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Blue thermally activated delayed fluorescence (TADF) top-emitting organic light-emitting diodes (TEOLEDs) have garnered widespread interest because of their 100% internal quantum efficiency (ηint) and high intensity due to microcavity effects. However, their external quantum efficiency (ηext) is limited by total internal reflection from the thin-film encapsulation (TFE) required by TEOLEDs and their susceptibility to moisture and oxygen. This paper introduces a novel approach in which TiO2nanoparticles (NPs) are incorporated into a negative photoresist, and Al2O3is applied to form a nanolaminated TFE layer. This TFE layer decreases the water vapor transmission rate (WVTR), enhances light extraction efficiency and viewing angle characteristics. Moreover, the optical property can be modulated by adjusting the TiO2NP content and a planarization layer, maintaining a total transmittance of approximately 70%. The barrier properties assessed via electrical Ca tests show that TFE achieves WVTRs below 10–5g m–2day–1under ambient conditions. When applied to blue TADF TEOLEDs, the TFE improves electroluminescence by 23.78% and external quantum efficiency by 32.31%. As the viewing angle shifts from 0° to 45°, the CIE coordinate and peak wavelength shifts decrease from 0.067 to 0.044 and 14 to 8 nm, respectively. These results can be theoretically explained based on Mie scattering efficiency and Snell’s law, as analyzed by finite-difference time-domain simulations.

Jacket platforms constitute the foundational infrastructure of offshore oil and gas field exploitation. How to efficiently and accurately monitor the mechanical properties of jacket structures is one of the key problems to be solved to ensure the safe operation of the platform. To address the practical engineering problem that it is difficult to monitor the stress response of the tubular joints of jacket platforms online, a digital twin reduced-order method for real-time prediction of the stress response of tubular joints is proposed. In the offline construction phase, multi-scale modeling and multi-parameter experimental design methods are used to obtain the stress response data set of the jacket structure. Proper orthogonal decomposition is employed to extract the main feature information from the snapshot matrix, resulting in a reduced-order basis. The leave-one-out cross-validation method is used to select the optimal modal order for constructing the reduced-order model (ROM). In the online prediction phase, a digital twin model of the tubular joint is established, and the prediction performance of the ROM is analyzed and verified through using random environmental load and field environmental monitoring data. The results indicate that, compared with traditional numerical simulations of tubular joints, the ROM based on the proposed reduced-order method is more efficient in predicting the stress response of tubular joints while ensuring accuracy and robustness.

The aim of this study was to systematically investigate the ultrasonographic features of nodular hidradenoma (NH). A retrospective analysis was used to systematically summarize the ultrasonographic data of 27 patients diagnosed with NH by surgical pathology, including 13 eccrine nodular hidradenomas (ENH) and 14 apocrine nodular hidradenomas (ANH). All instances of NH presented as solitary, well‐defined lesions that infiltrated the dermis and subcutaneous fat layer, characterized by a heterogeneous hypoechoic internal solid component on ultrasound imaging. Color Doppler ultrasound revealed blood flow signals of Grade 2 or higher within 74% (20/27) of the lesions. Solid + cystic (cystic >50%) NH (14/27, 51.4%) were typically large and predominantly had an oval shape (11/14, 78.5%). Their distinctive sonographic features included the presence of inner septa within the cystic area (8/14, 57.1%), “snow falling” sign (7/14, 50%), or “fluid–fluid level” sign (7/14, 50%). Solid + cystic (cystic ≤50%) NH exhibited a lobulated morphology in all instances (5/5, 100%). No inner septa, “snow falling” sign or “fluid–fluid level” sign was observed within the cystic regions. The solid NH (8/27, 29.7%) morphology predominantly featured lobulation (6 out of 8, 75%). Ultrasound analysis revealed distinct differences between ENH and ANH. ENH were more lobulated, while ANH were predominantly oval. ANH were mainly solid + cystic (cystic >50%), whereas ENH were mostly solid. Inner septa, “snow falling” sign, and “fluid–fluid level” sign frequencies were similar in both groups, correlating more with cystic‐solid composition than pathological subtype. Ultrasonographic features of lobulated morphology and the presence of inner septa, “snow falling” sign or “fluid–fluid level” sign in the cystic region provide strong support for the diagnosis of NH.

In this study, we systematically investigated the regioselective glycosylation of 2,4-OH mannoside and galactoside acceptors since regioselective protection of their 3- and 6-OHs is readily achieved. By altering the protecting groups at 1-, 3-, and 6-positions of such acceptors, we finally screened p-methoxyphenyl 3-OBn, 6-OTBDPS, α-mannoside, and β-galactoside acceptors whose 2-OHs exhibited excellent selectivity for glycosylation with various glycosyl donors, leading to 1,2-linked products in 70–82% yields. By utilizing such acceptors, a series of 2,4-linked trisaccharide products (53–65% yields over two steps) have been highly efficiently synthesized without the need for complex protection/deprotection operations at the 2- and 4-positions of these acceptors.

Shape memory polymers (SMPs) show promise in areas like wearable electronics and soft robotics but often have low (actuation) energy densities (<1 MJ/m3), limiting their maximum load. Recent work suggests that periodically incorporating directional H-bonds can enable high-energy-density SMPs by forming stable strain-induced supramolecular nanostructures. Here, we found that adding weaker H-bonding units to the polymer can tune its actuation temperature from 60°C to 25°C while maintaining ∼80% of the energy density of the original polymer and achieving self-healing at accessible temperatures (∼70°C). By using this self-healable, high-energy SMP, we realized rapid healing of macroscopic film damage (e.g., centimeter-sized knife punctures) that was not healable in polymers without high-energy shape-memory-assisted self-healing (SMASH) behavior. The self-healing SMP was used to fabricate a self-healable force sensor with high cyclability and sensitivity, marking a significant advancement in creating tunable and self-healable SMPs for smart, durable wearable devices.

AR, VR Headset for “metaverse” is one of candidates to widen human life experiences with virtual reality. Display requirement for headset is the high resolution as to 3000ppi that needs to enhance outcoupling efficiency of OLED (OLEDoS). We optimized 2D hole arrayed double anode structure for mass production with materials, thickness of anode and interlayer, process variation of etching top anode, and particles in double anodes. Outcoupling efficiency of optimized structure is improved to 1.377 folds than conventional TE‐OLEDs. We hope that this study promotes more approaches to excite SPPs of anode.

CARM1, belonging to the protein arginine methyltransferase (PRMT) family, is intricately associated with the progression of cancer and is viewed as a promising target for both cancer diagnosis and therapy. However, the number of specific and potent CARM1 inhibitors is limited. We herein discovered a CARM1 inhibitor, iCARM1, that showed better specificity and activity toward CARM1 compared to the known CARM1 inhibitors, EZM2302 and TP-064. Similar to CARM1 knockdown, iCARM1 suppressed the expression of oncogenic estrogen/ERα-target genes, whereas activated type I interferon (IFN) and IFN-induced genes (ISGs) in breast cancer cells. Consequently, iCARM1 potently suppressed breast cancer cell growth both in vitro and in vivo. The combination of iCARM1 with either endocrine therapy drugs or etoposide demonstrated synergistic effects in inhibiting the growth of breast tumors. In summary, targeting CARM1 by iCARM1 effectively suppresses breast tumor growth, offering a promising therapeutic approach for managing breast cancers in clinical settings.

Intrinsically stretchable electronics with skin-like mechanical properties have been identified as a promising platform for emerging applications ranging from continuous physiological monitoring to real-time analysis of health conditions, to closed-loop delivery of autonomous medical treatment1–7. However, current technologies could only reach electrical performance at amorphous-silicon level (that is, charge-carrier mobility of about 1 cm2V−1s−1), low integration scale (for example, 54 transistors per circuit) and limited functionalities8–11. Here we report high-density, intrinsically stretchable transistors and integrated circuits with high driving ability, high operation speed and large-scale integration. They were enabled by a combination of innovations in materials, fabrication process design, device engineering and circuit design. Our intrinsically stretchable transistors exhibit an average field-effect mobility of more than 20 cm2V−1s−1under 100% strain, a device density of 100,000 transistors per cm2, including interconnects and a high drive current of around 2 μA μm−1at a supply voltage of 5 V. Notably, these achieved parameters are on par with state-of-the-art flexible transistors based on metal-oxide, carbon nanotube and polycrystalline silicon materials on plastic substrates12–14. Furthermore, we realize a large-scale integrated circuit with more than 1,000 transistors and a stage-switching frequency greater than 1 MHz, for the first time, to our knowledge, in intrinsically stretchable electronics. Moreover, we demonstrate a high-throughput braille recognition system that surpasses human skin sensing ability, enabled by an active-matrix tactile sensor array with a record-high density of 2,500 units per cm2, and a light-emitting diode display with a high refreshing speed of 60 Hz and excellent mechanical robustness. The above advancements in device performance have substantially enhanced the abilities of skin-like electronics.

C32H44O6, monoclinic, P21(no. 4), a= 12.2561 Å, b= 10.3674(4) Å, c= 23.3835(8) Å, β= 98.463°, V= 2938.85(19) Å3, Z= 2, Rgt(F) = 0.0602, wRref(F2) = 0.1537, T = 273(2) K.

A ceramic coating on 60vol.%SiCP/2009 aluminum matrix composite was prepared by plasma electrolytic oxidation (PEO) in silicate solution. The microstructure, composition and wettability of the PEO coating and its corrosion resistance were investigated. The generation mechanism of spark discharge on the SiC particles surface was also discussed. It is found that the insoluble compound (SiO2) from silicate solution is vital for the ignition of spark discharge on the SiC particles surface. The defects in the Al−Si−O compounds provide a priority path for the conduction of discharge current on the SiC particles surface. A uniform coating of 5.5 μm was fabricated after 1200 s. Moreover, with the increase of time, the surface free energy of PEO coating reached the maximum of 37.10 mJ/cm2at 40 s, and then decreased to 25.95 mJ/cm2at 1200 s. Furthermore, the corrosion resistance of the composite was greatly improved by PEO treatment.

Deepsea mining has been proposed since the 1960s to alleviate the lack of resources on land. Vertical hydraulic transport of collected ores from the seabed to the sea surface is considered the most promising method for industrial applications. In the present study, an indoor model test of the vertical hydraulic transport of particles was conducted. A noncontact optical method has been proposed to measure the local characteristics of the particles inside a vertical pipe, including the local concentration and particle velocity. The hydraulic gradient of ore transport was evaluated with various particle size distributions, particle densities, feeding concentrations and mixture flow velocities. During transport, the local concentration is larger than the feeding concentration, whereas the particle velocity is less than the mixture velocity. The qualitative effects of the local concentration and local fluid velocity on the particle velocity and slip velocity were investigated. The local fluid velocity contributes significantly to particle velocity and slip velocity, whereas the effect of the local concentration is marginal. A higher feeding concentration and mixture flow velocity result in an increased hydraulic gradient. The effect of the particle size gradation is slight, whereas the particle density plays a crucial role in the transport.

The Doppler difference velocity between the measured solar spectrum and the measured asteroid spectrum is the measurement of the celestial Doppler difference navigation. Due to the absorption effect of the asteroid on the measured asteroid spectrum, the Doppler difference velocity is inevitably affected. In order to solve this problem, considering the fact that the asteroid reflectance is the ratio of the approximation of the asteroid spectrum to that of solar spectrum, we utilize the Gaussian genetic algorithm (GGA) and empirical mode decomposition (EMD) to extract the approximation of celestial spectra and propose an inversion method of celestial spectrum reflectance template based on GGA-EMD. First, the celestial spectrum is decomposed by EMD into the intrinsic mode functions (IMFs). A certain combination of IMFs can embody the spectrum reflectance. Then, we utilize the undetermined spectrum reflectance function and the measured solar spectrum to produce the virtual asteroid spectrum and adopt the Taylor method to estimate the Doppler difference velocity between the measured asteroid spectrum and the virtual one. Finally, to converge fast, we utilize the Gaussian function to optimize the mutation and crossover operations of genetic algorithm (GA) and propose the GGA. Using the Doppler difference velocity errors as the object of the fitness function, the GGA optimizes the combination of IMFs, which is used to construct an optimized celestial spectrum reflectance template. The experiment results show that the inversion method of celestial spectrum reflectance template based on GGA-EMD constructs the spectrum reflectance template effectively and improves the accuracy of the Doppler difference velocity.

Single-cell RNA sequencing (scRNA-seq) has rapidly emerged as a powerful technique for analyzing cellular heterogeneity at the individual cell level. In the analysis of scRNA-seq data, cell clustering is a critical step in downstream analysis, as it enables the identification of cell types and the discovery of novel cell subtypes. However, the characteristics of scRNA-seq data, such as high dimensionality and sparsity, dropout events and batch effects, present significant computational challenges for clustering analysis. In this study, we propose scGCC, a novel graph self-supervised contrastive learning model, to address the challenges faced in scRNA-seq data analysis. scGCC comprises two main components: a representation learning module and a clustering module. The scRNA-seq data is first fed into a representation learning module for training, which is then used for data classification through a clustering module. scGCC can learn low-dimensional denoised embeddings, which is advantageous for our clustering task. We introduce Graph Attention Networks (GAT) for cell representation learning, which enables better feature extraction and improved clustering accuracy. Additionally, we propose five data augmentation methods to improve clustering performance by increasing data diversity and reducing overfitting. These methods enhance the robustness of clustering results. Our experimental study on 14 real-world datasets has demonstrated that our model achieves extraordinary accuracy and robustness. We also perform downstream tasks, including batch effect removal, trajectory inference, and marker genes analysis, to verify the biological effectiveness of our model.

Chaotic semiconductor lasers have been widely investigated for high-speed random bit generation, which is applied for the generation of cryptographic keys for classical and quantum cryptography systems. Here, we propose and demonstrate a self-chaotic microlaser with enhanced chaotic bandwidth for high-speed random bit generation. By designing tri-mode interaction in a deformed square microcavity laser, we realize a self-chaotic laser caused by two-mode internal interaction, and achieve an enhanced chaotic standard bandwidth due to the photon–photon resonance effect by introducing the third mode. Moreover, 500 Gb/s random bit generation is realized and the randomness is verified by the NIST SP 800-22 statistics test. Our demonstration promises the applications of microlasers in secure communication, chaos radar, and optical reservoir computing, and also provides a platform for the investigations of multimode nonlinear laser dynamics.

We report the latest development in high-speed directly modulated tunable laser based on simple and compact hybrid square/rhombus-rectangular laser (HSRRL), aiming for low-cost deployment in next generation high-throughput optical interconnects, intra-/inter-data center networks, and optical fiber communication system. The HSRRL is composed of a deformed square whispering gallery mode (WGM) microcavity and a Fabry–Pérot (FP) cavity, and the WGM microcavity is used as an end face of the FP cavity with mode selection. A deformable WGM microcavity is designed to obtain a high reflectivity. The HSRRL, fabricated with deep-etching processing steps similar to FP laser without grating or epitaxial regrowth, has achieved 7-mA threshold current, 0.4-mW/mA slope efficiency, above 52-dB side mode suppression ratio (SMSR), and 16.2-GHz modulation bandwidth with injection currents of 34.3 mA and 60 mA for WGM microcavity and FP cavity, respectively. A wavelength tuning from 1537.40 nm to 1570.42 nm is realized by adjusting the injection currents simultaneously. In the tuning range, the laser has an SMSR greater than 40 dB, a linewidth less than 4 MHz, and a 3-dB modulation bandwidth greater than 14 GHz.

Zn-30Sn-2Cu-xNi (x=0, 0.5, 1.0, 1.5, wt.%) lead-free solder alloys were prepared via the casting method. Subsequently, their microstructures and corrosion behaviors in 0.5 mol/L NaCl solution were investigated. The electrochemical behaviour was studied using potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) techniques, to evaluate the influence of Ni content on the corrosion performances of the Zn-Sn-Cu alloy. The corrosion mechanism of the Zn-Sn-Cu-Ni alloys was analyzed by observing surface structure evolution during the corrosion process. The results revealed that the addition of 0.5 wt.% Ni improved the corrosion resistance of Zn-30Sn-2Cu alloy due to forming a denser and more homogeneous corrosion layer, which acted as a physical barrier. The phase identification confirmed that the main corrosion products consisted of ZnO, Zn(OH)2, and Zn5(OH)8Cl2·H2O. As the Ni content reached 1.0 and 1.5 wt.%, the corrosion resistance of Zn-30Sn-2Cu alloy declined, which could mainly be attributed to the galvanic corrosion between the (Ni, Cu)5Zn21intermetallics and Zn-rich phase, accelerating the dissolution of Zn-rich phase. Thus, Zn-30Sn-2Cu-0.5Ni solder alloy showed the best corrosion resistance.

The octahedral sites in Co3O4usually possess better oxygen evolution reaction (OER) activity, which have been identified as the active sites. Atomic cationic vacancy defects can elicit localized electron redistribution to regulate the electronic structure of the host materials. In this paper, Co3O4nanosheet arrays with cationic vacancies at octahedral sites (Vx-Co3O4) have been synthesized by a facile three-step reaction process. Density functional theory (DFT) computational results further indicate that the cationic vacancies can increase the number of electrons per Co atom and optimize the adsorption energy of the intermediate product. As a result, the optimized V0.3-Co3O4/CC electrode with abundant cationic vacancies displays excellent OER performance in alkaline electrolytes with an overpotential of 240 mV at 10 mA cm–2, Tafel slope of 72.2 mV dec–1, and superior stability, which are much better than those of the pure Co3O4and commercial IrO2.

Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads, such as wind, waves, and currents. This is particularly true for platforms that have been in service for an extended period. Identifying the modal parameters of offshore platforms is crucial for damage diagnosis, as it serves as a prerequisite and foundation for the process. Therefore, it holds great significance to prioritize the identification of these parameters. Aiming at the shortcomings of the traditional Fast Bayesian Fast Fourier Transform (FBFFT) method, this paper proposes a modal parameter identification method based on Automatic Frequency Domain Decomposition (AFDD) and optimized FBFFT. By introducing the AFDD method and Powell optimization algorithm, this method can automatically identify the initial value of natural frequency and solve the objective function efficiently and simply. In order to verify the feasibility and effectiveness of the proposed method, it is used to identify the modal parameters of the IASC-ASCE benchmark model and the jacket platform structure model, and the Most Probable Value (MPV) of the modal parameters and their respective posterior uncertainties are successfully identified. The identification results of the IASC-ASCE benchmark model are compared with the identification results of the MODE-ID method, which verifies the effectiveness and accuracy of the proposed method for identifying modal parameters. It provides a simple and feasible method for quantifying the influence of uncertain factors such as environmental parameters on the identification results, and also provides a reference for modal parameter identification of other large structures.

Resveratrol (RSV) has been shown to have a neuroprotective effect in various central nervous system disorders, although the role of RSV in diabetes‐induced cognitive dysfunction is still not fully elucidated. Here, we investigated whether RSV improved diabetes‐related cognitive dysfunction in vivo and in vitro. We induced a rat diabetic model with a high‐fat and high‐sucrose diet followed by intraperitoneal injection of streptozotocin and a diabetic neuron cell model by stimulation with high levels of glucose. We observed that RSV improved impairment in spatial learning and memory in the Morris water maze test (MWM) and novel object recognition test (ORT) in diabetic rats. RSV reversed the reduced miR‐146a‐5p and upregulated thioredoxin‐interacting protein (TXNIP) and inhibited the diabetes‐induced increase in interleukin (IL)‐1β and tumor necrosis factor (TNF)‐α levels in vivo and in vitro. RSV also inhibited diabetes‐induced endoplasmic reticulum stress (ESR) by reducing ESR‐related protein expression in vivo and in vitro. Moreover, inhibition of miR‐146a‐5p partially abolished the protective effects of RSV in HG‐treated primary neurons. Additionally, we used starBase to predict that miR‐146a‐5p interacts with TXNIP, which we then verified using a luciferase reporter gene assay. We further observed that miR‐146a‐5p regulates the mRNA and protein expression of TXNIP in vitro, indicating that the miR‐146a‐5p/TXNIP axis is involved in the regulation of cognitive dysfunction in a rat diabetic model. Collectively, these results demonstrate that RSV plays a neuroprotective role in diabetes‐associated cognitive dysfunction at least in part through regulation of the miR‐146a‐5p/TXNIP axis.

Insufficient activation of stimulator of interferon genes (STING) signaling pathway in tumor‐associated dendritic cells limits the efficiency of tumor immunotherapy. Herein, the “three‐in‐one” IAHA‐LaP/siPTPN6 NPs containing lanthanum ions (La3+), cGAMP, and PTPN6 siRNA are developed for triple amplification of the STING pathway. In vitro results demonstrate that La3+significantly promotes cGAMP‐mediated activation of the STING pathway by enhancing the phosphorylation of STING, TBK1, IRF3, and NF‐κB p65. Moreover, the IAHA‐LaP/siPTPN6 NPs further significantly enhance the phosphorylation of STING and NF‐κB p65 and augment K63‐linked ubiquitination of STING protein via siPTPN6‐mediated downregulation of SHP‐1 protein. Furthermore, NPs improve the secretion of IFNβ(2.4‐fold), IL‐6 (1.5‐fold), and TNF‐α(1.4‐fold), thereby promoting DCs maturation compared to the mixture of La3+and cGAMP. In vivo results show that the IAHA‐LaP/siPTPN6 NPs remarkably inhibit primary tumor growth by increasing the percentage of mature DCs in tumor‐draining lymph nodes, polarizing M2/M1 phenotype in TME, and promoting the infiltration of CD8+T cells into tumors. Moreover, these NPs dramatically prevent the growth of distal tumor by inducing systemic anti‐tumor immunity and generating a long‐term anti‐tumor memory for protection against tumor recurrence in mice bearing bilateral B16F10. These IAHA‐LaP/siPTPN6 NPs may offer a promising platform for robust anti‐tumor immune responses. The lanthanum ions, STING agonists (2′,3′‐cGAMP), and PTPN6 siRNA co‐loaded in a “three‐in‐one” nanomedicine is developed for enhancing tumor immunotherapy. Triple Amplification Effects of nanomedicine on the STING Signaling Pathway are proposed for restoring dendritic cells, and will be a potential and promising strategy for clinical tumor treatment.

Li-based batteries are ubiquitous in modern-day energy supply systems. However, the volatile and flammable nature of the electrolytes remains a safety challenge. Here, we report that anchored solvent molecules can increase the ionic conductivity of the electrolyte without undermining its non-flammability. Specifically, we developed a liquid-state polymer electrolyte composed of LiFSI salts, dimethoxyethane (DME) solvents, and polysiloxane tethered with ion-solvating moieties. DME coordinates with both the salt and the polymer, while, together with the salt, they synergistically plasticize the polymer to increase the ionic conductivity. The resulting non-flammable polymer electrolyte has a room-temperature ionic conductivity of 1.6 mS/cm and a wide operation window of 25°C–100°C. Benefiting from its liquid nature, our electrolyte can pair with commercially available electrodes without further cell engineering. Our work extends the ionic conductivity range of polymer electrolytes and shows a promising design pathway for next-generation safe and manufacturable electrolytes.

CircRNAs have a stable structure, which gives them a higher tolerance to nucleases. Therefore, the properties of circular RNAs are beneficial in disease diagnosis. However, there are few known associations between circRNAs and disease. Biological experiments identify new associations is time-consuming and high-cost. As a result, there is a need of building efficient and achievable computation models to predict potential circRNA-disease associations. In this paper, we design a novel convolution neural networks framework(DMFCNNCD) to learn features from deep matrix factorization to predict circRNA-disease associations. Firstly, we decompose the circRNA-disease association matrix to obtain the original features of the disease and circRNA, and use the mapping module to extract potential nonlinear features. Then, we integrate it with the similarity information to form a training set. Finally, we apply convolution neural networks to predict the unknown association between circRNAs and diseases. The five-fold cross-validation on various experiments shows that our method can predict circRNA-disease association and outperforms state of the art methods.

Background: The Booster Ring is further designed to store and accelerate protons up to 2 × 1012particles per pulse in the High-Intensity heavy-ion Accelerator Facility project, which was originally designed to accelerate high-intensity heavy ion beams. Purpose and Methods: To minimize the uncontrolled proton beam halo loss around the ring in operation, a two-stage collimation system is proposed to provide a well-shielded dump for localizing the proton beam halo loss. Results and conclusion: In this paper, the simulation is carried out to evaluate the collimation system which shows a 92.93% collimation efficiency. Finally, several factors that affect the collimation efficiency are taken into consideration, including the physical aperture, the offset and rotation errors of the collimators, the closed orbit distortion, as well as the Betatron tunes.

Lots of experimental studies have revealed the significant associations between lncRNAs and diseases. Identifying accurate associations will provide a new perspective for disease therapy. Calculation-based methods have been developed to solve these problems, but these methods have some limitations. In this paper, we proposed an accurate method, named MLGCNET, to discover potential lncRNA-disease associations. Firstly, we reconstructed similarity networks for both lncRNAs and diseases using top k similar information, and constructed a lncRNA-disease heterogeneous network (LDN). Then, we applied Multi-Layer Graph Convolutional Network on LDN to obtain latent feature representations of nodes. Finally, the Extra Trees was used to calculate the probability of association between disease and lncRNA. The results of extensive 5-fold cross-validation experiments show that MLGCNET has superior prediction performance compared to the state-of-the-art methods. Case studies confirm the performance of our model on specific diseases. All the experiment results prove the effectiveness and practicality of MLGCNET in predicting potential lncRNA-disease associations.

Recently, as a growing number of associations between microRNAs (miRNAs) and diseases are discovered, researchers gradually realize that miRNAs are closely related to several complicated biological processes and human diseases. Hence, it is especially important to construct availably models to infer associations between miRNAs and diseases. In this study, we presented Improved Graph Regression for miRNA-Disease Association Prediction (IGRMDA) to observe potential relationship between miRNAs and diseases. In order to reduce the inherent noise existing in the acquired biological datasets, we utilized matrix decomposition algorithm to process miRNA functional similarity and disease semantic similarity and then combining them with existing similarity information to obtain final miRNA similarity data and disease similarity data. Then, we applied miRNA-disease association data, miRNA similarity data and disease similarity data to form corresponding latent spaces. Furthermore, we performed improved graph regression algorithm in latent spaces, which included miRNA-disease association space, miRNA similarity space and disease similarity space. Non-negative matrix factorization and partial least squares were used in the graph regression process to obtain important related attributes. The cross validation experiments and case studies were also implemented to prove the effectiveness of IGRMDA, which showed that IGRMDA could predict potential associations between miRNAs and diseases.

Objective: To investigate the effect of pulmonary vein antrum enlargement combined with left atrial roof cryoballoon ablation in patients with persistent atrial fibrillation (PeAF) by analyzing the relationship between left atrial isolation area surface area (ISA) and early postoperative recurrence. Methods: 93 patients with PeAF were classified into recurrence and non-recurrence groups according to the results of the 1-year follow-up. Three-dimensional electroanatomical labeling map was constructed and merged with that of the left atrial pulmonary vein CTA, and the ISA and the left atrial surface area (LASA) were measured and analyzed to determine the relationship between ISA/LASA in relation to early postoperative recurrence. Results: 93 patients were included and followed up for 1 year with AF-free recurrence rate of 75.3%. The ISA of the recurrence group was lower than that of the non-recurrence group. Left atrial internal diameter (LAD), left common pulmonary vein, the ISA, the ISA/LASA and early-term recurrence had statistical significance in both groups. The factors that significantly predicted early-term recurrence were left common pulmonary vein and the ISA/LASA. ISA/LASA (HR 0, 95% CI 0–0.005, P= 0.008) and left common pulmonary vein trunk (HR 7.754, 95% CI 2.256–25.651, P= 0.001) were the independent risk factors for early recurrence. ROC curve analysis showed that ISA/LASA predicted the best early recurrence after operation with a cut-off value of 15.2%. Conclusion: A greater ISA/LASA reduces early recurrence after cryoablation in patients with PeAF. An ISA/LASA of 15.2% may be the best cut-off value for predicting early recurrence after cryoablation for PeAF.