H2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces

Temporal heterogeneous information network (temporal HIN) embedding, aiming to represent various types of nodes of different timestamps into low dimensional spaces while preserving structural and semantic information, is of vital importance in diverse real-life tasks. Researchers have made great efforts on temporal HIN embedding in Euclidean spaces and got some considerable achievements. However, there is always a fundamental conflict that many real-world networks show hierarchical property and power-law distribution, and are not isometric of Euclidean spaces. Recently, representation learning in hyperbolic spaces has been proved to be valid for data with hierarchical and power-law structure. Inspired by this character, we propose a hyperbolic heterogeneous temporal network embedding (H2TNE) model for temporal HINs. Specifically, we leverage a temporally and heterogeneously double-constrained random walk strategy to capture the structural and semantic information, and then calculate the embedding by exploiting hyperbolic distance in proximity measurement. Experimental results show that our method has superior performance on temporal link prediction and node classification compared with SOTA models.Comment: arXiv admin note: text overlap with arXiv:1705.08039 by other author

A Unified Framework for Integrating Semantic Communication and AI-Generated Content in Metaverse

As the Metaverse continues to grow, the need for efficient communication and intelligent content generation becomes increasingly important. Semantic communication focuses on conveying meaning and understanding from user inputs, while AI-Generated Content utilizes artificial intelligence to create digital content and experiences. Integrated Semantic Communication and AI-Generated Content (ISGC) has attracted a lot of attentions recently, which transfers semantic information from user inputs, generates digital content, and renders graphics for Metaverse. In this paper, we introduce a unified framework that captures ISGC two primary benefits, including integration gain for optimized resource allocation and coordination gain for goal-oriented high-quality content generation to improve immersion from both communication and content perspectives. We also classify existing ISGC solutions, analyze the major components of ISGC, and present several use cases. We then construct a case study based on the diffusion model to identify an optimal resource allocation strategy for performing semantic extraction, content generation, and graphic rendering in the Metaverse. Finally, we discuss several open research issues, encouraging further exploring the potential of ISGC and its related applications in the Metaverse.Comment: 8 pages, 6 figure

Comparative analysis of the secretomes of Schizophyllum commune and other wood-decay basidiomycetes during solid-state fermentation reveals its unique lignocellulose-degrading enzyme system

Additional file 3: Table S2. Identified proteins in the secretomes of four fungi during SSF on Jerusalem artichoke stalk

A Unified Blockchain-Semantic Framework for Wireless Edge Intelligence Enabled Web 3.0

Web 3.0 enables user-generated contents and user-selected authorities. With decentralized wireless edge computing architectures, Web 3.0 allows users to read, write, and own contents. A core technology that enables Web 3.0 goals is blockchain, which provides security services by recording content in a decentralized and transparent manner. However, the explosion of on-chain recorded contents and the fast-growing number of users cause increasingly unaffordable computing and storage resource consumption. A promising paradigm is to analyze the semantic information of contents that can convey precisely the desired meanings without consuming many resources. In this article, we propose a unified blockchain-semantic ecosystems framework for wireless edge intelligence-enabled Web 3.0. Our framework consists of six key components to exchange semantic demands. We then introduce an Oracle-based proof of semantic mechanism to implement on-chain and off-chain interactions of Web 3.0 ecosystems on semantic verification algorithms while maintaining service security. An adaptive Deep Reinforcement Learning-based sharding mechanism on Oracle is designed to improve interaction efficiency, which can facilitate Web 3.0 ecosystems to deal with varied semantic demands. Finally, a case study is presented to show that the proposed framework can dynamically adjust Oracle settings according to varied semantic demands.Comment: 8 pages, 5 figures, 1 tabl

Mixture of Experts for Network Optimization: A Large Language Model-enabled Approach

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLM-enabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems

Learning Lens Blur Fields

Optical blur is an inherent property of any lens system and is challenging to model in modern cameras because of their complex optical elements. To tackle this challenge, we introduce a high-dimensional neural representation of blur$-$$\textit{the lens blur field}$$-$and a practical method for acquiring it. The lens blur field is a multilayer perceptron (MLP) designed to (1) accurately capture variations of the lens 2D point spread function over image plane location, focus setting and, optionally, depth and (2) represent these variations parametrically as a single, sensor-specific function. The representation models the combined effects of defocus, diffraction, aberration, and accounts for sensor features such as pixel color filters and pixel-specific micro-lenses. To learn the real-world blur field of a given device, we formulate a generalized non-blind deconvolution problem that directly optimizes the MLP weights using a small set of focal stacks as the only input. We also provide a first-of-its-kind dataset of 5D blur fields$-$for smartphone cameras, camera bodies equipped with a variety of lenses, etc. Lastly, we show that acquired 5D blur fields are expressive and accurate enough to reveal, for the first time, differences in optical behavior of smartphone devices of the same make and model

Electrophysiology as a prognostic indicator of visual recovery in diabetic patients undergoing cataract surgery

Purpose: Visual outcomes after cataract surgery in diabetic patients with retinal or visual pathway disease are difficult to predict as the fundus may be obscured, and assessment of visual potential is challenging. This study assessed the value of visual electrophysiology as a prognostic indicator of visual recovery in diabetic patients with cataract, prior to cataract surgery. Methods: Forty-one diabetic patients (aged 52–80; 74 eyes) and 13 age-matched non-diabetic control patients (21 eyes) were examined prior to cataract surgery. Pre-surgical examinations included best-corrected visual acuity (BCVA), slit-lamp bio-microscopy, ISCEV-standard full-field electroretinography (ffERG), and flash visual evoked potential (flash VEP) testing. Electrophysiological assessments included quantification of the DA and LA ERG, oscillatory potentials (OPs; OP1, OP2, OP3, OP4) and flash VEP P1, P2, and P3 components. Post-operative BCVA was measured in all cases and the diabetic patients grouped according to the severity of visual acuity loss: mild (logMAR ≤ 0.1), moderate (0.1 < logMAR < 0.5), or severe (logMAR ≥ 0.5). A fourth group included those without diabetes. The pre-surgical electrophysiological data was compared between the four groups by analysis of variance. Results: The severity of post-surgical visual acuity loss in the diabetic patients was classified as mild (N=22 eyes), moderate (N=31 eyes), or severe (N=21 eyes). In the group without diabetes, post-surgical visual impairment was classified as mild (N=21 eyes). The pre-operative DA 10.0 ERG a-wave amplitudes, DA 3.0 ERG OP2 amplitudes, and the LA 3.0 a- and b-wave amplitudes showed best significant differences among the four groups. The flash VEP did not show significant difference between groups. Conclusion: Electrophysiological assessment of diabetic patients with cataract can provide a useful measure of retinal function. Full-field ERG components, including the DA 10.0 ERG a-wave, DA 3.0 ERG OP2 component, and the LA 3.0 a- and b-wave amplitudes, are of prognostic value in predicting post-surgical visual acuity, and may inform the surgical management of cataract patients with diabetes. [Figure not available: see fulltext.]

Cellular Differences in the Cochlea of CBA and B6 Mice May Underlie Their Difference in Susceptibility to Hearing Loss

Hearing is an extremely delicate sense that is particularly vulnerable to insults from environment, including drugs and noise. Unsurprisingly, mice of different genetic backgrounds show different susceptibility to hearing loss. In particular, CBA/CaJ (CBA) mice maintain relatively stable hearing over age while C57BL/6J (B6) mice show a steady decline of hearing, making them a popular model for early onset hearing loss. To reveal possible underlying mechanisms, we examined cellular differences in the cochlea of these two mouse strains. Although the ABR threshold and Wave I latency are comparable between them, B6 mice have a smaller Wave I amplitude. This difference is probably due to fewer spiral ganglion neurons found in B6 mice, as the number of ribbon synapses per inner hair cell (IHC) is comparable between the two mouse strains. Next, we compared the outer hair cell (OHC) function and we found OHCs from B6 mice are larger in size but the prestin density is similar among them, consistent with the finding that they share similar hearing thresholds. Lastly, we examined the IHC function and we found IHCs from B6 mice have a larger Ca2+ current, release more synaptic vesicles and recycle synaptic vesicles more quickly. Taken together, our results suggest that excessive exocytosis from IHCs in B6 mice may raise the probability of glutamate toxicity in ribbon synapses, which could accumulate over time and eventually lead to early onset hearing loss

Extracellular vesicles in the treatment and diagnosis of breast cancer: a status update

Breast cancer is one of the leading causes of cancer-related death in women. Currently, the treatment of breast cancer is limited by the lack of effectively targeted therapy and patients often suffer from higher severity, metastasis, and resistance. Extracellular vesicles (EVs) consist of lipid bilayers that encapsulate a complex cargo, including proteins, nucleic acids, and metabolites. These bioactive cargoes have been found to play crucial roles in breast cancer initiation and progression. Moreover, EV cargoes play pivotal roles in converting mammary cells to carcinogenic cells and metastatic foci by extensively inducing proliferation, angiogenesis, pre-metastatic niche formation, migration, and chemoresistance. The present update review mainly discusses EVs cargoes released from breast cancer cells and tumor-derived EVs in the breast cancer microenvironment, focusing on proliferation, metastasis, chemoresistance, and their clinical potential as effective biomarkers

Loneliness and depressive symptoms among men who have sex with men in China: A cross-sectional study

BackgroundWhile psychosocial problems and their related factors in men who have sex with men (MSM) have been well documented in developed countries, there are still not many studies addressing this issue in China and the results are inconsistent. This study aimed to assess the prevalence of loneliness and depressive symptoms among MSM, examine their associated factors, and investigate potential factors moderating the link between depressive symptoms and loneliness.MethodsA cross-sectional study was conducted in Taizhou of Zhejiang Province in China between April and November 2021. Loneliness was assessed using the 3-item UCLA Loneliness Scale (UCLA-3), and depressive symptoms were measured using the Patient Health Questionaire-9 (PHQ-9). Data from 655 MSM were eligible for analysis. Logistic regression models were used to examine the associations between independent variables and the outcomes of loneliness and depression. The interaction terms were added in the models to assess the moderating effects.ResultsOf the MSM sample, 13.28 and 7.48% perceived loneliness and reported moderate-to-severe depressive symptoms, respectively. We found that participants who experienced loneliness were more likely to have younger age (OR 0.44, 95% CI 0.21, 0.93, 15–32 years as reference group), low social support (OR 3.60, 95% CI 2.14, 6.04), low self-esteem (OR 3.03, 95% CI 1.45, 6.32) and moderate-to-severe depressive symptoms (OR 5.45, 95% CI 2.66, 11.15). The participants with moderate-to-severe depressive symptoms were more likely to have low self-esteem (OR 6.78, 95% CI 3.08, 14.95) and feelings of loneliness (OR 5.51, 95% CI 2.66, 11.40). Stratified analyzes showed that the magnitude of the associations between depressive symptoms and loneliness varied in MSM with different age, marital status, and self-esteem.ConclusionOur study suggests that we need to pay attention to feelings of loneliness and depressive symptoms and their closely associated factors such as social support and self-esteem among MSM in China. The MSM who were young, not married, and had low self-esteem were especially vulnerable to the impact of depressive symptoms on loneliness