The molecular pathology of genioglossus in obstructive sleep apnea

Obstructive sleep apnea (OSA) is a sleep respiratory disease characterized by sleep snoring accompanied by apnea and daytime sleeplessness. It is a complex disease, with the multifactorial etiology, and the pathology is incompletely understood. Genioglossus (GG), the largest dilator of upper airway, whose fatigue is strongly correlated to onset of OSA. This brief review was to investigate the pathogenesis of OSA targeting on GG from different risk factors as gender, obesity, and aging, and the molecular mechanism of GG injury in OSA pathogenesis. We hope to find the targeted molecular mechanism on GG in OSA treatment

Democracy and economic growth: a perspective of cooperation

Does democracy cause higher economic growth? We build a model taking culture and interpersonal cooperation into account and find that democracy increases economic productivity through giving people more equal rights, which allows people to build a larger interpersonal network so that they can reduce investment risk and employ high-productivity (high-risk) methods in production

The management of obstructive sleep apnea accompanied with mandibular retrognathia across the lifespan from orthodontic perspective

Obstructive sleep apnea (OSA) is a complex disease with complex etiology, which requires multidisciplinary cooperation in diagnosis and treatment. Mandibular retrognathia is strongly associated with OSA. Orthodontists can either correct the mandibular retrognathia of pediatric OSA via various kinds of orthodontic appliances, following adenoidectomy and tonsillectomy, or enlarge upper airway by mandibular advancement device (MAD) through repositioning the mandible and tongue of adult OSA patients. This mini review was to investigate the therapy of MAD to adult OSA as well as orthodontic treatment to pediatric OSA

Emotional Synchrony and Viewers’ Consumption: Evidence from Live Streaming of Virtual Idols

Live streaming has become increasingly popular in recent years for users to broadcast live events or experiences to an online audience in real-time. With the prevalence of the metaverse, virtual idols appear on the stage of live streaming. Unlike real human streamers, virtual idols are computer-generated avatars whose appearance does not exist in the physical world. Though existing literature has documented the effect of emotional displays of human streamers on viewers’ behavior, whether emotional displays of avatar streamers affect viewers’ behavior, especially viewers’ consumption behavior has not yet been explored. To fill this gap, we focus on the live streaming of virtual idols and conduct deep learning approaches to investigate the emotional synchrony between avatar streamers and their viewers based on moment-to-moment data. Our analysis of large-scale video data shows that the larger the discrepancy between the streamers’ acoustic- and text- emotions, the more likely the viewers will pay. This study sheds light on how avatars can be designed to leverage emotion to engage viewers in more consumption

Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic analysis using traditional methods (e.g., through classical machine-learning models) is much less effective under those settings, as the features picked manually are not distinctive any more. In this work, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue

Molecular Hybridization Used to Design and Synthesize Neo-tanshinlactone Derivatives as PD-1/PD-L1 Inhibitors

The overall goals of this research are to design and synthesize novel PD-1/PD-L1 interaction inhibitors with a neo-tanshinlactone backbone, to elucidate their structure-activityrelationship (SAR) correlations, to evaluate their inhibition of the PD-1/PD-L1 interaction, to compare their T cell cytotoxicity and T cell activation effects with positive control BMS-202, and to explore their anti-tumor effects in animal experiments.Neo-tanshinlactone (NTL), a natural product extracted from Salvia miltiorrhiza Bunge, and its analog 4-ethyl NTL were potent and selective against ER+ and/or HER2+ breast cancer. (in vitro anticancer SRB assay against MCF-7, ZR-751, and SK-BR-3) Previous development of ring-opened derivatives, especially D-ring opened derivatives, resulted in nanomolar activity against widespread cancer cell lines. The effectiveness against the MDA-MB-231 cell line, a triple negative breast cancer cell line, led to the continued design of D-ring opened derivatives, based on molecular targets that are specific to this cell line. Among all possible targets, the PD-1/PD-L1 checkpoint was chosen due to the potential of small molecule inhibitors and the limitations of the currently used antibody drugs. Designed by using molecular hybridization, four series of compounds (37 final products) were prepared and then screened with a homogenous time-resolved fluorescence (HTRF) method, resulting in three lead compounds (MZ52 IC50 74±4 nM; MZ58 IC50 134±17 ivnM; MZ61 IC50 225±19 nM) that were further evaluated. With the lowest T cell cytotoxicity coupled with the ability to activate CD8+ T cells in a T cell proliferation assay and a T cell functionality experiment, MZ58 emerged as the best candidate for animal experiments. In a subcutaneous transplantation tumor model, MZ58 exhibited better antitumor effects compared to those of positive control BMS-202 in preventing tumor growth. Additionally, MZ58 demonstrated significant effects in reducing T cell exhaustion. In conclusion, we hybridized the NTL backbone into a PD-1/PD-L1 interaction inhibitor design and, after in vivo and in vitro experiments, successfully acquired an effective candidate (MZ58) showing potent nanomole activity against PD-1/PD-L1 interaction and similarantitumor effects with low cytotoxicity toward T cells as well as the ability to activate CD8+ T cells and reduce T cell exhaustion, in comparison with positive control BMS-202.Doctor of Philosoph

Five-S-isotope evidence of two distinct mass-independent sulfur isotope effects and implications for the modern and Archean atmospheres.

The signature of mass-independent fractionation of quadruple sulfur stable isotopes (S-MIF) in Archean rocks, ice cores, and Martian meteorites provides a unique probe of the oxygen and sulfur cycles in the terrestrial and Martian paleoatmospheres. Its mechanistic origin, however, contains some uncertainties. Even for the modern atmosphere, the primary mechanism responsible for the S-MIF observed in nearly all tropospheric sulfates has not been identified. Here we present high-sensitivity measurements of a fifth sulfur isotope, stratospherically produced radiosulfur, along with all four stable sulfur isotopes in the same sulfate aerosols and a suite of chemical species to define sources and mechanisms on a field observational basis. The five-sulfur-isotope and multiple chemical species analysis approach provides strong evidence that S-MIF signatures in tropospheric sulfates are concomitantly affected by two distinct processes: an altitude-dependent positive 33S anomaly, likely linked to stratospheric SO2 photolysis, and a negative 36S anomaly mainly associated with combustion. Our quadruple stable sulfur isotopic measurements in varying coal samples (formed in the Carboniferous, Permian, and Triassic periods) and in SO2 emitted from combustion display normal 33S and 36S, indicating that the observed negative 36S anomalies originate from a previously unknown S-MIF mechanism during combustion (likely recombination reactions) instead of coal itself. The basic chemical physics of S-MIF in both photolytic and thermal reactions and their interplay, which were not explored together in the past, may be another ingredient for providing deeper understanding of the evolution of Earth's atmosphere and life's origin

Explainable Recommender with Geometric Information Bottleneck

Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanation generation or leverage the attention mechanism to extract important text spans from reviews as explanations. The extracted rationales are often confined to an individual review and may fail to identify the implicit features beyond the review text. To avoid the expensive human annotation process and to generate explanations beyond individual reviews, we propose to incorporate a geometric prior learnt from user-item interactions into a variational network which infers latent factors from user-item reviews. The latent factors from an individual user-item pair can be used for both recommendation and explanation generation, which naturally inherit the global characteristics encoded in the prior knowledge. Experimental results on three e-commerce datasets show that our model significantly improves the interpretability of a variational recommender using the Wasserstein distance while achieving performance comparable to existing content-based recommender systems in terms of recommendation behaviours