2,830 research outputs found

    Development of a Broad-Spectrum Antiviral Agent with Activity Against Herpesvirus Replication and Gene Expression

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    Purpose: To evaluate the broad-spectrum antiviral activity of peptide H9 (H9) in vitro in order to gain insight into its underlying molecular mechanisms.Method: Antiviral activity against Herpes simplex virus type 1 (HSV-1) was determined using thiazolyl blue (MTT) assay. Polymerase Chain Reaction (PCR) was employed to assay H9 antiviral activity against human cytomegalovirus (HCMV) and Epstein-Barr virus (EBV). The inhibitory effect of H9 on the replication of these viral genes including early genes was assayed by real time-Ppolymerase chain reaction (RT-PCR) and Western blot.Results: H9 possessed significant inhibitory effect on the four different herpesviruses with 50 % inhibitory concentration (IC50) of 1.21 ng/mL (HSV-1). AD169 infection was strongly inhibited with an EC50 value of 0.46 ng/ml. The anti-herpesviral activity of H9 was dose-dependent. The peptide acted primarily during the early stage of infection by detection of the early genes.Conclusion: The results demonstrate that H9 can inhibit the infection of HSV-1, EBV and HCMV. Furthermore, H9 has a broad-spectrum anti-herpesviral effect in vitro based on targeted killing of infected cells expressing genes.Keywords: Antagonist, Trapping receptor/ligand, Broad-spectrum, Anti-herpesvirus, H9 peptide, Gene expressio

    Multisensory causal inference in the brain

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    At any given moment, our brain processes multiple inputs from its different sensory modalities (vision, hearing, touch, etc.). In deciphering this array of sensory information, the brain has to solve two problems: (1) which of the inputs originate from the same object and should be integrated and (2) for the sensations originating from the same object, how best to integrate them. Recent behavioural studies suggest that the human brain solves these problems using optimal probabilistic inference, known as Bayesian causal inference. However, how and where the underlying computations are carried out in the brain have remained unknown. By combining neuroimaging-based decoding techniques and computational modelling of behavioural data, a new study now sheds light on how multisensory causal inference maps onto specific brain areas. The results suggest that the complexity of neural computations increases along the visual hierarchy and link specific components of the causal inference process with specific visual and parietal regions

    Emotions and Digital Well-being. The rationalistic bias of social media design in online deliberations

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    In this chapter we argue that emotions are mediated in an incomplete way in online social media because of the heavy reliance on textual messages which fosters a rationalistic bias and an inclination towards less nuanced emotional expressions. This incompleteness can happen either by obscuring emotions, showing less than the original intensity, misinterpreting emotions, or eliciting emotions without feedback and context. Online interactions and deliberations tend to contribute rather than overcome stalemates and informational bubbles, partially due to prevalence of anti-social emotions. It is tempting to see emotions as being the cause of the problem of online verbal aggression and bullying. However, we argue that social media are actually designed in a predominantly rationalistic way, because of the reliance on text-based communication, thereby filtering out social emotions and leaving space for easily expressed antisocial emotions. Based on research on emotions that sees these as key ingredients to moral interaction and deliberation, as well as on research on text-based versus non-verbal communication, we propose a richer understanding of emotions, requiring different designs of online deliberation platforms. We propose that such designs should move from text-centred designs and should find ways to incorporate the complete expression of the full range of human emotions so that these can play a constructive role in online deliberations

    A weak characterization of slow variables in stochastic dynamical systems

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    We present a novel characterization of slow variables for continuous Markov processes that provably preserve the slow timescales. These slow variables are known as reaction coordinates in molecular dynamical applications, where they play a key role in system analysis and coarse graining. The defining characteristics of these slow variables is that they parametrize a so-called transition manifold, a low-dimensional manifold in a certain density function space that emerges with progressive equilibration of the system's fast variables. The existence of said manifold was previously predicted for certain classes of metastable and slow-fast systems. However, in the original work, the existence of the manifold hinges on the pointwise convergence of the system's transition density functions towards it. We show in this work that a convergence in average with respect to the system's stationary measure is sufficient to yield reaction coordinates with the same key qualities. This allows one to accurately predict the timescale preservation in systems where the old theory is not applicable or would give overly pessimistic results. Moreover, the new characterization is still constructive, in that it allows for the algorithmic identification of a good slow variable. The improved characterization, the error prediction and the variable construction are demonstrated by a small metastable system

    Reducing bias in auditory duration reproduction by integrating the reproduced signal

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    Duration estimation is known to be far from veridical and to differ for sensory estimates and motor reproduction. To investigate how these differential estimates are integrated for estimating or reproducing a duration and to examine sensorimotor biases in duration comparison and reproduction tasks, we compared estimation biases and variances among three different duration estimation tasks: perceptual comparison, motor reproduction, and auditory reproduction (i.e. a combined perceptual-motor task). We found consistent overestimation in both motor and perceptual-motor auditory reproduction tasks, and the least overestimation in the comparison task. More interestingly, compared to pure motor reproduction, the overestimation bias was reduced in the auditory reproduction task, due to the additional reproduced auditory signal. We further manipulated the signal-to-noise ratio (SNR) in the feedback/comparison tones to examine the changes in estimation biases and variances. Considering perceptual and motor biases as two independent components, we applied the reliability-based model, which successfully predicted the biases in auditory reproduction. Our findings thus provide behavioral evidence of how the brain combines motor and perceptual information together to reduce duration estimation biases and improve estimation reliability
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