Hodgecannon1771

Research in visual word recognition has shown that phonological neighborhood density facilitates visual word recognition. The current research was designed to determine the electrophysiological effect of phonological neighborhood density (PND). In two experiments, participants made lexical decisions to words varying on phonological neighborhood while Event-related Potentials (ERPs) were recorded. Behaviorally, the results replicate previous research by showing that words with many phonological neighbors were responded to more rapidly than were words with few phonological neighbors. However, the main contribution of the current research is that it shows an effect of PND on the N400 and Late Positive Component Event-Related Potentials. In contrast to previous reports in the literature, the nature of the effect was such that the N400 was larger to words with few phonological neighbors than to words with many. Experiment 2 replicated these findings and provided estimates of the independent components' time course and source localization. The increased N400 for small neighborhood words is thought to reflect additional semantic processing required for these words due their weaker phonological representations.Charcot-Marie-Tooth disease type 2A (CMT2A) is an untreatable childhood peripheral neuropathy caused by mutations of the mitochondrial fusion protein, mitofusin (MFN) 2. Here, pharmacological activation of endogenous normal mitofusins overcame dominant inhibitory effects of CMT2A mutants in reprogrammed human patient motor neurons, reversing hallmark mitochondrial stasis and fragmentation independent of causal MFN2 mutation. In mice expressing human MFN2 T105M, intermittent mitofusin activation with a small molecule, MiM111, normalized CMT2A neuromuscular dysfunction, reversed pre-treatment axon and skeletal myocyte atrophy, and enhanced axon regrowth by increasing mitochondrial transport within peripheral axons and promoting in vivo mitochondrial localization to neuromuscular junctional synapses. MiM111-treated MFN2 T105M mouse neurons exhibited accelerated primary outgrowth and greater post-axotomy regrowth, linked to enhanced mitochondrial motility. MiM111 is the first pre-clinical candidate for CMT2A.Internal state alters sensory behaviors to optimize survival strategies. The neuronal mechanisms underlying hunger-dependent behavioral plasticity are not fully characterized. Here we show that feeding state alters C. elegans thermotaxis behavior by engaging a modulatory circuit whose activity gates the output of the core thermotaxis network. Feeding state does not alter the activity of the core thermotaxis circuit comprised of AFD thermosensory and AIY interneurons. Instead, prolonged food deprivation potentiates temperature responses in the AWC sensory neurons, which inhibit the postsynaptic AIA interneurons to override and disrupt AFD-driven thermotaxis behavior. Acute inhibition and activation of AWC and AIA, respectively, restores negative thermotaxis in starved animals. We find that state-dependent modulation of AWC-AIA temperature responses requires INS-1 insulin-like peptide signaling from the gut and DAF-16/FOXO function in AWC. Our results describe a mechanism by which functional reconfiguration of a sensory network via gut-brain signaling drives state-dependent behavioral flexibility.Effective learning requires using errors in a task-dependent manner, for example adjusting to errors that result from unpredicted environmental changes but ignoring errors that result from environmental stochasticity. Where and how the brain represents errors in a task-dependent manner and uses them to guide behavior are not well understood. We imaged the brains of human participants performing a predictive-inference task with two conditions that had different sources of errors. Their performance was sensitive to this difference, including more choice switches after fundamental changes versus stochastic fluctuations in reward contingencies. Using multi-voxel pattern classification, we identified task-dependent representations of error magnitude and past errors in posterior parietal cortex. These representations were distinct from representations of the resulting behavioral adjustments in dorsomedial frontal, anterior cingulate, and orbitofrontal cortex. Proteasome inhibitor The results provide new insights into how the human brain represents errors in a task-dependent manner and guides subsequent adaptive behavior.Insects utilize diverse food resources which can affect the evolution of their genomic repertoire, including leading to gene losses in different nutrient pathways. Here, we investigate gene loss in amino acid synthesis pathways, with special attention to hymenopterans and parasitoid wasps. Using comparative genomics, we find that synthesis capability for tryptophan, phenylalanine, tyrosine, and histidine was lost in holometabolous insects prior to hymenopteran divergence, while valine, leucine, and isoleucine were lost in the common ancestor of Hymenoptera. Subsequently, multiple loss events of lysine synthesis occurred independently in the Parasitoida and Aculeata. Experiments in the parasitoid Cotesia chilonis confirm that it has lost the ability to synthesize eight amino acids. Our findings provide insights into amino acid synthesis evolution, and specifically can be used to inform the design of parasitoid artificial diets for pest control.Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.