Utilizing multimodal AI to improve genetic analyses of cardiovascular traits.

Electronic health records, biobanks, and wearable biosensors contain multiple high-dimensional clinical data (HDCD) modalities (e.g., ECG, Photoplethysmography (PPG), and MRI) for each individual. Access to multimodal HDCD provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a novel multimodal deep learning method, M-REGLE, for discovering genetic associations from a joint representation of multiple complementary HDCD modalities. We showcase the effectiveness of this model by applying it to several cardiovascular modalities. M-REGLE jointly learns a lower representation (i.e., latent factors) of multimodal HDCD using a convolutional variational autoencoder, performs genome wide association studies (GWAS) on each latent factor, then combines the results to study the genetics of the underlying system. To validate the advantages of M-REGLE and multimodal learning, we apply it to common cardiovascular modalities (PPG and ECG), and compare its results to unimodal learning methods in which representations are learned from each data modality separately, but the downstream genetic analyses are performed on the combined unimodal representations. M-REGLE identifies 19.3% more loci on the 12-lead ECG dataset, 13.0% more loci on the ECG lead I + PPG dataset, and its genetic risk score significantly outperforms the unimodal risk score at predicting cardiac phenotypes, such as atrial fibrillation (Afib), in multiple biobanks.

Discovery and Development of Luvangetin from Zanthoxylum avicennae as a New Fungicide Candidate for Fusarium verticillioides.

Pathogenic fungi pose a significant threat to crop yields and human healthy, and the subsequent fungicide resistance has greatly aggravated these agricultural and medical challenges. Hence, the development of new fungicides with higher efficiency and greater environmental friendliness is urgently required. In this study, luvangetin, isolated and identified from the root of Zanthoxylum avicennae, exhibited wide-spectrum antifungal activity in vivo and in vitro. Integrated omics and in vitro and in vivo transcriptional analyses revealed that luvangetin inhibited GAL4-like Zn(II)2Cys6 transcriptional factor-mediated transcription, particularly the FvFUM21-mediated FUM cluster gene expression, and decreased the biosynthesis of fumonisins inFusarium verticillioides. Moreover, luvangetin binds to the double-stranded DNA helix in vitro in the groove mode. We isolated and identified luvangetin, a natural metabolite from a traditional Chinese edible medicinal plant and uncovered its multipathogen resistance mechanism. This study is the first to reveal the mechanism underlying the antifungal activity of luvangetin and provides a promising direction for the future use of plant-derived natural products to prevent and control plant and animal pathogenic fungi.

DyCARS: A dynamic context-aware recommendation system.

Dynamic recommendation systems aim to achieve real-time updates and dynamic migration of user interests, primarily utilizing user-item interaction sequences with timestamps to capture the dynamic changes in user interests and item attributes. Recent research has mainly centered on two aspects. First, it involves modeling the dynamic interaction relationships between users and items using dynamic graphs. Second, it focuses on mining their long-term and short-term interaction patterns. This is achieved through the joint learning of static and dynamic embeddings for both users and items. Although most existing methods have achieved some success in modeling the historical interaction sequences between users and items, there is still room for improvement, particularly in terms of modeling the long-term dependency structures of dynamic interaction histories and extracting the most relevant delayed interaction patterns. To address this issue, we proposed a Dynamic Context-Aware Recommendation System for dynamic recommendation. Specifically, our model is built on a dynamic graph and utilizes the static embeddings of recent user-item interactions as dynamic context. Additionally, we constructed a Gated Multi-Layer Perceptron encoder to capture the long-term dependency structure in the dynamic interaction history and extract high-level features. Then, we introduced an Attention Pooling network to learn similarity scores between high-level features in the user-item dynamic interaction history. By calculating bidirectional attention weights, we extracted the most relevant delayed interaction patterns from the historical sequence to predict the dynamic embeddings of users and items. Additionally, we proposed a loss function called the Pairwise Cosine Similarity loss for dynamic recommendation to jointly optimize the static and dynamic embeddings of two types of nodes. Finally, extensive experiments on two real-world datasets, LastFM, and the Global Terrorism Database showed that our model achieves consistent improvements over state-of-the-art baselines.

Terpolymer Containing a meta-Octyloxy-phenyl-Modified Dithieno[3,2-f:2',3'-h]quinoxaline Unit Enabling Efficient Organic Solar Cells.

With the rapid development of small-molecule electron acceptors, polymer electron donors are becoming more important than ever in organic photovoltaics, and there is still room for the currently available high-performance polymer donors. To further develop polymer donors with finely tunable structures to achieve better photovoltaic performances, random ternary copolymerization is a useful technique. Herein, by incorporating a new electron-withdrawing segment 2,3-bis(3-octyloxyphenyl)dithieno[3,2-f:2',3'-h]quinoxaline derivative (C12T-TQ) to PM6, a series of terpolymers were synthesized. It is worth noting that the introduction of the C12T-TQ unit can deepen the highest occupied molecular orbital energy levels of the resultant polymers. In addition, the polymer Z6 with a 10% C12T-TQ ratio possesses the highest film absorption coefficient (9.86 × 104 cm-1) among the four polymers. When blended with Y6, it exhibited superior miscibility and mutual crystallinity enhancement between Z6 and Y6, suppressed recombination, better exciton separation and charge collection characteristics, and faster hole transfer in the D-A interface. Consequently, the device of Z6:Y6 successfully achieved enhanced photovoltaic parameters and yielded an efficiency of 17.01%, higher than the 16.18% of the PM6:Y6 device, demonstrating the effectiveness of the meta-octyloxy-phenyl-modified dithieno[3,2-f:2',3'-h]quinoxaline moiety to build promising terpolymer donors for high-performance organic solar cells.

Binding of the plant-derived toxin simplexin to bovine protein kinase C: insights from molecular dynamics.

Pimelea poisoning of cattle is toxicologically linked to the activation of bovine protein kinase C (PKC) by the plant-derived toxin simplexin. To understand the affinity of PKC for simplexin, we performed molecular dynamics (MD) studies of simplexin, simplexin analogues, and several other activators of PKC. Binding enthalpy calculations indicated that simplexin had the strongest affinity for PKCα-C1B among the activators studied. Key to simplexin's affinity is its ability to form more hydrogen bonds to PKC, compared to the other activators. The C-3 carbonyl group and C-20 hydroxyl group of simplexin were identified as especially important for stabilizing the PKC binding interaction. The hydrophobic alkyl chain of simplexin induces deep membrane embedding of the PKC-simplexin complex, enhancing the protein-ligand hydrogen bonding. Our findings align with previous experiments on structure-activity relationships (SAR) for simplexin analogues, and provide insights that may guide the development of interventions or treatments for Pimelea poisoning.

Automatic origin prediction of liver metastases via hierarchical artificial-intelligence system trained on multiphasic CT data: a retrospective, multicentre study.

Background: Currently, the diagnostic testing for the primary origin of liver metastases (LMs) can be laborious, complicating clinical decision-making. Directly classifying the primary origin of LMs at computed tomography (CT) images has proven to be challenging, despite its potential to streamline the entire diagnostic workflow. Methods: We developed ALMSS, an artificial intelligence (AI)-based LMs screening system, to provide automated liver contrast-enhanced CT analysis for distinguishing LMs from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), as well as subtyping primary origin of LMs as six organ systems. We processed a CECT dataset between January 1, 2013 and June 30, 2022 (n = 3105: 840 HCC, 354 ICC, and 1911 LMs) for training and internally testing ALMSS, and two additional cohorts (n = 622) for external validation of its diagnostic performance. The performance of radiologists with and without the assistance of ALMSS in diagnosing and subtyping LMs was assessed. Findings: ALMSS achieved average area under the curve (AUC) of 0.917 (95% confidence interval [CI]: 0.899-0.931) and 0.923 (95% [CI]: 0.905-0.937) for differentiating LMs, HCC and ICC on both the internal testing set and external testing set, outperformed that of two radiologists. Moreover, ALMSS yielded average AUC of 0.815 (95% [CI]: 0.794-0.836) and 0.818 (95% [CI]: 0.790-0.842) for predicting six primary origins on both two testing sets. Interestingly, ALMSS assigned origin diagnoses for LMs with pathological phenotypes beyond the training categories with average AUC of 0.761 (95% [CI]: 0.657-0.842), which verify the model's diagnostic expandability. Interpretation: Our study established an AI-based diagnostic system that effectively identifies and characterizes LMs directly from multiphasic CT images. Funding: National Natural Science Foundation of China, Guangdong Provincial Key Laboratory of Medical Image Processing.

A hybrid transistor with transcriptionally controlled computation and plasticity.

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Visualization of Mesoscopic Conductivity Fluctuations in Amorphous Semiconductor Thin-Film Transistors.

Charge transport in amorphous semiconductors is considerably more complicated than the process in crystalline materials due to abundant localized states. In addition to device-scale characterization, spatially resolved measurements are important to unveiling electronic properties. Here, we report gigahertz conductivity mapping in amorphous indium gallium zinc oxide (a-IGZO) thin-film transistors by microwave impedance microscopy (MIM), which probes conductivity without Schottky barrier's influence. The difference between the dc and microwave conductivities reflects the efficacy of the injection barrier in an accumulation-mode transistor. The conductivity exhibits significant nanoscale inhomogeneity in the subthreshold regime, presumably due to trapping and release from localized states. The characteristic length scale of local fluctuations, as determined by the autocorrelation analysis, is about 200 nm. Using a random-barrier model, we can simulate the spatial variation of the potential landscape, which underlies the mesoscopic conductivity distribution. Our work provides an intuitive way to understand the charge transport mechanism in amorphous semiconductors at the microscopic level.

Characterizing Pairwise U-Turn Behavior in Fish: A Data-Driven Analysis.

We applied the time-series clustering method to analyze the trajectory data of rummy-nose tetra (Hemigrammus rhodostomus), with a particular focus on their spontaneous paired turning behavior. Firstly, an automated U-turn maneuver identification method was proposed to extract turning behaviors from the open trajectory data of two fish swimming in an annular tank. We revealed two distinct ways of pairwise U-turn swimming, named dominated turn and non-dominated turn. Upon comparison, the dominated turn is smoother and more efficient, with a fixed leader-follower relationship, i.e., the leader dominates the turning process. Because these two distinct ways corresponded to different patterns of turning feature parameters over time, we incorporated the Toeplitz inverse covariance-based clustering (TICC) method to gain deeper insights into this process. Pairwise turning behavior was decomposed into some elemental state compositions. Specifically, we found that the main influencing factor for a spontaneous U-turn is collision avoidance with the wall. In dominated turn, when inter-individual distances were appropriate, fish adjusted their positions and movement directions to achieve turning. Conversely, in closely spaced non-dominated turn, various factors such as changes in distance, velocity, and movement direction resulted in more complex behaviors. The purpose of our study is to integrate common location-based analysis methods with time-series clustering methods to analyze biological behavioral data. The study provides valuable insights into the U-turn behavior, motion characteristics, and decision factors of rummy-nose tetra during pairwise swimming. Additionally, the study extends the analysis of fish interaction features through the application of time-series clustering methods, offering a fresh perspective for the analysis of biological collective data.

Tunable Electronic Transport of New-Type 2D Iodine Materials Affected by the Doping of Metal Elements.

2D iodine structures under high pressures are more attractive and valuable due to their special structures and excellent properties. Here, electronic transport properties of such 2D iodine structures are theoretically studied by considering the influence of the metal-element doping. In equilibrium, metal elements in Group 1 can enhance the conductance dramatically and show a better enhancement effect. Around the Fermi level, the transmission probability exceeds 1 and can be improved by the metal-element doping for all devices. In particular, the device density of states explains well the distinctions between transmission coefficients originating from different doping methods. Contrary to the "big" site doping, the "small" site doping changes transmission eigenstates greatly, with pronounced electronic states around doped atoms. In non-equilibrium, the conductance of all devices is almost weaker than the equilibrium conductance, decreasing at low voltages and fluctuating at high voltages with various amplitudes. Under biases, K-big doping shows the optimal enhancement effect, and Mg-small doping exhibits the most effective attenuation effect on conductance. Contrastingly, the currents of all devices increase with bias linearly. The metal-element doping can boost current at low biases and weaken current at high voltages. These findings contribute much to understanding the effects of defects on electronic properties and provide solid support for the application of new-type 2D iodine materials in controllable electronics and sensors.

Collagenase-Responsive Hydrogel Loaded with GSK2606414 Nanoparticles for Periodontitis Treatment through Inhibiting Inflammation-Induced Expression of PERK of Periodontal Ligament Stem Cells.

GSK2606414 is a new, effective, highly selective PERK inhibitor with adenosine-triphosphate-competitive characteristics. It can inhibit endoplasmic reticulum stress and has the possibility of treating periodontitis. However, owing to its strong hydrophobicity and side effects, highly efficient pharmaceutical formulations are urgently needed to improve the bioavailability and therapeutic efficacy of GSK2606414 in the treatment of periodontitis. Herein, a novel local GSK2606414 delivery system was developed by synthesizing GSK2606414 nanoparticles (NanoGSK) and further loading NanoGSK into a collagenase-responsive hydrogel. The drug release results showed that the drug-loaded hydrogels had outstanding enzymatic responsive drug release profiles under the local microenvironment of periodontitis. Furthermore, in vitro studies showed that the drug-loaded hydrogel exhibited good cellular uptake and did not affect the growth and proliferation of normal cells, while the drug-loaded hydrogel significantly improved the osteogenic differentiation of inflammatory cells. In the evaluations of periodontal tissue repair, the drug-loaded hydrogels showed a great effect on inflammation inhibition, as well as alveolar bone regeneration. Therefore, this work introduces a promising strategy for the clinical treatment of periodontitis.

Co-embedding of edges and nodes with deep graph convolutional neural networks.

Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing framework exhibit a shallow structure, which hampers their ability to efficiently transmit information between distant nodes. To address this, we aim to propose a novel message-passing framework, enabling the construction of GNN models with deep architectures akin to convolutional neural networks (CNNs), potentially comprising dozens or even hundreds of layers. (2) Existing models often approach the learning of edge and node features as separate tasks. To overcome this limitation, we aspire to develop a deep graph convolutional neural network learning framework capable of simultaneously acquiring edge embeddings and node embeddings. By utilizing the learned multi-dimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features. To address these challenges, we propose the Co-embedding of Edges and Nodes with Deep Graph Convolutional Neural Networks (CEN-DGCNN). In our approach, we propose a novel message-passing framework that can fully integrate and utilize both node features and multi-dimensional edge features. Based on this framework, we develop a deep graph convolutional neural network model that prevents over-smoothing and obtains node non-local structural features and refined high-order node features by extracting long-distance dependencies between nodes and utilizing multi-dimensional edge features. Moreover, we propose a novel graph convolutional layer that can learn node embeddings and multi-dimensional edge embeddings simultaneously. The layer updates multi-dimensional edge embeddings across layers based on node features and an attention mechanism, which enables efficient utilization and fusion of both node and edge features. Additionally, we propose a multi-dimensional edge feature encoding method based on directed edges, and use the resulting multi-dimensional edge feature matrix to construct a multi-channel filter to filter the node information. Lastly, extensive experiments show that CEN-DGCNN outperforms a large number of graph neural network baseline methods, demonstrating the effectiveness of our proposed method.

A review of traditional Chinese medicine Curcumae Rhizoma for treatment of glioma.

Glioma is the most common primary central nervous tumor and its malignant and high recurrence rate are seriously threatening patient's life. The prognosis of glioma patients is still poor with a variety of modern treatments. Traditional Chinese medicine (TCM) is widely used in the adjuvant treatment or alternative medicine of glioma. Curcumae Rhizoma is one of the most commonly used in traditional Chinese medicine prescriptions for its anti-tumor characteristics. There are also many studies that reveals the anti-tumor effect of its active ingredients and some of which have been made into drugs and have been used in clinical practice. This review summarizes the new research progress on Curcumae Rhizoma for the treatment of glioma in recent years.

Synthetic 5-amino-6-D-ribitylaminouracil paired with inflammatory stimuli facilitates MAIT cell expansion in vivo.

Introduction: Mucosal-associated invariant T (MAIT) cells are a population of innate-like T cells, which mediate host immunity to microbial infection by recognizing metabolite antigens derived from microbial riboflavin synthesis presented by the MHC-I-related protein 1 (MR1). Namely, the potent MAIT cell antigens, 5-(2-oxopropylideneamino)-6-D-ribitylaminouracil (5-OP-RU) and 5-(2-oxoethylideneamino)-6-D-ribitylaminouracil (5-OE-RU), form via the condensation of the riboflavin precursor 5-amino-6-D-ribitylaminouracil (5-A-RU) with the reactive carbonyl species (RCS) methylglyoxal (MG) and glyoxal (G), respectively. Although MAIT cells are abundant in humans, they are rare in mice, and increasing their abundance using expansion protocols with antigen and adjuvant has been shown to facilitate their study in mouse models of infection and disease. Methods: Here, we outline three methods to increase the abundance of MAIT cells in C57BL/6 mice using a combination of inflammatory stimuli, 5-A-RU and MG. Results: Our data demonstrate that the administration of synthetic 5-A-RU in combination with one of three different inflammatory stimuli is sufficient to increase the frequency and absolute numbers of MAIT cells in C57BL/6 mice. The resultant boosted MAIT cells are functional and can provide protection against a lethal infection of Legionella longbeachae. Conclusion: These results provide alternative methods for expanding MAIT cells with high doses of commercially available 5-A-RU (± MG) in the presence of various danger signals.

The pharmacokinetic profile of brensocatib and its effect on pharmacodynamic biomarkers including NE, PR3, and CatG in various rodent species.

Brensocatib is a novel, oral, selective, reversible inhibitor of dipeptidyl peptidase 1 (DPP1), which activates several neutrophil serine proteases (NSPs), including neutrophil elastase (NE), proteinase 3 (PR3), and cathepsin G (CatG) in the bone marrow during the early stage of neutrophil maturation. These NSPs are associated with pathogen destruction and inflammatory mediation; their dysregulated activation can result in excess secretion of active NSPs causing damaging inflammation and contributing to neutrophil-mediated inflammatory and autoimmune diseases. Pharmacological inhibition of DPP1 in the bone marrow could therefore represent an attractive strategy for these neutrophil-driven diseases. A completed Phase 2 trial in non-cystic fibrosis bronchiectasis patients (ClinicalTrials.gov number NCT03218917; EudraCT number: 2017-002533-32) indeed demonstrated that administration of brensocatib attenuated the damaging effects of chronic inflammation by inhibiting the downstream activation of NSPs. To support a range of preclinical programs and further understand how rodent species and strains may affect brensocatib's pharmacokinetic (PK) profile and its pharmacodynamic (PD) effects on NE, PR3, and CatG, an extensive naïve dosing study with brensocatib at different dosing levels, frequencies, and durations was undertaken. Dose-dependent PK exposure responses (AUC and Cmax) were observed regardless of the rodent species and strain. Overall, mice showed greater reduction in NSP activities compared to rats. Both mice and rats dosed once daily (QD) had equivalent NSP activity reduction compared to BID (twice a day) dosing when the QD dose was 1.5-times the BID daily dose. For both mouse strains, CatG activity was reduced the most, followed by NE, then PR3; whereas, for both rat strains, PR3 activity was reduced the most, followed by CatG, and then NE. Maximum reduction in NSP activities was observed after ∼7 days and recoveries were nearly symmetrical. These results may facilitate future in vivo brensocatib study dosing considerations, such as the timing of prophylactic or therapeutic administration, choice of species, dosage and dosing frequency.

A Hybrid Transistor with Transcriptionally Controlled Computation and Plasticity.

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Brensocatib, an oral, reversible inhibitor of dipeptidyl peptidase 1, mitigates interferon-α-accelerated lupus nephritis in mice.

Neutrophils have been implicated in initiating and perpetuating systemic lupus erythematosus and the resultant kidney damage in lupus nephritis (LN) patients, in part through an excessive release of neutrophil serine proteases (NSPs). NSP zymogens are activated by dipeptidyl peptidase 1 (DPP1) during neutrophil maturation and released by mature neutrophils in response to inflammatory stimuli. Thus, a potential strategy to attenuate disease progression in LN would be to inhibit DPP1. We tested whether brensocatib, a highly selective and reversible DPP1 inhibitor, could mitigate LN progression in an interferon-alpha (IFNα)-accelerated NZB/W F1 mouse model. To confirm brensocatib's pharmacodynamic effect on NSPs in this mouse strain, repeated dose studies were conducted for 7 and 14 days in naïve NZB/W F1 mice via oral gavage twice a day. Brensocatib at 2 and 20 mg/kg/day achieved a significant reduction in bone marrow NSP activities after 7 days of daily administration. To initiate LN disease progression, the mice were injected with an IFNα-expressing adenovirus. After 2 weeks, three brensocatib doses (or vehicle) were administered for 6 more weeks. Throughout the 8-week study, brensocatib treatment (20 mg/kg/day) significantly reduced the occurrence of severe proteinuria compared to the vehicle control. Brensocatib treatment also entailed a significant reduction in the urine albumin-to-creatinine ratio, indicating decreased kidney damage, as well as a significant reduction in blood urea nitrogen level, suggesting improved renal function. Based on kidney histopathology analysis, brensocatib treatment significantly lowered both the renal tubular protein score and the nephropathy score compared to the vehicle group. A trend towards reduced glomerulonephritis score with brensocatib treatment was also observed. Lastly, brensocatib significantly reduced LN mouse kidney infiltration by various inflammatory cells. In conclusion, these results suggest that brensocatib alters disease progression in LN mice and warrant further evaluation of DPP1 inhibition in LN.

Noninvasive evaluation of neutrophil extracellular traps signature predicts clinical outcomes and immunotherapy response in hepatocellular carcinoma.

Background: Neutrophil extracellular traps (NETs) have been shown to play a pivotal role in promoting metastasis and immune escape in hepatocellular carcinoma (HCC). Therefore, noninvasive tests to detect the formation of NETs in tumors can have significant implications for the treatment and prognoses of patients. Here, we sought to develop and validate a computed tomography (CT)-based radiomics model to predict the gene expression profiles that regulate the formation of NETs in HCC. Methods: This study included 1133 HCC patients from five retrospective cohorts. Based on the mRNA expression levels of 69 biomarkers correlated with NET formation, a 6-gene score (NETs score, NETS) was constructed in cohort 1 from TCIA database (n=52) and validated in cohort 2 (n=232) from ICGC database and cohort 3 (n=365) from TCGA database. And then based on the radiomics features of CT images, a radiomics signature (RNETS) was developed in cohort 1 to predict NETS status (high- or low-NETS). We further employed two cohorts from Nanfang Hospital (Guangzhou, China) to evaluate the predictive power of RNETS in predicting prognosis in cohort 4 (n=347) and the responses to PD-1 inhibitor of HCC patients in cohort 5 (n=137). Results: For NETS, in cohort 1, the area under the curve (AUC) values predicting 1, 2, and 3-year overall survival (OS) were 0.836, 0.879, and 0.902, respectively. The low-NETS was associated with better survival and higher levels of immune cell infiltration. The RNETS yielded an AUC value of 0.853 in distinguishing between high-NETS or low-NETS and patients with low-RNETS were associated with significantly longer survival time in cohort 1 (P<0.001). Notably, the RNETS was competent in predicting disease-free survival (DFS) and OS in cohort 4 (P<0.001). In cohort 5, the RNETS was found to be an independent risk factor for progression-free survival (PFS) (P<0.001). In addition, the objective response rate of HCC patients treated with PD-1 inhibitor was significantly higher in the low-RNETS group (27.8%) than in the high-RNETS group (10.8%). Conclusions: This study revealed that RNETS as a radiomics biomarker could effectively predict prognosis and immunotherapy response in HCC patients.

Visual cortical LFP in relation to the hippocampal theta rhythm in track running rats.

Theta oscillations in the primary visual cortex (VC) have been observed during running tasks, but the mechanism behind their generation is not well understood. Some studies have suggested that theta in the VC is locally generated, while others have proposed that it is volume conducted from the hippocampus. The present study aimed to investigate the relationship between hippocampal and VC LFP dynamics. Analysis of power spectral density revealed that LFP in the VC was similar to that in the hippocampus, but with lower overall magnitude. As running velocity increased, both the power and frequency of theta and its harmonics increased in the VC, similarly to what is observed in the hippocampus. Current source density analysis triggered to theta did not identify distinct current sources and sinks in the VC, supporting the idea that theta in the VC is conducted from the adjacent hippocampus. Phase coupling between theta, its harmonics, and gamma is a notable feature in the hippocampus, particularly in the lacunosum moleculare. While some evidence of coupling between theta and its harmonics in the VC was found, bicoherence estimates did not reveal significant phase coupling between theta and gamma. Similar results were seen in the cross-region bicoherence analysis, where theta showed strong coupling with its harmonics with increasing velocity. Thus, theta oscillations observed in the VC during running tasks are likely due to volume conduction from the hippocampus.

A dynamic graph Hawkes process based on linear complexity self-attention for dynamic recommender systems.

The dynamic recommender system realizes the real-time recommendation for users by learning the dynamic interest characteristics, which is especially suitable for the scenarios of rapid transfer of user interests, such as e-commerce and social media. The dynamic recommendation model mainly depends on the user-item history interaction sequence with timestamp, which contains historical records that reflect changes in the true interests of users and the popularity of items. Previous methods usually model interaction sequences to learn the dynamic embedding of users and items. However, these methods can not directly capture the excitation effects of different historical information on the evolution process of both sides of the interaction, i.e., the ability of events to influence the occurrence of another event. In this work, we propose a Dynamic Graph Hawkes Process based on Linear complexity Self-Attention (DGHP-LISA) for dynamic recommender systems, which is a new framework for modeling the dynamic relationship between users and items at the same time. Specifically, DGHP-LISA is built on dynamic graph and uses Hawkes process to capture the excitation effects between events. In addition, we propose a new self-attention with linear complexity to model the time correlation of different historical events and the dynamic correlation between different update mechanisms, which drives more accurate modeling of the evolution process of both sides of the interaction. Extensive experiments on three real-world datasets show that our model achieves consistent improvements over state-of-the-art baselines.