Miracle-dz/UniFAR-FTUs huggingface dataset:
Longitudinal Changes in Diffusion Tensor Imaging Following Mild Traumatic Brain Injury and Correlation With Outcome

The chronic consequences of traumatic brain injury (TBI) may contribute to the increased risk for early cognitive decline and dementia, primarily due to diffusion axonal injury. Previous studies in mild TBI (mTBI) have been controversial in describing the white matter tract integrity changes occurring at acute and subacute post-injury. In this prospective longitudinal study, we aim to investigate the longitudinal changes of white matter (WM) using diffusion tensor imaging (DTI) and their correlations with neuropsychological tests. Thirty-three patients with subacute mTBI and 31 matched healthy controls were studied with an extensive imaging and clinical battery. Neuroimaging was obtained within 7 days post-injury for acute scans and repeated at 1 and 3 months post-injury. Using a region-of-interest-based approach, tract-based spatial statistics was used to conduct voxel-wise analysis on diffusion changes in mTBI and was compared to those of healthy matched controls, scanned during the same time period and rescanned with an interval similar to that of patients. We found decreased fractional anisotropy (FA) values in the left anterior limb of internal capsule (ALIC) and right inferior fronto-occipital fasciculus (IFOF) during the 7 days post-injury, which showed longitudinal evidence of recovery following 1 month post-injury. Increased FA values in these two tracts at 1 month post-injury were positively associated with better performance on cognitive information processing speed at initial assessment. By contrast, there were also some tracts (right anterior corona radiata, forceps major, and body of corpus callosum) exhibiting the continuing loss of integrity sustaining even beyond 3 months, which can predict the persisting post-concussion syndromes. Continuing loss of structural integrity in some tracts may contribute to the persistent post-concussion syndromes in mTBI patients, suggesting certain tracts providing an objective biomarker for tracking the pathological recovery process following mTBI.
A CRM1 Inhibitor Alleviates Cardiac Hypertrophy and Increases the Nuclear Distribution of NT-PGC-1α in NRVMs

Chromosomal maintenance 1 (CRM1) inhibitors display antihypertrophic effects and control protein trafficking between the nucleus and the cytoplasm. PGC-1α (peroxisome proliferator-activated receptor gamma coactivator-1alpha) is a type of transcriptional coactivator that predominantly resides in the nucleus and is downregulated during heart failure. NT-PGC-1α is an alternative splicing variant of PGC-1α that is primarily distributed in the cytoplasm. We hypothesized that the use of a CRM1 inhibitor could shuttle NT-PGC-1α into the nucleus and activate PGC-1α target genes to potentially improve cardiac function in a mouse model of myocardial infarction (MI). We showed that PGC-1α and NT-PGC-1α were decreased in MI-induced heart failure mice. Phenylephrine and angiotensin II were applied to induce hypertrophy in neonatal rat ventricular myocytes (NRVMs). The antihypertrophic effects of the CRM1-inhibitor Selinexor was verified through profiling the expression of β-MHC and through visualizing the cell cross-sectional area. NRVMs were transfected with adenovirus-NT-PGC-1α or adenovirus-NLS (nucleus localization sequence)-NT-PGC-1α and then exposed to Selinexor. Confocal microscopy was then used to observe the shuttling of NT-PGC-1α. After NT-PGC-1α was shuttled into the nucleus, there was increased expression of its related genes, including PPAR-α, Tfam, ERR-γ, CPT1b, PDK4, and Nrf2. The effects of Selinexor on post-MI C57BL/6j mice were determined by echocardiography and qPCR. We found that Selinexor showed antihypertrophic effects but did not influence the ejection fraction of MI-mice. Interestingly, the antihypertrophic effects of Selinexor might be independent of NT-PGC-1α transportation.
British Thoracic Society guidelines for the management of community acquired pneumonia in children: update 2011

The British Thoracic Society first published management guidelines for community acquired pneumonia in children in 2002 and covered available evidence to early 2000. These updated guidelines represent a review of new evidence since then and consensus clinical opinion where evidence was not found. This document incorporates material from the 2002 guidelines and supersedes the previous guideline document.
Decreased Glycogenolysis by miR-338-3p Promotes Regional Glycogen Accumulation Within the Spinal Cord of Amyotrophic Lateral Sclerosis Mice

Metabolic dysfunction is a hallmark of age-related neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). But the crosstalk between metabolic alteration and disease progression in ALS is still largely unknown. Glycogen, a branched polymer of glucose residues, is universally recognized as the energy reserve of the central nervous system (CNS), where its aberrant accumulation instigates neurodegeneration. Glycogen was reported to be accumulated in both CNS and visceral organs of SOD1G93A mice, a well-known ALS model, and contributes to the pathological process of ALS. However, the accumulative patterns and mechanisms are not well elucidated. Here, we provide extensive evidence to demonstrate that glycogen accumulated in the lumbar spinal cord of ALS mice along with the disease progression, but not in the motor cortex. This regional accumulation of glycogen was caused by deteriorated glycogenolysis, which was triggered by decreased glycogen phosphorylase, brain form (PYGB). Moreover, miR-338-3p, an elevated miRNA in the spinal cord of SOD1G93A mice, directly targeted PYGB and was responsible for the decreased glycogenolysis and subsequent glycogen accumulation. Our work is helpful for better understanding of of of metabolic dysfunctions in ALS and provides novel targets for the therapeutic intervention in the future.
Major Shifts in Pelagic Micronekton and Macrozooplankton Community Structure in an Upwelling Ecosystem Related to an Unprecedented Marine Heatwave

The community structure of pelagic zooplankton and micronekton may be a sensitive indicator of changes in environmental conditions within the California Current ecosystem. Substantial oceanographic changes in 2015 and 2016 due to the anomalously warm ocean conditions associated with the ‘warm blob’ and a major El Nino perturbation resulted in onshore and northward advection of warmer and more stratified surface waters resulting in reduced upwelling. Data from fine-mesh pelagic trawl surveys conducted off Oregon and Washington during early summer of 2011 and 2013-2016 were examined for interannual changes in spatial distribution and abundance of fish and invertebrate taxa. Overall species diversity was highest in 2015 and lowest in 2011, but 2016 was similar to the other years, although the evenness was somewhat lower. The community of taxa in both 2015 and 2016 was significantly different from the previously sampled years. Crustacean plankton densities (especially Euphausiidae) were extremely low in both years, and the invertebrate composition became dominated mostly by gelatinous zooplankton. Fishes and cephalopods showed mixed trends overall, but some species such as age-0 Pacific hake were found in relatively high abundances mainly along the shelf break in 2015 and 2016. These results suggest dramatically different pelagic communities were present during the recent warm years with a greater contribution from offshore taxa, especially gelatinous taxa, during 2015 and 2016. The substantial reorganization of the pelagic community has the potential to lead to major alterations in trophic functioning in this normally productive ecosystem.
Single visit nonsurgical endodontic therapy for periapical cysts: A clinical study

Aims: The aim of this study was to assess the outcome of single sitting root canal treatment (RCT) of asymptomatic teeth with periapical cysts. Materials and Methods: Ten maxillary anterior teeth showing periapical lesion on the radiograph was further screened by ultrasound with color power Doppler (CPD) for confirmation of a cyst. The average dimension of the lesions ranged from 1.3 to 1.9 cm. Single sitting RCT was performed on all the selected teeth. Postoperative healing was monitored at regular interval of 3 months, 6 months, and 1 year by using subjective feedback, radiograph, and ultrasound with CPD study. Results: Eight among the ten cases showed either signs of complete healing or healing in progress by the end of 6-12 months. Conclusions: It was observed that single sitting nonsurgical endodontic management of asymptomatic teeth with periapical cyst confirmed by ultrasound was successful in selected cases.
Why We Should Care About Regional Origins: Educational Selectivity Among Refugees and Labor Migrants in Western Europe

Immigrant selectivity describes the notion that migrants are not a random sample of the population at origin, but differ in certain traits such as educational attainment from individuals who stay behind. In this article, we move away from group-level descriptions of educational selectivity and measure it as an individual's relative position in the age- and gender-specific educational distribution of the country of origin. We describe the extent of educational selectivity for a selection of Western European destinations as well as a selection of origin groups ranging from recent refugee to labor migrant populations. By contrasting refugees to labor migrants, we address longstanding assumptions about typical differences in the degree of selectivity between different types of immigrants. According to our findings, there are few and only minor differences between refugee and labor migrants. However, these differences vary; and there are labor migrant groups that score similar or lower on selectivity than do the refugees covered in this study. Selectivity differences between refugees and labor migrants therefore seem less prominent than arguments in the literature suggest. Another key finding is that every origin group is composed of varying proportions of positively and negatively selected individuals. In most cases, the origin groups cover the whole spectrum of selectivity, so that characterizing them as either predominantly positively or negatively selected does not seem adequate. Furthermore, we show that using country-level educational distributions as opposed to sub-national regional-level distributions can lead to inaccurate measurements of educational selectivity. This problem does not occur universally, but only under certain conditions. That is, when high levels of outmigration from sub-national regions in which economic opportunities are considerably above or below the country average, measurement inaccuracy exceeds ignorable levels. In instances where researchers are not able to use sub-national regional measures, we provide them with practical guidance in the form of pre-trained machine-learning tools to assess the direction and the extent of the measurement inaccuracy that results from relying on country-level as opposed to sub-national regional-level educational distributions.
A Brief History of Simulation Neuroscience

Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain.
Two-tone spectroscopy of a SQUID metamaterial in the nonlinear regime

Compact microwave resonantors made of superconducting rings containing Josephson junctions (SQUIDs) are attractive candidates for building frequency tunable metamaterials with low losses and pronounced nonlinear properties. We explore the nonlinearity of a SQUID metamaterial by performing a two-tone resonant spectroscopy. The small-amplitude response of the metamaterial under strong driving by a microwave pump tone is investigated experimentally and theoretically. The transmission coefficient $S_{21}$ of a weak probe signal is measured in the presence of the pump tone. Increasing the power of the pump, we observe pronounced oscillations of the SQUID's resonance frequency $f_{\textrm{res}}$. The shape of these oscillations varies significantly with the frequency of the pump tone $f_{\textrm{dr}}$. The response to the probe signal displays instabilities and sidebands. A state with strong second harmonic generation is observed. We provide a theoretical analysis of these observations, which is in good agreement with the experimental results.
ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals

Mapping and localization are essential capabilities of robotic systems. Although the majority of mapping systems focus on static environments, the deployment in real-world situations requires them to handle dynamic objects. In this paper, we propose an approach for an RGB-D sensor that is able to consistently map scenes containing multiple dynamic elements. For localization and mapping, we employ an efficient direct tracking on the truncated signed distance function (TSDF) and leverage color information encoded in the TSDF to estimate the pose of the sensor. The TSDF is efficiently represented using voxel hashing, with most computations parallelized on a GPU. For detecting dynamics, we exploit the residuals obtained after an initial registration, together with the explicit modeling of free space in the model. We evaluate our approach on existing datasets, and provide a new dataset showing highly dynamic scenes. These experiments show that our approach often surpass other state-of-the-art dense SLAM methods. We make available our dataset with the ground truth for both the trajectory of the RGB-D sensor obtained by a motion capture system and the model of the static environment using a high-precision terrestrial laser scanner. Finally, we release our approach as open source code.
Photometric investigations on two totally eclipsing contact binaries: V342 UMa and V509 Cam

By analyzing two sets of complete BVRcIc light curves for V342 UMa and three sets of complete BVRcIc light curves for V509 Cam, we determined that the two systems are both W-subtype contact binaries and that V342 UMa manifests a shallow contact configuration, while V509 Cam exhibits a medium contact configuration. Given that both of them are totally eclipsing binaries, the physical parameters derived only by the photometric light curves are reliable. Meanwhile, the period changes of the two targets were analyzed based on all available eclipsing times. We discovered that V342 UMa shows long-term period decrease with a rate of −1.02(± 0.54) × 10−7 d yr−1 and that V509 Cam displays long-term period increase with a rate of 3.96(± 0.90) × 10−8 d yr−1. Both the conservative mass transfer and angular momentum loss via magnetic stellar winds can be used to interpret the long-term period decrease of V342 UMa. The long-term period increase of V509 Cam can be explained by mass transfer from the less massive star to the more massive one. The absolute parameters of the two binaries were estimated according to their Gaia distances and our derived photometric solution results. This method can be extended to other contact binaries without radial velocities but with reliable photometric solutions. Their evolutionary states were investigated and we found that they reveal properties that are identical to other W-subtype contact systems.
Topology of Networks in Generalized Musical Spaces

Abstract The abstraction of musical structures as mathematical objects in a geometrical space is one of the major accomplishments of contemporary music theory. The author generalizes the concept of musical spaces as networks and derives compositional design principles via network topology analysis. This approach provides a framework for analysis and quantification of similarity of musical objects and structures and suggests a way to relate such measures to human perception of different musical entities. Finally, network analysis provides alternative ways of interpreting the compositional process by quantifying emergent behaviors with well-established statistical mechanics. Interpreting the latter as probabilistic randomness in the network, the author develops novel compositional design frameworks.
Increasing older adults’ walking through primary care: results of a pilot randomized controlled trial

Background. Physical activity can positively influence health for older adults. Primary care is a good setting for physical activity promotion. Objective. To assess the feasibility of a pedometer-based walking programme in combination with physical activity consultations. Methods. Design: Two-arm (intervention/control) 12-week randomized controlled trial with a 12-week follow-up for the intervention group. Setting: One general practice in Glasgow, UK. Participants: Participants were aged ≥65 years. The intervention group received two 30-minute physical activity consultations from a trained practice nurse, a pedometer and a walking programme. The control group continued as normal for 12 weeks and then received the intervention. Both groups were followed up at 12 and 24 weeks. Outcome measures: Step counts were measured by sealed pedometers and an activPALTM monitor. Psychosocial variables were assessed and focus groups conducted. Results. The response rate was 66% (187/284), and 90% of those randomized (37/41) completed the study. Qualitative data suggested that the pedometer and nurse were helpful to the intervention. Step counts (activPAL) showed a significant increase from baseline to week 12 for the intervention group, while the control group showed no change. Between weeks 12 and 24, step counts were maintained in the intervention group, and increased for the control group after receiving the intervention. The intervention was associated with improved quality of life and reduced sedentary time. Conclusions. It is feasible to recruit and retain older adults from primary care and help them increase walking. A larger trial is necessary to confirm findings and consider cost-effectiveness.
Domain Specific Code Smells in Smart Contracts

Smart contracts are programs running on a blockchain. They are immutable to patch for bugs once deployed -- it is critical to ensure they are bug-free and well-designed before deploying. Code smells are symptoms in source code that possibly indicate deeper problems. The detection of code smells is a method to avoid potential bugs and improve the design of existing code. However, traditional code smell patterns are designed for centralized OO programs, e.g., Java or C++; while smart contracts are decentralized and contain numerous distinctive features, such as the gas system. To fill this gap, we collected smart-contract-related posts from Stack Exchange, as well as real-world smart contracts. We manually analyzed these posts and defined 20 kinds of \emph{code smells for smart contracts. We categorized these into security, architecture, and usability problems. To validate if practitioners consider these contract smells as harmful, we created an online survey and received 96 responses from 24 different countries. Feedback showed these code smells are harmful and removing them would improve quality and robustness of smart contracts. We manually identified our defined code smells in 587 contract accounts and publicly released our dataset. Finally, we summarized 5 impacts caused by contract code smells. These help developers better understand the symptoms of the smells and removal priority.
Remnants of Galactic Subhalos and Their Impact on Indirect Dark-Matter Searches

Dark-matter subhalos, predicted in large numbers in the cold-dark-matter scenario, should have an impact on dark-matter-particle searches. Recent results show that tidal disruption of these objects in computer simulations is overefficient due to numerical artifacts and resolution effects. Accounting for these results, we re-estimated the subhalo abundance in the Milky Way using semianalytical techniques. In particular, we showed that the boost factor for gamma rays and cosmic-ray antiprotons is increased by roughly a factor of two.
Statistically Discriminative Sub-trajectory Mining

We study the problem of discriminative sub-trajectory mining. Given two groups of trajectories, the goal of this problem is to extract moving patterns in the form of sub-trajectories which are more similar to sub-trajectories of one group and less similar to those of the other. We propose a new method called Statistically Discriminative Sub-trajectory Mining (SDSM) for this problem. An advantage of the SDSM method is that the statistical significance of the extracted sub-trajectories are properly controlled in the sense that the probability of finding a false positive sub-trajectory is smaller than a specified significance threshold alpha (e.g., 0.05), which is indispensable when the method is used in scientific or social studies under noisy environment. Finding such statistically discriminative sub-trajectories from massive trajectory dataset is both computationally and statistically challenging. In the SDSM method, we resolve the difficulties by introducing a tree representation among sub-trajectories and running an efficient permutation-based statistical inference method on the tree. To the best of our knowledge, SDSM is the first method that can efficiently extract statistically discriminative sub-trajectories from massive trajectory dataset. We illustrate the effectiveness and scalability of the SDSM method by applying it to a real-world dataset with 1,000,000 trajectories which contains 16,723,602,505 sub-trajectories.