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The energy research community is continuously pursuing improvements in power system resiliency and reliability. Microgrids offer a unique opportunity for enhanced reliability and resiliency by utilizing localized generation and energy storage when grid power is unavailable or too expensive. Energy management is a critical aspect of these systems to ensure proper balancing of sources and ensuring power supply to critical loads with minimum cost, especially in an islanded microgrid. This paper presents a hierarchical real-time optimization with mathematical formulations to achieve optimal operation for an islanded microgrid. The optimization is implemented using simple numerically tractable model predictive control strategies and enables appropriate decisions in response to constantly changing conditions. The optimization method is extended for experimentation within the real-time simulation. Simulation results show that the proposed resource management algorithm shows near-optimal performance while effectively dealing with uncertainties in forecasting.

Heterogeneous integration of microelectronic components provides a pathway to improve circuit/component performance; however, this comes with assembly challenges, in particular due to complex interfaces via subsurface bump bonds. The ability of these bonds to transmit electrical signals and conduct heat to the carrier substrate limits component performance. In this work, hyperspectral frequency‐domain thermoreflectance (FDTR) imaging is demonstrated as a robust technique for evaluating the quality of subsurface indium bump bonds in a surrogate microelectronic sample. By performing microscale FDTR imaging with coarse motion image stitching, thermal phase maps that cover a 4 mm by 4 mm field‐of‐view with subsurface feature sensitivity at depths greater than 50 µm are obtained. The resulting FDTR hyperspectral data contains more than three million pixels and reveal the quality of subsurface microbump arrays. Wide‐field analysis of bonded versus gap regions is enabled by deep neural network feature reconstruction, that after training, rapidly provides an interpretable representation of bond quality. Utility of noisy higher frequency FDTR phase maps, i.e., near the computationally predicted sensing depth limit, results in an average prediction error of 11%. Taken together, FDTR with neural network‐based analysis demonstrates subsurface bond monitoring at length scales relevant for heterogeneously integrated microelectronics. In this work, wide‐field (>1 mm2) frequency‐domain thermoreflectance (FDTR) hyperspectral imaging is used to image subsurface indium bump bonds 50 µm below the surface. Thermal analysis with Monte Carlo uncertainty propagation is used to evaluate bump quality, while a trained deep neural network (can rapidly reconstruct bump geometry contact area maps.

To capture the full spectrum of genetic risk for autism, we performed a two-stage analysis of rare de novo and inherited coding variants in 42,607 autism cases, including 35,130 new cases recruited online by SPARK. We identified 60 genes with exome-wide significance ( P  < 2.5 × 10 −6 ), including five new risk genes ( NAV3 , ITSN1 , MARK2 , SCAF1 and HNRNPUL2 ). The association of NAV3 with autism risk is primarily driven by rare inherited loss-of-function (LoF) variants, with an estimated relative risk of 4, consistent with moderate effect. Autistic individuals with LoF variants in the four moderate-risk genes ( NAV3 , ITSN1 , SCAF1 and HNRNPUL2 ; n  = 95) have less cognitive impairment than 129 autistic individuals with LoF variants in highly penetrant genes ( CHD8, SCN2A, ADNP, FOXP1 and SHANK3 ) (59% vs 88%, P  = 1.9 × 10 −6 ). Power calculations suggest that much larger numbers of autism cases are needed to identify additional moderate-risk genes. An integrated analysis of de novo and inherited coding variants in 42,607 individuals with autism spectrum disorder identifies 60 risk genes of which five have not previously been associated with neurodevelopmental disorders.

Most genes associated with neurodevelopmental disorders (NDDs) were identified with an excess of de novo mutations (DNMs) but the significance in case–control mutation burden analysis is unestablished. Here, we sequence 63 genes in 16,294 NDD cases and an additional 62 genes in 6,211 NDD cases. By combining these with published data, we assess a total of 125 genes in over 16,000 NDD cases and compare the mutation burden to nonpsychiatric controls from ExAC. We identify 48 genes (25 newly reported) showing significant burden of ultra-rare (MAF < 0.01%) gene-disruptive mutations (FDR 5%), six of which reach family-wise error rate (FWER) significance ( p  < 1.25E−06). Among these 125 targeted genes, we also reevaluate DNM excess in 17,426 NDD trios with 6,499 new autism trios. We identify 90 genes enriched for DNMs (FDR 5%; e.g., GABRG2 and UIMC1 ); of which, 61 reach FWER significance ( p  < 3.64E−07; e.g., CASZ1 ). In addition to doubling the number of patients for many NDD risk genes, we present phenotype–genotype correlations for seven risk genes ( CTCF , HNRNPU , KCNQ3 , ZBTB18 , TCF12 , SPEN , and LEO1 ) based on this large-scale targeted sequencing effort. For many neurodevelopmental disorder (NDD) risk genes, the significance for mutational burden is unestablished. Here, the authors sequence 125 candidate NDD genes in over 16,000 NDD cases; case-control mutational burden analysis identifies 48 genes with a significant burden of severe ultra-rare mutations.

We examined whether different profiles of quality of life (QoL) existed among youth referred to an autism spectrum disorder (ASD) specialty clinic and, if present, determined if these groups were associated with different characteristics. Data were from parental report of 5–17 year-old youth (N = 476) who were scheduled to receive an evaluation at an ASD clinic. Parents completed questionnaires, including the Pediatric Quality of Life Inventory, assessing child and family functioning; providers reported diagnostic impressions. A latent profile analysis found five distinct groups: Low Risk, School Problems, Only Social Emotional Problems, and two Physical/Social Emotional Problems. The groups differed on clinical characteristics and family functioning. These findings have implications for more efficient and effective evaluations in service delivery systems serving complex patients.

Most genetic studies consider autism spectrum disorder (ASD) and developmental disorder (DD) separately despite overwhelming comorbidity and shared genetic etiology. Here, we analyzed de novo variants (DNVs) from 15,560 ASD (6,557 from SPARK) and 31,052 DD trios independently and also combined as broader neurodevelopmental disorders (NDDs) using three models. We identify 615 NDD candidate genes (false discovery rate [FDR] < 0.05) supported by ≥1 models, including 138 reaching Bonferroni exome-wide significance (P < 3.64e–7) in all models. The genes group into five functional networks associating with different brain developmental lineages based on singlecell nuclei transcriptomic data.We find no evidence for ASD-specific genes in contrast to 18 genes significantly enriched for DD. There are 53 genes that show mutational bias, including enrichments for missense (n = 41) or truncating (n = 12) DNVs. We also find 10 genes with evidence of male- or female-bias enrichment, including 4 X chromosome genes with significant female burden (DDX3X, MECP2, WDR45, and HDAC8). This large-scale integrative analysis identifies candidates and functional subsets of NDD genes.

Background With the increasing number of genomic sequencing studies, hundreds of genes have been implicated in neurodevelopmental disorders (NDDs). The rate of gene discovery far outpaces our understanding of genotype–phenotype correlations, with clinical characterization remaining a bottleneck for understanding NDDs. Most disease-associated Mendelian genes are members of gene families, and we hypothesize that those with related molecular function share clinical presentations. Methods We tested our hypothesis by considering gene families that have multiple members with an enrichment of de novo variants among NDDs, as determined by previous meta-analyses. One of these gene families is the heterogeneous nuclear ribonucleoproteins (hnRNPs), which has 33 members, five of which have been recently identified as NDD genes ( HNRNPK , HNRNPU , HNRNPH1 , HNRNPH2 , and HNRNPR ) and two of which have significant enrichment in our previous meta-analysis of probands with NDDs ( HNRNPU and SYNCRIP ). Utilizing protein homology, mutation analyses, gene expression analyses, and phenotypic characterization, we provide evidence for variation in 12 HNRNP genes as candidates for NDDs. Seven are potentially novel while the remaining genes in the family likely do not significantly contribute to NDD risk. Results We report 119 new NDD cases (64 de novo variants) through sequencing and international collaborations and combined with published clinical case reports. We consider 235 cases with gene-disruptive single-nucleotide variants or indels and 15 cases with small copy number variants. Three hnRNP-encoding genes reach nominal or exome-wide significance for de novo variant enrichment, while nine are candidates for pathogenic mutations. Comparison of HNRNP gene expression shows a pattern consistent with a role in cerebral cortical development with enriched expression among radial glial progenitors. Clinical assessment of probands ( n  = 188–221) expands the phenotypes associated with HNRNP rare variants, and phenotypes associated with variation in the HNRNP genes distinguishes them as a subgroup of NDDs. Conclusions Overall, our novel approach of exploiting gene families in NDDs identifies new HNRNP -related disorders, expands the phenotypes of known HNRNP -related disorders, strongly implicates disruption of the hnRNPs as a whole in NDDs, and supports that NDD subtypes likely have shared molecular pathogenesis. To date, this is the first study to identify novel genetic disorders based on the presence of disorders in related genes. We also perform the first phenotypic analyses focusing on related genes. Finally, we show that radial glial expression of these genes is likely critical during neurodevelopment. This is important for diagnostics, as well as developing strategies to best study these genes for the development of therapeutics.

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. In addition, we identified variants that are possibly associated with ASD in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.1 provides statistical support for 26 ASD risk genes. While most of these genes are already known ASD risk genes, BRSK2 has the strongest statistical support and reaches genome-wide significance as a risk gene for ASD (p-value = 2.3e−06). Future studies leveraging the thousands of individuals with ASD who have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate ASD research that incorporates genetic etiology.

This study examined lifetime medical and psychiatric morbidity reported by caregivers of 2917 autistic adults participating in the US research cohort SPARK. Participants were 78.4% male, 47.3% had intellectual disability, and 32.1% had persistent language impairments. Childhood language disorders (59.7%), speech/articulation problems (32.8%), sleep (39.4%) and eating problems (29.4%), motor delays (22.8%) and history of seizure (15.5%) were the most frequently reported clinical features. Over two thirds (67.2%) had been diagnosed with at least one psychiatric disorder (anxiety disorders: 41.1%; ADHD: 38.7%). Compared to verbally fluent participants, those with language impairments had lower frequencies of almost all psychiatric disorders. Female sex and older age were associated with higher medical and psychiatric morbidity.

Wide‐Field Frequency Domain Thermoreflectance Wide‐field (≥1 mm2) frequency‐domain thermoreflectance hyperspectral imaging is used to image subsurface indium bump bonds 50 μm below the surface. Thermal analysis enables evaluation of bump quality in a surrogate heterogeneously integrated microelectronic. More details can be found in article 2401039 by Amun Jarzembski, Fabio Semperlotti, and co‐workers.