Recently, the development of the Metaverse has become a frontier spotlight, which is an important demonstration of the integration innovation of advanced technologies in the Internet. Moreover, artificial intelligence (AI) and 6G communications will be widely used in our daily lives. However, the effective interactions with the representations of multimodal data among users via 6G communications is the main challenge in the Metaverse. In this work, we introduce an intelligent cross-modal graph semantic communication approach based on generative AI and 3-dimensional (3D) point clouds to improve the diversity of multimodal representations in the Metaverse. Using a graph neural network, multimodal data can be recorded by key semantic features related to the real scenarios. Then, we compress the semantic features using a graph transformer encoder at the transmitter, which can extract the semantic representations through the cross-modal attention mechanisms. Next, we leverage a graph semantic validation mechanism to guarantee the exactness of the overall data at the receiver. Furthermore, we adopt generative AI to regenerate multimodal data in virtual scenarios. Simultaneously, a novel 3D generative reconstruction network is constructed from the 3D point clouds, which can transfer the data from images to 3D models, and we infer the multimodal data into the 3D models to increase realism in virtual scenarios. Finally, the experiment results demonstrate that cross-modal graph semantic communication, assisted by generative AI, has substantial potential for enhancing user interactions in the 6G communications and Metaverse.

In the evolving landscape of robotics and visual navigation, event cameras have gained important traction, notably for their exceptional dynamic range, efficient power consumption, and low latency. Despite these advantages, conventional processing methods oversimplify the data into 2 dimensions, neglecting critical temporal information. To overcome this limitation, we propose a novel method that treats events as 3D time-discrete signals. Drawing inspiration from the intricate biological filtering systems inherent to the human visual apparatus, we have developed a 3D spatiotemporal filter based on unsupervised machine learning algorithm. This filter effectively reduces noise levels and performs data size reduction, with its parameters being dynamically adjusted based on population activity. This ensures adaptability and precision under various conditions, like changes in motion velocity and ambient lighting. In our novel validation approach, we first identify the noise type and determine its power spectral density in the event stream. We then apply a one-dimensional discrete fast Fourier transform to assess the filtered event data within the frequency domain, ensuring that the targeted noise frequencies are adequately reduced. Our research also delved into the impact of indoor lighting on event stream noise. Remarkably, our method led to a 37% decrease in the data point cloud, improving data quality in diverse outdoor settings.

Utilizing renewable lignocellulosic resources for wastewater remediation is crucial to achieving sustainable social development. However, the resulting by-products and the synthetic process characterized by complexity, high cost, and environmental pollution limit the further development of lignocellulose-based materials. Here, we developed a sustainable strategy that involved a new functional deep eutectic solvent (DES) to deconstruct industrial xylose residue into cellulose-rich residue with carboxyl groups, lignin with carboxyl and quaternary ammonium salt groups, and DES effluent rich in lignin fragments. Subsequently, these fractions equipped with customized functionality were used to produce efficient wastewater remediation materials in cost-effective and environmentally sound manners, namely, photocatalyst prepared by carboxyl-modified cellulose residue, biochar-based adsorbent originated from modified lignin, and flocculant synthesized by self-catalytic in situ copolymerization of residual DES effluent at room temperature. Under the no-waste principle, this strategy upgraded the whole components of waste lignocellulose into high-value-added wastewater remediation materials with excellent universality. These materials in coordination with each other can stepwise purify high-hazardous mineral processing wastewater into drinkable water, including the removal of 99.81% of suspended solids, almost all various heavy metal ions, and 97.09% chemical oxygen demand, respectively. This work provided promising solutions and blueprints for lignocellulosic resources to alleviate water shortages while also advancing the global goal of carbon neutrality.

Neutrophils are primed for neutrophil extracellular trap (NET) formation during diabetes, and excessive NET formation from primed neutrophils compromises wound healing in patients with diabetes. Here, we demonstrate that trained immunity mediates diabetes-induced NET priming in neutrophils. Under diabetic conditions, neutrophils exhibit robust metabolic reprogramming comprising enhanced glycolysis via the pentose phosphate pathway and fatty acid oxidation, which result in the accumulation of acetyl-coenzyme A. Adenosine 5′-triphosphate-citrate lyase-mediated accumulation of acetyl-coenzyme A and histone acetyltransferases further induce the acetylation of lysine residues on histone 3 (AcH3K9, AcH3K14, and AcH3K27) and histone 4 (AcH4K8). The pharmacological inhibition of adenosine 5′-triphosphate-citrate lyase and histone acetyltransferases completely inhibited high-glucose-induced NET priming. The trained immunity of neutrophils was further confirmed in neutrophils isolated from patients with diabetes. Our findings suggest that trained immunity mediates functional changes in neutrophils in diabetic environments, and targeting neutrophil-trained immunity may be a potential therapeutic target for controlling inflammatory complications of diabetes.

To explore the complementary relationship between magnetic resonance imaging (MRI) radiomic and plasma biomarkers in the early diagnosis and conversion prediction of Alzheimer's disease (AD), our study aims to develop an innovative multivariable prediction model that integrates those two for predicting conversion results in AD. This longitudinal multicentric cohort study included 2 independent cohorts: the Sino Longitudinal Study on Cognitive Decline (SILCODE) project and the Alzheimer Disease Neuroimaging Initiative (ADNI). We collected comprehensive assessments, MRI, plasma samples, and amyloid positron emission tomography data. A multivariable logistic regression analysis was applied to combine plasma and MRI radiomics biomarkers and generate a new composite indicator. The optimal model's performance and generalizability were assessed across populations in 2 cross-racial cohorts. A total of 897 subjects were included, including 635 from the SILCODE cohort (mean [SD] age, 64.93 [6.78] years; 343 [63%] female) and 262 from the ADNI cohort (mean [SD] age, 73.96 [7.06] years; 140 [53%] female). The area under the receiver operating characteristic curve of the optimal model was 0.9414 and 0.8979 in the training and validation dataset, respectively. A calibration analysis displayed excellent consistency between the prognosis and actual observation. The findings of the present study provide a valuable diagnostic tool for identifying at-risk individuals for AD and highlight the pivotal role of the radiomic biomarker. Importantly, built upon data-driven analyses commonly seen in previous radiomics studies, our research delves into AD pathology to further elucidate the underlying reasons behind the robust predictive performance of the MRI radiomic predictor.

Porous substrates act as open “interfacial reactors” during the synthesis of polyamide composite membranes via interfacial polymerization. However, achieving a thin and dense polyamide nanofilm with high permeance and selectivity is challenging when using a conventional substrate with uniform wettability. To overcome this limitation, we propose the use of Janus porous substrates as confined interfacial reactors to decouple the local monomer concentration from the total monomer amount during interfacial polymerization. By manipulating the location of the hydrophilic/hydrophobic interface in a Janus porous substrate, we can precisely control the monomer solution confined within the hydrophilic layer without compromising its concentration. The hydrophilic surface ensures the uniform distribution of monomers, preventing the formation of defects. By employing Janus substrates fabricated through single-sided deposition of polydopamine/polyethyleneimine, we significantly reduce the thickness of the polyamide nanofilms from 88.4 to 3.8 nm by decreasing the thickness of the hydrophilic layer. This reduction leads to a remarkable enhancement in water permeance from 7.2 to 52.0 l/m²·h·bar while still maintaining ~96% Na₂SO₄ rejection. The overall performance of this membrane surpasses that of most reported membranes, including state-of-the-art commercial products. The presented strategy is both simple and effective, bringing ultrapermeable polyamide nanofilms one step closer to practical separation applications.

Intervertebral disc degeneration (IVDD) is a prevalent cause of low back pain and a leading contributor to disability. IVDD progression involves pathological shifts marked by low-grade inflammation, extracellular matrix remodeling, and metabolic disruptions characterized by heightened glycolytic pathways, mitochondrial dysfunction, and cellular senescence. Extensive posttranslational modifications of proteins within nucleus pulposus cells and chondrocytes play crucial roles in reshaping the intervertebral disc phenotype and orchestrating metabolism and inflammation in diverse contexts. This review focuses on the pivotal roles of phosphorylation, ubiquitination, acetylation, glycosylation, methylation, and lactylation in IVDD pathogenesis. It integrates the latest insights into various posttranslational modification-mediated metabolic and inflammatory signaling networks, laying the groundwork for targeted proteomics and metabolomics for IVDD treatment. The discussion also highlights unexplored territories, emphasizing the need for future research, particularly in understanding the role of lactylation in intervertebral disc health, an area currently shrouded in mystery.

The methylation of adenosine base at the nitrogen-6 position is referred to as “N6-methyladenosine (m⁶A)” and is one of the most prevalent epigenetic modifications in eukaryotic mRNA and noncoding RNA (ncRNA). Various m⁶A complex components known as “writers,” “erasers,” and “readers” are involved in the function of m⁶A. Numerous studies have demonstrated that m⁶A plays a crucial role in facilitating communication between different cell types, hence influencing the progression of diverse physiological and pathological phenomena. In recent years, a multitude of functions and molecular pathways linked to m⁶A have been identified in the osteogenic, adipogenic, and chondrogenic differentiation of bone mesenchymal stem cells (BMSCs). Nevertheless, a comprehensive summary of these findings has yet to be provided. In this review, we primarily examined the m⁶A alteration of transcripts associated with transcription factors (TFs), as well as other crucial genes and pathways that are involved in the differentiation of BMSCs. Meanwhile, the mutual interactive network between m⁶A modification, miRNAs, and lncRNAs was intensively elucidated. In the last section, given the beneficial effect of m⁶A modification in osteogenesis and chondrogenesis of BMSCs, we expounded upon the potential utility of m⁶A-related therapeutic interventions in the identification and management of human musculoskeletal disorders manifesting bone and cartilage destruction, such as osteoporosis, osteomyelitis, osteoarthritis, and bone defect.

Combined hyperlipidemia (CHL) manifests as elevated cholesterol and triglycerides, associated with fatty liver and cardiovascular diseases. Emerging evidence underscores the crucial role of the intestinal microbiota in metabolic disorders. However, the potential therapeutic viability of remodeling the intestinal microbiota in CHL remains uncertain. In this study, CHL was induced in low-density lipoprotein receptor-deficient (LDLR^-/-) hamsters through an 8-week high-fat and high-cholesterol (HFHC) diet or a 4-month high-cholesterol (HC) diet. Placebo or antibiotics were administered through separate or cohousing approaches. Analysis through 16S rDNA sequencing revealed that intermittent antibiotic treatment and the cohousing approach effectively modulated the gut microbiota community without impacting its overall abundance in LDLR^-/- hamsters exhibiting severe CHL. Antibiotic treatment mitigated HFHC diet-induced obesity, hyperglycemia, and hyperlipidemia, enhancing thermogenesis and alleviating nonalcoholic steatohepatitis (NASH), concurrently reducing atherosclerotic lesions in LDLR^-/- hamsters. Metabolomic analysis revealed a favorable liver lipid metabolism profile. Increased levels of microbiota-derived metabolites, notably butyrate and glycylglycine, also ameliorated NASH and atherosclerosis in HFHC diet-fed LDLR^-/- hamsters. Notably, antibiotics, butyrate, and glycylglycine treatment exhibited protective effects in LDLR^-/- hamsters on an HC diet, aligning with outcomes observed in the HFHC diet scenario. Our findings highlight the efficacy of remodeling gut microbiota through antibiotic treatment and cohousing in improving obesity, NASH, and atherosclerosis associated with refractory CHL. Increased levels of beneficial microbiota-derived metabolites suggest a potential avenue for microbiome-mediated therapies in addressing CHL-associated diseases.

In this study, we systematically investigated the interactions between Cu²⁺ and various biomolecules, including double-stranded DNA, Y-shaped DNA nanospheres, the double strand of the hybridization chain reaction (HCR), the network structure of cross-linked HCR (cHCR), and small molecules (PPi and His), using Cu²⁺ as an illustrative example. Our research demonstrated that the coordination between Cu²⁺ and these biomolecules not only is suitable for modulating luminescent material signals through complexation reactions with Cu²⁺ but also enhances signal intensities in materials based on chemical reactions by increasing spatial site resistance and local concentration. Building upon these findings, we harnessed the potential for signal amplification in self-assembled DNA nanospheres and the selective complexation modulation of calcein in conjunction with the aptamer targeting mucin 1 as a recognition probe. We applied this approach to the analysis of circulating tumor cells, with the lung cancer cell line A549 serving as a representative model. Our assay, utilizing both a fluorometer and a handheld detector, achieved impressive detection limits of ag/ml and single-cell levels for mucin 1 and A549 cells, and this approach was successfully validated using 46 clinical samples, yielding 100% specificity and 86.5% sensitivity. Consequently, our strategy has paved the way for more portable and precise disease diagnosis.