Improved change detection with nearby hands

Recent studies have suggested altered visual processing for objects that are near the hands. We present three experiments that test whether an observer’s hands near the display facilitate change detection. While performing the task, observers placed both hands either near or away from the display. When their hands were near the display, change detection performance was more accurate and they held more items in visual short-term memory (experiment 1). Performance was equally improved for all regions across the entire display, suggesting a stronger attentional engagement over all visual stimuli regardless of their relative distances from the hands (experiment 2). Interestingly, when only one hand was placed near the display, we found no facilitation from the left hand and a weak facilitation from the right hand (experiment 3). Together, these data suggest that the right hand is the main source of facilitation, and both hands together produce a nonlinear boost in performance (superadditivity) that cannot be explained by either hand alone. In addition, the presence of the right hand biased observers to attend to the right hemifield first, resulting in a right-bias in change detection performance (experiments 2 and 3)

Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared. Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests. Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively). Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa

Biochemical properties of Paracoccus denitrificans FnrP:Reactions with molecular oxygen and nitric oxide

In Paracoccus denitrificans, three CRP/FNR family regulatory proteins, NarR, NnrR and FnrP, control the switch between aerobic and anaerobic (denitrification) respiration. FnrP is a [4Fe-4S] cluster containing homologue of the archetypal O2 sensor FNR from E. coli and accordingly regulates genes encoding aerobic and anaerobic respiratory enzymes in response to O2, and also NO, availability. Here we show that FnrP undergoes O2-driven [4Fe-4S] to [2Fe-2S] cluster conversion that involves up to 2 O2 per cluster, with significant oxidation of released cluster sulfide to sulfane observed at higher O2 concentrations. The rate of the cluster reaction was found to be ~6-fold lower than that of E. coli FNR, suggesting that FnrP can remain transcriptionally active under microaerobic conditions. This is consistent with a role for FnrP in activating expression of the high O2 affinity cytochrome c oxidase under microaerobic conditions. Cluster conversion resulted in dissociation of the transcriptionally active FnrP dimer into monomers. Therefore, along with E. coli FNR, FnrP belongs to the subset of FNR proteins in which cluster type is correlated with association state. Interestingly, two key charged residues, Arg140 and Asp154, that have been shown to play key roles in the monomer-dimer equilibrium in E. coli FNR are not conserved in FnrP, indicating that different protomer interactions are important for this equilibrium. Finally, the FnrP [4Fe-4S] cluster is shown to undergo reaction with multiple NO molecules, resulting in iron nitrosyl species and dissociation into monomers

Different genes interact with particulate matter and tobacco smoke exposure in affecting lung function decline in the general population

BACKGROUND: Oxidative stress related genes modify the effects of ambient air pollution or tobacco smoking on lung function decline. The impact of interactions might be substantial, but previous studies mostly focused on main effects of single genes. OBJECTIVES: We studied the interaction of both exposures with a broad set of oxidative-stress related candidate genes and pathways on lung function decline and contrasted interactions between exposures. METHODS: For 12679 single nucleotide polymorphisms (SNPs), change in forced expiratory volume in one second (FEV(1)), FEV(1) over forced vital capacity (FEV(1)/FVC), and mean forced expiratory flow between 25 and 75% of the FVC (FEF(25-75)) was regressed on interval exposure to particulate matter >10 microm in diameter (PM10) or packyears smoked (a), additive SNP effects (b), and interaction terms between (a) and (b) in 669 adults with GWAS data. Interaction p-values for 152 genes and 14 pathways were calculated by the adaptive rank truncation product (ARTP) method, and compared between exposures. Interaction effect sizes were contrasted for the strongest SNPs of nominally significant genes (p(interaction)>0.05). Replication was attempted for SNPs with MAF<10% in 3320 SAPALDIA participants without GWAS. RESULTS: On the SNP-level, rs2035268 in gene SNCA accelerated FEV(1)/FVC decline by 3.8% (p(interaction) = 2.5x10(-6)), and rs12190800 in PARK2 attenuated FEV1 decline by 95.1 ml p(interaction) = 9.7x10(-8)) over 11 years, while interacting with PM10. Genes and pathways nominally interacting with PM10 and packyears exposure differed substantially. Gene CRISP2 presented a significant interaction with PM10 (p(interaction) = 3.0x10(-4)) on FEV(1)/FVC decline. Pathway interactions were weak. Replications for the strongest SNPs in PARK2 and CRISP2 were not successful. CONCLUSIONS: Consistent with a stratified response to increasing oxidative stress, different genes and pathways potentially mediate PM10 and tobac smoke effects on lung function decline. Ignoring environmental exposures would miss these patterns, but achieving sufficient sample size and comparability across study samples is challengin

Energy-efficient vertical handover parameters, classification and solutions over wireless heterogeneous networks: a comprehensive survey

In the last few decades, the popularity of wireless networks has been growing dramatically for both home and business networking. Nowadays, smart mobile devices equipped with various wireless networking interfaces are used to access the Internet, communicate, socialize and handle short or long-term businesses. As these devices rely on their limited batteries, energy-efficiency has become one of the major issues in both academia and industry. Due to terminal mobility, the variety of radio access technologies and the necessity of connecting to the Internet anytime and anywhere, energy-efficient handover process within the wireless heterogeneous networks has sparked remarkable attention in recent years. In this context, this paper first addresses the impact of specific information (local, network-assisted, QoS-related, user preferences, etc.) received remotely or locally on the energy efficiency as well as the impact of vertical handover phases, and methods. It presents energy-centric state-of-the-art vertical handover approaches and their impact on energy efficiency. The paper also discusses the recommendations on possible energy gains at different stages of the vertical handover process

Difference-based clustering of short time-course microarray data with replicates

<p>Abstract</p> <p>Background</p> <p>There are some limitations associated with conventional clustering methods for short time-course gene expression data. The current algorithms require prior domain knowledge and do not incorporate information from replicates. Moreover, the results are not always easy to interpret biologically.</p> <p>Results</p> <p>We propose a novel algorithm for identifying a subset of genes sharing a significant temporal expression pattern when replicates are used. Our algorithm requires no prior knowledge, instead relying on an observed statistic which is based on the first and second order differences between adjacent time-points. Here, a pattern is predefined as the sequence of symbols indicating direction and the rate of change between time-points, and each gene is assigned to a cluster whose members share a similar pattern. We evaluated the performance of our algorithm to those of K-means, Self-Organizing Map and the Short Time-series Expression Miner methods.</p> <p>Conclusions</p> <p>Assessments using simulated and real data show that our method outperformed aforementioned algorithms. Our approach is an appropriate solution for clustering short time-course microarray data with replicates.</p

Inferring stabilizing mutations from protein phylogenies : application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution

An Explicit Strategy Prevails When the Cerebellum Fails to Compute Movement Errors

In sensorimotor adaptation, explicit cognitive strategies are thought to be unnecessary because the motor system implicitly corrects performance throughout training. This seemingly automatic process involves computing an error between the planned movement and actual feedback of the movement. When explicitly provided with an effective strategy to overcome an experimentally induced visual perturbation, people are immediately successful and regain good task performance. However, as training continues, their accuracy gets worse over time. This counterintuitive result has been attributed to the independence of implicit motor processes and explicit cognitive strategies. The cerebellum has been hypothesized to be critical for the computation of the motor error signals that are necessary for implicit adaptation. We explored this hypothesis by testing patients with cerebellar degeneration on a motor learning task that puts the explicit and implicit systems in conflict. Given this, we predicted that the patients would be better than controls in maintaining an effective strategy assuming strategic and adaptive processes are functionally and neurally independent. Consistent with this prediction, the patients were easily able to implement an explicit cognitive strategy and showed minimal interference from undesirable motor adaptation throughout training. These results further reveal the critical role of the cerebellum in an implicit adaptive process based on movement errors and suggest an asymmetrical interaction of implicit and explicit processes