Estimation of the Concentration from a Moving Gaseous Source in the Atmosphere Using a Guided Sensing Aerial Vehicle

The estimation of the gas concentration (process-state) associated with a stationary or moving source using a sensing aerial vehicle (SAV) is considered. The dispersion from such a gaseous source into the ambient atmosphere is representative of an accidental or deliberate release of chemicals, or a release of gases from biological systems. Estimation of the concentration field provides a superior ability for source localization, assessment of possible adverse impacts, and eventual containment. The abstract and finite-dimensional approximation framework presented couples theoretical estimation and control with computational fluid dynamics methods. The gas dispersion (process) model is based on the advection-diffusion equation with variable eddy diffusivities and ambient winds. Cases are considered for a 2D and 3D domain. The state estimator is a modified Luenberger observer with a €�collocated€� filter gain that is parameterized by the position of the SAV. The process-state (concentration) estimator is based on a 2D and 3D adaptive, multigrid, multi-step finite-volume method. The grid is adapted with local refinement and coarsening during the process-state estimation in order to improve accuracy and efficiency. The motion dynamics of the SAV are incorporated into the spatial process and the SAV€™s guidance is directly linked to the performance of the state estimator. The computational model and the state estimator are coupled in the sense that grid-refinement is affected by the SAV repositioning, and the guidance laws of the SAV are affected by grid-refinement. Extensive numerical experiments serve to demonstrate the effectiveness of the coupled approach

Apixaban versus enoxaparin for thromboprophylaxis in medically ill patients

The efficacy and safety of prolonging prophylaxis for venous thromboembolism in medically ill patients beyond hospital discharge remain uncertain. We hypothesized that extended prophylaxis with apixaban would be safe and more effective than short-term prophylaxis with enoxaparin. METHODS: In this double-blind, double-dummy, placebo-controlled trial, we randomly assigned acutely ill patients who had congestive heart failure or respiratory failure or other medical disorders and at least one additional risk factor for venous thromboembolism and who were hospitalized with an expected stay of at least 3 days to receive apixaban, administered orally at a dose of 2.5 mg twice daily for 30 days, or enoxaparin, administered subcutaneously at a dose of 40 mg once daily for 6 to 14 days. The primary efficacy outcome was the 30-day composite of death related to venous thromboembolism, pulmonary embolism, symptomatic deep-vein thrombosis, or asymptomatic proximal-leg deep-vein thrombosis, as detected with the use of systematic bilateral compression ultrasonography on day 30. The primary safety outcome was bleeding. All efficacy and safety outcomes were independently adjudicated. RESULTS: A total of 6528 subjects underwent randomization, 4495 of whom could be evaluated for the primary efficacy outcome - 2211 in the apixaban group and 2284 in the enoxaparin group. Among the patients who could be evaluated, 2.71% in the apixaban group (60 patients) and 3.06% in the enoxaparin group (70 patients) met the criteria for the primary efficacy outcome (relative risk with apixaban, 0.87; 95% confidence interval [CI], 0.62 to 1.23; P = 0.44). By day 30, major bleeding had occurred in 0.47% of the patients in the apixaban group (15 of 3184 patients) and in 0.19% of the patients in the enoxaparin group (6 of 3217 patients) (relative risk, 2.58; 95% CI, 1.02 to 7.24; P = 0.04). CONCLUSIONS: In medically ill patients, an extended course of thromboprophylaxis with apixaban was not superior to a shorter course with enoxaparin. Apixaban was associated with significantly more major bleeding events than was enoxaparinSupported by Bristol-Myers Squibb and Pfize

Multiplicity Statistics of Stars in the Sagittarius Dwarf Spheroidal Galaxy: Comparison to the Milky Way

We use time-resolved spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) to examine the distribution of radial velocity (RV) variations in 249 stars identified as members of the Sagittarius (Sgr) dwarf spheroidal (dSph) galaxy by Hayes et al (2020). We select Milky Way (MW) stars that have stellar parameters ($log(g)$, $T_{eff}$, and $[Fe/H]$) similar to those of the Sagittarius members by means of a k-d tree of dimension 3. We find that the shape of the distribution of RV shifts in Sgr dSph stars is similar to that measured in their MW analogs, but the total fraction of RV variable stars in the Sgr dSph is larger by a factor of $\sim 2$. After ruling out other explanations for this difference, we conclude that the fraction of close binaries in the Sgr dSph is intrinsically higher than in the MW. We discuss the implications of this result for the physical processes leading to the formation of close binaries in dwarf spheroidal and spiral galaxies

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings

Framework for the employment and economic development strategy 1990/91 - a working document

SIGLEAvailable from British Library Document Supply Centre- DSC:OP/LG-5874 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

DBD-Hunter: a knowledge-based method for the prediction of DNA protein interactions

© 2008 The Authors. Published byOxford University Press. The definitive version is available online at: http://nar.oxfordjournals.org/cgi/content/full/36/12/3978doi:10.1093/nar/gkn332The structures of DNA–protein complexes have illuminated the diversity of DNA–protein binding mechanisms shown by different protein families. This lack of generality could pose a great challenge for predicting DNA–protein interactions. To address this issue, we have developed a knowledge-based method, DNA-binding Domain Hunter (DBD-Hunter), for identifying DNA-binding proteins and associated binding sites. The method combines structural comparison and the evaluation of a statistical potential, which we derive to describe interactions between DNA base pairs and protein residues. We demonstrate that DBD-Hunter is an accurate method for predicting DNA-binding function of proteins, and that DNA-binding protein residues can be reliably inferred from the corresponding templates if identified. In benchmark tests on ~4000 proteins, our method achieved an accuracy of 98% and a precision of 84%, which significantly outperforms three previous methods. We further validate the method on DNAbinding protein structures determined in DNA-free (apo) state. Weshow that the accuracy of our method is only slightly affected on apo-structures compared to the performance on holo-structures cocrystallized with DNA. Finally, we apply the method to ~1700 structural genomics targets and predict that 37 targets with previously unknown function are likely to be DNA-binding proteins

Public Opinion and Senate Confirmation of Supreme

We study the relationship between state-level public opinion and the roll call votes of senators on Supreme Court nominees. Applying recent advances in multilevel modeling, we use national polls on nine recent Supreme Court nominees to produce state-of-theart estimates of public support for the confirmation of each nominee in all 50 states. We show that greater public support strongly increases the probability that a senator will vote to approve a nominee, even after controlling for standard predictors of roll call voting. We also find that the impact of opinion varies with context: it has a greater effect on opposition party senators, on ideologically opposed senators, and for generally weak nominees. These results establish a systematic and powerful link between constituency opinion and voting on Supreme Court nominees

The kinetic basis for age-associated changes in quercetin and genistein glucuronidation by rat liver microsomes

The dietary bioavailability of the isoflavone genistein is decreased in older rats compared to young adults. Since flavonoids are metabolized extensively by the UDP-glucuronosyltransferases (UGTs), we hypothesized that UGT flavonoid conjugating activity changes with age. The effect of age on flavonoid glucuronidation was determined using hepatic microsomes from male F344 rats. Kinetic models of UGT activity toward the flavonol quercetin and the isoflavone genistein were established using pooled hepatic microsomal fractions of rats at different ages, and glucuronidation rates were determined using individual samples. Intrinsic clearance ( V max/ K m) values in 4-, 18- and 28-month-old rats were 0.100, 0.078 and 0.087 ml/min/mg for quercetin-7- O-glucuronide; 0.138, 0.133 and 0.088 for quercetin-3′- O-glucuronide; and 0.075, 0.077 and 0.057 for quercetin-4′- O-glucuronide, respectively. While there were no differences in formation rates of total quercetin glucuronides in individual samples, the production of the primary metabolite, quercetin-7- O-glucuronide, at 30 μM quercetin concentration was increased from 3.4 and 3.1 nmol/min/mg at 4 and 18 months to 3.8 nmol/min/mg at 28 months, while quercetin-3′- O-glucuronide formation at 28 months declined by a similar degree ( P≤.05). At 30 and 300 μM quercetin concentration, the rate of quercetin-4′- O-glucuronide formation peaked at 18 months at 0.9 nmol/min/mg. Intrinsic clearance values of genistein 7- O-glucuronide increased with age, in contrast to quercetin glucuronidation. Thus, the capacity for flavonoid glucuronidation by rat liver microsomes is dependent on age, UGT isoenzymes and flavonoid structure