Longitudinal analysis highlights structural changes in grey- and white-matter within military personnel exposed to blast.

Objective: The purpose of this study was to determine whether gray matter volume and diffusion-based metrics in associated white matter changed in breachers who had neuroimaging performed at two timepoints. A secondary purpose was to compare these changes in a group who had a one-year interval between their imaging timepoints to a group that had a two-year interval between imaging.

Methods: Between timepoints, clusters with significantly different gray matter volume were used as seeds for reconstruction of associated structural networks using diffusion metrics.

Family Management of Hypertension in Brazil: A Cross-Sectional Study.

In Brazil, research indicates that primary family members are the main source of support for individuals with chronic conditions such as hypertension (HTN). The burden of caregiving not only hinders effective HTN management but can also cause stress and anxiety, potentially leading to HTN in caregivers. Despite this, few studies have explored the impact of caregiving on these family members.

A technique for preserving network structure in randomized Hi-C data.

Chromatin interaction data are frequently analyzed as a network to study several aspects of chromatin structure. Hi-C experiments are costly and there is a need to create simulated networks for quality assessment or result validation purposes. Existing tools do not maintain network properties during randomization.

Characterizing behavioural differentiation in gene regulatory networks with representation graphs.

We introduce the formal notion of representation graphs, encapsulating the state space structure of gene regulatory network models in a compact and concise form that highlights the most significant features of stable states and differentiation processes leading to distinct stability regions. The concept has been developed in the context of a hybrid system-based gene network modelling framework; however, we anticipate that it can also be adapted to other approaches of modelling gene networks in discrete terms. We describe a practical algorithm for representation graph computation as well as two case studies demonstrating their real-world application and utility.