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Abstract Homophily, the tendency for individuals to preferentially interact with others similar to themselves is typically documented via self-report and, for children, adult report. Few studies have investigated homophily directly using objective measures of social movement. We quantified homophily in children with developmental disabilities (DD) and typical development (TD) using objective measures of position/orientation in preschool inclusion classrooms, designed to promote interaction between these groups of children. Objective measurements were collected using ultra-wideband radio-frequency tracking to determine social approach and social contact, measures of social movement and interaction. Observations of 77 preschoolers (47 with DD, and 30 TD) were conducted in eight inclusion classrooms on a total of 26 days. We compared DD and TD groups with respect to how children approached and shared time in social contact with peers using mixed-effects models. Children in concordant dyads (DD-DD and TD-TD) both moved toward each other at higher velocities and spent greater time in social contact than discordant dyads (DD-TD), evidencing homophily. DD-DD dyads spent less time in social contact than TD-TD dyads but were comparable to TD-TD dyads in their social approach velocities. Children’s preference for similar peers appears to be a pervasive feature of their naturalistic interactions. 

Data from high-energy observations are usually obtained as lists of photon events. A common analysis task for such data is to identify whether diffuse emission exists, and to estimate its surface brightness, even in the presence of point sources that may be superposed. We have developed a novel nonparametric event list segmentation algorithm to divide up the field of view into distinct emission components. We use photon location data directly, without binning them into an image. We first construct a graph from the Voronoi tessellation of the observed photon locations and then grow segments using a new adaptation of seeded region growing that we callSeeded Region Growing on Graph, after which the overall method is namedSRGonG. Starting with a set of seed locations, this results in an oversegmented data set, whichSRGonGthen coalesces using a greedy algorithm where adjacent segments are merged to minimize a model comparison statistic; we use the Bayesian Information Criterion. UsingSRGonGwe are able to identify point-like and diffuse extended sources in the data with equal facility. We validateSRGonGusing simulations, demonstrating that it is capable of discerning irregularly shaped low-surface-brightness emission structures as well as point-like sources with strengths comparable to that seen in typical X-ray data. We demonstrateSRGonG’s use on the Chandra data of the Antennae galaxies and show that it segments the complex structures appropriately.

 

Diadenosine tetraphosphate (Ap4A) is a putative second messenger molecule that is conserved from bacteria to man. Nevertheless, its physiological role, and the underlying molecular mechanisms, are poorly characterized. We investigated the molecular mechanism by which Ap4A regulates inosine-5’-monophosphate dehydrogenase (IMPDH, a key branching point enzyme for the biosynthesis of adenosine or guanosine nucleotides) in Bacillus subtilis. We solved the crystal structure of BsIMPDH bound to Ap4A at a resolution of 2.45 Å to show that Ap4A binds to the interface between two IMPDH subunits, acting as the glue that switches active IMPDH tetramers into less active octamers. Guided by these insights, we engineered mutant strains of B. subtilis that bypass Ap4A-dependent IMPDH regulation without perturbing intracellular Ap4A pools themselves. We used metabolomics suggesting that these mutants have a dysregulated purine, and in particular GTP, metabolome and phenotypic analysis showing increased sensitivity of B. subtilis IMPDH mutant strains to heat compared with wild-type. Our study identifies a central role for IMPDH in remodelling metabolism and heat resistance, and provides evidence that Ap4A can function as an alarmone. 

Abstract Background Personal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals’ geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method. Methods This study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios. Results The results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual’s home and all the other daily activity locations. Conclusions This new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing. 

Deep-learning methods enable the scaffolding of desired functional residues within a well-folded designed protein. 

The optical spectra of transition metal dichalcogenide monolayers are dominated by excitons and trions. Here, we establish the dependence of these optical transitions on the disorder from hyperspectral imaging of h-BN encapsulated monolayer MoSe₂. While both exciton and trion energies vary spatially, these two quantities are almost perfectly correlated, with spatial variation in the trion binding energy of only ∼0.18 meV. In contrast, variation in the energy splitting between the two lowest energy exciton states is one order of magnitude larger at ∼1.7 meV. Statistical analysis and theoretical modeling reveal that disorder results from dielectric and bandgap fluctuations, not electrostatic fluctuations. Our results shed light on disorder in high quality TMDC monolayers, its impact on optical transitions, and the many-body nature of excitons and trions.