A Happy Face Advantage With Male Caucasian Faces: It Depends on the Company You Keep

Happy faces are categorized faster as “happy” than angry faces as “angry,” the happy face advantage. Here, we show across three experiments that the size of the happy face advantage for male Caucasian faces varies as a function of the other faces they are presented with. A happy face advantage was present if the male Caucasian faces were presented among male African American faces, but absent if the same faces were presented among female faces, Caucasian or African American. The modulation of the happy face advantage for male Caucasian faces was observed even if the female Caucasian/male African American faces had neutral expressions. This difference in the happy face advantage for a constant set of faces as a function of the other faces presented indicates that it does not reflect on a stimulus-dependent bottom-up process but on the evaluation of the expressive faces within a specific context

Excess direct hospital cost of treating adult patients with ventilator associated respiratory infection (VARI) in Vietnam

INTRODUCTION: Ventilator associated respiratory infections (VARIs) are the most common hospital acquired infections in critical care worldwide. This work aims to estimate the total annual direct hospital cost of treating VARI throughout Vietnam. METHODS: A costing model was constructed to evaluate the excess cost of diagnostics and treatment of VARI in Vietnam. Model inputs included costs for extra lengths of stay, diagnostics, VARI incidence, utilisation of ventilators and antibiotic therapy. RESULTS: With the current VARI incidence rate of 21.7 episodes per 1000 ventilation-days, we estimated 34,428 VARI episodes in the 577 critical care units in Vietnam per year. The extra cost per VARI episode was $1,174.90 and the total annual excess cost was US$40.4 million. A 1% absolute reduction in VARI incidence density would save US$1.86 million annually. For each episode of VARI, the share of excess cost components was 45.1$117 per capita, VARI represents a substantial cost to the health service in Vietnam. Enhanced infection prevention and control and antimicrobial stewardship programmes should be implemented to reduce this

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Projet PARADI

Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices

The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well-annotated medical imaging datasets have to a rapid increase in the use of supervised machine learning in the 3D medical image analysis and diagnosis. In particular, deep convolutional neural networks (D-CNNs) have been key players and were adopted by the medical imaging community to assist clinicians and medical experts in disease diagnosis and treatment. However, training and inferencing deep neural networks such as D-CNN on high-resolution 3D volumes of Computed Tomography (CT) scans for diagnostic tasks pose formidable computational challenges. This challenge raises the need of developing deep learning-based approaches that are robust in learning representations in 2D images, instead 3D scans. In this work, we propose for the first time a new strategy to train \emph{slice-level} classifiers on CT scans based on the descriptors of the adjacent slices along the axis. In particular, each of which is extracted through a convolutional neural network (CNN). This method is applicable to CT datasets with per-slice labels such as the RSNA Intracranial Hemorrhage (ICH) dataset, which aims to predict the presence of ICH and classify it into 5 different sub-types. We obtain a single model in the top 4% best-performing solutions of the RSNA ICH challenge, where model ensembles are allowed. Experiments also show that the proposed method significantly outperforms the baseline model on CQ500. The proposed method is general and can be applied to other 3D medical diagnosis tasks such as MRI imaging. To encourage new advances in the field, we will make our codes and pre-trained model available upon acceptance of the paper.Comment: Accepted for presentation at the 22nd IEEE Statistical Signal Processing (SSP) worksho

Trending anti-E7 serology predicts mortality and recurrence of HPV-associated cancers of the oropharynx

High-risk human papillomavirus (HPV) is among the most common causes of head and neck cancer (HNC) with increasing incidence. HPV-associated HNC patients\u27 clinical response to treatment varies drastically, which has made treatment de-escalation clinical trials challenging. To address the need for noninvasive biomarkers that differentiate patient outcomes, serum antibodies to E7 oncoprotein levels were evaluated in serial serum specimens from HPV-positive HNC patients

Impact of culture towards disaster risk reduction

Number of natural disasters has risen sharply worldwide making the risk of disasters a global concern. These disasters have created significant losses and damages to humans, economy and society. Despite the losses and damages created by disasters, some individuals and communities do not attached much significance to natural disasters. Risk perception towards a disaster not only depends on the danger it could create but also the behaviour of the communities and individuals that is governed by their culture. Within this context, this study examines the relationship between culture and disaster risk reduction (DRR). A comprehensive literature review is used for the study to evaluate culture, its components and to analyse a series of case studies related to disaster risk. It was evident from the study that in some situations, culture has become a factor for the survival of the communities from disasters where as in some situations culture has acted as a barrier for effective DRR activities. The study suggests community based DRR activities as a mechanism to integrate with culture to effectively manage disaster risk

Deep Underground Science and Engineering Laboratory - Preliminary Design Report

The DUSEL Project has produced the Preliminary Design of the Deep Underground Science and Engineering Laboratory (DUSEL) at the rehabilitated former Homestake mine in South Dakota. The Facility design calls for, on the surface, two new buildings - one a visitor and education center, the other an experiment assembly hall - and multiple repurposed existing buildings. To support underground research activities, the design includes two laboratory modules and additional spaces at a level 4,850 feet underground for physics, biology, engineering, and Earth science experiments. On the same level, the design includes a Department of Energy-shepherded Large Cavity supporting the Long Baseline Neutrino Experiment. At the 7,400-feet level, the design incorporates one laboratory module and additional spaces for physics and Earth science efforts. With input from some 25 science and engineering collaborations, the Project has designed critical experimental space and infrastructure needs, including space for a suite of multidisciplinary experiments in a laboratory whose projected life span is at least 30 years. From these experiments, a critical suite of experiments is outlined, whose construction will be funded along with the facility. The Facility design permits expansion and evolution, as may be driven by future science requirements, and enables participation by other agencies. The design leverages South Dakota's substantial investment in facility infrastructure, risk retirement, and operation of its Sanford Laboratory at Homestake. The Project is planning education and outreach programs, and has initiated efforts to establish regional partnerships with underserved populations - regional American Indian and rural populations