Wilkinsonholdt2760

von Economo neurons (VENs) are bipolar, spindle-shaped neurons restricted to layer 5 of human frontoinsula and anterior cingulate cortex that appear to be selectively vulnerable to neuropsychiatric and neurodegenerative diseases, although little is known about other VEN cellular phenotypes. Single nucleus RNA-sequencing of frontoinsula layer 5 identifies a transcriptomically-defined cell cluster that contained VENs, but also fork cells and a subset of pyramidal neurons. Cross-species alignment of this cell cluster with a well-annotated mouse classification shows strong homology to extratelencephalic (ET) excitatory neurons that project to subcerebral targets. This cluster also shows strong homology to a putative ET cluster in human temporal cortex, but with a strikingly specific regional signature. Together these results suggest that VENs are a regionally distinctive type of ET neuron. Additionally, we describe the first patch clamp recordings of VENs from neurosurgically-resected tissue that show distinctive intrinsic membrane properties relative to neighboring pyramidal neurons.The flat periwinkles, Littorina fabalis and L. obtusata, comprise two sister gastropod species that have an enormous potential to elucidate the mechanisms involved in ecological speciation in the marine realm. However, the molecular resources currently available for these species are still scarce. In order to circumvent this limitation, we used RNA-seq data to characterize the transcriptome of four individuals from each species sampled in different locations across the Iberian Peninsula. click here Four de novo transcriptome assemblies were generated, as well as a pseudo-reference using the L. saxatilis reference transcriptome as backbone. After transcripts' annotation, variant calling resulted in the identification of 19,072 to 45,340 putatively species-diagnostic SNPs. The discriminatory power of a subset of these SNPs was validated by implementing an independent genotyping assay to characterize reference populations, resulting in an accurate classification of individuals into each species and in the identification of hybrids between the two. These data comprise valuable genomic resources for a wide range of evolutionary and conservation studies in flat periwinkles and related taxa.Asymmetries in motor behavior, such as human hand preference, are observed throughout bilateria. However, neural substrates and developmental signaling pathways that impose underlying functional lateralization on a broadly symmetric nervous system are unknown. Here we report that in the absence of over-riding visual information, zebrafish larvae show intrinsic lateralized motor behavior that is mediated by a cluster of 60 posterior tuberculum (PT) neurons in the forebrain. PT neurons impose motor bias via a projection through the habenular commissure. Acquisition of left/right identity is disrupted by heterozygous mutations in mosaic eyes and mindbomb, genes that regulate Notch signaling. These results define the neuronal substrate for motor asymmetry in a vertebrate and support the idea that haploinsufficiency for genes in a core developmental pathway destabilizes left/right identity.One primary reason that makes single-cell RNA-seq analysis challenging is dropouts, where the data only captures a small fraction of the transcriptome of each cell. Almost all computational algorithms developed for single-cell RNA-seq adopted gene selection, dimension reduction or imputation to address the dropouts. Here, an opposite view is explored. Instead of treating dropouts as a problem to be fixed, we embrace it as a useful signal. We represent the dropout pattern by binarizing single-cell RNA-seq count data, and present a co-occurrence clustering algorithm to cluster cells based on the dropout pattern. We demonstrate in multiple published datasets that the binary dropout pattern is as informative as the quantitative expression of highly variable genes for the purpose of identifying cell types. We expect that recognizing the utility of dropouts provides an alternative direction for developing computational algorithms for single-cell RNA-seq analysis.With more than 820 million undernourished people living in rural areas of low- and middle-income countries (LMICs), ending hunger and ensuring access to food by all is a global priority. In the past few decades, the adoption of technological innovations in the agricultural sector and related crop yield improvements have not led to expected improvements in the nutritional status of rural households in many LMICs. The increased energy expenditure associated with the adoption of productivity-enhancing innovations may provide an important explanation of the disconnect between agricultural productivity enhancements and improved nutritional outcomes. We develop a methodology for generating reliable livelihood energy/calorie expenditure profiles for rural agricultural households using research-grade accelerometer devices. We integrate the data on physical activity and energy expenditure in rural households with data on time-use and food intakes to generate a data set that provides a unique window into rural livelihoods. This can be a valuable resource to analyse agriculture-nutrition impact pathways and improve the welfare of rural and agricultural households.Efficient interconversion of both classical and quantum information between microwave and optical frequency is an important engineering challenge. The optomechanical approach with gigahertz-frequency mechanical devices has the potential to be extremely efficient due to the large optomechanical response of common materials, and the ability to localize mechanical energy into a micron-scale volume. However, existing demonstrations suffer from some combination of low optical quality factor, low electrical-to-mechanical transduction efficiency, and low optomechanical interaction rate. Here we demonstrate an on-chip piezo-optomechanical transducer that systematically addresses all these challenges to achieve nearly three orders of magnitude improvement in conversion efficiency over previous work. Our modulator demonstrates acousto-optic modulation with [Formula see text] = 0.02 V. We show bidirectional conversion efficiency of [Formula see text] with 3.3 μW  red-detuned optical pump, and [Formula see text] with 323 μW blue-detuned pump.