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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]

Polycystic ovary syndrome (PCOS), a common reproductive endocrine disease, has clinically heterogeneous characteristics. Recently, cuproptosis causes several diseases by killing cells. Hence, we aimed to explore cuproptosis-related molecular clusters in PCOS and construct a prediction model. Based on the GSE5090, GSE43264, GSE98421, and GSE124226 datasets, an analysis of cuproptosis regulators and immune features in PCOS was conducted. In 25 cases of PCOS, the molecular clusters of cuproptosis-related genes and the immune cell infiltration associated with PCOS were investigated. Weighted gene co-expression network analysis was used to identify differentially expressed genes within clusters. Next, we compared the performance of the random forest model, support vector machine model, generalized linear model, and eXtreme Gradient Boosting for deciding the optimum machine model. Validation of the predictive effectiveness was accomplished through nomogram, calibration curve, decision curve analysis, and using other two datasets. PCOS and non-PCOS controls differed in the dysregulation of cuproptosis-related genes and the activation of immunoreaction. Two cuproptosis-related molecular clusters associated with PCOS were identified. Significant heterogeneity was noted in immunity between the two clusters based on the analysis of immune infiltration. The immune-related pathways related to cluster-specific differentially expressed genes in Cluster1 were revealed by functional analysis. With a relatively low residual error and root mean square error and a higher area under the curve (1.000), the support vector machine model demonstrated optimal discriminative performance. An ultimate 5-gene-based support vector machine model was noted to perform satisfactorily in the other two validation datasets (area under the curve = 1.000 for both). Moreover, the nomogram, calibration curve, and decision curve analysis showed that PCOS subtypes can be accurately predicted. Our study results helped demonstrate a comprehensive understanding of the complex relationship between cuproptosis and PCOS and establish a promising prediction model for assessing the risk of cuproptosis in patients with PCOS. [ABSTRACT FROM AUTHOR]

Human epididymis protein 4 (HE4) is originally described as an epididymis specific protein and now clinically used as a serum marker for ovarian carcinoma. However, the expression of HE4 in neuroendocrine neoplasms (NENs) has not been studied. By immunohistochemistry, the expressions of HE4 in 94 normal tissues and 484 NENs which included 242 well-differentiated NENs and 242 poorly differentiated NENs were studied. HE4 was positive in 90/94 (95.7%) of the neuroendocrine cells in normal tissues, 228/242 (94.2%) of well-differentiated NENs, and 206/242 (85.1%) of poorly differentiated NENs, and the expression of HE4 decreased progressively with loss of histological differentiation, with the positive rate of 96.2%, 92.7%, 92.3%, 85.4%, and 84.4% in NET-G1/carcinoid, NET-G2/atypical carcinoid, NET-G3, NEC-LC, and NEC-SC respectively. In NET-G1 and NET-G2, HE4 staining showed a peculiar polarized distribution, with an extraordinarily strong granular staining in subnuclear cytoplasm. A diffuse and uniform cytoplastic HE4 staining was observed in NET-G3 and poorly differentiated NENs. The positive rate of HE4 in primary tumors (91.1%, 387/425) was significantly higher than that of metastases (79.7%, 47/59) (p < 0.05). In a series of 70 pure non-NENs poorly differentiated carcinomas, the specificity rate of HE4 was 92.9% (65/70), which was in line with that of Syn. The negative rate of HE4 was 87.0% (40/46) in the non-neuroendocrine components of the MiNEN cases, which was lower than that of the pure non-neuroendocrine carcinomas (92.9%, 65/70) but without statistical significance (p > 0.05). HE4 may prove to be a useful immunohistochemical marker of neuroendocrine differentiation, although comparative studies and a more extensive analysis of other tissue types are necessary. [ABSTRACT FROM AUTHOR]