A regression method for real-time video quality evaluation

No-Reference (NR) metrics provide a mechanism to assess video quality in an ever-growing wireless network. Their low computational complexity and functional characteristics make them the primary choice when it comes to realtime content management and mobile streaming control. Unfortunately, common NR metrics suer from poor accuracy, particularly in network-impaired video streams. In this work, we introduce a regression-based video quality metric that is simple enough for real-time computation on thin clients, and comparably as accurate as state-of-the-art Full-Reference (FR) metrics, which are functionally and computationally inviable in real-time streaming. We benchmark our metric against the FR metric VQM (Video Quality Metric), finding a very strong correlation factor

Cognitive impairment and World Trade Centre-related exposures

On 11 September 2001 the World Trade Center (WTC) in New York was attacked by terrorists, causing the collapse of multiple buildings including the iconic 110-story ‘Twin Towers’. Thousands of people died that day from the collapse of the buildings, fires, falling from the buildings, falling debris, or other related accidents. Survivors of the attacks, those who worked in search and rescue during and after the buildings collapsed, and those working in recovery and clean-up operations were exposed to severe psychological stressors. Concurrently, these ‘WTC-affected’ individuals breathed and ingested a mixture of organic and particulate neurotoxins and pro-inflammogens generated as a result of the attack and building collapse. Twenty years later, researchers have documented neurocognitive and motor dysfunctions that resemble the typical features of neurodegenerative disease in some WTC responders at midlife. Cortical atrophy, which usually manifests later in life, has also been observed in this population. Evidence indicates that neurocognitive symptoms and corresponding brain atrophy are associated with both physical exposures at the WTC and chronic post-traumatic stress disorder, including regularly re-experiencing traumatic memories of the events while awake or during sleep. Despite these findings, little is understood about the long-term effects of these physical and mental exposures on the brain health of WTC-affected individuals, and the potential for neurocognitive disorders. Here, we review the existing evidence concerning neurological outcomes in WTC-affected individuals, with the aim of contextualizing this research for policymakers, researchers and clinicians and educating WTC-affected individuals and their friends and families. We conclude by providing a rationale and recommendations for monitoring the neurological health of WTC-affected individuals

Solving the chemical master equation using sliding windows

<p>Abstract</p> <p>Background</p> <p>The chemical master equation (CME) is a system of ordinary differential equations that describes the evolution of a network of chemical reactions as a stochastic process. Its solution yields the probability density vector of the system at each point in time. Solving the CME numerically is in many cases computationally expensive or even infeasible as the number of reachable states can be very large or infinite. We introduce the sliding window method, which computes an approximate solution of the CME by performing a sequence of local analysis steps. In each step, only a manageable subset of states is considered, representing a "window" into the state space. In subsequent steps, the window follows the direction in which the probability mass moves, until the time period of interest has elapsed. We construct the window based on a deterministic approximation of the future behavior of the system by estimating upper and lower bounds on the populations of the chemical species.</p> <p>Results</p> <p>In order to show the effectiveness of our approach, we apply it to several examples previously described in the literature. The experimental results show that the proposed method speeds up the analysis considerably, compared to a global analysis, while still providing high accuracy.</p> <p>Conclusions</p> <p>The sliding window method is a novel approach to address the performance problems of numerical algorithms for the solution of the chemical master equation. The method efficiently approximates the probability distributions at the time points of interest for a variety of chemically reacting systems, including systems for which no upper bound on the population sizes of the chemical species is known a priori.</p

Hybrid video quality prediction: reviewing video quality measurement for widening application scope

A tremendous number of objective video quality measurement algorithms have been developed during the last two decades. Most of them either measure a very limited aspect of the perceived video quality or they measure broad ranges of quality with limited prediction accuracy. This paper lists several perceptual artifacts that may be computationally measured in an isolated algorithm and some of the modeling approaches that have been proposed to predict the resulting quality from those algorithms. These algorithms usually have a very limited application scope but have been verified carefully. The paper continues with a review of some standardized and well-known video quality measurement algorithms that are meant for a wide range of applications, thus have a larger scope. Their individual artifacts prediction accuracy is usually lower but some of them were validated to perform sufficiently well for standardization. Several difficulties and shortcomings in developing a general purpose model with high prediction performance are identified such as a common objective quality scale or the behavior of individual indicators when confronted with stimuli that are out of their prediction scope. The paper concludes with a systematic framework approach to tackle the development of a hybrid video quality measurement in a joint research collaboration.Polish National Centre for Research and Development (NCRD) SP/I/1/77065/10, Swedish Governmental Agency for Innovation Systems (Vinnova

Adrenal function recovery after durable oral corticosteroid sparing with benralizumab in the PONENTE study

Background Oral corticosteroid (OCS) dependence among patients with severe eosinophilic asthma can cause adverse outcomes, including adrenal insufficiency. PONENTE's OCS reduction phase showed that, following benralizumab initiation, 91.5% of patients eliminated corticosteroids or achieved a final dosage ≤5 mg·day-1 (median (range) 0.0 (0.0-40.0) mg). Methods The maintenance phase assessed the durability of corticosteroid reduction and further adrenal function recovery. For ~6 months, patients continued benralizumab 30 mg every 8 weeks without corticosteroids or with the final dosage achieved during the reduction phase. Investigators could prescribe corticosteroids for asthma exacerbations or increase daily dosages for asthma control deteriorations. Outcomes included changes in daily OCS dosage, Asthma Control Questionnaire (ACQ)-6 and St George's Respiratory Questionnaire (SGRQ), as well as adrenal status, asthma exacerbations and adverse events. Results 598 patients entered PONENTE; 563 (94.1%) completed the reduction phase and entered the maintenance phase. From the end of reduction to the end of maintenance, the median (range) OCS dosage was unchanged (0.0 (0.0-40.0) mg), 3.2% (n=18/563) of patients experienced daily dosage increases, the mean ACQ-6 score decreased from 1.26 to 1.18 and 84.5% (n=476/563) of patients were exacerbation free. The mean SGRQ improvement (-19.65 points) from baseline to the end of maintenance indicated substantial quality-of-life improvements. Of patients entering the maintenance phase with adrenal insufficiency, 32.4% (n=104/321) demonstrated an improvement in adrenal function. Adverse events were consistent with previous reports. Conclusions Most patients successfully maintained maximal OCS reduction while achieving improved asthma control with few exacerbations and maintaining or recovering adrenal function

Helicobacter pylori CagA Triggers Expression of the Bactericidal Lectin REG3γ via Gastric STAT3 Activation

Background: Most of what is known about the Helicobacter pylori (H. pylori) cytotoxin, CagA, pertains to a much-vaunted role as a determinant of gastric inflammation and cancer. Little attention has been devoted to potential roles of CagA in the majority of H. pylori infected individuals not showing oncogenic progression, particularly in relation to host tolerance. Regenerating islet-derived (REG)3c encodes a secreted C-type lectin that exerts direct bactericidal activity against Grampositive bacteria in the intestine. Here, we extend this paradigm of lectin-mediated innate immunity, showing that REG3c expression is triggered by CagA in the H. pylori-infected stomach. Methodology/Principal Findings: In human gastric mucosal tissues, REG3c expression was significantly increased in CagApositive, compared to CagA-negative H. pylori infected individuals. Using transfected CagA-inducible gastric MKN28 cells, we recapitulated REG3c induction in vitro, also showing that tyrosine phosphorylated, not unphosphorylated CagA triggers REG3c transcription. In concert with induced REG3c, pro-inflammatory signalling downstream of the gp130 cytokine coreceptor via the signal transducer and activator of transcription (STAT)3 and transcription of two cognate ligands, interleukin(IL)-11 and IL-6, were significantly increased. Exogenous IL-11, but not IL-6, directly stimulated STAT3 activation and REG3c transcription. STAT3 siRNA knockdown or IL-11 receptor blockade respectively abrogated or subdued CagAdependent REG3c mRNA induction, thus demonstrating a requirement for uncompromised signalling via the IL-11/STAT

Precise parameter synthesis for stochastic biochemical systems

We consider the problem of synthesising rate parameters for stochastic biochemical networks so that a given time-bounded CSL property is guaranteed to hold, or, in the case of quantitative properties, the probability of satisfying the property is maximised or minimised. Our method is based on extending CSL model checking and standard uniformisation to parametric models, in order to compute safe bounds on the satisfaction probability of the property. We develop synthesis algorithms that yield answers that are precise to within an arbitrarily small tolerance value. The algorithms combine the computation of probability bounds with the refinement and sampling of the parameter space. Our methods are precise and efficient, and improve on existing approximate techniques that employ discretisation and refinement. We evaluate the usefulness of the methods by synthesising rates for three biologically motivated case studies: infection control for a SIR epidemic model; reliability analysis of molecular computation by a DNA walker; and bistability in the gene regulation of the mammalian cell cycle