278 research outputs found

    Routes for breaching and protecting genetic privacy

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    We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.Comment: Draft for comment

    Lipid analogs reveal features critical for hemolysis and diminish granadaene mediated Group B Streptococcus infection

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    Although certain microbial lipids are toxins, the structural features important for cytotoxicity remain unknown. Increased functional understanding is essential for developing therapeutics against toxic microbial lipids. Group B Streptococci (GBS) are bacteria associated with preterm births, stillbirths, and severe infections in neonates and adults. GBS produce a pigmented, cytotoxic lipid, known as granadaene. Despite its importance to all manifestations of GBS disease, studies towards understanding granadaene’s toxic activity are hindered by its instability and insolubility in purified form. Here, we report the synthesis and screening of lipid derivatives inspired by granadaene, which reveal features central to toxin function, namely the polyene chain length. Furthermore, we show that vaccination with a non-toxic synthetic analog confers the production of antibodies that inhibit granadaene-mediated hemolysis ex vivo and diminish GBS infection in vivo. This work provides unique structural and functional insight into granadaene and a strategy to mitigate GBS infection, which will be relevant to other toxic lipids encoded by human pathogens.This work was supported by funding from the National Institutes of Health Grants R01AI112619, R01AI133976, R01AI100989, and R21AI125907 and seed funds from Seattle Childrens Research Institute to L.

    Stimulus-Dependent Adjustment of Reward Prediction Error in the Midbrain

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    Previous reports have described that neural activities in midbrain dopamine areas are sensitive to unexpected reward delivery and omission. These activities are correlated with reward prediction error in reinforcement learning models, the difference between predicted reward values and the obtained reward outcome. These findings suggest that the reward prediction error signal in the brain updates reward prediction through stimulus–reward experiences. It remains unknown, however, how sensory processing of reward-predicting stimuli contributes to the computation of reward prediction error. To elucidate this issue, we examined the relation between stimulus discriminability of the reward-predicting stimuli and the reward prediction error signal in the brain using functional magnetic resonance imaging (fMRI). Before main experiments, subjects learned an association between the orientation of a perceptually salient (high-contrast) Gabor patch and a juice reward. The subjects were then presented with lower-contrast Gabor patch stimuli to predict a reward. We calculated the correlation between fMRI signals and reward prediction error in two reinforcement learning models: a model including the modulation of reward prediction by stimulus discriminability and a model excluding this modulation. Results showed that fMRI signals in the midbrain are more highly correlated with reward prediction error in the model that includes stimulus discriminability than in the model that excludes stimulus discriminability. No regions showed higher correlation with the model that excludes stimulus discriminability. Moreover, results show that the difference in correlation between the two models was significant from the first session of the experiment, suggesting that the reward computation in the midbrain was modulated based on stimulus discriminability before learning a new contingency between perceptually ambiguous stimuli and a reward. These results suggest that the human reward system can incorporate the level of the stimulus discriminability flexibly into reward computations by modulating previously acquired reward values for a typical stimulus

    Delay aversion but preference for large and rare rewards in two choice tasks: implications for the measurement of self-control parameters

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    BACKGROUND: Impulsivity is defined as intolerance/aversion to waiting for reward. In intolerance-to-delay (ID) protocols, animals must choose between small/soon (SS) versus large/late (LL) rewards. In the probabilistic discount (PD) protocols, animals are faced with choice between small/sure (SS) versus large/luck-linked (LLL) rewards. It has been suggested that PD protocols also measure impulsivity, however, a clear dissociation has been reported between delay and probability discounting. RESULTS: Wistar adolescent rats (30- to 46-day-old) were tested using either protocol in drug-free state. In the ID protocol, animals showed a marked shift from LL to SS reward when delay increased, and this despite adverse consequences on the total amount of food obtained. In the PD protocol, animals developed a stable preference for LLL reward, and maintained it even when SS and LLL options were predicted and demonstrated to become indifferent. We demonstrate a clear dissociation between these two protocols. In the ID task, the aversion to delay was anti-economical and reflected impulsivity. In the PD task, preference for large reward was maintained despite its uncertain delivery, suggesting a strong attraction for unitary rewards of great magnitude. CONCLUSION: Uncertain delivery generated no aversion, when compared to delays producing an equivalent level of large-reward rarefaction. The PD task is suggested not to reflect impulsive behavior, and to generate patterns of choice that rather resemble the features of gambling. In summary, present data do indicate the need to interpret choice behavior in ID and PD protocols differently

    Classifying perinatal mortality using verbal autopsy: is there a role for nonphysicians?

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    <p>Abstract</p> <p>Background</p> <p>Because of a physician shortage in many low-income countries, the use of nonphysicians to classify perinatal mortality (stillbirth and early neonatal death) using verbal autopsy could be useful.</p> <p>Objective</p> <p>To determine the extent to which underlying perinatal causes of deaths assigned by nonphysicians in Guatemala, Pakistan, Zambia, and the Democratic Republic of the Congo using a verbal autopsy method are concordant with underlying perinatal cause of death assigned by physician panels.</p> <p>Methods</p> <p>Using a train-the-trainer model, 13 physicians and 40 nonphysicians were trained to determine cause of death using a standardized verbal autopsy training program. Subsequently, panels of two physicians and individual nonphysicians from this trained cohort independently reviewed verbal autopsy data from a sample of 118 early neonatal deaths and 134 stillbirths. With the cause of death assigned by the physician panel as the reference standard, sensitivity, specificity, positive and negative predictive values, and cause-specific mortality fractions were calculated to assess nonphysicians' coding responses. Robustness criteria to assess how well nonphysicians performed were used.</p> <p>Results</p> <p>Causes of early neonatal death and stillbirth assigned by nonphysicians were concordant with physician-assigned causes 47% and 57% of the time, respectively. Tetanus filled robustness criteria for early neonatal death, and cord prolapse filled robustness criteria for stillbirth.</p> <p>Conclusions</p> <p>There are significant differences in underlying cause of death as determined by physicians and nonphysicians even when they receive similar training in cause of death determination. Currently, it does not appear that nonphysicians can be used reliably to assign underlying cause of perinatal death using verbal autopsy.</p

    Assessing the Potential Impacts to Riparian Ecosystems Resulting from Hemlock Mortality in Great Smoky Mountains National Park

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    Hemlock Woolly Adelgid (Adelges tsugae) is spreading across forests in eastern North America, causing mortality of eastern hemlock (Tsuga canadensis [L.] Carr.) and Carolina hemlock (Tsuga caroliniana Engelm.). The loss of hemlock from riparian forests in Great Smoky Mountains National Park (GSMNP) may result in significant physical, chemical, and biological alterations to stream environments. To assess the influence of riparian hemlock stands on stream conditions and estimate possible impacts from hemlock loss in GSMNP, we paired hardwood- and hemlock-dominated streams to examine differences in water temperature, nitrate concentrations, pH, discharge, and available photosynthetic light. We used a Geographic Information System (GIS) to identify stream pairs that were similar in topography, geology, land use, and disturbance history in order to isolate forest type as a variable. Differences between hemlock- and hardwood-dominated streams could not be explained by dominant forest type alone as forest type yields no consistent signal on measured conditions of headwater streams in GSMNP. The variability in the results indicate that other landscape variables, such as the influence of understory Rhododendron species, may exert more control on stream conditions than canopy composition. The results of this study suggest that the replacement of hemlock overstory with hardwood species will have minimal impact on long-term stream conditions, however disturbance during the transition is likely to have significant impacts. Management of riparian forests undergoing hemlock decline should, therefore, focus on facilitating a faster transition to hardwood-dominated stands to minimize long-term effects on water quality

    Using C. elegans to discover therapeutic compounds for ageing-associated neurodegenerative diseases

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    Age-associated neurodegenerative disorders such as Alzheimer’s disease are a major public health challenge, due to the demographic increase in the proportion of older individuals in society. However, the relatively few currently approved drugs for these conditions provide only symptomatic relief. A major goal of neurodegeneration research is therefore to identify potential new therapeutic compounds that can slow or even reverse disease progression, either by impacting directly on the neurodegenerative process or by activating endogenous physiological neuroprotective mechanisms that decline with ageing. This requires model systems that can recapitulate key features of human neurodegenerative diseases that are also amenable to compound screening approaches. Mammalian models are very powerful, but are prohibitively expensive for high-throughput drug screens. Given the highly conserved neurological pathways between mammals and invertebrates, Caenorhabditis elegans has emerged as a powerful tool for neuroprotective compound screening. Here we describe how C. elegans has been used to model various human ageing-associated neurodegenerative diseases and provide an extensive list of compounds that have therapeutic activity in these worm models and so may have translational potential

    Temporal-Difference Reinforcement Learning with Distributed Representations

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    Temporal-difference (TD) algorithms have been proposed as models of reinforcement learning (RL). We examine two issues of distributed representation in these TD algorithms: distributed representations of belief and distributed discounting factors. Distributed representation of belief allows the believed state of the world to distribute across sets of equivalent states. Distributed exponential discounting factors produce hyperbolic discounting in the behavior of the agent itself. We examine these issues in the context of a TD RL model in which state-belief is distributed over a set of exponentially-discounting “micro-Agents”, each of which has a separate discounting factor (γ). Each µAgent maintains an independent hypothesis about the state of the world, and a separate value-estimate of taking actions within that hypothesized state. The overall agent thus instantiates a flexible representation of an evolving world-state. As with other TD models, the value-error (δ) signal within the model matches dopamine signals recorded from animals in standard conditioning reward-paradigms. The distributed representation of belief provides an explanation for the decrease in dopamine at the conditioned stimulus seen in overtrained animals, for the differences between trace and delay conditioning, and for transient bursts of dopamine seen at movement initiation. Because each µAgent also includes its own exponential discounting factor, the overall agent shows hyperbolic discounting, consistent with behavioral experiments
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