Microstructure，mechanical property and oxidation behavior of HfZrTiTaBx HEAs

The unique structural and thermal features of high-entropy alloys (HEAs) conduce to their excellent stability and mechanical properties. Recent researches have suggested that the high-entropy alloys composed of refractory metals exhibit competitive phase-stability and strength at elevated temperatures, which made them the promising candidate materials for high-temperature structural applications at even higher temperatures compared with the Ni-based superalloys. However, the alloys barely consisting of refractory metal elements are usually oxidized easily in oxidizing environment at high temperatures. This work aims to prepare a refractory HEA with both excellent mechanical properties and outstanding oxidation resistance by alloying of B element. In this study, an equimolar quaternary HfZrTiTa alloy and three kinds of HfZrTiTaBx(x=1.1, 2.3, 4.7) alloys with different amounts of B-addition were produced by vacuum arc melting technique in argon atmosphere. The structures of the prepared alloys were characterized via X-Ray diffraction and TEM. The oxidation behaviors of these alloys were investigated by differential scanning calorimeter (DSC)from 25℃ to 1300℃ in air. Their mechanical properties at room temperature and phase-stability at different annealing temperatures from 800℃ to 1600℃ were also examined. The results show that the HfZrTiTa alloy consists of a fully disordered body-centered cubic (BCC) solid solution phase due to the high mixing entropy, while the alloys with B addition have some nano particles uniformly distributed in the BCC solid solution matrix. The lattice parameters and Vicker hardness of the B-containing alloys increase with increasing B content due to the interstitial solid solution strengthening of B element and nanoprecipitation strengthening. The BCC structure of all alloy samples remains stable up to 1200℃. The quaternary HfZrTiTa alloy has a flexural strength of 2.3GPa with a typical dimple fracture morphology, indicating that the alloy shows ductile to some extent. The oxidation rates of the HfZrTiTaBx (x=1.1, 2.3, 4.7) alloys at 1300℃ were about 0.13~0.15g•mm-2•h-1, obviously lower than that of the HfZrTiTa alloy (0.454g•mm-2•h-1)

How Can Recommender Systems Benefit from Large Language Models: A Survey

Recommender systems (RS) play important roles to match users' information needs for Internet applications. In natural language processing (NLP) domains, large language model (LLM) has shown astonishing emergent abilities (e.g., instruction following, reasoning), thus giving rise to the promising research direction of adapting LLM to RS for performance enhancements and user experience improvements. In this paper, we conduct a comprehensive survey on this research direction from an application-oriented view. We first summarize existing research works from two orthogonal perspectives: where and how to adapt LLM to RS. For the "WHERE" question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i.e., feature engineering, feature encoder, scoring/ranking function, and pipeline controller. For the "HOW" question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria, i.e., whether to tune LLMs or not, and whether to involve conventional recommendation model (CRM) for inference. Detailed analysis and general development trajectories are provided for both questions, respectively. Then, we highlight key challenges in adapting LLM to RS from three aspects, i.e., efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects. We also actively maintain a GitHub repository for papers and other related resources in this rising direction: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys.Comment: 15 pages; 3 figures; summarization table in appendi

Dysregulated Bone Metabolism Is Related to High Expression of miR-151a-3p in Severe Adolescent Idiopathic Scoliosis

Adolescent idiopathic scoliosis (AIS) is a common complex disease, and bone homeostasis plays an important role in its pathogenesis. Recent advances in epigenetic research show that dysregulated miRNAs may participate in the development of orthopedic diseases and AIS. The aim of this study was to detect differentially expressed miRNAs in severe AIS and elucidate the mechanism of miRNA deregulation in the pathogenesis of AIS. In the present study, miRNA expression profiles were detected in severe and mild AIS patients as well as healthy controls by miRNA sequencing. Candidate miRNAs were validated in a larger cohort. Primary osteoblasts from severe AIS patients were extracted and isolated to determine the effect of the candidate miRNAs on bone metabolism. Finally, we determined the methylation level in primary osteoblasts from severe AIS patients. The result showed that miR-151a-3p was overexpressed in severe AIS patients. Reduced GREM1 expression was observed in primary osteoblasts from severe AIS patients. miR-151a-3p directly inhibited GREM1 in primary osteoblasts. Relatively lower methylation levels were detected in primary osteoblasts from severe AIS patients. In conclusion, our study revealed that plasma miR-151a-3p levels may serve as a biomarker for severe AIS. Overexpression of miR-151a-3p may interrupt bone homeostasis via inhibiting GREM1 expression. Our result may provide a new biomarker for the early detection of AIS and increase our understanding of the pathogenesis of AIS

An integrated document retrieval method combining entity annotation and keyword index: A KIM platform implementation

Purpose: The objective of this paper is to testify the effect of ontology-based semantic annotation on the performance of document retrieval.Design/methodology/approach: An integrated document retrieval method is put forward in this paper, in which the entities of documents are annotated by the upper ontology and domain ontology, then the documents are further indexed by the entity annotation as well as traditional keywords.Findings: The research result shows that the structured entity retrieval and relation retrieval can be realized by the ontology-based entity index, which is beyond the ability of the tradition keyword-based retrieval. Meanwhile, the experiment shows that the recall and precision of document retrieval are improved effectively.Research limitations: Due to the small amount of our current tourism domain ontology, the document retrieval with the ontology-based semantic index is limited by the size of ontology and the precision of semantic annotation. Meanwhile, the semantic annotation algorithm mainly relies on the current information extraction strategy of KIM Platform. Therefore, the performance of disambiguation and relation extraction algorithm need to be further improved.Practical implications: Our method can improve the efficiency of document retrieval system, which facilitates the knowledge and document management in corporations, governments and other organizations.Originality/value: The integrated document retrieval method proposed in the paper can combine the entity index based on the general ontology with domain ontology and the keyword index. Our result verified the effectiveness of the combined index strategy.Purpose: The objective of this paper is to testify the effect of ontology-based semantic annotation on the performance of document retrieval.Design/methodology/approach: An integrated document retrieval method is put forward in this paper, in which the entities of documents are annotated by the upper ontology and domain ontology, then the documents are further indexed by the entity annotation as well as traditional keywords.Findings: The research result shows that the structured entity retrieval and relation retrieval can be realized by the ontology-based entity index, which is beyond the ability of the tradition keyword-based retrieval. Meanwhile, the experiment shows that the recall and precision of document retrieval are improved effectively.Research limitations: Due to the small amount of our current tourism domain ontology, the document retrieval with the ontology-based semantic index is limited by the size of ontology and the precision of semantic annotation. Meanwhile, the semantic annotation algorithm mainly relies on the current information extraction strategy of KIM Platform. Therefore, the performance of disambiguation and relation extraction algorithm need to be further improved.Practical implications: Our method can improve the efficiency of document retrieval system, which facilitates the knowledge and document management in corporations, governments and other organizations.Originality/value: The integrated document retrieval method proposed in the paper can combine the entity index based on the general ontology with domain ontology and the keyword index. Our result verified the effectiveness of the combined index strategy.</p

Preparation and Properties of Highly Elastic, Lightweight, and Thermally Insulating SiO₂ Fibrous Porous Materials

Fibrous porous materials are one of the most commonly used high-temperature insulation materials because of their high porosity and low thermal conductivity. Due to their wide applications in the aerospace and energy industries, the investigation of high-elastic thermally insulating porous materials has attracted increasing attention. In order to improve the elasticity of fibrous porous materials, quartz fibers with high aspect ratio were used as matrix, sodium hexametaphosphate (SHMP) was selected as dispersant. We innovatively reported that a unique three-dimensional skeleton structure was constructed by adjusting the dispersion of fibers in the slurry, and the lightweight, thermal insulating and elastic SiO2 fibrous porous material was then prepared by the compression molding method. The characterization results of zeta potential and absorbance showed that the addition of SHMP was an effective method to enhance the dispersibility of quartz fibers in the slurry. SiO2 fibrous porous materials with 0.4 wt% SHMP content exhibited an ideal three-dimensional skeleton structure, which endowed the porous material with high porosity (89.39%), low density (0.04751 g/cm3), and low thermal conductivity (0.0356 W·m−1·K−1). The three-dimensional skeleton structure formed by overlapping fibers with high aspect ratios endowed the porous material with excellent elasticity. SiO2 fibrous porous materials with 0.4 wt% SHMP content could undergo large strains of 30% and achieved a resilience ratio of 81.69% under the 30th compression cycle. Moreover, after heat treatment at 800 °C, SiO2 fibrous porous materials also maintained good elasticity with a resilience ratio of more than 80%

Cervical <i>Staphylococcus aureus</i> Infection after Receiving the Third Dose of COVID-19 Vaccination: A Case Report

Introduction: Vaccination is one of the most effective ways to control the COVID-19 pandemic. However, as the number of people vaccinated against COVID-19 continues to increase, there are more reports on the safety of vaccines. So far, there have been no reported cases of spinal infection associated with COVID-19 vaccination. Recently, we admitted a patient who developed cervical Staphylococcus aureus infection resulting in high paraplegia after receiving the third dose of COVID-19 vaccine when the symptoms of cold did not completely disappear. Case presentation: The patient was a 70-year-old man who received the third injection of COVID-19 vaccine when the cold symptoms were not completely gone. On the day after the injection, the patient developed severe neck and shoulder pain, accompanied by numbness and fatigue in the limbs. MRI examination of the cervical spine on day 6 after vaccination showed no obvious signs of infection. The patient had progressive weakness in the extremities. On the ninth day after vaccination, the patient developed paralysis of both lower limbs and significant sensory loss. Cervical abscess and cervical spinal cord injury were considered for cervical CT and MRI examination on the 15th day after vaccination. We used an anterior approach to remove as much of the lesion as possible. Staphylococcus aureus was detected and antibiotic treatment was continued after surgery. The patient’s pain symptoms were significantly relieved, which prevented the abscess from further pressing the spinal cord and provided possible conditions for the recovery of neurological function in the later stage. Conclusion: This case is the first reported cervical Staphylococcus aureus infection resulting in high paraplegia after receiving the third dose of COVID-19 vaccine with low immunity. This case raises awareness of this rare but potentially life-threatening adverse reaction, and reminds people to hold off when their immune system is weakened

Engineering chimeric antigen receptor T cells for solid tumour therapy

Abstract Cell‐based immunotherapy, for example, chimeric antigen receptor T (CAR‐T) cell immunotherapy, has revolutionized cancer treatment, particularly for blood cancers. However, factors such as insufficient T cell tracking, tumour heterogeneity, inhibitory tumour microenvironment (TME) and T cell exhaustion limit the broad application of CAR‐based immunotherapy for solid tumours. In particular, the TME is a complex and evolving entity, which is composed of cells of different types (e.g., cancer cells, immune cells and stromal cells), vasculature, soluble factors and extracellular matrix (ECM), with each component playing a critical role in CAR‐T immunotherapy. Thus, developing approaches to mitigate the inhibitory TME factors is critical for future success in applying CAR‐T cells for solid tumour treatment. Accordingly, understanding the bilateral interaction of CAR‐T cells with the TME is in pressing need to pave the way for more efficient therapeutics. In the following review, we will discuss TME‐associated aspects with an emphasis on T cell trafficking, ECM barriers, abnormal vasculature, solid tumour heterogenicity and immune suppressive microenvironment. We will then summarize current engineering strategies to overcome the challenges posed by the TME‐associated factors. Lastly, the future directions for engineering efficient CAR‐T cells for solid tumour therapy will be discussed

Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry.

A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clustering (DCAEC) model, to cluster label-free IFC images without any prior knowledge of input labels. The DCAEC model first encodes the input images into the latent representations and then clusters based on the latent representations. Using the DCAEC model, we achieve a balanced accuracy of 91.9% for human white blood cell (WBC) clustering and 97.9% for WBC/leukemia clustering using the 3D IFC images and 3D DCAEC model. Above all, although no human recognizable features can separate the clusters of cells with protein localization, we demonstrate the fused DCAEC model can achieve a cluster balanced accuracy of 85.3% from the label-free 2D transmission and 3D side scattering images. To reveal how the neural network recognizes features beyond human ability, we use the gradient-weighted class activation mapping method to discover the cluster-specific visual patterns automatically. Evaluation results show that the automatically identified salient image regions have strong cluster-specific visual patterns for different clusters, which we believe is a stride for the interpretable neural network for cell analysis with high-throughput IFCs

Correlation of Surface Toll-Like Receptor 9 Expression with IL-17 Production in Neutrophils during Septic Peritonitis in Mice Induced by E. coli

IL-17 is a proinflammatory cytokine produced by various immune cells. Polymorphonuclear neutrophils (PMNs) are the first line of defense in bacterial infection and express surface Toll-like receptor 9 (sTLR9). To study the relationship of sTLR9 and IL-17 in PMNs during bacterial infection, we infected mice with E. coli intraperitoneally to establish a septic peritonitis model for studying the PMNs response in peritoneal cavity. We found that PMNs and some of “giant cells” were massively accumulated in the peritoneal cavity of mice with fatal septic peritonitis induced by E. coli. Kinetically, the CD11b+ PMNs were increased from 20–40% at 18 hours to >80% at 72 hours after infection. After E. coli infection, sTLR9 expression on CD11b+ and CD11b− PMNs and macrophages in the PLCs were increased at early stage and deceased at late stage; IL-17 expression was also increased in CD11b+ PMNs, CD11b− PMNs, macrophages, and CD3+ T cells. Using experiments of in vitro blockage, qRT-PCR and cell sorting, we confirmed that PMNs in the PLCs did increase their IL-17 expression during E. coli infection. Interestingly, sTLR9−CD11b+Ly6G+ PMNs, not sTLR9+CD11b+Ly6G+ PMNs, were found to be able to increase their IL-17 expression. Together, the data may help understand novel roles of PMNs in septic peritonitis