Brain networks and intelligence: A graph neural network based approach to resting state fMRI data.

Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions.

Exploring the role of grafting in abiotic stress management: Contemporary insights and automation trends.

Grafting is a technique that involves attaching a rootstock to the aerial part of another genotype or species (scion), leading to improved crop performance and sustainable growth. The ability to tolerate abiotic stresses depends on cell membrane stability, a reduction in electrolyte leakage, and the species of scion and rootstock chosen. This external mechanism, grafting, serves as a beneficial tool in influencing crop performance by combining nutrient uptake and translocation to shoots, promoting sustainable plant growth, and enhancing the potential yield of both fruit and vegetable crops.

st-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks.

Objective: Modeling dynamic neuronal activity within brain networks enables the precise tracking of rapid temporal fluctuations across different brain regions. However, current approaches in computational neuroscience fall short of capturing and representing the spatiotemporal dynamics within each brain network. We developed a novel weakly supervised spatiotemporal dense prediction model capable of generating personalized 4D dynamic brain networks from fMRI data, providing a more granular representation of brain activity over time.

Pregnancy Outcomes and All-Cause Mortality After Pregnancy Among US-Born Women With Perinatally Acquired HIV.

Background: Maternal and pregnancy outcomes among women with perinatally acquired HIV (PHIV) versus women with HIV acquired through other routes (NPHIV) are not fully understood.

Setting: US-born women during 2005-2015 in New York City.

Methods: We used data from the New York City HIV surveillance registry, Expanded Perinatal Surveillance database, and Vital Statistics, to compare pregnancy and all-cause mortality outcomes among women with PHIV versus NPHIV delivering infants during 2005-2015.

Adolescent brain maturation associated with environmental factors: a multivariate analysis.

Human adolescence marks a crucial phase of extensive brain development, highly susceptible to environmental influences. Employing brain age estimation to assess individual brain aging, we categorized individuals ( = 7,435, aged 9-10 years old) from the Adolescent Brain and Cognitive Development (ABCD) cohort into groups exhibiting either accelerated or delayed brain maturation, where the accelerated group also displayed increased cognitive performance compared to their delayed counterparts. A 4-way multi-set canonical correlation analysis integrating three modalities of brain metrics (gray matter density, brain morphological measures, and functional network connectivity) with nine environmental factors unveiled a significant 4-way canonical correlation between linked patterns of neural features, air pollution, area crime, and population density.