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Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the “big data” revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC networkmore »has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

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Composite polymer electrolytes (CPEs) for solid-state Li metal batteries (SSLBs) still suffer from gradually increased interface resistance and unconstrained Li dendrite growth. Herein, we addressed the challenges by designing a LiF-rich inorganic solid-electrolyte interphase (SEI) through introducing a fluoride-salt concentrated interlayer on CPE film. The rigid and flexible CPE helps accommodate the volume change of electrodes, while the polymeric high-concentrated electrolyte (PHCE) surface-layer regulates Li-ion flux due to the formation of a stable LiF-rich SEI via anion reduction. The designed CPE-PHCE presents enhanced ionic conductivity and high oxidation stability of > 5.0V (vs. Li/Li+). What’s more, it dramatically reduces the interfacial resistance and achieves a high critical current density of 4.5 mA cm-2 for dendrite-free cycling. The SSLBs, fabricated with thin CPE-PHCE membrane (< 100 μm) and Co-free LiNiO2 cathode, exhibit exceptional electrochemical performance and long cycling stability. This approach of SEI design can also be applied to other types of batteries.