Pain perception enhancement in consecutive second-eye phacoemulsification cataract surgeries under topical anesthesia.

Cataract is the main cause of visual impairment and blindness worldwide while the only effective cure for cataract is still surgery. Consecutive phacoemulsification under topical anesthesia has been the routine procedure for cataract surgery. However, patients often grumbled that they felt more painful during the second-eye surgery compared to the first-eye surgery. The intraoperative pain experience has negative influence on satisfaction and willingness for second-eye cataract surgery of patients with bilateral cataracts. Intraoperative ocular pain is a complicated process induced by the nociceptors activation in the peripheral nervous system. Immunological, neuropsychological, and pharmacological factors work together in the enhancement of intraoperative pain. Accumulating published literatures have focused on the pain enhancement during the second-eye phacoemulsification surgeries. In this review, we searched PubMed database for articles associated with pain perception differences between consecutive cataract surgeries published up to Feb. 1, 2024. We summarized the recent research progress in mechanisms and interventions for pain perception enhancement in consecutive second-eye phacoemulsification cataract surgeries. This review aimed to provide novel insights into strategies for improving patients' intraoperative experience in second-eye cataract surgeries.

Drug repositioning based on residual attention network and free multiscale adversarial training.

BACKGROUND: Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently needed. RESULTS: This paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease associations. CONCLUSIONS: The comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor for identifying unobserved disease-drug associations.

Scalable, Controlled Bimodal Pore-Structured Polymer Coating for Efficient Passive Daytime Radiative Cooling.

Outdoor thermal irritation poses a serious threat to public health, with the frequent occurrence of increasingly intense heat waves. With the global goal of carbon peaking and carbon neutrality, there is an urgent need for a strategy that is efficient and can provide localized outdoor cooling without an intensive energy input. This paper demonstrated a rapidly formable polyurethane-based coating with controlled bimodal spherical micropores. Nano-Al2O3 particles (300 nm) embedded in the polymer were used for targeted enhancement of reflectance at 0.38-0.5 wavelengths. The enhanced film reflected 93% solar irradiance and selectively transmitted 95% thermal radiation (8-13 µm), enabling rapid cooling and the creation of a comfortable thermal microclimate to avoid overheating of 6-11 °C during daytime conditions. The ultrawide material compatibility and excellent adaptive mechanical strength of polyurethane-based coatings are expected to benefit the sustainable development of society in a wide range of fields, from health to economics.

Ultrafast Dual-Shock Chemistry Synthesis of Ordered/Disordered Hybrid Carbon Anodes: High-Rate Performance of Li-Ion Batteries.

Graphite exhibits crystal anisotropy, which impedes the mass transfer of ion intercalation and extraction processes in Li-ion batteries. Herein, a dual-shock chemical strategy has been developed to synthesize the carbon anode. This approach comprised two key phases: (1) a thermal shock utilizing ultrahigh temperature (3228 K) can thermodynamically facilitate graphitization; (2) a mechanical shock (21.64 MPa) disrupting the π-π interactions in the aromatic chains of carbon can result in hybrid-structured carbon composed of crystalline and amorphous carbon. The optimized carbon (DSC-200-0.3) demonstrates a capacity of 208.61 mAh/g at a 10C rate, with a significant enhancement comparing with 15 mAh/g of the original graphite. Impressively, it maintains 81.06% capacity even after 3000 charge-discharge cycles. Dynamic process analysis reveals that this superior rate performance is attributed to a larger interlayer spacing facilitating ion transport comparing with the original graphite, disordered amorphous carbon for additional lithium storage sites, and crystallized carbon for enhanced charge transfer. The dual-shock chemical approach offers a cost-effective and efficient method to rapidly produce hybrid-structured carbon anodes, enabling 10C fast charging capabilities in lithium-ion batteries.

Prediction of Adolescents' Fluid Intelligence Scores based on Deep Learning with Reconstruction Regularization.

Objective: The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents. Methods: We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.0. A total of 148 ROIs were selected and 604 features were proposed by FreeSurfer segmentation. The training and testing sets were divided in a ratio of 7:3. To predict fluid intelligence scores, we used AE, MLP and classic machine learning models, and compared their performance on the test set. In addition, we explored their performance across gender subpopulations. Moreover, we evaluated the importance of features using the SHapley Additive Explain method. Results: The proposed model achieves optimal performance on the test set for predicting actual fluid intelligence scores (PCC = 0.209 ± 0.02, MSE = 105.212 ± 2.53). Results show that autoencoders with refactoring regularization are significantly more effective than MLPs and classical machine learning models. In addition, all models performed better on female adolescents than on male adolescents. Further analysis of relevant characteristics in different populations revealed that this may be related to gender differences in underlying fluid intelligence mechanisms. Conclusions: We construct a weak but stable correlation between brain structural features and raw fluid intelligence using autoencoders. Future research may need to explore ensemble regression strategies utilizing multiple machine learning algorithms on multimodal data in order to improve the predictive performance of fluid intelligence based on neuroimaging features.

EcoDetect-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Domestic Waste Exposure in Intricate Environmental Landscapes.

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model's capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO's adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development.

Neural network enabled fringe projection through scattering media.

The projection of fringes plays an essential role in many applications, such as fringe projection profilometry and structured illumination microscopy. However, these capabilities are significantly constrained in environments affected by optical scattering. Although recent developments in wavefront shaping have effectively generated high-fidelity focal points and relatively simple structured images amidst scattering, the ability to project fringes that cover half of the projection area has not yet been achieved. To address this limitation, this study presents a fringe projector enabled by a neural network, capable of projecting fringes with variable periodicities and orientation angles through scattering media. We tested this projector on two types of scattering media: ground glass diffusers and multimode fibers. For these scattering media, the average Pearson's correlation coefficients between the projected fringes and their designed configurations are 86.9% and 79.7%, respectively. These results demonstrate the effectiveness of the proposed neural network enabled fringe projector. This advancement is expected to broaden the scope of fringe-based imaging techniques, making it feasible to employ them in conditions previously hindered by scattering effects.

Investigating the Impact of Pineapple-Whey Protein Fermentation Products on Cefixime-Induced Intestinal Flora Dysbiosis in Mice Using 16S Sequencing and Untargeted Metabolomics Techniques.

In our previous study, a new fermented food (PWF) created by utilizing pineapple by-products and whey proteins as a matrix via co-fermentation with lactic acid bacteria and yeast was developed, and, in the current study, we examined the impact of a pineapple-whey protein fermentation product on a cefixime-induced dysbiosis model in mice using 16S sequencing and untargeted metabolomics techniques. The results indicated that the pineapple-whey protein fermentation product played a positive role in restoring the intestinal flora. In this study, cefixime reduced the overall abundance of intestinal flora and decreased the relative abundance of probiotics in the gut, while also inhibiting amino acid metabolism. The addition of PWF normalized the intestinal flora to a steady state, significantly increasing the populations of Weissella, Lactococcus, Faecalibaculum, and Bacteroides acidophilus, while decreasing the numbers of Akkermansia and Escherichia-Shigella. Additionally, PWF modulated microbial metabolites, such as L-glutamate and L-threonine, and upregulated amino-acid-related metabolic pathways, including those involving glycine, serine, and threonine. In conclusion, PWF can alleviate intestinal flora dysbiosis and metabolic disturbances induced by antibiotic interventions. It is suggested that PWF could be a potential dietary strategy for patients with antibiotic-associated diarrhea.

Approaching Ultimate Synthesis Reaction Rate of Ni-Rich Layered Cathodes for Lithium-Ion Batteries.

Nickel-rich layered oxide LiNixCoyMnzO2 (NCM, x + y + z = 1) is the most promising cathode material for high-energy lithium-ion batteries. However, conventional synthesis methods are limited by the slow heating rate, sluggish reaction dynamics, high energy consumption, and long reaction time. To overcome these challenges, we first employed a high-temperature shock (HTS) strategy for fast synthesis of the NCM, and the approaching ultimate reaction rate of solid phase transition is deeply investigated for the first time. In the HTS process, ultrafast average reaction rate of phase transition from Ni0.6Co0.2Mn0.2(OH)2 to Li- containing oxides is 66.7 (% s-1), that is, taking only 1.5 s. An ultrahigh heating rate leads to fast reaction kinetics, which induces the rapid phase transition of NCM cathodes. The HTS-synthesized nickel-rich layered oxides perform good cycling performances (94% for NCM523, 94% for NCM622, and 80% for NCM811 after 200 cycles at 4.3 V). These findings might also assist to pave the way for preparing effectively Ni-rich layered oxides for lithium-ion batteries.

Brain-Controlled Hand Exoskeleton Based on Augmented Reality-Fused Stimulus Paradigm.

Advancements in brain-machine interfaces (BMIs) have led to the development of novel rehabilitation training methods for people with impaired hand function. However, contemporary hand exoskeleton systems predominantly adopt passive control methods, leading to low system performance. In this work, an active brain-controlled hand exoskeleton system is proposed that uses a novel augmented reality-fused stimulus (AR-FS) paradigm as a human-machine interface, which enables users to actively control their fingers to move. Considering that the proposed AR-FS paradigm generates movement artifacts during hand movements, an enhanced decoding algorithm is designed to improve the decoding accuracy and robustness of the system. In online experiments, participants performed online control tasks using the proposed system, with an average task time cost of 16.27 s, an average output latency of 1.54 s, and an average correlation instantaneous rate (CIR) of 0.0321. The proposed system shows 35.37% better efficiency, 8.03% reduced system delay, and 35.28% better stability than the traditional system. This study not only provides an efficient rehabilitation solution for people with impaired hand function but also expands the application prospects of brain-control technology in areas such as human augmentation, patient monitoring, and remote robotic interaction. The video in Graphical Abstract Video demonstrates the user's process of operating the proposed brain-controlled hand exoskeleton system.

Coupling Antisite Defect and Lattice Tensile Stimulates Facile Isotropic Li-Ion Diffusion.

Despite widely used as a commercial cathode, the anisotropic 1D channel hopping of lithium ions along the [010] direction in LiFePO4 prevents its application in fast charging conditions. Herein, an ultrafast nonequilibrium high-temperature shock technology is employed to controllably introduce the Li-Fe antisite defects and tensile strain into the lattice of LiFePO4. This design makes the study of the effect of the strain field on the performance further extended from the theoretical calculation to the experimental perspective. The existence of Li-Fe antisite defects makes it feasible for Li+ to move from the 4a site of the edge-sharing octahedra across the ab plane to 4c site of corner-sharing octahedra, producing a new diffusion channel different from [010]. Meanwhile, the presence of a tensile strain field reduces the energy barrier of the new 2D diffusion path. In the combination of electrochemical experiments and first-principles calculations, the unique multiscale coupling structure of Li-Fe antisite defects and lattice strain promotes isotropic 2D interchannel Li+ hopping, leading to excellent fast charging performance and cycling stability (high-capacity retention of 84.4% after 2000 cycles at 10 C). The new mechanism of Li+ diffusion kinetics accelerated by multiscale coupling can guide the design of high-rate electrodes.

Paediatric off-label use of drugs in Gansu, China: a multicentre cross-sectional study.

OBJECTIVE: To examine the current prevalence and cost of paediatric off-label drug prescriptions in Gansu, China, and the potential influencing factors. DESIGN: The prevalence of off-label prescriptions in paediatrics was evaluated according to the National Medical Products Administration drug instructions in the China Pharmaceutical Reference (China Pharmaceutical Reference, MCDEX) database. The evidence of the prescription was determined by existing clinical practice guidelines and the Thomson Grade in the Micromedex 2021 compendium. We used logistic regression to investigate the characteristics that influence paediatric off-label drug use after single-factor regression analysis. SETTING: A multicentre cross-sectional study of outpatient paediatric prescriptions in 196 secondary and tertiary hospitals in Gansu Province, China, in March and September 2020. RESULTS: We retrieved 104 029 paediatric prescriptions, of which 39 480 (38.0%) contained off-label use. The most common diseases treated by off-label drugs were respiratory system diseases (n=15 831, 40.1%). A quarter of off-label prescriptions had adequate evidence basis (n=10 130, 25.6%). Unapproved indications were the most common type of off-label drug use (n=25 891, 65.6%). A total of 1177 different drugs were prescribed off-label, with multienzyme tablets being the most common drug (n=1790, 3.5%). The total cost of the prescribed off-label drugs was ¥106 116/day. Off-label prescriptions were less frequent in tertiary than in secondary hospitals. Topical preparations were more commonly prescribed off-label than other types of drugs. Senior-level clinicians prescribed drugs off-label more often than intermediate and junior clinicians. CONCLUSION: Off-label drug use is widespread in paediatric practice in China. Three-quarters of the prescriptions may potentially include inappropriate medication use, resulting in a daily economic burden of about ¥81 000 in 2020 in Gansu Province with 25 million inhabitants. The management of off-label drug use in paediatrics in China needs improvement.

Lightweight and robust cellulose/MXene/polyurethane composite aerogels as personal protective wearable devices for electromagnetic interference shielding.

The increasing electromagnetic pollution is urgently needed as an electromagnetic interference shielding protection device for wearable devices. Two-dimensional transition metal carbides and nitrides (MXene), due to their interesting layered structure and high electrical conductivity, are ideal candidates for constructing efficient conductive networks in electromagnetic interference shielding materials. In this work, lightweight and robust cellulose/MXene/polyurethane composite aerogels were prepared by mixing cellulose nanofiber (CNF) suspensions with MXene, followed by freeze-drying and coating with polyurethane. In this process, CNF effectively assembled MXene nanosheets into a conductive network by enhancing the interactions between MXene nanosheets. The prepared aerogel exhibited the shielding effectiveness of 48.59 dB in the X-band and an electrical conductivity of 0.34 S·cm-1. Meanwhile, the composite aerogel also possessed excellent thermal insulation, infrared stealth, mechanical and hydrophobic properties, and can be used as a wearable protective device to protect the human body from injuries in different scenarios while providing electromagnetic interference shielding protection.

A Dual-mode platform for the rapid detection of Escherichia coli O157:H7 based on CRISPR/Cas12a and RPA.

Escherichia coli O157:H7 (E. coli O157:H7) is a foodborne pathogenic microorganism that is commonly found in the environment and poses a significant threat to human health, public safety, and economic stability worldwide. Thus, early detection is essential for E. coli O157:H7 control. In recent years, a series of E. coli O157:H7 detection methods have been developed, but the sensitivity and portability of the methods still need improvement. Therefore, in this study, a rapid and efficient testing platform based on the CRISPR/Cas12a cleavage reaction was constructed. Through the integration of recombinant polymerase amplification and lateral flow chromatography, we established a dual-interpretation-mode detection platform based on CRISPR/Cas12a-derived fluorescence and lateral flow chromatography for the detection of E. coli O157:H7. For the fluorescence detection method, the limits of detection (LODs) of genomic DNA and E. coli O157:H7 were 1.8 fg/µL and 2.4 CFU/mL, respectively, within 40 min. Conversely, for the lateral flow detection method, LODs of 1.8 fg/µL and 2.4 × 102 CFU/mL were achieved for genomic DNA and E. coli O157:H7, respectively, within 45 min. This detection strategy offered higher sensitivity and lower equipment requirements than industry standards. In conclusion, the established platform showed excellent specificity and strong universality. Modifying the target gene and its primers can broaden the platform's applicability to detect various other foodborne pathogens.

Dynamics of nutrients, sensory quality and microbial communities and their interactions during co-fermentation of pineapple by-products and whey protein.

In this study, a new fermented food was developed using pineapple by-products and whey protein (2.6%) as raw materials through the co-fermentation of autochthonous lactic acid bacteria and yeast. To better understand the fermentation mechanism and the impact of microorganisms on the entire fermentation system, we tracked the changes in carbohydrate and amino acid profiles, organoleptic quality and microbial community during the fermentation process. Compared with unfermented samples, dietary fiber and free amino acids increased significantly as fermentation proceeded. The fermented samples were significantly lower in astringency and bitterness and significantly higher in sourness, umami and richness. The fermented products were richer in volatile compounds with floral, cheesy, fruity and other flavors. Relevant analyses showed that the core microbial community was highly correlated with the quality attributes of the fermented products. Microorganisms such as Lactococcus, Weissella, Hanseniaspora, Saccharomyces and Lachancea contributed significantly to the fermented products.

Curvature-enhanced graph convolutional network for biomolecular interaction prediction.

Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local geometric properties and enhance the learning capability of GCNs. More specifically, ORCs are evaluated based on the local topology from node neighborhoods, and further incorporated into the weight function for the feature aggregation in message-passing procedure. Our CGCN model is extensively validated on fourteen real-world bimolecular interaction networks and analyzed in details using a series of well-designed simulated data. It has been found that our CGCN can achieve the state-of-the-art results. It outperforms all existing models, as far as we know, in thirteen out of the fourteen real-world datasets and ranks as the second in the rest one. The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negative-curvature ratios, network densities, and network sizes (when larger than 500).

Sd-net: a semi-supervised double-cooperative network for liver segmentation from computed tomography (CT) images.

INTRODUCTION: The automatic segmentation of the liver is a crucial step in obtaining quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This task is challenging due to the frequent presence of noise and sampling artifacts in computerized tomography (CT) images, as well as the complex background, variable shapes, and blurry boundaries of the liver. Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such a learning framework is built on laborious manual annotation with strict requirements for expertise, leading to insufficient high-quality labels. METHODS: To overcome such limitation and exploit massive weakly labeled data, we relaxed the rigid labeling requirement and developed a semi-supervised double-cooperative network (SD- Net). SD-Net is trained to segment the complete liver volume from preoperative abdominal CT images by using limited labeled datasets and large-scale unlabeled datasets. Specifically, to enrich the diversity of unsupervised information, we construct SD-Net consisting of two collaborative network models. Within the supervised training module, we introduce an adaptive mask refinement approach. First, each of the two network models predicts the labeled dataset, after which adaptive mask refinement of the difference predictions is implemented to obtain more accurate liver segmentation results. In the unsupervised training module, a dynamic pseudo-label generation strategy is proposed. First each of the two models predicts unlabeled data and the better prediction is considered as pseudo-labeling before training. RESULTS AND DISCUSSION: Based on the experimental findings, the proposed method achieves a dice score exceeding 94%, indicating its high level of accuracy and its suitability for everyday clinical use.

Comprehensive analysis of the miRNA-mRNA regulatory network involved in spontaneous recovery of an H2O2-induced zebrafish cataract model.

OBJECTIVE: To identify the hub miRNAs and mRNAs contributing to the spontaneous recovery of an H2O2-induced zebrafish cataract model. METHODS: Zebrafishes were divided into three groups, i.e., Group A, which included normal control fish (day 0), and Groups B and C, where fish were injected with 2.5% hydrogen peroxide into the anterior chamber and reared for 14 and 30 days, respectively. Fish eyes were examined by stereomicroscope photography and optical coherence tomography (OCT). RNA profiles of fish lenses were detected by RNA sequencing. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs) were identified among three groups. The DEGs and DEmiRs, which changed in opposite positions between "B vs. A" and "C vs. B" were defined as ODGs (opposite positions changed DEGs) and ODmiRs (opposite positions changed DEmiRs). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) analysis were carried out by R language. The protein-protein interaction network (PPI) was constructed using STRING. Potential targets of miRNAs were obtained using miRanda. miRNA-mRNA networks were constructed by Cytoscape. RESULTS: The fish lens opacity formed on day 14 and recovered to transparent on day 30 after injection. Compared to group B, 1366 DEGs and 54 DEmiRs were identified in group C. "C vs. B" DEGs were enriched in gene clusters related to development and oxidative phosphorylation. Target genes of DEmiRs were enriched in clusters such as development and cysteine metabolism. Among three groups, 786 ODGs and 27 ODmiRs were identified, and 480 ODGs were predicted as targets of ODmiRs. Target ODGs were enriched in pathways related to methionine metabolism, ubiquitin, sensory system development, and structural constituents of the eye lens. In addition, we established an ODmiRs-ODGs regulation network. CONCLUSION: We identified several hub mRNAs and altered miRNAs in the formation and reversal of zebrafish cataracts. These hub miRNAs/mRNAs could be potential targets for the non-surgical treatment of ARC.

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction.

BACKGROUND: Long noncoding RNAs (lncRNAs) are integral to a plethora of critical cellular biological processes, including the regulation of gene expression, cell differentiation, and the development of tumors and cancers. Predicting the relationships between lncRNAs and diseases can contribute to a better understanding of the pathogenic mechanisms of disease and provide strong support for the development of advanced treatment methods. RESULTS: Therefore, we present an innovative Node-Adaptive Graph Transformer model for predicting unknown LncRNA-Disease Associations, named NAGTLDA. First, we utilize the node-adaptive feature smoothing (NAFS) method to learn the local feature information of nodes and encode the structural information of the fusion similarity network of diseases and lncRNAs using Structural Deep Network Embedding (SDNE). Next, the Transformer module is used to capture potential association information between the network nodes. Finally, we employ a Transformer module with two multi-headed attention layers for learning global-level embedding fusion. Network structure coding is added as the structural inductive bias of the network to compensate for the missing message-passing mechanism in Transformer. NAGTLDA achieved an average AUC of 0.9531 and AUPR of 0.9537 significantly higher than state-of-the-art methods in 5-fold cross validation. We perform case studies on 4 diseases; 55 out of 60 associations between lncRNAs and diseases have been validated in the literatures. The results demonstrate the enormous potential of the graph Transformer structure to incorporate graph structural information for uncovering lncRNA-disease unknown correlations. CONCLUSIONS: Our proposed NAGTLDA model can serve as a highly efficient computational method for predicting biological information associations.

Origin and early divergence of tandem duplicated sorbitol transporter genes in Rosaceae: insights from evolutionary analysis of the SOT gene family in angiosperms.

Sorbitol is a critical photosynthate and storage substance in the Rosaceae family. Sorbitol transporters (SOTs) play a vital role in facilitating sorbitol allocation from source to sink organs and sugar accumulation in sink organs. While prior research has addressed gene duplications within the SOT gene family in Rosaceae, the precise origin and evolutionary dynamics of these duplications remain unclear, largely due to the complicated interplay of whole genome duplications and tandem duplications. Here, we investigated the synteny relationships among all identified Polyol/Monosaccharide Transporter (PLT) genes in 61 angiosperm genomes and SOT genes in representative genomes within the Rosaceae family. By integrating phylogenetic analyses, we elucidated the lineage-specific expansion and syntenic conservation of PLTs and SOTs across diverse plant lineages. We found that Rosaceae SOTs, as PLT family members, originated from a pair of tandemly duplicated PLT genes within Class III-A. Furthermore, our investigation highlights the role of lineage-specific and synergistic duplications in Amygdaloideae in contributing to the expansion of SOTs in Rosaceae plants. Collectively, our findings provide insights into the genomic origins, duplication events, and subsequent divergence of SOT gene family members. Such insights lay a crucial foundation for comprehensive functional characterizations in future studies.