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Abstract Knowledge-aware recommendation systems often face challenges owing to sparse supervision signals and redundant entity relations, which can diminish the advantages of utilizing knowledge graphs for enhancing recommendation performance. To tackle these challenges, we propose a novel recommendation model named Dual-Intent-View Contrastive Learning network (DIVCL), inspired by recent advancements in contrastive and intent learning. DIVCL employs a dual-view representation learning approach using Graph Neural Networks (GNNs), consisting of two distinct views: a local view based on the user-item interaction graph and a global view based on the user-item-entity knowledge graph. To further enhance learning, a set of intents are integrated into each user-item interaction as a separate class of nodes, fulfilling three crucial roles in the GNN learning process: (1) providing fine-grained representations of user-item interaction features, (2) acting as evaluators for filtering relevant relations in the knowledge graph, and (3) participating in contrastive learning to strengthen the model’s ability to handle sparse signals and redundant relations. Experimental results on three benchmark datasets demonstrate that DIVCL outperforms state-of-the-art models, showcasing its superior performance. The implementation is available at: https://github.com/yzxx667/DIVCL .

Abstract Single-atom skeletal editing is an increasingly powerful tool for scaffold hopping-based drug discovery. However, the insertion of a functionalized carbon atom into heteroarenes remains rare, especially when performed in complex chemical settings. Despite more than a century of research, Ciamician-Dennstedt (C-D) rearrangement remains limited to halocarbene precursors. Herein, we report a general methodology for the Ciamician-Dennstedt reaction using α-halogen-free carbenes generated in situ from N-triftosylhydrazones. This one-pot, two-step protocol enables the insertion of various carbenes, including those previously unexplored in C-D skeletal editing chemistry, into indoles/pyrroles scaffolds to access 3-functionalized quinolines/pyridines. Mechanistic studies reveal a pathway involving the intermediacy of a 1,4-dihydroquinoline intermediate, which could undergo oxidative aromatization or defluorinative aromatization to form different carbon-atom insertion products.

The rheumatoid arthritis (RA) microenvironment is often followed by a vicious circle of high inflammation, endogenous gas levels imbalance, and poor treatment. To break the circle, we develop a dual-gas-mediated injectable hydrogel for modulating the immune microenvironment of RA and simultaneously releasing therapeutic drugs. The hydrogel (DNRS gel) could be broken down on-demand by consuming excessive nitric oxide (NO) and releasing therapeutic hydrogen sulfide (H2S), resulting in endogenous gas restoration, inflammation alleviation, and macrophage polarization to M2 type. Additionally, the hydrogel could suppress osteoclastogenesis and enhance osteogenesis. Furthermore, the intra-articularly injected hydrogel with methotrexate (MTX/DNRS gel) significantly alleviated inflammation and clinical symptoms and promoted the repair of bone erosion in the collagen-induced arthritis rat model. As a result, in vivo results demonstrated that MTX/DNRS gel restored the microenvironment and improved the therapeutic effect of MTX. This study provides a novel understanding of developing versatile smart delivery platforms for RA treatment.

Short-term energy-consumption prediction is the basis of anomaly detection, real-time scheduling, and energy-saving control in manufacturing systems. Most existing methods focus on single-node energy-consumption prediction and suffer from difficult parameter collection and modelling. Although several methods have been presented for multinode energy-consumption prediction, their prediction performance needs to be improved owing to a lack of appropriate knowledge guidance and learning networks for complex spatiotemporal relationships. This study presents a symmetric spatiotemporal learning network (SSTLN) with a sparse meter graph (SMG) (SSTLN-SMG) that aims to predict multiple nodes based on energy-consumption time series and general process knowledge. The SMG expresses process knowledge by abstracting production nodes, material flows, and energy usage, and provides initial guidance for the SSTLN to extract spatial features. SSTLN, a symmetrical stack of graph convolutional networks (GCN) and gated linear units (GLU), is devised to achieve a trade-off not only between spatial and temporal feature extraction but also between detail capture and noise suppression. Extensive experiments were performed using datasets from an aluminium profile plant. The experimental results demonstrate that the proposed method allows multinode energy-consumption prediction with less prediction error than state-of-the-art methods, methods with deformed meter graphs, and methods with deformed learning networks.

Typical loess generally shows the property of strong seismic subsidence due to its special microstructure characteristics. Fuping County which is located in the connection zone of Weihe Basin and Ordos Platform possesses complex seismic tectonic settings, high seismic fortification intensity, and widely distributed loess strata. Consequently, the risk of loess seismic subsidence is relatively high for the loess site of the region. In this paper, the regions of Fuping City and Fuping-Yanliang new area were chosen as the study area. The technical methods, such as data collection, field investigation, laboratory test, and theoretical analysis, were adopted to determine the seismic geological conditions of the region. The ground motion parameters with different transcendental probability levels were obtained by adopting the probability analysis method of seismic risk. The seismic subsidence properties of loess from various landforms and strata were studied based on a large number of dynamic triaxial tests. On this basis, Evaluate the seismic subsidence of loess sites under strong earthquakes in Fuping County. Under the condition of a probability of exceeding 10% in 50 years, mild and moderate seismic subsidence areas in the study area are distributed in the Fuyuan and Weihe secondary terraces. Under the condition of a probability of exceeding 2% in 50 years, the mild and moderate loess seismic subsidence areas in the study area are mainly distributed in the third to fourth level terraces of the Shichuan River, the floating plateau, and the second level terraces of the Wei River. Severe seismic subsidence areas are mainly distributed in the second level terraces of the Weihe River, the third to fourth level terraces of the Shichuan River, and the floating plateau. Based on the seismic subsidence level, the loess seismic subsidence sub regions of the study area were given at the 10% and 2% probability of exceedance in 50 years. Attention should be paid to this part of potential earthquake geological disaster in the design of prevention and foundation.

Abstract Antimicrobial resistance (AMR) from pathogenic bacterial biofilms has become a global health issue while developing novel antimicrobials is inefficient and costly. Combining existing multiple drugs with enhanced efficacy and/or reduced toxicity may be a promising approach to treat AMR. D‐amino acids mixtures coupled with antibiotics can provide new therapies for drug‐resistance infection with reduced toxicity by lower drug dosage requirements. However, iterative trial‐and‐error experiments are not tenable to prioritize credible drug formulations, owing to the extremely large number of possible combinations. Herein, a new avenue is provide to accelerate the exploration of desirable antimicrobial formulations via high‐throughput screening and machine learning optimization. Such an intelligent method can navigate the large search space and rapidly identify the D‐amino acid mixtures with the highest anti‐biofilm efficiency and also the synergisms between D‐amino acid mixtures and antibiotics. The optimized drug cocktails exhibit high antimicrobial efficacy while remaining non‐toxic, which is demonstrated not only from in vitro assessments but also the first in vivo study using a lung infection mouse model.

Multivariate ridge regression (MR), linear discriminant analysis (LDA) and extreme learning machine (ELM) have been widely used in hyperspectral image (HSI) classification. However, these methods do not consider the influence of noise in HSIs, spatial information, and the internal relationship between samples. As a result, the sample distribution is not ideal and the classification effect cannot be improved. This article extends LDA and MR to the field of tensors, that can not only use the spatial information of the sample, but also can make the distribution of homogeneous samples more concentrated. Besides, this article analyzes the relationship between the number of neurons in the hidden layer of ELM and the classification accuracy. Finally, singular spectral analysis (SSA) is chosen to improve classification accuracy. The tensor discriminant ridge regression model with ELM and SSA for HSI classification is proposed. Experiments show compared with tensor-based classifiers, ELM and other state-of-the-art methods, the proposed method is efficient and effective.

Converting low-cost inorganic chemicals into value-added organic chemicals is a longstanding goal in chemistry. Here the authors describe a silver-catalysed chemoselective carbene N−H insertion reaction, providing access to primary amines from aqueous ammonia and diazo compounds.

Biodegradable magnesium alloys have excellent properties with respect to biodegradability, biocompatibility, and biomechanics, which may indicate a possibility of its application in intestinal stents. Investigation of Mg-Zn-Y-Nd alloy's application in intestinal stents has been performed. This study aims to investigate the degradation behavior of Mg-Zn-Y-Nd alloy intestinal stents coated with poly(L-lactide)/paclitaxel in the intestinal environment and its biocompatibility with intestinal tissue. In this paper, Mg-Zn-Y-Nd alloy's corrosion properties were evaluated by the immersion test in human feces, SEM and XRD, and animal tests. In vitro results showed that when the Mg-Zn-Y-Nd alloy was immersed in human feces for two weeks, its corrosion resistance could be improved by micro arc oxidation (MAO) and poly-l-lactide (PLLA) dual coating. Additionally, this result was also confirmed in vivo experiments by rabbit model. And animal tests also demonstrated that the Mg-Zn-Y-Nd alloy with MAO/PLLA/paclitaxel dual coating drug-eluting stents could inhibit the proliferation of local intestinal tissue around the stents. However, in vivo studies illustrated that the intestinal stents gradually degraded in rabbit model within 12 days. Considering the degradation rate of the stent was faster than expected in rabbits, the support performance of the scaffold requires further improvement.

Abstract Background The cellular dynamics in the tumour microenvironment (TME) along with non‐small cell lung cancer (NSCLC) progression remain unclear. Methods Multiplex immunofluorescence test detecting 10 immune‐related markers on 553 primary tumour (PT) samples of NSCLC was conducted and spatial information in TME was assessed by the StarDist depth learning model. The single‐cell transcriptomic atlas of PT (n = 4) and paired tumour‐draining lymph nodes (TDLNs) (n = 5 for tumour‐invaded, n = 3 for tumour‐free) microenvironment was profiled. Various bioinformatics analyses based on Gene Expression Omnibus, TCGA and Array‐Express databases were also used to validate the discoveries. Results Spatial distances of CD4+ T cells–CD38+ T cells, CD4+ T cells–neutrophils and CD38+ T cells–neutrophils prolonged and they were replaced by CD163+ macrophages in PT along with tumour progression. Neutrophils showed unique stage and location‐dependent prognostic effects. A high abundance of stromal neutrophils improved disease‐free survival in the early‐stage, whereas high intratumoural neutrophil infiltrates predicted poor prognosis in the mid‐to‐late‐stage. Significant molecular and functional reprogramming in PT and TDLN microenvironments was observed. Diverse interaction networks mediated by neutrophils were found between positive and negative TDLNs. Five phenotypically and functionally heterogeneous subtypes of tumour‐associated neutrophil (TAN) were further identified by pseudotime analysis, including TAN‐0 with antigen‐presenting function, TAN‐1 with strong expression of interferon (IFN)‐stimulated genes, the pro‐tumour TAN‐2 subcluster, the classical subset (TAN‐3) and the pro‐inflammatory subtype (TAN‐4). Loss of IFN‐stimulated signature and growing angiogenesis activity were discovered along the transitional trajectory. Eventually, a robust six neutrophil differentiation relevant genes‐based model was established, showing that low‐risk patients had longer overall survival time and may respond better to immunotherapy. Conclusions The cellular composition, spatial location, molecular and functional changes in PT and TDLN microenvironments along with NSCLC progression were deciphered, highlighting the immunoregulatory roles and evolutionary heterogeneity of TANs.

PurposeTo develop a deep learning system to differentiate demyelinating optic neuritis (ON) and non-arteritic anterior ischemic optic neuropathy (NAION) with overlapping clinical profiles at the acute phase.MethodsWe developed a deep learning system (ONION) to distinguish ON from NAION at the acute phase. Color fundus photographs (CFPs) from 871 eyes of 547 patients were included, including 396 ON from 232 patients and 475 NAION from 315 patients. Efficientnet-B0 was used to train the model, and the performance was measured by calculating the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Also, Cohen’s kappa coefficients were obtained to compare the system’s performance to that of different ophthalmologists.ResultsIn the validation data set, the ONION system distinguished between acute ON and NAION achieved the following mean performance: time-consuming (23 s), AUC 0.903 (95% CI 0.827–0.947), sensitivity 0.796 (95% CI 0.704–0.864), and specificity 0.865 (95% CI 0.783–0.920). Testing data set: time-consuming (17 s), AUC 0.902 (95% CI 0.832–0.944), sensitivity 0.814 (95% CI 0.732–0.875), and specificity 0.841 (95% CI 0.762–0.897). The performance (κ = 0.805) was comparable to that of a retinal expert (κ = 0.749) and was better than the other four ophthalmologists (κ = 0.309–0.609).ConclusionThe ONION system performed satisfactorily distinguishing ON from NAION at the acute phase. It might greatly benefit the challenging differentiation between ON and NAION.

High Temperature may exert a great impact on the service life and reliability of friction materials during friction sliding. Tribological behavior of a new developed copper metal matrix composite (Cu-MMC) against a steel disc was investigated using a home-built pin-on-disc tribometer in the ambient temperature range of 25–800 °C. Detailed analyses of worn surfaces and subsurfaces were performed using scanning electron microscope, optical microscope, X-ray diffractometer and 3D optical profiler. The worn surface of Cu-MMC exhibited different features at various ambient temperatures, resulting in a significant difference in tribological properties. The friction coefficient increased slightly from 0.563 to 0.640 as the ambient temperature varied from 25 °C to 400 °C, then a further increase in the ambient temperature led to a sharp decrease in the friction coefficient to 0.323 at 800 °C. A prompt increase in the mass loss of Cu-MMC was evident with increasing the ambient temperature from 25 °C to 400 °C, and then a slight increase occurred in mass loss of Cu-MMC upon increasing the ambient temperature up to 600 °C after which a significant declining trend of Cu-MMC was observed. The worn surfaces with a rugged and fluctuant morphology contributed to the high and unstable friction coefficient, as well as the high mass loss of Cu-MMC. The dominant wear mechanisms of Cu-MMC were severe oxidation, abrasive wear and adhesive wear at 400 °C and 600 °C, then delamination wear and oxidation at 800 °C.

Abstract Background Epigenetic reprogramming is involved in multiple steps of human cancer evolution and is mediated by a variety of chromatin-modifying enzymes. Specifically, the histone lysine methyltransferase KMT2D is among the most frequently mutated genes in oral squamous cell carcinoma (OSCC). However, the mechanisms by which KMT2D affects the development of OSCC remain unclear. Results In the present study, we found that the expression of KMT2D was elevated in OSCC compared to paracancerous specimens and was correlated with a more advanced tumor grade. More importantly, knockdown of KMT2D impaired their reconstitution in patient-derived organoids and decreased the expression of CD133 and β-catenin in OSCC cells. In in vitro and in vivo models, knockdown of KMT2D reduced the colony formation, migration and invasion abilities of OSCC cells and delayed tumor growth. Mechanistically, the dual-luciferase reporter and co-immunoprecipitation assays in two individual OSCC cell lines indicated that KMT2D may cooperate with MEF2A to promote the transcription activity of CTNNB1, thereby enhancing WNT signaling. Conclusion The upregulation of KMT2D contributes to stem-like properties in OSCC cells by sustaining the MEF2A-mediated transcriptional activity of CTNNB1.

Abstract Background Conventional immunohistochemistry technologies were limited by the inability to simultaneously detect multiple markers and the lack of identifying spatial relationships among cells, hindering understanding of the biological processes in cancer immunology. Methods Tissue slices of primary tumours from 553 IA∼IIIB non‐small cell lung cancer (NSCLC) cases were stained by multiplex immunofluorescence (mIF) assay for 10 markers, including CD4, CD38, CD20, FOXP3, CD66b, CD8, CD68, PD‐L1, CD133 and CD163, evaluating the amounts of 26 phenotypes of cells in tumour nest and tumour stroma. StarDist depth learning model was utilised to determine the spatial location of cells based on mIF graphs. Single‐cell RNA sequencing (scRNA‐seq) on four primary NSCLC cases was conducted to investigate the putative cell interaction networks. Results Spatial proximity among CD20+ B cells, CD4+ T cells and CD38+ T cells (r2 = 0.41) was observed, whereas the distribution of regulatory T cells was associated with decreased infiltration levels of CD20+ B cells and CD38+ T cells (r2 = −0.45). Univariate Cox analyses identified closer proximity between CD8+ T cells predicted longer disease‐free survival (DFS). In contrast, closer proximity between CD133+ cancer stem cells (CSCs), longer distances between CD4+ T cells and CD20+ B cells, CD4+ T cells and neutrophils, and CD20+ B cells and neutrophils were correlated with dismal DFS. Data from scRNA‐seq further showed that spatially adjacent N1‐like neutrophils could boost the proliferation and activation of T and B lymphocytes, whereas spatially neighbouring M2‐like macrophages showed negative effects. An immune‐related risk score (IRRS) system aggregating robust quantitative and spatial prognosticators showed that high‐IRRS patients had significantly worse DFS than low‐IRRS ones (HR 2.72, 95% CI 1.87–3.94, p

To efficiently manipulate parametric mode mixing is an intriguing physical issue for phonon-cavity electromechanics, which is also crucial for various technological applications ranging from frequency synthesis to nonlinear spectroscopy. Here, we theoretically investigate the dependence of phononic high-order sidebands on intra- and inter-mode couplings in two interconnected electromechanical resonators. When the system operates in a strong inter-mode coupling regime, we report that two inter-mode phonon-scattering pathways allow us to selectively obtain either odd- or even-order sidebands at different output ports. The signal intensities of the separated sidebands can be independently modulated by introducing harmonic actuation forces acting on the interconnected resonators. Once the intra-mode coupling becomes totally dominant, the direct transition between intra-mode phonons only triggers the emergence of full-order sidebands in the nonlinear responses. In this case, increasing the quality factor of the resonators is beneficial for amplifying a few low order sidebands nearby the principal modes of the resonators but sacrificing the order of the sidebands.

Abstract Background Targeted optical imaging offers a noninvasive and accurate method for the early detection of gastrointestinal tumors, especially for flat appearances. In our previous study, a sequence of SNFYMPL (SNF) was identified as a specific peptide to bind to esophageal carcinoma using phage-display technology. This study aimed to evaluate the tumor-targeting efficacy of Cy5.5-conjugated SNF probe for imaging of esophageal carcinoma in vitro and in vivo. Methods The SNF-Cy5.5 probe was synthesized and then identified using High Performance Liquid Chromatography (HPLC) and mass spectrometry (MS). Confocal fluorescence imaging and Flow cytometry analysis were performed to evaluate the binding specificity and the receptor binding affinity of SNF-Cy5.5 to OE33. In vivo imaging was performed to evaluate the targeting ability of SNF-Cy5.5 to esophageal carcinoma. Results The confocal imaging and flow cytometry analysis showed that SNF-Cy5.5 bound specifically to the plasma membrane of OE33 cells with a high affinity. In vivo, for non-block group, SNF-Cy5.5 probe exhibited rapid OE33 tumor targeting during 24 h p.i. and excellent tumor-to-background contrast at 2 h p.i. For the block group, SNF-Cy5.5 was not observed in the mice after 4 h p.i. Ex vivo imaging also revealed that a higher fluorescent signal intensity value of the tumors was clearly observed in the non-block group than that in the block group (2.6 ± 0.32 × 109 vs. 0.8 ± 0.08 × 109, p

Set-valued data is extremely important and widely used in sensor technology and application. Recently, privacy protection for set-valued data under differential privacy (DP) has become a research hotspot. However, the DP model assumes that the data center is trustworthy, consequently, increasingly attention has been paid to the application of the local differential privacy model (LDP) for set-valued data. Constrained by the local differential privacy model, most methods randomly respond to the subset of set-valued data, and the data collector conducts statistics on the received data. There are two main problems with this kind of method: one is that the utility function used in the random response loses too much information; the other is that the privacy protection of the set-valued data category is usually ignored. To solve these problems, this paper proposes a set-valued data collection method (SetLDP) based on the category hierarchy under the local differential privacy model. The core idea is to first make a random response to the existence of the category, continue to disturb the item count if the category exists, and finally randomly respond to a candidate itemset based on the new utility function. Theory analysis and experimental results show that the SetLDP can not only preserve more information, but also protect the category private information in set-valued data.

We propose an effective scheme to enhance the efficiency of optical second-order sideband (OSS) via optomechanically induced absorption (OMIA) in a double-cavity optomechanical system. In this double-cavity optomechanical configuration, the optomechanical cavity is driven by a strong control field and a weak probe pulse, while the assisted cavity is excited by another strong control field. Using experimentally achievable parameters, we identify that the present optomechanical system can create the OMIA effect and improve the efficiency of the upper OSS beyond that is achieved by employing optomechanically induced transparency (OMIT). With the aid of OMIA, we also report that the maximum efficiency and linewidth of the upper OSS peak can be modulated by the double-cavity coupling strength and the detuning of the driving field. The present proposal offers a new path to optimize optical nonlinearity and design chip-scale signal processing.

Aims: The study aimed to determine whether the microRNA miR21-5p (MiR21) mediates temporomandibular joint osteoarthritis (TMJ-OA) by targeting growth differentiation factor 5 (Gdf5). Methods: TMJ-OA was induced in MiR21 knockout (KO) mice and wild-type (WT) mice by a unilateral anterior crossbite (UAC) procedure. Mouse tissues exhibited histopathological changes, as assessed by: Safranin O, toluidine blue, and immunohistochemistry staining; western blotting (WB); and quantitative real-time polymerase chain reaction (RT-qPCR). Mouse condylar chondrocytes were transfected with a series of MiR21 mimic, MiR21 inhibitor, Gdf5 siRNA (si-GDF5), and flag-GDF5 constructs. The effects of MiR-21 and Gdf5 on the expression of OA related molecules were evaluated by immunofluorescence, alcian blue staining, WB, and RT-qPCR. Results: UAC altered the histological structure and extracellular matrix content of cartilage in the temporomandibular joint (TMJ), and KO of MiR21 alleviated this effect (p < 0.05). Upregulation of MiR21 influenced the expression of TMJ-OA related molecules in mandibular condylar chondrocytes via targeting Gdf5 (p < 0.05). Gdf5 overexpression significantly decreased matrix metalloproteinase 13 (MMP13) expression (p < 0.05) and reversed the effects of MiR21 (p < 0.05). Conclusion: MiR21, which acts as a critical regulator of Gdf5 in chondrocytes, regulates TMJ-OA related molecules and is involved in cartilage matrix degradation, contributing to the progression of TMJ-OA.

Abstract Background Due to the lack of research on the pathological mechanism of temporomandibular joint osteoarthritis (TMJOA), there are few effective treatment measures in the clinic. In recent years, microRNAs (miRs) have been demonstrated to play an important role in the pathogenesis of osteoarthritis (OA) by regulating a variety of target genes, and the latest evidence shows that miR-21-5p is specifically overexpressed in OA. The purpose of this project was to clarify whether miR-21-5p can regulate the TMJOA process by targeting Spry1. Methods TMJOA was induced by a unilateral anterior crossbite (UAC) model, and the effect of miR-21-5p knockout on TMJOA was evaluated by toluidine blue (TB), immunohistochemical (IHC) staining, Western blotting (WB) and RT-qPCR. Primary mouse condylar chondrocytes (MCCs) were isolated, cultured and transfected with a series of mimics, inhibitors, siRNA-Spry1 or cDNA Spry1. WB, RT-qPCR, IHC and TB were used to detect the effect of miR-21-5p and its target gene Spry1 on the expression of MMP-13, VEGF and p-ERK1/2 in TMJOA. The effect of miR-21-5p on angiogenesis was evaluated by chick embryo chorioallantoic membrane (CAM) assay and WB. Results In the UAC model, the cartilage thickness and extracellular matrix of miR-21-5p knockout mice were less damaged, and miR-21-5p and UAC model were shown to affect the expression of Spry1, IL-1β, MMP-13, and VEGF. Luciferase experiments confirmed that Spry1 was the direct target of miR-21-5p. The expression levels of Spry1, MMP-13, VEGF and p-ERK1/2 in MCCs transfected with miR-21-5p mimic were higher than those in the inhibitor group. Under the simulated inflammatory environment of IL-1β, the expression levels of MMP-13, VEGF and p-ERK1/2 were positively correlated with miR-21-5p, while Spry1 was negatively correlated with miR-21-5p. Inhibition of miR-21-5p expression and overexpression of Spry1 enhanced the inhibition of MMP-13, VEGF and p-ERK1/2 expression. MiR-21-5p had a significant role in promoting angiogenesis in the chick embryo CAM assay, and this role was clearly mediated by the ERK-MAPK signalling pathway. Conclusion This study verified that miR-21-5p can promote the process of TMJOA by targeting Spry1, which provides a new direction for future research on the treatment of this disease.

Long noncoding RNAs (lncRNAs) are increasingly recognized as decisive factors in the progression of head and neck squamous cell carcinoma (HNSCC), and they participate in the epithelial–mesenchymal transformation (EMT) of HNSCC. LncRNAs are closely related to the prognosis of patients with HNSCC; thus, it is essential to identify EMT-related lncRNAs with prognostic value for HNSCC. The coexpression network of EMT-related lncRNAs was constructed using The Cancer Genome Atlas (TCGA). An EMT-related eight-lncRNA-based prognostic signature was constructed using LASSO Cox regression and Cox proportional hazards analyses. Univariate and multivariate analyses and stratified prognosis confirmed that the prognostic signature was an independent predictive factor. Subsequently, we performed immune cell infiltration analysis, gene set enrichment analysis (GSEA), and single-sample GSEA (ssGSEA) pathway enrichment analysis to uncover the potential molecular mechanisms of prognostic differences in the high- and low-risk groups. Next, we discussed the relationship between the prognostic signature and immune checkpoint-related genes, their TIDE scores, and the sensitivity of common chemotherapeutics. Finally, we further verified the expression differences in lncRNAs that were included in our signature via RT–qPCR in eighteen paired tissues. In summary, this prognostic signature provides powerful prognostic biomarkers for HNSCC and could serve as a predictor for the sensitivity of common chemotherapeutics and immunotherapy responses as well as providing a reference for further personalized treatment.

Subretinal fluid (SRF) can lead to irreversible visual loss in patients with central serous chorioretinopathy (CSC) if not absorbed in time. Early detection and intervention of SRF can help improve visual prognosis and reduce irreversible damage to the retina. As fundus image is the most commonly used and easily obtained examination for patients with CSC, the purpose of our research is to investigate whether and to what extent SRF depicted on fundus images can be assessed using deep learning technology. In this study, we developed a cascaded deep learning system based on fundus image for automated SRF detection and macula-on/off serous retinal detachment discerning. The performance of our system is reliable, and its accuracy of SRF detection is higher than that of experienced retinal specialists. In addition, the system can automatically indicate whether the SRF progression involves the macula to provide guidance of urgency for patients. The implementation of our deep learning system could effectively reduce the extent of vision impairment resulting from SRF in patients with CSC by providing timely identification and referral.

To predict visual acuity (VA) and post-therapeutic optical coherence tomography (OCT) images 1, 3, and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC) by artificial intelligence (AI). Real-world clinical and imaging data were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data obtained from ZOC (416 eyes of 401 patients) were used as the training set; the data obtained from XEC (64 eyes of 60 patients) were used as the test set. Six different machine learning algorithms and a blending algorithm were used to predict VA, and a pix2pixHD method was adopted to predict post-therapeutic OCT images in patients after laser treatment. The data for VA predictions included clinical features obtained from electronic medical records (20 features) and measured features obtained from fundus fluorescein angiography, indocyanine green angiography, and OCT (145 features). The data for OCT predictions included 480 pairs of pre- and post-therapeutic OCT images. The VA and OCT images predicted by AI were compared with the ground truth. In the VA predictions of XEC dataset, the mean absolute errors (MAEs) were 0.074–0.098 logMAR (within four to five letters), and the root mean square errors were 0.096–0.127 logMAR (within five to seven letters) for the 1-, 3-, and 6-month predictions, respectively; in the post-therapeutic OCT predictions, only about 5.15% (5 of 97) of synthetic OCT images could be accurately identified as synthetic images. The MAEs of central macular thickness of synthetic OCT images were 30.15 ± 13.28 μm and 22.46 ± 9.71 μm for the 1- and 3-month predictions, respectively. This is the first study to apply AI to predict VA and post-therapeutic OCT of patients with CSC. This work establishes a reliable method of predicting prognosis 6 months in advance; the application of AI has the potential to help reduce patient anxiety and serve as a reference for ophthalmologists when choosing optimal laser treatments.

Flash−butt welded rail is widely used in railway transportation; however, the welded joint is vulnerable after a long time of service, and its damage mechanism is controversial. Here, tensile and reciprocating friction tests were carried out to analyze the mechanical and tribological behaviors between the welded joint and the base metal of a U75VG rail. The results show that flash−butt welding promotes the pearlite to transform into ferrite, leading to a relatively low hardness value but high plasticity. In addition, the yielding and strength of the all−weld−metal specimen are 385 MPa and 1090 MPa, respectively, which are about 24.51% and 7.63% lower than that of the base metal specimen. It is worth noting that the elongation of the all−weld−metal specimen is 57.1% higher than that of the base metal specimen, and more dimples and tearing ridges can be detected on the fracture morphology of the all−weld−metal specimen, while the fracture morphology of the base metal specimen is filled with shallow dimples and cleavage planes. Moreover, the weld metal has a relatively higher COF (coefficient of friction), and its fluctuation amplitude is 1.25 times higher than that of the base metal, which is due to the rougher worn surface. Furthermore, the introduction of flash−butt welding changes the wear mechanism of the U75VG rail from adhesive wear and oxidation to fatigue wear and slight oxidation, and ultimately leads to more serious damage.

Due to that the performance of differential evolution (DE) significantly depends on offspring generation strategies, various DE variants have been reported with the improved mutation operators. However, on the one hand, the mutation operators in most DE variants are guided by the elites in terms of the fitness value, without considering their distribution information in the fitness landscape. It may lead to the population be evolving towards the unpromising areas more frequently if these elites are clustered in a locally optimal region. On the other hand, in most DE variants, the evolutionary information of the potential trial vectors is not fully utilized to guide the search, which will hamper the local exploitation in the promising regions that they are located in. To overcome these weaknesses, this article proposes an enhanced DE framework (DELDG) with a leaders-detection-and-guidance mechanism that consists of an adaptive leaders detection (ALD) and a neighborhood-based tournament selection (NTS). With these two novel operators, DELDG can not only guide the mutation process of each individual with multiple promising leaders detected by ALD, but also accelerate the convergence speed with the competition among the potential trial vectors by NTS. Therefore, DELDG is characterized by the explicit detection of the promising leaders according to their fitness values and distribution information and the effective use of the potential trial vectors in the neighborhood of each leader. Compared with 36 excellent DE variants and evolutionary algorithms (EAs), the experimental results on 28 IEEE CEC2013 real-parameter functions and 17 IEEE CEC2011 real-world problems have demonstrated the competitive performance of DELDG.

Polymer flooding is drawing lots of attention because of the technical maturity in some reservoirs. The first commercial polymer flooding in China was performed in the Daqing oilfield and is one of the largest applications in the world. Some laboratory tests from Daqing researchers in China showed that the viscoelasticity of high molecular weight polymers plays a significant role in increasing displacement efficiency. Hence, encouraged by the conventional field applications and new findings on the viscoelasticity effect of polymers on residual oil saturation (ROS), some high-concentration high-molecular-weight (HCHMW) polymer-flooding field tests have been conducted. Although some field tests were well-documented, subsequent progress was seldom reported. It was recently reported that HCHMW has a limited application in Daqing, which does not agree with observations from laboratory core flooding and early field tests. However, the cause of this discrepancy is unclear. Thus, a systematic summary of polymer-flooding mechanisms and field tests in China is necessary. This paper explained why HCHMW is not widely used when considering new understandings of polymer-flooding mechanisms. Different opinions on the viscoelasticity effect of polymers on ROS reduction were critically reviewed. Other mechanisms of polymer flooding, such as wettability change and gravity stability effect, were discussed with regard to widely reported laboratory tests, which were explained in terms of the viscoelasticity effects of polymers on ROS. Recent findings from Chinese field tests were also summarized. Salt-resistance polymers (SRPs) with good economic performance using produced water to prepare polymer solutions were very economically and environmentally promising. Notable progress in SRP flooding and new amphiphilic polymer field tests in China were summarized, and lessons learned were given. Formation blockage, represented by high injection pressure and produced productivity ability, was reported in several oil fields due to misunderstanding of polymers’ injectivity. Although the influence of viscoelastic polymers on reservoir conditions is unknown, the injection of very viscous polymers to displace medium-to-high viscosity oils is not recommended. This is especially important for old wells that could cause damage. This paper clarified misleading notions on polymer-flooding implementations based on theory and practices in China.

From the aspect of calculation theory, the beam–spring model method and modified routine method of shield tunnel segment structure calculation were improved, and an efficient integrated software system for segment structure calculation of shield tunnel was developed. The beam–spring method is generally calculated according to the assumption of continuous displacement between beams and joints, and the existing modified routine method assumes that the lateral pressure gradient is constant generally, which does not consider the variation in lateral pressure gradient caused by the difference in the lateral pressure coefficient of soil layers or the water level height, which has a certain deviation from the actual situation. The existing beam–spring method and modified routine method theory were improved, the discontinuous displacement between beams and joints in the beam–spring method was taken into account, and the problem of lateral pressure gradient change in the modified routine method was solved. The calculation software system developed by C# and python programming language was proposed to improve the accuracy and efficiency of segment structure calculation. Based on the actual monitoring data of the internal force of the shield tunnel segment and the adjacent shield tunnel segments under construction in Changsha, China, the segments of the shield tunnel with different cross-section sizes and different hydrogeological conditions are calculated to verify the reliability of the calculation software system. At the same time, combined with the calculation results of the software system and field test data, the stiffness reduction coefficient and equivalent foundation resistance coefficient in the modified routine method were derived to further improve the accuracy of the calculation results, which provided a new idea for the calculation of segment structure of shield tunnel with different diameters under different hydrogeological conditions.

Atrial fibrillation (AF) is the most common cardiac arrhythmia, yet the molecular signature of the vulnerable atrial substrate is not well understood. Here, we delineated a distinct transcriptional signature in right versus left atrial cardiomyocytes (CMs) at baseline and identified chamber-specific gene expression changes in patients with a history of AF in the setting of end-stage heart failure (AF+HF) that are not present in heart failure alone (HF). We observed that human left atrial (LA) CMs exhibited Notch pathway activation and increased ploidy in AF+HF but not in HF alone. Transient activation of Notch signaling within adult CMs in a murine genetic model is sufficient to increase ploidy in both atrial chambers. Notch activation within LA CMs generated a transcriptomic fingerprint resembling AF, with dysregulation of transcription factor and ion channel genes, including Pitx2, Tbx5, Kcnh2, Kcnq1, and Kcnip2. Notch activation also produced distinct cellular electrophysiologic responses in LA versus right atrial CMs, prolonging the action potential duration (APD) without altering the upstroke velocity in the left atrium and reducing the maximal upstroke velocity without altering the APD in the right atrium. Our results support a shared human/murine model of increased Notch pathway activity predisposing to AF.

Abstract We analyze the destructive interference in monolayer graphene under Landau quantization in a time-dependent way by using the Bloch-Maxwell formalism. Based on this analysis, we investigate the dynamics control of an infrared probe and a terahertz (THz) switch pulses in graphene. In presence of the THz switch pulse, the destructive interference take places and can be optimized so that the monolayer graphene is completely transparent to the infrared probe pulse. In absence of the THz switch pulse, however, the infrared probe pulse is absorbed due to such a interference does not take place. Furthermore, we provide a clear physics insight of this destructive interference by using the classical dressed-state theory. Conversely, the present model may be rendered either absorbing or transparent to the THz switch pulse. By choosing appropriate wave form of the probe and switch pulses, we show that both infrared probe and THz switch pulses exhibit the steplike transitions between absorption and transparency. Such steplike transitions can be used to devise a versatile quantum interference-based solid-state optical switching with distinct wave-lengths for optical communication devices.

Loess is a special geotechnical material with strong structural properties, and the microstructural characteristics of loess significantly influence its macroscopic physical features, mechanical properties, and catastrophic behavior. In this paper, serial samples were extracted from the continuous loess and paleosol strata of the Bai Lu tableland; with these samples, the optical microscopy-based serial sectioning method was adopted to study the quantitative characterization and variation in the loess microstructure. Three-dimensional characteristics and quantitative parameters of the particles, pores, and throats of the loess were obtained. The results indicate that the volume, Eq-Radius, and major-minor axis ratio of the loess particles satisfy third-order, third-order, and second-order Gaussian distributions, respectively. The Eq-Radius of the loess pores and throats satisfies a first-order Gaussian distribution, and the throat channel length satisfies a gamma distribution. With increasing stratum depth, the particles become more flattened, the throat radii become larger and the pore channels become slenderer. The variation in fitting parameters and the correlations between the macrophysical and mechanical properties of loess were then explored. The study of the microstructure of loess contributes to a better understanding of the catastrophic behavior of loess and the physical mechanism of geologic hazards in this area.

The main causes of cartilage destruction during temporomandibular joint osteoarthritis (TMJOA) are extracellular matrix degradation and angiogenesis, accompanied by an increased level of matrix-degrading enzymes and proangiogenic factors. Interleukin 6 and extracellular signal–regulated kinase (ERK) signaling pathways may play a critical role in these two processes simultaneously, but researchers have not clearly determined the mechanism. We hypothesized that estrogen-related receptor γ (ERRγ) is involved in both cartilage degeneration and angiogenesis in TMJOA. The interactions between ERRγ and the Mmp9 and Vegfa promoter regions were investigated using a chromatin immunoprecipitation (ChIP) assay. A chick embryo chorioallantoic membrane (CAM) assay was performed to investigate the inhibitory effects of U0126 and GSK5182 on angiogenesis. Western blotting, reverse transcription–quantitative PCR (RT-qPCR), immunofluorescence staining, toluidine blue staining, and transfection with cDNAs or small interfering RNAs (siRNAs) were performed on primary mandibular condylar chondrocytes (MCCs). Unilateral anterior crossbite–induced TMJOA models were established in rats, and Western blotting, RT-qPCR, immunohistochemistry, and Safranin O-Fast Green staining were performed to evaluate changes in vivo. ERK1/2 activated matrix metalloproteinase 9 (MMP9) and vascular endothelial growth factor A (VEGFA), which are involved in cartilage destruction, through ERRγ. Based on the ChIP assay results, ERRγ directly activated the transcription of the Mmp9 and Vegfa genes. In chick embryo CAM models, U0126 and GSK5182 significantly inhibited angiogenesis. In conclusion, ERRγ is a downstream transcription factor of ERK1/2, and its upregulation leads to extracellular matrix degradation and angiogenesis in TMJOA. This study identified a common factor between inflammation and vascularization in OA as well as a new therapeutic target for OA: ERRγ.

Density and porosity have an important influence on powder metallurgy products, and it is feasible to predict the tap density of powder by simulating the packing of powder. Analysis models of spherical and simple geometric have been developed to predict the packing density of the powder. In this study, a novel particle model with complex three-dimensional (3D) morphology was established to simulate the packing process of titanium hydride powder with binary particle size distribution. The results have indicated that when the particle size ratio and the volume ratio of fine powder to coarse powder were 1:3 and 1:1, respectively, the sintered sample reaches its optimum sintered density of 99.24%. Compared with these samples sintered by single-scale powder, the size and distribution of residual pores reduced significantly with the application of mixed powders, which is beneficial to the mechanical properties of powder metallurgy products.

It is widely acknowledged that the blended elemental (BE) powder metallurgy (PM) Ti6Al4V alloy attracted unusual due to its low cost and comprehensive mechanical properties. However, the high porosity and mediocre mechanical properties of traditional vacuum sintering limited its application. To achieve better mechanical performance, the central composite designs (CCDs) method was employed to analyze the influence of sintering parameters, such as sintering temperature (St), heating rate (Hr), and holding time (Ht). The results indicated that St makes the most significant contribution to compressive strength and sintering density, accounting for 95.5% and 86.54% respectively. In addition, Ht makes the most significant contribution to compression ratio, which accounted for 89.35%. Through the analysis of response surface methodology (RSM), the optimum sintering parameters (St, Ht, Hr) could be considered to be 1300 °C, 148 min and 5 °C/min. In addition, verification experiments were carried out under the optimum conditions, and the experimental results were in good agreement with the predicted values, since the deviation of the predicted and experimental values was less than 4.9%. Therefore, the results of this study could certify the reliability of CCDs method, which would contribute to the development of titanium alloys with low cost and high mechanical properties.

Indoor scene classification plays a vital part in environment cognition of service robot. With the development of deep learning, fine-tuning CNN (Convolutional Neural Network) on target datasets has become a popular way to solve classification problems. However, this method cannot obtain satisfying indoor scene classification results because of overfitting when scene training datasets are insufficient. To solve this problem, an indoor scene classification method is proposed in this paper, which utilizes CNN feature of scene images to generate scene category features to classify scenes by a novel feature matching algorithm. The novel feature matching algorithm can further improve the speed of scene classification. In addition, overfitting is eliminated by our method even though the training data is limited. The presented method was evaluated on two benchmark scene datasets, Scene 15 dataset and MIT 67 dataset, acquiring 96.49% and 81.69% accuracy, respectively. The experiment results showed that our method was superior to other scene classification methods in terms of accuracy, speed, and robustness. To further evaluate our method, test experiments on unknown scene images from SUN 397 dataset had been done, and the models based on different training datasets obtained 94.34% and 79.80% test accuracy severally, which proved that the proposed method owned good performance in indoor scene classification.

This paper presents a multi-sensor model combination system with class-specific voting for physical activity monitoring, which combines multiple classifiers obtained by splicing sensor data from different nodes into new data frames to improve the diversity of model inputs. Data obtained from a wearable multi-sensor wireless integrated measurement system (WIMS) consisting of two accelerometers and one ventilation sensor have been analysed to identify 10 different activity types of varying intensities performed by 110 voluntary participants. It is noted that each classifier shows better performance on some specific activity classes. Through class-specific weighted majority voting, the recognition accuracy of 10 PA types has been improved from 86% to 92% compared with the non-combination approach. Furthermore, the combination method has shown to be effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition and has better performance in monitoring physical activities of varying intensities than traditional homogeneous classifiers.

Hypoxia is an important and influential factor in development. The embryonic kidney is exposed to a hypoxic environment throughout its development. The Wnt/β-catenin pathway plays vital roles in the differentiation and self-renewal of metanephrogenic mesenchymal stem cells (MMSCs) from which the kidney is derived. Thus, we hypothesized that hypoxia can regulate the differentiation and pluripotency of MMSCs through the Wnt/β-catenin pathway. To test this hypothesis, MMSCs from rats at embryonic day 18.5 were cultured in normoxic (21% O2) and hypoxic (1% O2) conditions. The effects of hypoxia on differentiation, stemness, proliferation, and apoptosis of cultured MMSCs and on the activity of the Wnt/β-catenin pathway were tested. Our results revealed that the hypoxic condition increased the number of epithelial cells (E-cadherin+ or CK18+) as well the expression of markers of renal tubule epithelia cells (CDH6, Aqp1, and OPN), decreased the number and proliferation of stem cells (SIX-2+ or CITED1+), and induced apoptosis. Additionally, hypoxia reduced the expression of Wnt4 as well as its downstream molecules β-catenin, LEF-1, and Axin2. Activation of the Wnt/β-catenin pathway by LiCl or BIO modified the effects of hypoxia on the differentiation and self-renewal of MMSCs. Thus, we concluded that hypoxia induces the differentiation and inhibits the self-renewal of MMSCs by inhibiting the Wnt/β-catenin pathway. The observations further our understanding of the effects of hypoxia on kidney.

BACKGROUNDS: Despite the potential therapeutic benefits, cell therapy in renal ischemia-reperfusion (I/R) injury is currently limited by low rates of cell engraftment after systemic delivery. In this study, we investigate whether locally administration through renal artery can enhance the migration and therapeutic potential of mesenchymal stem cells (MSCs) in ischemic kidney. METHODS: The model of renal I/R injury was induced by 45 min occlusion of the left renal pedicle and right nephrectomy in rat. Followed by reperfusion, graded doses of CM-Dil labeled MSCs were implanted via three routes: tail vein (TV), carotid artery (CA), and renal artery (RA). Renal blood flow was evaluated by color and spectral Doppler ultrasound at 1 h and 24 h post-I/R. All the samples were collected for analysis at 24 h post-I/R. RESULTS: After injection of 1×10(6) MSCs, RA group showed obviously increased renal retention of grafted MSCs compared with TV and CA group; however, the renal function was even further deteriorated. When graded doses of MSCs, the maximal therapeutic efficiency was achieved with renal artery injection of 1×10(5) MSCs, which was significantly better than TV and CA group of 1×10(6) MSCs. In addition, further fluorescent microscopic and ultrasonic examination confirmed that the aggravated renal dysfunction in RA group was due to renal hypoperfusion caused by cell occlusion. CONCLUSION: Administration route and dosage are two critical factors determining the efficiency of cell therapy and 1×10(5) MSCs injected through renal artery produces the most dramatic improvement in renal function and morphology in rat model of renal I/R injury.

This paper reviews the research of monitoring technologies for bolted structural connections. The acoustoelastic effect based method, the piezoelectric active sensing method, and the piezoelectric impedance method are the three commonly used to monitor bolted connections. The basic principle and the applications of these three methods are discussed in detail in this paper. In addition, this paper presents a comparison of these methods and discusses their suitability for in situ or real-time bolt connection monitoring.

Mesenchymal stem cell (MSC) administration is known to enhance the recovery of the kidney following injury. Here we tested the potential of hypoxic-preconditioned-MSC transplantation to enhance the efficacy of cell therapy on acute kidney injury (AKI) by improving MSC migration to the injured kidney. Cobalt was used as hypoxia mimetic preconditioning (HMP). MSC were subjected to HMP through 24 h culture in 200 µmol/L cobalt. Compared to normoxia cultured MSC (NP-MSC), HMP significantly increased the expression of HIF-1α and CXCR4 in MSC and enhanced the migration of MSC in vitro. This effect was lost when MSC were treated with siRNA targeting HIF-1α or CXCR4 antagonist. SPIO labeled MSC were administered to rats with I/R injury followed immediately by magnetic resonance imaging. Imaging clearly showed that HMP-MSC exhibited greater migration and a longer retention time in the ischemic kidney than NP-MSC. Histological evaluation showed more HMP-MSC in the glomerular capillaries of ischemic kidneys than in the kidneys receiving NP-MSC. Occasional tubules showed iron labeling in the HMP group, while no tubules had iron labeling in NP group, indicating the possibility of tubular transdifferentiation after HMP. These results were also confirmed by fluorescence microscopy study using CM-DiI labeling. The increased recruitment of HMP-MSC was associated with reduced kidney injury and enhanced functional recovery. This effect was also related to the increased paracrine action by HMP-MSC. Thus we suggest that by enhancing MSC migration and prolonging kidney retention, hypoxic preconditioning of MSC may be a useful approach for developing AKI cell therapy.

Piezoceramic based active sensing methods have been researched to monitor preload on bolt connections. However, there is a saturation problem involved with this type of method. The transmitted energy is sometimes saturated before the maximum preload which is due to it coming into contact with flat surfaces. When it comes to flat contact surfaces, the true contact area will easily saturate with the preload. The design of a new type of bolt looseness monitoring sensor, a smart washer, is to mitigate the saturation problem. The smart washer is composed of two annular disks with contact surfaces that are machined into convex and concave respectively, to eliminate the complete flat contact surfaces and to reduce the saturation effect. One piezoelectric patch is bonded on the non-contact surface of each annular disk. These two mating annular disks form a smart washer. One of the two piezoelectric patches serves as an actuator to generate an ultrasonic wave that propagates through the contact surface; the other one serves as a sensor to detect the propagated waves. The wave energy propagated through the contact surface is proportional to the true contact area which is determined by the bolt preload. The time reversal method is used to extract the peak of the focused signal as the index of the transmission wave energy; then, the relationship between the signal peak and bolt preload is obtained. Experimental results show that the focused signal peak value changes with the bolt preload and presents an approximate linear relationship when the saturation problem is experienced. The proposed smart washer can monitor the full range of the rated preload.