Miracle-dz/UniFAR-FTUs huggingface dataset:
Hemorrhagic shock alters fracture callus composition and activates the IL6 and RANKL/OPG pathway in mice

Background Fracture and hemorrhagic shock often lead to impaired fracture healing. To elucidate underlying pathogenesis, this study aimed to analyze histological properties during fracture healing after hemorrhagic shock and involved signaling pathways in mice. METHODS Male C57BL/6NCrl mice were assigned into five groups. Control group underwent no interventions. Sham group had a catheter and external fixator but neither blood loss nor osteotomy. Trauma-hemorrhage (TH) group received a pressure-controlled hemorrhagic shock; osteotomy (Fx) group, an osteotomy and fixator; and combined trauma (THFx) group, both hemorrhagic shock and externally fixed osteotomy. After 1, 2, 3, and 4 weeks, the animals were killed. Undecalcified bones were analyzed histologically and signaling pathways relevant for fracture healing by polymerase chain reaction and Western blot. Statistical significance was set at 0.05 or less. Comparisons were performed using the Mann-Whitney U or Kruskal-Wallis test. RESULTS In the THFx group, a decreased bone formation after 3 weeks, a reduction of both bone and cartilage after 2 weeks, and an enhanced activation of the RANKL/OPG and IL6 signaling pathway after 1 week were shown in comparison to Fx. CONCLUSIONS Hemorrhagic shock has a retarding effect on fracture healing in the early phase of fracture healing and leads to activation of the IL6 and RANKL/OPG signaling pathways.
2-D Magnetomechanical Transient Study of a Motor Suffering a Bar Breakage

The analysis of the vibration response of electrical machines has importance in noise prediction and more recently, diagnosis of electrical faults, especially in the industrial environment, where it is a well-known technique. This work assesses the performance of a strongly coupled two-dimensional (2-D) magnetomechanical approach, as directly available in multiphysics software, for the simulation of an induction machine under heavy operational conditions: a direct-on-line startup. Both healthy and broken bar states are simulated in a time span long enough to allow the detailed study of the varying frequency components. The results yield, in addition to the usual electrical and magnetic quantities, electromagnetic-induced vibration components in the stator. A comparison with current and vibration experimental data is also performed showing a good agreement with variable frequency components and certain limitations concerning their amplitude.
Attentional Bias to Threat-Related Information Among Individuals With Dental Complaints: The Role of Pain Expectancy

Expecting pain can be perceived as a threat may involve recruitment of cognitive strategies (such as attentional avoidance) which might help the person to reduce distress. The ecological validity of the paradigms aiming to study the attentional biases toward or away from threatening stimuli by manipulating the perception of threat in experimental settings has been questioned. Therefore, the current study aims to investigate the attentional bias toward or away from the threat when a confrontation with a real threatening and painful condition would be expected (i.e., dental treatment). One hundred and twenty-seven patients referred to three dentistry clinics for a dental treatment (experiment participants) and 30 individuals with no dental complaints (control participants) completed this study. Patients were randomly allocated to a high pain expectancy (HPE: n = 65) or a low pain expectancy (LPE: n = 62) expectancy condition. All participants completed questionnaires of distress, fear of pain, and fear of dental pain. Furthermore, they participated in a dot-probe task that assessed their attention to painful faces, dental pictures, and happy faces. In addition, before the treatment, participants reported their anticipated pain intensity and after the treatment, they reported the pain intensity that they perceived during the treatment using two separate visual analog scales. Patients in the HPE group showed a bias away from dental pictures compared to LPE and control group participants. HPE group patients also reported greater pain intensity during the treatment compared to LPE patients. Greater attentional bias away from dental pictures among HPE patients was associated with higher levels of fear of pain, fear of dental pain, and stress. Avoidance of highly salient threatening images can be seen as an unhelpful emotion-regulation strategy that individuals use to manage their fears. However, in this study, avoidance was associated with poorer outcomes.
Amplified Pilot Head Vibration and the Effects of Vibration Mitigation on Neck Muscle Strain.

INTRODUCTION Rotary wing pilot neck strain is increasing in prevalence due to the combined effects of head supported mass (e.g., Night Vision Goggles, head mounted displays) and whole-body vibration. This study examined the physiological responses of pilots during exposure to whole-body vibration (WBV) representative of the National Research Council's Bell 412 helicopter in forward flight. WBV levels were measured and evaluated using the ISO-2631-1-1997 WBV standards. METHODS Twelve pilots (aged 20-59 yr, 7 of the 12 with 20+ years flight experience) underwent six 15-min vibration trials on a human rated shaker platform. Participants were exposed to three vibration levels (-25%, normal, and +25% amplitude; Levels 1-3, respectively) while seated on an Original Equipment Manufacturer (OEM) or vibration mitigating (MIT) cushion. Upper back and neck electromyography (EMG) and acceleration were continuously recorded. RESULTS Normalized EMG amplitude was higher using the OEM compared to the MIT during Level 2 (0.18 vs. -0.27) and Level 3 (0.24 vs. -0.14) for the anterior neck muscles. Health weighted vibration amplitude at the head (Mean of 3 levels: OEM = 1.19 and MIT = 1.11 m · s-2) was larger than the vibration amplitude at the seat (Mean of 3 levels: OEM = 0.77 and MIT = 0.70 m · s-2). DISCUSSION The amplification of head vibration relative to the seat, and the significant effects of vibration level, as well as the vibration mitigation cushion, on neck EMG amplitude support the need for revisions to the ISO-2631-1 standard to account for the head and neck response to whole-body vibration.Wright Beatty HE, Law AJ, Thomas JR, Wickramasinghe V. Amplified pilot head vibration and the effects of vibration mitigation on neck muscle strain. Aerosp Med Hum Perform. 2018; 89(6):510-519.
Stochastic Dosimetry Based on Low Rank Tensor Approximations for the Assessment of Children Exposure to WLAN Source

In this study, the exposure of a female eight-year old child to a WLAN access point located in an unknown position in a realistic indoor environment was investigated. A stochastic dosimetry approach based on low rank tensor approximation was used to develop surrogate models to quickly estimate the specific absorption rate in all the possible positions of the WLAN source and the child. Results showed that, for all the possible positions in the room, the exposure values were significantly below the limits of the International Commission of Non-Ionizing Radiation Protection guidelines for general public exposure. The variation of the positions of the source and the child greatly influenced the exposure, resulting in quartile coefficient of dispersion values always higher than 65%. The WLAN source positions, among all the possible ones, that minimize the exposure of the child staying in the rectangular room were found to be on the sides of the smaller wall of the room.
Continental Boundary Ice Sheet / Ice Cap Boundary Bering Sea Bering Strait Great Bear Lake East Siberian

P. J. Bartlein, M. E. Edwards, S. W. Hostetler, S. L. Shafer, P. M. Anderson, L. B. Brubaker, and A. V. Lozhkin Department of Geography, University of Oregon, Eugene, Oregon, USA Geography and Environment, University of Southampton, Southampton, UK College of Natural Sciences and Mathematics, University of Alaska, Fairbanks, Alaska, USA U.S. Geological Survey, Corvallis, Oregon, USA Quaternary Research Center, University of Washington, Seattle, Washington, USA School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA Northeast Interdisciplinary Research Institute, Far East Branch of the Russian Academy of Sciences, Magadan, Russia
Small Set Expansion in The Johnson Graph

This paper studies expansion properties of the (generalized) Johnson Graph. For natural numbers t < ` < k, the nodes of the graph are sets of size ` in a universe of size k. Two sets are connected if their intersection is of size t. The Johnson graph arises often in combinatorics and theoretical computer science: it represents a “slice” of the noisy hypercube, and it is the graph that underlies direct product tests as well as a candidate hard unique game. We prove that any small set of vertices in the graph either has near perfect edge expansion or is not pseudorandom. Here “not pseudorandom” means that the set becomes denser when conditioning on containing a small set of elements. In other words, we show that slices of the noisy hypercube – while not small set expanders like the noisy hypercube – only have non-expanding small sets of a certain simple structure. This paper is related to a recent line of work establishing the 2-to-2 Theorem in PCP. The result was motivated, in part, by [7] which hypothesized and made partial progress on similar result for the Grassmann graph. In turn, our result for the Johnson graphs served as a crucial step towards the full result for the Grassmann graphs completed subsequently in [20].
Automatic Selection of Process Corner Simulations for Faster Design Verification

Integrated circuit designs are verified in simulation over a set of process corners, which are combinations of expected transistor properties, power supply voltages, and die temperatures. The simulation time per corner can be long and semiconductor processes can have more than 1000 corners. Simulation is thus a serious bottleneck in design verification. We propose an algorithm that selects the smallest number of process corner simulations that are required to estimate minimum and/or maximum values of the output functions that model circuit behavior. Using our best corner selection algorithm, the required number of process corner simulations is reduced by an average of 79% (a speed-up of 4.71) with respect to a set of 46 output functions from nine industrial benchmark circuits.
Provider and patient satisfaction with the integration of ambulatory and hospital EHR systems

Objective The installation of EHR systems can disrupt operations at clinical practice sites, but also lead to improvements in information availability. We examined how the installation of an ambulatory EHR at OB/GYN practices and its subsequent interface with an inpatient perinatal EHR affected providers' satisfaction with the transmission of clinical information and patients' ratings of their care experience. Methods We collected data on provider satisfaction through 4 survey rounds during the phased implementation of the EHR. Data on patient satisfaction were drawn from Press Ganey surveys issued by the healthcare network through a standard process. Using multivariable models, we determined how provider satisfaction with information transmission and patient satisfaction with their care experience changed as the EHR system allowed greater information flow between OB/GYN practices and the hospital. Results Outpatient OB/GYN providers became more satisfied with their access to information from the inpatient perinatal triage unit once system capabilities included automatic data flow from triage back to the OB/GYN offices. Yet physicians were generally less satisfied with how the EHR affected their work processes than other clinical and non-clinical staff. Patient satisfaction dropped after initial EHR installation, and we find no evidence of increased satisfaction linked to system integration. Conclusions Dissatisfaction of providers with an EHR system and difficulties incorporating EHR technology into patient care may negatively impact patient satisfaction. Care must be taken during EHR implementations to maintain good communication with patients while satisfying documentation requirements.
The Evolution of Gene Expression Underlying Vision Loss in Cave Animals

Abstract Dissecting the evolutionary genetic processes underlying eye reduction and vision loss in obligate cave-dwelling organisms has been a long-standing challenge in evolutionary biology. Independent vision loss events in related subterranean organisms can provide critical insight into these processes as well as into the nature of convergent loss of complex traits. Advances in evolutionary developmental biology have illuminated the significant role of heritable gene expression variation in the evolution of new forms. Here, we analyze gene expression variation in adult eye tissue across the freshwater crayfish, representing four independent vision-loss events in caves. Species and individual expression patterns cluster by eye function rather than phylogeny, suggesting convergence in transcriptome evolution in independently blind animals. However, this clustering is not greater than what is observed in surface species with conserved eye function after accounting for phylogenetic expectations. Modeling expression evolution suggests that there is a common increase in evolutionary rates in the blind lineages, consistent with a relaxation of selective constraint maintaining optimal expression levels. This is evidence for a repeated loss of expression constraint in the transcriptomes of blind animals and that convergence occurs via a similar trajectory through genetic drift.
Power-Efficient Linear Phase FIR Notch Filter Design Using the LARS Scheme

In this paper, an effective paradigm based on the Least Angle Regression (LARS) scheme is developed to iteratively compute the power-efficient linear phase FIR notch filters. At each iteration, we compute the equiangular vector and the step size to be taken which are then used to modify our previous prediction of the filter coefficients along the computed equiangular direction. The iteration of the LARS scheme stops when the error defined as the L2-norm of the difference between the computed prediction of the notch filter and the desired notch filter is less than the pre-chosen design error ε . The simulation results demonstrate that the proposed LARS scheme is an effective paradigm to compute power-efficient linear phase notch FIR filters. Key–Words: Least angle regression scheme, notch filter, FIR filter, linear phase, power-efficient,filter design
Top-Down Disconnectivity in Schizophrenia During P300 Tasks

Cognitive deficits in schizophrenia are correlated with the dysfunctions of distinct brain regions including anterior cingulate cortex (ACC) and prefrontal cortex (PFC). Apart from the dysfunctions of the intrinsic connectivity of related areas, how the coupled neural populations work is also crucial in related processes. Twenty-four patients with schizophrenia (SZs) and 24 matched healthy controls (HCs) were recruited in our study. Based on the electroencephalogram (EEG) datasets recorded, the Dynamic Causal Modeling (DCM) was then adopted to estimate how the brain architecture adapts among related areas in SZs and to investigate the mechanism that accounts for their cognitive deficits. The distinct winning models in SZs and HCs consistently emphasized the importance of ACC in regulating the elicitations of P300s. Specifically, comparing to that in HCs, the winning model in SZs uncovered a compensatory pathway from dorsolateral PFC to intraparietal sulcus that promised the SZs' accomplishing P300 tasks. The findings demonstrated that the “disconnectivity hypothesis” is helpful and useful in explaining the cognitive deficits in SZs, while the brain architecture adapted with related compensatory pathway promises the limited brain cognitions in SZs. This study provides a new viewpoint that deepens our understanding of the cognitive deficits in schizophrenia.
IoT Healthcare Communication System for IEEE 11073 PHD and IHE PCD-01 Integration Using CoAP

With the proliferation of the Internet of Things (IoT) healthcare devices, significant interoperability issue arises where devices use proprietary data transfer protocols. The IHE PCD-01 standard has been suggested for the exchange of healthcare data in ISO/IEEE 11073 PHD data model. However, the PCD-01 is not efficient to be used in the IoT environment. This is because the use of SOAP for PCD-01 may be too complex to be implemented in the resource-constrained IoT healthcare devices. In this paper, we have designed a communication system to implement ISO/IEEE 11073 and IHE PCD-01 integration using the IETF CoAP. More specifically, we have designed the architecture and procedures, using CoAP, to seamlessly transmit the bio-signal from the tiny resource-constrained IoT healthcare devices to the server in a standardized way. We have also built the agent, gateway, and PCD-01 interface at the server, all of which are using the CoAP as a communication protocol. In order to evaluate the performance of the proposed system, we have used the PCD data to be transmitted over CoAP, MQTT, and HTTP. The evaluation of the system performance shows that the use of CoAP results in faster transaction and lesser cost than other protocols, with less battery power consumption.
Shape regression for vertebra fracture quantification

Accurate and reliable identification and quantification of vertebral fractures constitute a challenge both in clinical trials and in diagnosis of osteoporosis. Various efforts have been made to develop reliable, objective, and reproducible methods for assessing vertebral fractures, but at present there is no consensus concerning a universally accepted diagnostic definition of vertebral fractures. In this project we want to investigate whether or not it is possible to accurately reconstruct the shape of a normal vertebra, using a neighbouring vertebra as prior information. The reconstructed shape can then be used to develop a novel vertebra fracture measure, by comparing the segmented vertebra shape with its reconstructed normal shape. The vertebrae in lateral x-rays of the lumbar spine were manually annotated by a medical expert. With this dataset we built a shape model, with equidistant point distribution between the four corner points. Based on the shape model, a multiple linear regression model of a normal vertebra shape was developed for each dataset using leave-one-out cross-validation. The reconstructed shape was calculated for each dataset using these regression models. The average prediction error for the annotated shape was on average 3%.
Machine Learning Based Predictive Maintenance Strategy: A Super Learning Approach with Deep Neural Networks

Effective predictive maintenance (PdM) strategy is needed in microelectronic manufacturing to reduce cost and loss of cycle time associated with unplanned maintenance events in fab. Adoption of recent advances in big data analytics methods will help in developing an effective PdM strategy. This paper discusses advanced machine learning based PdM strategy. Various approaches to formulate the PdM problem and solve it are discussed. The performance of respective machine learning algorithms on equipment data is evaluated. To benefit from the strengths of various algorithms a super learning based approach with deep neural networks is proposed. This approach is robust compared to single method selection and handles changing production environment well. This cross validation based stacked model ensemble predictive maintenance strategy is also easy to automate.
JHU/APL CONSENSE: The Confidence-Based System Ensembler

CONSENSE, JHU/APL’s submission for the TAC KBP 2015 slot filling validation (SFV) and ensembling track, is presented. CONSENSE’s design revolves around a constrained optimization framework for validation and ensembling, based on confidence values produced by slot filling systems. One advantage of this design is that no prior knowledge is required of how the confidence values were estimated. CONSENSE consists of three phases: feature-based filtering of SFV queries, weighted confidence scaling using constrained optimization, and confidencebased validation and ensembling. Details of these phases are provided, along with results of a preliminary evaluation of CONSENSE indicating its suitability for a variety of use cases.
Autophagosomal YKT6 is required for fusion with lysosomes independently of syntaxin 17

Macroautophagy is an evolutionarily conserved catabolic mechanism that delivers intracellular constituents to lysosomes using autophagosomes. To achieve degradation, lysosomes must fuse with closed autophagosomes. We previously reported that the soluble N-ethylmaleimide–sensitive factor attachment protein receptor (SNARE) protein syntaxin (STX) 17 translocates to autophagosomes to mediate fusion with lysosomes. In this study, we report an additional mechanism. We found that autophagosome–lysosome fusion is retained to some extent even in STX17 knockout (KO) HeLa cells. By screening other human SNAREs, we identified YKT6 as a novel autophagosomal SNARE protein. Depletion of YKT6 inhibited autophagosome–lysosome fusion partially in wild-type and completely in STX17 KO cells, suggesting that YKT6 and STX17 are independently required for fusion. YKT6 formed a SNARE complex with SNAP29 and lysosomal STX7, both of which are required for autophagosomal fusion. Recruitment of YKT6 to autophagosomes depends on its N-terminal longin domain but not on the C-terminal palmitoylation and farnesylation that are essential for its Golgi localization. These findings suggest that two independent SNARE complexes mediate autophagosome–lysosome fusion.
Continuous Blood Pressure Estimation from PPG Signal

Given the importance of blood pressure (BP) as a direct indicator of hypertension, regular monitoring is encouraged for healthy people and mandatory for patients at risk from cardiovascular diseases. We propose a system in which photoplethysmogram (PPG) is used to continuously estimate BP. A PPG sensor can be easily embedded in a modern wearable device, which can be used in such an approach. A set of features describing the PPG signal on a per-cycle basis is computed to be used in regression models. The predictive performance of the models is improved by rst using the RReliefF algorithm to select a subset of relevant features. Afterwards, personalization of the models is considered to further improve the performance. The approach is validated using two distinct datasets, one from a hospital environment and the other collected during every-day activities. Using the MIMIC hospital dataset, the best achieved mean absolute errors (MAE) in a leave-one-subject-out (LOSO) experiment were 4.47 +- 5.85 mmHg for systolic and 2.02 +- 2.94 mmHg for diastolic BP, at maximum personalization. For everyday-life dataset, the lowest errors in the same LOSO experiment were 8.57 +- 7.93 mmHg for systolic and 4.42 +- 3.61 mmHg for diastolic BP, again using maximum personalization.
Adept Automatic Knowledge Discovery System for Cold Start Knowledge Base Population

We submitted to the TAC 2017 Cold Start Knowledge Base Population (CSKB) track with our Adept Automatic Knowledge Discovery (A2KD) system. A2KD is an end-to-end knowledge base population system that uses diverse informationextraction (IE) algorithms provided by various universities, and integrates their output in a coherent way in order to populate a knowledge base. By the time of submission, the A2KD system had algorithms for Entity Discovery and Linking (EDL), Slot Filling (SF), and Event Argument Linking (EAL) for English and Chinese only. In addition to submitting system output for monolingual English and Chinese evaluations, we also submitted a bilingual variant which used English and Chinese but no Spanish.