Targeted disruption of MCPIP1/Zc3h12a results in fatal inflammatory disease

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141347/1/imcb201311.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141347/2/imcb201311-sup-0001.pd

Multicentre evaluation of the Naída Ci Q70 sound processor: Feedback from cochlear implant users and professionals

The aim of this survey was to gather data from both implant recipients and professionals on the ease of use of the Naída CI Q70 (Naída CI) sound processor from Advanced Bionics and on the usefulness of the new functions and features available. A secondary objective was to investigate fitting practices with the new processor. A comprehensive user satisfaction survey was conducted in a total of 186 subjects from 24 centres. In parallel, 23 professional questionnaires were collected from 11 centres. Overall, there was high satisfaction with the Naída CI processor from adults, children, experienced and new CI users as well as from professionals. The Naída CI processor was shown as being easy to use by all ages of recipients and by professionals. The majority of experienced CI users rated the Naída CI processor as being similar or better than their previous processor in all areas surveyed. The Naída CI was recommended by the professionals for fitting in all populations. Features like UltraZoom, ZoomControl and DuoPhone would not be fitted to very young children in contrast to adults. Positive ratings were obtained for ease of use, comfort and usefulness of the new functions and features of the Naída CI sound processor. Seventy-seven percent of the experienced CI users rated the new processor as being better than their previous sound processor from a general point of view. The survey also showed that fitting practices were influenced by the age of the user

Coupling a model of human thermoregulation with computational fluid dynamics for predicting human-environment interaction

This paper describes the methods developed to couple a commercial CFD program with a multi-segmented model of human thermal comfort and physiology. A CFD model is able to predict detailed temperatures and velocities of airflow around a human body, whilst a thermal comfort model is able to predict the response of a human to the environment surrounding it. By coupling the two models and exchanging information about the heat transfer at the body surface the coupled system can potentially predict the response of a human body to detailed local environmental conditions. This paper presents a method of exchanging data, using shared files, to provide a means of dynamically exchanging simulation data with the IESD-Fiala model during the CFD solution process. Additional code is used to set boundary conditions for the CFD simulation at the body surface as determined by the IESD-Fiala model and to return information about local environmental conditions adjacent to the body surface as determined by the CFD simulation. The coupled system is used to model a human subject in a naturally ventilated environment. The resulting ventilation flow pattern agrees well with other numerical and experimental work

Backward bending structure of Phillips Curve in Japan, France, Turkey and the U.S.A.

This work aims to analyse the cointegration and the causality relationship between inflation and unemployment by using nonlinear A.R.D.L. and two popular nonlinear causality tests for the period from 1960 to 2016 in Japan, Turkey, the U.S.A. and from 1970 to 2016 in France. This study complements the previous empirical papers. However, it differs from the existing literature with simultaneous use of nonlinear A.R.D.L. and causality methods. Nonlinear A.R.D.L. determined that there is a long run relationship between inflation and unemployment; between economic growth and unemployment for Japan, France, the U.S.A. and Turkey

Unterstützung kommunalplanerischer Prozesse mit CityGLM-basierter Anbindung Modelica-getriebener Quartierssimulationen

Eine integrale Planung städtischer (Energie-)Systeme bedarf einer planungsbegleitenden Unterstützung durch IT-basierte Planungs- und Simulationswerkzeuge. Die durchgängige Anwendung dieser digitalen Planungshilfsmittel wird allerdings bislang insbesondere durch den sehr hohen Aufwand bei der Spezifizierung und Erfassung benötigter Datengrundlagen sowie eine mangelhafte Interoperabilität zwischen den Systemen gehemmt. Im Rahmen eines Forschungsverbundprojektes wird dieses Problemfeld mittels praxisbezogener Prozessanalysen genauer spezifiziert und die technische und fachliche Integration durch die prozessbezogene Spezifikation relevanter Informationsbedarfe sowie die Entwicklung einer darauf aufbauenden, bidirektionalen Schnittstelle auf Basis des etablierten virtuellen Stadtmodellstandards CityGML verbessert. Als exemplarisches Anwendungsszenario innerhalb kommunaler Planungsprozesse wurde die Ausweisung von Vorranggebieten der Fernwärmenutzung basierend auf einer räumlichen Analyse des Wärmebedarfs für verschiedene Entwicklungsszenarien mittels einer bidirektionalen standard-basierten Koppelung von CityGML und Modelica ausgearbeitet

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesized that regression-based DL outperforms classification-based DL. Therefore, we developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images in 11,671 patients across nine cancer types. We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology