Gender and Weight Shape Brain Dynamics during Food Viewing

Hemodynamic imaging results have associated both gender and body weight to variation in brain responses to food-related information. However, the spatio-temporal brain dynamics of gender-related and weight-wise modulations in food discrimination still remain to be elucidated. We analyzed visual evoked potentials (VEPs) while normal-weighted men (n = 12) and women (n = 12) categorized photographs of energy-dense foods and non-food kitchen utensils. VEP analyses showed that food categorization is influenced by gender as early as 170 ms after image onset. Moreover, the female VEP pattern to food categorization co-varied with participants' body weight. Estimations of the neural generator activity over the time interval of VEP modulations (i.e. by means of a distributed linear inverse solution [LAURA]) revealed alterations in prefrontal and temporo-parietal source activity as a function of image category and participants' gender. However, only neural source activity for female responses during food viewing was negatively correlated with body-mass index (BMI) over the respective time interval. Women showed decreased neural source activity particularly in ventral prefrontal brain regions when viewing food, but not non-food objects, while no such associations were apparent in male responses to food and non-food viewing. Our study thus indicates that gender influences are already apparent during initial stages of food-related object categorization, with small variations in body weight modulating electrophysiological responses especially in women and in brain areas implicated in food reward valuation and intake control. These findings extend recent reports on prefrontal reward and control circuit responsiveness to food cues and the potential role of this reactivity pattern in the susceptibility to weight gain

A Modeling Approach to Examining the Effect of Viruses on Marine Bacterial Populations in Different Nutrient-limited Environments

Viruses are responsible for about half of the bacteria mortality in the ocean, with ten to twenty percent of bacteria lysed every day. The lysis of these bacteria releases nutrients into the water, which can then be reused by other bacteria. Previous models and experiments have shown that viruses could thereby cause an increase in the productivity and abundance of bacteria. However, not all nutrients are equal in this process. Because of a stoichiometric difference between viruses and bacteria, proportionally less phosphorus than nitrogen is released into the water during lysis. If phosphorus rather than nitrogen was limiting, this could result in a lower stimulatory effect of viruses. In this study, I use multitrophic models to compare the effect of viruses in nitrogen- and phosphorus-limited conditions. Viruses have a net stimulatory effect on heterotrophic bacteria abundance in the nitrogen-limited system but no net effect in the phosphorus-limited system. In both systems, viruses cause a decrease in cyanobacteria and zooplankton abundance, and an increase in inorganic and organic nutrients. The increase in inorganic nutrients is significantly larger in the nitrogen-limited system than in the phosphorus-limited system. These results are consistent with the hypothesis that nutrient release during lysis is lower in a phosphorus-limited system than in a nitrogen-limited system. However, viruses have the same positive effect on nutrient recycling in both the nitrogen- and the phosphorus-limited system. The presence of viruses causes an increase in primary productivity and carbon sink while causing a decrease in nutrient export in both systems. Virus abundance is a good predictor for carbon sink, which is otherwise difficult to measure. Including an explicit viral class in global models could help provide better estimates for carbon sink and thus a better understanding of the climate

Sexual dimorphism and sex pheromone detection in Aphidoletes aphidimyza

Sexual dimorphism, particularly at the level of sensory and locomotor organs, is usually attributed to sexual selection. Antennae are notably developed in males of species that need to detect a sex pheromone at low concentration or at long distance. In addition to their role in intrasexual selection, antennae can be seen as important ornaments in intersexual selection. Antennae of Aphidoletes aphidimyza are clearly sexually dimorphic (males have longer antennae than females, with highly developed sensilla) while females emit a sex pheromone for mating. Males with longer and more symmetrical antennae than others could be more successful in reaching the source of sex pheromone, especially if they can fly properly. A morphometric study was first conducted, to apprehend the variability of antennae, wings and tibias in lab conditions. The length of the antennae of male A. aphidimyza is impressive and the right antenna is longer than the left antenna. Secondly, choice experiments were conducted in a Y-shaped olfactometer with males of A. aphidimyza facing the sex pheromone. The relationship between choice patterns and morphology of males was then studied, but no link was found between the morphology of males and their behaviour while exposed to the sex pheromone, although males were indeed attracted by the olfactometer arm containing the sex pheromone

Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors: Evidence from rural Madagascar

International audienceWhile much progress has been achieved over the last decades, malaria surveillance and control remain a challenge in countries with limited health care access and resources. Highresolution predictions of malaria incidence using routine surveillance data could represent a powerful tool to health practitioners by targeting malaria control activities where and when they are most needed. Here, we investigate the predictors of spatio-temporal malaria dynamics in rural Madagascar, estimated from facility-based passive surveillance data. Specifically, this study integrates climate, land-use, and representative household survey data to explain and predict malaria dynamics at a high spatial resolution (i.e., by Fokontany, a cluster of villages) relevant to health care practitioners. Combining generalized linear mixed models (GLMM) and path analyses, we found that socioeconomic , land use and climatic variables are all important predictors of monthly malaria incidence at fine spatial scales, via both direct and indirect effects. In addition, out-of-sample predictions from our model were able to identify 58% of the Fokontany in the top quintile for malaria incidence and account for 77% of the variation in the Fokontany incidence rank. These results suggest that it is possible to build a predictive framework using environmental and social predictors that can be complementary to standard surveillance systems and help inform control strategies by field actors at local scales

Generating controlled image sets in cognitive neuroscience research.

The investigation of perceptual and cognitive functions with non-invasive brain imaging methods critically depends on the careful selection of stimuli for use in experiments. For example, it must be verified that any observed effects follow from the parameter of interest (e.g. semantic category) rather than other low-level physical features (e.g. luminance, or spectral properties). Otherwise, interpretation of results is confounded. Often, researchers circumvent this issue by including additional control conditions or tasks, both of which are flawed and also prolong experiments. Here, we present some new approaches for controlling classes of stimuli intended for use in cognitive neuroscience, however these methods can be readily extrapolated to other applications and stimulus modalities. Our approach is comprised of two levels. The first level aims at equalizing individual stimuli in terms of their mean luminance. Each data point in the stimulus is adjusted to a standardized value based on a standard value across the stimulus battery. The second level analyzes two populations of stimuli along their spectral properties (i.e. spatial frequency) using a dissimilarity metric that equals the root mean square of the distance between two populations of objects as a function of spatial frequency along x- and y-dimensions of the image. Randomized permutations are used to obtain a minimal value between the populations to minimize, in a completely data-driven manner, the spectral differences between image sets. While another paper in this issue applies these methods in the case of acoustic stimuli (Aeschlimann et al., Brain Topogr 2008), we illustrate this approach here in detail for complex visual stimuli