Catch-Up Growth Following Fetal Growth Restriction Promotes Rapid Restoration of Fat Mass but Without Metabolic Consequences at One Year of Age

BACKGROUND: Fetal growth restriction (FGR) followed by rapid weight gain during early life has been suggested to be the initial sequence promoting central adiposity and insulin resistance. However, the link between fetal and early postnatal growth and the associated anthropometric and metabolic changes have been poorly studied. METHODOLOGY/PRINCIPAL FINDINGS: Over the first year of post-natal life, changes in body mass index, skinfold thickness and hormonal concentrations were prospectively monitored in 94 infants in whom the fetal growth velocity had previously been measured using a repeated standardized procedure of ultrasound fetal measurements. 45 infants, thinner at birth, had experienced previous FGR (FGR+) regardless of birth weight. Growth pattern in the first four months of life was characterized by greater change in BMI z-score in FGR+ (+1.26+/-1.2 vs +0.58 +/-1.17 SD in FGR-) resulting in the restoration of BMI and of fat mass to values similar to FGR-, independently of caloric intakes. Growth velocity after 4 months was similar and BMI z-score and fat mass remained similar at 12 months of age. At both time-points, fetal growth velocity was an independent predictor of fat mass in FGR+. At one year, fasting insulin levels were not different but leptin was significantly higher in the FGR+ (4.43+/-1.41 vs 2.63+/-1 ng/ml in FGR-). CONCLUSION: Early catch-up growth is related to the fetal growth pattern itself, irrespective of birth weight, and is associated with higher insulin sensitivity and lower leptin levels after birth. Catch-up growth promotes the restoration of body size and fat stores without detrimental consequences at one year of age on body composition or metabolic profile. The higher leptin concentration at one year may reflect a positive energy balance in children who previously faced fetal growth restriction

The cell biology of smell

The olfactory system detects and discriminates myriad chemical structures across a wide range of concentrations. To meet this task, the system utilizes a large family of G protein–coupled receptors—the odorant receptors—which are the chemical sensors underlying the perception of smell. Interestingly, the odorant receptors are also involved in a number of developmental decisions, including the regulation of their own expression and the patterning of the olfactory sensory neurons' synaptic connections in the brain. This review will focus on the diverse roles of the odorant receptor in the function and development of the olfactory system

A Corticothalamic Circuit Model for Sound Identification in Complex Scenes

The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics

Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex (V1). However, the computational and ecological principles underlying contextual effects are incompletely understood. We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics, and we interpret the firing rates of V1 neurons as performing optimal recognition in this model. We show that this leads to a substantial generalization of divisive normalization, a computation that is ubiquitous in many neural areas and systems. A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence. We optimized the parameters of the model on natural image patches, and then simulated neural and perceptual responses on stimuli used in classical experiments. The model reproduces some rich and complex response patterns observed in V1, such as the contrast dependence, orientation tuning and spatial asymmetry of surround suppression, while also allowing for surround facilitation under conditions of weak stimulation. It also mimics the perceptual salience produced by simple displays, and leads to readily testable predictions. Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs, and lends statistical support to the theory that V1 computes visual salience

The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain

The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100–500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a “router” network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates

CMS Data Processing Workflows during an Extended Cosmic Ray Run

Peer reviewe

Aligning the CMS Muon Chambers with the Muon Alignment System during an Extended Cosmic Ray Run

Peer reviewe