Characterization of intestinal fungal community diversity in people living with HIV/AIDS (PLWHA).

Acquired Immune Deficiency Syndrome (AIDS) is a highly dangerous infectious disease caused by the Human Immunodeficiency Virus (HIV), a virus that attacks the human immune system. To explore the correlation between intestinal fungal community and immune function (Immune cells and inflammatory factors) in people living with HIV/AIDS (PLWHA). The feces and blood samples were collected from two groups of subjects: PLWHA and healthy controls. High-throughput sequencing of the internal transcribed spacer 1, flow cytometry, and ELISA were performed to analyze the differences and correlations between fungal microbiota, cellular immune status and serum inflammatory factors in the two groups. There were significant differences in the composition of fungal microbiota between the two groups. The relative abundance of Candida, Bjerkandera, and Xeromyces in PLWHA was significantly higher than that of healthy volunteers (P < 0.01), while the relative abundance of Mycospaerella, Xeroxysium, Penicillium, and Glomerella in PLWHA was significantly lower than that of healthy volunteers. The correlation analysis results show that Mycospaerella and Xeromyces are significantly positively correlated with CD4+/CD8+ T cells and the anti-inflammatory cytokine IL-4. On the other hand, Candida was positively correlated with pro-inflammatory factors negatively correlated with CD4+/CD8+ T cells and the anti-inflammatory cytokine IL-4, while it is positively correlated with pro-inflammatory cytokines. The significant increase in the relative abundance of Candida may be one of the important causes of intestinal damage in PLWHA. The results of this study contribute to the understanding of the relationship between fungal microbiota structure and immune function in the gut ecology of PLWHA.

A "flexible" compulsory dental basic research course for undergraduate students: a three-year retrospective study.

INTRODUCTION: Since 2015, the Guanghua School of Stomatology has established an elective dental basic research course (EDBRC). To make all students benefit from the dental basic research course without causing excessive academic burden, the "flexible" compulsory dental basic research course (CDBRC) was settled in 2020. This study intends to introduce the "flexible" compulsory teaching module and assessment system of CDBRC, and analyze its effectiveness over 3 years. MATERIALS AND METHODS: The grade point average (GPA), course pressure, level of basic research knowledge and skills, and students' research achievements were collected and analyzed between EDBRC and CDBRC. The unpaired t test was used to analyze the difference. RESULTS: The "flexible" CDBRC has been successfully constructed with compulsory teaching module and hierarchical assessment system. The CDBRC has not caused significant course pressure to students compared with the EDBRC. Besides, the "flexible" CDBRC can improve the students' GPA, basic research knowledge, and research achievements. CONCLUSIONS: The "flexible" CDBRC can improve students' academic performance and basic research abilities without causing significant course pressure, which can be conducted in dental schools with similar backgrounds. TRIAL REGISTRATION: Not applicable.

Assessment of the combined vulnerability to droughts and heatwaves in Shandong Province in summer from 2000 to 2018.

Droughts and heat waves exhibit synergistic effects and are among the world's most costly disasters. To explore the spatiotemporal differences and formation mechanisms of the combined vulnerability to droughts and heat waves in Shandong Province over the past 20 years, a vulnerability scoping diagram (VSD) model with three dimensions-exposure, sensitivity, and adaptability-was constructed to assess and compare the combined vulnerability to high-temperature and drought events, considering economic and social conditions. The results showed that (1) over the past 20 years, heat waves and droughts have increased in Shandong Province. The number of high-temperature events significantly increased in the west and decreased along the eastern coast, and drought change was characterized by an increase in the south and a decrease in the north. (2) The combined exposure to summer droughts and heat waves in Shandong Province showed a significant increasing trend (P < 0.05) at a rate of approximately 0.072/10a; the combined sensitivity significantly decreased (P < 0.05) at a rate of approximately 0.137/10a, and the combined adaptability continued to increase at a rate of approximately 0.481/10a. (3) The combined vulnerability to summer droughts and heat waves in the western inland area of Shandong Province was high and gradually decreased toward the southeastern coast. The overall decrease trend was nonsignificant with a decrease of approximately 0.126/10a, and the decline rate decreased from northwest to southeast, in which Laiwu, Yantai, Jinan, and Zibo cities exhibited a significant decreasing trend (P < 0.05). Although the compound vulnerability of Shandong Province has decreased insignificantly, the frequency of combined drought and heat wave events has increased, and the combined vulnerability will increase in the future.

Effect of symmetry breaking on multi-plasmon-induced transparency based on single-layer graphene metamaterials with strips and rings.

A single-layer graphene metamaterial consisting of a horizontal graphene strip, four vertical graphene strips, and two graphene rings is proposed to realize tunable multi-plasma-induced transparency (MPIT) by the coupled mode theory and the finite-difference time-domain method. A switch with three modulation modes is realized by dynamically adjusting the Fermi level of graphene. Moreover, the effect of symmetry breaking on MPIT is investigated by controlling the geometric parameters of graphene metamaterials. Triple-PIT, dual-PIT, single-PIT can be transformed into each other. The proposed structure and results provide guidance for applications such as designing photoelectric switches and modulators.

Diffusion Probabilistic Modeling for Video Generation.

Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation. This paper showcases their ability to sequentially generate video, surpassing prior methods in perceptual and probabilistic forecasting metrics. We propose an autoregressive, end-to-end optimized video diffusion model inspired by recent advances in neural video compression. The model successively generates future frames by correcting a deterministic next-frame prediction using a stochastic residual generated by an inverse diffusion process. We compare this approach against six baselines on four datasets involving natural and simulation-based videos. We find significant improvements in terms of perceptual quality and probabilistic frame forecasting ability for all datasets.

Dynamic neural reconfiguration for distinct strategies during competitive social interactions.

Information exchange between brain regions is key to understanding information processing for social decision-making, but most analyses ignore its dynamic nature. New insights on this dynamic might help us to uncover the neural correlates of social cognition in the healthy population and also to understand the malfunctioning neural computations underlying dysfunctional social behavior in patients with mental disorders. In this work, we used a multi-round bargaining game to detect switches between distinct bargaining strategies in a cohort of 76 healthy participants. These switches were uncovered by dynamic behavioral modeling using the hidden Markov model. Proposing a novel model of dynamic effective connectivity to estimate the information flow between key brain regions, we found a stronger interaction between the right temporoparietal junction (rTPJ) and the right dorsolateral prefrontal cortex (rDLPFC) for the strategic deception compared with the social heuristic strategies. The level of deception was associated with the information flow from the Brodmann area 10 to the rTPJ, and this association was modulated by the rTPJ-to-rDLPFC information flow. These findings suggest that dynamic bargaining strategy is supported by dynamic reconfiguration of the rDLPFC-and-rTPJ interaction during competitive social interactions.

THz broadband and dual-channel perfect absorbers based on patterned graphene and vanadium dioxide metamaterials.

This paper proposes a novel and perfect absorber based on patterned graphene and vanadium dioxide hybrid metamaterial, which can not only achieve wide-band perfect absorption and dual-channel absorption in the terahertz band, but also realize their conversion by adjusting the temperature to control the metallic or insulating phase of VO2. Firstly, the absorption spectrum of the proposed structure is analyzed without graphene, where the absorption can reach as high as 100% at one frequency point (f = 5.956 THz) when VO2 is in the metal phase. What merits attention is that the addition of graphene above the structure enhances the almost 100% absorption from one frequency point (f = 5.956 THz) to a wide frequency band, in which the broadband width records 1.683 THz. Secondly, when VO2 is the insulating phase, the absorption of the metamaterial structure with graphene outperforms better, and two high absorption peaks are formed, logging 100% and 90.7% at f3 = 5.545 THz and f4 = 7.684 THz, respectively. Lastly, the adjustment of the Fermi level of graphene from 0.8 eV to 1.1 eV incurs an obvious blueshift of the absorption spectra, where an asynchronous optical switch can be achieved at fK1 = 5.782 THz and fK2 = 6.898 THz. Besides, the absorber exhibits polarization sensitivity at f3 = 5.545 THz, and polarization insensitivity at f4 = 7.684 THz with the shift in the polarization angle of incident light from 0° to 90°. Accordingly, this paper gives insights into the new method that increases the high absorption width, as well as the great potential in the multifunctional modulator.

The Diagnostic Value of the Systemic Immune-Inflammation Index for Venous Thromboembolism in Lung Cancer Patients: A Retrospective Study.

Background: Venous thromboembolism (VTE) is considered a common complication in lung cancer patients. Despite its widespread use, the Khorana score performed moderately in predicting VTE risk. This study aimed to determine the diagnostic utility of the Systemic Immunoinflammatory Index (SII) and to create a novel nomogram for predicting VTE in patients with pulmonary carcinoma. Materials and Methods: The data, like clinical features and laboratory indicators, of inpatients diagnosed with lung cancer from March 2019 to March 2020 were collected and analyzed. Univariate and multivariate logistic analyses were performed to confirm the risk factors and then construct a nomogram model. The calibration curve and clinical decision curve analysis (DCA) were used to assess the model's fitting performance. The receiver-operating characteristic (ROC) curve and the area under the ROC curve (AUC) were used to evaluate the diagnostic value of SII and the nomogram. Results: This study enrolled 369 lung patients with a VTE morbidity rate of 23.33%. The patients with VTE had higher SII levels than the non-VTE group (1441.47 ± 146.28 vs. 626.76 ± 26.04, P < 0.001). SII is the stronger correlator for VTE among inflammatory markers, of which the optimal cut-off value was 851.51. Univariate and multivariate analysis revealed that the age, metastasis, antitumor treatment, hemoglobin<100 g/L, SII>851.51 × 109/L, and D-dimer>2 folds were independent risk factors for lung cancer-related VTE, and a new prediction nomogram model was constructed based on them. ROC curve analysis showed the AUC of the new model and Khorana score were 0.708 (0.643-0.772) and 0.600 (0.531-0.699). Conclusion: The SII was a simple and valuable biomarker for VTE, and the new nomogram model based on it can accurately forecast the occurrence of VTE. They can be utilized in clinical practice to identify those at high risk of VTE in lung cancer patients.

Ectodysplasin A (EDA) Signaling: From Skin Appendage to Multiple Diseases.

Ectodysplasin A (EDA) signaling is initially identified as morphogenic signaling regulating the formation of skin appendages including teeth, hair follicles, exocrine glands in mammals, feathers in birds and scales in fish. Gene mutation in EDA signaling causes hypohidrotic ectodermal dysplasia (HED), a congenital hereditary disease with malformation of skin appendages. Interestingly, emerging evidence suggests that EDA and its receptors can modulate the proliferation, apoptosis, differentiation and migration of cancer cells, and thus may regulate tumorigenesis and cancer progression. More recently, as a newly discovered hepatocyte factor, EDA pathway has been demonstrated to be involved in the pathogenesis of nonalcoholic fatty liver disease (NAFLD) and type II diabetes by regulating glucose and lipid metabolism. In this review, we summarize the function of EDA signaling from skin appendage development to multiple other diseases, and discuss the clinical application of recombinant EDA protein as well as other potential targets for disease intervention.

Selective Adsorption of C6, C8, and C10 Linear α-Olefins from Binary Liquid-Phase Olefin/Paraffin Mixtures Using Zeolite Adsorbents: Experiment and Simulations.

The adsorption separation of gaseous olefin/paraffin using porous materials has been extensively studied from both experimental and molecular simulation perspectives, while the adsorption separation of liquid-phase olefin/paraffin has been much less studied. One of the most important reasons for this is that it is difficult to measure the actual adsorption capacity of liquid-phase adsorption separation directly through experiments, and the simulation results of most studies are compared to gas-phase measurements. In this paper, the selective adsorption of linear α-olefins from three binary liquid-phase olefin/paraffin mixtures, 1-hexene/n-hexane (C6), 1-octene/n-octane (C8), and 1-decene/n-decane (C10), by zeolite adsorbents was systematically investigated using batch adsorption experiments and configurational-bias grand canonical Monte Carlo (CB-GCMC) simulations. In the batch experiments, based on the liquid-phase measurement method of the actual adsorption capacity that we developed, a modified commercial 5A zeolite with a relatively large pore volume and surface area was used for adsorption. The results showed that the modified 5A zeolite had larger actual adsorption capacities for C6 and C8 linear α-olefins, which increased by 51% and 56%, respectively, than the standard 5A zeolite that was used in our previous work. The adsorption isotherms of C6, C8, and C10 in the 5A and 13X zeolites were calculated by CB-GCMC simulations. The visualized results of density profiles showed that the olefin molecules were densely distributed at the edge of the zeolite cages and that there were cases where a single molecule was adsorbed over two adjacent cages. The good agreement between the experimental and simulated data proves the completeness of the liquid-phase measurement method that we developed and the reliability of the simulation prediction.

Heat- or light-induced acylarylation of unactivated alkenes towards 3-(α-acyl) indolines.

A heat- or photoredox/iron dual catalysis-enabled dehydrogenative acylarylation of N-allyl anilines leading to 2-substituted 3-(α-acyl) indolines with a quaternary stereogenic center is presented, with unactivated alkenic bonds as radical acceptors and simple aldehydes as radical precursors. This reaction features high yields, a broad substrate scope, and a great exo selectivity, and gram-scale syntheses could be readily carried out.

Deep-Learning-Based Drug-Target Interaction Prediction.

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

The model adaptive space shrinkage (MASS) approach: a new method for simultaneous variable selection and outlier detection based on model population analysis.

Variable selection and outlier detection are important processes in chemical modeling. Usually, they affect each other. Their performing orders also strongly affect the modeling results. Currently, many studies perform these processes separately and in different orders. In this study, we examined the interaction between outliers and variables and compared the modeling procedures performed with different orders of variable selection and outlier detection. Because the order of outlier detection and variable selection can affect the interpretation of the model, it is difficult to decide which order is preferable when the predictabilities (prediction error) of the different orders are relatively close. To address this problem, a simultaneous variable selection and outlier detection approach called Model Adaptive Space Shrinkage (MASS) was developed. This proposed approach is based on model population analysis (MPA). Through weighted binary matrix sampling (WBMS) from model space, a large number of partial least square (PLS) regression models were built, and the elite parts of the models were selected to statistically reassign the weight of each variable and sample. Then, the whole process was repeated until the weights of the variables and samples converged. Finally, MASS adaptively found a high performance model which consisted of the optimized variable subset and sample subset. The combination of these two subsets could be considered as the cleaned dataset used for chemical modeling. In the proposed approach, the problem of the order of variable selection and outlier detection is avoided. One near infrared spectroscopy (NIR) dataset and one quantitative structure-activity relationship (QSAR) dataset were used to test this approach. The result demonstrated that MASS is a useful method for data cleaning before building a predictive model.

Research hotspots and trends of nanomaterials in stomatology: A bibliometric analysis from 2000 to 2023.

Background: Nanomaterials (NMs) have emerged as highly promising candidates for stomatology due to their excellent quality and remarkable progress in recent years. However, with the rapid expansion of the research scale, challenges arise in the technological decision-making and research management processes, and therefore difficulty for researchers to understand the knowledge structure and research hotspots has increased significantly. This study aims to make a comprehensive summary of authors, institutions, journals, research topics, development trends, and research hotspots of NMs in stomatology through bibliometric analysis for the sake of providing references for scientific decision-making, research management, and academic exploration in this filed. Methods: Studies on research and application of NMs in stomatology were retrieved from the Web of Science Core Collection (WoSCC) from January 1, 2000 to April 27, 2023. Bibliometric analysis and visualization were conducted using CiteSpace and VOSviewer. Results: A total of 620 articles were included in this study, showing a gradual increase in the number of publications focusing on NMs in stomatology. Globally, China ranked first with 130 publications, and the United States (US) enjoyed the highest citation count (n = 5218) and average citation per paper (ACP) (n = 52.18). The top three institutions with the highest publication output were the University of Sao Paulo (n = 22), the Chinese Academy of Sciences (n = 20), and Shanghai Jiaotong University (n = 13). The journals MATERIALS and NANOMATERIALS emerged as the most popular in this field (n = 20), and BIOMATERIALS had the highest co-citations (n = 1597). The most prolific author was Dos Reis and Andrea Candido (n = 7), while Thomas J. Webster enjoyed the highest co-citations (n = 94). Burstness analysis of the references revealed a prominent research focus on nanoparticle drug delivery systems (specifically lipid nanoparticles). Keyword burstness analysis identified "oxide nanoparticle" as the primary frontier keyword in this field. Conclusion: This is the first study of using bibliometric analysis to summarize the research trends and frontiers of NMs in stomatology. With progressive advancements in the research and application of NMs in oral healthcare, their academic impact is steadily increasing. China and the US maintain a leading position in this field. Future directions could primarily focus on the development and application of nanoparticle drug delivery systems (especially lipid nanoparticles) and metal oxide nanoparticles (especially in antibacterial aspects). We hope that this bibliometric analysis could provide researchers with a panoramic view and useful references for future research, thus promoting the development of NMs in stomatology.

Reversal of T-cell exhaustion: Mechanisms and synergistic approaches.

T cells suffer from long-term antigen stimulation and insufficient energy supply, leading to a decline in their effector functions, memory capabilities, and proliferative capacity, ultimately resulting in T cell exhaustion and an inability to perform normal immune functions in the tumor microenvironment. Therefore, exploring how to restore these exhausted T cells to a state with effector functions is of great significance. Exhausted T cells exhibit a spectrum of molecular alterations, such as heightened expression of inhibitory receptors, shifts in transcription factor profiles, and modifications across epigenetic, metabolic, and transcriptional landscapes. This review provides a comprehensive overview of various strategies to reverse T cell exhaustion, including immune checkpoint blockade, and explores the potential synergistic effects of combining multiple approaches to reverse T cell exhaustion. It offers new insights and methods for achieving more durable and effective reversal of T cell exhaustion.

Traditional Chinese medicine (Xielikang) reduces diarrhea symptoms in acquired immune deficiency syndrome (AIDS) patients by regulating the intestinal microbiota.

Diarrheal acquired immune deficiency syndrome (AIDS) seriously affects the quality of life of patients. In this study, we analyzed the differences in the intestinal microbiota among healthy individuals, AIDS patients without diarrhea and AIDS patients with diarrhea through high-throughput sequencing. The microbial diversity in the intestines of patients in the AIDS diarrhea group was significantly increased, and after treatment with Xielikang, the intestinal microbial diversity returned to the baseline level. At the phylum level, compared those in to the healthy (ZC) and AIDS non diarrhea (FN) groups, the relative abundances of Bacteroidetes and Verrucomirobia in the AIDS diarrhea (FA) group before treatment were significantly increased, while the relative abundance of Firmicutes was significantly decreased. Similarly, compared with those in the FA group, the relative abundances of Bacteroidea and Firmicutes in the AIDS diarrhea (FB) group after treatment were significantly increased, while the relative abundance of Firmicutes was significantly decreased after treatment. Additionally, there was no significant difference between the ZC and FN groups. At the genus level, compared with those in the ZC group, the relative abundance of Prevotella and Escherichia_Shigella in the FA group was significantly increased, while the relative abundances of Megamonas and Bifidobacterium was significantly decreased compared to that in the ZC group. After treatment with Xielikang, the relative abundance of Prevotella and Escherichia_Shigella in the FB group were significantly decreased, while the relative abundances of Megamonas and Bifidobacteria were significantly increased than those in the FA group; moreover, there was no significant difference between the ZC and FN groups. The functional prediction results showed that the ketodeoxyoctonate (Kdo) transfer to lipid IVA III and the superpathway of N-acetylglucosamine pathways in the AIDS diarrhea group were significantly altered. The correlation analysis results showed that Dorea was positively correlated with inflammatory factors, while Streptococcus and Lactobacillus were negatively correlated with inflammatory factors. The composition and function of the intestinal microbiota changed significantly in AIDS diarrhea patients, which affected the immune function of the host. The Xielikang capsule modulated the composition of the intestinal microbiota in AIDS diarrhea patients and thus improved immune function and reduced diarrheal symptoms.

"Osteo-Organogenesis Niche" Hyaluronic Acid Engineered Materials Directing Re-Osteo-Organogenesis via Manipulating Macrophage CD44-MAPK/ERK-ETV1/5-MRC1 Axis.

The strategy of re-organogenesis provides an optimal framework for restoring complex organ structures and functions in adult damage. While the focus has often been on restoring organogenesis stem cells, there is limited investigations of reverting the environmental niche to support this approach. The guiding principle of "Nature selects the fittest to survive" drives the intricate dynamic changes in cellular events within the niche environment, especially through immune surveillance. The extracellular matrix (ECM) serves as the "self-associated molecular patterns" of the niche, containing extensive data on cell-niche reaction data and acting as the active tuner of immune surveillance. In this study, hyaluronic acid (HA) is identified as a unique component of the ECM in cranial osteo-organogenesis. Mechanistically, HA activates the Cluster of Differentiation 44 (CD44)-Mitogen-Activated Protein Kinase (MAPK)/Extracellular Signal-Regulated Kinase (ERK)-Ets Variant 1/5 (ETV1/5)- Mannose Receptor C-Type 1 (MRC1) axis in macrophages, establishing a distinct immune surveillance during osteo-organogenesis. Furthermore, HA is utilized as a novel engineered material for an "Osteo-organogenesis niche", restoring immune surveillance and synergistically regulating stem cells to achieve re-osteo-organogenesis in cranial defects of rats. Taken together, the study unveils a previously unknown strategy for leveraging re-organogenesis by utilizing "organogenesis niche" ECM engineered materials to manipulate immune surveillance, thereby comprehensively regulating stem cells and other tissue cells effectively for re-organogenesis.

Edaravone Dexborneol mitigates pathology in animal and cell culture models of Alzheimer's disease by inhibiting neuroinflammation and neuronal necroptosis.

BACKGROUND: Alzheimer's disease (AD) is the most prevalent neurodegenerative disease with limited disease-modifying treatments. Drug repositioning strategy has now emerged as a promising approach for anti-AD drug discovery. Using 5×FAD mice and Aß-treated neurons in culture, we tested the efficacy of Y-2, a compounded drug containing the antioxidant Edaravone (Eda), a pyrazolone and (+)-Borneol, an anti-inflammatory diterpenoid from cinnamon, approved for use in amyotrophic lateral sclerosis patients. RESULTS: We examined effects of Y-2 versus Eda alone by i.p. administered in 8-week-old 5×FAD mice (females) for 4 months by comparing cognitive function, Aß pathologies, neuronal necroptosis and neuroinflammation. Using primary neurons and astrocytes, as well as neuronal and astrocytic cell lines, we elucidated the molecular mechanisms of Y-2 by examining neuronal injury, astrocyte-mediated inflammation and necroptosis. Here, we find that Y-2 improves cognitive function in AD mice. Histopathological data show that Y-2, better than Eda alone, markedly ameliorates Aß pathologies including Aß burden, astrogliosis/microgliosis, and Tau phosphorylation. In addition, Y-2 reduces Aß-induced neuronal injury including neurite damage, mitochondrial impairment, reactive oxygen species production and NAD+ depletion. Notably, Y-2 inhibits astrocyte-mediated neuroinflammation and attenuates TNF-α-triggered neuronal necroptosis in cell cultures and AD mice. RNA-seq further demonstrates that Y-2, compared to Eda, indeed upregulates anti-inflammation pathways in astrocytes. CONCLUSIONS: Our findings infer that Y-2, better than Eda alone, mitigates AD pathology and may provide a potential drug candidate for AD treatment.

Insights From Generative Modeling for Neural Video Compression.

While recent machine learning research has revealed connections between deep generative models such as VAEs and rate-distortion losses used in learned compression, most of this work has focused on images. In a similar spirit, we view recently proposed neural video coding algorithms through the lens of deep autoregressive and latent variable modeling. We present these codecs as instances of a generalized stochastic temporal autoregressive transform, and propose new avenues for further improvements inspired by normalizing flows and structured priors. We propose several architectures that yield state-of-the-art video compression performance on high-resolution video and discuss their tradeoffs and ablations. In particular, we propose (i) improved temporal autoregressive transforms, (ii) improved entropy models with structured and temporal dependencies, and (iii) variable bitrate versions of our algorithms. Since our improvements are compatible with a large class of existing models, we provide further evidence that the generative modeling viewpoint can advance the neural video coding field.

EDA ligand triggers plasma membrane trafficking of its receptor EDAR via PKA activation and SNAP23-containing complexes.

BACKGROUND: Ectodysplasin-A (EDA), a skin-specific TNF ligand, interacts with its membrane receptor EDAR to trigger EDA signaling in skin appendage formation. Gene mutations in EDA signaling cause Anhidrotic/Hypohidrotic Ectodermal Dysplasia (A/HED), which affects the formation of skin appendages including hair, teeth, and several exocrine glands. RESULTS: We report that EDA triggers the translocation of its receptor EDAR from a cytosolic compartment into the plasma membrane. We use protein affinity purification to show that upon EDA stimulation EDAR associates with SNAP23-STX6-VAMP1/2/3 vesicle trafficking complexes. We find that EDA-dependent PKA activation is critical for the association. Notably, either of two HED-linked EDAR mutations, T346M and R420W, prevents EDA-induced EDAR translocation; and both EDA-induced PKA activation and SNAP23 are required for Meibomian gland (MG) growth in a skin appendage model. CONCLUSIONS: Overall, in a novel regulatory mechanism, EDA increases plasma membrane translocation of its own receptor EDAR, augmenting EDA-EDAR signaling in skin appendage formation. Our findings also provide PKA and SNAP23 as potential targets for the intervention of HED.