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Viewport-dependent streaming (VDS) of immersive video typically devises the attentive viewport (or FoV - Field of View) with high-quality compression but low-quality compressed content outside of it to reduce bandwidth. It, however, assumes uniform compression within the viewport, completely neglecting visual redundancy caused by non-uniform perception in central and peripheral vision areas when consuming the content using a head-mounted display (HMD). Our work models the unequal retinal perception within the instantaneous viewport and explores using it in the VDS system for non-uniform viewport compression to further save the data volume. To this end, we assess the just-noticeable-distortion moment of the rendered viewport frame by carefully adapting image quality-related compression factors like quantization stepsize q and/or spatial resolution s zone-by-zone to explicitly derive the imperceptible quality perception threshold with respect to the eccentric angle. Independent validations show that the visual perception of the immersive images with non-uniform FoV quality guided by our model is indistinguishable from that of images with default uniform FoV quality. Our model can be flexibly integrated with the tiling strategy in popular video codecs to facilitate non-uniform viewport compression in practical VDS systems for significant bandwidth reduction (e.g., about 40% reported in our experiments) at similar visual quality. [ABSTRACT FROM AUTHOR]

Endometriosis (EMT) is a prevalent gynecological disorder characterized by pain and infertility associated with the menstrual cycle. Pyroptosis, an emerging cell death mechanism, has been implicated in the pathogenesis of diverse diseases, highlighting its pivotal role in disease progression. Therefore, our study aimed to investigate the impact of pyroptosis in EMT using a comprehensive bioinformatics approach. We initially obtained two datasets from the Gene Expression Omnibus database and performed differential expression analysis to identify pyroptosis-related genes (PRGs) that were differentially expressed between EMT and non-EMT samples. Subsequently, several machine learning algorithms, namely least absolute shrinkage selection operator regression, support vector machine-recursive feature elimination, and random forest algorithms were used to identify a hub gene to construct an effective diagnostic model for EMT. Receiver operating characteristic curve analysis, nomogram, calibration curve, and decision curve analysis were applied to validate the performance of the model. Based on the selected hub gene, differential expression analysis between high- and low-expression groups was conducted to explore the functions and signaling pathways related to it. Additionally, the correlation between the hub gene and immune cells was investigated to gain insights into the immune microenvironment of EMT. Finally, a pyroptosis-related competing endogenous RNA network was constructed to elucidate the regulatory interactions of the hub gene. Our study revealed the potential contribution of a specific PRG to the pathogenesis of EMT, providing a novel perspective for clinical diagnosis and treatment of EMT. [ABSTRACT FROM AUTHOR]

Formaldehyde (HCHO) is a serious hazardous air pollutant and crucial precursor of PM2.5 and ozone compound pollution. There has been a dearth of HCHO research in Tibet where pressing need to protect the unique ecosystem. Therefore, this study aims to investigate the spatial-temporal distribution of HCHO from 2013 to 2021 and identify its influencing factors using satellite observations. Our findings reveal a noteworthy annual growth rate of 2.25% yr−1 in HCHO vertical column density (VCD) in Tibet. This rate is comparable to that in India and even surpasses levels observed in many other regions worldwide, including eastern China. Furthermore, unlike other areas, the eastern region of Tibet exhibits no discernible seasonal pattern in HCHO VCD. The anomalous variation in HCHO concentrations in Tibet can primarily be attributed to long-distance transnational transport originating from incomplete combustion in India Assam. Our research underscores the urgent need for enhanced atmospheric environmental management in Tibet. [ABSTRACT FROM AUTHOR]

Spacer acquisition step in CRISPR-Cas system involves the recognition and subsequent integration of protospacer by the Cas1-Cas2 complex in CRISPR-Cas systems. Here we report an anti-CRISPR protein, AcrVA5, and reveal the mechanisms by which it strongly inhibits protospacer integration. Our biochemical data shows that the integration by Cas1-Cas2 was abrogated in the presence of AcrVA5. AcrVA5 exhibits low binding affinity towards Cas2 and acetylates Cas2 at Lys55 on the binding interface of the Cas2 and AcrVA5 N-terminal peptide complex to inhibit the Cas2-mediated endonuclease activity. Moreover, a detailed structural comparison between our crystal structure and homolog structure shows that binding of AcrVA5 to Cas2 causes steric hindrance to the neighboring protospacer resulting in the partial disassembly of the Cas1-Cas2 and protospacer complex, as demonstrated by electrophoretic mobility shift assay. Our study focuses on this mechanism of spacer acquisition inhibition and provides insights into the biology of CRISPR-Cas systems. Here, the authors characterize an anti-CRISPR protein that prevents protospacer integration by Cas1-Cas2, providing structural insights that may benefit CRISPR-Cas systems research. [ABSTRACT FROM AUTHOR]

Loki is a state-of-the-art adaptive bitrate algorithm for the transmission of real-time-communication (RTC) video. It fuses traditional heuristic methods with a learning-based model to maximize the quality of experience (QoE) under diverse network conditions. However, a recurring rebound pattern is observed in Loki’s decision-making process where the decision frequently oscillates between the two boundaries of the action space, making Loki fail to adapt to the fluctuating network bandwidth. To address this issue, we propose Loki+, which improves both the fusion mechanism and the design of the learning-based actor. Specifically, we replace the element-wise multiplication with a simple but effective trend fusion and further optimize the design of reward and loss functions for training Loki+. Extensive simulation results show that Loki+ significantly improves the QoE in the aspects of reducing the stall rate by 20% ∼ 60% and the frame delay by 3.5% ∼ 30.5% while maintaining a similar sending bitrate or video quality, compared with Loki. [ABSTRACT FROM AUTHOR]