Spin caloritronics with superconductors: Enhanced thermoelectric effects, generalized Onsager response-matrix, and thermal spin currents

It has recently been proposed and experimentally demonstrated that it is possible to generate large thermoelectric effects in ferromagnet/superconductor structures due to a spin-dependent particle-hole asymmetry. Here, we theoretically show that quasiparticle tunneling between two spin-split superconductors enhances the thermoelectric response manyfold compared to when only one such superconductor is used, generating Seebeck coefficients ($\mathcal{S} > 1$ mV/K) and figures of merit ($ZT \simeq 40$) far exceeding the best bulk thermoelectric materials, and also becomes more resilient toward inelastic scattering processes. We present a generalized Onsager response-matrix which takes into account spin-dependent voltage and temperature gradients. Moreover, we show that thermally induced spin currents created in such junctions, even in the absence of a polarized tunneling barrier, also become largest in the case where a spin-dependent particle-hole asymmetry exists on both sides of the barrier. We determine how these thermal spin currents can be tuned both in magnitude and sign by several parameters, including the external field, temperature, and the superconducting phase-difference.Comment: 7 pages, 5 figures. v2: Added several new results, such as the response matrix for spin-dependent biases and the evaluation of thermal spin currents. Accepted for publication in Phys. Rev.

StackInsights: Cognitive Learning for Hybrid Cloud Readiness

Hybrid cloud is an integrated cloud computing environment utilizing a mix of public cloud, private cloud, and on-premise traditional IT infrastructures. Workload awareness, defined as a detailed full range understanding of each individual workload, is essential in implementing the hybrid cloud. While it is critical to perform an accurate analysis to determine which workloads are appropriate for on-premise deployment versus which workloads can be migrated to a cloud off-premise, the assessment is mainly performed by rule or policy based approaches. In this paper, we introduce StackInsights, a novel cognitive system to automatically analyze and predict the cloud readiness of workloads for an enterprise. Our system harnesses the critical metrics across the entire stack: 1) infrastructure metrics, 2) data relevance metrics, and 3) application taxonomy, to identify workloads that have characteristics of a) low sensitivity with respect to business security, criticality and compliance, and b) low response time requirements and access patterns. Since the capture of the data relevance metrics involves an intrusive and in-depth scanning of the content of storage objects, a machine learning model is applied to perform the business relevance classification by learning from the meta level metrics harnessed across stack. In contrast to traditional methods, StackInsights significantly reduces the total time for hybrid cloud readiness assessment by orders of magnitude

Conversion pathways of primary defects by annealing in proton-irradiated n-type 4H-SiC

The development of defect populations after proton irradiation of n-type 4H-SiC and subsequent annealing experiments is studied by means of deep level transient (DLTS) and photoluminescence (PL) spectroscopy. A comprehensive model is suggested describing the evolution and interconversion of irradiation-induced point defects during annealing below 1000{\deg}C. The model proposes the EH4 and EH5 traps frequently found by DLTS to originate from the (+/0) charge transition level belonging to different configurations of the carbon antisite-carbon vacancy (CAV) complex. Furthermore, we show that the transformation channel between the silicon vacancy (VSi) and CAV is effectively blocked under n-type conditions, but becomes available in samples where the Fermi level has moved towards the center of the band gap due to irradiation-induced donor compensation. The annealing of VSi and the carbon vacancy (VC) is shown to be dominated by recombination with residual self-interstitials at temperatures of up to 400{\deg}C. Going to higher temperatures, a decay of the CAV pair density is reported which is closely correlated to a renewed increase of VC concentration. A conceivable explanation for this process is the dissociation of the CAV pair into separate carbon anitisites and VC defects. Lastly, the presented data supports the claim that the removal of free carriers in irradiated SiC is due to introduced compensating defects and not passivation of shallow nitrogen donors

The Impact of Vitamin K2 on Energy Metabolism

Environmental and behavioral adaptations introduced during the last decades have synergistically enhanced man’s lifespan, but also paved the ground for disease states involving impairment of multiple organs, which are both modulating and depending on homeostatic calorie “accounting.

TOFA: Transfer-Once-for-All

Weight-sharing neural architecture search aims to optimize a configurable neural network model (supernet) for a variety of deployment scenarios across many devices with different resource constraints. Existing approaches use evolutionary search to extract a number of models from a supernet trained on a very large data set, and then fine-tune the extracted models on the typically small, real-world data set of interest. The computational cost of training thus grows linearly with the number of different model deployment scenarios. Hence, we propose Transfer-Once-For-All (TOFA) for supernet-style training on small data sets with constant computational training cost over any number of edge deployment scenarios. Given a task, TOFA obtains custom neural networks, both the topology and the weights, optimized for any number of edge deployment scenarios. To overcome the challenges arising from small data, TOFA utilizes a unified semi-supervised training loss to simultaneously train all subnets within the supernet, coupled with on-the-fly architecture selection at deployment time

The Circulation in Keehi Lagoon, Oahu, Hawaii, During July and August, 1968

The data from seven oceanographic field surveys taken during July and August, 1968 in Keehi Lagoon, Oahu, Hawaii, and the results of an analysis of these data are presented in this report. The primary objectives of the work were to determine the volume transports to and from the lagoon and to find the circulation both in the lagoon and in the area adjacent to the entrance. The surface circulation was found to be strongly dependent upon the prevailing winds. A westward flow of surface water was observed in most areas of the lagoon except during periods of weak winds. The subsurface flow (below 2.5 meters) was strongly dependent upon the bathymetry. This flow was either to or from the lagoon depending on whether a flooding or ebbing tide was in progress. However, on the eastern side of the lagoon, the incoming transport was greater than the outgoing transport, particularly in a dredged ship channel that crosses the lagoon entrance reef. In contrast, the outgoing transport was greater than the incoming transport on the western side of the lagoon. These conditions result in a limited amount of daily flushing of the lagoon from the east to west. The tide records showed a large number of high amplitude free oscillations of the lagoon surface. The contribution to the circulation from these free oscillations was examined and found to be nominal throughout most of the lagoon, but significant at a few locations in the lagoon. The stratification in the lagoon was also examined and found to be of importance only in the dredged seaplane channels bordering the lagoon and in the area outside and west of the lagoon entrance. Two contributing factors causing the existing stratification are stream runoff from the Moanalua and Kalihi Streams, and warming of the surface water due to surface heat exchange. Most of the warming of the surface water takes place over the large centrally located mud flats in the lagoon. This warm water subsequently flows into the seaplane channels during ebbing tides and later moves westward around Ahua Point

Performance of magnetic resonance imaging-based prostate cancer risk calculators and decision strategies in two large European medical centres

Objectives: To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa). Patients and Methods: We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%–20%. Results: A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate-specific antigen (PSA) level, and PSA density (PSAD) were 68 (63–72) years, 9 (7–15) ng/mL, and 0.20 (0.13–0.32) ng/mL2, respectively. In all, 18 multivariable risk calculators (MRI-RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI-RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI-findings and PSAD. Conclusions: Even in this high-risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI-RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD.</p

Metabolic characterization of triple negative breast cancer

Background: The aims of this study were to characterize the metabolite profiles of triple negative breast cancer (TNBC) and to investigate the metabolite profiles associated with human epidermal growth factor receptor-2/neu (HER-2) overexpression using ex vivo high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). Metabolic alterations caused by the different estrogen receptor (ER), progesterone receptor (PgR) and HER-2 receptor statuses were also examined. To investigate the metabolic differences between two distinct receptor groups, TNBC tumors were compared to tumors with ERpos/PgR(pos)/HER-2(pos) status which for the sake of simplicity is called triple positive breast cancer (TPBC).Methods: The study included 75 breast cancer patients without known distant metastases. HR MAS MRS was performed for identification and quantification of the metabolite content in the tumors. Multivariate partial least squares discriminant analysis (PLS-DA) modeling and relative metabolite quantification were used to analyze the MR data.Results: Choline levels were found to be higher in TNBC compared to TPBC tumors, possibly related to cell proliferation and oncogenic signaling. In addition, TNBC tumors contain a lower level of Glutamine and a higher level of Glutamate compared to TPBC tumors, which indicate an increase in glutaminolysis metabolism. The development of glutamine dependent cell growth or "Glutamine addiction" has been suggested as a new therapeutic target in cancer. Our results show that the metabolite profiles associated with HER-2 overexpression may affect the metabolic characterization of TNBC. High Glycine levels were found in HER-2(pos) tumors, which support Glycine as potential marker for tumor aggressiveness.Conclusions: Metabolic alterations caused by the individual and combined receptors involved in breast cancer progression can provide a better understanding of the biochemical changes underlying the different breast cancer subtypes. Studies are needed to validate the potential of metabolic markers as targets for personalized treatment of breast cancer subtypes

Vestibular disease in dogs: association between neurological examination, MRI lesion localisation and outcome.

OBJECTIVES To determine whether the neurological examination correctly distinguishes between central and peripheral vestibular lesions in dogs. MATERIALS AND METHODS Retrospective study on dogs with vestibular disease presenting to two referral clinics in Germany. RESULTS Ninety-three dogs were included; neurological examination suggested central vestibular disease in 62 and a peripheral lesion in 31. MRI diagnosis was central vestibular disease in 68 dogs and peripheral in 25. Of the 62 dogs with a lesion localisation diagnosed as central vestibular by neurological exam, 61 were correctly identified (98.4%). Twenty-four of the 31 dogs diagnosed with a peripheral lesion by neurological exam had a consistent lesion on MRI (77.4%). CLINICAL SIGNIFICANCE The neurological examination is efficient at identifying lesions in the central vestibular system but less so for peripheral lesions. Therefore it is prudent to recommend imaging in dogs that show signs of peripheral vestibular syndrome but do not rapidly respond to treatment

Современные подходы в диагностике и лечении тромбозов системы нижней полой вены

ТРОМБОЗ ВЕНОЗНЫЙ /ДИАГН /ТЕРПОЛАЯ ВЕНА, НИЖНЯЯТРОМБОЭМБОЛИЯЛЕГОЧНАЯ ЭМБОЛИ