Gerber R.A.,Pfizer |
Perry R.,Adelphi |
Thompson R.,Pfizer |
Bainbridge C.,Pulvertaft Hand Center
BMC Musculoskeletal Disorders | Year: 2011
Background: Dupuytren's disease is a fibro-proliferative disorder affecting ∼3-5% of the UK population. Current surgical treatments for Dupuytren's contracture (DC) include fasciectomy and fasciotomy. We assessed the clinical management of DC in England over a 5-year period; associated NHS costs were assessed for a 1-year period. Methods. Hospital Episode Statistics were extracted from April 2003 to March 2008 for patients with Palmar Fascial Fibromatosis (ICD10 = M720) and DC-related procedures. Variables included demographics, OPCS, patient status and physician specialty. To estimate 2010-2011 costs, HRG4 codes and the National Schedule of Tariff 2010-11-NHS Trusts were applied to the 2007-2008 period. Results: Over 5 years, 75,157 DC admissions were recorded; 64,506 were analyzed. Mean admissions per year were 12,901 and stable. Day cases increased from 42% (2003-2004) to 62% (2007-2008). The percent of patients having two or more admissions per year increased from 5.5% in 2003-2004 to 26.1% in 2007-2008. Between 2003 and 2007, 91% of procedures were Fasciectomy. Revision of Fasciectomy and Fasciotomy each accounted for ∼4%; Amputation for 1%. In 2007, classification was extended to identify Digital Fasciectomy, its Revision and Dermofasciectomy. In 2007-2008, admissions were: 70% Palmar Fasciectomy, 16% Digital Fasciectomy, 1.3% Other Fasciectomy, 4.4% Revision of Palmar Fasciectomy, 1.3% Revision of Digital Fasciectomy, 3.8% Division of Palmar Fascia, 2.6% Dermofasciectomy and 1.1% Amputation. 79% of cases were overseen by trauma and orthopaedic surgeons, 19% by plastic surgeons. Mean (±SD) inpatient hospital length of stay was 1.5 (±1.4) days in 2003-2004 and 1.0 (±1.3) days in 2007-2008. Total estimated costs for 1 year (2010-2011) were 41,576,141. Per-patient costs were €2,885 (day case) and 3,534 (inpatient). Costs ranged from €2,736 (day-case Fasciectomy) to €9,210 (day-case Revision Digital). Conclusions: Between 2003 and 2008, fasciectomy was the most common surgical procedure for DC in England. While procedure rates and physician specialties varied little, there was a reversal in surgical venue: inpatient operations decreased as day-case procedures increased. The change is likely due to economic trends and changes to the healthcare system. Estimated costs for 2010-2011 varied by procedure type and patient status. These findings can be used to understand clinical management of DC and guide healthcare policy. © 2011 Gerber et al; licensee BioMed Central Ltd. Source
Hudson P.,VU University Amsterdam |
Botzen W.J.W.,VU University Amsterdam |
Kreibich H.,German Research Center for Geosciences |
Bubeck P.,Adelphi |
H. Aerts J.C.J.,VU University Amsterdam
Natural Hazards and Earth System Sciences | Year: 2014
The employment of damage mitigation measures (DMMs) by individuals is an important component of integrated flood risk management. In order to promote efficient damage mitigation measures, accurate estimates of their damage mitigation potential are required. That is, for correctly assessing the damage mitigation measures' effectiveness from survey data, one needs to control for sources of bias. A biased estimate can occur if risk characteristics differ between individuals who have, or have not, implemented mitigation measures. This study removed this bias by applying an econometric evaluation technique called propensity score matching (PSM) to a survey of German households along three major rivers that were flooded in 2002, 2005, and 2006. The application of this method detected substantial overestimates of mitigation measures' effectiveness if bias is not controlled for, ranging from nearly EUR 1700 to 15 000 per measure. Bias-corrected effectiveness estimates of several mitigation measures show that these measures are still very effective since they prevent between EUR 6700 and 14 000 of flood damage per flood event. This study concludes with four main recommendations regarding how to better apply propensity score matching in future studies, and makes several policy recommendations. © Author(s) 2014. Source
Home > Press > UMD & Army researchers discover salty solution to better, safer batteries: Greatest potential uses seen in safety-critical, automotive and grid-storage applications Abstract: A team of researchers from the University of Maryland (UMD) and the U.S. Army Research Laboratory (ARL) have devised a groundbreaking "Water-in-Salt" aqueous Lithium ion battery technology that could provide power, efficiency and longevity comparable to today's Lithium-ion batteries, but without the fire risk, poisonous chemicals and environmental hazards of current Lithium batteries. The team of researchers, led by Chunsheng Wang, an associate professor in UMD's Department of Chemical & Biomolecular Engineering, and Kang Xu, senior research chemist at the Sensor and Electron Devices Directorate of ARL, said their work, published this week in the journal Science, demonstrates a major advance in the long history of water-based (aqueous) batteries by doubling the voltage, or power, of an aqueous battery. The researchers said their technology holds great promise, particularly in applications that involve large energies at kilowatt or megawatt levels, such as electric vehicles, or grid-storage devices for energy harvest systems, and in applications where battery safety and toxicity are primary concerns, such as safe, non-flammable batteries for airplanes, naval vessels or spaceships, and in medical devices like pacemakers. "Through this work we were able to increase the electrochemical window of aqueous electrolyte from less than 1.5 Volts to ~ 3.0 Volts and demonstrated high voltage aqueous full Lithium-ion cell with 2.3 Volts, showing for the first time that aqueous batteries could seriously compete in terms of power and energy density with the non-aqueous lithium-ion batteries that power our mobile, digital lifestyle" said Wang, who also is affiliated with the University of Maryland Energy Research Center and the Maryland NanoCenter. According to Lt. Col. (Retired) Edward Shaffer, who heads the Army Research Laboratory's Energy and Power Division, the significant potential advantages this new approach has over current batteries "could lead to thermally, chemically and environmentally safer batteries carried and worn by soldiers; safe, reduced-footprint energy storage for confined spaces, particularly submarines; and novel hybrid power solutions for military platforms and systems." Researchers Wang, Xu and colleagues found that the key to their breakthrough was the use of a type of water-based electrolyte containing ultrahigh concentrations of a carefully selected Lithium salt. This approach transformed the battery's chemistry, resulting in the formation of a thin protective film on the anode electrode for the very first time in a water-based battery. Known in battery science as a "Solid Electrolyte Interphase (SEI)," such a protective and stabilizing film is essential to the high performance characteristics of state-of-the-art Li-ion batteries. It previously has been achieved only in non-aqueous electrolytes. "What's most important about our work is the breakthrough made at the fundamental level," said UMD Postdoctoral Research Associate Liumin Suo, who is a member of Wang's research group and first author of the Science paper. "Prior to this work no one thought it possible to form SEI in water-based [batteries], but we demonstrated that it can happen." The UMD & ARL team compared the performance of their new "Water-in-Salt" battery with that of other aqueous battery systems. They showed that high stability of other aqueous batteries was achieved only at the expense of voltage and energy density and vice versa. However, the formation of an anode/electrolyte interphase in their "Water-in-Salt" electrolyte allowed them to break this inverse relationship between cycling stability and high voltage and to achieve both simultaneously. "Researchers in the Li-ion battery field have recently found that previously "useless" solvents could be made functional in Li-ion cells through the addition of high concentrations of salts. The work by Suo et al., extends this idea to the case of the solvent, water. By extending the operational voltage window to approximately 3 Volts, it is possible that a new generation of safer and possibly less expensive Li-ion cells could result," said Dalhousie University (Nova Scotia) Professor Jeff Dahn, a leading battery researcher, who was not involved in the study. "Only further R&D efforts will be able to verify the practicality of this discovery, so prudence is needed in assessing the potential of this, or any basic research advance," said Dahn, the NSERC-3M Canada Industrial Research Chair in Materials for Advanced Batteries. "Our finding opens an entirely new avenue to aqueous electrochemical devices, not only batteries, but also devices like supercapacitors and electroplating devices," said Xu. ### This research received funding from the Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E) (DEAR0000389) and support from the Maryland NanoCenter and its Nanoscale Imaging Spectroscopy & Properties Laboratory. This UMD lab is supported in part by the National Science Foundation. Modeling efforts were supported by the ARL Enterprise for Multiscale Research of Materials. "Water-in-Salt" Electrolyte Enables High Voltage Aqueous Li-ion Chemistries Liumin Suo,(1) Oleg Borodin,(2) Tao Gao,(1) Marco Olguin,(2) Janet Ho,(2) Xiulin Fan,(1) Chao Luo,(1) Chunsheng Wang,(1), * Kang Xu (2), * (1)Department of Chemical and Biomolecular Engineering, University of Maryland College Park (2) Electrochemistry Branch, Sensor and Electron Devices Directorate, Power and Energy Division U.S. Army Research Laboratory Adelphi, Maryland For more information, please click If you have a comment, please us. Issuers of news releases, not 7th Wave, Inc. or Nanotechnology Now, are solely responsible for the accuracy of the content.
The U.S. Naval Research Laboratory (NRL) welcomed Under Secretary of Defense for Acquisition, Technology and Logistics, USD(AT&L), Frank Kendall, to its Washington D.C., headquarters, Feb. 25, to meet with top NRL scientists and researchers and tour key laboratories on the 130-acre campus situated along the Potomac River. Kendall is the leader in the effort to increase the buying power of the Department of Defense (DoD) and improve the performance of the defense acquisition enterprise. Kendall has over 40 years of experience in engineering, management, defense acquisition, and national security affairs in private industry, government, and the military. Prior to receiving a first-hand tour featuring research in autonomous systems, cyber and national security, space sciences, and ISR technologies, Kendall recognized three NRL scientists with 'Spotlight Awards' for significant accomplishments in acquisitions and support of USD(AT&L) "Better Buying Power" initiatives. "From their inception, the 'Better Buying Power' initiatives have been about getting the most value possible from our available capital," said Kendall. "We achieve this by continuing to strengthen our culture of costs conscious professionals and our technical excellence, ingredients necessary to bring innovative solutions to our warfighters." The Spotlight Recognition Award is designed to recognize civilian and military DoD professionals for their significant acquisition accomplishments in any of the AT&L priorities and/or Better Buying Power initiatives. The essential element of the award is to recognize individuals or team members who are doing great work for the DoD, but are not necessarily in the "spotlight" on a day-to-day basis. The NRL recipients of the Spotlight Award for fiscal year 2015 include: Dr. Glen Henshaw - for conceiving and executing research for in-space autonomous inspection and grapple of satellites and for excellence in contribution toward the advancement of technical developments of the Defense Advanced Research Projects Agency's geosynchronous Earth orbit (GEO) robotics program, aimed at changing U.S. operations in space. As NRL's chief space roboticist, Henshaw is additionally recognized for advancements and enhancements made to space robotics hardware, software, and algorithms that serve as the core of DoD satellite servicing technologies. Dr. Igor Medintz - for achievements in creating chemistries that allow biological molecules to be attached to nanoparicles in an ordered manner and elucidating how such materials engage in different types of energy transfer. These materials display incredible promise for new technologies, attributing to scaling of functional devices down to the nanometer or single molecule level. Translating, for example, into unimolecular energy harvesting assemblies and sensors small enough to circulate, unattended inside a living cell. The results of this research are considered key enablers and are beginning to show promise for a wide range of technologies as they are steadily being incorporated worldwide. Dr. Christina J. Naify - for her role in team leadership in overlapping research that has led to several exciting new sonar and communications concepts: An electromagnetic leaky-wave antenna, promising a new capability to image complex underwater environments using a single source and single receiving element to lower the electrical complexity and increase the speed of imaging systems by performing the imaging task in analog; and acoustic transduction for undersea warfare that can be utilized to achieve high data-rate secured acoustic communications, promising substantially increased underwater acoustic communication bit rates. In addition to the Spotlight Awards, Kendall also recognized eight NRL scientists and researchers for the new USD(AT&L) Laboratory University Collaboration Initiative (LUCI) Awards. This new initiative is designed to foster collaboration between National Security Science and Engineering Faculty Fellows (NSSEFF), and service laboratory researchers on basic research topics in areas of critical interest to DoD. The 2016 LUCI Award NRL recipients and corresponding citations are: "The work and dedication awarded here today has had a significant impact on the AT&L mission," Kendall said. "NRL is a great institution with a great reputation." All LUCI winners will be invited to the National Security Science and Engineering Faculty Fellows (NSSEFF) spring meeting and dinner on April 5-6, 2016, which will be held at the Army Research Laboratory (ARL), in Adelphi, Md. Kendall is a Distinguished Graduate of the U.S. Military Academy at West Point and holds a Master of Science degree in aerospace engineering from California Institute of Technology, a Master of Business Administration degree from the C.W. Post Center of Long Island University, and a Juris Doctor degree from Georgetown University Law Center. About the U.S. Naval Research Laboratory The U.S. Naval Research Laboratory provides the advanced scientific capabilities required to bolster our country's position of global naval leadership. The Laboratory, with a total complement of approximately 2,500 personnel, is located in southwest Washington, D.C., with other major sites at the Stennis Space Center, Miss., and Monterey, Calif. NRL has served the Navy and the nation for over 90 years and continues to advance research further than you can imagine. For more information, visit the NRL website or join the conversation on Twitter, Facebook, and YouTube.
Preliminary results from a single-trial rapid serial visual representation task demonstrate the potential for enabling generalized human-autonomy sensor fusion across multiple subjects. Brain–computer interfaces (BCIs) have traditionally been used to enable communication and control for paralyzed patients.1 However, it is also thought that BCIs hold promise for fulfilling the longstanding goal of creating artificial systems (i.e., which can perform with the adaptability, robustness, and general intelligence of humans). To augment the sensing and processing capabilities of such artificial systems, BCI systems can thus be used on healthy individuals. In this way, the biological machinery that enables human cognition can be leveraged. Image triage—a visual target search over a set of images—is a prime application for this new class of BCI. Humans can effortlessly identify target objects in scenes that stymie even the best machine vision techniques. Manual inspection by humans, however, is limited by the speed at which targets can be consciously detected and reported by a behavioral response. For example, when targets are identified by pressing a button, the button is typically pressed two to five images after the target image is shown (when the image stream is presented at 5Hz). This forces the observer to assume a distribution of images for the several images that precede the button press.2 In addition, humans perform inconsistently because of exogenous distractions or endogenous factors (e.g., fatigue), whereas computer vision algorithms offer constant and predictable performance. As an alternative, machine learning approaches can be applied to raw human neurophysiological data and thus reveal signals that are relevant to the detection of target images. Ultimately, this can increase both the accuracy and the response rate of image triage classification tasks. Indeed, in recent work, it has been shown that the classification performance in a rapid serial visual presentation (RSVP) image triage task (see Figure 1) can be improved by combining human neurophysiological data with machine vision classifiers.2 To date, such methods have relied on the late fusion of human and machine-generated classifier outputs. In other words, the classifiers for image and human data are trained separately and their outputs are later fused. It may be possible to improve the classification performance even further if the complementary information carried in the human signals and the image data can be trained in tandem. To realize this aim, however, relevant neurophysiological data (which carries a discriminatory signal) and the ability to process and convert these signals to useful task determinants is required. In addition, it is necessary to have a common framework, within which it is possible to train a classifier that directly learns combined models of human neurophysiological data and image data. Figure 1. Illustrating the display of images during a rapid serial visual presentation (RSVP) task. In this case, the images are presented at 5Hz and the subjects are required to indicate (via a behavioral response) when an—infrequently occurring—target image is shown. The aim of our work3 is to improve human-autonomy classification performance by developing a single framework that builds codependent models of human neuophysiological information and image data to generate fused target estimates. CNNs are a type of supervised deep-learning architecture that have set record benchmarks in many domains, including speech recognition, drug discovery, genomics, and visual object recognition.4 CNNs enable automatic feature selection and extraction from raw data. This is achieved by hierarchically stacking linear and nonlinear filtering modules to form a network, where each layer transforms an input into a representation at a higher and more abstract level. The resulting non-convex optimization is performed through the iterative application of a back-propagation algorithm until the maximum performance is achieved or the network converges. Given the success of using CNNs in machine vision for object recognition, as well as recent work in which CNNs are used for multimodal fusion,5–7 we believe that CNNs are thus promising for early fusion models of EEG data and computer vision. As a first step in our study, we investigated the use of CNNs for multiclass single-trial classification of EEG recordings across multiple subjects during an RSVP task. Our results suggest that the EEG RSVP CNN classifier was able to meet—and exceed—the performance of other classifiers. This was the case for a single generalized model across 18 subjects (where our method learned one model and each of the other classifiers learned 18 models). Our classifier also out-performed the other classifiers for automatically selecting features that do not explicitly rely on the detection of any known features. A sample of the filters learned by our network in these tests is shown in Figure 2. Figure 2. Three sample layers of spatial filters learned by the electroencephalograph (EEG) RSVP convolution neural network. The filters are shown mapped to the position of EEG electrodes that were placed on the scalp of subjects. Dark red and dark blue indicate larger magnitudes of activation (no units). Black dots denote the true spatial location of each electrode on the scalp. Our CNN design includes four convolutional layers, two fully connected layers, and a readout layer. We provide a more detailed rationale for this network architecture, and its hyperparameters, elsewhere.3 Our preliminary results (see Figure 3) show that—compared with the other classifiers—our CNN achieves the highest performance level. The area-under-the-curve (AUC) value we obtain (0.72) is higher than for the second-best classifier (0.71), despite the fact that we use a generalized model and individual models are used for the other classifier. In addition, when we train our classifier to convergence, we see overfitting and a corresponding increase in loss. To prevent this, we only train the classifier until the loss on the validation set starts to increase. Figure 3. Area under the curve (AUC) and loss of the EEG RSVP CNN classifier during training iteration on the evaluation testing set. The dashed red line indicates when the maximum AUC value was reached. In summary, we have designed a CNN deep-learning classifier that learns a single generalized model across multiple subjects for single-trial RSVP EEG classification. We have demonstrated that our CNN is a viable alternative to existing neural classifiers, by showing that it meets and exceeds the classification performance of several leading classifiers. By designing a CNN classifier to automatically detect the features that maximally separate target and non-target samples from raw data, we hypothesized that our framework would learn a feature set that enabled high performance. We also predicted that the classifier would have an increased robustness across subjects. Our preliminary analysis, however, is not sufficient to make meaningful statistical comparisons between the performance of our network and the state-of-the-art methods. Nonetheless, we are able to achieve a similar performance with our CNN (i.e., without requiring individualized models for each subject or explicit reliance on known features in the raw data). We are currently extending this work to statistically validate the performance of our CNN. We will quantify the contributions of different spatial weights and analyze the significance of the features that the network has learned. US Army Research Laboratory (ARL) Adelphi, MD Jared Shamwell earned his BA in economics and philosophy from Columbia University in 2009, and is currently pursuing a PhD in neuroscience at the University of Maryland, College Park. He is also a researcher at ARL, with research interests in machine learning, computational neuroscience, and robotics. Hyungtae Lee received his BS in electrical engineering and mechanical engineering from Sogang University, Republic of Korea, in 2006, his MS in electrical engineering from the Korea Advanced Institute of Science and Technology in 2008, and his PhD in electrical and computer engineering from the University of Maryland, College Park, in 2014. He is currently employed as an electrical engineering senior consultant by Booz Allen Hamilton Inc. (working at ARL). His research interests include object, action, event, and pose recognition, computer vision, and pattern recognition. Heesung Kwon is a team leader of the imagery analytics team in the Image Processing Branch at ARL. His current research interests include image/video analytics, human-autonomy interactions, deep learning, and machine learning. He has published about 100 journal articles, book chapters, and conference papers on these topics. Vernon Lawhern is currently working as a mathematical statistician in the Human Research and Engineering Directorate at ARL. He is interested in machine learning, statistical signal processing, and data mining of large neurophysiological data collections for the development of improved brain–computer interfaces. William Nothwang is currently the team leader for the Electronics for Sense and Control Team within the Sensors and Electron Devices Directorate at ARL. His team conducts basic and applied scientific research in distributed state estimation for the dismounted soldier and microair vehicles (specifically applied to microautonomous air systems and human physiological state monitoring). Amar Marathe is currently a biomedical engineer in the Human Research and Engineering Directorate at ARL. He is interested in using modern machine learning approaches to characterize and quantify human variability. US Army Research Laboratory (ARL)Adelphi, MDJared Shamwell earned his BA in economics and philosophy from Columbia University in 2009, and is currently pursuing a PhD in neuroscience at the University of Maryland, College Park. He is also a researcher at ARL, with research interests in machine learning, computational neuroscience, and robotics. 1. J. R. Wolpaw, D. J. McFarland, Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans, Proc. Nat'l Acad. Sci. USA 101, p. 17849-17854, 2004.