Mcleodmaurer2587

Flourite-structure ferroelectrics (FEs) and antiferroelectrics (AFEs) such as HfO2 and its variants have gained copious attention from the semiconductor community, because they enable complementary metal-oxide-semiconductor (CMOS)-compatible platforms for high-density, high-performance non-volatile and volatile memory technologies. While many individual experiments have been conducted to characterize and understand fluorite-structure FEs and AFEs, there has been little effort to aggregate the information needed to benchmark and provide insights into their properties. We present a fast and robust modeling framework that automatically fits the Preisach model to the experimental polarization ([Formula see text]) versus electric field ([Formula see text]) hysteresis characterizations of fluorite-structure FEs. The modifications to the original Preisach model allow the double hysteresis loops in fluorite-structure antiferroelectrics to be captured as well. By fitting the measured data reported in the literature, we observe that ferroelectric polarization and dielectric constant decrease as the coercive field rises in general.This paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase (1) first, the healthcare cost of a new patient's treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.The early identification of patients suffering from SARS-CoV-2 infection in primary care is of outmost importance in the current pandemic. The objective of this study was to describe the clinical characteristics of primary care patients who tested positive for SARS-CoV-2. We conducted a cross-sectional study between March 24 and May 7, 2020, involving consecutive patients undergoing RT-PCR testing in two community-based laboratories in Lyon (France) for a suspicion of COVID-19. We examined the association between symptoms and a positive test using univariable and multivariable logistic regression, adjusted for clustering within laboratories, and calculated the diagnostic performance of these symptoms. Of the 1561 patients tested, 1543 patients (99%) agreed to participate. Among them, 253 were positive for SARS-CoV-2 (16%). The three most frequently reported 'ear-nose-throat' and non-'ear-nose-throat' symptoms in patients who tested positive were dry throat (42%), loss of smell (36%) and loss of taste (31%), respectively fever (58%), cough (52%) and headache (45%). In multivariable analyses, loss of taste (OR 3.8 [95% CI 3.3-4.4], p-value  less then  0.001), loss of smell (OR 3.0 [95% CI 1.9-4.8], p  less then  0.001), muscle pain (OR 1.6 [95% CI 1.2-2.0], p = 0.001) and dry nose (OR 1.3 [95% CI 1.1-1.6], p = 0.01) were significantly associated with a positive result. In contrast, sore throat (OR 0.6 [95% CI 0.4-0.8], p = 0.003), stuffy nose (OR 0.6 [95% CI 0.6-0.7], p  less then  0.001), diarrhea (OR 0.6 [95% CI 0.5-0.6], p  less then  0.001) and dyspnea (OR 0.5 [95% CI 0.3-0.7], p  less then  0.001) were inversely associated with a positive test. The combination of loss of taste or smell had the highest diagnostic performance (OR 6.7 [95% CI 5.9-7.5], sensitivity 44.7% [95% CI 38.4-51.0], specificity 90.8% [95% CI 89.1-92.3]). No other combination of symptoms had a higher performance. U18666A inhibitor Our data could contribute to the triage and early identification of new clusters of cases.There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively included DWI data of patients with acute ischemic lesions from six centers. Dataset A (n = 2986) and B (n = 3951) included data from Siemens and GE MRI scanners, respectively. The datasets were split into the training (80%), validation (10%), and internal test (10%) sets, and six neuroradiologists created ground-truth masks. Models A and B were the proposed neural networks trained on datasets A and B. The models subsequently fine-tuned across the datasets using their validation data. Another radiologist performed the segmentation on the test sets for comparisons. The median Dice scores of models A and B were 0.858 and 0.857 for the internal tests, which were non-inferior to the radiologist's performance, but demonstrated lower performance than the radiologist on the external tests. Fine-tuned models A and B achieved median Dice scores of 0.832 and 0.846, which were non-inferior to the radiologist's performance on the external tests. The present work shows that the inter-vendor operability of deep learning for the segmentation of ischemic lesions on DWI might be enhanced via transfer learning; thereby, their clinical applicability and generalizability could be improved.Studies on microbial communities are pivotal to understand the role and the evolutionary paths of the host and their associated microorganisms in the ecosystems. Meta-genomics techniques have proven to be one of the most effective tools in the identification of endosymbiotic communities of host species. The microbiome of the highly exploited topshell Phorcus sauciatus was characterized in the Northeastern Atlantic (Portugal, Madeira, Selvagens, Canaries and Azores). Alpha diversity analysis based on observed OTUs showed significant differences among regions. The Principal Coordinates Analysis of beta-diversity based on presence/absence showed three well differentiated groups, one from Azores, a second from Madeira and the third one for mainland Portugal, Selvagens and the Canaries. The microbiome results may be mainly explained by large-scale oceanographic processes of the study region, i.e., the North Atlantic Subtropical Gyre, and specifically by the Canary Current. Our results suggest the feasibility of microbiome as a model study to unravel biogeographic and evolutionary processes in marine species with high dispersive potential.