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Primary osteosarcomas involving the base of the skull in middle-aged patients are rare. We describe the case of a 59-year-old Asian woman presenting with lethargy, epistaxis, left maxillary and mandibular pain, and headache. Computed tomography (CT) and magnetic resonance imaging (MRI) showed a large left greater sphenoid wing tumour with extensive local infiltration and intracranial extension. The tumour was diagnosed as osteosarcoma based on histological examination. In this report, we discuss the clinical presentations, radiological features, and imaging differential diagnoses of this case.

Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data. While such plausible control models were able to simulate and explain many basic locomotion behaviors (e.g. walking, running, and climbing stairs), modeling higher layer controls (e.g. processing environment cues, planning long-term motion strategies, and coordinating basic motor skills to navigate in dynamic and complex environments) remains a challenge. Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has been rarely applied in neuromechanical simulation to model human control. In this paper, we review the current state of neuromechanical simulations, along with the fundamentals of reinforcement learning, as it applies to human locomotion. We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations. This “Learn to Move” competition was an official competition at the NeurIPS conference from 2017 to 2019 and attracted over 1300 teams from around the world. Top teams adapted state-of-the-art deep reinforcement learning techniques and produced motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical simulations without utilizing reference motion data. We close with a discussion of future opportunities at the intersection of human movement simulation and reinforcement learning and our plans to extend the Learn to Move competition to further facilitate interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research

Electron transport in a nanowire exhibits a distinct behavior following the irradiation of intense laser pulse. Using particle-in-cell simulation, we observe a large-amplitude particle-driven wakefield excitation followed by electron acceleration in the solid density. Besides, we observed the quiver of the electrons across the nanowire under the action of the surrounding laser electric field facilitating deeper wakefield propagation in the nanowire with $2.5\ifmmode\times\else\texttimes\fi{}$ energy gain over a flat target. These results open insights into the laser-energy coupling with nanostructure targets and radiation sources, and motivate the wakefield acceleration in solid density plasma.

BackgroundThe ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate, and capable of assessing and mitigating drift.MethodsWe computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-minute trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subject’s RMS differences over time.ResultsIMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3-6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60 to 0.87). We observed minimal drift in the RMS differences over ten minutes; the average slopes of the linear fits to these data were near zero (−0.14 to 0.17 deg/min).ConclusionsOur workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, obviating the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments.

A halogen-free ionic liquid (IL) designed with long alkyl chain anion is reported. 1-methylimidazolium stearate (MIM stearate) synthesized through Bronsted acid-base reaction has shown improved lipophilic character and is able to penetrate bacterial cell walls. Antimicrobial activities against Gram-negative bacteria, Escherichia coli, and Pseudomonas aeruginosa were observed. Anti-biofilm assays showed effectivity against the biofilm of Pseudomonas aeruginosa. At 50 µg/mL the %biofilm inhibition of MIM stearate towards P. aeruginosa biofilm formation is comparable to the Bromofuran positive control. Brine shrimp lethality assay showed weak toxicity indicating the IL to be safe and benign. The synthesized MIM stearate showed good promise as an antimicrobial and anti-biofilm agent.

In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector. Top participants described their algorithms in this paper. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each team implemented different modifications of the known algorithms by, for example, dividing the task into subtasks, learning low-level control, or by incorporating expert knowledge and using imitation learning.

Background The ageing population and the changing disease profile have been driving the demand for community nurses. However, few nursing students in Singapore aspire to have a career in community nursing following graduation. Objectives To explore undergraduate nursing students' perceptions of a career in community nursing and to identify strategies to promote community nursing among nursing students. Methods An exploratory qualitative design was used. Four focus group discussions were conducted with a purposive sample of 24 undergraduate nursing students from a university. Interview transcripts were analysed using thematic analysis. Findings Four key themes emerged as follows: 'mid-life career', 'limited career progression', 'a difficult transition' and 'international nurses-dominated workforce'. Discussion While most participants rejected a career in community nursing immediately after graduation, they planned to join it at the later stages of their lives to accommodate changing life priorities. Limited career progression and increased difficulty in career transition from community nursing to acute care nursing were identified as deterrent factors in the participants' choices of community nursing as a career. Feelings of marginalization and a lack of role models in community nursing were perceived to be the result of the international nurses-dominated community workforce. Conclusion Community nursing remains an underrated career. There is a need to foster an optimistic career outlook and mobility in community nursing to entice nursing students into this career track. Implications for nursing policy Strategies to enhance community nursing recruitment should focus on providing more quality and diverse community placement opportunities in various community care settings, implementing a clearly defined career developmental plan to elucidate the role of community nurses, and improving community care and cultural competencies to develop a skilled and diverse community nursing workforce.

This study evaluates factors that influence door to operation theatre (OT) time in a tertiary referral centre following activation of trauma team. Specific factors observed in this study were association of the injury severity score (ISS), activation of trauma team and the number of referred specialty to door to operation theatre time.Retrospective chart review that evaluates all trauma patients which required immediate operative intervention from January 2011 to December 2015. Trauma patients were selected from the resuscitation log book and data were collected by chart review of selected patients.Only 5 out of 279 patients (1.8%) achieved optimal door to OT time. (60 minutes) Mean door to OT time was 299.27 minutes (95% CI: 280.52, 318.52). Trauma team activation has shown significant improvement in door to OT time (p=0.047). Time of multiple team referrals (p=0.023) and time of operative decision (p0.001) both had significant impact on door to OT time. Other factors included were demographics, ISS score, Glasgow Coma Scale (GCS), mechanism of injury and systolic blood pressure on arrival all which showed no significance.Trauma team activation in a tertiary centre improved trauma care by reducing door to OT time to less than 60 minutes. Implementation of an effective trauma team activation system in all hospitals throughout Malaysia is recommended.

Goal: Technologies that augment human performance are the focus of intensive research and development, driven by advances in wearable robotic systems. Success has been limited by the challenge of understanding human-robot interaction. To address this challenge, we developed an optimization framework to synthesize a realistic human standing long jump and used the framework to explore how simulated wearable robotic devices might enhance jump performance. Methods: A planar, five-segment, seven-degree-of-freedom model with physiological torque actuators, which have variable torque capacity depending on joint position and velocity, was used to represent human musculoskeletal dynamics. An active augmentation device was modeled as a torque actuator that could apply a single pulse of up to 100 Nm of extension torque. A passive design was modeled as rotational springs about each lower limb joint. Dynamic optimization searched for physiological and device actuation patterns to maximize jump distance. Results: Optimization of the nominal case yielded a 2.27 m jump that captured salient kinematic and kinetic features of human jumps. When the active device was added to the ankle, knee, or hip, jump distance increased to between 2.49 and 2.52 m. Active augmentation of all three joints increased the jump distance to 3.10 m. The passive design increased jump distance to 3.32 m by adding torques of 135, 365, and 297 Nm to the ankle, knee, and hip, respectively. Conclusion: Dynamic optimization can be used to simulate a standing long jump and investigate human-robot interaction. Significance: Simulation can aid in the design of performance-enhancing technologies.

A simultaneous sensing of multiple Mach-Zehnder interferometer sensors is proposed by using spatial division multiplexing setup. Two MZI sensors were installed on a hardened steel bar to conduct a curvature measurement. The percentage differences observed in the sensors before and after the curvature test was carried out are 1.5% and 0.3%, respectively. The mapping of pristine to multiplexed sensor signal shows a regression coefficient, r2 of 96.16%. These results indicated that the SDM of MZI sensor arrays is reliable and repeatable.

In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms., Comment: 27 pages, 17 figures

Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?

Synthesizing physiologically-accurate human movement in a variety of conditions can help practitioners plan surgeries, design experiments, or prototype assistive devices in simulated environments, reducing time and costs and improving treatment outcomes. Because of the large and complex solution spaces of biomechanical models, current methods are constrained to specific movements and models, requiring careful design of a controller and hindering many possible applications. We sought to discover if modern optimization methods efficiently explore these complex spaces. To do this, we posed the problem as a competition in which participants were tasked with developing a controller to enable a physiologically-based human model to navigate a complex obstacle course as quickly as possible, without using any experimental data. They were provided with a human musculoskeletal model and a physics-based simulation environment. In this paper, we discuss the design of the competition, technical difficulties, results, and analysis of the top controllers. The challenge proved that deep reinforcement learning techniques, despite their high computational cost, can be successfully employed as an optimization method for synthesizing physiologically feasible motion in high-dimensional biomechanical systems.

The effects of radiation reaction (RR) have been studied extensively by using the ultraintense laser interacts with the counter-propagating relativistic electron. At the laser intensity at the order of $10^{23}$ W/cm$^2$, the effects of RR are significant in a few laser period for a relativistic electron. However, the laser at such intensity is tightly focused and the laser energy is usually assumed to be fixed. Then, the signal of RR and energy conservation cannot be guaranteed. To assess the effects of RR in a tightly focused laser pulse and the evolution of the laser energy, we simulate this interaction with a beam of $10^9$ electrons by means of Particle-in-Cell (PIC) method. We observed that the effects of RR are suppressed due to the ponderomotive force and accompanied by a non-negligible amount of laser field energy reduction. This is due to the ponderomotive force that prevents the electrons from approaching the center of the laser pulse and leads to the interaction at weaker field region. At the same time, the laser energy is absorbed through ponderomotive acceleration. Thus, the kinetic energy of the electron beam has to be carefully selected such that the effects of RR become obvious., Comment: 6 pages, 3 figures

Background Idiopathic Parkinson's disease (IPD) is characterized by the clinical motor symptoms of hypokinesia, rigidity, and tremor. Apart from these motor symptoms, cognitive deficits often occur in IPD. The positive effect of cholinesterase inhibitors on cognitive deficits in IPD and findings of earlier molecular imaging studies suggest that the cholinergic system plays an important role in the origin of cognitive decline in IPD. Methods Twenty-five non-demented patients with IPD underwent a 5-[123I]iodo-3-[2(S)-2-azetidinylmethoxy]pyridine (5-I-A-85380) SPECT to visualize α4β2 nicotinic acetylcholine receptors (nAchR) and cognitive testing with the CERAD (Consortium to Establish a Registry for Alzheimer's Disease) battery to identify domains of cognitive dysfunction. Results In the CERAD, the IPD patients exhibited deficits in non-verbal memory, attention, psychomotor velocity, visuoconstructive ability, and executive functions. After Bonferroni correction for multiple comparisons, we found significant correlations between performance of the CERAD subtests Boston Naming Test (a specific test for visual perception and for detection of word-finding difficulties) and Word List Intrusions (a specific test for learning capacity and memory for language information) vs binding of α4β2 nAchR in cortical (the right superior parietal lobule) and subcortical areas (the left thalamus, the left posterior subcortical region, and the right posterior subcortical region). Conclusions These significant correlations between the results of the CERAD subtests and the cerebral α4β2 nAchR density, as assessed by 5-I-A-85380 SPECT, indicate that cerebral cholinergic pathways are relevant to cognitive processing in IPD.

The energy conversion in laser-electron beam collision is typically small. However, with a properly chosen parameter of the laser and electron beam, the energy conversion can be optimized. In this paper, the laser and electron parameters are selected such that the ponderomotive force is compensated by the radiation reaction force in the head-on collision configuration. Then, the relativistic electron beam can quiver in the laser pulse for a longer time to increase the energy conversion. To access the laser energy evolution, simulations of laser-electron beam collision by the Particle-in-Cell method are performed. The optimum of laser field energy depletion is observed at γ0 = a0 ∼ 400 and limited beyond this point due to the impenetrability threshold. The total energy conversion into radiation emission is optimum at γ0 = a0 ∼ 250. We estimated that the conversion efficiency can be up to 11% for an electron bunch with charge of the order of 100 nC. The efficient gamma-ray sources are of great interest for applications in photonuclear experiments.The energy conversion in laser-electron beam collision is typically small. However, with a properly chosen parameter of the laser and electron beam, the energy conversion can be optimized. In this paper, the laser and electron parameters are selected such that the ponderomotive force is compensated by the radiation reaction force in the head-on collision configuration. Then, the relativistic electron beam can quiver in the laser pulse for a longer time to increase the energy conversion. To access the laser energy evolution, simulations of laser-electron beam collision by the Particle-in-Cell method are performed. The optimum of laser field energy depletion is observed at γ0 = a0 ∼ 400 and limited beyond this point due to the impenetrability threshold. The total energy conversion into radiation emission is optimum at γ0 = a0 ∼ 250. We estimated that the conversion efficiency can be up to 11% for an electron bunch with charge of the order of 100 nC. The efficient gamma-ray sources are of great interest for ...

We present a novel and simple method for amplifying RNA targets (named by its acronym, SMART), and for detection, using engineered amplification probes that overcome existing limitations of current RNA-based technologies. This system amplifies and detects optimal engineered ssDNA probes that hybridize to target RNA. The amplifiable probe-target RNA complex is captured on magnetic beads using a sequence-specific capture probe and is separated from unbound probe using a novel microfluidic technique. Hybridization sequences are not constrained as they are in conventional target-amplification reactions such as nucleic acid sequence amplification (NASBA). Our engineered ssDNA probe was amplified both off-chip and in a microchip reservoir at the end of the separation microchannel using isothermal NASBA. Optimal solution conditions for ssDNA amplification were investigated. Although KCl and MgCl(2) are typically found in NASBA reactions, replacing 70 mmol/L of the 82 mmol/L total chloride ions with acetate resulted in optimal reaction conditions, particularly for low but clinically relevant probe concentrations (≤100 fmol/L). With the optimal probe design and solution conditions, we also successfully removed the initial heating step of NASBA, thus achieving a true isothermal reaction. The SMART assay using a synthetic model influenza DNA target sequence served as a fundamental demonstration of the efficacy of the capture and microfluidic separation system, thus bridging our system to a clinically relevant detection problem.

There are many artificial intelligence approaches used in the development of Automatic Speech Recognition (ASR), and hybrid approach is one of them. The common hybrid method in speech recognition is the combination of Hidden Markov Model (HMM) and Artificial Neural Network (ANN). The hybrid HMM/ANN is able to combine the strength of HMM in sequential modeling structure and ANN in pattern classification. Thus, this paper proposed a speaker independent and continuous Malay language speech recogniser by using the hybrid HMM/ANN method. In addition to that, this paper presents a study on Standard Malay's phonetic and phonology to help in the recognition of Malay words. The CSLU toolkit is utilized for building the recogniser, and the experimental results showed that the proposed HMM/ANN model outperformed the conventional HMM model. The performances of the recognisers are measured in term of word accuracy and sentence accuracy.

Movement is fundamental to human and animal life, emerging through interaction of complex neural, muscular, and skeletal systems. Study of movement draws from and contributes to diverse fields, including biology, neuroscience, mechanics, and robotics. OpenSim unites methods from these fields to create fast and accurate simulations of movement, enabling two fundamental tasks. First, the software can calculate variables that are difficult to measure experimentally, such as the forces generated by muscles and the stretch and recoil of tendons during movement. Second, OpenSim can predict novel movements from models of motor control, such as kinematic adaptations of human gait during loaded or inclined walking. Changes in musculoskeletal dynamics following surgery or due to human-device interaction can also be simulated; these simulations have played a vital role in several applications, including the design of implantable mechanical devices to improve human grasping in individuals with paralysis. OpenSim is an extensible and user-friendly software package built on decades of knowledge about computational modeling and simulation of biomechanical systems. OpenSim's design enables computational scientists to create new state-of-the-art software tools and empowers others to use these tools in research and clinical applications. OpenSim supports a large and growing community of biomechanics and rehabilitation researchers, facilitating exchange of models and simulations for reproducing and extending discoveries. Examples, tutorials, documentation, and an active user forum support this community. The OpenSim software is covered by the Apache License 2.0, which permits its use for any purpose including both nonprofit and commercial applications. The source code is freely and anonymously accessible on GitHub, where the community is welcomed to make contributions. Platform-specific installers of OpenSim include a GUI and are available on simtk.org.

PURPOSE: Transient elastography (FibroScan, [TE]) and serum fibrosis markers such as the FibroTest (FT) are established methods for the noninvasive staging of liver fibrosis. A study using real-time elastography (HI-RTE), which is integrated in a conventional ultrasound system, was recently published with comparable results to transient elastography. The aim of the present study was to validate real-time elastography using the formulas calculated in previous studies and to compare the results to transient elastography and FibroTest for the noninvasive assessment of liver fibrosis. MATERIALS AND METHODS: One hundred and thirty-four patients with chronic liver disease and either histological assessment of liver fibrosis (n = 112) or proven liver cirrhosis (n = 22) were included in the study. All patients received TE, HI-RTE, and biochemical evaluation on the same day as presentation. The calculation of the elasticity score of real-time elastography was performed in accordance with the two previously published studies. RESULTS: The Spearman correlation coefficient between transient elastography, real-time elastography and FibroTest with the histological Chevallier score was statistically significant with 0.78, 0.34, and 0.67, respectively (p < 0.01). The diagnostic accuracy expressed as areas under ROC curves was 0.84, 0.69 and 0.85 for the diagnosis of significant fibrosis (F ≥ 2), and 0.97, 0.65, and 0.83 for the diagnosis of cirrhosis, respectively. CONCLUSION: Real-time elastography in its present form cannot replace transient elastography for noninvasive assessment of liver fibrosis.

Ice thickness estimates and impactor dynamics indicate that some impacts must breach Europas ice crust; and outcomes of impact experiments using ice-over-water targets range from simple craters to chaos-like destroyed zones, depending on impact energy and ice competence. Firstorder impacts--into thick ice or at low impact energy--produce craters. Second-order impacts punch through the ice, making holes that resemble raft-free chaos areas. Third-order impacts--into thinnest ice or at highest energy--produce large irregular raft-filled zones similar to platy chaos. Other evidence for an impact origin for chaos areas comes from the size-frequency distribution of chaos+craters on Europa, which matches the impact production functions of Ganymede and Callisto; and from small craters around the large chaos area Thera Macula, which decrease in average size and density per unit area as a function of distance from Theras center. There are no tiny chaos areas and no craters >50 km diameter. This suggests that small impactors never penetrate, whereas large ones (berPenetrators: >2.5 km diameter at average impact velocity) always do. Existence of both craters and chaos areas in the size range 2-40 km diameter points to spatial/temporal variation in crust thickness. But in this size range, craters are progressively outnumbered by chaos areas at larger diameters, suggesting that probability of penetration increases with increasing scale of impact. If chaos areas do represent impact sites, then Europas surface is older than previously thought. The recalculated resurfacing age is 480 (-302/+960) Ma: greater than prior estimates, but still very young by solar system standards.

Background We investigated whether acupuncture as a supportive antiemetic approach reduces the need for antiemetic rescue medication during highly emetogenic chemotherapy in pediatric oncology. We report on a multicenter crossover study at 5 tertiary hospitals in Germany. Procedure Twenty-three children (13.6 y,+/- 2.9) receiving highly emetogenic chemotherapy for treatment of solid malignant tumors were included. Patients were randomly allocated to receive acupuncture treatment during either the second or third identical chemotherapy course together with standard antiemetic medication. The main outcome measure was the amount of additional antiemetic medication during chemotherapy. Secondary outcome measure was the number of episodes of vomiting per course. Results Fourty-six chemotherapy courses with or without acupuncture were compared. The need for rescue antiemetic medication was significantly lower in acupuncture courses compared to control courses (p=0.001) Episodes of vomiting per course were also significantly lower in courses with acupuncture (p=0.01). Except for pain from needling (4/23) no side effects occurred. Patients acceptance of acupuncture was high. Conclusions Acupuncture as applied here seems to be effective in preventing nausea and vomiting in pediatric cancer patients.

Calcite-twinning analysis of Paleozoic limestones from 42 sites reveals that the change in regional strike along the frontal edge of the Pennsylvania salient is accompanied by an equal-magnitude rotation of paleostress directions of up to 60 degrees. The rotations, recorded at 22 reliable sites, show no discernible difference between sites with rocks of Cambrian-Ordovician and Silurian-Devonian age. Evidence for similarly fanned orientations is not present in foreland sites. Scatter in the data is attributed to grain-scale rotations and compaction overprinting, as demonstrated in prior studies, and it was reduced by data-cleaning methods as well as by the use of contouring and data-averaging methods. Comparisons of paleostress directions within the belt reveal only minor rotations in the southwest region of the salient, and the bulk of rotation is accommodated by the northern limb. We hypothesize that these rotations resulted from convergence in the thrust wedge against a northerly bounding, rigid basement block around Pennsylvanian times. This created a structural anisotropy in the evolving belt that guided the post-rotational formation of folds in Early Permian times and produced the current pattern of the salient. This model of decoupled thrusting/rotation and folding explains a variety of previously conflicting observations in the area while offering a kinematically consistent scenario that may also apply to other curved orogenic belts.

This two-pronged study seeks to ascertain the research psychographic characteristics of a select group of nurse professoriate (n=37) in a comprehensive university in the Philippines. The quantitative part of the study had an adapted survey questionnaire, which profiled the demographic and psychographic characteristics of the respondents. Defining the qualitative aspect of the inquiry was an in-depth interview with five (5) of the respondents chosen purposively to triangulate the data yielded by the questionnaire. Data were treated statistically and interpretively to describe the phenomenon under inquiry. It is interesting to note that while the nurse professoriate had shown a positive attitude towards research, variables such as time constraints, lack of research knowledge, research support structure and motivation hinder them from doing research activities. Implications of the study to reculturing, restructuring and reformulating efforts in research are also discussed.