Assessing the persistence of a contaminant plume generated by linear aquifer source depletion and back diffusion from an aquitard.

This study presents analytical solutions for describing contaminant storage and release from an aquitard with linear source depletion (LSD) boundary conditions. We investigated three scenarios for trichloroethylene (TCE) mass exchange before and after the LSD period in an aquifer bounded by an adjacent aquitard based on the LSD dynamics, a resistance coefficient, and the aquitard thickness. The developed analytical solutions showed good agreement with measured profiles and breakthrough curves from a previous study.

Neurodevelopmental imprints of sociomarkers in adolescent brain connectomes.

Neural consequences of social disparities are not yet rigorously investigated. How socioeconomic conditions influence children's connectome development remains unknown. This paper endeavors to gauge how precisely the connectome structure of the brain can predict an individual's social environment, thereby inversely assessing how social influences are engraved in the neural development of the Adolescent brain.

ADTR: Adaptive Decision Transformer for Dynamic Treatment Regimes in Sepsis.

Dynamic treatment regimes (DTRs), which comprise a series of decisions taken to select adequate treatments, have attracted considerable attention in the clinical domain, especially from sepsis researchers. Existing sepsis DTR learning studies are mainly based on offline reinforcement learning (RL) approaches working on electronic healthcare records data. However, a trained policy may choose a treatment different from a human clinician's prescription.

EEG-Oriented Self-Supervised Learning With Triple Information Pathways Network.

Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have attracted widespread attention for monitoring the clinical condition of users and identifying their intention/emotion. Nevertheless, the existing methods generally model EEG signals with limited viewpoints or restricted concerns about the characteristics of the EEG signals, and thus represent complex spectro-/spatiotemporal patterns and suffer from high variability. In this work, we propose the novel EEG-oriented self-supervised learning methods and a novel deep architecture to learn rich representation, including information about the diverse spectral characteristics of neural oscillations, the spatial properties of electrode sensor distribution, and the temporal patterns of both the global and local viewpoints.

Deep learning model for individualized trajectory prediction of clinical outcomes in mild cognitive impairment.

Objectives: Accurately predicting when patients with mild cognitive impairment (MCI) will progress to dementia is a formidable challenge. This work aims to develop a predictive deep learning model to accurately predict future cognitive decline and magnetic resonance imaging (MRI) marker changes over time at the individual level for patients with MCI.

Methods: We recruited 657 amnestic patients with MCI from the Samsung Medical Center who underwent cognitive tests, brain MRI scans, and amyloid-β (Aβ) positron emission tomography (PET) scans.