Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

We present a sensitivity analysis and discuss the probabilistic forecast capabilities of the novel sea ice model neXtSIM used in hindcast mode. The study pertains to the response of the model to the uncertainty on winds using probabilistic forecasts of ice trajectories. neXtSIM is a continuous Lagrangian numerical model that uses an elasto-brittle rheology to simulate the ice response to external forces. The sensitivity analysis is based on a Monte Carlo sampling of 12 members. The response of the model to the uncertainties is evaluated in terms of simulated ice drift distances from their initial positions, and from the mean position of the ensemble, over the mid-term forecast horizon of 10 days. The simulated ice drift is decomposed into advective and diffusive parts that are characterised separately both spatially and temporally and compared to what is obtained with a free-drift model, that is, when the ice rheology does not play any role in the modelled physics of the ice. The seasonal variability of the model sensitivity is presented and shows the role of the ice compactness and rheology in the ice drift response at both local and regional scales in the Arctic. Indeed, the ice drift simulated by neXtSIM in summer is close to the one obtained with the free-drift model, while the more compact and solid ice pack shows a significantly different mechanical and drift behaviour in winter. For the winter period analysed in this study, we also show that, in contrast to the free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoy's trajectories and compared to the capability of the free-drift model. We found that neXtSIM performs significantly better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search and rescue operations, although the sources of uncertainties assumed for the present experiment are not sufficient for complete coverage of the observed IABP positions

Distribution and prevalence of microorganisms causing diabetic foot infection in Hospital Serdang and Hospital Ampang for the year 2010 to 2014

Background: In developing countries like Malaysia, the prevalence of diabetes mellitus is increasing at an alarming rate. Various complications develop in patients diagnosed with diabetes. Diabetic foot is one such complication that is a threat to morbidity and mortality rate owing to its risk of amputation. Understanding the microbiology of diabetic foot infection becomes an essential part of management as it can help to channel the exact treatment rather than empirical treatment. Aim: To determine the distribution and prevalence of microorganism causing diabetic foot infection in Hospital Serdang and Hospital Ampang for the year 2010 till 2014. Methodology: This was a cross-sectional study using retrospective data from January 2010 to December 2014 of 885 patients with diabetic foot infection in Hospital Serdang and Hospital Ampang, tertiary hospitals in Klang Valley. Data were analyzed using IBM SPSS Statistics version 22.0 for Windows. Results: A total of 1356 pathogens were isolated from 885 patients, with a rate of 1.53 isolates per culture (IPC). The prevalence of gram-negative bacteria was predominant in DFI accounting for 71.27% whereas gram-positive was only 28.73%. Among the gram-negative isolates, the most common pathogen was Pseudomonas aeroginosa accounting for 24.49% followed by Proteus mirabilis (14.34%) and Klebsiella spp. (11.12%). Gram-positive isolates consist of Staphylococcus aureus with a percentage of 66.77% and Streptococcus spp. 33.23%. The Methicillin-Resistant Staphylococcus aureus (MRSA) accounts for 26.24% of the isolates. There were more monomicrobial cultures than polymicrobial culture (465 vs. 420). The most common antibiotic prescribed is ampicillin/sulbactam (55.57%) followed by cloxacillin (13.29%) and penicillin (10.77%). Conclusion: The prevalence of gram-negative bacteria in DFI is higher than gram-positive bacteria. The most common gram-negative bacteria is Pseudomonas aeroginosa followed by Proteus mirabilis and Klebsiella spp. whereas the most common gram-positive bacteria is Staphylococcus aureus. The rate of monomicrobial infection is slightly higher than polymicrobial infection. Ampicillin/sulbactam is the most commonly prescribed antibiotic for a patient with DFI

Prevalence, awareness, treatment and control of hypertension in Malaysia: a national study of 16,440 subjects,”

Summary Study design: A cross-sectional study was conducted in all states of Malaysia to determine the prevalence, awareness, treatment and control of hypertension. A stratified two-stage cluster sampling design with proportional allocation was used. Methods: Trained nurses obtained two blood pressure measurements from each subject. Hypertension was defined as mean systolic blood pressure 4140 mmHg, diastolic blood pressure 490 mmHg, or a self-reported diagnosis of hypertension and taking antihypertensive medication. All data were analysed using Stata 9.2 software and took the complex survey design into account. A two-sided P-value of o0.05 was considered to be statistically significant. Results: The overall prevalence of hypertension for subjects aged X15 years was 27.8% (95% confidence interval (CI) 26.9-28.8). The prevalence of hypertension was significantly higher in males (29.6%, 95% CI 28.3-31.0) compared with females (26.0%, 95% CI 25.0-27.1). Multivariate logistic regression showed that the odds of having hypertension increased with increasing age, in males, in subjects with a family history of hypertension, with increasing body mass index, in non-smokers and with decreasing levels of education. Only 34.6% of the subjects with hypertension were aware of their hypertensive status, and 32.4 were taking antihypertensive medication. Amongst the latter group, only 26.8% had their blood pressure under control. The prevalence of hypertension amongst those aged X30 years has increased from 32.9% in 1996 to 40.5% in 2004. Conclusion: In Malaysia, the prevalence of hypertension is high, but levels of awareness, treatment and control are low. There is an urgent need for a ARTICLE IN PRES

Data assimilation using adaptive, non-conservative, moving mesh models

Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or removes mesh nodes according to specific rules reflecting constraints in the numerical solver. In this case, the number of mesh nodes will change during the integration and, as a result, the dimension of the model's state vector will not be conserved. This work presents a novel approach to the formulation of ensemble data assimilation (DA) for models with this underlying computational structure. The challenge lies in the fact that remeshing entails a different state space dimension across members of the ensemble, thus impeding the usual computation of consistent ensemble-based statistics. Our methodology adds one forward and one backward mapping step before and after the ensemble Kalman filter (EnKF) analysis, respectively. This mapping takes all the ensemble members onto a fixed, uniform reference mesh where the EnKF analysis can be performed. We consider a high-resolution (HR) and a low-resolution (LR) fixed uniform reference mesh, whose resolutions are determined by the remeshing tolerances. This way the reference meshes embed the model numerical constraints and are also upper and lower uniform meshes bounding the resolutions of the individual ensemble meshes. Numerical experiments are carried out using 1-D prototypical models: Burgers and Kuramoto–Sivashinsky equations and both Eulerian and Lagrangian synthetic observations. While the HR strategy generally outperforms that of LR, their skill difference can be reduced substantially by an optimal tuning of the data assimilation parameters. The LR case is appealing in high dimensions because of its lower computational burden. Lagrangian observations are shown to be very effective in that fewer of them are able to keep the analysis error at a level comparable to the more numerous observers for the Eulerian case. This study is motivated by the development of suitable EnKF strategies for 2-D models of the sea ice that are numerically solved on a Lagrangian mesh with remeshing

Data assimilation using adaptive, non-conservative, moving mesh models

Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or removes mesh nodes according to specific rules reflecting constraints in the numerical solver. In this case, the number of mesh nodes will change during the integration and, as a result, the dimension of the model's state vector will not be conserved. This work presents a novel approach to the formulation of ensemble data assimilation (DA) for models with this underlying computational structure. The challenge lies in the fact that remeshing entails a different state space dimension across members of the ensemble, thus impeding the usual computation of consistent ensemble-based statistics. Our methodology adds one forward and one backward mapping step before and after the ensemble Kalman filter (EnKF) analysis, respectively. This mapping takes all the ensemble members onto a fixed, uniform reference mesh where the EnKF analysis can be performed. We consider a high-resolution (HR) and a low-resolution (LR) fixed uniform reference mesh, whose resolutions are determined by the remeshing tolerances. This way the reference meshes embed the model numerical constraints and are also upper and lower uniform meshes bounding the resolutions of the individual ensemble meshes. Numerical experiments are carried out using 1-D prototypical models: Burgers and Kuramoto-Sivashinsky equations and both Eulerian and Lagrangian synthetic observations. While the HR strategy generally outperforms that of LR, their skill difference can be reduced substantially by an optimal tuning of the data assimilation parameters. The LR case is appealing in high dimensions because of its lower computational burden. Lagrangian observations are shown to be very effective in that fewer of them are able to keep the analysis error at a level comparable to the more numerous observers for the Eulerian case. This study is motivated by the development of suitable EnKF strategies for 2-D models of the sea ice that are numerically solved on a Lagrangian mesh with remeshing

The National Pediatric Surgery Simulation Program in France: A tool to develop resident training in pediatric surgery

BACKGROUND/PURPOSE: To implement resident curriculum in France based on theoretical teaching and bed side training, the national council known as the "Collège Hospitalier et Universitaire de Chirurgie Pédiatrique" examined the relevance and feasibility of systematically introducing simulation program in the pediatric surgery resident training. MATERIAL AND METHODS: A national simulation training program was developed and took place in a 2-day session organized in 7 simulation centers in France. The program included technical (laparoscopic/suturing technique on low-fidelity models) and nontechnical (6 scenarios for standardized consultation, and a team work scenario based on errors prevention in the operative room) skills. Evaluation of the program (Likert scale from 1 (bad) to 5 (excellent) and notation on 20 points) concerned trainees and trainers. RESULTS: 40 residents (95% of all pediatric surgery French residents) attended with a ratio of trainees/trainer of ½. The training objectives earned a score of 4.46/5. The pedagogical value of the seminar scored 4.7/5, teaching quality 17.95/20, and the overall seminar score was 17.35/20. CONCLUSION: This program, unique nationally, was assessed very favorably by the participating residents and by the involved trainers. To our knowledge, it represents the first mandatory national simulation training program included within a surgical training model. LEVEL OF EVIDENCE: Level IV

Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020

Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve state estimates and consequent forecasts. We interface the deterministic ensemble Kalman filter (DEnKF) to the Lagrangian neXt generation Sea Ice Model, neXtSIM. The ensemble is generated by perturbing the atmospheric and oceanic forcing throughout the simulations and randomly initialized ice cohesion. Our ensemble–DA system assimilates sea ice concentration (SIC) from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and sea ice thickness (SIT) from the merged CryoSat-2 and SMOS datasets (CS2SMOS). Because neXtSIM is computationally solved on a time-dependent evolving mesh, it is a challenging application for ensemble–DA. As a solution, we perform the DEnKF analysis on a fixed and regular reference mesh, on which model variables are interpolated before the DA and then back to each member's mesh after the DA. We evaluate the impact of assimilating different types of sea ice observations on the model's forecast skills of the Arctic sea ice by comparing satellite observations and a free-run ensemble in an Arctic winter period, 2019–2020. Significant improvements in modeled SIT indicate the importance of assimilating weekly CS2SMOS SIT, while the improvements of SIC and ice extent are moderate but benefit from daily ingestion of the OSI-SAF SIC. For most of the winter, the correlation between SIT and SIC is weaker, which results in little cross-inference between the two variables in the assimilation step. Overall, the ensemble–DA system based on the stand-alone sea ice model demonstrates the feasibility of winter Arctic sea ice prediction with good computational efficiency. These results open the path toward operational implementation and the extension to multi-year assimilation.</p

Interaction of Saccharomyces boulardii with Salmonella enterica Serovar Typhimurium Protects Mice and Modifies T84 Cell Response to the Infection

BACKGROUND: Salmonella pathogenesis engages host cells in two-way biochemical interactions: phagocytosis of bacteria by recruitment of cellular small GTP-binding proteins induced by the bacteria, and by triggering a pro-inflammatory response through activation of MAPKs and nuclear translocation of NF-kappaB. Worldwide interest in the use of functional foods containing probiotic bacteria for health promotion and disease prevention has increased significantly. Saccharomyces boulardii is a non-pathogenic yeast used as a probiotic in infectious diarrhea. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we reported that S. boulardii (Sb) protected mice from Salmonella enterica serovar Typhimurium (ST)-induced death and prevented bacterial translocation to the liver. At a molecular level, using T84 human colorectal cancer cells, we demonstrate that incubation with Sb before infection totally abolished Salmonella invasion. This correlates with a decrease of activation of Rac1. Sb preserved T84 barrier function and decreased ST-induced IL-8 synthesis. This anti-inflammatory effect was correlated with an inhibitory effect of Sb on ST-induced activation of the MAPKs ERK1/2, p38 and JNK as well as on activation of NF-kappaB. Electron and confocal microscopy experiments showed an adhesion of bacteria to yeast cells, which could represent one of the mechanisms by which Sb exerts its protective effects. CONCLUSIONS: Sb shows modulating effects on permeability, inflammation, and signal transduction pathway in T84 cells infected by ST and an in vivo protective effect against ST infection. The present results also demonstrate that Sb modifies invasive properties of Salmonella