Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning.

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability.

Prioritizing disease-related rare variants by integrating gene expression data.

Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in diseased patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

Prioritizing disease-related rare variants by integrating gene expression data.

Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

Genomic data resources of the Brain Somatic Mosaicism Network for neuropsychiatric diseases.

Somatic mosaicism is defined as an occurrence of two or more populations of cells having genomic sequences differing at given loci in an individual who is derived from a single zygote. It is a characteristic of multicellular organisms that plays a crucial role in normal development and disease. To study the nature and extent of somatic mosaicism in autism spectrum disorder, bipolar disorder, focal cortical dysplasia, schizophrenia, and Tourette syndrome, a multi-institutional consortium called the Brain Somatic Mosaicism Network (BSMN) was formed through the National Institute of Mental Health (NIMH). In addition to genomic data of affected and neurotypical brains, the BSMN also developed and validated a best practices somatic single nucleotide variant calling workflow through the analysis of reference brain tissue. These resources, which include >400 terabytes of data from 1087 subjects, are now available to the research community via the NIMH Data Archive (NDA) and are described here.

Integrative analyses highlight functional regulatory variants associated with neuropsychiatric diseases.

Noncoding variants of presumed regulatory function contribute to the heritability of neuropsychiatric disease. A total of 2,221 noncoding variants connected to risk for ten neuropsychiatric disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, bipolar disorder, borderline personality disorder, major depression, generalized anxiety disorder, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder and schizophrenia, were studied in developing human neural cells. Integrating epigenomic and transcriptomic data with massively parallel reporter assays identified differentially-active single-nucleotide variants (daSNVs) in specific neural cell types. Expression-gene mapping, network analyses and chromatin looping nominated candidate disease-relevant target genes modulated by these daSNVs. Follow-up integration of daSNV gene editing with clinical cohort analyses suggested that magnesium transport dysfunction may increase neuropsychiatric disease risk and indicated that common genetic pathomechanisms may mediate specific symptoms that are shared across multiple neuropsychiatric diseases.

Pan-conserved segment tags identify ultra-conserved sequences across assemblies in the human pangenome.

The human pangenome, a new reference sequence, addresses many limitations of the current GRCh38 reference. The first release is based on 94 high-quality haploid assemblies from individuals with diverse backgrounds. We employed a k-mer indexing strategy for comparative analysis across multiple assemblies, including the pangenome reference, GRCh38, and CHM13, a telomere-to-telomere reference assembly. Our k-mer indexing approach enabled us to identify a valuable collection of universally conserved sequences across all assemblies, referred to as "pan-conserved segment tags" (PSTs). By examining intervals between these segments, we discerned highly conserved genomic segments and those with structurally related polymorphisms. We found 60,764 polymorphic intervals with unique geo-ethnic features in the pangenome reference. In this study, we utilized ultra-conserved sequences (PSTs) to forge a link between human pangenome assemblies and reference genomes. This methodology enables the examination of any sequence of interest within the pangenome, using the reference genome as a comparative framework.

Mutations in human DNA methyltransferase DNMT1 induce specific genome-wide epigenomic and transcriptomic changes in neurodevelopment.

DNA methyltransferase type 1 (DNMT1) is a major enzyme involved in maintaining the methylation pattern after DNA replication. Mutations in DNMT1 have been associated with autosomal dominant cerebellar ataxia, deafness and narcolepsy (ADCA-DN). We used fibroblasts, induced pluripotent stem cells (iPSCs) and induced neurons (iNs) generated from patients with ADCA-DN and controls, to explore the epigenomic and transcriptomic effects of mutations in DNMT1. We show cell type-specific changes in gene expression and DNA methylation patterns. DNA methylation and gene expression changes were negatively correlated in iPSCs and iNs. In addition, we identified a group of genes associated with clinical phenotypes of ADCA-DN, including PDGFB and PRDM8 for cerebellar ataxia, psychosis and dementia and NR2F1 for deafness and optic atrophy. Furthermore, ZFP57, which is required to maintain gene imprinting through DNA methylation during early development, was hypomethylated in promoters and exhibited upregulated expression in patients with ADCA-DN in both iPSC and iNs. Our results provide insight into the functions of DNMT1 and the molecular changes associated with ADCA-DN, with potential implications for genes associated with related phenotypes.

Evaluation of C4 Gene Copy Number in Pediatric Acute Neuropsychiatric Syndrome.

Pediatric acute-onset neuropsychiatric syndrome (PANS) is an abrupt-onset neuropsychiatric disorder. PANS patients have an increased prevalence of comorbid autoimmune illness, most commonly arthritis. In addition, an estimated one-third of PANS patients present with low serum C4 protein, suggesting decreased production or increased consumption of C4 protein. To test the possibility that copy number (CN) variation contributes to risk of PANS illness, we compared mean total C4A and total C4B CN in ethnically matched subjects from PANS DNA samples and controls (192 cases and 182 controls). Longitudinal data from the Stanford PANS cohort (n = 121) were used to assess whether the time to juvenile idiopathic arthritis (JIA) or autoimmune disease (AI) onset was a function of total C4A or C4B CN. Lastly, we performed several hypothesis-generating analyses to explore the correlation between individual C4 gene variants, sex, specific genotypes, and age of PANS onset. Although the mean total C4A or C4B CN did not differ in PANS compared to controls, PANS patients with low C4B CN were at increased risk for subsequent JIA diagnosis (hazard ratio = 2.7, p value = 0.004). We also observed a possible increase in risk for AI in PANS patients and a possible correlation between lower C4B and PANS age of onset. An association between rheumatoid arthritis and low C4B CN has been reported previously. However, patients with PANS develop different types of JIA: enthesitis-related arthritis, spondyloarthritis, and psoriatic arthritis. This suggests that C4B plays a role that spans these arthritis types.

Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes.

X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of the absorber atom. In this work, we develop a database of sulfur K-edge XAS spectra of crystalline and amorphous lithium thiophosphate materials based on the atomic structures reported in Chem. Mater., 34, 6702 (2022). The XAS database is based on simulations using the excited electron and core-hole pseudopotential approach implemented in the Vienna Ab initio Simulation Package. Our database contains 2681 S K-edge XAS spectra for 66 crystalline and glassy structure models, making it the largest collection of first-principles computational XAS spectra for glass/ceramic lithium thiophosphates to date. This database can be used to correlate S spectral features with distinct S species based on their local coordination and short-range ordering in sulfide-based solid electrolytes. The data is openly distributed via the Materials Cloud, allowing researchers to access it for free and use it for further analysis, such as spectral fingerprinting, matching with experiments, and developing machine learning models.

Lead-Free, Luminescent Perovskite Nanocrystals Obtained through Ambient Condition Synthesis.

Heterovalently substituting toxic lead is an increasingly popular design strategy to obtain environmentally sustainable variants of the exciting material class of halide perovskites. Perovskite nanocrystals (NCs) obtained through solution-based methods exhibit exceedingly high optical quality. Unfortunately, most of these synthesis routes still require reaction under inert gas and at very high temperatures. Herein a novel synthesis routine for lead-free double perovskite (LFDP) NCs is presented. An approach based upon the hot injection and ligand-assisted reprecipitation (LARP) methods to achieve a low-temperature and ambient atmosphere-based synthesis for manganese-doped Cs2 NaBiCl6 NCs is presented. Mn incorporation is critical for the otherwise non-emissive material, with a 9:1 Bi:Mn precursor ratio maximizing the bright orange photoluminescence (PL) and quantum yield (QY). Higher synthesis temperatures slightly increase the material's performance, yet NCs synthesized at room temperature are still emissive, highlighting the versatility of the synthetic approach. While the material's indirect bandgap limits its appeal for optoelectronics, this feature could benefit photocatalysis due to longer carrier lifetimes. Moreover, the developed synthesis is facile and can rapidly be adapted to other more viable material compositions and up-scaled to realize applications directly.

Rapid Data-Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion.

With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. With sheer limitless possibilities for material combinations and synthetic procedures, obtaining novel, highly functional materials has been a tedious trial and error process. Recently, machine learning has emerged as a powerful tool to help optimize syntheses; however, most approaches require a substantial amount of input data, limiting their pertinence. Here, three well-known machine-learning models are merged with Bayesian optimization into one to optimize the synthesis of CsPbBr3 nanoplatelets with limited data demand. The algorithm can accurately predict the photoluminescence emission maxima of nanoplatelet dispersions using only the three precursor ratios as input parameters. This allows us to fabricate previously unobtainable seven and eight monolayer-thick nanoplatelets. Moreover, the algorithm dramatically improves the homogeneity of 2-6-monolayer-thick nanoplatelet dispersions, as evidenced by narrower and more symmetric photoluminescence spectra. Decisively, only 200 total syntheses are required to achieve this vast improvement, highlighting how rapidly material properties can be optimized. The algorithm is highly versatile and can incorporate additional synthetic parameters. Accordingly, it is readily applicable to other less-explored nanocrystal syntheses and can help rapidly identify and improve exciting compositions' quality.

Artificial Intelligence-Aided Mapping of the Structure-Composition-Conductivity Relationships of Glass-Ceramic Lithium Thiophosphate Electrolytes.

Lithium thiophosphates (LPSs) with the composition (Li2S) x (P2S5)1-x are among the most promising prospective electrolyte materials for solid-state batteries (SSBs), owing to their superionic conductivity at room temperature (>10-3 S cm-1), soft mechanical properties, and low grain boundary resistance. Several glass-ceramic (gc) LPSs with different compositions and good Li conductivity have been previously reported, but the relationship among composition, atomic structure, stability, and Li conductivity remains unclear due to the challenges in characterizing noncrystalline phases in experiments or simulations. Here, we mapped the LPS phase diagram by combining first-principles and artificial intelligence (AI) methods, integrating density functional theory, artificial neural network potentials, genetic-algorithm sampling, and ab initio molecular dynamics simulations. By means of an unsupervised structure-similarity analysis, the glassy/ceramic phases were correlated with the local structural motifs in the known LPS crystal structures, showing that the energetically most favorable Li environment varies with the composition. Based on the discovered trends in the LPS phase diagram, we propose a candidate solid-state electrolyte composition, (Li2S) x (P2S5)1-x (x ∼ 0.725), that exhibits high ionic conductivity (>10-2 S cm-1) in our simulations, thereby demonstrating a general design strategy for amorphous or glassy/ceramic solid electrolytes with enhanced conductivity and stability.

Thickness-Dependent Dark-Bright Exciton Splitting and Phonon Bottleneck in CsPbBr3-Based Nanoplatelets Revealed via Magneto-Optical Spectroscopy.

The optimized exploitation of perovskite nanocrystals and nanoplatelets as highly efficient light sources requires a detailed understanding of the energy spacing within the exciton manifold. Dark exciton states are particularly relevant because they represent a channel that reduces radiative efficiency. Here, we apply large in-plane magnetic fields to brighten optically inactive states of CsPbBr3-based nanoplatelets for the first time. This approach allows us to access the dark states and directly determine the dark-bright splitting, which reaches 22 meV for the thinnest nanoplatelets. The splitting is significantly less for thicker nanoplatelets due to reduced exciton confinement. Additionally, the form of the magneto-PL spectrum suggests that dark and bright state populations are nonthermalized, which is indicative of a phonon bottleneck in the exciton relaxation process.

Energy Transfer in Stability-Optimized Perovskite Nanocrystals.

Outstanding optoelectronic properties and a facile synthesis render halide perovskite nanocrystals (NCs) a promising material for nanostructure-based devices. However, the commercialization is hindered mainly by the lack of NC stability under ambient conditions and inefficient charge carrier injection. Here, we investigate solutions to both problems, employing methylammonium lead bromide (MAPbBr3) NCs encapsulated in diblock copolymer core-shell micelles of tunable size. We confirm that the shell does not prohibit energy transfer, as FRET efficiencies between these NCs and 2D CsPbBr3 nanoplatelets (NPLs) reach 73.6%. This value strongly correlates to the micelle size, with thicker shells displaying significantly reduced FRET efficiencies. Those high efficiencies come with a price, as the thinnest shells protect the encapsulated NCs less from environmentally induced degradation. Finding the sweet spot between efficiency and protection could lead to the realization of tailored energy funnels with enhanced carrier densities for high-power perovskite NC-based optoelectronic applications.

Analysis of somatic mutations in 131 human brains reveals aging-associated hypermutability.

We analyzed 131 human brains (44 neurotypical, 19 with Tourette syndrome, 9 with schizophrenia, and 59 with autism) for somatic mutations after whole genome sequencing to a depth of more than 200×. Typically, brains had 20 to 60 detectable single-nucleotide mutations, but ~6% of brains harbored hundreds of somatic mutations. Hypermutability was associated with age and damaging mutations in genes implicated in cancers and, in some brains, reflected in vivo clonal expansions. Somatic duplications, likely arising during development, were found in ~5% of normal and diseased brains, reflecting background mutagenesis. Brains with autism were associated with mutations creating putative transcription factor binding motifs in enhancer-like regions in the developing brain. The top-ranked affected motifs corresponded to MEIS (myeloid ectopic viral integration site) transcription factors, suggesting a potential link between their involvement in gene regulation and autism.

Maternal attachment insecurity, maltreatment history, and depressive symptoms are associated with broad DNA methylation signatures in infants.

The early environment, including maternal characteristics, provides many cues to young organisms that shape their long-term physical and mental health. Identifying the earliest molecular events that precede observable developmental outcomes could help identify children in need of support prior to the onset of physical and mental health difficulties. In this study, we examined whether mothers' attachment insecurity, maltreatment history, and depressive symptoms were associated with alterations in DNA methylation patterns in their infants, and whether these correlates in the infant epigenome were associated with socioemotional and behavioral functioning in toddlerhood. We recruited 156 women oversampled for histories of depression, who completed psychiatric interviews and depression screening during pregnancy, then provided follow-up behavioral data on their children at 18 months. Buccal cell DNA was obtained from 32 of their infants for a large-scale analysis of methylation patterns across 5 × 106 individual CpG dinucleotides, using clustering-based significance criteria to control for multiple comparisons. We found that tens of thousands of individual infant CpGs were alternatively methylated in association with maternal attachment insecurity, maltreatment in childhood, and antenatal and postpartum depressive symptoms, including genes implicated in developmental patterning, cell-cell communication, hormonal regulation, immune function/inflammatory response, and neurotransmission. Density of DNA methylation at selected genes from the result set was also significantly associated with toddler socioemotional and behavioral problems. This is the first report to identify novel regions of the human infant genome at which DNA methylation patterns are associated longitudinally both with maternal characteristics and with offspring socioemotional and behavioral problems in toddlerhood.

Electron-Hole Binding Governs Carrier Transport in Halide Perovskite Nanocrystal Thin Films.

Two-dimensional halide perovskite nanoplatelets (NPLs) have exceptional light-emitting properties, including wide spectral tunability, ultrafast radiative decays, high quantum yields (QY), and oriented emission. Due to the high binding energies of electron-hole pairs, excitons are generally considered the dominant species responsible for carrier transfer in NPL films. To realize efficient devices, it is imperative to understand how exciton transport progresses therein. We employ spatially and temporally resolved optical microscopy to map exciton diffusion in perovskite nanocrystal (NC) thin films between 15 °C and 55 °C. At room temperature (RT), we find the diffusion length to be inversely correlated to the thickness of the nanocrystals (NCs). With increasing temperatures, exciton diffusion declines for all NC films, but at different rates. This leads to specific temperature turnover points, at which thinner NPLs exhibit higher diffusion lengths. We attribute this anomalous diffusion behavior to the coexistence of excitons and free electron hole-pairs inside the individual NCs within our temperature range. The organic ligand shell surrounding the NCs prevents charge transfer. Accordingly, any time an electron-hole pair spends in the unbound state reduces the FRET-mediated inter-NC transfer rates and, consequently, the overall diffusion. These results clarify how exciton diffusion progresses in strongly confined halide perovskite NC films, emphasizing critical considerations for optoelectronic devices.

Hyperexcitable arousal circuits drive sleep instability during aging.

Sleep quality declines with age; however, the underlying mechanisms remain elusive. We found that hyperexcitable hypocretin/orexin (Hcrt/OX) neurons drive sleep fragmentation during aging. In aged mice, Hcrt neurons exhibited more frequent neuronal activity epochs driving wake bouts, and optogenetic activation of Hcrt neurons elicited more prolonged wakefulness. Aged Hcrt neurons showed hyperexcitability with lower KCNQ2 expression and impaired M-current, mediated by KCNQ2/3 channels. Single-nucleus RNA-sequencing revealed adaptive changes to Hcrt neuron loss in the aging brain. Disruption of Kcnq2/3 genes in Hcrt neurons of young mice destabilized sleep, mimicking aging-associated sleep fragmentation, whereas the KCNQ-selective activator flupirtine hyperpolarized Hcrt neurons and rejuvenated sleep architecture in aged mice. Our findings demonstrate a mechanism underlying sleep instability during aging and a strategy to improve sleep continuity.