2,779 research outputs found
Development of a Broad-Spectrum Antiviral Agent with Activity Against Herpesvirus Replication and Gene Expression
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
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
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
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Use of Ex Vivo Normothermic Perfusion for Quality Assessment of Discarded Human Donor Pancreases.
A significant number of pancreases procured for transplantation are deemed unsuitable due to concerns about graft quality and the associated risk of complications. However, this decision is subjective and some declined grafts may be suitable for transplantation. Ex vivo normothermic perfusion (EVNP) prior to transplantation may allow a more objective assessment of graft quality and reduce discard rates. We report ex vivo normothermic perfusion of human pancreases procured but declined for transplantation, with ABO-compatible warm oxygenated packed red blood cells for 1-2 h. Five declined human pancreases were assessed using this technique after a median cold ischemia time of 13 h 19 min. One pancreas, with cold ischemia over 30 h, did not appear viable and was excluded. In the remaining pancreases, blood flow and pH were maintained throughout perfusion. Insulin secretion was observed in all four pancreases, but was lowest in an older donation after cardiac death pancreas. Amylase levels were highest in a gland with significant fat infiltration. This is the first study to assess the perfusion, injury, as measured by amylase, and exocrine function of human pancreases using EVNP and demonstrates the feasibility of the approach, although further refinements are required.This study was financially supported by a grant from the Mason Medical Research Foundation.This is the author accepted manuscript. The final version is available via Wiley at http://onlinelibrary.wiley.com/doi/10.1111/ajt.13303/abstract
Exploring patterns of recurrent melanoma in Northeast Scotland to inform the introduction a digital self-examination intervention
Peer reviewedPublisher PD
A weak characterization of slow variables in stochastic dynamical systems
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
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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A demonstration of 'broken' visual space
It has long been assumed that there is a distorted mapping between real and ‘perceived’ space, based on demonstrations of systematic errors in judgements of slant, curvature, direction and separation. Here, we have applied a direct test to the notion of a coherent visual space. In an immersive virtual environment, participants judged the relative distance of two squares displayed in separate intervals. On some trials, the virtual scene expanded by a factor of four between intervals although, in line with recent results, participants did not report any noticeable change in the scene. We found that there was no consistent depth ordering of objects that can explain the distance matches participants made in this environment (e.g. A > B > D yet also A < C < D) and hence no single one-to-one mapping between participants’ perceived space and any real 3D environment. Instead, factors that affect pairwise comparisons of distances dictate participants’ performance. These data contradict, more directly than previous experiments, the idea that the visual system builds and uses a coherent 3D internal representation of a scene
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