RESUMEN
Membrane-based organic solvent separations are rapidly emerging as a promising class of technologies for enhancing the energy efficiency of existing separation and purification systems. Polymeric membranes have shown promise in the fractionation or splitting of complex mixtures of organic molecules such as crude oil. Determining the separation performance of a polymer membrane when challenged with a complex mixture has thus far occurred in an ad hoc manner, and methods to predict the performance based on mixture composition and polymer chemistry are unavailable. Here, we combine physics-informed machine learning algorithms (ML) and mass transport simulations to create an integrated predictive model for the separation of complex mixtures containing up to 400 components via any arbitrary linear polymer membrane. We experimentally demonstrate the effectiveness of the model by predicting the separation of two crude oils within 6-7% of the measurements. Integration of ML predictors of diffusion and sorption properties of molecules with transport simulators enables for the rapid screening of polymer membranes prior to physical experimentation for the separation of complex liquid mixtures.
RESUMEN
Atypical functional connectivity (FC) and an imbalance of excitation-to-inhibition (E/I) have been previously reported in cerebro-cerebellar circuits in autism spectrum disorder (ASD). The current investigation used resting state fMRI and proton magnetic resonance spectroscopy (1H-MRS) to examine the relationships between E/I (glutamate + glutamine/GABA) and FC of the dorsolateral prefrontal cortex and posterolateral cerebellar hemisphere from 14 adolescents/adults with ASD and 12 age/sex/IQ-matched controls. In this pilot sample, cerebro-cerebellar FC was positively associated with cerebellar E/I and listening comprehension abilities in individuals with ASD but not controls. Additionally, a subgroup of individuals with ASD and low FC (n = 5) exhibited reduced E/I and impaired listening comprehension. Thus, altered functional coherence of cerebro-cerebellar circuits in ASD may be related with a cerebellar E/I imbalance.
Asunto(s)
Trastorno del Espectro Autista/fisiopatología , Cerebelo/fisiopatología , Corteza Prefrontal/fisiopatología , Adolescente , Adulto , Femenino , Humanos , Imagen por Resonancia Magnética , Masculino , Inhibición NeuralRESUMEN
S100A1 is a member of the S100 family of Ca2+-binding proteins and regulates several cellular processes, including those involved in Ca2+ signaling and cardiac and skeletal muscle function. In Alzheimer's disease, brain S100A1 is overexpressed and gives rise to disease pathologies, making it a potential therapeutic target. The 2.25â Å resolution crystal structure of Ca2+-S100A1 is solved here and is compared with the structures of other S100 proteins, most notably S100B, which is a highly homologous S100-family member that is implicated in the progression of malignant melanoma. The observed structural differences in S100A1 versus S100B provide insights regarding target protein-binding specificity and for targeting these two S100 proteins in human diseases using structure-based drug-design approaches.