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The emergence of vision transformers (ViTs) in image classification has shifted the methodologies for visual representation learning. In particular, ViTs learn visual representation at full receptive field per layer across all the image patches, in comparison to the increasing receptive fields of CNNs across layers and other alternatives (e.g., large kernels and atrous convolution). In this work, for the first time we explore the global context learning potentials of ViTs for dense visual prediction (e.g., semantic segmentation). Our motivation is that through learning global context at full receptive field layer by layer, ViTs may capture stronger long-range dependency information, critical for dense prediction tasks. We first demonstrate that encoding an image as a sequence of patches, a vanilla ViT without local convolution and resolution reduction can yield stronger visual representation for semantic segmentation. For example, our model, termed as SEgmentation TRansformer (SETR), excels on ADE20K (50.28% mIoU, the first position in the test leaderboard on the day of submission) and performs competitively on Cityscapes. However, the basic ViT architecture falls short in broader dense prediction applications, such as object detection and instance segmentation, due to its lack of a pyramidal structure, high computational demand, and insufficient local context. For tackling general dense visual prediction tasks in a cost-effective manner, we further formulate a family of Hierarchical Local-Global (HLG) Transformers, characterized by local attention within windows and global-attention across windows in a pyramidal architecture. Extensive experiments show that our methods achieve appealing performance on a variety of dense prediction tasks (e.g., object detection and instance segmentation and semantic segmentation) as well as image classification. Our code and models are available at https://github.com/fudan-zvg/SETR. [ABSTRACT FROM AUTHOR]

Background: Autophagy is a crucial mechanism for maintaining cellular homeostasis and responding to environmental stress, and it is closely linked to tumor drug resistance. Through multi-omics analysis, this study explores the expression patterns, functions, and potential role of the autophagy-related gene Angiotensinogen (AGT) in colorectal cancer (CRC), particularly in relation to chemotherapy resistance. Methods: This study first compared AGT expression between CRC and normal tissues using the GTEx and TCGA databases. Differences in expression were assessed using Wilcoxon Rank Sum Tests, and the prognostic impact of AGT was evaluated through univariate Cox survival analysis and meta-analysis. Functional enrichment was performed using the limma and fgsea packages. Drug sensitivity analysis was conducted based on the CTRP database, while immune infiltration was assessed using the CIBERSORT and ESTIMATE methods. Spatial transcriptomic characteristics were explored through 10x Visium technology and deconvolution analysis to investigate the correlation between AGT expression levels and tumor cell content.scRNA-seq data from CRC tissues were sourced from Tumor Immune Single Cell Hub (TISCH).Functional annotation was performed with Single-sample gene set enrichment analysis (SSGSEA), and pseudotime analysis using Monocle 2 mapped their developmental trajectories. The potential of AGT inhibitors in the treatment of CRC was analyzed using drug-target Mendelian randomization.Finally, Phenome-Wide Association Study (PheWAS) was conducted to evaluate genetic associations and potential side effects of AGT inhibitors. Results: AGT expression was significantly higher in CRC tissues compared to normal tissues and was associated with shorter recurrence-free survival (RFS). Autophagy signaling pathways were markedly enriched in the high AGT expression group. AGT expression was positively correlated with resistance to chemotherapeutic agents such as gemcitabine, cisplatin, paclitaxel, and 5-fluorouracil. Spatial transcriptomic analysis revealed that AGT was predominantly expressed in malignant tumor regions. Single-cell analysis identified 21 distinct cell subpopulations across 13 major types. AGT expression was significantly higher in tumor samples, especially in the fibroblast C6 subpopulation. Tumor-related pathways were enriched in C1, C5, C6, and C8 subpopulations. Pseudotime analysis revealed that these subpopulations, particularly C6, were in terminal developmental stages.Drug-target Mendelian randomization analysis indicated a negative causal relationship between AGT inhibitors and the risk of both heart failure(ORdrug = 0.950, 95% CI, 0.912–0.990; P = 0.014) and CRC(ORdrug = 0.874, 95% CI: 0.792–0.964; P = 0.007).PheWAS analysis showed no genetic associations between AGT inhibitors and other traits, indicating its specificity and low risk of side effects. Conclusion: Elevated AGT expression in CRC is associated with resistance to chemotherapy, and its inhibition may offer a therapeutic avenue for the treatment of colorectal cancer. [ABSTRACT FROM AUTHOR]

The burgeoning practice of unauthorized acquisition and utilization of personal textual data (e.g., social media comments and search histories) by certain entities has become a discernible trend. To uphold data protection regulations such as the Asia–Pacific Privacy Initiative (APPI) and to identify instances of unpermitted exploitation of personal data, we propose a novel and efficient audit framework that helps users conduct cognitive analysis to determine if their textual data was used for data augmentation. and training a discriminative model. In particular, we focus on auditing models that use BERT as the backbone for discriminating text and are at the core of popular online services. We first propose an accumulated discrepancy score, which involves not only the response of the target model to the auditing sample but also the responses between pre-trained and finetuned models, to identify membership. We implement two types of audit methods (i.e., sample-level and user-level) according to our framework and conduct comprehensive experiments on two downstream applications to evaluate the performance. The experimental results demonstrate that our sample-level auditing achieves an AUC of 89.7% and an accuracy of 83%, whereas the user-level method can audit membership with an AUC of 89.7% and an accuracy of 88%. Additionally, we undertake an analysis of how augmentation methods impact auditing performance and expound upon the underlying reasons for these observations. [ABSTRACT FROM AUTHOR]

This study aimed to explore the potential association between Endothelin type A receptor (EDNRA) genetic polymorphisms and susceptibility to large artery atherosclerotic stroke (LAA), as well as the involvement of inflammation mechanisms. We recruited Han Chinese patients with LAA and age- and sex-matched controls. The distribution of alleles and genotypes for 16 single nucleotide polymorphisms (SNPs) in EDNRA was analyzed using dominant, recessive, and co-dominant genetic models between cases and controls. We quantified the mRNA and protein levels of EDNRA and NLRP3 genes, and concentrations of inflammatory factors (TNFα, IL-1β, IL-6, IL-8, IL-10, IL-18, and CCL18) in peripheral blood samples randomly selected from cases and controls. We also investigated the relationship between these SNPs, gene expression patterns and inflammatory factor levels. A total of 428 LAA cases and 434 controls were enrolled in this study. The results showed that rs5343 TT genotype of EDNRA was significantly associated with an increased risk of LAA (OR = 3.243, 95%CI = 1.608–6.542, P = 0.001). It also demonstrated a significant upregulation level of NLRP3 as well as higher concentrations of IL-10, IL-18, and CCL-18 in cases compared to controls. Besides, we discovered that the EDNRA polymorphisms were linked to NLRP3, IL-6, IL-10, and IL-18 levels in cases. There existed a positive correlation between EDNRA transcription levels and both NLRP3 transcript levels (r = 0.437, p < 0.001) and IL-18 concentrations (r = 0.212, p < 0.001). EDNRA is linked to susceptibility of LAA. This association may be attributed to the NLRP3-mediated inflammatory pathway. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the potential causal relationships between coronary artery disease (CAD), myocardial infarction (MI), urate levels, and aortic aneurysm (AA), abdominal aortic aneurysm (AAA), thoracic aortic aneurysm(TAA), aortic dissection (AD) in individuals of European ancestry. To examine the potential causal relationships between CAD, MI, and urate levels with AA, AAA, TAA, AD, respectively, we performed a two-sample Mendelian randomization (MR) analysis. Genetic instruments that reached genome-wide significance (p < 5 × 10 − 8) for risk factors were obtained from genome-wide association studies(GWASs) conducted on individuals of European origin. On the other hand, genetic instruments of AA, AAA, TAA or AD were chosen from the FinnGen cohort. The primary analysis employed the inverse-variance weighted MR method, while sensitivity analyses were conducted using MR-Egger, weighted median MR, MR pleiotropy residual sum and outlier, and Phenoscanner searching. In addition, we performed the MR-Egger intercept analysis to identify potential pleiotropy and utilized Cochran's Q statistics to evaluate heterogeneity. Additionally, we conducted bidirectional Mendelian randomization experiments to mitigate the potential influence of reverse causation. According to the results of our study, there were statistically significant higher risks for AA in relation to CAD/MI(odds ratio (OR) with 95% confidence interval (CI): 1.309 (1.150–1.490), and 1.255 (1.147–1.373). Similarly, there were statistically significant higher risks for AAA in relation to CAD and MI (OR with 95% CI: 1.383 (1.189–1.609), and 1.352 (1.178–1.552). The sensitivity analysis demonstrated that the causative effects of CAD/MI, and AA /AAA, were robust. A positive causal link was observed between CAD/MI, and AA/AAA. Nevertheless, no causal link was found between CAD, MI, urate levels, and TAA. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are susceptible to adversarial examples generally generated by adding imperceptible perturbations to the clean images, resulting in the degraded performance of DNNs models. To generate adversarial examples, most methods utilize the L p norm to limit the perturbations and satisfy such imperceptibility. However, the L p norm cannot fully guarantee the semantic authenticity of adversarial examples. Defenses may take advantage of this defect to weaken the attack capability of adversarial examples. Moreover, existing methods with L p restriction have poor generalization ability in white-box attacks and have inferior aggressiveness in black-box attacks. To solve the problems mentioned above, we propose a multiple feature interpolation method to generate face adversarial examples. In the proposed method, we perform the multiple feature interpolation to generate face adversarial examples with new semantics in the process of original image reconstruction and conditional attribute-guided image generation based on StarGAN. Experimental results demonstrate that adversarial examples generated by our method possess new attribute-guided semantics and satisfactory attack success rates under both white-box and black-box settings. [ABSTRACT FROM AUTHOR]

Mycoplasmapneumoniae pneumonia (MPP) is a common respiratory tract infection disease in children. To date, there have been few studies on the relationship between cytological changes in bronchoalveolar lavage fluid (BALF) and clinical features. The objective of this study is to investigate the correlation between changes in the proportion of cell classifications in BALF and the clinical features in children with severe MPP (SMPP). In total, the study included 64 children with SMPP requiring bronchoalveolar lavage who were admitted to our hospital between March and September 2022 (study group) and 11 children with bronchial foreign bodies without co-infection (control group), who were admitted during the same period. The proportion of cell classifications in BALF was determined by microscopic examination after performing Wright-Giemsa staining. Patients were grouped according to different clinical characteristics, and between-group comparisons were made regarding the variations in the proportion of cell classifications in BALF. The levels of blood routine neutrophil percentage (GRA%), C-reactive protein, D-dimer and lactate dehydrogenase in the study group were higher than those in the control group (P < 0.05). There were differences in the GRA% and macrophage percentage in the BALF between the two groups (P < 0.05). The GRA% and blood lymphocyte percentage were associated with pleural effusion. Multiple indicators correlated with extrapulmonary manifestations (P < 0.05). Moreover, the percentage of lymphocytes in the BALF correlated with pleural effusion, extrapulmonary manifestations and refractory MPP (RMPP) (P < 0.05). Logistic regression showed that BALF lymphocytes were protective factors for RMPP, while serum amyloid A and extrapulmonary manifestations were risk factors (P < 0.05). Conclusion: The BALF of children with SMPP is predominantly neutrophilic. A lower percentage of lymphocytes is related to a higher incidence of pleural effusion, extrapulmonary manifestations and progression to RMPP, as well as a longer length of hospitalisation. What Is Known: • Mycoplasma pneumonia in children is relatively common in clinical practice. Bronchoalveolar lavage (BAL) is a routine clinical procedure. What Is New: However, there are relatively few studies focusing on the cytomorphological analysis of cells in BAL fluid. [ABSTRACT FROM AUTHOR]

Vision transformers (ViTs) have pushed the state-of-the-art for visual perception tasks. The self-attention mechanism underpinning the strength of ViTs has a quadratic complexity in both computation and memory usage. This motivates the development of approximating the self-attention at linear complexity. However, an in-depth analysis in this work reveals that existing methods are either theoretically flawed or empirically ineffective for visual recognition. We identify that their limitations are rooted in the inheritance of softmax based self-attention during approximations, that is, normalizing the scaled dot-product between token feature vectors using the softmax function. As preserving the softmax operation challenges any subsequent linearization efforts. By this insight, a family of SOftmax-Free Transformers (SOFT) are proposed. Specifically, a Gaussian kernel function is adopted to replace the dot-product similarity, enabling a full self-attention matrix to be approximated under low-rank matrix decomposition. For computational robustness, we estimate the Moore–Penrose inverse using an iterative Newton–Raphson method in the forward process only, while calculating its theoretical gradients only once in the backward process. To further expand applicability (e.g., dense prediction tasks), an efficient symmetric normalization technique is introduced. Extensive experiments on ImageNet, COCO and ADE20K show that our SOFT significantly improves the computational efficiency of existing ViT variants. With linear complexity, much longer token sequences are permitted by SOFT, resulting in superior trade-off between accuracy and complexity. Code and models are available at https://github.com/fudan-zvg/SOFT. [ABSTRACT FROM AUTHOR]

Strongly correlated materials respond sensitively to external perturbations such as strain, pressure, and doping. In the recently discovered superconducting infinite-layer nickelates, the superconducting transition temperature can be enhanced via only ~ 1% compressive strain-tuning with the root of such enhancement still being elusive. Using resonant inelastic x-ray scattering (RIXS), we investigate the magnetic excitations in infinite-layer PrNiO2 thin films grown on two different substrates, namely SrTiO3 (STO) and (LaAlO3)0.3(Sr2TaAlO6)0.7 (LSAT) enforcing different strain on the nickelates films. The magnon bandwidth of PrNiO2 shows only marginal response to strain-tuning, in sharp contrast to the enhancement of the superconducting transition temperature Tc in the doped superconducting samples. These results suggest the bandwidth of spin excitations of the parent compounds is similar under strain while Tc in the doped ones is not, and thus provide important empirics for the understanding of superconductivity in infinite-layer nickelates. Nickelates have been shown to host unconventional superconductivity, and recently it has been found that the choice of substrate can significantly change the superconducting critical temperature. This suggests, that like some Cuprates, strain could be important. Here Gao, Fan, Wang, and coauthors find that magnetic excitations in a parent Nickelate are insensitive to substrate choice, and therefore strain, which differs markedly from the case of Cuprates. [ABSTRACT FROM AUTHOR]

Increasing numbers of artificial intelligence systems are employing collaborative machine learning techniques, such as federated learning, to build a shared powerful deep model among participants, while keeping their training data locally. However, concerns about integrity and privacy in such systems have significantly hindered the use of collaborative learning systems. Therefore, numerous efforts have been presented to preserve the model’s integrity and reduce the privacy leakage of training data throughout the training phase of various collaborative learning systems. This survey seeks to provide a systematic and comprehensive evaluation of security and privacy studies in collaborative training, in contrast to prior surveys that only focus on one single collaborative learning system. Our survey begins with an overview of collaborative learning systems from various perspectives. Then, we systematically summarize the integrity and privacy risks of collaborative learning systems. In particular, we describe state-of-the-art integrity attacks (e.g., Byzantine, backdoor, and adversarial attacks) and privacy attacks (e.g., membership, property, and sample inference attacks), as well as the associated countermeasures. We additionally provide an analysis of open problems to motivate possible future studies. [ABSTRACT FROM AUTHOR]

Neuroinflammation is a key factor in cognitive dysfunction and neurodegenerative diseases such as Alzheimer's disease (AD), so inhibiting neuroinflammation is considered as a potential treatment for AD. Epigallocatechin-3-gallate (EGCG), a polyhydroxyphenol of green tea, has been found to exhibit anti-oxidative, anti-inflammatory and neuroprotective effects. The aim of this study was to investigate the inhibitory effect of EGCG on inflammation and its mechanism. In this study, BV2 cells were simultaneously exposed to lipopolysaccharides (LPS) and the amyloid-β oligomer (AβO) to induce inflammatory microenvironments. Inflammatory cytokines and NLRP3 inflammasome-related molecules were detected by RT-PCR and Western Blot. The results show that EGCG inhibits LPS/AβO-induced inflammation in BV2 cells through regulating IL-1β, IL-6, and TNF-α. Meanwhile, EGCG reduces the activation of the NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3) inflammasome and levels of intracellular ROS in BV2 cells treated with LPS/AβO by affecting the mitochondrial membrane potential (MMP). Further research found that EGCG inhibited MMP through regulating thioredoxin-interacting protein (TXNIP) in LPS/AβO-induced neuroinflammation. In conclusion, EGCG may alleviate LPS/AβO-induced microglial neuroinflammation by suppressing the ROS/ TXNIP/ NLRP3 pathway. It may provide a potential mechanism underlying the anti-inflammatory properties of EGCG for alleviating AD. [ABSTRACT FROM AUTHOR]

Similar-dissimilar (SD) classification aims to train a binary classifier from only similar and dissimilar data pairs, which indicate whether two instances belong to the same class (similar) or not (dissimilar). Although effective learning methods have been proposed for SD classification, they cannot deal with online learning scenarios with sequential data that can be frequently encountered in real-world applications. In this paper, we provide the first attempt to investigate the online SD classification problem. Specifically, we first adapt the unbiased risk estimator of SD classification to online learning scenarios with a conservative regularization term, which could serve as a naive method to solve the online SD classification problem. Then, by further introducing a margin criterion for whether to update the classifier or not with the received cost, we propose two improvements (one with linearly scaled cost and the other with quadratically scaled cost) that result in two online SD classification methods. Theoretically, we derive the regret, mistake, and relative loss bounds for our proposed methods, which guarantee the performance on sequential data. Extensive experiments on various datasets validate the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

The superconducting gap symmetry is crucial in understanding the underlying superconductivity mechanism. Angle-resolved photoemission spectroscopy (ARPES) has played a key role in determining the gap symmetry in unconventional superconductors. However, it has been considered so far that ARPES can only measure the magnitude of the superconducting gap but not its phase; the phase has to be detected by other phase-sensitive techniques. Here we propose a method to directly detect the superconducting gap sign by ARPES. This method is successfully validated in a cuprate superconductor Bi2Sr2CaCu2O8+δ with a well-known d-wave gap symmetry. When two bands have a strong interband interaction, the resulted electronic structures in the superconducting state are sensitive to the relative gap sign between the two bands. Our present work provides an approach to detect the gap sign and can be applied to various superconductors, particularly those with multiple orbitals like the iron-based superconductors. According to conventional wisdom, angle-resolved photoemission spectroscopy (ARPES) can only measure the magnitude of the superconducting gap but not its phase. Here, the authors propose a new method to directly detect the superconducting gap phase using ARPES and validate this technique on a cuprate superconductor. [ABSTRACT FROM AUTHOR]

Cloud storage auditing research is dedicated to solving the data integrity problem of outsourced storage on the cloud. In recent years, researchers have proposed various cloud storage auditing schemes using different techniques. While these studies are elegant in theory, they assume an ideal cloud storage model; that is, they assume that the cloud provides the storage and compute interfaces as required by the proposed schemes. However, this does not hold for mainstream cloud storage systems because these systems only provide read and write interfaces but not the compute interface. To bridge this gap, this work proposes a serverless computing-based cloud storage auditing system for existing mainstream cloud object storage. The proposed system leverages existing cloud storage auditing schemes as a basic building block and makes two adaptations. One is that we use the read interface of cloud object storage to support block data requests in a traditional cloud storage auditing scheme. Another is that we employ the serverless computing paradigm to support block data computation as traditionally required. Leveraging the characteristics of serverless computing, the proposed system realizes economical, pay-as-you-go cloud storage auditing. The proposed system also supports mainstream cloud storage upper layer applications (e.g., file preview) by not modifying the data formats when embedding authentication tags for later auditing. We prototyped and open-sourced the proposed system to a mainstream cloud service, i.e., Tencent Cloud. Experimental results show that the proposed system is efficient and promising for practical use. For 40 GB of data, auditing takes approximately 98 s using serverless computation. The economic cost is 120.48 CNY per year, of which serverless computing only accounts for 46%. In contrast, no existing studies reported cloud storage auditing results for real-world cloud services. [ABSTRACT FROM AUTHOR]

Neural networks do not generalize well to unseen data with domain shifts—a longstanding problem in machine learning and AI. To overcome the problem, we propose MixStyle, a simple plug-and-play, parameter-free module that can improve domain generalization performance without the need to collect more data or increase model capacity. The design of MixStyle is simple: it mixes the feature statistics of two random instances in a single forward pass during training. The idea is grounded by the finding from recent style transfer research that feature statistics capture image style information, which essentially defines visual domains. Therefore, mixing feature statistics can be seen as an efficient way to synthesize new domains in the feature space, thus achieving data augmentation. MixStyle is easy to implement with a few lines of code, does not require modification to training objectives, and can fit a variety of learning paradigms including supervised domain generalization, semi-supervised domain generalization, and unsupervised domain adaptation. Our experiments show that MixStyle can significantly boost out-of-distribution generalization performance across a wide range of tasks including image recognition, instance retrieval and reinforcement learning. The source code is released at https://github.com/KaiyangZhou/mixstyle-release. [ABSTRACT FROM AUTHOR]

Machine learning makes multimedia data (e.g., images) more attractive, however, multimedia data is usually distributed and privacy sensitive. Multiple distributed multimedia clients can resort to federated learning (FL) to jointly learn a global shared model without requiring to share their private samples with any third-party entities. In this paper, we show that FL suffers from the cross-client generative adversarial networks (GANs)-based (C-GANs) attack, in which a malicious client (i.e., adversary) can reconstruct samples with the same distribution as the training samples from other clients (i.e., victims). Since a benign client's data can be leaked to the adversary, this attack brings the risk of local data leakage for clients in many security-critical FL applications. Thus, we propose Fed-EDKD (i.e., Federated Ensemble Data-free Knowledge Distillation) technique to improve the current popular FL schemes to resist C-GANs attack. In Fed-EDKD, each client submits a local model to the server for obtaining an ensemble global model. Then, to avoid model expansion, Fed-EDKD adopts data-free knowledge distillation techniques to transfer knowledge from the ensemble global model to a compressed model. By this way, Fed-EDKD reduces the adversary's control capability over the global model, so Fed-EDKD can effectively mitigate C-GANs attack. Finally, the experimental results demonstrate that Fed-EDKD significantly mitigates C-GANs attack while only incurring a slight accuracy degradation of FL. [ABSTRACT FROM AUTHOR]

Excessive proliferation and migration of vascular smooth muscle cells (VSMCs) contribute to the intimal hyperplasia in type 2 diabetes mellitus (T2DM) patients after percutaneous coronary intervention. We aimed to investigate the role of lncRNA cyclin-dependent kinase inhibitor 2B antisense RNA 1 (CDKN2B-AS1) in VSMC proliferation and migration, as well as the underlying mechanism. T2DM model mice with carotid balloon injury were used in vivo and mouse aortic vascular smooth muscle cells (MOVAS) stimulated by insulin were used in vitro to assess the role of CDKN2B-AS1 in VSMC proliferation and migration following vascular injury in T2DM state. To investigate cell viability and migration, MTT assay and Transwell assay were conducted. To elucidate the underlying molecular mechanisms, the methylation-specific polymerase chain reaction, RNA immunoprecipitation, RNA-pull down, co-immunoprecipitation, and chromatin immunoprecipitation were performed. In vivo, CDKN2B-AS1 was up-regulated in common carotid artery tissues. In vitro, insulin treatment increased CDKN2B-AS1 level, enhanced MOVAS cell proliferation and migration, while the promoting effect was reversed by CDKN2B-AS1 knockdown. CDKN2B-AS1 forms a complex with enhancer of zeste homolog 2 (EZH2) and DNA methyltransferase (cytosine-5) 1 (DNMT1) to regulate smooth muscle 22 alpha (SM22α) methylation levels. In insulin-stimulated cells, SM22α knockdown abrogated the inhibitory effect of CDKN2B-AS1 knockdown on cell viability and migration. Injection of lentivirus-sh-CDKN2B-AS1 relieved intimal hyperplasia in T2DM mice with carotid balloon injury. Up-regulation of CDKN2B-AS1 induced by insulin promotes cell proliferation and migration by targeting SM22α through forming a complex with EZH2 and DNMT1, thereby aggravating the intimal hyperplasia after vascular injury in T2DM. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is the most common neurodegenerative disease. Acanthopanax senticosus, also known as Ciwujia or Siberian ginseng in Chinese, has a wide range of antioxidant and anti-inflammatory activities. The study aims to explore the action mechanism of A. senticosus against Alzheimer's disease using network pharmacology and molecular docking. The active ingredients and targets of A. senticosus were searched through the ETCM database, and Alzheimer's disease-related targets were obtained through the OMIM and GeneCards databases. The Cytoscape 3.7.2 software was used to construct a "drug-component-target" relationship network, and the target genes of A. senticosus against Alzheimer's disease were imported into the String database to establish a protein interaction (PPI) network. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes gene enrichment analyses were performed through the Metascape database to obtain potential pathways of action of A. senticosus for the treatment of Alzheimer's disease, and the ability of these active ingredients to bind to core targets was then verified by molecular docking. 51 active ingredients were screened from A. senticosus, and 88 effective targets for Alzheimer's disease were screened. Topological and pathway-enrichment analyses revealed that A. senticosus could play a beneficial role in the treatment of Alzheimer's disease by regulating apoptosis and inflammation. Molecular docking results showed that Ciwujianoside B, Chiisanoside, and Ciwujianoside D1 had strong binding abilities to key target proteins (TNFα, IL1β, and CASP3). Collectively, A. senticosus is feasible in the treatment of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

The mechanism of unconventional superconductivity in correlated materials remains a great challenge in condensed matter physics. The recent discovery of superconductivity in infinite-layer nickelates, as an analog to high-Tc cuprates, has opened a new route to tackle this challenge. By growing 8 nm Pr0.8Sr0.2NiO2 films on the (LaAlO3)0.3(Sr2AlTaO6)0.7 substrate, we successfully raise the superconducting onset transition temperature Tc in the widely studied SrTiO3-substrated nickelates from 9 K into 15 K, which indicates compressive strain is an efficient protocol to further enhance superconductivity in infinite-layer nickelates. Additionally, the x-ray absorption spectroscopy, combined with the first-principles and many-body simulations, suggest a crucial role of the hybridization between Ni and O orbitals in the unconventional pairing. These results also suggest the increase of Tc be driven by the change of charge-transfer nature that would narrow the origin of general unconventional superconductivity in correlated materials to the covalence of transition metals and ligands. The recent discovery of superconductivity in the nickelates provides another angle to investigate this phenomenon in the high-Tc cuprates and hopefully help solve the mechanism of their unconventional superconductivity. Here, the authors report an increase in Tc for Pr0.8Sr0.2NiO2 where strain from the underlying LSAT substrate plays a possible role, supporting simulations also reveal the contributing role Ni and O orbitals hybridisation play in the unconventional pairing. [ABSTRACT FROM AUTHOR]

Here we report the first observation of the concurrent breakdown of the strange metal (SM) normal state and superconductivity at a pressure-induced quantum critical point in Ca10(Pt4As8)((Fe0.97Pt0.03)2As2)5 superconductor. We find that, upon suppressing the superconducting state, the power exponent (α) changes from 1 to 2, and the slope of the temperature-linear resistivity per FeAs layer (A□) gradually diminishes. At a critical pressure, A□ and superconducting transition temperature (Tc) go to zero concurrently, where a quantum phase transition from a superconducting state with a SM normal state to a non-superconducting Fermi liquid state occurs. Scaling analysis reveals that the change of A□ with Tc obeys the relation of Tc ~ (A□)0.5, similar to what is seen in other chemically doped unconventional superconductors. These results suggest that there is a simple but powerful organizational principle of connecting the SM normal state with the high-Tc superconductivity. The strange metal state, characterized by a linear-in-temperature resistivity, is often seen in the normal state of high-temperature superconductors. Here, the authors report the breakdown of both the strange metal and superconducting states at a pressure-induced quantum critical point in an iron-pnictide superconductor. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) brought a healthy crisis all around the world. It's not only affected healthcare utilization, but also some disease's incidence. We collected pre-hospital emergency data in Chengdu from January 2016 to December 2021, and studied the demand for emergency medical services (EMSs), emergency response times (ERTs), and the disease spectrum in the city proper of Chengdu as a whole. A total of 1,122,294 prehospital emergency medical service (EMS) instances met the inclusion criteria. Under the influence of COVID-19, notably in 2020, the epidemiological characteristics of prehospital emergency services in Chengdu were considerably altered. However, as the pandemic was brought under control, they returned to normal or even earlier in 2021.The COVID-19 pandemic had an effect on people's medical care-seeking behavior and the performance of prehospital emergency services. Although the indicators associated with prehospital emergency services eventually recovered as the epidemic was brought under control, they remained marginally different from before the outbreak. [ABSTRACT FROM AUTHOR]

Purpose: To investigate abnormalities in cortical and subcortical structures of the brain in preschool children with MRI-negative epilepsy. Methods: Cortical thickness, cortical mean curvature, cortical surface area, cortical volume, and volumes of subcortical structures were measured using Freesurfer software in preschool children with epilepsy and age-matched controls. Results: Findings showed cortical thickening in the left fusiform gyrus, left middle temporal gyrus, right suborbital sulcus, and right gyrus rectus, and cortical thinning mainly in the parietal lobe of preschool children with epilepsy compared to controls. The difference in cortical thickness in the left superior parietal lobule remained after correction for multiple comparisons and was negatively correlated with duration of epilepsy. Cortical mean curvature, surface area, and volume were mainly altered in the frontal and temporal lobes. Changes in mean curvature in the right pericallosal sulcus were positively correlated with age at seizure onset, and changes in mean curvature in the left intraparietal sulcus and transverse parietal sulcus were positively correlated with frequency of seizures. There were no significant differences in the volumes of the subcortical structures. Conclusion: Changes in preschool children with epilepsy occur in the cortical rather than subcortical structures of the brain. These findings further our understanding of the effects of epilepsy in preschool children and will inform management of epilepsy in this patient population. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) directly learn from an unknown real distribution through adversarial training. However, training the generator only by the feedback of the discriminator cannot make GANs learn adaptively from the unknown complex real distribution, and for this reason the quality of generated images is unsatisfactory sometimes. To address this problem, we propose a framework for training GANs with an adaptive learning strategy from simpleness to complexity. First, we employ a pre-trained encoder and a generator to construct a simple task that looks like a real image. Second, an adaptive learning strategy is designed based on the mathematical expectation of the discriminating results of the real image and the simple task. The designed adaptive learning strategy is well compatible with various GANs architectures. Experimental results demonstrate the proposed method can improve the performance of existing GANs. [ABSTRACT FROM AUTHOR]

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To investigate the application of combined audiological examination and a self-reported symptoms survey in the evaluation of hyperacusis. Patients who visited the outpatient department of Otolaryngology Head and Neck Surgery and Otological medicine, Affiliated Hospital of Southwest Medical University, from January 2019 to March 2021 were divided into a hyperacusis group and a normal control group. We measured the loudness discomfort level (LDL) and hearing threshold (HT) of the subjects and investigated their self-reported symptoms. We compared the demographic characteristics, loudness discomfort level, and hearing threshold of the two groups and analyzed the self-reported symptoms and audiological characteristics of hyperacusis. We considered 87 subjects, comprising 40 patients with hyperacusis and 47 healthy individuals. Among the hyperacusis patients, bilateral disease was predominant. Among them, 33 were females, 23 had hearing loss, and 20 had tinnitus. Patients are mainly in the 21–60 age group. Patients with hyperacusis had low discomfort thresholds at all frequencies except 500 Hz (P ≤ 0.05, mean LDL decreased by 6.14–1.37 dB HL for all frequencies). The incidences of feeling upset, pain, and anxiety or fear were 95%, 65%, and 82.5%, respectively, in patients with hyperacusis. The severity of symptoms varies between patients with hyperacusis and healthy individuals. A combination of LDL measurements and self-reported symptom surveys allows for an accurate and comprehensive assessment of hyperacusis. Trial registration: This study was retrospectively registered (TRN: ChiCTR2100047391) on June 13, 2021. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "The correlation between the level of cellular classification in bronchoalveolar lavage fluid of children with severe Mycoplasma pneumoniae and clinical characteristics" published in the European Journal of Pediatrics. The correction adds funding information for the study, stating that it was supported by the Henan Provincial Science and Technology Research Project. The original article has been corrected. The publisher, Springer Nature, remains neutral regarding jurisdictional claims and institutional affiliations. The authors of the article are Kun Ma, Xue Liu, Xiang-Tao Wu, Tuan-Jie Wang, Kai-Li Li, Dao-Bin Wang, and Shu-Jun Li. [Extracted from the article]

Smart Healthcare (SHC) plays an increasingly greater role in improving the quality of health care, which has been widely concerned by researchers, hospitals and governments. In SHC, it is crucial that a patient's health data is readily accessible to authorized nurses, doctors, and emergency services. To realize the easy access while protecting the privacy of patients' data, ciphertext-policy attribute-based encryption (CP-ABE) has been widely used to achieve secure data sharing and support fine-grained access control. However, the existing CP-ABE schemes have three flaws for SHC. First, CP-ABE with partially hidden of access policies may also leak user's attribute privacy. Second, malicious user may disclose patient's health records and these records can not be traced. Third, it is less efficient that the data user, who does not have right to access data, downloads the whole ciphertext. In this paper, we design STEAC to address the above problems. To solve the first problem, we introduce the garbled Bloom filter method to realize fully hidden of access policies. For solving the second problem, we use the transaction-based blockchain scheme to trace the ciphertext storage and access. And before the real decryption, a decryption test operation is added to overcome the third flaw. Finally, security analysis and comprehensive performance evaluation also demonstrate STEAC is secure in standard model and is also more efficient than the previous schemes. [ABSTRACT FROM AUTHOR]

The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI". Artificial intelligence approaches inspired by human cognitive function have usually single learned ability. The authors propose a multimodal foundation model that demonstrates the cross-domain learning and adaptation for broad range of downstream cognitive tasks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Marked evolution of properties with minute changes in the doping level is a hallmark of the complex chemistry that governs copper oxide superconductivity as manifested in the celebrated superconducting domes and quantum criticality taking place at precise compositions1–4. The strange-metal state, in which the resistivity varies linearly with temperature, has emerged as a central feature in the normal state of copper oxide superconductors5–9. The ubiquity of this behaviour signals an intimate link between the scattering mechanism and superconductivity10–12. However, a clear quantitative picture of the correlation has been lacking. Here we report the observation of precise quantitative scaling laws among the superconducting transition temperature (Tc), the linear-in-T scattering coefficient (A1) and the doping level (x) in electron-doped copper oxide La2–xCexCuO4 (LCCO). High-resolution characterization of epitaxial composition-spread films, which encompass the entire overdoped range of LCCO, has enabled us to systematically map its structural and transport properties with unprecedented accuracy and with increments of Δx = 0.0015. We have uncovered the relations Tc ~ (xc – x)0.5 ~ (A1□)0.5, where xc is the critical doping in which superconductivity disappears and A1□ is the coefficient of the linear resistivity per CuO2 plane. The striking similarity of the Tc versus A1□ relation among copper oxides, iron-based and organic superconductors may be an indication of a common mechanism of the strange-metal behaviour and unconventional superconductivity in these systems.Precise quantitative scaling laws are observed between the normalized T-linear coefficient and Tc among copper oxides, pnictides and a class of organic superconductors, suggesting a common underlying physics at work in these unconventional superconductors. [ABSTRACT FROM AUTHOR]

Vehicle ad hoc network (VANET) is an open communication environment. Any user can broadcast messages, which means that it can be easily attacked by malicious users. Therefore, the authentication of vehicles is needed. In this paper, we propose a Chebyshev polynomial-based conditional privacy-preserving authentication and group-key agreement scheme for VANET. Specifically, we solve three problems in VANET: (1) we improve the effectiveness of TA by using Chebyshev polynomial to authenticate vehicles; (2) we reduce the computational burden of TA by using Chinese remainder theorem to manage group members; (3) we provide conditional privacy for users by using traceable pseudonym scheme. Theoretical and experimental results show that the proposed scheme is more efficient than most existing schemes. [ABSTRACT FROM AUTHOR]

No-reference image quality assessment (NR-IQA) based on deep learning attracts a great research attention recently. However, its performance in terms of accuracy and efficiency is still under exploring. To address these issues, in this paper, we propose a quality-distinguishing and patch-comparing NR-IQA approach based on convolutional neural network (QDPC-CNN). We improve the prediction accuracy by two proposed mechanisms: quality-distinguishing adaption and patch-comparing regression. The former trains multiple models from different subsets of a dataset and adaptively selects one for predicting quality score of a test image according to its quality level, and the latter generates patch pairs for regression under different combination strategies to make better use of reference images in network training and enlarge training data at the same time. We further improve the efficiency of network training by a new patch sampling way based on the visual importance of each patch. We conduct extensive experiments on several public databases and compare our proposed QDPC-CNN with existing state-of-the-art methods. The experimental results demonstrate that our proposed method outperforms the others both in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

High temperature superconductivity in cuprates arises from doping a parent Mott insulator by electrons or holes. A central issue is how the Mott gap evolves and the low-energy states emerge with doping. Here we report angle-resolved photoemission spectroscopy measurements on a cuprate parent compound by sequential in situ electron doping. The chemical potential jumps to the bottom of the upper Hubbard band upon a slight electron doping, making it possible to directly visualize the charge transfer band and the full Mott gap region. With increasing doping, the Mott gap rapidly collapses due to the spectral weight transfer from the charge transfer band to the gapped region and the induced low-energy states emerge in a wide energy range inside the Mott gap. These results provide key information on the electronic evolution in doping a Mott insulator and establish a basis for developing microscopic theories for cuprate superconductivity. How a Mott insulating state evolves into a conducting or superconducting state is a central issue in doping a Mott insulator and important to understand the physics in high temperature cuprate superconductors. Here, the authors visualize the electronic structure evolution of a Mott insulator within the full Mott gap region and address the fundamental issues. [ABSTRACT FROM AUTHOR]

The problem of fine-grained sketch-based image retrieval (FG-SBIR) is defined and investigated in this paper. In FG-SBIR, free-hand human sketch images are used as queries to retrieve photo images containing the same object instances. It is thus a cross-domain (sketch to photo) instance-level retrieval task. It is an extremely challenging problem because (i) visual comparisons and matching need to be executed under large domain gap, i.e., from black and white line drawing sketches to colour photos; (ii) it requires to capture the fine-grained (dis)similarities of sketches and photo images while free-hand sketches drawn by different people present different levels of deformation and expressive interpretation; and (iii) annotated cross-domain fine-grained SBIR datasets are scarce, challenging many state-of-the-art machine learning techniques, particularly those based on deep learning. In this paper, for the first time, we address all these challenges, providing a step towards the capabilities that would underpin a commercial sketch-based object instance retrieval application. Specifically, a new large-scale FG-SBIR database is introduced which is carefully designed to reflect the real-world application scenarios. A deep cross-domain matching model is then formulated to solve the intrinsic drawing style variability, large domain gap issues, and capture instance-level discriminative features. It distinguishes itself by a carefully designed attention module. Extensive experiments on the new dataset demonstrate the effectiveness of the proposed model and validate the need for a rigorous definition of the FG-SBIR problem and collecting suitable datasets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to unseen ones so that the latter can be recognised without any training samples. This is made possible by learning a projection function between a feature space and a semantic space (e.g. attribute space). Considering the seen and unseen classes as two domains, a big domain gap often exists which challenges ZSL. In this work, we propose a novel inductive ZSL model that leverages superclasses as the bridge between seen and unseen classes to narrow the domain gap. Specifically, we first build a class hierarchy of multiple superclass layers and a single class layer, where the superclasses are automatically generated by data-driven clustering over the semantic representations of all seen and unseen class names. We then exploit the superclasses from the class hierarchy to tackle the domain gap challenge in two aspects: deep feature learning and projection function learning. First, to narrow the domain gap in the feature space, we define a recurrent neural network over superclasses and then plug it into a convolutional neural network for enforcing the superclass hierarchy. Second, to further learn a transferrable projection function for ZSL, a novel projection function learning method is proposed by exploiting the superclasses to align the two domains. Importantly, our transferrable feature and projection learning methods can be easily extended to a closely related task—few-shot learning (FSL). Extensive experiments show that the proposed model outperforms the state-of-the-art alternatives in both ZSL and FSL tasks. [ABSTRACT FROM AUTHOR]

Orf virus (ORFV) infects sheep and goat tissues, resulting in severe proliferative lesions. To analyze cellular protein expression in ORFV-infected goat skin fibroblast (GSF) cells, we used two-dimensional liquid chromatography-tandem mass spectrometry coupled with isobaric tags for relative and absolute quantification (iTRAQ). The proteomics approach was used along with quantitative reverse transcription polymerase chain reaction (RT-qPCR) to detect differentially expressed proteins in ORFV-infected GSF cells and mock-infected GSF cells. A total of 282 differentially expressed proteins were identified. It was found that 222 host proteins were upregulated and 60 were downregulated following viral infection. We confirmed that these proteins were differentially expressed and found that heat shock 70-kDa protein 1B (HSPA1B) was differentially expressed and localized in the cytoplasm. It was also noted that HSPA1B caused inhibition of viral proliferation, in the middle and late stages of viral infection. The differentially expressed proteins were associated with the biological processes of viral binding, cell structure, signal transduction, cell adhesion, and cell proliferation. [ABSTRACT FROM AUTHOR]

This case study is about a landslide that occurred after 4 days of heavy rainfall, in the morning of June 29, 2012, in Cengong County, Guizhou Province of China, geographical coordinated 108°20′-109°03′E, 27°09′-27°32′N, with an estimated volume of 3.3×106 m3. To fully investigate the landslide process and formation mechanism, detailed geotechnical and geophysical investigations were performed including borehole drilling, sampling, and laboratory tests coupled with monitoring of displacement. Also, a combined seepage-slope stability modeling was performed to study the behavior of the landslide. After the heavy rainfall event, the sliding process started in this area. The landslide development can be divided into different parts. The man-made fill area, spatially distributed in the south side of the landslide area with low elevations, slid first along the interface between the slope debris and the strongly weathered bedrock roughly in the EW direction. Consequently, due to severe lateral shear disturbance, the slope in the main sliding zone slid next towards the SW direction, along the sliding surface developed within the strongly weathered calcareous shale formation located at a depth of 25-35 m. This means it was a rainfall triggered deep-seated landslide. Finally, retrogressive failure of a number of upstream blocks occurred, which moved in more than one direction. The initial failure of the man-made fill area was the 'engine' of the whole instability framework. This artificial material with low permeability, piled up in the accumulation area of surface and sub-surface and destroyed the drainage capacity of the groundwater. The numerical modeling results agreed with the analysis results obtained from the laboratory and field investigations. A conceptual model is given to illustrate the formation mechanism and development process of the landslide. [ABSTRACT FROM AUTHOR]

In the era of big data remarked by high dimensionality and large sample size, the least absolute shrinkage and selection operator (LASSO) problems demand efficient algorithms. Both static and dynamic strategies based on screening test principle have been proposed recently, in order to safely filter out irrelevant atoms from the dictionary. However, such strategies only work well for LASSO problems with large regularization parameters, and lose their efficiency for those with small regularization parameters. This paper presents a novel greedy screening test strategy to accelerate solving LASSO problems with small regularization parameters, as well as its effectiveness through adoption of a relatively larger regularization parameter which filters out irrelevant atoms in every iteration. Further more, the convergence proof of the greedy strategy is given, and the computational complexity of LASSO solvers integrated with this strategy is investigated. Numerical experiments on both synthetic and real data sets support the effectiveness of this greedy strategy, and the results show it outperforms both the static and dynamic strategies for LASSO problems with small regularization parameters. [ABSTRACT FROM AUTHOR]

A Gram-stain negative, non-motile, asporogenous, aerobic and rod-shaped bacterial strain, designated ZY113T, was isolated from the surface of a marine red alga collected from the coast in Weihai, Shandong Province, China. Strain ZY113T was found to grow at 4–37 °C (optimum at 28–30 °C), with 1.0–7.0% (w/v) NaCl (optimum 2.0–3.0%) and at pH 6.0–9.0 (optimum 7.0–8.0). Phylogenetic analysis based on 16S rRNA gene sequences revealed that strain ZY113T is a member of the genus Polaribacter, with Polaribacter dokdonensis KCTC 12392T as a close relative (97.4% similarity). The sole respiratory quinone was found to be menaquinone 6 (MK-6) and the major fatty acids were identified as iso-C15:0, iso-C15:0 3-OH and iso-C13:0. The polar lipids were found to consist of phosphatidylethanolamine, two unidentified aminolipids and three unidentified lipids. The G + C content of the genomic DNA was determined to be 30.1 mol%. On the basis of phenotypic, genotypic and phylogenetic analysis, strain ZY113T is considered to represent a novel species of the genus Polaribacter, for which the name Polaribacter aquimarinus sp. nov. is proposed. The type strain is ZY113T (= KCTC 62374T = MCCC 1H00296T). [ABSTRACT FROM AUTHOR]

We found that the high solid solution Al-9 Mg alloy with a bimodal grain structure processed by hard-plate rolling (HPR) exhibited enhanced thermal stability despite annealing at 275 °C for 24 hours. The notable thermal stability originates from the inherent stable deformation microstructure dominated by (101)-oriented coarse grains induced by a high solute Mg content, which further stabilizes the microstructure upon static annealing. Moreover, recovery occurs preferentially in coarse grains, leading to slow coarsening of the ultrafine grains. This paper provides a new way to improve thermal stability of severely deformed Al-Mg alloys by tailoring grain orientations. [ABSTRACT FROM AUTHOR]

The deammonification process is a promising technology, while achieving stable performance is still a challenge for domestic sewage treatment. To investigate the stability of deammonification in the plug flow system, which can be updated from A/O or A/A/O bioreactor, a plug flow fixed biofilm reactor was started-up and fed with synthetic low-strength wastewater. As a result, average ammonium removal efficiency of 90.0 ± 10.0% and total nitrogen removal efficiency of 79.4 ± 9.3% were achieved, while the nitrate production ratio (∆Nitrate/∆Ammonium) was at superior levels (9.5 ± 3.4%). Candidatus Jettenia and Candidatus Brocadia were the anammox bacteria in this reactor, and Candidatus Jettenia was the predominant anammox bacteria. Anammox bacteria were dominated in three of the four sampling points except the first one. Relative abundance of NOB increased along the reactor. The result of the present work implied that the plug flow system was able to maintain stable deammonification process, and NOB was suppressed by higher residual ammonium concentration in the front of reactor while the suppression weakened along the reactor. [ABSTRACT FROM AUTHOR]

A quenching–tempering–partitioning (Q–T–P) process was performed on a Fe-13Cr-3Ni-2Cu-0.1C alloy to investigate the effect of the partitioning process on the reversed austenite and mechanical properties. A quenching and tempering (Q–T) process was also carried out for comparison. The amount and characteristics of the reversed austenite were studied with XRD and TEM. The results showed that the volume fraction of reversed austenite in the Q–T–P-treated samples was higher than that in the Q–T-treated ones. Finer film-like and blocky austenite formed in the Q–T–P-treated samples, and fine Cu-rich precipitates were also well distributed in the matrix. However, wide band and blocky austenite formed in the Q–T-treated samples. Hence, the Q–T–P-treated samples had better comprehensive mechanical properties than the Q–T-treated ones. [ABSTRACT FROM AUTHOR]