Mapping morphological cortical networks with joint probability distributions from multiple morphological features.

Morphological features sourced from structural magnetic resonance imaging can be used to infer human brain connectivity. Although integrating different morphological features may theoretically be beneficial for obtaining more precise morphological connectivity networks (MCNs), the empirical evidence to support this supposition is scarce. Moreover, the incorporation of different morphological features remains an open question. In this study, we proposed a method to construct cortical MCNs based on multiple morphological features. Specifically, we adopted a multi-dimensional kernel density estimation algorithm to fit regional joint probability distributions (PDs) from different combinations of four morphological features, and estimated inter-regional similarity in the joint PDs via Jensen-Shannon divergence. We evaluated the method by comparing the resultant MCNs with those built based on different single morphological features in terms of topological organization, test-retest reliability, biological plausibility, and behavioral and cognitive relevance. We found that, compared to MCNs built based on different single morphological features, MCNs derived from multiple morphological features displayed less segregated, but more integrated network architecture and different hubs, had higher test-retest reliability, encompassed larger proportions of inter-hemispheric edges and edges between brain regions within the same cytoarchitectonic class, and explained more inter-individual variance in behavior and cognition. These findings were largely reproducible when different brain atlases were used for cortical parcellation. Further analysis of macaque MCNs revealed weak, but significant correlations with axonal connectivity from tract-tracing, independent of the number of morphological features. Altogether, this paper proposes a new method for integrating different morphological features, which will be beneficial for constructing MCNs.

An atmospheric water harvesting system based on the "Optimal Harvesting Window" design for worldwide water production.

Atmospheric water harvesting (AWH) is a promising solution to the water shortage problem. Current sorption-based AWH (SAWH) systems seldom obtain both wide climatic adaptability and high energy efficiency due to the lack of thermodynamic optimization. To achieve the ideal harvesting circulation in SAWH systems, the "optimal harvesting window" (OHW) design based on thermodynamic analysis was first proposed and validated by our prototype. The "OHW" theory indicates the water production rate and energy efficiency could be improved by properly reducing the adsorption temperature. As the humidity increases, the optimal adsorption temperature should be closer to the dew point of the environment. Experimental results revealed that, loaded with 3 kg widely adopted silica gel, the daily water production could reach 5.76-17.64 L/d with ultrahigh energy efficiency of 0.46-1.5 L/kWh. This prototype could also achieve optimal performance in wide climatic conditions in terms of 13-35 °C and 18%-72% RH. Lastly, the performance of photovoltaic (PV)-driven SAWH was evaluated. Results showed that a 1 m2 PV panel could generate 0.66-2 L water per day in Shanghai throughout the year, the highest in opening literature. Notably, this work introduces a promising concept that can help achieve large-scale, ultra-fast, energy-efficient AWH worldwide.

Exercise intervention in middle-aged and elderly individuals with insomnia improves sleep and restores connectivity in the motor network.

Exercise is a potential treatment to improve sleep quality in middle-aged and elderly individuals. Understanding exercise-induced changes in functional plasticity of brain circuits that underlie improvements in sleep among middle-aged and older adults can inform treatment of sleep problems. The aim of the study is to identify the effects of a 12-week exercise program on sleep quality and brain functional connectivity in middle-aged and older adults with insomnia. The trial was registered with Chinese Clinical Trial Register (ChiCTR2000033652). We recruited 84 healthy sleepers and 85 individuals with insomnia. Participants with insomnia were assigned to receive either a 12-week exercise intervention or were placed in a 12-week waitlist control condition. Thirty-seven middle-aged and older adults in the exercise group and 30 in the waitlist group completed both baseline and week 12 assessments. We found that middle-aged and older adults with insomnia showed significantly worse sleep quality than healthy sleepers. At the brain circuit level, insomnia patients showed decreased connectivity in the widespread motor network. After exercise intervention, self-reported sleep was increased in the exercise group (P < 0.001) compared to that in the waitlist group. We also found increased functional connectivity of the motor network with the cerebellum in the exercise group (P < 0.001). Moreover, we observed significant correlations between improvement in subjective sleep indices and connectivity changes within the motor network. We highlight exercise-induced improvement in sleep quality and functional plasticity of the aging brain.