Transfusion thresholds and beyond

Comment on Liberal transfusion strategy improves survival in perioperative but not in critically ill patients. A meta-analysis of randomised trials. [Br J Anaesth. 2015

Exploring Food Detection using CNNs

One of the most common critical factors directly related to the cause of a chronic disease is unhealthy diet consumption. In this sense, building an automatic system for food analysis could allow a better understanding of the nutritional information with respect to the food eaten and thus it could help in taking corrective actions in order to consume a better diet. The Computer Vision community has focused its efforts on several areas involved in the visual food analysis such as: food detection, food recognition, food localization, portion estimation, among others. For food detection, the best results evidenced in the state of the art were obtained using Convolutional Neural Network. However, the results of all these different approaches were gotten on different datasets and therefore are not directly comparable. This article proposes an overview of the last advances on food detection and an optimal model based on GoogLeNet Convolutional Neural Network method, principal component analysis, and a support vector machine that outperforms the state of the art on two public food/non-food datasets

Active Vibration Control of Structures using an Impedance Matching Control Technique

Active vibration control of structures has gained a lot of interest in recent years. This paper presents an active vibration control methodology of a structure using piezoelectric actuators. The proposed methodology is useful in practical applications where the system to be controlled is difficult to model due to the presence of complex boundary conditions. The impedance matching control technique uses a power flow approach wherein the controller is designed such that the power flow into the structure is minimized. The system transfer function is obtained from the experimental collocated actuator/sensor pair data using Eigen Realisation Algorithm (ERA). The controller is designed for the system transfer function according to impedance matching theory. The above approach is targeted towards the vibration control of wind tunnel stings, which suffer from flow-induced vibration. A wind tunnel sting model is designed and fabricated for this study. The real time implementation of the impedance matching controller has been carried out using dSPACE® Digital Signal Processor (DSP) card. The results are encouraging and demonstrate the feasibility of applying this technique in the wind tunne

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.Comment: 12 pages. Statistical Paper

Use of denaturing gradient gel electrophoresis in screening unknown β-thalassemia mutations in Egyptian patients

The molecular defects resulting in a β-thalassemia phenotype, in the Egyptian population show a clear heterogenic pattern. Many studies have embarked on the molecular detection and characterization of these mutations, using a wide array of the available techniques with successful detection of both known and unknown mutations. PCR based techniques, as well as direct DNA sequencing are effective with some limitations as regards the time, effort and high cost to reach a final diagnosis. Intermediary screening techniques have proved to be effective tools to overcome these drawbacks. This study aims to assess the use of the denaturing gradient gel electrophoresis (DGGE)1 to detect b-thalassemia mutations prior to the performance of direct sequencing to minimize the cost and workload involved in the process. In this study, forty-two previously genotyped patients in a study by El-Gawhary et al. in 2007, have been analyzed by DGGE for fragment 2 then 1. These are the β-globin gene fragments showing the majority of the β-thalassemia mutations. Sixty-eight alleles out of 79 mutant alleles in total were detected within these two fragments. The 11 undetected alleles comprise 9 alleles that require further examination using other DGGE fragments (0, 4 and 5) and correspond to -87(C > G), intervening sequence (IVS)II-1(G >A), IVS II-745, and IVS II-848(C > A). The remaining two that failed detection correspond to codon (CD) 37(G> A). Although, IVS-II 745(C >G) is undetectable within these two fragments, its 100% linkage polymorphism (+20 C> T) was detected in fragment 1 gel. DGGE is a sensitive technique to screen for β-thalassemia mutations. For simultaneous analysis of multiple samples with unknown mutations, it is recommended that direct DNA sequencing be coupled with DGGE whenever available to reduce time, effort and cost.Keywords: β-Thalassemia; DGGE; PCR; Mutations; Polymorphism; Screenin

Detecting and characterizing lateral phishing at scale

We present the first large-scale characterization of lateral phishing attacks, based on a dataset of 113 million employee-sent emails from 92 enterprise organizations. In a lateral phishing attack, adversaries leverage a compromised enterprise account to send phishing emails to other users, benefit-ting from both the implicit trust and the information in the hijacked user's account. We develop a classifier that finds hundreds of real-world lateral phishing emails, while generating under four false positives per every one-million employee-sent emails. Drawing on the attacks we detect, as well as a corpus of user-reported incidents, we quantify the scale of lateral phishing, identify several thematic content and recipient targeting strategies that attackers follow, illuminate two types of sophisticated behaviors that attackers exhibit, and estimate the success rate of these attacks. Collectively, these results expand our mental models of the 'enterprise attacker' and shed light on the current state of enterprise phishing attacks

Melt conditioning by advanced shear technology (MCAST) for refining solidification microstructures

MCAST (melt conditioning by advanced shear technology) is a novel processing technology developed recently by BCAST at Brunel University for conditioning liquid metal prior to solidification processing. The MCAST process uses a twin screw mechanism to impose a high shear rate and a high intensity of turbulence to the liquid metal, so that the conditioned liquid metal has uniform temperature, uniform chemical composition and well-dispersed and completely wetted oxide particles with a fine size and a narrow size distribution. The microstructural refinement is achieved through an enhanced heterogeneous nucleation rate and an increased nuclei survival rate during the subsequent solidification processing. In this paper we present the MCAST process and its applications for microstructural refinement in both shape casting and continuous casting of light alloys

Duality relations for the ASEP conditioned on a low current

We consider the asymmetric simple exclusion process (ASEP) on a finite lattice with periodic boundary conditions, conditioned to carry an atypically low current. For an infinite discrete set of currents, parametrized by the driving strength $s_K$, $K \geq 1$, we prove duality relations which arise from the quantum algebra $U_q[\mathfrak{gl}(2)]$ symmetry of the generator of the process with reflecting boundary conditions. Using these duality relations we prove on microscopic level a travelling-wave property of the conditioned process for a family of shock-antishock measures for $N>K$ particles: If the initial measure is a member of this family with $K$ microscopic shocks at positions $(x_1,\dots,x_K)$, then the measure at any time $t>0$ of the process with driving strength $s_K$ is a convex combination of such measures with shocks at positions $(y_1,\dots,y_K)$. which can be expressed in terms of $K$-particle transition probabilities of the conditioned ASEP with driving strength $s_N$.Comment: 26 page

Gender-related variability in information processing of disclosure documents

Disclosure is used worldwide as a tool to increase transparency and help investors to make their decisions, thus partially overcoming asymmetric information in financial markets. This research seeks to explore gender–related variability in visual attention allocation to the Key Investor Information Document, and in the evaluation of product financial attractiveness. We exploited the eye–tracking methodology to collect neural data, responding to the call for considering new data sources. The analysis shows that men tend to dedicate more attention to the sections Objectives and Past performance while women spend more time to scan the sections Risk–reward profile and Costs and charges; when evaluating product financial attractiveness, women, with respect to men, tend to evaluate more often products as poorly financially attractive. Results reveal the existence of gender–related variability in the visual search strategy for relevant information, which, in turn, can impact on the phase of product evaluation. These findings highlight the professional responsibility of regulators and supervisors to monitor sellers’ and marketers’ behaviours when they interact with consumers. Moreover, this study could provide support to develop financial disclosure documents considering individual differences and ensuring that adequate attention is allocated by investors to all financial information sources, thus raising the level of investor protection. Eventually, the study stimulates innovations to be embedded in the world–wide ongoing regulatory developments that aim at increasing transparency requirements

Graviton Propagation and Vacuum Polarization in Curved Space

The effects of vacuum polarization arising from loops of massive scalar particles on graviton propagation in curved space are considered. Physically, they are due to curvature induced tidal forces acting on the cloud of virtual scalar particles surrounding the graviton. The effects are tractable in a WKB and large mass limit and the results can be written as an effective refractive index for the graviton modes with both a real and imaginary part. The imaginary part of the refractive index is a curvature induced contribution to the wavefunction renormalization of the graviton in real affine time and can have the effect of dressing or un-dressing the graviton. The real part of the refractive index increases logarithmically at high frequency as long as the null energy condition is satisfied by the background.Comment: 21 pages, typos correcte