The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Association between HLA Class I and Class II Alleles and the Outcome of West Nile Virus Infection: An Exploratory Study

BACKGROUND: West Nile virus (WNV) infection is asymptomatic in most individuals, with a minority developing symptoms ranging from WNV fever to serious neuroinvasive disease. This study investigated the impact of host HLA on the outcome of WNV disease. METHODS: A cohort of 210 non-Hispanic mostly white WNV(+) subjects from Canada and the U.S. were typed for HLA-A, B, C, DP, DQ, and DR. The study subjects were divided into three WNV infection outcome groups: asymptomatic (AS), symptomatic (S), and neuroinvasive disease (ND). Allele frequency distribution was compared pair-wise between the AS, S, and ND groups using χ2 and Fisher's exact tests and P values were corrected for multiple comparisons (Pc). Allele frequencies were compared between the groups and the North American population (NA) used as a control group. Logistic regression analysis was used to evaluate the potential synergistic effect of age and HLA allele phenotype on disease outcome. RESULTS: The alleles HLA-A*68, C*08 and DQB*05 were more frequently associated with severe outcomes (ND vs. AS, P(A*68) = 0.013/Pc = 0.26, P(C*08) = 0.0075/Pc = 0.064, and P(DQB1*05) = 0.029/Pc = 0.68), However the apparent DQB1*05 association was driven by age. The alleles HLA-B*40 and C*03 were more frequently associated with asymptomatic outcome (AS vs. S, P(B*40) = 0.021/Pc = 0.58 and AS vs. ND P(C*03) = 0.039/Pc = 0.64) and their frequencies were lower within WNV(+) subjects with neuroinvasive disease than within the North American population (NA vs. S, P(B*40) = 0.029 and NA vs. ND, P(C*03) = 0.032). CONCLUSIONS: Host HLA may be associated with the outcome of WNV disease; HLA-A*68 and C*08 might function as "susceptible" alleles, whereas HLA-B*40 and C*03 might function as "protective" alleles

Nut production in Bertholletia excelsa across a logged forest mosaic: implications for multiple forest use

Although many examples of multiple-use forest management may be found in tropical smallholder systems, few studies provide empirical support for the integration of selective timber harvesting with non-timber forest product (NTFP) extraction. Brazil nut (Bertholletia excelsa, Lecythidaceae) is one of the world’s most economically-important NTFP species extracted almost entirely from natural forests across the Amazon Basin. An obligate out-crosser, Brazil nut flowers are pollinated by large-bodied bees, a process resulting in a hard round fruit that takes up to 14 months to mature. As many smallholders turn to the financial security provided by timber, Brazil nut fruits are increasingly being harvested in logged forests. We tested the influence of tree and stand-level covariates (distance to nearest cut stump and local logging intensity) on total nut production at the individual tree level in five recently logged Brazil nut concessions covering about 4000 ha of forest in Madre de Dios, Peru. Our field team accompanied Brazil nut harvesters during the traditional harvest period (January-April 2012 and January-April 2013) in order to collect data on fruit production. Three hundred and ninety-nine (approximately 80%) of the 499 trees included in this study were at least 100 m from the nearest cut stump, suggesting that concessionaires avoid logging near adult Brazil nut trees. Yet even for those trees on the edge of logging gaps, distance to nearest cut stump and local logging intensity did not have a statistically significant influence on Brazil nut production at the applied logging intensities (typically 1–2 timber trees removed per ha). In one concession where at least 4 trees ha-1 were removed, however, the logging intensity covariate resulted in a marginally significant (0.09) P value, highlighting a potential risk for a drop in nut production at higher intensities. While we do not suggest that logging activities should be completely avoided in Brazil nut rich forests, when a buffer zone cannot be observed, low logging intensities should be implemented. The sustainability of this integrated management system will ultimately depend on a complex series of socioeconomic and ecological interactions. Yet we submit that our study provides an important initial step in understanding the compatibility of timber harvesting with a high value NTFP, potentially allowing for diversification of forest use strategies in Amazonian Perù

Sensitivity and Bias in Decision-Making under Risk: Evaluating the Perception of Reward, Its Probability and Value

BACKGROUND: There are few clinical tools that assess decision-making under risk. Tests that characterize sensitivity and bias in decisions between prospects varying in magnitude and probability of gain may provide insights in conditions with anomalous reward-related behaviour. OBJECTIVE: We designed a simple test of how subjects integrate information about the magnitude and the probability of reward, which can determine discriminative thresholds and choice bias in decisions under risk. DESIGN/METHODS: Twenty subjects were required to choose between two explicitly described prospects, one with higher probability but lower magnitude of reward than the other, with the difference in expected value between the two prospects varying from 3 to 23%. RESULTS: Subjects showed a mean threshold sensitivity of 43% difference in expected value. Regarding choice bias, there was a 'risk premium' of 38%, indicating a tendency to choose higher probability over higher reward. An analysis using prospect theory showed that this risk premium is the predicted outcome of hypothesized non-linearities in the subjective perception of reward value and probability. CONCLUSIONS: This simple test provides a robust measure of discriminative value thresholds and biases in decisions under risk. Prospect theory can also make predictions about decisions when subjective perception of reward or probability is anomalous, as may occur in populations with dopaminergic or striatal dysfunction, such as Parkinson's disease and schizophrenia

Place, space, and foreign direct investment into peripheral cities

Perspectives drawn from the economic geography literature are increasingly used to generate insights into locational issues in international business. In this paper, we seek to integrate these literatures further by investigating the locational determinants of foreign direct investment (FDI) into peripheral cities within an emerging economy. Peripheral cities in emerging economies are attracting a growing proportion of global FDI flows, but the international business literature lacks a framework for understanding subnational determinants of FDI, particularly into non-core locations. We draw on the core-periphery model to build and test theory on how spatial interdependencies between subnational locations impact on the distribution of FDI inflows into a large and heterogeneous country China. Our results show that whilst peripheral cities tend to have a negative effect on FDI, this effect is positively moderated by proximity to core cities. The results highlight the importance of considering interactions between place and space when investigating locational issues in international business

Neural processes mediating contextual influences on human choice behaviour

Contextual influences on choice are ubiquitous in ecological settings. Current evidence suggests that subjective values are normalized with respect to the distribution of potentially available rewards. However, how this context-sensitivity is realised in the brain remains unknown. To address this, here we examine functional magnetic resonance imaging (fMRI) data during performance of a gambling task where blocks comprise values drawn from one of two different, but partially overlapping, reward distributions or contexts. At the beginning of each block (when information about context is provided), hippocampus is activated and this response is enhanced when contextual influence on choice increases. In addition, response to value in ventral tegmental area/substantia nigra (VTA/SN) shows context-sensitivity, an effect enhanced with an increased contextual influence on choice. Finally, greater response in hippocampus at block start is associated with enhanced context sensitivity in VTA/SN. These findings suggest that context-sensitive choice is driven by a brain circuit involving hippocampus and dopaminergic midbrain

Determinants of the Incidence of Hand, Foot and Mouth Disease in China Using Geographically Weighted Regression Models

Child population density and climate factors are potential determinants of the HFMD incidence in most areas in China. The strength and direction of association between these factors and the incidence of HFDM is spatially heterogeneous at the local geographic level, and child population density has a greater influence on the incidence of HFMD than the climate factors

Stimulation of Midbrain Dopaminergic Structures Modifies Firing Rates of Rat Lateral Habenula Neurons

Ventral tegmental area (VTA) and substantia nigra pars compacta (SNpc) are midbrain structures known to be involved in mediating reward in rodents. Lateral habenula (LHb) is considered as a negative reward source and it is reported that stimulation of the LHb rapidly induces inhibition of firing in midbrain dopamine neurons. Interestingly, the phasic fall in LHb neuronal activity may follow the excitation of dopamine neurons in response to reward-predicting stimuli. The VTA and SNpc give rise to dopaminergic projections that innervate the LHb, which is also known to be involved in processing painful stimuli. But it's unclear what physiological effects these inputs have on habenular function. In this study we distinguished the LHb pain-activated neurons of the Wistar rats and assessed their electrophysiological responsiveness to the stimulation of the VTA and SNpc with either single-pulse stimulation (300 µA, 0.5 Hz) or tetanic stimulation (80 µA, 25 Hz). Single-pulse stimulation that was delivered to either midbrain structure triggered transient inhibition of firing of ∼90% of the LHb pain-activated neurons. However, tetanic stimulation of the VTA tended to evoke an elevation in neuronal firing rate. We conclude that LHb pain-activated neurons can receive diverse reward-related signals originating from midbrain dopaminergic structures, and thus participate in the regulation of the brain reward system via both positive and negative feedback mechanisms