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U.S. national and state population-based estimates of adults living with autism spectrum disorder (ASD) are nonexistent due to the lack of existing surveillance systems funded to address this need. Therefore, we estimated national and state prevalence of adults 18–84 years living with ASD using simulation in conjunction with Bayesian hierarchal models. In 2017, we estimated that approximately 2.21% (95% simulation interval (SI) 1.95%, 2.45%) or 5,437,988 U.S. adults aged 18 and older have ASD, with state prevalence ranging from 1.97% (95% SI 1.55%, 2.45%) in Louisiana to 2.42% (95% SI 1.93%, 2.99%) in Massachusetts. Prevalence and case estimates of adults living with ASD (diagnosed and undiagnosed) can help states estimate the need for diagnosing and providing services to those unidentified.

Spatial positions of cells in tissues strongly influence function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking.We developed Slide-seq, a method for transferring RNA from tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the locations of the RNA to be inferred by sequencing. Using Slide-seq,we localized cell types identified by single-cell RNA sequencing datasets within the cerebellum and hippocampus, characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, and defined the temporal evolution of cell type–specific responses in a mouse model of traumatic brain injury.These studies highlight how Slide-seq provides a scalablemethod for obtaining spatially resolved gene expression data at resolutions comparable to the sizes of individual cells.

Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations. The method introduces a second network during training to provide suitable \"target\" dynamics useful for performing the task. Because it exploits the full recurrent connectivity, the method produces networks that perform tasks with fewer neurons and greater noise robustness than traditional least-squares (FORCE) approaches. In addition, we show how introducing additional input signals into the target-generating network, which act as task hints, greatly extends the range of tasks that can be learned and provides control over the complexity and nature of the dynamics of the trained, task-performing network.

This paper considers how to identify statistical outliers in psychophysical datasets where the underlying sampling distributions are unknown. Eight methods are described, and each is evaluated using Monte Carlo simulations of a typical psychophysical experiment. The best method is shown to be one based on a measure of spread known as S n . This is shown to be more sensitive than popular heuristics based on standard deviations from the mean, and more robust than non-parametric methods based on percentiles or interquartile range. Matlab code for computing S n is included.

In this meta-analysis, we systematically reviewed research on digital games and learning for K–16 students. We synthesized comparisons of game versus nongame conditions (i.e., media comparisons) and comparisons of augmented games versus standard game designs (i.e., value-added comparisons). We used random-effects meta-regression models with robust variance estimates to summarize overall effects and explore potential moderator effects. Results from media comparisons indicated that digital games significantly enhanced student learning relative to nongame conditions (ḡ = 0.33, 95% confidence interval [0.19, 0.48], k = 57, n = 209). Results from value-added comparisons indicated significant learning benefits associated with augmented game designs (ḡ = 0.34, 95% confidence interval [0.17, 0.51], k = 20, n = 40). Moderator analyses demonstrated that effects varied across various game mechanics characteristics, visual and narrative characteristics, and research quality characteristics. Taken together, the results highlight the affordances of games for learning as well as the key role of design beyond medium.

This meta-analysis provides an extensive and organized summary of intervention studies in education that are grounded in motivation theory. We identified 74 published and unpublished papers that experimentally manipulated an independent variable and measured an authentic educational outcome within an ecologically valid educational context. Our analyses included 92 independent effect sizes with 38,377 participants. Our results indicated that interventions were generally effective, with an average mean effect size of d = 0.49 (95% confidence interval = [0.43, 0.56]). Although there were descriptive differences in the effect sizes across several moderator variables considered in our analyses, the only significant difference found was for the type of experimental design, with randomized designs having smaller effect sizes than quasi-experimental designs. This work illustrates the extent to which interventions and accompanying theories have been tested via experimental methods and provides information about appropriate next steps in developing and testing effective motivation interventions in education.

Insects are the most speciose group of animals, but the phylogenetic relationships of many major lineages remain unresolved. We inferred the phylogeny of insects from 1478 protein-coding genes. Phylogenomic analyses of nucleotide and amino acid sequences, with site-specific nucleotide or domain-specific amino acid substitution models, produced statistically robust and congruent results resolving previously controversial phylogenetic relationships. We dated the origin of insects to the Early Ordovician [~479 million years ago (Ma)], of insect flight to the Early Devonian (~406 Ma), of major extant lineages to the Mississippian (~345 Ma), and the major diversification of holometabolous insects to the Early Cretaceous. Our phylogenomic study provides a comprehensive reliable scaffold for future comparative analyses of evolutionary innovations among insects.

Practitioners and policymakers rely on meta-analyses to inform decision making around the allocation of resources to individuals and organizations. It is therefore paramount to consider the validity of these results. A well-documented threat to the validity of research synthesis results is the presence of publication bias, a phenomenon where studies with large and/or statistically significant effects, relative to studies with small or null effects, are more likely to be published. We investigated this phenomenon empirically by reviewing meta-analyses published in top-tier journals between 1986 and 2013 that quantified the difference between effect sizes from published and unpublished research. We reviewed 383 meta-analyses of which 81 had sufficient information to calculate an effect size. Results indicated that published studies yielded larger effect sizes than those from unpublished studies (d; = 0.18, 95% confidence interval [0.10, 0.25]). Moderator analyses revealed that the difference was larger in meta-analyses that included a wide range of unpublished literature. We conclude that intervention researchers require continued support to publish null findings and that meta-analyses should include unpublished studies to mitigate the potential bias from publication status.

Image scanning microscopy (ISM) can improve the effective spatial resolution of confocal microscopy to its theoretical limit. However, current implementations are not robust or versatile, and are incompatible with fluorescence lifetime imaging (FLIM). We describe an implementation of ISM based on a single-photon detector array that enables super-resolution FLIM and improves multicolor, live-cell and in-depth imaging, thereby paving the way for a massive transition from confocal microscopy to ISM.A single-photon detector array enables robust and versatile image scanning microscopy (ISM) on any confocal microscope. This implementation makes super-resolution FLIM possible and eases a transition from confocal microscopy to ISM.

Key Points The 'robustness' of a phenotypic trait is the absence or low level of variation when faced with a given incoming variation. Analysis of the propagation of variation in a biological system has been approached differently in evolutionary quantitative genetics and in systems biology studies, but a new unifying approach is now possible. The robustness of a downstream phenotype to a range of variation in an upstream component may be explained by pervasive nonlinearities and plateaus in quantitative relationships between system components. Many recent reports of robustness-conferring genes do not distinguish between effects on phenotypic mean and variance, and therefore are just another way to refer to the low penetrance and condition-dependence of mutations. A robust feature is not necessarily an evolutionary advantage and may have arisen under neutral evolution or through pleiotropy. Robustness of a phenotypic trait is characterized as lack of, or low, variance in that phenotype under a particular genetic or environmental perturbation. The authors review recent studies characterizing robustness, provide guidance in reporting robust features and insights into how variation propagates across biological systems. Robustness is characterized by the invariant expression of a phenotype in the face of a genetic and/or environmental perturbation. Although phenotypic variance is a central measure in the mapping of the genotype and environment to the phenotype in quantitative evolutionary genetics, robustness is also a key feature in systems biology, resulting from nonlinearities in quantitative relationships between upstream and downstream components. In this Review, we provide a synthesis of these two lines of investigation, converging on understanding how variation propagates across biological systems. We critically assess the recent proliferation of studies identifying robustness-conferring genes in the context of the nonlinearity in biological systems.