Notes

The auxin signaling circle means low-sugar-state enter straight into compensated mobile growth inside the fugu5 cotyledon.
CMMG contains the most comprehensive collection of consistently functionally annotated species of the mouse and human microbiome to date, setting the ground for analysis of new and reanalysis of existing datasets at an unprecedented depth.A central goal in sensory neuroscience is to understand the neuronal signal processing involved in the encoding of natural stimuli. A critical step towards this goal is the development of successful computational encoding models. For ganglion cells in the vertebrate retina, the development of satisfactory models for responses to natural visual scenes is an ongoing challenge. Standard models typically apply linear integration of visual stimuli over space, yet many ganglion cells are known to show nonlinear spatial integration, in particular when stimulated with contrast-reversing gratings. We here study the influence of spatial nonlinearities in the encoding of natural images by ganglion cells, using multielectrode-array recordings from isolated salamander and mouse retinas. We assess how responses to natural images depend on first- and second-order statistics of spatial patterns inside the receptive field. This leads us to a simple extension of current standard ganglion cell models. We show that taking not only the weighted average of light intensity inside the receptive field into account but also its variance over space can partly account for nonlinear integration and substantially improve response predictions of responses to novel images. For salamander ganglion cells, we find that response predictions for cell classes with large receptive fields profit most from including spatial contrast information. Finally, we demonstrate how this model framework can be used to assess the spatial scale of nonlinear integration. Our results underscore that nonlinear spatial stimulus integration translates to stimulation with natural images. Furthermore, the introduced model framework provides a simple, yet powerful extension of standard models and may serve as a benchmark for the development of more detailed models of the nonlinear structure of receptive fields.[This corrects the article DOI 10.1371/journal.pbio.3001548.].This study applied the extended parallel process model (EPPM) to investigate the factors affecting people's preventive behaviors against COVID-19, and thereby, draw relevant policy implications for current and future other epidemics. The EPPM was used to examine the danger control and fear control responses, along with the separate effects of their sub-factors (perceived susceptibility, perceived severity, response efficacy, and self-efficacy) on personal hygiene behaviors, social distancing measures, and fatalism. In total, data from an online survey of 813 adults were analyzed. The results of multiple regression analysis showed a strong effect of self-efficacy on danger control (ß = 0.23 for personal hygiene behaviors, β = 0.26 for social distancing) and fear control responses (ß = -0.13 for fatalism). However, based on the type of control response, the effect of perceived susceptibility and perceived severity, which were the main factors in threat appraisal, was insignificant or marginally significant. Further, a higher perceived severity was associated with higher fatalism in the fear control response (ß = 0.09). Those who were currently employed performed fewer social distancing measures compared to those who did not (ß = -0.11), whereas there was no difference in personal hygiene behaviors. These results suggest that risk communication in emerging infectious disease crises should provide customized information on people who are hard to comply with social distancing. Besides delivering the message of self-efficacy, policies should be implemented to create a social environment in which individuals can practice social distancing without constraints.One of the most difficult sensorimotor behaviors exhibited by flying animals is the ability to track another flying animal based on its sound emissions. From insects to mammals, animals display this ability in order to localize and track conspecifics, mate or prey. The pursuing individual must overcome multiple non-trivial challenges including the detection of the sounds emitted by the target, matching the input received by its (mostly) two sensors, localizing the direction of the sound target in real time and then pursuing it. All this has to be done rapidly as the target is constantly moving. In this project, we set to mimic this ability using a physical bio-mimetic autonomous drone. We equipped a miniature commercial drone with our in-house 2D sound localization electronic circuit which uses two microphones (mimicking biological ears) to localize sound signals in real-time and steer the drone in the horizontal plane accordingly. We focus on bat signals because bats are known to eavesdrop on conspecifics and follow them, but our approach could be generalized to other biological signals and other man-made signals. Using two different experiments, we show that our fully autonomous aviator can track the position of a moving sound emitting target and pursue it in real-time. Building an actual robotic-agent, forced us to deal with real-life difficulties which also challenge animals. We thus discuss the similarities and differences between our and the biological approach.Onchocercidae nematodes are heteroxenous parasites with worldwide distribution, and some of the species associated to animals may present zoonotic potential. Climatic changes and anthropic influences on the environment may result in vectors' proliferation, facilitating the spillover to humans and/or non-typical animal hosts. The Iguaçu National Park (PARNA Iguaçu), one of the most important Brazilian natural remanescents of Atlantic rainforest, is strongly affected by human activities such as tourism and agriculture. The complexity of this area is especially characterized by the close nexus between the rich wildlife, humans, and domestic animals, especially domestic dogs. Based on this, this research aimed to diagnose the Onchocercidae nematodes in wild carnivores and domestic dogs in the PARNA Iguaçu and the surrounding areas. For this, we collected 162 samples of seven species of wild carnivores and 225 samples of domestic dogs. The presence of microfilariae in the blood samples was diagnosed by the modifience on forest remnants.The Joint Program Executive Office for Chemical, Biological, Radiological, and Nuclear Defense (JPEO-CBRND) began development of a broad-spectrum antiviral countermeasure against deliberate use of high-consequence viral hemorrhagic fevers (VHFs) in 2016. The effort featured comprehensive preclinical research, including laboratory testing and rapid advancement of lead molecules into nonhuman primate (NHP) models of Ebola virus disease (EVD). Remdesivir (GS-5734, Veklury, Gilead Sciences) was the first small molecule therapeutic to successfully emerge from this effort. Remdesivir is an inhibitor of RNA-dependent RNA polymerase, a viral enzyme that is essential for viral replication. Its robust potency and broad-spectrum antiviral activity against certain RNA viruses including Ebola virus and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) led to its clinical evaluation in randomized, controlled trials (RCTs) in human patients during the 2018 EVD outbreak in the Democratic Republic of the Congo (DRC against highly lethal, infectious agents.
As prevalence decreases in pre-elimination settings, identifying the spatial distribution of remaining infections to target control measures becomes increasingly challenging. By measuring multiple antibody responses indicative of past exposure to different pathogens, integrated serological surveys enable simultaneous characterisation of residual transmission of multiple pathogens.

Here, we combine integrated serological surveys with geostatistical modelling and remote sensing-derived environmental data to estimate the spatial distribution of exposure to multiple diseases in children in Northern Ghana. The study utilised the trachoma surveillance survey platform (cross-sectional two-stage cluster-sampled surveys) to collect information on additional identified diseases at different stages of elimination with minimal additional cost. Geostatistical modelling of serological data allowed identification of areas with high probabilities of recent exposure to diseases of interest, including areas previously unknown to control programmes. We additionally demonstrate how serological surveys can be used to identify areas with exposure to multiple diseases and to prioritise areas with high uncertainty for future surveys. Modelled estimates of cluster-level prevalence were strongly correlated with more operationally feasible metrics of antibody responses.

This study demonstrates the potential of integrated serological surveillance to characterise spatial distributions of exposure to multiple pathogens in low transmission and elimination settings when the probability of detecting infections is low.
This study demonstrates the potential of integrated serological surveillance to characterise spatial distributions of exposure to multiple pathogens in low transmission and elimination settings when the probability of detecting infections is low.Perception is often characterized computationally as an inference process in which uncertain or ambiguous sensory inputs are combined with prior expectations. Although behavioral studies have shown that observers can change their prior expectations in the context of a task, robust neural signatures of task-specific priors have been elusive. Here, we analytically derive such signatures under the general assumption that the responses of sensory neurons encode posterior beliefs that combine sensory inputs with task-specific expectations. Specifically, we derive predictions for the task-dependence of correlated neural variability and decision-related signals in sensory neurons. The qualitative aspects of our results are parameter-free and specific to the statistics of each task. The predictions for correlated variability also differ from predictions of classic feedforward models of sensory processing and are therefore a strong test of theories of hierarchical Bayesian inference in the brain. AG 825 in vitro Importantly, we find that Bayesian learning predicts an increase in so-called "differential correlations" as the observer's internal model learns the stimulus distribution, and the observer's behavioral performance improves. This stands in contrast to classic feedforward encoding/decoding models of sensory processing, since such correlations are fundamentally information-limiting. We find support for our predictions in data from existing neurophysiological studies across a variety of tasks and brain areas. Finally, we show in simulation how measurements of sensory neural responses can reveal information about a subject's internal beliefs about the task. Taken together, our results reinterpret task-dependent sources of neural covariability as signatures of Bayesian inference and provide new insights into their cause and their function.
Recent evidence highlighting high HIV incidence and prevalence in informal settlements suggests that they are environments that foster HIV risk. Given growing urbanization in sub-Saharan Africa, there is a critical need to assess the successes and challenges of implementing HIV testing, prevention and treatment interventions in these contexts.

We randomly selected a household-based sample of 1528 adult men (18-35) and women (18-24) living in 18 randomly selected communities in KZN, South Africa. After the baseline interview, communities were randomized to one of three intervention rollout arms in a stepped wedge design. At approximately 8-month intervals, the Asibonisane Community Responses Program (and in particular the implementation of Stepping Stones, a participatory HIV prevention program focused on strengthening relationships and communication) was rolled at by intervention phase. Using data from this evaluation, we describe levels and trends in HIV testing and treatment during follow-up, and we use fixed effects models to estimate the effects of participation in the program on testing.
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