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Causal Structure Discovery (CSD) is the problem of identifying causal relationships from large quantities of data through computational methods. With the limited ability of traditional association-based computational methods to discover causal relationships, CSD methodologies are gaining popularity. The goal of the study was to systematically examine whether (i) CSD methods can discover the known causal relationships from observational clinical data and (ii) to offer guidance to accurately discover known causal relationships. We used Alzheimer's disease (AD), a complex progressive disease, as a model because the well-established evidence provides a "gold-standard" causal graph for evaluation. We evaluated two CSD methods, Fast Causal Inference (FCI) and Fast Greedy Equivalence Search (FGES) in their ability to discover this structure from data collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used structural equation models (which is not designed for CSD) as control. We applied these methods under three scenarios defined by increasing amounts of background knowledge provided to the methods. The methods were evaluated by comparing the resulting causal relationships with the "gold standard" graph that was constructed from literature. Dedicated CSD methods managed to discover graphs that nearly coincided with the gold standard. For best results, CSD algorithms should be used with longitudinal data providing as much prior knowledge as possible.

Abstract: Nonvolatile optoelectronic memories integrated with the functions of sensing, data storage, and data processing are promising for the potential Internet of things (IoT) applications. To meet the requirements of IoT devices, multifunctional memory devices with low power consumption and secure data storage are highly desirable. This study demonstrates an optoelectronic resistive switching memory integrated with sensing and logic operations by adopting organic–inorganic hybrid CH3NH3PbI3−xClx perovskites, which possess unusual defect physics and excellent light absorption. The CH3NH3PbI3−xClx cell exhibits low operation voltage of 0.1 V with the assistance of light illumination, long‐term retention property, and multiple resistance states. Its unique optoelectronic characteristics enable to perform logic operation for inputting one electrical pulse and one optical signal, and detect the coincidence of electrical and optical signal as well. This design provides possibilities for smart sensor in IoT application. [ABSTRACT FROM AUTHOR]

Robust hydrogen evolution reaction (HER) in alkaline solution using a cost-effective catalyst is still a critical challenge for industrial H2O electrolysis. The dramatic depletion of interfacial H2O and hindered OH− diffusion bring about a harsh interfacial environment and thereby lead to sluggish hydrogen evolution. Here we develop heterogeneous catalysts based on defect-rich three-dimensional graphene (M/3D graphene) and achieve improved HER kinetics via surface hydroxyl group modification on the graphene surface. Our theoretical studies suggest that the surface hydroxyl groups derived from the hydroxylation process effectively attract multi-overlayer H2O clusters without any thermodynamic barrier and form a reservoir to continuously supply H2O for catalytic sites. Electrochemical characterizations indicate that the HER kinetics per active electrochemical surface area have been greatly improved after crafting abundant hydroxyl groups on the 3D graphene framework, which can be attributed to sufficient proton donors and an ameliorative interfacial pH environment. Benefiting from the surface hydroxyl group modification, the MoS2/3D graphene structure facilitates the HER at a low onset potential of 60 mV in alkaline medium and exhibits excellent durability after 3000 cycles. This surface modification strategy reveals the importance of interfacial H2O supply in HER kinetics and provides general guidance on designing highly efficient catalysts in alkaline medium. [ABSTRACT FROM AUTHOR]