“Snowed In” in Russia: A Historical Analysis of American and Russian Extradition and How the Snowden Saga Might Impact the Future

After Edward Snowden—an American citizen charged with theft and unauthorized communication of classified defense information (among other things)—was granted asylum by the Russian Federation, relations between the United States and Russia deteriorated rapidly. This Note analyzes the history of American and Russian extradition agreements and provides a sample extradition agreement that, if enacted prior to Russia’s asylum grant, may have altered the outcome

Image fusion for a nighttime driving display

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (leaves 39-40).An investigation into image fusion for a nighttime driving display application was performed. Most of the image fusion techniques being investigated in this application were developed for other purposes. When comparing the images of a typical night driving scene to the images used in medical or satellite image fusion it becomes apparent that most of the techniques developed for other fields would be overkill in a nighttime driving display application. This observation motivated the development of a set of image fusion techniques specifically for the nighttime driving display application using the computationally simple Discrete Haar Wavelet Transform. This thesis presents the techniques that were developed and the details of their implementation.by William Frederick Herrington, Jr.S.M

Optical Detection of Degraded Therapeutic Proteins

The quality of therapeutic proteins such as hormones, subunit and conjugate vaccines, and antibodies is critical to the safety and efficacy of modern medicine. Identifying malformed proteins at the point-of-care can prevent adverse immune reactions in patients; this is of special concern when there is an insecure supply chain resulting in the delivery of degraded, or even counterfeit, drug product. Identification of degraded protein, for example human growth hormone, is demonstrated by applying automated anomaly detection algorithms. Detection of the degraded protein differs from previous applications of machine-learning and classification to spectral analysis: only example spectra of genuine, high-quality drug products are used to construct the classifier. The algorithm is tested on Raman spectra acquired on protein dilutions typical of formulated drug product and at sample volumes of 25 μL, below the typical overfill (waste) volumes present in vials of injectable drug product. The algorithm is demonstrated to c orrectly classify anomalous recombinant human growth hormone (rhGH) with 92% sensitivity and 98% specificity even when the algorithm has only previously encountered high-quality drug product.United States. Defense Advanced Research Projects Agency (Contract N66001-13-C-4025

Closing the loop – the role of pathologists in digital and computational pathology research

An increasing number of manuscripts related to digital and computational pathology are being submitted to The Journal of Pathology: Clinical Research as part of the continuous evolution from digital imaging and algorithm-based digital pathology to computational pathology and artificial intelligence. However, despite these technological advances, tissue analysis still relies heavily on pathologists’ annotations. There are three crucial elements to the pathologist’s role during annotation tasks: granularity, time constraints, and responsibility for the interpretation of computational results. Granularity involves detailed annotations, including case level, regional, and cellular features; and integration of attributions from different sources. Time constraints due to pathologistshortages have led to the development of techniques to expedite annotation tasks from cell-level attributions up to so-called unsupervised learning. The impact of pathologists may seem diminished, but their role is crucial inproviding ground truth and connecting pathological knowledge generation with computational advancements. Measures to display results back to pathologists and reflections about correctly applied diagnostic criteria are mandatory to maintain fidelity during human–machine interactions. Collaboration and iterative processes, such as human-in-the-loop machine learning are key for continuous improvement, ensuring the pathologist’s involvement in evaluating computational results and closing the loop for clinical applicability. The journal is interested particularly in the clinical diagnostic application of computational pathology and invites submissions that address the issues raised in this editoria

Bioimpedance in CKD: an untapped resource?

No abstract available

Thermally enhanced blue light-emitting diode

We investigate thermoelectric pumping in wide-bandgap GaN based light-emitting diodes (LEDs) to take advantage of high junction temperature rather than avoiding the problem of temperature-induced efficiency droop through external cooling. We experimentally demonstrate a thermally enhanced 450 nm GaN LED, in which nearly fourfold light output power is achieved at 615 K (compared to 295 K room temperature operation), with nearly no reduction in the wall-plug efficiency (i.e., electrical-optical energy conversion efficiency) at bias V< ℏ ω/q. The LED is shown to work in a mode similar to a thermodynamic heat engine operating with charged carriers pumped into the active region by a combination of electrical work and Peltier heat (phonons) drawn from the lattice. In this optimal operating regime at 615 K, the LED injection current (3.26 A/cm[superscript 2]) is of similar magnitude to the operating point of common high power GaN based LEDs (5–35 A/cm[superscript 2]). This result suggests the possibility of removing bulky heat sinks in current high power LED products thus realizing a significant cost reduction for solid-state lighting.Bose (Firm)Singapore. Agency for Science, Technology and Researc

Predictors of atherosclerotic events in patients on haemodialysis: post hoc analyses from the AURORA Study

Background: Patients on haemodialysis (HD) are at high risk for cardiovascular events, but heart failure and sudden death are more common than atherosclerotic events. The A Study to Evaluate the Use of Rosuvastatinin in Subjects on Regular Hemodialysis: An Assessment of Survival and Cardiovascular Events (AURORA) trial was designed to assess the effect of rosuvastatin on myocardial infarction and death from any cardiac cause in 2773 HD patients. We studied predictors of the atherosclerotic cardiovascular events in AURORA. Methods: We readjudicated all deaths and presumed myocardial infarctions according to the criteria used in the Study of Heart and Renal Protection (SHARP); these were specifically developed to separate atherosclerotic from non-atherosclerotic cardiovascular events. The readjudicated atherosclerotic end point included the first event of the following: non-fatal myocardial infarction, fatal coronary heart disease, non-fatal and fatal non-haemorrhagic stroke, coronary revascularization procedures and death from ischaemic limb disease. Stepwise Cox regression analysis was used to identify the predictors of such events. Results: During a mean follow-up of 3.2 years, 506 patients experienced the new composite atherosclerotic outcome. Age, male sex, prevalent diabetes, prior cardiovascular disease, weekly dialysis duration, baseline albumin [hazard ratio (HR) 0.96; 95% confidence interval (CI) 0.94–0.99 per g/L increase], high-sensitivity C-reactive protein (HR 1.13; 95% CI 1.04–1.22 per mg/L increase) and oxidized low-density lipoprotein (LDL) cholesterol (HR 1.09; 95% CI 1.03–1.17 per 10 U/L increase) were selected as significant predictors in the model. Neither LDL cholesterol nor allocation to placebo/rosuvastatin therapy predicted the outcome. Conclusions: Even with the use of strict criteria for end point definition, non-traditional risk factors, but not lipid disturbances, predicted atherosclerotic events in HD patients