Search

Recently, mask-fill-based 3D Molecular Generation (MG) methods have become very popular in virtual drug design. However, the existing MG methods ignore the chemical properties of atoms and contain inappropriate atomic position training data, which limits their generation capability. To mitigate the above issues, this paper presents a novel mask-fill-based 3D molecule generation model driven by atomic chemical properties (APMG). Specifically, we construct a new attention-MPNN-based encoder and introduce the electronic information into atom representations to enrich chemical properties. Also, a multi-functional classifier is designed to predict the electronic information of each generated atom, guiding the type prediction of elements and bonds. By design, the proposed method uses the chemical properties of atoms and their correlations for high-quality molecule generation. Second, to optimize the atomic position training data, we propose a novel atomic training position generation approach using the Chi-Square distribution. We evaluate our APMG method on the CrossDocked dataset and visualize the docking states of the pockets and generated molecules. The obtained results demonstrate the superiority and merits of APMG over the state-of-the-art approaches.

Federated learning (FL), an emerging machine learning paradigm that trains a global model across distributed clients without violating data privacy, has recently attracted significant attention. However, FL's distributed nature and iterative training extensively increase the attacking surface for Byzantine and inference attacks. Existing FL defense methods can hardly protect FL from both Byzantine and inference attacks due to their fundamental conflicts. The noise injected to defend against inference attacks interferes with model weights and training data, obscuring model analysis that Byzantine-robust methods utilize to detect attacks. Besides, the practicability of existing Byzantine-robust methods is limited since they heavily rely on model analysis. In this article, we present Siren$^+$+, a new robust FL system that defends against a wide spectrum of Byzantine attacks and inference attacks by jointly utilizing a proactive alarming mechanism and local differential privacy (LDP). The proactive alarming mechanism orchestrates clients and the FL server to collaboratively detect attacks using distributed alarms, which are free from the noise interference injected by LDP. Compared with the state-of-the-art defense methods, Siren$^+$+ can protect FL from Byzantine and inference attacks from a higher proportion of malicious clients in the system while keeping the global model performing normally. Extensive experiments with diverse settings and attacks on real-world datasets show that Siren$^+$+ outperforms existing defense methods when attacked by Byzantine and inference attacks.

The electrochemical behavior of MgCl2and the co-deposition mechanisms of Mg and Sr in the SrCl2−KCl eutectic system were evaluated using various electrochemical techniques, including cyclic voltammetry (CV), square wave voltammetry (SWV), and open-circuit potential (OCP) analysis. It was observed the Mg(II) reduction on the tungsten electrode in SrCl2−KCl melt occurred in a single-step process involving the transfer of two electrons, exhibiting a quasi-reversible mode. The electrochemical co-deposition of Mg and Sr in the SrCl2−KCl−MgCl2melt involved the formation of two types of Mg−Sr intermetallic compounds. The evolution in the co-deposition mechanism of the nucleation type with changing overpotential was explored through chronoamperometry (CA). It is shown that the nucleation pattern on the electrode surface was depended on the substrate materials and the electrode reaction products. Early-stage nucleation was attributed to Mg(II) reduction under all overpotential conditions. With the co-deposition of Mg and Sr, Mg(II) was reduced to form the Mg nucleus on the electrode surface, followed by underpotential deposition of Sr(II) on the Mg surface accompanied by Mg deposition simultaneously. Additionally, increasing MgCl2concentration in the SrCl2−KCl melt resulted in a higher current density during the Mg−Sr co-deposition process.

ABSTRACTIL-33 has been associated with pro- and anticancer functions in cancer. However, its role in pancreatic cancer metastasis remains unknown. This study aimed to explore the role of miR-548t-5p/IL-33 axis in the metastasis of pancreatic cancer. Luciferase activity assay, qRT-PCR, Western blot and ELISA were performed to prove whether IL-33 is the target of miR-548t-5p. In vivo metastasis assay and cellular transwell assay were performed to explore the role of miR-548t-5p/IL-33 axis in the invasion and metastasis of pancreatic cancer. Co-culture experiments and immunohistochemistry were performed to observe whether IL-33 affects cell invasion and metastasis dependent on the involvement of M2 macrophages. THP-1 cell induction experiment and flow cytometry were performed to explore the effect of IL-33 on macrophage polarization. CCK-8, colony formation, cell apoptosis, cell cycle, cell wound healing and transwell assay were performed to investigate the effect of IL-33 induced M2 macrophages on cell malignant biological behavior by coculturing pancreatic cancer cells with the conditioned medium (CM) from macrophages. We found that miR-548t-5p regulated the expression and secretion of IL-33 in pancreatic cancer cells by directly targeting IL-33 mRNA. IL-33 secreted by cancer cells promoted the recruitment and activation of macrophages to a M2-like phenotype. In turn, IL-33 induced M2 macrophages promoted the migration and invasion of cancer cells. Moreover, IL-33 affected pancreatic cancer cell invasion dependent on the involvement of M2 macrophages in the co-culture system. Thus, our study suggested that manipulation of this IL-33-dependent crosstalk has a therapeutic potential for the treatment of pancreatic cancer metastasis.

Layered compounds characterized by van der Waals gaps are often associated with relatively weak interlayer particle interactions. However, in specific scenarios, these seemingly feeble forces can exert an impact on interlayer interactions through subtle energy fluctuations, which can give rise to a diverse range of physical and chemical properties, particularly intriguing in the context of thermal transport. In this study, taking a natural superlattice composed of alternately stacked PbS and SnS2sublayers as a model, we proposed that in a superlattice, there is strong hybridization between acoustic phonons of heavy sublayers and optical phonons of light sublayers. We identified newly generated vibration modes in the superlattice, such as interlayer shear and breathing, which exhibit lower sound velocity and contribute less to heat transport compared to their parent materials, which significantly alters the thermal behaviors of the superlattice compared to its bulk counterparts. Our findings on the behavior of interlayer phonons in superlattices not only can shed light on developing functional materials with enhanced thermal dissipation capabilities but also contribute to the broader field of condensed matter physics, offering insights into various fields, including thermoelectrics and phononic devices, and may pave the way for technological advancements in these areas.

Existing caricature-visual face recognition methods train the models based on caricature-visual image pairs from the same identities. Unfortunately, in many real-world applications, facial caricatures and visual facial images are usually unpaired in the training set due to the difficulty of collecting facial caricatures drawn by artists. In this paper, we study caricature-visual face recognition under the practical setting that only unpaired facial caricature and visual facial images are available as training samples, and define this setting as unpaired caricature-visual face recognition. To this end, we develop a novel feature decomposition-restoration-decomposition method (FDRD), which mainly consists of a backbone network, an identity-oriented feature decomposition module, and a modality-oriented feature restoration module, to extract modality-irrelevant identity features. To effectively train FDRD in the case of limited facial caricature training samples, we develop a two-stage learning framework. In the first stage, we perform single-modality restoration, enabling the model to have the basic ability of feature decomposition and restoration for each modality. In the second stage, we perform cross-modality recognition by exchanging new modality features between the two modalities, facilitating the model to focus on the decoupling of identity features and modality features. Experimental results demonstrate that our method performs favorably against several state-of-the-art face recognition methods and cross-modality methods. Our code is available at https://github.com/Capricorn-Karma/FDRD.

With the fast development of AI technologies, deep learning is widely applied for biomedical data analytics and digital healthcare. However, there remain gaps between AI-aided diagnosis and real-world healthcare demands. For example, hemodynamic parameters of the middle cerebral artery (MCA) have significant clinical value for diagnosing adverse perinatal results. Nevertheless, the current measurement procedure is tedious for sonographers. To reduce the workload of sonographers, we propose MCAS-GP, a deep learning-empowered framework that tackles the Middle Cerebral Artery Segmentation and Gate Proposition. MCAS-GP can automatically segment the region of the MCA and detect the corresponding position of the gate in the procedure of fetal MCA Doppler assessment. In MCAS-GP, a novel learnable atrous spatial pyramid pooling (LASPP) module is designed to adaptively learn multi-scale features. We also propose a novel evaluation metric, Affiliation Index, for measuring the effectiveness of the position of the output gate. To evaluate our proposed MCAS-GP, we build a large-scale MCA dataset, collaborating with the International Peace Maternity and Child Health Hospital of China welfare institute (IPMCH). Extensive experiments on the MCA dataset and two other public surgical datasets demonstrate that MCAS-GP can achieve considerable performance improvement in both accuracy and inference time.

Three-dimensional direct numerical simulations of the wake flow downstream of a near-wall circular cylinder at different gap ratios and boundary layer thicknesses are carried out by using the iterative immersed boundary method. The non-dimensional gap between the cylinder and the wall, G/D= 0.2, 0.6 and 1.0, the non-dimensional boundary layer thickness, δ/D= 0.0, 0.7 and 1.6, the Reynolds number, Re=350, and the aspect ratio of the cylinder, L/D= 25 are adopted. High-resolution visualizations of the complex vortex structures at different δ/Dand G/Dare presented. The transition of the streamwise vortex mode, the combined effects of δ/Dand G/Don the flow statistics, the pressure and shear stress distribution and the hydrodynamic forces are analyzed. Results show that with decreasing G/Dand increasing δ/D, the gap flow and its vortex-shedding are significantly weakened, together with an elongated wake and an enlarged low-velocity area near the wall, leading to the wake mode transition from the two-sided to one-sided vortex-shedding. Different relative positions of the cylinder regarding the boundary layer alter the flow features of the shear layers. With an increase in δ/D, the front stagnation point shifts to the upper surface, and the distance between the flow divergence point and the maximum pressure position increases. The mean drag coefficient and r.m. s. values of drag and lift coefficients decrease with a decrease in G/Dand an increase in δ/D, while the mean lift coefficient increases with decreasing G/Dbut decreases with increasing δ/D.

Data augmentation has been observed playing a crucial role in achieving better generalization in many machine learning tasks, especially in unsupervised domain adaptation (DA). It is particularly effective on visual object recognition tasks as images are high-dimensional with an enormous range of variations that can be simulated. Existing data augmentation techniques, however, are not explicitly designed to address the differences between different domains. Expert knowledge about the data is required, as well as manual efforts in finding the optimal parameters. In this article, we propose a novel domain-adaptive augmentation method by making use of a state-of-the-art style transfer method and domain discrepancy measurement. Specifically, we measure the discrepancy between source and target domains, and use it as a guide to augment the original source samples using style transferred source-to-target samples. The proposed domain-adaptive augmentation method is data and model agnostic that can be easily incorporated with state-of-the-art DA algorithms. We show empirically that, by using this domain-adaptive augmentation, we are able to gradually reduce the discrepancy between the source and target samples, and further boost the adaptation performance using different DA algorithms on three popular domain adaption datasets.

Silibinin has shown various pharmacological effects that could be attributed to its antioxidant, anti‐inflammatory, and immunoregulatory properties. However, the therapeutic potential of silibinin for periodontitis has not been investigated. The therapeutic effects of silibinin in ligation‐induced experimental periodontitis were investigated using biochemical, histological, and immunohistochemical methods. The effects of silibinin on the osteoclastogenesis of RAW264.7 cells were investigated using TRAP staining, quantitative polymerase chain reaction (qPCR), pit formation, and immunoblotting. Moreover, its effects on inflammatory cytokine production, RANKL expression, and oxidative stress in lipopolysaccharide (LPS)‐stimulated human gingival fibroblasts (HGFs) were evaluated using qPCR and flow cytometry. A coculture system was established to elucidate the effects of silibinin on the crosstalk between LPS‐stimulated HGFs and undifferentiated monocytes. Silibinin significantly reduced the alveolar bone loss, decreased the gingival inflammation and RANKL expression, and decreased the RANKL/osteoprotegerin ratio in gingival tissues in experimental periodontitis. The in vitro results showed that silibinin inhibited RANKL‐induced osteoclast differentiation and function of RAW264.7 cells and suppressed RANKL‐induced nuclear factor of activated T cells 1 (NFATc1) induction and translocation through the nuclear factor‐κB and mitogen‐activated protein kinase signaling pathways. Silibinin decreased the inflammatory cytokine level and oxidative stress production in LPS‐stimulated HGFs; significantly suppressed membrane‐bound RANKL expression on LPS‐stimulated HGFs; and significantly disrupted TRAP+cell differentiation in the coculture system. Silibinin effectively inhibits inflammation‐induced bone loss in experimental periodontitis based on the regulation of stimulated HGFs by inhibiting the expression of inflammatory and osteoclastogenic mediators. Collectively, targeting the inflamed HGF resolution that mediates osteogenesis may use silibinin as a potential drug‐repurposing candidate for modulating alveolar bone destruction in periodontitis. Silibinin effectively inhibits inflammation‐induced bone loss in experimental periodontitis based on the regulation of stimulated HGFs by inhibiting the expression of inflammatory and osteoclastogenic mediators.

Chiral perovskites with exceptional structural flexibility and optoelectrical properties are regarded as promising materials for optoelectronic and spintronic devices. However, the effects of achiral organic ligands on the chiroptical activity of chiral perovskites have been rarely explored. Here, we present four one-dimensional lead bromide perovskites R-/S-(AMP)2Pb3Br10(1-Rand 1-S) with mono-cation and R-/S-AMP(DMA)PbBr5(2-Rand 2-S) with mixed-cation templated by chiral R-/S-2-aminomethylpyrrolidine (R-/S-AMP) and achiral dimethylammonium (DMA). All four compounds demonstrate broadband emissions from self-trapped excitons (STEs). Derived from the stronger chirality transfer synergistically induced by achiral DMA, amplified bathochromically-shifted circular dichroism signals and strong warm-white circularly polarized emissions have been observed in 2-Rand 2-S. This mixed-cation structural engineering provides a new route toward developing chiral perovskites for optoelectronic applications.

With the maturity of design and manufacturing technology development, the drive for lower cost of wind energy continues to increase. To further reduce cost of energy, some wind turbine blade manufacturers adopt the so-called “mould case sharing” strategy, which means that blades with different size share a certain length range of baseline mould cases to improve the reuse rate of mould cases and thus control the average cost of blades. This study develops a deep learning platform for predicting blade performance based on the blade mould-sharing strategy concept. Firstly, a large number of blade samples are generated by randomly sampling geometric parameters such as chord length, twist angle and pre-bending of the blades. Subsequently, a multi-functional simulation platform computes the corresponding key parameters, such as aerodynamic efficiency, blade tip deformation and aerodynamic noise. Afterwards, a blade performance prediction model based on deep neural networks is constructed and trained to achieve better prediction accuracy. Finally, an intelligent algorithm is invoked to design a new larger blade. As compared with traditional modular design methods based on physical solvers, this data-driven approach can significantly reduce the computational cost and design cycles, it has potential application advantages to reduce the unnecessary mould cases design and production.

Sufficient annotated data is critical to the success of deep learning methods. Annotating for vessel segmentation in X-ray coronary angiograms is extremely difficult because of the small and complex structures to be processed. Although unsupervised domain adaptation methods can be utilized to alleviate the annotation burden by using data in other domains, e.g., eye fundus images, these methods cannot perform well due to the characteristic of medical images. Data augmentation can help improve the similarity of source domain and target domain in unsupervised domain adaptation tasks. Existing data augmentation methods play a limited role in improving domain adaptation performance, especially for special medical image segmentation tasks. In this paper, we propose an effective perceptual data augmentation method to improve the similarity between eye fundus images and coronary angiograms by synthesizing virtual samples. Auto Foreground Augment method is designed to search for geometric transformations that improve the similarity between foreground vessels of eye fundus images and coronary angiograms. The Haar Wavelet-Based Perceptual Similarity Index is utilized to guide the synthesis of virtual samples in foreground and background mixup. Extensive experiments show that our data augmentation method can synthesize high-quality virtual samples and thus improve the domain adaptation performance. To our best knowledge, this is the first work to apply perceptual data augmentation to vessel segmentation in coronary angiograms.

Recently, deep learning has become the mainstream methodology for Compound-Protein Interaction (CPI) prediction. However, the existing compound-protein feature extraction methods have some issues that limit their performance. First, graph networks are widely used for structural compound feature extraction, but the chemical properties of a compound depend on functional groups rather than graphic structure. Besides, the existing methods lack capabilities in extracting rich and discriminative protein features. Last, the compound-protein features are usually simply combined for CPI prediction, without considering information redundancy and effective feature mining. To address the above issues, we propose a novel CPInformer method. Specifically, we extract heterogeneous compound features, including structural graph features and functional class fingerprints, to reduce prediction errors caused by similar structural compounds. Then, we combine local and global features using dense connections to obtain multi-scale protein features. Last, we apply ProbSparse self-attention to protein features, under the guidance of compound features, to eliminate information redundancy, and to improve the accuracy of CPInformer. More importantly, the proposed method identifies the activated local regions that link a CPI, providing a good visualisation for the CPI state. The results obtained on five benchmarks demonstrate the merits and superiority of CPInformer over the state-of-the-art approaches.

Nb-containing catalysts have the potential to catalyze carbon dioxide (CO2) reduction due to their strong surface acidity and CO2activation sites. Still, they have not been widely used in the development and design of catalysts due to the theoretical/cost/safety limitations. Related advances have been continuously reported in the literature, demonstrating to some extent the promise of catalytic applications of Nb-containing catalysts in this area. In this minireview, we discuss the structure–activity relationships of Nb-containing catalysts for photo-, electro-, and thermocatalytic reduction of CO2. The engineering strategies of Nb-containing catalysts for enhancing the conversion and selectivity of CO2reduction are discussed, ranging from Nb doping, noble metal decoration, surface acidity adjustment, oxygen vacancy engineering, and heterojunction construction to Nb or Nb2O5particle decoration. The theoretical calculation research for the possible reaction paths and product selectivity is also discussed. Finally, the prospects for designing and optimizing Nb-containing catalysts are proposed. With a deep understanding of catalytic activity and reaction mechanism, this minireview is expected to present the optimization of the Nb-containing catalysts for efficient and highly selective CO2reduction.

Fault diagnosis is key to enhancing the performance and safety of battery storage systems. However, it is challenging to realize efficient fault diagnosis for lithium-ion batteries because the accuracy diagnostic algorithm is limited and the features of the different faults are similar. The model-based method has been widely used for degradation mechanism analysis, state estimation, and life prediction of lithium-ion battery systems due to the fast speed and high development efficiency. This paper reviews the mainstream modeling approaches used for battery diagnosis. First, a review of the battery’s degradation mechanisms and the external factors affecting the aging rate is presented. Second, the different modeling approaches are summarized, from microscopic to macroscopic scales, including density functional theory, molecular dynamics, X-ray computed tomography technology, electrochemical model, equivalent circuit model, distributed model and neural network algorithm. Subsequently, the advantages and disadvantages of these model approaches are discussed for fault detection and diagnosis of batteries in different application scenarios. Finally, the remaining challenges of model-based battery diagnosis and the future perspective of using cloud control and battery intelligent networking to enhance diagnostic performance are discussed.

Atherosclerotic cardiovascular disease resulting from dysregulated lipid metabolism is the leading cause of morbidity and mortality worldwide. Apolipoprotein E (ApoE) plays a critical role in cholesterol metabolism. Knockouts in lipid-metabolizing proteins including ApoE in multiple model organisms such as mice and rats exhibiting elevated levels of cholesterol have been widely used for dissecting the pathology of atherosclerosis, but few of these animal models exhibit advanced atherosclerotic plaques leading to ischemia-induced clinical symptoms, limiting their use for translational studies. Here we report hypercholesterolemia and severe atherosclerosis characterized by stenosis and occlusion of arteries, together with clinical manifestations of stroke and gangrene, in ApoE knockout dogs generated by CRISPR/Cas9 and cloned by somatic cell nuclear transfer technologies. Importantly, the hypercholesterolemia and atherosclerotic complications in F0 mutants are recapitulated in their offspring. As the ApoE-associated atherosclerosis and clinical manifestations in mutant dogs are more similar to that in human patients compared with those in other animal models, these mutant dogs will be invaluable in developing and evaluating new therapies, including endovascular procedures, against atherosclerosis and related disorders.

Background: In recent years, indocyanine green fluorescence lymphography has been introduced for lymphatic mapping in gastric cancer surgery. The aim of this study was to investigate the efficacy of ICGFL in lymph node dissection during minimally invasive surgery for gastric cancer. Methods: A systematic review of electronic databases including PubMed, Embase, Web of Science, the Cochrane Library, and China National Knowledge Infrastructure was performed from the inception to January 2021 for all studies comparing ICGFL with non-ICGFL in GC patients undergoing minimal access gastrectomy. The primary outcome was the total number of harvested lymph nodes. The secondary endpoints were the number of metastatic LNs, operative time, estimated blood loss, and postoperative complications. The registration number of this protocol is PROSPERO CRD42020203443. Results: A total of 13 studies including 1882 participants were included. In this meta-analysis, the use of ICGFL was associated with a higher number of harvested LNs (40.33 vs. 33.40; MD = 6.93; 95%CI: 4.28 to 9.58; P< 0.0001; I2= 86%). No significant difference was found between the ICGFL and control groups in terms of metastatic LNs (2.63 vs. 2.42; MD = 0.21; 95%CI: −0.46 to 0.87; P= 0.54; I2= 0%). In addition, the use of ICGFL could be safely performed without increasing the operative time (P= 0.49), estimated blood loss (P= 0.26) and postoperative complications (P= 0.54). Conclusion: The use of ICGFL may be a useful tool facilitating complete lymph node dissection during minimally invasive GC resection. However, more high-quality RCTs with large sample size are needed to validate this issue.

In order to develop a refiner of Mg– Al alloys, an Al – 1C (in wt.%) master alloy was synthesized using a casting method. The microstructure and grain-refining performance of the Al – 1C master alloy were investigated using X-ray diffraction (XRD), electron probe microanalysis (EPMA) and a grain-refining test. The microstructure of the Al – 1C master alloy is composed of α-Al solid solution, Al4C3particles, and graphite phases. After grain refinement of AZ63B alloy by the Al – 1C master alloy, the mean grain size reached a limit when 2 wt.% Al – C master alloy was added at 800 °C and held for 20 min in the melt before casting. The minimum mean grain size is approximately 48 μm at the one-half radius of the ingot and is about 17% of that of the unrefined alloy. The Al – 1C master alloy results in better grain refinement than C2Cl6and MgCO3carbon-containing refiners.

ABSTRACTA growing body of research has illuminated that non-coding RNAs (ncRNAs) plays an important role in the development of drug resistance in hepatocellular carcinoma (HCC) cells. The expression profiles of differential expressed genes (DEGs) and ncRNAs related to the sorafenib resistance in HCC cells were analyzed according to the Gene Expression Omnibus (GEO) dataSets and The Cancer Genome Atlas (TCGA) datasets. Bioinformatics technology was used to construct the interaction network of DEGs and ncRNAs. Cell transfection, dual-luciferase reporter assay, Western blot, cell counting kit-8 (CCK-8), flow cytometry and quantitative real-time polymerase chain reaction(qRT-PCR) were used to study the mechanism of sorafenib resistance in HepG2 cells and Huh-7 cells. The expression of reelin (RELN) and secretagogin (SCGN) were the only down-regulated in sorafenib-resistant HCC cells. The results showed that RELNgene demethylation reversed the cytotoxic of sorafenib on HepG2 cells and Huh-7 cells. Hsa_circRNA_102049 over-expression promoted the sensitivity of HepG2 cells and Huh-7 cells to sorafenib, hsa_circRNA_102049 up-regulated the expression of RELNgene by sponging hsa-miR-214-3p. The resistance to sorafenib in RELN knockout HepG2 cells and Huh-7 cells could be reverted by has-circRNA_102049. These findings support targeting of hsa_circRNA_102049 and RELN in sorafenib-treated HCC cells as a novel intervention, which is expected to overcome sorafenib resistance of HCC cells.

The development of flexible electronic skins with high performance and multifunctional sensing capabilities is of great significance for applications ranging from healthcare monitoring to artificial intelligence. To mimic and surpass the high-gauge-factor sensing properties of human skin, structure design and appropriate material selection of sensors are both essentially required. Here, we present an efficient, low-cost fabrication strategy to construct an ultra-highly sensitive, flexible pressure sensor by embedding the aligned nickel-coated carbon fibers (NICFs) in a polydimethylsiloxane (PDMS) substrate. Our design substantially contributes to ultrahigh sensitivity through the parallel circuit formed by aligned NICFs as well as surface spinosum microstructure molded by sandpaper. As a result, the sensor exhibits excellent sensitivity (15 525 kPa–1), a fast response time (30 ms), and good stability over 3000 loading–unloading cycles. Furthermore, these superior sensing properties trigger applications in water quality and wave monitoring in conjunction with mechanical flexibility and robustness. As a precedent for adjusting the sensitivities of the sensor, the NICFs/PDMS sensor provides a promising method for multiscenario healthcare monitoring, multiscale pressure spatial distribution, and human–machine interfacing.

This study evaluated the atherosclerosis phenotype by vascular duplex ultrasonography (VDU) in ApoE-deficient dogs.

The two-dimensional (2D) structure often leads to unusual phenomena for the impact of confined mean free path of carrier scattering. As a quasi-2D layered material, AgCrSe2has a liquid-like phonon behavior for its unstable Ag atoms at service temperature, leading to the promising candidate for thermoelectricity and fast ionic conductor. However, the inferior electronic performance constrains its application prospects as a functional semiconductor, which provides broad opportunity to tune its electric behaviors by defect chemistry. In this work, we revealed abundant electric transport behaviors of AgCrSe2with different types of intrinsic defects. For example, the AgCrSe2changes from Anderson insulator to metal when Se defects become prevailing and the magnetoresistance alters its sign depending on the relative ratio of Ag and Cr defects. Our results reported here can give salutary boosting on regulating the electric properties of ternary transition metal selenide by defect chemistry.

This study investigated the degradation mechanism of the surrounding rock of a heavy-haul railway under a water-rich condition, based on the construction of the Taihangshan tunnel for the Wari Railway, a heavy-haul railway that used standard construction practices for axle loads of 30 t. Remote monitoring demonstrated that the coupling effect between the dynamic load of a heavy-haul train and the groundwater leads to the deterioration and hollowing of the surrounding rock. This study clarified the void evolution process and deterioration mechanism of the basement rock under the comprehensive influence of the groundwater–train dynamic load using a refined discrete element numerical simulation. The results revealed that the groundwater was the primary influencing factor in the deterioration of the lower part of the heavy-haul railway tunnel. Rock particles were gradually lost under the effects of long-term erosion due to groundwater and heavy-haul trains, which inevitably damaged the basement rock after the construction was completed. Based on this observation, the critical conditions for the deterioration and attenuation law of the physical parameters of the basement rock were obtained. The results of this study can provide ideas and serve as a reference for the forecasting and disaster treatment of basement rock damage in heavy-haul railway tunnels.

Flexible pressure sensors have attracted intense attention because of their widespread applications in electronic skin, human–machine interfaces, and healthcare monitoring. Conductive porous structures are always utilized as active layers to improve the sensor sensitivities. However, flexible pressure sensors derived from traditional foaming techniques have limited structure designability. Besides, random pore distribution causes difference in structure and signal repeatability between different samples even in one batch, therefore limiting the batch production capabilities. Herein, we introduce a structure designable lattice structure pressure sensor (LPS) produced by bottom-up digital light processing (DLP) 3D printing technique, which is capable of efficiently producing 55 high fidelity lattice structure models in 30 min. The LPS shows high sensitivity (1.02 kPa–1) with superior linearity over a wide pressure range (0.7 Pa to 160 kPa). By adjusting the design parameters such as lattice type and layer thickness, the electrical sensitivities and mechanical properties of LPS can be accurately controlled. In addition, the LPS endures up to 60000 compression cycles (at 10 kPa) without any obvious electrical signal degradation. This benefits from the firm carbon nanotubes (CNTs) coating derived from high-energy ultrasonic probe and the subsequent thermal curing process of UV-heat dual-curing photocurable resin. For practical applications, the LPS is used for real time pulse monitoring, voice recognition and Morse code communication. Furthermore, the LPS is also integrated to make a flexible 4 × 4 sensor arrays for detecting spatial pressure distribution and a flexible insole for foot pressure monitoring.

TRIzol is used for the extraction of RNA, DNA and proteins from tissues or cells. Here, we present a simple picking method to extract DNA from tissues using TRIzol. Spectrophotometric analysis showed that the 260/280 and 260/230 nm optical density ratio of the picking method's DNA is ideal and better than that obtained by the classic TRIzol method. Gel electrophoresis showed that there was no RNA contamination, and the DNA had not degraded. DNA extracted by the picking method had the same performance in restriction enzyme digestion and quantitative PCR as that obtained by the traditional method. Viral DNA in the infected tissue was also obtained. This modified method facilitates various molecular biology assays.

Lithium metal anodes are of great interest for advanced high-energy density batteries such as lithium-air, lithium-sulfur and solid-state batteries, due to their low electrode potential and ultra-high theoretical capacity. There are, however, several challenges limiting their practical applications, which include low coulombic efficiency, the uncontrollable growth of dendrites and poor rate capability. Here, a rational design of 3D structured lithium metal anodes comprising of in-situ growth of cobalt-decorated nitrogen-doped carbon nanotubes on continuous carbon nanofibers is demonstrated via electrospinning. The porous and free-standing scaffold can enhance the tolerance to stresses resulting from the intrinsic volume change during Li plating/stripping, delivering a significant boost in both charge/discharge rates and stable cycling performance. A binary Co-Li alloying phase was generated at the initial discharge process, creating more active sites for the Li nucleation and uniform deposition. Characterization and density functional theory calculations show that the conductive and uniformly distributed cobalt-decorated carbon nanotubes with hierarchical structure can effectively reduce the local current density and more easily absorb Li atoms, leading to more uniform Li nucleation during plating. The current work presents an advance on scalable and cost-effective strategies for novel electrode materials with 3D hierarchical microstructures and mechanical flexibility for lithium metal anodes.

Background and purpose While drug-eluting stents (DES) have been widely applicated in coronary stenosis, uncertainty persists concerning the relative performance and clinical benefit in patients undergoing vertebral artery stenting when compared with a bare metal stent (BMS). We sought to compare in-stent restenosis (ISR) rates of DES and BMS in the treatment of vertebral artery ostium (VAO) stenosis.Materials and Methods This study analyzed a single-center prospective cohort. Over 1.5-year period (January 2014-June 2015), 137 consecutive patients underwent VAO stenting involving deployment of 76 DES and 74 BMS. Patient demographics, comorbidities, stenosis severity, stent diameters and lengths, periprocedural complications, imaging and duplex ultrasonography follow-up and recurrent symptoms were assessed.Results Technical success was achieved in all patients. Mean VAO stenosis at presentation were 82.4 ± 7.2% in the DES group and 83.3 ± 7.5% in the BMS group and were reduced to 12.5 ± 4.5% and 11.3 ± 4.0%. Mean stent diameter was 3.53 ± 0.40 mm in DES and 5.05 ± 0.40 mm in BMS (p < 0.0001). Mean follow-up was 12.3 months for DES and 11.7 months for BMS. The use of DES was associated with significant lower ISR rates compared with BMS (18.4% vs. 31.1%; OR = 2.628, p = 0.021). Recurrent symptoms rates were similar in DES vs. BMS (2.6% vs 2.7%, p = 0.680). Stent type and stent diameter were independent risk factors for ISR (P = 0.026).Conclusion Our results suggest superior efficacy of deploying DES for the treatment of VAO stenosis with lower ISR rates as compared to BMS, but do not support significant differences in periprocedural risk and recurrent symptoms rate.

Skin sensors are of paramount importance for flexible wearable electronics, which are active in medical diagnosis and healthcare monitoring. Ultrahigh sensitivity, large measuring range, and high skin conformability are highly desirable for skin sensors. Here, an ultrathin flexible piezoresistive sensor with high sensitivity and wide detection range is reported based on hierarchical nanonetwork structured pressure-sensitive material and nanonetwork electrodes. The hierarchical nanonetwork material is composed of silver nanowires (Ag NWs), graphene (GR), and polyamide nanofibers (PANFs). Among them, Ag NWs are evenly interspersed in a PANFs network, forming conductive pathways. Also, GR acts as bridges of crossed Ag NWs. The hierarchical nanonetwork structure and GR bridges of the pressure-sensitive material enable the ultrahigh sensitivity for the pressure sensor. More specifically, the sensitivity of 134 kPa–1(0–1.5 kPa) and the low detection of 3.7 Pa are achieved for the pressure sensor. Besides, the nanofibers act as a backbone, which provides effective protection for Ag NWs and GR as pressure is applied. Hence, the pressure sensor possesses an excellent durability (>8000 cycles) and wide detection range (>75 kPa). Additionally, ultrathin property (7 μm) and nanonetwork structure provide high skin conformability for the pressure sensor. These superior performances lay a foundation for the application of pressure sensors in physiological signal monitoring and pressure spatial distribution detection.

This article proposes a novel sentence semantic equivalence identification (SSEI) method by using the semantic difference features between sentences. The lexical differences of a sentence pair are first extracted, and the bidirectional long short term memory (BiLSTM) network is then applied on them to generate the semantic difference representations. Finally, an efficient gate mechanism is proposed to integrate the semantic differences with existing models (called base model) to enhance their encoding capability in the SSEI task. Exhaustive experiments conducted on the standard Quora corpus, and the Large-scale Chinese Question Matching Corpus (LCQMC) show that the proposed gated semantic difference (GSD) method brings significant improvement for different existing state-of-the-art models. When the bidirectional encoder representations from transformers model (BERT) is used as the base model, the accuracy for SSEI on Quora is improved from 90.63% to 91.98%, and the F1 score on the LCQMC is improved from 87.0% to 87.7%, which outperforms the best-published results.

Here, an innovative noncontact interactive visualized sensor based on an electret is reported. Visualization of a floating external object, such as the contour, the separation distance and the velocity, is achieved in a completely non-contact way. Besides, identification of hidden patterns inside the device, including letters and a bar code, is successfully acquired through a non-contact scanning method.

Combustion characteristics are significantly affected by ambient pressure, which is usually much higher than the atmosphere in the engine cylinder. In this work, the effects of pressure on the flame structure and soot behavior in a methane diffusion flame doped with n-heptane were numerically investigated by a detailed chemical mechanism and a sectional soot model. The results show that the high-temperature region moves toward the wings of flame and the radius of the flame decreases as ambient pressure increases. Soot volume fraction increases significantly with its peak value scaled with p2.25for the pure methane flame and p1.60for the methane flame doped with n-heptane. In addition, the height at which the initial soot forms moves upstream, while the height at which soot particles are completely oxidized moves downstream, resulting in a larger sooting region. The increase of soot concentration is mainly due to the increase of the mixture density as pressure changes, and therefore, the collision frequency between gas species and particles increases, which in turn accelerates the inception rate and surface growth rate of particles. Primary number density also increases because of the increase in the inception rate. The aggregate characteristics, which are evaluated by the number of primary particles per aggregate, increase because of the increase in the particle number density that leads to an acceleration in particle coagulation. Soot mass addition, dominated by hydrogen-abstraction–carbon-addition reactions, increases because of the increase of absolute concentrations of C2H2and H radicals, while soot mass consumption, dominated by OH oxidation, increases with a lower rate than that of soot addition because of the increase of the collision frequency and efficiency between the OH radicals and particles.

The rapid development of flexible and wearable electronics calls for a sustainable solution of the power supply. In recent years, the energy-harvesting triboelectric nanogenerator (TENG) has attracted increasing attentions due to its sustainability, flexibility, and versatility. However, achieving both high electric output and flexibility at the same time remains to be a challenge. In this work, we reported a corona charging enhanced flexible triboelectric nanogenerator (EF-TENG) to harvest mechanical energy from human motions. The EF-TENG relied on the repeated contacts between a poly(tetrafluoroethylene)/poly(dimethylsiloxane) nanocomposite electret and a nanofibers/AgNWs electrode on arrayed silicone pyramids. When the EF-TENG (3.5 × 3.5 cm2) was pressed, the open-circuit voltage (Voc), the short-circuit current (Isc), and the power density could reach 275 V, 9.5 μA, and 802.31 mW/m2, respectively. The Vocof the EF-TENG was improved by 244% compared to the device of which the electret was not corona charged. Major factors that affected the electric output of the EF-TENG were discussed, including the height of the pyramids, the configuration of the pyramids array, and the properties of the electret nanocomposite. The EF-TENG only had an overall thickness of 1.3 mm and a weight of 1.7 g, making it especially suitable to be attached onto human body for harvesting mechanical energy from biomechanical motions.

Current therapies primarily targeting inflammation often fail to address the root relationship between intestinal mucosal integrity and the resulting dysregulated cell death and ensuing inflammation in ulcerative colitis (UC). First, UC tissues from human and mice models in this article both emphasize the crucial role of Gasdermin E (GSDME)-mediated pyroptosis in intestinal epithelial cells (IECs) as it contributes to colitis by releasing proinflammatory cytokines, thereby compromising the intestinal barrier. Then, 4-octyl-itaconate (4-OI), exhibiting potential for anti-inflammatory activity in inhibiting pyroptosis, was encapsulated by butyrate-modified liposome (4-OI/BLipo) to target delivery for IECs. In brief, 4-OI/BLipo exhibited preferential accumulation in inflamed colonic epithelium, attributed to over 95% of butyrate being produced and absorbed in the colon. As expected, epithelium barriers were restored significantly by alleviating GSDME-mediated pyroptosis in colitis. Accordingly, the permeability of IECs was restored, and the resulting inflammation, mucosal epithelium, and balance of gut flora were reprogrammed, which offers a hopeful approach to the effective management of UC.

This paper uses the enthalpy-porosity technique to conduct a three-dimensional numerical investigation on the effect of adding longitudinal fins in energy storage units to enhance the melting process, with a specific focus on natural convection. By reducing the extension length of the upper part of the fins and increasing the extension length at the bottom, the work aims to enhance heat transfer while reducing obstruction to convection. The effects of rectangular, trapezoidal, and triangular longitudinal fins with thicknesses of 0.2, 0.25, and 0.3 mm on the melting characteristics were compared while maintaining a constant filling volume of RT42 phase change material (PCM) within the rectangular container. The results indicate that the enhanced heat transfer capacity of fins with different structures increases as the fin length increases. Triangular fins exhibit the most significant heat transfer enhancement effect, with the least hindrance to convection. When the fin thickness is 0.2 mm, the triangular fin decreases the melting completion time by 15.7 %, increases the heat storage rate by 18.7 % compared to the rectangular fin, and exhibits optimal natural convection intensity and temperature distribution in the energy storage unit. Additionally, the maximum flow velocity and average Nusselt number can be increased by 21.2 % and 18.5 %, respectively. It was also found that triangular fins are more suitable for high-temperature working conditions.

This study aims to figure out some safety issues by investigating the roof shape of infrastructures such as zero-carbon huts that people are already using to prevent dangerous hydrogen clouds from building up in places with little airflow when there is a leak. We designed three different obstacles. The horizontal and vertical concentration distributions of the free-jet hydrogen cloud when encountering different obstacles were determined by concentration measurements. In addition, the diffusion characteristics of the hydrogen jet in the presence of obstacles were investigated by using the Background Oriented Schlieren (BOS). The results show that there is a linear relationship between the measured axial concentration of the hydrogen jet and the leakage parameters at lower flow rates. The measured radial concentration of the hydrogen jet shows a “Gaussian-like” distribution when encountering a flat plate obstacle, a “W” distribution when encountering a flat plate obstacle with sidewalls, and a “W-like” distribution when encountering a dome-type obstacle. A method of inverting the radial concentration distribution of the hydrogen jet using the BOS is also proposed, and the error between the theoretical and experimental values is calculated to be 3.81 %. This study stresses the significance of geometric and dimensional parameters on hydrogen leakage characteristics and, as a result, provides insights into developing performance standards for the availability and reliability of safety-critical systems.

Two novel fungal metabolites, asperochones A and B, were obtained from an Aspergillussp. Their structures were determined by 1D/2D nuclear magnetic resonance (NMR) spectroscopy, high resolution electrospray ionization mass spectroscopy (HRESIMS), and single-crystal X-ray diffraction analysis. Asperochone A possesses an intriguing skeleton bearing 5/6/6/6/7/5/5/5 octacyclic ring system, and asperochone B also exhibits an unusual carbon skeleton with five stereochiral centers. Their structures were proposed as heterotrimeric and heterodimeric products of aromatic polyketides. In addition, asperochone A exhibited a potential anti-tuberculosis effect since it showed a moderate potency against Mycobacterium smegmatis.

Transforming dynamic mechanical interactions into visualized luminescence represents a research frontier in the detection of tactile stimuli. Here, we report a self-powered high-resolution triboelectrification-induced electroluminescence (HR-TIEL) sensor for visualizing the contact profile and dynamic trajectory of a contact object. As dynamic interactions occur, triboelectric charges at the contact interface generate a transient electric field that excites the phosphor. From the numerical simulation, a conductive layer based on transparent silver nanowires (AgNWs) guides the direction of the electric field and confines it within the profile boundary of the connect object. As a result, a sharp change of the electric field at the profile boundary greatly promotes the luminescence intensity as well as the lateral spatial resolution. Compared to a triboelectrification-induced electroluminescence (TIEL) sensor without the conductive layer, the luminescence intensity is enhanced by 90%, and the lateral spatial resolution of ∼500 μm is achieved. The HR-TIEL sensor is then demonstrated to reveal the surface texture on a nitrile glove. It relies on neither additional power supplies nor complex wiring/circuit design. This work paves the way for the feasible detection of tactile stimuli such as touch and slipping, which will be potentially used in robotics, human–machine interface, flexible and wearable electronics, and so forth.

ABSTRACTLongfin batfish (Platax teira) is a coastal marine fish life. The aims of this study are to investigate the compositions and content of amino acid and fatty acid in three parts of P. teira. The results showed that P. teiramuscle protein content was as high as 21.00%. For amino acid composition, seventeen kinds of amino acids were detected in three parts. The essential amino acid score (EAAS) and essential amino acids (EAA) / total amino acids (TAA) ratio of P. teirawere both above 70 and 0.4. EAAS in the upper part of the abdominal muscle showed higher than those two parts. The content of saturated fatty acids (SFA) and the polyunsaturated fatty acids (PUFA) in the upper part of the abdominal muscle was higher than that in other groups. No significant differences in monounsaturated fatty acids (MUFA) and n-3/n- 6 were detected among three parts. Overall, the upper part of the abdominal muscle was thought to have a higher nutritious value. These results showed P. teirais a high quality marine fish with high protein content and low fat, abundant amino acids with proper proportion, however, it was regarded as an excellent breeding varieties.

ObjectiveThe study was designed to investigate if vascular occlusion in the internal carotid artery (ICA) or the contralateral vertebral artery (VA) contribute to developing in-stent restenosis (ISR) in patients with vertebral artery ostium stenosis (VAOS).Methods420 consecutive patients treated with VAOS stents (from a population of 8145 patients with VAOS) from January 2013 to December 2014 were analyzed in this retrospective study; 216 with drug eluted stents and 204 with bare metal stents. Based on pre-stent DSA findings, patients were divided into four groups: both carotid and vertebral arteries patent (PAT), ICA occlusion (ICA-OCC), contralateral VA occlusion (CVA-OCC), and combined occlusions (C-OCC). The incidence of ISR (stenosis >50%) was compared between groups using Cox regression analysis.ResultsOf the 420 patients, the mean incidence of ISR was 36.4%, with a median 12 months of follow-up (IQR 3–12). Logistic regression analysis showed that drug eluting stent had less ISR than bare metal stent (OR=0.38, 95% CI 0.19 to 0.75, P=0.01). Cox regression analysis showed that CVA-OCC (HR=1.63, P=0.02) and C-OCC (HR=3.30, P=0.001) were risk factors for ISR but not ICA-OCC (P=0.31). In the CVA-OCC and C-OCC groups, in-stent peak systolic velocity (PSV) ≥140 cm/s, 1 day after successful stenting, was associated with subsequent development of ISR (OR=2.81, 95% CI 1.06 to 7.43, P=0.04).ConclusionContralateral VA occlusion at the time of stenting increased the risk of ISR, especially if stent PSV on day 1 was >140 cm/s. Bare metal stents had more ISR than drug eluting stents.

Flexible piezoelectric nanogenerators have drawn considerable attention for their wide applications in harvesting ambient mechanical energy. Here, we report a flexible porous nanogenerator (FPNG) based on the dual effect of ferroelectricity and piezoelectricity. The electric output originated from the combination of the above two effects can be constructively added up, resulting in an enhancement of the electric output. With dimensions of 2 × 2 × 0.3 cm3, the FPNG can generate an open-circuit voltage (Voc) of 29 V and a short-circuit current (Isc) of 116 nA under a compressive force of 30 N. Besides, the FPNG is applicable to other forms of mechanical stimuli, including twisting and bending. Harvesting energy from a rowing bicycle tire is demonstrated in this report. This work provides a new route to promoting the electric output of piezoelectric nanogenerators.

In the process of anaerobic digestion of wastewater, effluent chemical oxygen demand (COD) and gas production are important parameters to measure the effect of anaerobic biological treatment, and are also important indicators for evaluating the performance of water treatment. At present, most of these values in anaerobic biological treatment systems for wastewater are often obtained through manual tests. The disadvantage of manual assays is the long detection time and poor stability. Therefore, the prediction of water COD and gas production based on back propagation neural network (BPNN) is proposed in this paper. Then, aiming at the problems of speed sluggishness and lopsided one-sided minimization in traditional BP neural networks, an improved BP neural network prediction model based on genetic algorithm (GA-BPNN) is proposed. Experimental results show that the performance of GA-BPNN is better than traditional BPNN. In effluent COD prediction, the mean absolute percent error (MAPE) of BP neural network prediction is 60.7234%, while the MAPE of GA-BPNN algorithm is only 20.9854%. In the prediction of gas production, the MAPE of BP neural network prediction is 10.5521%, while the MAPE of GA-BPNN algorithm is only 7.5677%. Moreover, both the effluent COD prediction and the gas production forecasting, GA-BPNN algorithm’s mean square error (MSE), root mean square error (RMSE) and Pearson’s correlation coefficient are all better than BP neural network.

Background: Outcomes of laparoscopic surgery in advanced gastric cancer patients who received neoadjuvant therapy represent a controversial issue. We performed an updated meta-analysis to evaluate the perioperative and long-term survival outcomes of laparoscopic gastrectomy (LG) versus conventional open gastrectomy (OG) in this subset of patients. Methods: Electronic databases including PubMed, Embase, Web of Science, the Cochrane Central Register of Controlled Trials and China National Knowledge Infrastructure were comprehensively searched up to May 2023. The short-term and long-term outcomes of LG versus OG in advanced gastric cancer patients undergoing neoadjuvant therapy were evaluated. Effect sizes with 95% confidence intervals were always assessed using random-effects model. The prospective protocol was registered with PROSPERO (CRD42022359126). Results: Eighteen studies (2 randomized controlled trials and 16 cohort studies) involving 2096 patients were included. In total, 933 patients were treated with LG and 1163 patients were treated with OG. In perioperative outcomes, LG was associated with less estimated blood loss (MD = − 65.15; P&lt; 0.0001), faster time to flatus (MD = − 0.56; P&lt; 0.0001) and liquid intake (MD = − 0.42; P= 0.02), reduced hospital stay (MD = − 2.26; P&lt; 0.0001), lower overall complication rate (OR = 0.70; P= 0.002) and lower minor complication rate (OR = 0.69; P= 0.006), while longer operative time (MD = 25.98; P&lt; 0.0001). There were no significant differences between the two groups in terms of proximal margin, distal margin, R1/R2 resection rate, retrieved lymph nodes, time to remove gastric tube and drainage tube, major complications and other specific complications. In survival outcomes, LG and OG were not significantly different in overall survival, disease-free survival and recurrence-free survival. Conclusion: LG can be a safe and feasible technique for the treatment of advanced gastric cancer patients receiving neoadjuvant therapy. However, more high-quality randomized controlled trials are still needed to further validate the results of our study.

Accurately simulating and characterizing the thermal behavior of lithium batteries is vital for thermal design and management. Currently, the widely used simulation method for studying battery module thermal behavior and management is the virtual electrical connection. However, this method overlooks the presence of the busbar. Therefore, this study utilizes a physical electrical connection simulation method to investigate the impact of busbar heat generation and transfer on the thermoelectric behavior of the battery module. The results indicate that the busbar's heat production and transfer significantly affect the current density and temperature distribution, as well as the temperature uniformity of the battery module. The physical electrical connection simulation method accurately models the actual current transfer process between the battery electrodes, aligning with the real-world operation. In comparison to the virtual electrical connection simulation method, the physical electrical connection approach shows elevated local temperatures at the battery electrodes and the upper area in proximity to the busbar. It also exhibits more significant temperature gradients and worse temperature uniformity. Under real-world driving conditions, the temperature and heat generation of the battery undergo more frequent and wider fluctuations compared with constant-current discharge. The impact of the busbar's heat production and transfer on battery thermal behavior becomes more evident in these conditions. For example, under the FTP75, NEDC, and WLTC conditions, the maximum temperature increased by 8.04 °C, 28.43 °C, and 27.35 °C respectively, and the maximum temperature difference increased by 7.91 °C, 28.41 °C, and 27.19 °C respectively. Thus, the physical electrical connection simulation method significantly enhances the realism of battery module thermoelectric behavior simulations, contributing to the accuracy and effectiveness of thermal management analysis and product design.