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We surveyed New York physicians to study their perceptions of reporting requirements related to their own mental health care on professional applications, including whether they were experiencing symptoms of burnout. Over half of the responding physicians reported experiencing symptoms of burnout and these physicians were at increased odds of perceiving a barrier to seeking mental health care if they had to report such care on professional applications and renewals for medical licensure, malpractice, and hospital privileges and credentialing compared to physicians not experiencing symptoms of burnout. As state medical boards, hospitals, and insurers seek information to help assess risks posed by physicians, it is essential to strike an appropriate balance between their duty to protect the public and the physician's right to confidentiality. This balance can be assessed based on the questions that are asked on various professional applications and how information gleaned through physician responses is used. Overly intrusive questions, though well intentioned to protect the public, may run counter to current interpretations of federal law and may inhibit care-seeking among physicians, which is critical to both patient safety and physician health.

With the aging of the population in the United States, an increasing number of individuals suffer from mobility challenges. For such individuals, the difficulty of standing up from a seated position is a major barrier for their daily physical activities. In the paper, a novel assistive device, namely Semi-Wearable Sit-to-Stand Assist (SW-SiStA), is presented, which provides effective lower-limb assistance to overcome such difficulty for the mobility-challenged individuals. Unlike traditional exoskeletons, the SW-SiStA can be easily detached after the completion of the sit-to-stand process, and thus will not cause additional burden to the user during the subsequent ambulation. The SW-SiStA is powered with a pneumatic actuator, leverage its advantages of low cost and high power/force density. The complexity of the device is reduced by the use of a simple solenoid valve in combination with two adjustable needle valves, providing the desired individualized adjustability without the expensive proportional valves. Human testing of the device indicated that the SW-SiStA was able to provide effective sit-to-stand assistance in a natural way, and the users were able to expend significantly less muscle efforts in the process.

Robotic exoskeletons have the capability to improve community ambulation in aging individuals. These exoskeleton controllers utilize different environmental information such as walking speeds and slope inclines to provide corresponding assistance. Several numerical approaches for estimating this environmental information have been implemented; however, they tend to be limited during dynamic changes. A possible solution is a machine learning model utilizing the user's electromyography (EMG) signals along with mechanical sensor data. We developed a neural network-based walking speed and slope estimator for a powered hip exoskeleton and explored the EMG signal contributions in both static and dynamic settings while wearing the device. We also analyzed the performance of different EMG electrode placements. The resulting machine learning model achieved error rates below 0.08 m/s RMSE and 1.3 RMSE. Our study findings from four able-bodied and two elderly subjects indicate that EMG can improve the performance by reducing the error rate by 14.8% compared to the model using only mechanical sensors. Additionally, results show that using EMG electrode configuration within the exoskeleton interface region is sufficient for the EMG model performance.

Purpose: Lack of specialty board certification has been reported as a significant physician-level predictor of receiving a disciplinary action from a state medical board. This study investigated the association between family physicians receiving a disciplinary action from a state medical board and certification by the American Board of Family Medicine (ABFM)., Method: Three datasets were merged and a series of logistic regressions were conducted examining the relationship between certification status and disciplinary actions when adjusting for covariates. Data were available from 1976 to 2017. Predictor variables were gender, age, medical training degree type, medical school location, and the severity of the action., Results: Of the family physicians in this sample, 95% (114,454/120,443) had never received any disciplinary action. Having ever been certified was associated with a reduced likelihood of ever receiving an action (odds ratio [OR] = 0.35; 95% confidence interval [CI] = 0.30, 0.40; P < .001), and having held a prior but not current certification at the time of the action was associated with an increase in receiving the most severe type of action (OR = 3.71; 95% CI = 2.24, 6.13; P < .001)., Conclusions: Disciplinary actions are uncommon events. Family physicians who had ever been ABFM certified were less likely to receive an action. The most severe actions were associated with decreased odds of being board certified at the time of the action. Receiving the most severe action type increased the likelihood of physicians holding a prior but not current certification.

Myoelectric signals are a standard input for volitional control of prosthetic devices. As an information-rich signal, feature selection plays a decisive role in the performance of motion classification. In this paper, we evaluate feature selection in the classification of simultaneous motions produced from combinations of wrist and elbow flexion/extension, radio-ulnar pronation/supination, and hand opening/closing aiming to determine a common set of recommendations for the implementation of motion classification from EMG signals for prosthetic control. Chow-Liu trees and forward feature selection are used as the methods for selecting features, and six different classification algorithms are evaluated as the wrapping component. We analyzed the performance of different linear and non-linear kernel algorithms in terms of the accuracy with respect to the feature selection, observing that feature selection was critical for improving accuracy levels to above 95%. Chow-Liu trees demonstrated to be a strategy that enables a combination of a low number of iterations with comparable accuracy to what is achieved with a forward selection search. In addition, we found a trend for waveform length and entropy as the most relevant types of features to consider and found evidence that simultaneous motion classification should be handled using non-linear classification approaches. Our study serves to improve the feature and algorithm selection for surface EMG signals in the classification of simultaneous motions generating a viable approach in the recognition of combinations of actions from the upper limb.

Purpose: Physicians must pass the United States Medical Licensing Examination (USMLE) to obtain an unrestricted license to practice allopathic medicine in the United States. Little is known, however, about how well USMLE performance relates to physician behavior in practice, particularly conduct inconsistent with safe, effective patient care. The authors examined the extent to which USMLE scores relate to the odds of receiving a disciplinary action from a U.S. state medical board., Method: Controlling for multiple factors, the authors used non-nested multilevel logistic regression analyses to estimate the relationships between scores and receiving an action. The sample included 164,725 physicians who graduated from U.S. MD-granting medical schools between 1994 and 2006., Results: Physicians had a mean Step 1 score of 214 (standard deviation [SD] = 21) and a mean Step 2 Clinical Knowledge (CK) score of 213 (SD = 23). Of the physicians, 2,205 (1.3%) received at least one action. Physicians with higher Step 2 CK scores had lower odds of receiving an action. A 1-SD increase in Step 2 CK scores corresponded to a decrease in the chance of disciplinary action by roughly 25% (odds ratio = 0.75; 95% CI = 0.70-0.80). After accounting for Step 2 CK scores, Step 1 scores were unrelated to the odds of receiving an action., Conclusions: USMLE Step 2 CK scores provide useful information about the odds a physician will receive an official sanction for problematic practice behavior. These results provide validity evidence supporting current interpretation and use of Step 2 CK scores.

Powered lower limb prostheses can assist users in a variety of ambulation modes by providing knee and/or ankle joint power. This study's goal was to develop a flexible control system to allow users to perform a variety of tasks in a natural, accurate, and reliable way. Six transfemoral amputees used a powered knee-ankle prosthesis to ascend/descend a ramp, climb a 3- and 4-step staircase, perform walking and standing transitions to and from the staircase, and ambulate at various speeds. A mode-specific classification architecture was developed to allow seamless transitions at four discrete gait events. Prosthesis mode transitions (i.e., the prosthesis' mechanical response) were delayed by 90 ms. Overall, users were not affected by this small delay. Offline classification results demonstrate significantly reduced error rates with the delayed system compared to the non-delayed system (p < 0.001). The average error rate for all heel contact decisions was 1.65% [0.99%] for the non-delayed system and 0.43% [0.23%] for the delayed system. The average error rate for all toe off decisions was 0.47% [0.16%] for the non-delayed system and 0.13% [0.05%] for the delayed system. The results are encouraging and provide another step towards a clinically viable intent recognition system for a powered knee-ankle prosthesis.

Background: Despite a large increase in robotic exoskeleton research, there are few studies that have examined human performance with different control strategies on the same exoskeleton device. Direct comparison studies are needed to determine how users respond to different types of control. The purpose of this study was to compare user performance using a robotic hip exoskeleton with two different controllers: a controller that targeted a biological hip torque profile and a proportional myoelectric controller., Methods: We tested both control approaches on 10 able-bodied subjects using a pneumatically powered hip exoskeleton. The state machine controller targeted a biological hip torque profile. The myoelectric controller used electromyography (EMG) of lower limb muscles to produce a proportional control signal for the hip exoskeleton. Each subject performed two 30-min exoskeleton walking trials (1.0 m/s) using each controller and a 10-min trial with the exoskeleton unpowered. During each trial, we measured subjects' metabolic cost of walking, lower limb EMG profiles, and joint kinematics and kinetics (torques and powers) using a force treadmill and motion capture., Results: Compared to unassisted walking in the exoskeleton, myoelectric control significantly reduced metabolic cost by 13% ( p  = 0.005) and biological hip torque control reduced metabolic cost by 7% ( p  = 0.261). Subjects reduced muscle activity relative to the unpowered condition for a greater number of lower limb muscles using myoelectric control compared to the biological hip torque control. More subjects subjectively preferred the myoelectric controller to the biological hip torque control., Conclusion: Myoelectric control had more advantages (metabolic cost and muscle activity reduction) compared to a controller that targeted a biological torque profile for walking with a robotic hip exoskeleton. However, these results were obtained with a single exoskeleton device with specific control configurations while level walking at a single speed. Further testing on different exoskeleton hardware and with more varied experimental protocols, such as testing over multiple types of terrain, is needed to fully elucidate the potential benefits of myoelectric control for exoskeleton technology.

Background: The American Board of Anesthesiology administers written and oral examinations for its primary certification. This retrospective cohort study tested the hypothesis that the risk of a disciplinary action against a physician's medical license is lower in those who pass both examinations than those who pass only the written examination., Methods: Physicians who entered anesthesiology training from 1971 to 2011 were followed up to 2014. License actions were ascertained via the Disciplinary Action Notification Service of the Federation of State Medical Boards., Results: The incidence rate of license actions was relatively stable over the study period, with approximately 2 to 3 new cases per 1,000 person-years. In multivariable models, the risk of license actions was higher in men (hazard ratio = 1.88 [95% CI, 1.66 to 2.13]) and lower in international medical graduates (hazard ratio = 0.73 [95% CI, 0.66 to 0.81]). Compared with those passing both examinations on the first attempt, those passing neither examination (hazard ratio = 3.60 [95% CI, 3.14 to 4.13]) and those passing only the written examination (hazard ratio = 3.51 [95% CI, 2.87 to 4.29]) had an increased risk of receiving an action from a state medical board. The risk was no different between the latter two groups (P = 0.81), showing that passing the oral but not the written primary certification examination is associated with a decreased risk of subsequent license actions. For those with residency performance information available, having at least one unsatisfactory training record independently increased the risk of license actions., Conclusions: These findings support the concept that an oral examination assesses domains important to physician performance that are not fully captured in a written examination.

A broad goal in the field of powered lower limb exoskeletons is to reduce the metabolic cost of walking. Ankle exoskeletons have successfully achieved this goal by correctly timing a plantarflexor torque during late stance phase. Hip exoskeletons have the potential to assist with both flexion and extension during walking gait, but the optimal timing for maximally reducing metabolic cost is unknown. The focus of our study was to determine the best assistance timing for applying hip assistance through a pneumatic exoskeleton on human subjects. Ten non-impaired subjects walked with a powered hip exoskeleton, and both hip flexion and extension assistance were separately provided at different actuation timings using a simple burst controller. The largest average across-subject reduction in metabolic cost for hip extension was at 90% of the gait cycle (just prior to heel contact) and for hip flexion was at 50% of the gait cycle; this resulted in an 8.4 and 6.1% metabolic reduction, respectively, compared to walking with the unpowered exoskeleton. However, the ideal timing for both flexion and extension assistance varied across subjects. When selecting the assistance timing that maximally reduced metabolic cost for each subject, average metabolic cost for hip extension was 10.3% lower and hip flexion was 9.7% lower than the unpowered condition. When taking into account user preference, we found that subject preference did not correlate with metabolic cost. This indicated that user feedback was a poor method of determining the most metabolically efficient assistance power timing. The findings of this study are relevant to developers of exoskeletons that have a powered hip component to assist during human walking gait.

Research on robotic exoskeletons has rapidly expanded over the previous decade. Advances in robotic hardware and energy supplies have enabled viable prototypes for human testing. This review paper describes current lower limb robotic exoskeletons, with specific regard to common trends in the field. The preponderance of published literature lacks rigorous quantitative evaluations of exoskeleton performance, making it difficult to determine the disadvantages and drawbacks of many of the devices. We analyzed common approaches in exoskeleton design and the convergence, or lack thereof, with certain technologies. We focused on actuators, sensors, energy sources, materials, and control strategies. One of the largest hurdles to be overcome in exoskeleton research is the user interface and control. More intuitive and flexible user interfaces are needed to increase the success of robotic exoskeletons. In the last section, we discuss promising future solutions to the major hurdles in exoskeleton control. A number of emerging technologies could deliver substantial advantages to existing and future exoskeleton designs. We conclude with a listing of the advantages and disadvantages of the emerging technologies and discuss possible futures for the field.

Although there have been many lower limb robotic exoskeletons that have been tested for human walking, few devices have been tested for assisting running. It is possible that a pseudo-passive elastic exoskeleton could benefit human running without the addition of electrical motors due to the spring-like behavior of the human leg. We developed an elastic lower limb exoskeleton that added stiffness in parallel with the entire lower limb. Six healthy, young subjects ran on a treadmill at 2.3 m/s with and without the exoskeleton. Although the exoskeleton was designed to provide ~50% of normal leg stiffness during running, it only provided 24% of leg stiffness during testing. The difference in added leg stiffness was primarily due to soft tissue compression and harness compliance decreasing exoskeleton displacement during stance. As a result, the exoskeleton only supported about 7% of the peak vertical ground reaction force. There was a significant increase in metabolic cost when running with the exoskeleton compared with running without the exoskeleton (ANOVA, P < .01). We conclude that 2 major roadblocks to designing successful lower limb robotic exoskeletons for human running are human-machine interface compliance and the extra lower limb inertia from the exoskeleton.

Purpose: Little is known about the attrition of physicians trained in internal medicine (IM). The authors sought to examine career paths, disciplinary actions, and American Board of Medical Specialties (ABMS) certification status of IM residents., Method: Three datasets were combined to study 66,881 residents in Accreditation Council for Graduate Medical Education-accredited IM residency programs nationwide from 1995 to 2004. Group differences (among an American Board of Internal Medicine [ABIM]-certified cohort; an ABMS-certified cohort (but not ABIM-certified); and a noncertified cohort) in IM residency performance ratings, specialty certification status, year of initial IM training, and medical board disciplinary actions were examined. Analyses included chi-square tests, analysis of variance, pairwise comparisons, and logistic regressions., Results: Ninety-five percent of IM residents obtained ABIM certification; 1.6% received ABMS certification in another specialty; 3.4% received no ABMS specialty certification, of which 74.3% have a current medical license; and 66.6% self-reported IM as their primary specialty. During residency, the ABIM cohort performed better than those who never obtained ABIM certification. Disciplinary actions were lowest for the ABIM cohort (1.2%), 2.4% for the ABMS cohort, and highest and more severe for the noncertified cohort (6.0%)., Conclusions: Only 5% of IM residents do not achieve IM certification. IM resident attrition minimally impacts physician supply, though those without certification appear to contribute disproportionately to poor physician performance indicators. Improved tracking of the U.S. physician workforce could aid policy makers in predicting manpower shifts in certain specialty areas, both during and after residency training.

Powered lower limb prosthesis technologies hold the promise of providing greater ability and mobility to transfemoral amputees. Intent recognition systems for these devices may allow amputees to perform automatic, seamless transitions between locomotion modes. Prior studies in which pattern recognition algorithms have been trained to recognize subject-specific patterns within device-mounted sensor data have shown the feasibility of such systems. While effective, these strategies require substantial training regimens. To reduce this training burden, we developed and evaluated user-independent intent recognition systems. A novel mode-specific classification system was developed that allowed each locomotion transition to be statistically considered its own class. Various pattern recognition algorithms were trained with sensor data from a pool of eight lower limb amputees and performance was tested using data on a novel subject. For both user-dependent and user-independent classification, mode-specific classification reduced error ( ) on transitional steps by ∼ 50% without affecting steady-state classification. Incorporating sensor time history and level-ground walking data from the novel subject into the training data resulted in decreasing errors ( ) on steady-state classification by over 60% without affecting transitional error. These strategies were combined to demonstrate significant overall system improvements from baseline conditions presented in prior research.

Importance: Some patients with lower leg amputations may be candidates for motorized prosthetic limbs. Optimal control of such devices requires accurate classification of the patient's ambulation mode (eg, on level ground or ascending stairs) and natural transitions between different ambulation modes., Objective: To determine the effect of including electromyographic (EMG) data and historical information from prior gait strides in a real-time control system for a powered prosthetic leg capable of level-ground walking, stair ascent and descent, ramp ascent and descent, and natural transitions between these ambulation modes., Design, Setting, and Participants: Blinded, randomized crossover clinical trial conducted between August 2012 and November 2013 in a research laboratory at the Rehabilitation Institute of Chicago. Participants were 7 patients with unilateral above-knee (n = 6) or knee-disarticulation (n = 1) amputations. All patients were capable of ambulation within their home and community using a passive prosthesis (ie, one that does not provide external power)., Interventions: Electrodes were placed over 9 residual limb muscles and EMG signals were recorded as patients ambulated and completed 20 circuit trials involving level-ground walking, ramp ascent and descent, and stair ascent and descent. Data were acquired simultaneously from 13 mechanical sensors embedded on the prosthesis. Two real-time pattern recognition algorithms, using either (1) mechanical sensor data alone or (2) mechanical sensor data in combination with EMG data and historical information from earlier in the gait cycle, were evaluated. The order in which patients used each configuration was randomized (1:1 blocked randomization) and double-blinded so patients and experimenters did not know which control configuration was being used., Main Outcomes and Measures: The main outcome of the study was classification error for each real-time control system. Classification error is defined as the percentage of steps incorrectly predicted by the control system., Results: Including EMG signals and historical information in the real-time control system resulted in significantly lower classification error (mean, 7.9% [95% CI, 6.1%-9.7%]) across a mean of 683 steps (range, 640-756 steps) compared with using mechanical sensor data only (mean, 14.1% [95% CI, 9.3%-18.9%]) across a mean of 692 steps (range, 631-775 steps), with a mean difference between groups of 6.2% (95% CI, 2.7%-9.7%] (P = .01)., Conclusions and Relevance: In this study of 7 patients with lower limb amputations, inclusion of EMG signals and temporal gait information reduced classification error across ambulation modes and during transitions between ambulation modes. These preliminary findings, if confirmed, have the potential to improve the control of powered leg prostheses.

Improving lower-limb prostheses is important to enhance the mobility of amputees. The purpose of this paper is to introduce an impedance-based control strategy (consisting of four novel algorithms) for an active knee and ankle prosthesis and test its generalizability across multiple walking speeds, walking surfaces, and users. The four algorithms increased ankle stiffness throughout stance, decreased knee stiffness during terminal stance, as well as provided powered ankle plantarflexion and knee swing initiation through modifications of equilibrium positions of the ankle and knee, respectively. Seven amputees (knee disarticulation and transfemoral levels) walked at slow, comfortable, and hurried speeds on level and inclined (10°) surfaces. The prosthesis was tuned at their comfortable level ground walking speed. We further quantified trends in prosthetic knee and ankle kinematics, and kinetics across conditions. Subjects modulated their walking speed by ±25% (average) from their comfortable speeds. As speed increased, increasing ankle angles and velocities as well as stance phase ankle power and plantarflexion torque were observed. At slow and comfortable speeds, plantarflexion torque was increased on the incline. At slow and comfortable speeds, stance phase positive knee power was increased and knee torque more flexor on the incline. As speed increased, knee torque became less flexor on the incline. These algorithms were shown to generalize well across speed, produce gait mechanics that compare favorably with non-amputee data, and display evidence of scalable device function. They have the potential to reduce the challenge of clinically configuring such devices and increase their viability during daily use.

Targeted muscle reinnervation (TMR) is a surgical technique that creates myoelectric prosthesis control sites for high-level amputees. The electromyographic (EMG) signal patterns provided by the reinnervated muscles are well-suited for pattern recognition control. Pattern recognition allows for control of a greater number of degrees of freedom (DOF) than the conventional, EMG amplitude-based approach. Previous pattern recognition studies have shown benefit in placing electrodes directly over the reinnervated muscles. Localizing the optimal TMR locations is inconvenient and time consuming. In this contribution, we demonstrate that a clinically practical grid arrangement of electrodes yields real-time control performance that is equivalent to, or better than, the site-specific electrode placement for simultaneous control of multiple DOFs using pattern recognition. Additional findings indicate that grid-like electrode arrangement yields significantly lower classification errors for classifiers with a large number of movement classes ( > 9). These findings suggest that a grid electrode arrangement can be effectively used to control a multi-DOF upper limb prosthesis while reducing the time and effort associated with fitting the prosthesis due to clinical localization of control sites on amputee patients.

Lower limb prostheses that can generate net positive mechanical work may restore more ambulation modes to amputees. However, configuration of these devices imposes an additional burden on clinicians relative to conventional prostheses; devices for transfemoral amputees that require configuration of both a knee and an ankle joint are especially challenging. In this paper, we present an approach to configuring such powered devices. We developed modified intrinsic control strategies--which mimic the behavior of biological joints, depend on instantaneous loads within the prosthesis, or set impedance based on values from previous states, as well as a set of starting configuration parameters. We developed tables that include a list of desired clinical gait kinematics and the parameter modifications necessary to alter them. Our approach was implemented for a powered knee and ankle prosthesis in five ambulation modes (level-ground walking, ramp ascent/descent, and stair ascent/descent). The strategies and set of starting configuration parameters were developed using data from three individuals with unilateral transfemoral amputations who had previous experience using the device; this approach was then tested on three novice unilateral transfemoral amputees. Only 17% of the total number of parameters (i.e., 24 of the 140) had to be independently adjusted for each novice user to achieve all five ambulation modes and the initial accommodation period (i.e., time to configure the device for all modes) was reduced by 56%, to 5 hours or less. This approach and subsequent reduction in configuration time may help translate powered prostheses into a viable clinical option where amputees can more quickly appreciate the benefits such devices can provide.

Recently developed lower-limb prostheses are capable of actuating the knee and ankle joints, allowing amputees to perform advanced locomotion modes such as step-over-step stair ascent and walking on sloped surfaces. However, transitions between these locomotion modes and walking are neither automatic nor seamless. This study describes methods for construction and training of a high-level intent recognition system for a lower-limb prosthesis that provides natural transitions between walking, stair ascent, stair descent, ramp ascent, and ramp descent. Using mechanical sensors onboard a powered prosthesis, we collected steady-state and transition data from six transfemoral amputees while the five locomotion modes were performed. An intent recognition system built using only mechanical sensor data was 84.5% accurate using only steady-state training data. Including training data collected while amputees performed seamless transitions between locomotion modes improved the overall accuracy rate to 93.9%. Training using a single analysis window at heel contact and toe off provided higher recognition accuracy than training with multiple analysis windows. This study demonstrates the capability of an intent recognition system to provide automatic, natural, and seamless transitions between five locomotion modes for transfemoral amputees using powered lower limb prostheses.

L-arginine metabolism including the nitric oxide (NO) synthase and arginase pathways is important in the maintenance of airways function. We have previously reported that accumulation of asymmetric dimethylarginine (ADMA) in airways, resulting in changes in L-arginine metabolism, contributes to airways obstruction in asthma and cystic fibrosis. Herein, we assessed L-arginine metabolism in airways of patients with chronic obstructive pulmonary disease (COPD). Lung function testing, measurement of fractional exhaled NO (FeNO) and sputum NO metabolites, as well as quantification of L-arginine metabolites (L-arginine, L-ornithine, L-citrulline, ADMA and symmetric dimethylarginine) using liquid chromatography-mass spectrometry (LC-MS) were performed. Concentrations of L-ornithine, the product of arginase activity, correlated directly with L-arginine and ADMA sputum concentrations. FeNO correlated directly with pre- and post-bronchodilator forced expiratory volume in one second (FEV1). Sputum arginase activity correlated inversely with total NO metabolite (NOx) and nitrite concentrations in sputum, and with pre- and post-bronchodilator FEV1. These findings suggest that ADMA in COPD airways results in a functionally relevant shift of L-arginine breakdown by the NO synthases towards the arginase pathway, which contributes to airway obstruction in these patients.

New computerized and powered lower limb prostheses are being developed that enable amputees to perform multiple locomotion modes. However, current lower limb prosthesis controllers are not capable of transitioning these devices automatically and seamlessly between locomotion modes such as level-ground walking, stairs and slopes. The focus of this study was to evaluate different intent recognition interfaces, which if configured properly, may be capable of providing more natural transitions between locomotion modes. Intent recognition can be accomplished using a multitude of different signals from mechanical sensors on the prosthesis. Since these signals are non-stationary over any given stride, and gait is cyclical, time history information may improve locomotion mode recognition. The authors propose a dynamic Bayesian network classification strategy to incorporate prior sensor information over the gait cycle with current sensor information. Six transfemoral amputees performed locomotion circuits comprising level-ground walking and ascending/descending stairs and ramps using a powered knee and ankle prosthesis. Using time history reduced steady-state misclassifications by over half (p < 0.01), when compared to strategies that did not use time history, without reducing intent recognition performance during transitions. These results suggest that including time history information across the gait cycle can enhance locomotion mode intent recognition performance.

Myoelectric control has been used for decades to control powered upper limb prostheses. Conventional, amplitude-based control has been employed to control a single prosthesis degree of freedom (DOF) such as closing and opening of the hand. Within the last decade, new and advanced arm and hand prostheses have been constructed that are capable of actuating numerous DOFs. Pattern recognition control has been proposed to control a greater number of DOFs than conventional control, but has traditionally been limited to sequentially controlling DOFs one at a time. However, able-bodied individuals use multiple DOFs simultaneously, and it may be beneficial to provide amputees the ability to perform simultaneous movements. In this study, four amputees who had undergone targeted motor reinnervation (TMR) surgery with previous training using myoelectric prostheses were configured to use three control strategies: 1) conventional amplitude-based myoelectric control, 2) sequential (one-DOF) pattern recognition control, 3) simultaneous pattern recognition control. Simultaneous pattern recognition was enabled by having amputees train each simultaneous movement as a separate motion class. For tasks that required control over just one DOF, sequential pattern recognition based control performed the best with the lowest average completion times, completion rates and length error. For tasks that required control over 2 DOFs, the simultaneous pattern recognition controller performed the best with the lowest average completion times, completion rates and length error compared to the other control strategies. In the two strategies in which users could employ simultaneous movements (conventional and simultaneous pattern recognition), amputees chose to use simultaneous movements 78% of the time with simultaneous pattern recognition and 64% of the time with conventional control for tasks that required two DOF motions to reach the target. These results suggest that when amputees are given the ability to control multiple DOFs simultaneously, they choose to perform tasks that utilize multiple DOFs with simultaneous movements. Additionally, they were able to perform these tasks with higher performance (faster speed, lower length error and higher completion rates) without losing substantial performance in 1 DOF tasks.

The clinical application of robotic technology to powered prosthetic knees and ankles is limited by the lack of a robust control strategy. We found that the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles in a patient who had undergone knee amputation improved control of a robotic leg prosthesis. EMG signals were decoded with a pattern-recognition algorithm and combined with data from sensors on the prosthesis to interpret the patient's intended movements. This provided robust and intuitive control of ambulation--with seamless transitions between walking on level ground, stairs, and ramps--and of the ability to reposition the leg while the patient was seated.

Advanced upper limb prostheses capable of actuating multiple degrees of freedom (DOFs) are now commercially available. Pattern recognition algorithms that use surface electromyography (EMG) signals show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to activate only one DOF at a time. This study introduces a novel classifier based on Bayesian theory to provide classification of simultaneous movements. This approach and two other classification strategies for simultaneous movements were evaluated using nonamputee and amputee subjects classifying up to three DOFs, where any two DOFs could be classified simultaneously. Similar results were found for nonamputee and amputee subjects. The new approach, based on a set of conditional parallel classifiers was the most promising with errors significantly less (p < 0.05) than a single linear discriminant analysis (LDA) classifier or a parallel approach. For three-DOF classification, the conditional parallel approach had error rates of 6.6% on discrete and 10.9% on combined motions, while the single LDA had error rates of 9.4% on discrete and 14.1% on combined motions. The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements.

Background: Hevea brasiliensis, a member of the Euphorbiaceae family, is the major commercial source of natural rubber (NR). NR is a latex polymer with high elasticity, flexibility, and resilience that has played a critical role in the world economy since 1876., Results: Here, we report the draft genome sequence of H. brasiliensis. The assembly spans ~1.1 Gb of the estimated 2.15 Gb haploid genome. Overall, ~78% of the genome was identified as repetitive DNA. Gene prediction shows 68,955 gene models, of which 12.7% are unique to Hevea. Most of the key genes associated with rubber biosynthesis, rubberwood formation, disease resistance, and allergenicity have been identified., Conclusions: The knowledge gained from this genome sequence will aid in the future development of high-yielding clones to keep up with the ever increasing need for natural rubber.

Despite recent guidelines promoting online professionalism, consequences for specific violations by physicians have not been explored. In this article, the authors gauged consensus among state medical boards in the United States (response rate, 71%) about the likelihood of investigations for violations of online professionalism by using 10 hypothetical vignettes. High consensus was defined as more than 75% of respondents indicating that investigation was "likely" or "very likely," moderate consensus as 50% to 75% indicating this, and low consensus as fewer than 50% indicating this. Four online vignettes demonstrated high consensus: Citing misleading information about clinical outcomes (81%; 39/48), using patient images without consent (79%; 38/48), misrepresenting credentials (77%; 37/48), and inappropriately contacting patients (77%; 37/48). Three demonstrated moderate consensus for investigation: depicting alcohol intoxication (73%; 35/48), violating patient confidentiality (65%; 31/48), and using discriminatory speech (60%; 29/48). Three demonstrated low consensus: using derogatory speech toward patients (46%; 22/48), showing alcohol use without intoxication (40%; 19/48), and providing clinical narratives without violation of confidentiality (16%; 7/48). Areas of high consensus suggest "online behaviors" that physicians should never engage in, whereas moderate- and low-consensus areas provide useful contextual information about "gray areas." Increased awareness of these specific behaviors may reduce investigations and improve online professionalism for physicians.

Powered lower limb prostheses, capable of multiple locomotion modes, are being developed for transfemoral amputees. Current devices do not seamlessly transition between modes such as level walking, stairs and slopes. The purpose of this study was to develop an intent recognition system and test its performance across five different modes. A Dynamic Bayesian Network (DBN) was used for classification of neural and mechanical signals while four amputees completed a circuit containing level-walking, ramp ascent, ramp descent, stair ascent and stair descent. Our results indicate that transitional and steady-state stair steps had a high recognition rate (>99%), while ramp steps were significantly more difficult to classify (p<0.01) (13.7% error on transition steps and 1.3% on steady-state steps). With all five modes trained into the same system, the transitional error rate was 11.3%. Transitional error could be reduced by 31% by training the ramp ascent mode as level walking, and 92% by training both ramp ascent and descent as level walking. This is a viable solution when the level-walking mode can accommodate ramp modes which is currently the case with the ramp ascent. The high recognition rates for recognizing stairs shown in this study demonstrates the potential for an intent recognition system using neural information to allow amputees to naturally transition between locomotion modes on powered prostheses.

Saprospira grandis is a coastal marine bacterium that can capture and prey upon other marine bacteria using a mechanism known as 'ixotrophy'. Here, we present the complete genome sequence of Saprospira grandis str. Lewin isolated from La Jolla beach in San Diego, California. The complete genome sequence comprises a chromosome of 4.35 Mbp and a plasmid of 54.9 Kbp. Genome analysis revealed incomplete pathways for the biosynthesis of nine essential amino acids but presence of a large number of peptidases. The genome encodes multiple copies of sensor globin-coupled rsbR genes thought to be essential for stress response and the presence of such sensor globins in Bacteroidetes is unprecedented. A total of 429 spacer sequences within the three CRISPR repeat regions were identified in the genome and this number is the largest among all the Bacteroidetes sequenced to date.

Pattern recognition of myoelectric signals for prosthesis control has been extensively studied in research settings and is close to clinical implementation. These systems are capable of intuitively controlling the next generation of dexterous prosthetic hands. However, pattern recognition systems perform poorly in the presence of electrode shift, defined as movement of surface electrodes with respect to the underlying muscles. This paper focused on investigating the optimal interelectrode distance, channel configuration, and electromyography feature sets for myoelectric pattern recognition in the presence of electrode shift. Increasing interelectrode distance from 2 to 4 cm improved pattern recognition system performance in terms of classification error and controllability (p < 0.01). Additionally, for a constant number of channels, an electrode configuration that included electrodes oriented both longitudinally and perpendicularly with respect to muscle fibers improved robustness in the presence of electrode shift (p < 0.05). We investigated the effect of the number of recording channels with and without electrode shift and found that four to six channels were sufficient for pattern recognition control. Finally, we investigated different feature sets for pattern recognition control using a linear discriminant analysis classifier and found that an autoregressive set significantly (p < 0.01) reduced sensitivity to electrode shift compared to a traditional time-domain feature set.

Introduction: In response to the need for physician education on proper opioid prescribing, the Federation of State Medical Boards (FSMB) and the FSMB Foundation, the philanthropic arm of the FSMB, commissioned and distributed Responsible Opioid Use: A Physician's Guide to more than 165, 000 licensed physicians in the United States. The book, written by pain management specialist Scott Fishman, MD, seeks tofurtherphysicians' continuing medical education by providing information on how to properly prescribe opioids to treat patients in pain. Although the book has been widely distributed, there have been no systematic studies of its impact. To address this knowledge gap, the authors surveyed licensed physicians in Georgia who received a copy of the book to determine whether it added to their knowledge about prescribing opioids, and if they planned to make changes in theirpractice based on reading the book., Methods: Six weeks after licensed physicians in Georgia received the book, a survey was sent to 12,666 of them via e-mail., Results: A total of508 physicians completed the online survey. Of these, 82.1 percent rated the book either "very good" or "good" on providing pragmatic steps for improved care forpatients in pain, and more than 80 percent agreed that the book is a useful educational tool. Almost one-third (32.2percent) claimed that they intend to make changes to theirpractice after reading the book. The analysis also showed physicians in a solo practice were more likely to make changes (41.8percent) than their counterparts in office-based group practice (33.3 percent) and hospital-based (25.0 percent) settings. Primary care providers (41.6 percent) were also much more likely to make changes than physicians working in other specialty areas of medicine (22.8 percent). Well over half (57. 7percent) of the respondents indicated the book was better than other publications they had read on opioid prescribing and pain management., Conclusions: The results from this state-wide survey of licensed physicians demonstrate the value of educating physicians about how to appropriately prescribe, document, and treat patients who need opioid medications for pain management. The findings should be of value to organizations seeking to better educate physicians about appropriate opioid prescribing by providing insight into which physician population would be the most receptive to the type of information presented in Dr. Fishman's book. When faced with limited resources, an organizational strategy that first targets solo and primary care practitioners may improve physician educational efforts about prescribing opioids better than a strategy targeting medical and surgical specialists or those physicians participating in group practice settings.

Myoelectric pattern recognition systems for prosthesis control are often studied in controlled laboratory settings, but obstacles remain to be addressed before they are clinically viable. One important obstacle is the difficulty of maintaining system usability with socket misalignment. Misalignment inevitably occurs during prosthesis donning and doffing, producing a shift in electrode contact locations. We investigated how the size of the electrode detection surface and the placement of electrode poles (electrode orientation) affected system robustness with electrode shift. Electrodes oriented parallel to muscle fibers outperformed electrodes oriented perpendicular to muscle fibers in both shift and no-shift conditions (p < 0.01). Another finding was the significant difference (p < 0.01) in performance for the direction of electrode shift. Shifts perpendicular to the muscle fibers reduced classification accuracy and real-time controllability much more than shifts parallel to the muscle fibers. Increasing the size of the electrode detection surface was found to help reduce classification accuracy sensitivity to electrode shifts in a direction perpendicular to the muscle fibers but did not improve the real-time controllability of the pattern recognition system. One clinically important result was that a combination of longitudinal and transverse electrodes yielded high controllability with and without electrode shift using only four physical electrode pole locations.

Myoelectric pattern recognition control can potentially provide upper limb amputees with intuitive control of multiple prosthetic functions. However, the lack of robustness of myoelectric pattern recognition algorithms is a barrier for clinical implementation. One issue that can contribute to poor system performance is electrode shift, which is a change in the location of the electrodes with respect to the underlying muscles that occurs during donning and doffing and daily use. We investigated the effects of interelectrode distance and feature choice on system performance in the presence of electrode shift. Increasing the interelectrode distance from 2 cm to 4 cm significantly (p<0.01) improved classification accuracy in the presence of electrode shifts of up to 2 cm. In a controllability test, increasing the interelectrode distance from 2 cm to 4 cm improved the user's ability to control a virtual prosthesis in the presence of electrode shift. Use of an autoregressive feature set significantly (p<0.01) reduced sensitivity to electrode shift when compared to use of a traditional time-domain feature set.

Purpose: The aim of this study was to systematically review the literature on the effect of statins on mortality in patients with infection and/or sepsis., Materials and Methods: MEDLINE, EMBASE, PapersFirst, and the Cochrane collaboration and the Cochrane Register of controlled trials were searched and were current as of December 2009. Randomized, double-blind or single-blind, placebo-controlled studies; observational cohort studies (retrospective and prospective); and case-controlled studies were included. Types of participants included adult and pediatric subjects with sepsis or various other types of infection. Exposure was defined as the use of a statin for any indication. The primary outcome chosen was mortality from any cause, and secondary outcomes included 30-day mortality, in-hospital mortality, mortality from pneumonia, mortality from bacteremia, mortality from sepsis, and mortality from mixed infection., Results: A total of 20 studies were included in the analysis, 18 being cohort studies (12 retrospective, 6 prospective), 1 matched cohort study with 2 case-control studies, and 1 randomized control trial. Meta-analysis for various infection-related outcomes revealed the following pooled odds ratios all in favor of statin use vs non: 0.61 (95% confidence interval [CI], 0.48-0.73) for 30-day mortality (n = 7), 0.38 (95% CI, 0.13-0.64) for in-hospital mortality (n = 7), 0.63 (95% CI, 0.55-0.71) for pneumonia-related mortality (n = 7), 0.33 (95% CI, 0.09-0.75) for bacteremia-related mortality (n = 4), 0.40 (95% CI, 0.23-0.57) for sepsis-related mortality (n = 4), and 0.50 (95% CI, 0.18-0.83) for mixed infection-related mortality (n = 4)., Conclusions: This meta-analysis demonstrated a protective effect for statins in patients with sepsis and/or other infections compared to placebo for various infection-related outcomes. However, our results are limited by the cohort design of the selected studies and the degree of heterogeneity among them, and as a result, further randomized trials are needed to validate the use of statins for sepsis and/or other infections., (Copyright © 2010 Elsevier Inc. All rights reserved.)

In this work we quantified the in vitro calibration relationships between high frequency electrical stimulation and GABA and glutamate release in both mature retinoic acid differentiated P19 neurons and immortalized embryonic cortical cells engineered to express glutamic acid decarboxylase, GAD65. Extracellular glutamate and GABA was quantified by 2D gas chromatography and time of flight mass spectrometry after stimulation at varying amplitudes and frequencies. Amplitude sweeps resulted in a linear calibration for P19 neurons; the level of neurotransmitter varied over one order of magnitude from ~ 200 pg/neuron to ~ 1.2 ng/neuron for glutamate and ~ 1 ng/neuron to ~ 2 ng/neuron for GABA, depending on the stimulation amplitude. Frequency sweeps resulted in a peak release at 250 Hz for glutamate and 400 Hz for GABA in P19 cells. The GABA transporter inhibitor, nipecotic acid, increased extracellular GABA levels and decrease glutamate. In contrast the embryonic cortical cells had a strongly nonlinear dependency of release on stimulation amplitude, and a weak dependence on frequency. These cells had roughly equal extracellular glutamate and GABA levels after stimulation despite the expression of GAD65. In addition glutamate and GABA levels were insensitive to nipecotic acid. These results demonstrate an ability to calibrate and tune neurotransmitter release from neural cells using high frequency stimulation parameters.

Of 30 baculovirus genomes that have been sequenced to date, the only nonlepidopteran baculoviruses include the dipteran Culex nigripalpus nucleopolyhedrovirus and two hymenopteran nucleopolyhedroviruses that infect the sawflies Neodiprion lecontei (NeleNPV) and Neodiprion sertifer (NeseNPV). This study provides a complete sequence and genome analysis of the nucleopolyhedrovirus that infects the balsam fir sawfly Neodiprion abietis (Hymenoptera, Symphyta, Diprionidae). The N. abietis nucleopolyhedrovirus (NeabNPV) is 84,264 bp in size, with a G+C content of 33.5%, and contains 93 predicted open reading frames (ORFs). Eleven predicted ORFs are unique to this baculovirus, 10 ORFs have a putative sequence homologue in the NeleNPV genome but not the NeseNPV genome, and 1 ORF (neab53) has a putative sequence homologue in the NeseNPV genome but not the NeleNPV genome. Specific repeat sequences are coincident with major genome rearrangements that distinguish NeabNPV and NeleNPV. Genes associated with these repeat regions encode a common amino acid motif, suggesting that they are a family of repeated contiguous gene clusters. Lepidopteran baculoviruses, similarly, have a family of repeated genes called the bro gene family. However, there is no significant sequence similarity between the NeabNPV and bro genes. Homologues of early-expressed genes such as ie-1 and lef-3 were absent in NeabNPV, as they are in the previously sequenced hymenopteran baculoviruses. Analyses of ORF upstream sequences identified potential temporally distinct genes on the basis of putative promoter elements.