Armstrong Bjerring (lunchchange5)

The examination of this number at different distances from the surface of the lacunae demonstrates branching in the canaliculi network. We analyzed the impact of spatial resolution on quantification by comparing parameters extracted from the same samples imaged with 120 nm and 30 nm voxel sizes. To avoid any bias related to the analysis region, the volumes at 120 nm and 30 nm were registered and cropped to the same field of view. Our results show that the measurements at 120 and 30 nm are strongly correlated in our data set but that the highest spatial resolution provides more accurate information on the canaliculi network and its branching properties.Forecasting healthcare utilization has the potential to anticipate care needs, either accelerating needed care or redirecting patients toward care most appropriate to their needs. While prior research has utilized clinical information to forecast readmissions, analyzing digital footprints from social media can inform our understanding of individuals' behaviors, thoughts, and motivations preceding a healthcare visit. We evaluate how language patterns on social media change prior to emergency department (ED) visits and inpatient hospital admissions in this case-crossover study of adult patients visiting a large urban academic hospital system who consented to share access to their history of Facebook statuses and electronic medical records. An ensemble machine learning model forecasted ED visits and inpatient admissions with out-of-sample cross-validated AUCs of 0.64 and 0.70 respectively. Prior to an ED visit, there was a significant increase in depressed language (Cohen's d = 0.238), and a decrease in informal language (d = 0.345). Facebook posts prior to an inpatient admission showed significant increase in expressions of somatic pain (d = 0.267) and decrease in extraverted/social language (d = 0.357). These results are a first step in developing methods to utilize user-generated content to characterize patient care-seeking context which could ultimately enable better allocation of resources and potentially early interventions to reduce unplanned visits.Despite its popularity, the construct of biological motion (BM) and its putative anomalies in autism spectrum disorder (ASD) are not completely clarified. In this article, we present a meta-analysis investigating the putative anomalies of BM perception in ASD. Through a systematic literature search, we found 30 studies that investigated BM perception in both ASD and typical developing peers by using point-light display stimuli. A general meta-analysis including all these studies showed a moderate deficit of individuals with ASD in BM processing, but also a high heterogeneity. This heterogeneity was explored in different additional meta-analyses where studies were grouped according to levels of complexity of the BM task employed (first-order, direct and instrumental), and according to the manipulation of low-level perceptual features (spatial vs. temporal) of the control stimuli. Results suggest that the most severe deficit in ASD is evident when perception of BM is serving a secondary purpose (e.g., inferring intentionality/action/emotion) and, interestingly, that temporal dynamics of stimuli are an important factor in determining BM processing anomalies in ASD. Our results question the traditional understanding of BM anomalies in ASD as a monolithic deficit and suggest a paradigm shift that deconstructs BM into distinct levels of processing and specific spatio-temporal subcomponents.An amendment to this paper has been published and can be accessed via a link at the top of the paper.Pollination and seed dispersal patterns determine gene flow within plant populations. In tropical forests, a high proportion of trees are dioecious, insect pollinated and dispersed by vertebrates. Dispersal vectors and density dependent factors may modulate realized gene flow and influ