Boosted hydrogen evolution kinetics of heteroatom-doped carbons with isolated Zn as an accelerant.

Carbon-based single-atom catalysts, a promising candidate in electrocatalysis, offer insights into electron-donating effects of metal center on adjacent atoms. Herein, we present a practical strategy to rationally design a model catalyst with a single zinc (Zn) atom coordinated with nitrogen and sulfur atoms in a multilevel carbon matrix. The Zn site exhibits an atomic interface configuration of ZnN4S1, where Zn's electron injection effect enables thermal-neutral hydrogen adsorption on neighboring atoms, pushing the activity boundaries of carbon electrocatalysts toward electrochemical hydrogen evolution to an unprecedented level. Experimental and theoretical analyses confirm the low-barrier Volmer-Tafel mechanism of proton reduction, while the multishell hollow structures facilitate the hydrogen evolution even at high current intensities. This work provides insights for understanding the actual active species during hydrogen evolution reaction and paves the way for designing high-performance electrocatalysts.

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules.

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

Preclinical Pharmacology Characterization of Sovleplenib (HMPL-523), an Orally Available Syk Inhibitor.

Spleen tyrosine kinase (Syk) is an intracellular tyrosine kinase involved in the signal transduction in immune cells mainly. Its aberrant regulation is associated with diversified allergic disorders, autoimmune diseases and B cell malignancies. Therefore, inhibition of Syk is considered a reasonable approach to treat autoimmune/inflammatory diseases and B cell malignancies. Here we described the preclinical characterization of sovleplenib, a novel, highly potent and selective, oral Syk inhibitor, in several rodent autoimmune disease models. Sovleplenib potently inhibited Syk activity in a recombinant enzymatic assay and Syk-dependent cellular functions in various immune cell lines and human whole blood in vitro. Furthermore, sovleplenib, by oral administration, demonstrated strong in vivo efficacies in murine models of immune thrombocytopenia (ITP), autoimmune hemolytic anemia (AIHA), and chronic graft-versus-host disease (cGVHD), and a rat model of collagen induced arthritis (CIA) respectively, in a dose-dependent manner. Collectively, these results clearly supported sovleplenib as a therapeutic agent in the treatment of autoimmune diseases. Sovleplenib is being globally developed for ITP (Phase III, NCT05029635, Phase Ib/II, NCT03951623), wAIHA (Phase II/III, NCT05535933) and B-cell lymphoma (Phase I, NCT02857998, NCT03779113). SIGNIFICANCE STATEMENT: Syk is a key mediator of signaling pathways downstream of a wide array of receptors important for immune functions, including the B cell receptor, immunoglobulin receptors bearing Fc receptors. Inhibition of Syk could provide a novel therapeutic approach for autoimmune diseases and hematologic malignancies. The manuscript describes the preclinical pharmacology characterization of sovleplenib, a novel Syk inhibitor, in enzymatic and cellular assays in vitro and several murine autoimmune disease models in vivo.

Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction.

BACKGROUND: There is still limited research on the prognostic value of Presepsin as a biomarker for predicting the outcome of COVID-19 patients. Additionally, research on the combined predictive value of Presepsin with clinical scoring systems and inflammation markers for disease prognosis is lacking. METHODS: A total of 226 COVID-19 patients admitted to Beijing Youan Hospital's emergency department from May to November 2022 were screened. Demographic information, laboratory measurements, and blood samples for Presepsin levels were collected upon admission. The predictive value of Presepsin, clinical scoring systems, and inflammation markers for 28-day mortality was analyzed. RESULTS: A total of 190 patients were analyzed, 83 (43.7%) were mild, 61 (32.1%) were moderate, and 46 (24.2%) were severe/critically ill. 23 (12.1%) patients died within 28 days. The Presepsin levels in severe/critical patients were significantly higher compared to moderate and mild patients (p < 0.001). Presepsin showed significant predictive value for 28-day mortality in COVID-19 patients, with an area under the ROC curve of 0.828 (95% CI: 0.737-0.920). Clinical scoring systems and inflammation markers also played a significant role in predicting 28-day outcomes. After Cox regression adjustment, Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR, and LCR were identified as independent predictors of 28-day mortality in COVID-19 patients (all p-values < 0.05). Combining Presepsin with clinical scoring systems and inflammation markers further enhanced the predictive value for patient prognosis. CONCLUSION: Presepsin is a favorable indicator for the prognosis of COVID-19 patients, and its combination with clinical scoring systems and inflammation markers improved prognostic assessment.

TMEM160 promotes tumor immune evasion and radiotherapy resistance via PD-L1 binding in colorectal cancer.

BACKGROUND: The effectiveness of anti-programmed cell death protein 1(PD-1)/programmed cell death 1 ligand 1(PD-L1) therapy in treating certain types of cancer is associated with the level of PD-L1. However, this relationship has not been observed in colorectal cancer (CRC), and the underlying regulatory mechanism of PD-L1 in CRC remains unclear. METHODS: Binding of TMEM160 to PD-L1 was determined by co-immunoprecipitation (Co-IP) and GST pull-down assay.The ubiquitination levels of PD-L1 were verified using the ubiquitination assay. Phenotypic experiments were conducted to assess the role of TMEM160 in CRC cells. Animal models were employed to investigate how TMEM160 contributes to tumor growth.The expression and clinical significance of TMEM160 and PD-L1 in CRC tissues were evaluated by immunohistochemistry(IHC). RESULTS: In our study, we made a discovery that TMEM160 interacts with PD-L1 and plays a role in stabilizing its expression within a CRC model. Furthermore, we demonstrated that TMEM160 hinders the ubiquitination-dependent degradation of PD-L1 by competing with SPOP for binding to PD-L1 in CRC cells. Regarding functionality, the absence of TMEM160 significantly inhibited the proliferation, invasion, metastasis, clonogenicity, and radioresistance of CRC cells, while simultaneously enhancing the cytotoxic effect of CD8 + T cells on tumor cells. Conversely, the upregulation of TMEM160 substantially increased these capabilities. In severely immunodeficient mice, tumor growth derived from lentiviral vector shTMEM160 cells was lower compared with that derived from shNC control cells. Furthermore, the downregulation of TMEM160 significantly restricted tumor growth in immune-competent BALB/c mice. In clinical samples from patients with CRC, we observed a strong positive correlation between TMEM160 expression and PD-L1 expression, as well as a negative correlation with CD8A expression. Importantly, patients with high TMEM160 expression exhibited a worse prognosis compared with those with low or no TMEM160 expression. CONCLUSIONS: Our study reveals that TMEM160 inhibits the ubiquitination-dependent degradation of PD-L1 that is mediated by SPOP, thereby stabilizing PD-L1 expression to foster the malignant progress, radioresistance, and immune evasion of CRC cells. These findings suggest that TMEM160 holds potential as a target for the treatment of patients with CRC.

Regulation of SiO2 Nanoparticles on the Adsorptive Fractionation of Dissolved Organic Matter by Goethite.

SiO2 nanoparticles (SiO2NPs) are most widely available and coexisting with DOM at the mineral-water interface; however, the role of SiO2NPs in DOM fractionation and the underlying mechanisms have not been fully understood. Using Fourier transform ion cyclotron resonance mass spectrometry, combined with Fourier transform infrared spectroscopy and X-ray adsorption fine structure spectroscopy, was employed to investigate the adsorptive fractionation of litter layer-derived DOM on goethite coexisting with SiO2NPs under different pH conditions. Results indicated that the inhibitory effect of the coexisting SiO2NPs on OM sorbed by goethite was waning as environmental pH increased due to the reduced steric interactions and the concurrent elevated hydrogen bonding/hydrophobic partitioning interactions on the goethite surface. We observed the coexisting SiO2NPs inhibited the adsorption of high carboxylic-containing condensed aromatic/aromatics compounds on goethite under different pH conditions while improving the adsorption of highly unsaturated aliphatic/phenolic and carbohydrate-like compounds in an alkaline and/or circumneutral environment. More nitrogen-containing structures may favor the adsorption of phenolic and nonaromatic compounds to goethite by counteracting the negative effect of SiO2NPs. These findings suggest that DOM sequestration may be significantly regulated by the coexisting SiO2NPs at the mineral-water interface, which may further influence the carbon-nitrogen cycling and contaminant fate in natural environments.

Artificial Intelligence-Assisted RFID Tag-Integrated Multi-Sensor for Quality Assessment and Sensing.

Radio frequency identification (RFID) is well known as an identification, track, and trace approach and is considered to be the key physical layer technology for the industrial internet of things (IIoT). However, IIoT systems have to introduce additional complex sensor networks for pervasive monitoring, and there are still challenges related to item-level sensing and data recording. To overcome the shortage, this work proposes an artificial intelligence (AI)-assisted RFID-based multi-sensing technology. Both passive and semi-passive RFID tag-integrated multi-sensors are developed. The main contributions and the novelty of this investigation are as follows. A UHF RFID tag-integrated multi-sensor with a boosted charge pump is proposed; it enables high RF signal sensitivity and a long operational range. The whole hardware design, including the antenna and energy harvester, are studied. Moreover, a demonstration with real-world ham product sensing data is conducted. This work also proposes and successfully demonstrates the integration of machine learning algorithms, specifically the NARX neural network, with RFID sensing data for food product quality assessment and sensing (QAS). This application of machine learning to RFID-generated data for quality assessment is also a novel aspect of the research. The deployment of an autoregressive model with an exogenous input (NARX) neural network model, tailored for nonlinear processes, emerges as the most effective, achieving a root mean square error (RMSE) of 0.007 and an R-squared value of 0.99 for ham product QAS. By deploying the technology, low-cost, timely, and flexible product QAS can be achieved in manufacturing industries, which helps product quality improvement and the optimization of the manufacturing line and supply chain.

Urban network noise control based on road grade optimization considering comprehensive traffic environment benefit.

A double-decision optimization model based on the road grade optimization strategy and considered comprehensive traffic environment benefit is proposed to control the traffic noise. The upper-level model maximizes the comprehensive traffic environment benefit, including network noise emission and traffic efficiency. Adjusting the emphasis on noise optimization benefits and traffic efficiency in road network planning through setting weights. The lower-level resolves the question of network traffic flow assignment using a stochastic user-equilibrium model. The increase of traffic environment demand, network noise emissions decrease and travel time rises. In the case, with a low environmental requirement (weighting with 1.1), the sound pressure emission of the network decreases by 9.23% with only a 4.01% increase in travel time. Under the high environmental requirement (weighting with 0.2), the sound pressure decreases by 26.8%, but the travel time rises by as high as 30.9%. The network is optimized towards road grade degradation and is the first to optimize the arterial roads. In addition, it is found that the influence of speed on traffic noise is greater than that of traffic volume through case validation. This method proposing traffic noise optimization strategies at the road network planning level provides technical support for the proactive governance of traffic noise pollution and the improvement of traffic sound environment quality.

Correlating Structural Disorder in Metal (Oxy)hydroxides and Catalytic Activity in Electrocatalytic Oxygen Evolution.

Understanding the correlation between the structural evolution of electrocatalysts and their catalytic activity is both essential and challenging. In this study, we investigate this correlation in the context of the oxygen evolution reaction (OER) by examining the influence of structural disorder during and after dynamic structural evolution on the OER activity of Fe-Ni (oxy)hydroxide catalysts using operando X-ray absorption spectroscopy, alongside other experiments and theoretical calculations. The Debye-Waller factors obtained from extended X-ray absorption fine structure analyses reflect the degree of structural disorder and exhibit a robust correlation with the intrinsic OER activities of the electrocatalysts. The enhanced OER activity of in situ-generated metal (oxy)hydroxides derived from different pre-catalysts is linked to increased structural disorder, offering a promising approach for designing efficient OER electrocatalysts. This strategy may inspire similar investigations in related electrocatalytic energy-conversion systems.

Evaluating test-retest reliability and sex-/age-related effects on temporal clustering coefficient of dynamic functional brain networks.

The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.

Ultra-Highly Active Ni-Doped MOF-5 Heterogeneous Catalysts for Ethylene Dimerization.

Here, an ultra-highly active Ni-MOF-5 catalyst with high Ni loading for ethylene dimerization is reported. The Ni-MOF-5 catalysts are synthesized by a facile one-pot co-precipitation method at room temperature, where Ni2+ replaces Zn2+ in MOF-5. Unlike Zn2+ with tetrahedral coordination in MOF-5, Ni2+ is coordinated with extra solvent molecules except for four-oxygen from the framework. After removing coordinated solvent molecules, Ni-MOF-5 achieves an ethylene turnover frequency of 352 000 h-1 , corresponding to 9040 g of product per gram of catalyst per hour, at 35 °C and 50 bar, far exceeding the activities of all reported heterogeneous catalysts. The high Ni loading and full exposure structure account for the excellent catalytic performance. Isotope labeling experiments reveal that the catalytic process follows the Cossee-Arlman mechanism, rationalizing the high activity and selectivity of the catalyst. These results demonstrate that Ni-MOF-5 catalysts are very promising for industrial catalytic ethylene dimerization.

Downregulation of glycoprotein non-metastatic melanoma protein B prevents high glucose-induced angiogenesis in diabetic retinopathy.

Diabetic retinopathy (DR), a microvascular complication characterized by abnormal angiogenesis, is the most common reason for irreversible blindness. Glycoprotein non-metastatic melanoma protein B (GPNMB), as a transmembrane protein, was found to be associated with angiogenesis. This study aims to investigate the role of GPNMB in DR. The levels of GPNMB and Integrin ß1 were detected by real-time PCR and western blot and were found to be increased in human retinal microvascular endothelial cells (HRMECs) with high glucose (HG, 25 mmol/L) treatment. Knockdown of GPNMB was mediated by lentivirus carrying shRNA targeting GPNMB in vivo and in vitro. Functional experiments, including cell counting kit-8 (CCK-8), scratch, and tube formation assays, showed the anti-proliferative, anti-migrative, and anti-angiogenic roles of GPNMB knockdown in HRMECs using the lentivirus system following HG challenge. Additionally, increased GPNMB levels were detected in the retina of DR rats induced by a single intraperitoneal injection of streptozotocin (60 mg/kg) using real-time PCR, western blot, and immunofluorescence assays. Downregulation of GPNMB inhibited the angiogenesis and vascular endothelial growth factor production in the retina of rats with DR. Furthermore, overexpression of Integrin ß1 led to increased angiogenesis in DR. Integrin ß1 was indicated as a target protein of GPNMB. Upregulated-Integrin ß1 restored GPNMB knockdown-induced inhibition of cell viability, migration, and tube formation in HRMECs. In conclusion, we provide evidence for the angiogenic role of GPNMB and demonstrate that silencing GPNMB may represent a therapeutic potential in the treatment of DR.

Density functional theory based computational investigations on the stability of highly active trimetallic PtPdCu nanoalloys for electrochemical oxygen reduction.

Activity, cost, and durability are the trinity of catalysis research for the electrochemical oxygen reduction reaction (ORR). While studies towards increasing activity and reducing cost of ORR catalysts have been carried out extensively, much effort is needed in durability investigation of highly active ORR catalysts. In this work, we examined the stability of a trimetallic PtPdCu catalyst that has demonstrated high activity and incredible durability during ORR using density functional theory (DFT) based computations. Specifically, we studied the processes of dissolution/deposition and diffusion between the surface and inner layer of Cu species of Pt20Pd20Cu60 catalysts at electrode potentials up to 1.2 V to understand their role towards stabilizing Pt20Pd20Cu60 catalysts. The results show there is a dynamic Cu surface composition range that is dictated by the interplay of the four processes, dissolution, deposition, diffusion from the surface to inner layer, and diffusion from the inner layer to the surface of Cu species, in the stability and observed oscillation of lattice constants of Cu-rich PtPdCu nanoalloys.

Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides.

The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.

Molecular size-dependent compositions and lead (II) binding behaviors of two origins of organic fertilizers-derived dissolved organic matter.

The application of organic fertilizers caused large amounts of dissolved organic matter (DOM) entering the soil environment and influencing the behaviors and fates of heavy metals. Here, we investigated the molecular weight-dependent (high molecular weight [HMW], 1 kDa-0.7 µm; low molecular weight [LMW], <1 kDa) compositions and lead (Pb) binding behaviors of DOM derived from sheep manure-based (SMOF) and shrimp peptide-based organic fertilizers (SPOF) using chromophoric and fluorescent spectroscopy, Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and two-dimensional correlation spectroscopy (2D-COS). Results showed that SMOF released more DOM with higher aromaticity and hydrophobicity, containing more fluvic-like components, carboxylic-rich alicyclic molecules (CRAMs) and lignin phenolic compounds compared to SPOF-DOM with more microbially-transformed heteroatom-containing compounds (CHON, CHONS and CHOS). Furthermore, there was more aromatic compounds with ample carboxyl and hydroxyl groups in HMW-DOM but abundant protein-like components and heteroatom-containing compounds (CHONS and CHOS) in LMW-DOM. SMOF-DOM exhibited more obvious MW-dependent heterogeneity in molecular components compared to SPOF-DOM with higher molecular diversity. Moreover, 2D-COS indicated phenol and carboxyl groups in SMOF-DOM and polysaccharides in SPOF-DOM exhibited superior binding affinities for Pb. Pb binding to HMW-DOM derived from SMOF first occurred in the phenolic groups in fulvic-like substances, while polysaccharides in LMW-DOM first participated in the binding of Pb. In contrast, irrespective of MWs, polysaccharides and humic-like substances with aromatic (CC) groups in SPOF-DOM displayed a faster response to Pb. Furthermore, the polysaccharides which preferentially participated in the binding of Pb to SPOF-DOM and SMOF-derived LMW-DOM may pose a higher risk of Pb in the environment. These results were helpful to understand the effects of sources and size-dependent compositions of DOM on the associated risks of heavy metals in the environments.

An Acoustic Localization Sensor Based on MEMS Microphone Array for Partial Discharge.

Partial discharge (PD) localization is important for monitoring and maintaining high-voltage equipment, which can help to prevent accidents. In this work, an acoustic localization sensor based on microelectromechanical system (MEMS) microphone array is proposed, which can detect and locate the partial discharge through a beam-forming algorithm. The MEMS microphone array consists of eight commercial MEMS microphones (SPV08A0LR5H-1, Knowles Electronics, IL, USA) with an aperture size of about 0.1 m × 0.1 m, allowing for a small hardware size and low cost. In order to optimize the acoustic performance of the array, a random array topology is designed. The simulation analysis indicates that the designed random topology is superior to several commonly used topologies. In terms of the localization algorithm, a deconvolution method called Fourier-based fast iterative shrinkage thresholding algorithm (FFT-FISTA) is applied. Simulation and experiment results demonstrate that FFT-FISTA used in the proposed acoustic localization sensor has significant advantages over the conventional beam-forming algorithm on spatial resolution and sidelobe suppression. Experimental results also show that the average localization error of the proposed scheme is about 0.04 m, which can meet the demands of practical application.

Comprehensive Analysis of Cellular Senescence-Related Genes in Prognosis, Molecular Characterization and Immunotherapy of Hepatocellular Carcinoma.

BACKGROUND: Cellular senescence is a tumor suppressive response in which the cell cycle is in a state of permanent arrest and can inhibit tumor cell proliferation. In recent years, induction of cellular senescence has been shown to be important for antitumor therapy, and the link between cellular senescence and clinical prognosis and immunotherapy of hepatocellular carcinoma is still unknown. METHODS: We performed enrichment analysis of genes in three cellular senescence gene sets, screened for gene sets significantly enriched in hepatocellular carcinoma and extracted genes from them. Signature were constructed using senescence-related genes, and their expression was verified at the protein and RNA levels. Survival, clinical staging and grading, immune infiltration, immunotherapy, and drug sensitivity were also analyzed between risk groups. RESULTS: The q-PCR and immunohistochemistry results revealed significant differences in the expression of the signature genes between normal and tumor tissues. Significant differences in clinicopathological features, prognosis and immune infiltration were observed between risk groups. In the low-risk group, better OS and lower TMB scores were demonstrated, while the high-risk group had higher immune checkpoint expression, as well as lower risk of immune escape. In addition, we found that the High-risk group was more sensitive to sorafenib. CONCLUSION: In summary, the signature constructed using aging-related genes can reliably predict patient prognosis and immunotherapy efficacy, providing a new idea for immune system therapy of hepatocellular carcinoma.

An integrative multi-omics analysis based on liquid-liquid phase separation delineates distinct subtypes of lower-grade glioma and identifies a prognostic signature.

BACKGROUND: Emerging evidences have indicated that the aberrant liquid-liquid phase separation (LLPS) leads to the dysfunction of biomolecular condensates, thereby contributing to the tumorigenesis and progression. Nevertheless, it remains unclear whether or how the LLPS of specific molecules affects the prognosis and tumor immune microenvironment (TIME) of patients with lower-grade glioma (LGG). METHODS: We integrated the transcriptome information of 3585 LLPS-related genes to comprehensively evaluate the LLPS patterns of 423 patients with LGG in The Cancer Genome Atlas (TCGA) cohort. Then, we systematically demonstrated the differences among four LLPS subtypes based on multi-omics analyses. In addition, we constructed the LLPS-related prognostic risk score (LPRS) for individualized integrative assessment. RESULTS: Based on the expression profiles of 85 scaffolds, 355 regulators, and 3145 clients in LGG, we identified four LLPS subtypes, namely LS1, LS2, LS3 and LS4. We confirmed that there were significant differences in prognosis, clinicopathological features, cancer hallmarks, genomic alterations, TIME patterns and immunotherapeutic responses among four LLPS subtypes. In addition, a prognostic signature called LPRS was constructed for individualized integrative assessment. LPRS exhibited a robust predictive capacity for prognosis of LGG patients in multiple cohorts. Moreover, LPRS was found to be correlated with clinicopathological features, cancer hallmarks, genomic alterations and TIME patterns of LGG patients. The predictive power of LPRS in response to immune checkpoint inhibitor (ICI) therapy was also prominent. CONCLUSIONS: This study provided a novel classification of LGG patients based on LLPS. The constructed LPRS might facilitate individualized prognosis prediction and better immunotherapy options for LGG patients.

Unidirectional coupling and efficient detection of near-infrared surface plasmon polaritons for on-chip optoelectronic interconnection.

Plasmonic interconnection is one kind of the possible methods to construct next-generation optoelectronic integrated circuits. In this paper, the plasmonic interconnection device based on Ge in infrared band is constructed, through efficient electron-hole pair generation, the device can achieve high photocurrent response (0.25A/W). Because of the low plasmon coupling efficiency of the conventional basic periodic gratings, this paper optimized the design of the coupling structure and improved the coupling efficiency by 4 times through constructing a binary Bragg/periodic grating coupler which can realize unidirectional plasmon coupling with a simulated extinction ratio of 12.5 dB. The devices can be easily fabricated by single-step electron beam lithography and lift-off process. The experimental results verified a 3.5 times improvement in the SPPs current of the designed plasmonic interconnection device, which provides a technical path to realize efficient plasmon transmission and detection for on-chip optoelectronic interconnection.

Compact multispectral photodetectors based on nanodisk arrays atop optical cavity substrates.

It is challenging for the multi-spectral photodetector to have a compact structure, high spectral resolution, and high detection efficiency. This paper reports on a new approach for compact multi-spectral visible light detecting based on the hexagonal lattice silver nanodisk arrays atop optical cavity substrates. Through numerical calculations and optimizations of experiments, we verified that the narrow band responsivity of the photodetector was caused by coupling the surface plasmonic resonances and cavity mode. The multi-spectral photodetector exhibited that the minimum FWHM and the maximum responsivity of was achieved to be 80 nm and 91.5 mA·W-1, respectively. Besides, we also analyzed the influence of the proposed structure on the energy wastage by numerical comparison. The proposed way for multi-spectral photodetector is promising to be an excellent design for the narrow band spectral detection. The design can also be easily integrated with CMOS devices and applied to other spectral regimes for different applications.