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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]

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]

Purpose: This study aims to overcome the challenges of the current oral targeted drug delivery system, such as the complex preparation process, poor biocompatibility, and delayed drug release. Methods: Here, a non-covalent polymer hydrogel was prepared using the mechanochemical method, and the solid phase loading of 5-amino salicylic acid (5-ASA) was realized. Results: The results obtained from the thermodynamics study, particle size analysis, and electron microscopy show that chitosan (CS) and sodium alginate (SA) form a pH-sensitive hydrogel under the mechanochemical force and also maintain good stability in aqueous solution. Fluorescent tracers study showed that the pH-sensitive hydrogel could achieve the targeted drug release in the colon and the retention time was over 12 h. Next, in vivo efficacy studies, change in mice body weight, DAI (disease activity index) score, thymus, and spleen index, and the diseased state of the mice colon revealed that the pH-sensitive hydrogel is an improved drug delivery system over 5-ASA API commercial preparations as observed in the efficacy and toxicological studies. Conclusion: This method uses an innovative preparation technology that without the need of cross-linking agent to produce an efficient colon-targeted drug delivery system for the treatment of ulcerative colitis. [ABSTRACT FROM AUTHOR]

Hypoxia-inducible factor 1 alpha (HIF-1α) is closely related to chemoresistance of ovarian cancers. Although it is reported that HIF-1α can be regulated by Sentrin/SUMO-specific protease 1 (SENP1), the effects of SENP1 on HIF-1α is still controversial. In this study, we identified that SENP1 positively regulated the expression of HIF-1α by deSUMOylation and weakened the sensitivity of hypoxic ovarian cancer cells to cisplatin. These results indicate that SENP1 is a positive regulator of HIF-1α and plays a negative role in ovarian cancer chemotherapy. [ABSTRACT FROM AUTHOR]

A bio-enabled, environmentally-friendly, and maximally mild layer-by-layer approach has been developed to surface modify inherently hydrophobic Kapton HN substrates to allow for great printability of both water- and organic solvent-based inks thus facilitating the full-inkjet-printing of flexible electronic devices. Different from the traditional Kapton surface modification approaches which are structure-compromising and use harsh conditions to target, and oxidize and/or remove part of, the surface polyimide of Kapton, the present Kapton surface modification approach targeted the surface electric charges borne by its additive particles, and was not only the first to utilize environmentally-friendly clinical biomolecules to build up a thin film of protamine-heparin complex on Kapton, but also the first to be conducted under minimally destructive and maximally mild conditions. Besides, for electrically charged ink particles, the present surface modification method can enhance the uniformity of the inkjet-printed films by reducing the 'coffee ring effect'. As a proof-of-concept demonstration, reduced graphene oxide-based gas sensors, which were flexible, ultra-lightweight, and miniature-sized, were fully-inkjet-printed on surface modified Kapton HN films and tested for their sensitivity to dimethyl methylphosphonate (a nerve agent simulant). Such fabricated sensors survived a Scotch-tape peel test and were found insensitive to repeated bending to a small 0.5 cm radius. [ABSTRACT FROM AUTHOR]

We identified the Rho GTPase Rnd1 as a candidate metastasis suppressor in basal-like and triple-negative breast cancer through bioinformatics analysis. Depletion of Rnd1 disrupted epithelial adhesion and polarity, induced epithelial-to-mesenchymal transition, and cooperated with deregulated expression of c-Myc or loss of p53 to cause neoplastic conversion. Mechanistic studies revealed that Rnd1 suppresses Ras signalling by activating the GAP domain of Plexin B1, which inhibits Rap1. Rap1 inhibition in turn led to derepression of p120 Ras-GAP, which was able to inhibit Ras. Inactivation of Rnd1 in mammary epithelial cells induced highly undifferentiated and invasive tumours in mice. Conversely, Rnd1 expression inhibited spontaneous and experimental lung colonization in mouse models of metastasis. Genomic studies indicated that gene deletion in combination with epigenetic silencing or, more rarely, point mutation inactivates RND1 in human breast cancer. These results reveal a previously unappreciated mechanism through which Rnd1 restrains activation of Ras-MAPK signalling and breast tumour initiation and progression. [ABSTRACT FROM AUTHOR]