Mesoscale cortical dynamics reflect the interaction of sensory evidence and temporal expectation during perceptual decision-making

How sensory evidence is transformed across multiple brain regions to influence behavior remains poorly understood. We trained mice in a visual change detection task designed to separate the covert antecedents of choices from activity associated with their execution. Wide-field calcium imaging across the dorsal cortex revealed fundamentally different dynamics of activity underlying these processes. Although signals related to execution of choice were widespread, fluctuations in sensory evidence in the absence of overt motor responses triggered a confined activity cascade, beginning with transient modulation of visual cortex and followed by sustained recruitment of the secondary and primary motor cortex. Activation of the motor cortex by sensory evidence was modulated by animals’ expectation of when the stimulus was likely to change. These results reveal distinct activation timescales of specific cortical areas by sensory evidence during decision-making and show that recruitment of the motor cortex depends on the interaction of sensory evidence and temporal expectation

Challenges of future multimedia QoE monitoring for internet service providers

The ever-increasing network traffic and user expectations at reduced cost make the delivery of high Quality of Experience (QoE) for multimedia services more vital than ever in the eyes of Internet Service Providers (ISPs). Real-time quality monitoring, with a focus on the user, has become essential as the first step in cost-effective provisioning of high quality services. With the recent changes in the perception of user privacy, the rising level of application-layer encryption and the introduction and deployment of virtualized networks, QoE monitoring solutions need to be adapted to the fast changing Internet landscape. In this contribution, we provide an overview of state-of-the-art quality monitoring models and probing technologies, and highlight the major challenges ISPs have to face when they want to ensure high service quality for their customers

Antimicrobial activity against oral pathogens and immunomodulatory effects and toxicity of geopropolis produced by the stingless bee Melipona fasciculata Smith

<p>Abstract</p> <p>Background</p> <p>Native bees of the tribe Meliponini produce a distinct kind of propolis called geopropolis. Although many pharmacological activities of propolis have already been demonstrated, little is known about geopropolis, particularly regarding its antimicrobial activity against oral pathogens. The present study aimed at investigating the antimicrobial activity of <it>M. fasciculata </it>geopropolis against oral pathogens, its effects on <it>S. mutans </it>biofilms, and the chemical contents of the extracts. A gel prepared with a geopropolis extract was also analyzed for its activity on <it>S. mutans </it>and its immunotoxicological potential.</p> <p>Methods</p> <p>Antimicrobial activities of three hydroalcoholic extracts (HAEs) of geopropolis, and hexane and chloroform fractions of one extract, were evaluated using the agar diffusion method and the broth dilution technique. Ethanol (70%, v/v) and chlorhexidine (0.12%, w/w) were used as negative and positive controls, respectively. Total phenol and flavonoid concentrations were assayed by spectrophotometry. Immunotoxicity was evaluated in mice by topical application in the oral cavity followed by quantification of biochemical and immunological parameters, and macro-microscopic analysis of animal organs.</p> <p>Results</p> <p>Two extracts, HAE-2 and HAE-3, showed inhibition zones ranging from 9 to 13 mm in diameter for <it>S. mutans </it>and <it>C. albicans</it>, but presented no activity against <it>L</it>. <it>acidophilus</it>. The MBCs for HAE-2 and HAE-3 against <it>S. mutans </it>were 6.25 mg/mL and 12.5 mg/mL, respectively. HAE-2 was fractionated, and its chloroform fraction had an MBC of 14.57 mg/mL. HAE-2 also exhibited bactericidal effects on <it>S. mutans </it>biofilms after 3 h of treatment. Significant differences (p < 0.05) in total phenol and flavonoid concentrations were observed among the samples. Signs toxic effects were not observed after application of the geopropolis-based gel, but an increase in the production of IL-4 and IL-10, anti-inflammatory cytokines, was detected.</p> <p>Conclusions</p> <p>In summary, geopropolis produced by <it>M. fasciculata </it>can exert antimicrobial action against <it>S. mutans </it>and <it>C. albicans</it>, with significant inhibitory activity against <it>S. mutans </it>biofilms. The extract with the highest flavonoid concentration, HAE-2, presented the highest antimicrobial activity. In addition, a geopropolis-based gel is not toxic in an animal model and displays anti-inflammatory effect.</p

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Locomotion-dependent remapping of distributed cortical networks

The interactions between neocortical areas are fluid and state-dependent, but how individual neurons couple to cortex-wide network dynamics remains poorly understood. We correlated the spiking of neurons in primary visual (V1) and retrosplenial (RSP) cortex to activity across dorsal cortex, recorded simultaneously by widefield calcium imaging. Neurons were correlated with distinct and reproducible patterns of activity across the cortical surface; while some fired predominantly with their local area, others coupled to activity in distal areas. The extent of distal coupling was predicted by how strongly neurons correlated with the local network. Changes in brain state triggered by locomotion strengthened affiliations of V1 neurons with higher visual and motor areas, while strengthening distal affiliations of RSP neurons with sensory cortices. Thus, the diverse coupling of individual neurons to cortex-wide activity patterns is restructured by running in an area-specific manner, resulting in a shift in the mode of cortical processing during locomotion

BUFFEST : Predicting Buffer Conditions and Real-time Requirements of HTTP(S) Adaptive Streaming Clients

Stalls during video playback are perhaps the most important indicator of a client's viewing experience. To provide the best possible service, a proactive network operator may therefore want to know the buffer conditions of streaming clients and use this information to help avoid stalls due to empty buffers. However, estimation of clients' buffer conditions is complicated by most streaming services being rate-adaptive, and many of them also encrypted. Rate adaptation reduces the correlation between network throughput and client buffer conditions. Usage of HTTPS prevents operators from observing information related to video chunk requests, such as indications of rate adaptation or other HTTP-level information.; AB@This paper presents BUFFEST, a novel classification framework that can be used to classify and predict streaming clients' buffer conditions from both HTTP and HTTPS traffic. To illustrate the tradeoffs between prediction accuracy and the available information used by classifiers, we design and evaluate classifiers of different complexity. At the core of BUFFEST is an event-based buffer emulator module for detailed analysis of clients' buffer levels throughout a streaming session, as well as for automated training and evaluation of online packet-level classifiers. We then present example results using simple threshold-based classifiers and machine learning classifiers that only use TCP/IP packet-level information. Our results are encouraging and show that BUFFEST can distinguish streaming clients with low buffer conditions from clients with significant buffer margin during a session even when HTTPS is used