: This study explored cultural and gendered experiences of distress among Syrian refugees in Jordan to inform mental health and psychosocial support services with the population. We sought to understand perceived causes of distress, salient expressions used to describe distress, and ways of coping.

Methods: Eight focus group discussions (FGDs) were conducted with adult Syrian refugees (four male, four female).

Systematic review of nutrition interventions in older patients with cancer: A synthesis of evidence and a future research priority.

Introduction: Older patients with cancer (65 years and older) are a growing population with unique nutrition-and treatment-related issues that accelerate aging. Nutrition interventions attenuate nutritional decline, muscle loss, and risk of malnutrition and sarcopenia in patients with cancer, however the evidence for older patients with cancer is limited. The aim of this systematic review was to evaluate the efficacy of nutrition interventions on nutritional status, body weight/composition and clinical outcomes in older patients with cancer and to identify future research priority areas.

Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.

Background: Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.

Methods: To address this gap, we present ALIGATEHR, which models inferred family relations in a graph attention network augmented with an attention-based medical ontology representation, thus accounting for the complex influence of genetics, shared environmental exposures, and disease dependencies.