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Postoperative Pain Management: Efficacy associated with Caudal Tramadol throughout Kid Lower Abdominal Surgery: A Randomized Medical Research.
Our understanding of synaptic connectivity in the brain relies on the ability to accurately trace sparsely labeled neurons from 3D optical microscopy stacks of images. A variety of automated algorithms and software tools have been developed for this task. These algorithms can capture the general layout of neurites with high fidelity, but the resulting traces often contain topological errors such as broken and incorrectly merged branches. Even a small number of isolated topological errors can drastically alter the connectivity, and therefore, their detection and correction are paramount for connectomics studies. Here, we describe an automated trace proofreading approach that utilizes machine learning to correct trace topology. Multiple stacks of neuron images were traced by two users to create a labeled dataset and assess the baseline of inter-user variability. All traces were then disconnected at branch points and a deep neural network was trained to detect the correct way of reconnecting the branches. Custom morphological features were generated for each cluster of branch points, in a way that is dependent on a merging scenario but invariant to translations, rotations, and reflections of the cluster in the imaging plane. The features and image volume centered at the branch point were used for training a neural network that concatenates these input streams and outputs the confidence measure for different branch merging scenarios. The designed method significantly reduces the number of topological errors in automated traces and comes close to the accuracy achieved by expert users which is the gold standard in the field.Yields of organoindium reagents synthesized from indium metal were previously reported to be highly dependent on metal batch and supplier due to the presence or absence of anticaking agent. Here, single-particle fluorescence microscopy established that MgO, an additive in some batches nominally for anticaking, significantly increased the physisorption of small-molecule organics onto the surface of the resulting MgO-coated indium metal particles. An inert and relatively nonpolar boron dipyrromethene fluorophore with a hydrocarbon tail provided a sensitive probe for this surface physisorption. SEM images revealed markedly different surface properties of indium particles either with or without MgO, consistent with their different physisorption properties observed by fluorescence microscopy. We further documented incomplete commercial bottle labeling regarding the presence and composition of this anticaking agent, both within our laboratory and previously in the literature, which may complicate reproducibility between laboratories. Trimethylsilyl chloride pretreatment, a step employed in a subset of reported synthetic procedures, removed the anticaking agent and produced particles with similar physisorption properties as commercial batches of indium powder distributed without the anticaking agent. These data indicate the possibility of an additional substrate/catalyst physisorption mechanism by which the anticaking agent may be influencing synthetic procedures that generate organoindium reagents from indium metal, in addition to simple anticaking.Youth in foster care often experience more difficulty in school compared to their non-foster care peers. Difficulties exist across domains of academic functioning, including both performance (e.g., low grades) and behavioral health (e.g., high externalizing concerns) in school. One factor that has shown to be associated with positive academic functioning in the general population but remains to be comprehensively examined among youth in foster care is social support. This includes examining specific sources of support for youth in foster care and taking into consideration the context of the frequent placement disruptions many children in foster care experience. This study sought to determine which sources of social support are associated with academic functioning for youth in foster care by examining child-report of social support from parents, teachers, friends, and classmates in relation to school grades and teacher-reported behavioral health outcomes. Information on each source of social support was obtained from the self-report of 257 youth in foster care, and information on placement characteristics were obtained from child welfare casefiles. Teachers provided information on youth's behavioral health in school, and academic grades were obtained from school records. Results suggested that youth reported teacher social support, as compared to parent, friend, or classmate social support, was most influential for both performance and behavioral health in school. Findings highlight the need for additional research on the important role of teachers for promoting academic success amongst youth in foster care, as well as the importance of placement changes in relation to academic functioning.Although the rattling of rattlesnakes (Crotalus and Sistrurus) is widely accepted as being aposematic, the hypothesis that rattling deters approach from the snake's potentially dangerous adversaries has not been well tested. In a controlled study using rattling recorded from captive rattlesnakes (C. oreganus helleri) and a variety of comparison sounds or no-sound controls, domestic dogs (Canis familiaris) showed no hesitation to approach camouflaged speakers projecting the recorded rattles. The dogs were equally likely to approach speakers projecting rattling as they were to approach speakers playing control sounds, or speakers that were silent. Furthermore, the dogs spent no less time in front of the speakers projecting the rattles than they did in front of speakers projecting control sounds or no sound. The dogs' reactions may not be representative of other species with whom rattlesnakes come into contact, but the data suggest a need for some circumspection about the role of rattling in the rattlesnake's defensive repertoire. Our results also suggest that dogs may be vulnerable to envenomation because they fail to react to the sound of rattling with avoidance.Eddy covariance (EC) measurements of ecosystem-atmosphere carbon dioxide (CO2) exchange provide the most direct assessment of the terrestrial carbon cycle. Measurement biases for open-path (OP) CO2 concentration and flux measurements have been reported for over 30 years, but their origin and appropriate correction approach remain unresolved. Here, we quantify the impacts of OP biases on carbon and radiative forcing budgets for a sub-boreal wetland. Comparison with a reference closed-path (CP) system indicates that a systematic OP flux bias (0.54 μmol m-2 s-1) persists for all seasons leading to a 110% overestimate of the ecosystem CO2 sink (cumulative error of 78 gC m-2). Two potential OP bias sources are considered Sensor-path heat exchange (SPHE) and analyzer temperature sensitivity. We examined potential OP correction approaches including i) Fast temperature measurements within the measurement path and sensor surfaces; ii) Previously published parameterizations; and iii) Optimization algorithms. The measurements revealed year-round average temperature and heat flux gradients of 2.9 °C and 16 W m-2 between the bottom sensor surfaces and atmosphere, indicating SPHE-induced OP bias. However, measured SPHE correlated poorly with the observed differences between OP and CP CO2 fluxes. While previously proposed nominally universal corrections for SPHE reduced the cumulative OP bias, they led to either systematic under-correction (by 38.1 gC m-2) or to systematic over-correction (by 17-37 gC m-2). The resulting budget errors exceeded CP random uncertainty and change the sign of the overall carbon and radiative forcing budgets. Analysis of OP calibration residuals as a function of temperature revealed a sensitivity of 5 μmol m-3 K-1. This temperature sensitivity causes CO2 calibration errors proportional to sample air fluctuations that can offset the observed growing season flux bias by 50%. Consequently, we call for a new OP correction framework that characterizes SPHE- and temperature-induced CO2 measurement errors.CoCr alloy-based femoral heads have failed prematurely due to galvanic-induced corrosion when coupled with a titanium hip stem. Coupling a titanium based-femoral head with the titanium hip stem is ideal in addressing this failure mode. Ti6Al4V (Ti64) alloy was reinforced with zirconia-toughened alumina (ZTA) by directed-energy deposition (DED)-based additive manufacturing (AM) to address that concern. Preliminary materials processing work resulted in failed samples due to cracking, porosity, and delamination. After careful parameter optimization, a Ti64+5wt.%ZTA (5ZTA) composition produced a metallurgically sound and coherent interface, minimal porosity, and bulk structures. Hardness was observed to increase by 27%, normalized wear rate reduced by 25%, and contact resistance increased during in vitro tribological testing along with faster surface re-passivation.The directed energy deposition (DED)-based additive manufacturing (AM) was used to create compositionally graded pure Al-12Si to pure Al2O3 structures varying the powder feed rates during deposition. Thermal diffusivity of Al-12Si+Al2O3 structures was reduced by >60% compared to pure Al-12Si. With a pure Al2O3 ceramic layer on Al-12Si+Al2O3, our results confirm the feasibility of designing and manufacturing metal-ceramic composites via AM with tailored thermal properties.Causal inference with observational longitudinal data and time-varying exposures is often complicated by time-dependent confounding and attrition. The G-computation formula is one approach for estimating a causal effect in this setting. The parametric modeling approach typically used in practice relies on strong modeling assumptions for valid inference, and moreover depends on an assumption of missing at random, which is not appropriate when the missingness is missing not at random (MNAR) or due to death. In this work we develop a flexible Bayesian semi-parametric G-computation approach for assessing the causal effect on the subpopulation that would survive irrespective of exposure, in a setting with MNAR dropout. The approach is to specify models for the observed data using Bayesian additive regression trees, and then use assumptions with embedded sensitivity parameters to identify and estimate the causal effect. The proposed approach is motivated by a longitudinal cohort study on cognition, health, and aging, and we apply our approach to study the effect of becoming a widow on memory. We also compare our approach to several standard methods.
We consider the prevalence of family complexity and its association with children's externalizing behavior problems over children's life course and over historical time.

A growing literature has demonstrated the prevalence and multidimensional nature of family complexity and its association with child behavior. The nature/strength of this association may have changed in recent cohorts as family complexity has become more common.

Data are from the 1997 and 2014 cohorts of the Panel Study of Income Dynamics Child Development Supplement. Samples represent U.S. Lifirafenib children ages 0-12 years born since 1985 (N=5,030). Ordinary least squares regression estimated change in the association between family complexity and behavior between cohorts. Difference-in-difference models estimated baseline and longitudinal differences in children's behavior as linked to family complexity.

The prevalence of family complexity has stabilized over the last two decades, and the antecedents to parental repartnering and complex sibship organization remain similar.
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