Appropriate counseling approach to disclose HIV status to HIV-infected children: Chiradzulu, Malawi

Mexico AIDS Conference 200

An olfactory subsystem that detects carbon disulfide and mediates food-related social learning

In mammals, pheromones and other social cues can promote mating or aggression behaviors; can communicate information about social hierarchies, genetic identity and health status; and can contribute to associative learning. However, the molecular, cellular, and neural mechanisms underlying many olfactory-mediated social interactions remain poorly understood. Here, we show a specialized olfactory subsystem that includes olfactory sensory neurons (OSNs) expressing the receptor guanylyl cyclase GC-D, the cyclic nucleotide-gated channel subunit CNGA3, and the carbonic anhydrase isoform CAII (GC-D(+) OSNs) is required for the acquisition of socially transmitted food preferences (STFPs) in mice

Electron electric dipole moment experiment using electric-field quantized slow cesium atoms

A proof-of-principle electron electric dipole moment (e-EDM) experiment using slow cesium atoms, nulled magnetic fields, and electric field quantization has been performed. With the ambient magnetic fields seen by the atoms reduced to less than 200 pT, an electric field of 6 MV/m lifts the degeneracy between states of unequal mF and, along with the low (approximately 3 m/s) velocity, suppresses the systematic effect from the motional magnetic field. The low velocity and small residual magnetic field have made it possible to induce transitions between states and to perform state preparation, analysis, and detection in regions free of applied static magnetic and electric fields. This experiment demonstrates techniques that may be used to improve the e-EDM limit by two orders of magnitude, but it is not in itself a sensitive e-EDM search, mostly due to limitations of the laser system.Comment: 9 pages, 8 figures, accepted for publication in Phys. Rev.

A Preliminary Assessment of Perceived and Objectively Scaled Workload of a Voice-Based Driver Interface

Interaction with a voice-command interface for radio control, destination entry, MP3 song selection, and phone dialing was assessed along with traditional manual radio control and a multi-level audio–verbal calibration task (nback) on-road in 60 drivers. Subjective workload, compensatory behavior, and physiological indices of cognitive workload suggest that there may be both potential benefits and cautions in the implementation of a representative production level interface

Synthetic ozone deposition and stomatal uptake at flux tower sites

We develop and evaluate a method to estimate O-3 deposition and stomatal O-3 uptake across networks of eddy covariance flux tower sites where O-3 concentrations and O-3 fluxes have not been measured. The method combines standard micrometeorological flux measurements, which constrain O-3 deposition velocity and stomatal conductance, with a gridded dataset of observed surface O-3 concentrations. Measurement errors are propagated through all calculations to quantify O-3 flux uncertainties. We evaluate the method at three sites with O(3 )flux measurements: Harvard Forest, Blodgett Forest, and Hyytiala Forest. The method reproduces 83 % or more of the variability in daily stomatal uptake at these sites with modest mean bias (21 % or less). At least 95 % of daily average values agree with measurements within a factor of 2 and, according to the error analysis, the residual differences from measured O-3 fluxes are consistent with the uncertainty in the underlying measurements. The product, called synthetic O-3 flux or SynFlux, includes 43 FLUXNET sites in the United States and 60 sites in Europe, totaling 926 site years of data. This dataset, which is now public, dramatically expands the number and types of sites where O-3 fluxes can be used for ecosystem impact studies and evaluation of air quality and climate models. Across these sites, the mean stomatal conductance and O-3 deposition velocity is 0.03-1.0 cm s(-1). The stomatal O-3 flux during the growing season (typically April-September) is 0.5-11.0 nmol O-3 m(-2) s(-1) with a mean of 4.5 nmol O(3 )m(-2) s(-1) and the largest fluxes generally occur where stomatal conductance is high, rather than where O-3 concentrations are high. The conductance differences across sites can be explained by atmospheric humidity, soil moisture, vegetation type, irrigation, and land management. These stomatal fluxes suggest that ambient O-3 degrades biomass production and CO2 sequestration by 20 %-24 % at crop sites, 6 %-29 % at deciduous broadleaf forests, and 4 %-20 % at evergreen needleleaf forests in the United States and Europe.Peer reviewe

Power-Based Droop Control in DC Microgrids Enabling Seamless Disconnection From Upstream Grids

This paper proposes a local power-based droop controller for distributed energy resource converters in dc microgrids that are connected to upstream grids by grid-interface converters. During normal operation, the grid-interface converter imposes the microgrid bus voltage, and the proposed controller allows power flow regulation at distributed energy resource converters\u2019 output. On the other hand, during abnormal operation of the grid-interface converter (e.g., due to faults in the upstream grid), the proposed controller allows bus voltage regulation by droop control. Notably, the controller can autonomously convert from power flow control to droop control, without any need of bus voltage variation detection schemes or communication with other microgrid components, which enables seamless transitions between these two modes of operation. Considering distributed energy resource converters employing the power-based droop control, the operation modes of a single converter and of the whole microgrid are defined and investigated herein. The controller design is also introduced. Furthermore, the power sharing performance of this control approach is analyzed and compared with that of classical droop control. The experimental results from a laboratory-scale dc microgrid prototype are reported to show the final performances of the proposed power-based droop control

Trimethylamine-N-oxide postprandial response in plasma and urine is lower after fermented compared to non-fermented dairy consumption in healthy adults

Trimethylamine-N-oxide (TMAO) can be produced by the gut microbiota from dietary substrates and is associated with cardiovascular disease. While dairy products contain TMAO precursors, the effect of fermented dairy on TMAO metabolism remains unclear. We used plasma and urine samples collected for two randomised cross-over studies to evaluate the effects of fermented dairy consumption on TMAO metabolism. In Study 1, thirteen healthy young men tested a yogurt and an acidified milk during postprandial tests and a two-week daily intervention. In Study 2, ten healthy adults tested milk and cheese during postprandial tests. TMAO and five related metabolites were measured in plasma and urine by LC-MS/MS and NMR. Faecal microbiota composition was assessed in Study 1 (16S rRNA metagenomics sequencing). Fermented milk products were associated with lower postprandial TMAO responses than non-fermented milks in urine (Study 1, p = 0.01; Study 2, p = 0.02) and in plasma, comparing yogurt and acidified milk (Study 1, p = 0.04). Daily consumption of dairy products did not differentially affect fasting TMAO metabolites. Significant correlations were observed between microbiota taxa and circulating or urinary TMAO concentrations. Fermentation of dairy products appear, at least transiently, to affect associations between dairy products and circulating TMAO levels

A Deep Learning Parameterization for Ozone Dry Deposition Velocities

The loss of ozone to terrestrial and aquatic systems, known as dry deposition, is a highly uncertain process governed by turbulent transport, interfacial chemistry, and plant physiology. We demonstrate the value of using Deep Neural Networks (DNN) in predicting ozone dry deposition velocities. We find that a feedforward DNN trained on observations from a coniferous forest site (Hyytiala, Finland) can predict hourly ozone dry deposition velocities at a mixed forest site (Harvard Forest, Massachusetts) more accurately than modern theoretical models, with a reduction in the normalized mean bias (0.05 versus similar to 0.1). The same DNN model, when driven by assimilated meteorology at 2 degrees x 2.5 degrees spatial resolution, outperforms the Wesely scheme as implemented in the GEOS-Chem model. With more available training data from other climate and ecological zones, this methodology could yield a generalizable DNN suitable for global models. Plain Language Summary Ozone in the lower atmosphere is a toxic pollutant and greenhouse gas. In this work, we use a machine learning technique known as deep learning, to simulate the loss of ozone to Earth's surface. We show that our deep learning simulation of this loss process outperforms existing traditional models and demonstrate the opportunity for using machine learning to improve our understanding of the chemical composition of the atmosphere.Peer reviewe