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In order to further improve the bending performance of the traditional re-entrant (RE) honeycomb, a novel auxetic honeycomb architecture, called RE-L honeycomb, was proposed by adding an additional link-wall structure to the RE cell. The bending behaviors of the novel RE-L honeycomb, including the properties under linear elastic deformation and the bending behaviors under large deformation, were comprehensively investigated by the analytical, numerical and experimental models. Results show that the proposed RE-L honeycomb significantly improves the bending compliance in the x-direction due to the highly flexible performance of the additional structure, where the bending rigidity and the maximum bending force are only 23% and 29.4% of those of the RE honeycomb, respectively. Besides, the additional structure obviously improves the designability and orthotropic property of the original auxetic honeycomb. In conclusion, the proposed RE-L shows improved bending performance, which deserves more attention in future research and related applications.

Based on the gradual normalization of voluntary performance letter disclosure, this paper chooses the data of Chinese main board-listed companies from 2013 to 2020 and uses multiple regression analysis to explore the impact of preliminary annual results quality on audit fees and the regulating mechanism of property rights. The results show that the quality of preliminary annual results can assist auditors to assess audit risk levels, and then affect audit fees. In addition, in the periodic regression analysis, there is a significantly positive correlation between the preliminary annual results material misstatement and audit fee. By comparison, private enterprises have a more significant positive correlation between material misstatement and audit fees.

Cancer cells take up amino acids from the extracellular space to drive cell proliferation and viability. Similar mechanisms are applied by immune cells, resulting in the competition between conventional T cells, or indeed chimeric antigen receptor (CAR) T cells and tumor cells, for the limited availability of amino acids within the environment. We demonstrate that T cells can be re-engineered to express SLC7A5 or SLC7A11 transmembrane amino acid transporters alongside CARs. Transporter modifications increase CAR T-cell proliferation under low tryptophan or cystine conditions with no loss of CAR cytotoxicity or increased exhaustion. Transcriptomic and phenotypic analysis reveals that downstream, SLC7A5/SLC7A11–modified CAR T cells upregulate intracellular arginase expression and activity. In turn, we engineer and phenotype a further generation of CAR T cells that express functional arginase 1/arginase 2 enzymes and have enhanced CAR T-cell proliferation and antitumor activity. Thus, CAR T cells can be adapted to the amino acid metabolic microenvironment of cancer, a hitherto recognized but unaddressed barrier for successful CAR T-cell therapy.

Autologous Active Specific Immunotherapy (AASI) is a type of immunotherapy that targets complementary autoantibodies which suppress the specific immune response using anti-idiotypic antibodies. AASI entails removing immune cells (dendritic cells) from the patient's blood and subjecting them in a lab setting to a particular tumour antigen (proteins detected on the surface of cancer cells). AASI is a personalized treatment approach that has been used to treat various types of cancer, including melanoma, osteosarcoma and breast cancer. Clinical trials have shown promising results, with some patients experiencing complete remission or long-term disease control. Although AASI has shown potential as a cancer treatment, further research is needed to optimize its effectiveness and safety. AASI is a complex and expensive therapy, and its use is currently limited to specialized cancer treatment centres.

This comprehensive review chronologically summarizes the visible-light-induced cascade reactions with literature coverage up to October 2022.

Honeyword (or decoy password) based authentication, first introduced by Juels and Rivest in 2013, has emerged as a security mechanism that can provide security against server-side threats on the password-files. From the theoretical perspective, this security mechanism reduces attackers’ efficiency to a great extent as it detects the threat on a password-file so that the system administrator can be notified almost immediately as an attacker tries to take advantage of the compromised file. This paper aims to present a comprehensive survey of the relevant research and technological developments in honeyword-based authentication techniques. We cover twenty-three techniques related to honeyword, reported under different research articles since 2013. This survey paper helps the readers to (i) understand how honeyword based security mechanism works in practice, (ii) get a comparative view on the existing honeyword based techniques, and (iii) identify the existing gaps that have yet to be filled and the emergent research opportunities.

Colorectal cancer (CRC) is one of the most common cancers worldwide. The tumor microenvironment exerts crucial effects in driving CRC progression. Cancer-associated fibroblasts (CAFs) serve as one of the most important tumor microenvironment components promoting CRC progression. This study aimed to elucidate the novel molecular mechanisms of CAF-secreted insulin-like growth factor (IGF) 2 in colorectal carcinogenesis. Our results indicated that IGF2 was a prominent factor upregulated in CAFs compared with normal fibroblasts. CAF-derived conditioned media (CM) promoted tumor growth, migration, and invasion of HCT 116 and DLD-1 cells. IGF1R expression is significantly increased in CRC, serving as a potent receptor in response to IGF2 stimulation and predicting unfavorable outcomes for CRC patients. Apart from the PI3K-AKT pathway, RNA-seq analysis revealed that the YAP1-target signature serves as a prominent downstream effector to mediate the oncogenic signaling of IGF2-IGF1R. By single-cell RNA sequencing (scRNA-seq) and immunohistochemical validation, IGF2 was found to be predominantly secreted by CAFs, whereas IGF1R was expressed mainly by cancer cells. IGF2 triggers the nuclear accumulation of YAP1 and upregulates YAP1 target signatures; however, these effects were abolished by either IGF1R knockdown or inhibition with picropodophyllin (PPP), an IGF1R inhibitor. Using CRC organoid and in vivo studies, we found that cotargeting IGF1R and YAP1 with PPP and verteporfin (VP), a YAP1 inhibitor, enhanced antitumor effects compared with PPP treatment alone. In conclusion, this study revealed a novel molecular mechanism by which CAFs promote CRC progression. The findings highlight the translational potential of the IGF2-IGF1R-YAP1 axis as a prognostic biomarker and therapeutic target for CRC. © 2022 The Pathological Society of Great Britain and Ireland.

Today, the rapid growth of vehicles connected to the Internet enables provides various services to consumers, including traffic management, traffic safety, and entertainment. Vehicular Ad-Hoc Network (VANET) is one of the most prominent and emerging technologies on the Internet of Vehicular Things (IoVT). This technology offers to fulfill requirements such as robust information exchange, and infotainment among vehicles for the smart city environment. Still, it has some challenges such as centralization, storage, security, and privacy because all city vehicular networks send vehicles and road-related information data directly to the cloud. This article proposes BIIoVT: Blockchain-based Secure Storage Architecture for Intelligent Internet of Vehicular Things (IIVoT) to mitigate the above-mention issues. Blockchain provides security and privacy at each city's vehicular networks and decentralized storage at the cloud layer with a Distributed Hash Table (DHT). It also examines how the vehicular network offers a secure platform. The validation results of the proposed architecture show an outstanding balance of secure storage and efficiency for the IoVT compared to existing methods.

In this paper, we study divergence properties of the Fourier series on Cantor-type fractal measure, also called the mock Fourier series. We give a sufficient condition under which the mock Fourier series for doubling spectral measure is divergent on a set of strictly positive measure. In particular, there exists an example of the quarter Cantor measure whose mock Fourier sums are not almost everywhere convergent.

Identifying native-like protein-ligand complexes (PLCs) from an abundance of docking decoys is critical for large-scale virtual drug screening in early-stage drug discovery lead searching efforts. Providing reliable prediction is still a challenge for most current affinity predicting models because of a lack of non-binding data during model training, lost critical physical-chemical features, and difficulties in learning abstract information with limited neural layers. In this work, we proposed a deep learning model, DeepBindBC, for classifying putative ligands as binding or non-binding. Our model incorporates information on non-binding interactions, making it more suitable for real applications. ResNet model architecture and more detailed atom type representation guarantee implicit features can be learned more accurately. Here, we show that DeepBindBC outperforms Autodock Vina, Pafnucy, and DLSCORE for three DUD.E testing sets. Moreover, DeepBindBC identified a novel human pancreatic α-amylase binder validated by a fluorescence spectral experiment (K

In the era of big data, data analysis of high-dimensional data-sets often suffers from Curse-of-dimensionality. In the current article, we propose a dimensionality-reduction method through feature selection from high-dimensional gene expression (GE) data-set using a Multi-objective optimization based Multi-view Co-Clustering algorithm (named M2Co-Clus). A popular evolutionary technique- Non-dominated Sorting Genetic Algorithm-II or NSGA-II has been utilized as the underlying optimization strategy of the proposed method. First, we construct two views of the chosen data-set, utilizing knowledge from two different biological data sources. Next, we develop the M2Co-Clus algorithm considering the constructed views to identify a set of good co-clustering solutions. Finally, based on a concept of consensus operation on co-clustering outcome, a small number of most relevant and non-redundant features are extracted from the original feature-space. The reduced dimension formed by new feature-space causes to decrease the computational burden and noise level of original data. For experimental analysis, we have chosen three benchmark GE data-sets. Our feature selection strategy's effectiveness is evaluated through sample-classification accuracy, accompanied by visualization tests like cluster profile plot/Eisen plot, and biological significance test. A thorough comparative analysis with existing feature selection algorithms using external and internal evaluation metrics supports the potency of our proposed method.

Studies have found that the addition of plant essential oils to feed had a positive effect on intestinal microflora and immunity in mice. However, the effect of different ways of ingestion of orange essential oil on mice has seldom been reported. In the present study, we investigated the effects of ingestion of orange essential oil by gavage, sniffing and feeding on intestinal microflora and immunity in mice.The results obtained showed that a low concentration of essential oil feeding significantly increased the spleen index of mice (P 0.05). The effect of different ways of ingestion on the thymus index, immunoglobulin G and immunoglobulin M of mice was not significant (P 0.05). High and medium concentrations of essential oil feeding increased the level of interleukin-2 in mice (P 0.05). HCompared with gavage and feeding, sniffing had a significant effect on immunoglobulins in mice. All the three ingestion methods could affect the intestinal microflora of mice and increase the abundance of Lactobacillus. © 2022 Society of Chemical Industry.

We have designed and developed OceanBase, a distributed relational database system from the very basics for a decade. Being a scale-out multi-tenant system, OceanBase is cross-region fault tolerant, which is based on the shared-nothing architecture. Besides sharing many similar goals with alternative distributed DBMS, such as horizontal scalability, fault-tolerance, etc., our design has been driven by the demands of typical RDBMS compatibility as well as both on-premise and off-premise deployments. OceanBase has fulfilled its design goal. It implements the salient features of certain mainstream classical RDBMS, and most applications on them can run on OceanBase, with or without a few minor modifications. Tens of thousands of OceanBase servers have been deployed in Alipay.com as well as many other commercial organizations. It has also successfully passed the TPC-C benchmark test and seized the first place with more than 707 million tpmC. This paper presents the goals, design criteria, infrastructure, and key components of OceanBase including its engines for storage and transaction processing. Further, it details how OceanBase achieves the above leading TPC-C benchmark in a distributed cluster with more than 1,500 servers from 3 zones. It also describes lessons what we have learnt in building OceanBase for more than a decade.

Background: Breast cancer is one cancer that develops from breast tissue and one of the major reasons for the death of women all over the world. The tumor infiltrating lymphocytes in tumor immune microenvironment are correlated with the prognosis in breast cancer patients, and play an important role in the occurrence and development of breast cancer. Method: In this study, by integrated the immune gene expression of 20 breast cancer cohorts from the public dataset, an immune-based prognostic score was established. This immune-based prognostic score was found to be correlated with prognosis, stromal score, tumor purity, three famous immune checkpoints, and immune escape mechanism in breast cancer patients. Results: The clinical application of the prognostic score was verified by the breast cancer patients treated with chemotherapy, and good therapeutic benefit of the prognostic score was obtained. In addition, the XGBoost classifier was used to construct for predicting the high and low prognostic score subtypes, and the predictive results indicated that the XGBoost was suitable to predict these two subtypes in breast cancer patients. Conclusion: Based on these results, we believed that the prognostic score may be used as an effective prognostic marker and may provide great help for chemotherapy treatment of breast cancer patients

Background Triple-negative breast cancer (TNBC) possesses special biological behavior and clinicopathological characteristics, which is highly invasive and propensity to metastasize to lymph nodes, leading to a worse prognosis than other types of breast cancer. Thus, the development of an effective therapeutic method is significant to improve the survival rate of TNBC patients. Results In this work, a liposome-based theranostic nanosystem (ILA@Lip) was successfully prepared by simultaneously encapsulating IR 780 as the photosensitizer and lenvatinib as an anti-angiogenic agent, together with banoxantrone (AQ4N) molecule as the hypoxia-activated prodrug. The ILA@Lip can be applied for the near-infrared (NIR) fluorescence diagnostic imaging of TNBC and its lymph node metastasis for multimodal therapy. Lenvatinib in ILA@Lip can inhibit angiogenesis by cutting oxygen supply, thereby leading to enhanced hypoxia levels. Meanwhile, large amounts of reactive oxygen species (ROS) were produced while IR 780 was irradiated by an 808 nm laser, which also rapidly exhausted oxygen in tumor cells to worsen tumor hypoxia. Through creating an extremely hypoxic in TNBC, the conversion of non-toxic AQ4N to toxic AQ4 was much more efficiency for hypoxia-activated chemotherapy. Cytotoxicity assay of ILA@Lip indicated excellent biocompatibility with normal cells and tissues, but showed high toxicity in hypoxic breast cancer cells. Also, the in vivo tumors treated by the ILA@Lip with laser irradiation were admirably suppressed in both subcutaneous tumor model and orthotopic tumor models. Conclusion Utilizing ILA@Lip is a profound strategy to create an extremely hypoxic tumor microenvironment for higher therapeutic efficacy of hypoxia-activated chemotherapy, which realized collective suppression of tumor growth and has promising potential for clinical translation.