Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Electromagnetic Field Treatment on Recovery from Spinal CordInjury in a Rat Model Clinical and Imaging Findings

Background: Spinal cord injury (SCI) refers to spinal cord damage arising from trauma, disease or degeneration. At present, there is still no treatment for any paraplegia resulting from SCI. We have previously shown that very low intensity, low frequency, electromagnetic field treatment (VLIFE) promotes neuronal plasticity after stroke and as a result, improves clinical recovery. Objective: In this paper we studied an innovative electromagnetic field treatment for SCI in an animal model. Methods: SCI was caused to 20 rats by hemi crush. The animals were divided into three groups, 7 animals were not treated, two groups received VLIFE treatment for two months, 7 rats received 15.72 Hz, and 6 rats received a dedicated treatment of 26 Hz. Clinical evaluation was performed weekly, and imaging assessment monthly. Results: Clinical performance assessed by a locomotor test, show significant clinical improvement of the neurological function following treatment with VLIFE (p < 0.05). Imaging results after two months of treatment, by MRI including DTI analysis, show that the non-treated (sham) spinal cord has not recovered, while in the treated animal the fibers of the spinal cord were preserved and rewired. VLIFE treatment has major benefits on injured spinal cord: preservation of the spinal cord from further degradation caused by the edema and internal cord scars, and rewiring of the spinal cord resulting with rehabilitation and improved clinical performance. Conclusions: Low intensity low frequency electromagnetic field treatment may be beneficial for rehabilitation from SCI, human clinical trials are planned.
On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor †

In sports motion analysis, observation is a prerequisite for understanding the quality of motions. This paper introduces a novel approach to detect and segment sports motions using a wearable sensor for supporting systematic observation. The main goal is, for convenient analysis, to automatically provide motion data, which are temporally classified according to the phase definition. For explicit segmentation, a motion model is defined as a sequence of sub-motions with boundary states. A sequence classifier based on deep neural networks is designed to detect sports motions from continuous sensor inputs. The evaluation on two types of motions (soccer kicking and two-handed ball throwing) verifies that the proposed method is successful for the accurate detection and segmentation of sports motions. By developing a sports motion analysis system using the motion model and the sequence classifier, we show that the proposed method is useful for observation of sports motions by automatically providing relevant motion data for analysis.
A Pad-Based Multi-Device Collaborative Teaching Software Architecture for Smart Classroom

With the development of distributed computing, mobile computing, pervasive computing technology, Pad are gradually becoming the mainstream device in smart classroom in the post PC era. However, the current pad application researches seldom involve teaching control of classroom activities, and learning process monitoring, multi-device collaboration. Therefore, based on teacher-led and student-centered teaching ideas, this paper proposes pad-based multi-device collaborative teaching software architecture for smart classroom, and then which is applied to prototype development. The practice has proved that this architecture can be helpful in developing pad-based multi-device collaborative teaching software.
Radical and lunatic fringes modulate notch ligands to support mammalian intestinal homeostasis

Notch signalling maintains stem cell regeneration at the mouse intestinal crypt base and balances the absorptive and secretory lineages in the upper crypt and villus. Here we report the role of Fringe family of glycosyltransferases in modulating Notch activity in the two compartments. At the crypt base, RFNG is enriched in the Paneth cells and increases cell surface expression of DLL1 and DLL4. This promotes Notch activity in the neighbouring Lgr5+ stem cells assisting their self-renewal. Expressed by various secretory cells in the upper crypt and villus, LFNG promotes DLL surface expression and suppresses the secretory lineage . Hence, in the intestinal epithelium, Fringes are present in the ligand-presenting ‘sender’ secretory cells and promote Notch activity in the neighbouring ‘receiver’ cells. Fringes thereby provide for targeted modulation of Notch activity and thus the cell fate in the stem cell zone, or the upper crypt and villus.
CAM: Cloud-Assisted Privacy Preserving Mobile Health Monitoring

Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing mobile communications and cloud computing technologies to provide feedback decision support, has been considered as a revolutionary approach to improving the quality of healthcare service while lowering the healthcare cost. Unfortunately, it also poses a serious risk on both clients' privacy and intellectual property of monitoring service providers, which could deter the wide adoption of mHealth technology. This paper is to address this important problem and design a cloud-assisted privacy preserving mobile health monitoring system to protect the privacy of the involved parties and their data. Moreover, the outsourcing decryption technique and a newly proposed key private proxy reencryption are adapted to shift the computational complexity of the involved parties to the cloud without compromising clients' privacy and service providers' intellectual property. Finally, our security and performance analysis demonstrates the effectiveness of our proposed design.
Thermal modelling of dual-stator five-phase permanent magnet synchronous generator

In present scenario permanent magnet (PM) machines are widely used due to its higher power density, better speed versus torque characteristics, high dynamic response and high efficiency. Thermal model is generally used for the design improvement of a machine and to assess the loading capability under different working conditions. This paper presents the thermal modelling, based on lumped parameters, for a natural-cooled dual stator five phase permanent magnet synchronous generator (PMSG). The model proposed in this paper has twelve nodes to find temperature in different parts of the machine. Model is developed in MATLAB environment and the results are validated using Finite element method(FEM) results. Though Finite element thermal model is considered to be more accurate than lumped parameter thermal model, it takes significant time for simulation and design improvement.
Risk Factors for Training-Related Injuries During U.S. Army Basic Combat Training.

Background Injuries during basic combat training (BCT) impact military health and readiness in the U.S. Army. Identifying risk factors is crucial for injury prevention, but few Army-wide studies to identify risk factors for injury during BCT have been completed to date. This study examines associations between individual and training-related characteristics and injuries during Army BCT. Methods Using administrative data from the Total Army Injury and Health Outcomes Database (TAIHOD), we identified individuals who apparently entered BCT for the first time between 1 January 2002 and 30 September 2007, based on review of administrative records. Injuries were identified and categorized based on coded medical encounter data. When combined with dates of medical services, we could count injuries per person, identify unique injuries, and identify the quantity and type of medical care delivered. Regression models produced odds ratios (ORs) and 95% confidence intervals (CIs) to identify risk factors for injury during BCT (yes/no), adjusted for potential confounders. Results Of the 278,045 (83.4%) men and 55,302 (16.6%) women who were apparently first-time trainees, 39.5% (n = 109,760) of men and 60.9% (n = 33,699) of women were injured during training based on over 2 million recorded medical encounters entries. The large cohort yielded statistically significant, small magnitude associations between injury and all individual and training-related covariates for men, and all but medical accession waivers and weight for women. After adjustment, largest magnitude effects among men were due to age > 25 yr vs. 17-18 yr (OR = 1.83, 95% CI: 1.75, 1.91); having been married in the past vs. being single (OR = 1.36, 95% CI: 1.24, 1.49); rank E4-E7 vs. E1 (OR = 0.56, 95% CI: 0.53. 0.59); training at Ft. Jackson (OR = 0.66, 95% CI: 0.64, 0.69), Ft. Leonard Wood (OR = 0.67, 95% CI: 0.65, 0.70), or Ft. Knox (OR = 0.69, 95% CI: 066, 0.72) vs. Ft. Benning. Odds of injury were highest during 2005, 2006, and 2007. After adjustment for weight and body mass index, taller men had higher odds of BCT injury than average height men (OR = 1.08, 95% CI: 1.05, 1.11). Among women, short stature (OR = 1.11; 95% CI: 1.04, 1.19), training at Ft Leonard Wood (OR = 1.10; 95% CI: 1.04, 1.16) and evidence of injury prior to training based on accession waiver (OR = 1.12; 95% CI: 1.00, 1.26) increased injury risk. Conclusions This Army-wide analysis reveals higher BCT-related injury rates for both men and women than prior studies and identifies risk factors for injuries during BCT. The large data set allows adjustment for many covariates, but because statistical analysis may yield significant findings for small differences, results must be interpreted based on minimally important differences determined by military and medical professionals. Results provide information that may be used to adapt training or medical screening and examination procedures for basic trainees.
Urban Area Detection in Very High Resolution Remote Sensing Images Using Deep Convolutional Neural Networks

Detecting urban areas from very high resolution (VHR) remote sensing images plays an important role in the field of Earth observation. The recently-developed deep convolutional neural networks (DCNNs), which can extract rich features from training data automatically, have achieved outstanding performance on many image classification databases. Motivated by this fact, we propose a new urban area detection method based on DCNNs in this paper. The proposed method mainly includes three steps: (i) a visual dictionary is obtained based on the deep features extracted by pre-trained DCNNs; (ii) urban words are learned from labeled images; (iii) the urban regions are detected in a new image based on the nearest dictionary word criterion. The qualitative and quantitative experiments on different datasets demonstrate that the proposed method can obtain a remarkable overall accuracy (OA) and kappa coefficient. Moreover, it can also strike a good balance between the true positive rate (TPR) and false positive rate (FPR).
The Effect of Patient Cost Sharing on Utilization, Health, and Risk Protection

This paper exploits a sharp reduction in patient cost sharing at age 70 in Japan, using a regression discontinuity design to examine its effect on utilization, health, and financial risk arising from out-of-pocket expenditures. Due to the national policy, cost sharing is 60-80 percent lower at age 70 than at age 69. I find that both outpatient and inpatient care are price sensitive among the elderly. While I find little impact on mortality and other health outcomes, the results show that reduced cost sharing is associated with lower out-of-pocket expenditures, especially at the right tail of the distribution.
Development and Characterization of an Endotoxemia Model in Zebra Fish

Endotoxemia is a condition in which endotoxins enter the blood stream and cause systemic and sometimes lethal inflammation. Zebra fish provides a genetically tractable model organism for studying innate immunity, with additional advantages in live imaging and drug discovery. However, a bona fide endotoxemia model has not been established in zebra fish. Here, we have developed an acute endotoxemia model in zebra fish by injecting a single dose of LPS directly into the circulation. Hallmarks of human acute endotoxemia, including systemic inflammation, extensive tissue damage, circulation blockade, immune cell mobilization, and emergency hematopoiesis, were recapitulated in this model. Knocking out the adaptor protein Myd88 inhibited systemic inflammation and improved zebra fish survival. In addition, similar alternations of pathways with human acute endotoxemia were detected using global proteomic profiling and MetaCore™ pathway enrichment analysis. Furthermore, treating zebra fish with a protein tyrosine phosphatase nonreceptor type 11 (Shp2) inhibitor decreased systemic inflammation, immune mobilization, tissue damage, and improved survival in the endotoxemia model. Together, we have established and characterized the phenotypic and gene expression changes of a zebra fish endotoxemia model, which is amenable to genetic and pharmacological discoveries that can ultimately lead to a better mechanistic understanding of the dynamics and interplay of the innate immune system.
FATIGUE PROCESS MONITORING FOR COMPOSITE LAMINATES WITH FIBER BRAGG GRATINGS

The internal strain of composite laminates during a constant stress amplitude fatigue testing process was monitored with fiber Bragg gratings (FBGs) as a fatigue indicator to reveal Young's modulus decrease of the specimen, which is a direct index of damage. It is demonstrated that FBGs can not only be embedded in composite structures to detect fatigue damage during fatigue process, but also with excellent durability compared with other sensors such as foil gauges, which is most frequently used in strain measurement. After 1 million cycles, the FBGs can still keep good sensibility. It can be concluded that it is a novel way to monitor, evaluate and give crash alert for the health state of composite structures during their whole service life with FBGs as a fatigue indicator.
Atmospheric Chemistry and Physics Technical Note: Recursive Rediscretisation of Geo-scientific Data in the Modular Earth Submodel System (messy)

This technical note describes a method for the re-discretisation of " gridded " geo-scientific data. A recursive algorithm (NREGRID) is derived to solve the rediscretisa-tion problem for orthogonal grids (including curvilinear) of arbitrary dimension. The algorithm is used within the program NCREGRID to handle geo-scientific data. These data are typically 2-dimensional (latitude-longitude grid), or 3-dimensional (latitude-longitude grid with a vertical pressure, or hybrid pressure coordinate, as used in atmospheric modelling). NCREGRID can be used as stand-alone program for the transformation (" regridding ") of data from and to data files in netCDF format. Moreover, NCREGRID constitutes the core of the Modular Earth Submodel System (MESSy) data import interface, providing a powerful tool for accessing data independently of the applied grid resolution (" automatic regridding ").
TWEAK Negatively Regulates Human Dicer

The ribonuclease Dicer plays a central role in the microRNA pathway by processing microRNA precursors (pre-microRNAs) into microRNAs, a class of 19- to 24-nucleotide non-coding RNAs that regulate expression of ≈60% of the genes in humans. To gain further insights into the function and regulation of Dicer in human cells, we performed a yeast two-hybrid (Y2HB) screen using human Dicer double-stranded RNA-binding domain (dsRBD) as bait. This approach identified tumor necrosis factor (TNF)-like weak inducer of apoptosis (TWEAK) as a Dicer-interacting protein candidate. Confocal immunofluorescence microscopy revealed the colocalization of Dicer and TWEAK proteins at the perinuclear region of HeLa cells. The Dicer-TWEAK protein interaction was confirmed by coimmunoprecipitation and found not likely to be mediated by RNA. TWEAK dose-dependently reduced pre-microRNA conversion into mature microRNA in Dicer activity assays using extracts of transfected human HEK 293 cells. TWEAK expression also impaired microRNA-guided RNA silencing of a reporter gene induced by a pre-microRNA. These findings suggest a role for TWEAK—a pro-inflammatory cytokine—in regulating Dicer function and microRNA biogenesis, and its possible involvement in regulating gene expression during inflammatory processes and diseases.
Asymmetric Item-Item Similarity Measure for Linked Open Data Enabled Collaborative Filtering

The boom in Linked Open Data (LOD) has recently stimulated the research of a new generation of recommender systems—LOD-enabled recommender systems, in which the similarity measure for LOD is one of the core issues. The partitioned information content (PIC)-based semantic similarity (PICSS) is a newly developed symmetric similarity measure for LOD. However, recent studies have shown that asymmetric similarity measures are more effective than symmetric similarity measures in solving recommendation problems. In this paper we develop an asymmetric item-item similarity measure for LOD—the asymmetric PIC-based semantic similarity measure (APICSS), which applies our proposed two notions: the proportion of common PIC between two resources in the PIC of a resource and the PIC difference between two resources, on the basis of the notion of PIC. Experimental evaluation with the item-based collaborative filtering method on the MovieLens 100k dataset, the DBpedia 2016-04 release, and the DBpedia-MovieLens 100k dataset shows that our APICSS measure outperforms the PICSS measure in terms of both Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The average RMSE accuracy has an increase of 1.58% and the maximum RMSE accuracy has an increase of 2.07%, compared to PICSS. The average MAE accuracy has an increase of 1.63% and the maximum MAE accuracy has an increase of 2.19%, compared to PICSS.
Hyperactivity and impulsivity in adult attention-deficit/hyperactivity disorder is related to glutamatergic dysfunction in the anterior cingulate cortex

Abstract Objectives: Attention-deficit/hyperactivity disorder (ADHD) is closely linked to the dysregulation of dopaminergic and noradrenergic neurotransmission in the fronto-striatal neural network, including the anterior cingulate cortex (ACC) and the dorsolateral prefrontal cortex (DLPFC). Additionally, increasing evidence supports the involvement of the glutamatergic system in the pathophysiology of ADHD. Impulsivity, a core symptom in patients with ADHD, has been repeatedly associated with glutamatergic neurotransmission, and pharmacological treatment of ADHD has been shown to reduce glutamate levels in the prefrontal cortex. Methods: We investigated glutamate levels in the ACC and the DLPFC in 30 adults with ADHD and 30 healthy controls using single-voxel proton magnetic resonance spectroscopy on a 3T scanner. Results: The ADHD group showed a significant increase in glutamate in the ACC compared to controls, no significant differences in metabolites were observed in the DLPFC. Overall, glutamate levels in the ACC were positively correlated with ADHD symptomatology, especially hyperactivity and impulsivity symptoms. Conclusions: Increased levels of glutamate in the ACC, which were positively correlated with hyperactivity and impulsivity, support the hypothesis that dysfunctional glutamatergic neurotransmission is at least partially responsible for ADHD symptomatology. Modulation of glutamatergic neurotransmission might therefore be a promising avenue for future pharmacological interventions.
cdma2000/sup /spl reg// high rate broadcast packet data air interface design

The Third Generation Partnership Project 2 recently baselined the specification for a cdma2000/sup /spl reg// high-rate broadcast packet data air interface ("cdma2000 High Rate Broadcast-Multicast Packet Data Air Interface Specification", 3GPP2 C.50054). The standard allows high-speed delivery of packet data to multiple access terminals. The article describes the air interface design adopted by 3GPP2, and presents simulation results that predict the performance of the high-speed broadcast system.
DeepMimic

A longstanding goal in character animation is to combine data-driven specification of behavior with a system that can execute a similar behavior in a physical simulation, thus enabling realistic responses to perturbations and environmental variation. We show that well-known reinforcement learning (RL) methods can be adapted to learn robust control policies capable of imitating a broad range of example motion clips, while also learning complex recoveries, adapting to changes in morphology, and accomplishing user-specified goals. Our method handles keyframed motions, highly-dynamic actions such as motion-captured flips and spins, and retargeted motions. By combining a motion-imitation objective with a task objective, we can train characters that react intelligently in interactive settings, e.g., by walking in a desired direction or throwing a ball at a user-specified target. This approach thus combines the convenience and motion quality of using motion clips to define the desired style and appearance, with the flexibility and generality afforded by RL methods and physics-based animation. We further explore a number of methods for integrating multiple clips into the learning process to develop multi-skilled agents capable of performing a rich repertoire of diverse skills. We demonstrate results using multiple characters (human, Atlas robot, bipedal dinosaur, dragon) and a large variety of skills, including locomotion, acrobatics, and martial arts.
Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie