Optimized H.264 Video Encoding and Packetization for Video Transmission Over Pipeline Forwarding Networks

Previous works showed that the quality-of-service (QoS) requirements of multimedia applications can be optimally satisfied by pipeline forwarding (PF) by providing end-to-end delay guarantees as well as high network resource utilization. However, the unavoidable mismatch between reserved resources and the unpredictable traffic profile of a video stream has an impact on the resulting application layer quality. Therefore, a new low-complexity H.264 video encoding and packetization scheme based on a distortion-optimized macroblock grouping technique is designed here to maximize the performance of video transmission on PF networks. The scheme considers the perceptual importance of the different parts of the video data to group the most important information in few packets that are the natural candidates to receive the deterministic service provided by PF. Results show peak signal-to-noise ratio (PSNR) gains up to 2.5 dB over traditional video encoding and packetization schemes, as well as more graceful degradation in case of high network load.     

Molecular Pathogenesis of Glioblastoma in Adults and Future Perspectives: A Systematic Review

Glioblastoma (GBM) is the most common and malignant tumour of the central nervous system. Recent appreciation of the heterogeneity amongst these tumours not only changed the WHO classification approach, but also created the need for developing novel and personalised therapies. This systematic review aims to highlight recent advancements in understanding the molecular pathogenesis of the GBM and discuss related novel treatment targets. A systematic search of the literature in the PubMed library was performed following the PRISMA guidelines for molecular pathogenesis and therapeutic advances. Original and meta-analyses studies from the last ten years were reviewed using pre-determined search terms. The results included articles relevant to GBM development focusing on the aberrancy in cell signaling pathways and intracellular events. Theragnostic targets and vaccination to treat GBM were also explored. The molecular pathophysiology of GBM is complex. Our systematic review suggests targeting therapy at the stemness, p53 mediated pathways and immune modulation. Exciting novel immune therapy involving dendritic cell vaccines, B-cell vaccines and viral vectors may be the future of treating GBM.     

Content-adaptive traffic prioritization of spatio-temporal scalable video for robust communications over QoS-provisioned 802.11e networks

In this work a low complexity traffic prioritization strategy to transmit H.264 scalable video coding (SVC) video over 802.11e wireless networks is presented (the approach applies to any DiffServ-like network). The distinguishing feature of the proposed strategy is the ability to adapt the amount of error protection to the changing characteristics of the video content. First, we estimate the perceptual impact of data losses in the different types of enhancement layers, i.e., temporal or spatial, for a large set of H.264/SVC videos. The experiments show that perceptual impairments are highly correlated with the level of motion activity in the video sequence. Then, we propose a content-adaptive traffic prioritization strategy based on the identification of the most important parts of the enhancement layers of the video sequence by means of a low complexity macroblock analysis process. The prioritization algorithm is tested by simulating a realistic 802.11e ad hoc network scenario. Simulation results show that the proposed traffic prioritization strategy consistently outperforms, particularly for dynamic video sequences, fixed a priori approaches, as well as a traditional FEC-based UEP strategy.     

Diet, Helicobacter pylori, and p53 mutations in gastric cancer: a molecular epidemiology study in Italy

A series of 105 gastric cancer (GC) cases with paraffin-embedded specimens interviewed in a previous population-based case-control study conducted in a high-risk area around Florence, Italy, was examined for the presence of p53 mutations. Overall, 33 of 105 cases had a mutation (p53+) identified by single-strand conformational polymorphism and confirmed by sequencing (Y-H. Shiao et al., submitted for publication). p53+ cases had a more traditional dietary pattern (i.e., corn meal mush, meat soup, and other homemade dishes) and reported less frequent consumption of raw vegetables (particularly lettuce and raw carrots). A positive association with a high nitrite intake and a negative association with raw vegetables and diffuse type histology persisted in a multivariate analysis. In addition, p53+ cases tended to be located in the upper portion of the stomach and to be associated with advanced age and blood group A. No relation was found between the presence of p53 mutations and histologically defined Helicobacter pylori infection, smoking history, family history of gastric cancer, education, and social class. Of the 33 p53+ cases, 19 had G:C-->A:T transitions at CpG sites. These tumors tended to occur in females and in association with H. pylori infection but not other risk factors. The remaining 14 cases with a p53 mutation had mainly transversions but also two deletions and two transitions at non-CpG sites. These tumors showed a strong positive association with a traditional dietary pattern and with the estimated intake of selected nutrients (nitrite, protein, and fat, particularly from animal sources). The findings of this case-case analysis suggest that p53 mutations at non-CpG sites are related to exposure to alkylating compounds from diet, whereas p53 mutations at CpG sites might be related to H. pylori infection.     

2D recurrent neural networks: a high-performance tool for robust visual tracking in dynamic scenes

This paper proposes a novel method for robust visual tracking of arbitrary objects, based on the combination of image-based prediction and position refinement by weighted correlation. The effectiveness of the proposed approach is demonstrated on a challenging set of dynamic video sequences, extracted from the final of triple jump at the London 2012 Summer Olympics. A comparison is made against five baseline tracking systems. The novel system shows remarkable superior performances with respect to the other methods, in all considered cases characterized by changing background, and a large variety of articulated motions. The novel architecture, from here onward named 2D Recurrent Neural Network (2D-RNN), is derived from the well-known recurrent neural network model and adopts nearest neighborhood connections between the input and context layers in order to store the temporal information content of the video. Starting from the selection of the object of interest in the first frame, neural computation is applied to predict the position of the target in each video frame. Normalized cross-correlation is then applied to refine the predicted target position. 2D-RNN ensures limited complexity, great adaptability and a very fast learning time. At the same time, it shows on the considered dataset fast execution times and very good accuracy, making this approach an excellent candidate for automated analysis of complex video streams.

     

Modeling and estimating the subjects’ diversity of opinions in video quality assessment: a neural network based approach

Subjective experiments are considered the most reliable way to assess the perceived visual quality. However, observers’ opinions are characterized by large diversity: in fact, even the same observer is often not able to exactly repeat his first opinion when rating again a given stimulus. This makes the Mean Opinion Score (MOS) alone, in many cases, not sufficient to get accurate information about the perceived visual quality. To this aim, it is important to have a measure characterizing to what extent the observed or predicted MOS value is reliable and stable. For instance, the Standard deviation of the Opinions of the Subjects (SOS) could be considered as a measure of reliability when evaluating the quality subjectively. However, we are not aware of the existence of models or algorithms that allow to objectively predict how much diversity would be observed in subjects’ opinions in terms of SOS. In this work we observe, on the basis of a statistical analysis made on several subjective experiments, that the disagreement between the quality as measured by means of different objective video quality metrics (VQMs) can provide information on the diversity of the observers’ ratings on a given processed video sequence (PVS). In light of this observation we: i) propose and validate a model for the SOS observed in a subjective experiment; ii) design and train Neural Networks (NNs) that predict the average diversity that would be observed among the subjects’ ratings for a PVS starting from a set of VQMs values computed on such a PVS; iii) give insights into how the same NN based approach can be used to identify potential anomalies in the data collected in subjective experiments.