Usage-based theories assume that all aspects of language processing are shaped by the distributional properties of the language. The frequency not only of words but also of larger chunks plays a major role in language processing. These theories predict that the frequency of phrases influences the time needed to prepare these phrases for production and their acoustic duration. By contrast, dominant psycholinguistic models of utterance production predict no such effects. In these models, the system keeps track of the frequency of individual words but not of co-occurrences. This study investigates the extent to which the frequency of phrases impacts naming latencies and acoustic duration with a balanced design, where the same words are recombined to build high- and low-frequency phrases. The brain signal of participants is recorded so as to obtain information on the electrophysiological bases and functional locus of frequency effects. Forty-seven participants named pictures using high- and low-frequency adjective-noun phrases. Naming latencies were shorter for high-frequency than low-frequency phrases. There was no evidence that phrase frequency impacted acoustic duration. The electrophysiological signal differed between high- and low-frequency phrases in time windows that do not overlap with conceptualization or articulation processes. These findings suggest that phrase frequency influences the preparation of phrases for production, irrespective of the lexical properties of the constituents, and that this effect originates at least partly when speakers access and encode linguistic representations. Moreover, this study provides information on how the brain signal recorded during the preparation of utterances changes with the frequency of word combinations. Recent work on US Whites from clinical samples used obstructive sleep apnea (OSA) symptoms to generate phenotypes for individuals with moderate-severe OSA which suggested 3 to 5 symptom classes. However, it is unknown whether similar classes generalize to diverse Hispanics/Latino adults. Therefore, we sought to fill this gap by empirically deriving sleep phenotypes among a large sample of diverse Hispanics/Latinos. We used data from The Hispanic Community Health Study/Study of Latinos (HCHS/SOL; 2008-2011), a prospective cohort study designed using a multisite multistage probability sample of adults 18-74 years old. The subpopulation of interest included participants with moderate-severe OSA symptoms (≥15 respiratory event index (REI) events per hour; n=1,605). We performed latent class analysis for complex survey data using 15 common OSA symptoms (e.g. Epworth Sleepiness Scale) and four comorbidities to identify phenotype classes. Average age was 52.4 ± 13.9 years and 34.0% were female. Mean respiratory event index was 33.8 ± 22.5 events per hour. Fit statistics and clinical significance suggested that a three-class solution provided best fit to the data. The three phenotypes were 1) Minimally Symptomatic (47.7%), 2) Excessive sleepiness (37.1%), and (3) Disturbed Sleep (15.2%). Sensitivity models were consistent with main proposed solution. Derived sleep phenotypes among diverse Hispanic/Latinos were consistent with recent findings from the Sleep Apnea Global Interdisciplinary Consortium, but we found notable differences in class prevalence relative to Whites. Further research is needed to link derived sleep phenotypes to health comorbidities in diverse populations.Derived sleep phenotypes among diverse Hispanic/Latinos were consistent with recent findings from the Sleep Apnea Global Interdisciplinary Consortium, but we found notable differences in class prevalence relative to Whites. Verteporfin manufacturer Further research is needed to link derived sleep phenotypes to health comorbidities in diverse populations.Reward enhances stimulus processing in the visual cortex, but the mechanisms through which this effect occurs remain unclear. Reward prospect can both increase the deployment of voluntary attention and increase the salience of previously neutral stimuli. In this study, we orthogonally manipulated reward and voluntary attention while human participants performed a global motion detection task. We recorded steady-state visual evoked potentials to simultaneously measure the processing of attended and unattended stimuli linked to different reward probabilities, as they compete for attentional resources. The processing of the high rewarded feature was enhanced independently of voluntary attention, but this gain diminished once rewards were no longer available. Neither the voluntary attention nor the salience account alone can fully explain these results. Instead, we propose how these two accounts can be integrated to allow for the flexible balance between reward-driven increase in salience and voluntary attention.GC sites distant from 8-oxo-7,8-dihydroguanine (G O, 8-hydroxyguanine) are frequently mutated when the lesion-bearing plasmid DNA is replicated in human cells with reduced Werner syndrome (WRN) protein. To detect the untargeted mutations preferentially, the oxidized guanine base was placed downstream of the reporter supF gene and the plasmid DNA was introduced into WRN-knockdown cells. The total mutant frequency seemed higher in the WRN-knockdown cells as compared to the control cells. Mutation analyses revealed that substitution mutations occurred at the GC pairs of 5'-GpA-3'/5'-TpC-3' sites, the preferred sequence for the apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like 3 (APOBEC3)-family cytosine deaminases, in the supF gene in both control and knockdown cells. These mutations were observed more frequently at G sites than C sites on the DNA strand where the G O base was originally located. This tendency was promoted by the knockdown of the WRN protein. The present results imply the possible involvement of APOBEC3-family cytosine deaminases in the action-at-a-distance (untargeted) mutations at GC (or G) sites induced by G O and in cancer initiation by oxidative stress.Deep neural networks (DNNs) trained on object recognition provide the best current models of high-level visual cortex. What remains unclear is how strongly experimental choices, such as network architecture, training, and fitting to brain data, contribute to the observed similarities. Here, we compare a diverse set of nine DNN architectures on their ability to explain the representational geometry of 62 object images in human inferior temporal (hIT) cortex, as measured with fMRI. We compare untrained networks to their task-trained counterparts and assess the effect of cross-validated fitting to hIT, by taking a weighted combination of the principal components of features within each layer and, subsequently, a weighted combination of layers. For each combination of training and fitting, we test all models for their correlation with the hIT representational dissimilarity matrix, using independent images and subjects. Trained models outperform untrained models (accounting for 57% more of the explainable variance), suggesting that structured visual features are important for explaining hIT.