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Background: Our previous research showed that Porphyromonas gingivalis (P. gingivalis) infection can activate the inflammatory signaling pathway and promotes the malignancy development of esophageal squamous cell carcinoma (ESCC). However, the prognostic significance of inflammatory response-related genes (IRRGs) in P. gingivalis -infected ESCC requires further elucidation. Hence, our study constructed a prognostic signature based on P. gingivalis and IRRGs to forecast the survival of patients with ESCC, which may provide insight into new treatment options for ESCC patients. Methods: Differentially expressed genes (DEGs) were identified in P.gingivalis -infected and P.gingivalis -uninfected ESCC cell by RNA sequencing. A risk model was constructed and validated using the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database by using univariate Cox regression analysis, LASSO, and the multivariate Cox regression analysis. Kaplan-Meier analysis was carried out to compare the overall survival (OS) between high-risk and low-risk groups. Single-sample gene set enrichment analysis was used to analyze the immune cell infiltration. The Genomics of Drug Sensitivity in Cancer database was used to predict drug sensitivity. Results: There were 365 DEGs between the P.gingivalis -infected and P.gingivalis -uninfected groups. Four genes including DKK1, ESRRB, EREG, and RELN were identified to construct the prognostic risk model (P =.012, C-index = 0.73). In both the training and validation sets, patients had a considerably shorter OS in the high-risk group than those in the low-risk group (P <.05). A nomogram was established using the risk score, gender, and N stage which could effectively forecast the prognosis of patients (P =.016, C-index = 0.66). The high-risk group displayed lower immune infiltrating cells, such as activated dendritic cells, type 2 T helper cells, and neutrophils (P <.05). A total of 41 drugs, including dactinomycin, luminespib, and sepantronium bromide, had a significant difference in IC50 between the 2 subgroups. Conclusion: We demonstrated the potential of a novel signature constructed from 4 P. gingivalis -related IRRGs for prognostic prediction in ESCC patients. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the impact of selected data augmentation techniques on the learning performance of neural networks for dynamic signature verification. The paper investigates selected data augmentation techniques in deep learning for verification purpose of dynamic signature. Two neural networks were used as classifiers: MLP and LSTM-FCN. Investigation of five selected augmentation methods and experiments were performed on the open source signature database SVC2004. The authors tested both classifiers without augmentation and then with data augmentation for three extensions of the learning set and three sizes of the user database. They presented the results of the experiments in tabular form for each augmentation method. The results were compared with the existing dynamic signature verification methods and given in the paper. [ABSTRACT FROM AUTHOR]

Based on Rodolfo Sacco's passionate research on cryptotypes and mute law, the essay aims to propose an onto-aesthetic understanding of latency in law. Inspired by a transdisciplinary way of thinking, the Author elevates comparative law to the privileged site where a critique of the unexpressed can be performed; the degree of figurality of the law can be uncovered and measured; an economy of surplus can be disclosed as the proper segnatorial trait of the material appearances of the law. In this direction, comparative law combines a phenomenology of latency with a hermeneutics of the implicit/implied and becomes the place where it is possible to uncover the communality among plural symbolic universes (literature, arts, music) against the tyranny of disciplinary strategy. [ABSTRACT FROM AUTHOR]

The rapid advancement of internet technology has raised attention to the importance of electronic voting in maintaining democracy and fairness in elections. E-voting refers to the use of electronic technology to facilitate the casting and counting of votes in elections. The need for designated verification arises from concerns about voter privacy, auditability, and the prevention of manipulation. Traditional e-voting systems use cryptographic techniques for security but lack verifiable proof of integrity. Integrating e-voting with a quantum designated verifier could address these challenges by leveraging the principles of quantum mechanics to enhance security and trustworthiness. In light of this, we propose a quantum e-voting scheme that uses a designated verifier signature. To ensure the confidentiality and authenticity of the voting process, the scheme uses quantum features like the no-cloning theorem and quantum key distribution. The proposed scheme has security properties like source hiding, non-transferability, and message anonymity. The proposed scheme is resistant to many quantum attacks, such as eavesdropping and impersonation. Due to designated verification, the scheme minimizes the risk of tempering. This paper provides a detailed description of the proposed scheme and analyzes its security properties. Therefore, the proposed scheme is efficient, practical, and secure. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is a highly heterogeneous disease with poor clinical outcomes. To comprehensively dissect the molecular landscape of GBM and heterogeneous macrophage clusters in the progression of GBM, this study integrates single-cell and bulk transcriptome data to recognize a distinct pro-tumor macrophage cluster significantly associated with the prognosis of GBM and develop a GBM prognostic signature to facilitate prior subtypes. Leveraging glioma single-cell sequencing data, we identified a novel pro-tumor macrophage subgroup, marked by S100A9, which might interact with endothelial cells to facilitate tumor progression via angiogenesis. To further benefit clinical application, a prognostic signature was established with the genes associated with pro-tumor macrophages. Patients classified within the high-risk group characterized with enrichment in functions related to tumor progression, including epithelial-mesenchymal transition and hypoxia, displays elevated mutations in the TERT promoter region, reduced methylation in the MGMT promoter region, poorer prognoses, and diminished responses to temozolomide therapy, thus effectively discriminating between the prognostic outcomes of GBM patients. Our research sheds light on the intricate microenvironment of gliomas and identifies potential molecular targets for the development of novel therapeutic approaches. [ABSTRACT FROM AUTHOR]

Programmed cell death (PCD) is a process that eliminates infected, damaged, or possibly neoplastic cells to sustain homeostatic multicellular organisms. Although long noncoding RNAs (lncRNAs) are involved in various types of PCD and regulate tumor growth, invasion, and migration, the role of PCD-related lncRNAs in bladder cancer still lacks systematic exploration. In this research, we integrated multiple types of PCD as pan-PCD and identified eight pan-PCD-related lncRNAs (LINC00174, HCP5, HCG27, UCA1, SNHG15, GHRLOS, CYB561D2, and AGAP11). Then, we generated a pan-PCD-related lncRNA prognostic signature (PPlncPS) with excellent predictive power and reliability, which performed equally well in the E-MTAB-4321 cohort. In comparison with the low-PPlncPS score group, the high-PPlncPS score group had remarkably higher levels of angiogenesis, matrix, cancer-associated fibroblasts, myeloid cell traffic, and protumor cytokine signatures. In addition, the low-PPlncPS score group was positively correlated with relatively abundant immune cell infiltration, upregulated expression levels of immune checkpoints, and high tumor mutation burden (TMB). Immunogenomic profiles revealed that patients with both low PPlncPS scores and high TMB had the best prognosis and may benefit from immune checkpoint inhibitors. Furthermore, for patients with high PPlncPS scores, docetaxel, staurosporine, and luminespib were screened as potential therapeutic candidates. In conclusion, we generated a pan-PCD-related lncRNA signature, providing precise and individualized prediction for clinical prognosis and some new insights into chemotherapy and immune checkpoint inhibitor therapy for bladder cancer. [ABSTRACT FROM AUTHOR]

The increase in signature forgery cases can be attributed to the escape of forged signatures from manual signature verification systems. Researchers have developed various machine learning and deep learning methods to verify the authenticity of signatures, one of which uses convolutional neural networks (CNNs). This research aims to develop a mobile application for handwritten signature verification using CNN architecture by adding a batch normalization technique to its layer. The performance of our proposed method achieved a verification accuracy of 86.36%, with a 0.061 false acceptance rate (FAR), 0.303 false rejection rate (FRR), and 0.182 equal error rate (EER), which is compatible to be embedded in smartphones. However, there is still a need for further development of the CNN model and its integration with mobile applications. [ABSTRACT FROM AUTHOR]

We develop techniques to construct explicit symplectic Lefschetz fibrations over the 2-sphere with any prescribed signature σ and any spin type when σ is divisible by 16. This solves a long-standing conjecture on the existence of such fibrations with positive signature. As applications, we produce symplectic 4-manifolds that are homeomorphic but not diffeomorphic to connected sums of S² ×S², with the smallest topology known to date, as well as larger examples as symplectic Lefschetz fibrations. [ABSTRACT FROM AUTHOR]

Background: Endoplasmic reticulum stress (ERS) could be a strategy for treating malignant tumors. Moreover, long noncoding RNAs (lncRNAs) can promote tumorigenesis and progression, and forecast the prognosis of cancers. Nevertheless, the prognostic value of ERS‐related lncRNAs has not been reported in lung adenocarcinoma (LUAD). Methods: The messenger RNA (mRNA), microRNA (miRNA) and lncRNA expression data related to LUAD were obtained in public databases (TCGA and GEO databases). Prognostic ERS‐related differentially expressed lncRNAs (ERS‐DELs) were obtained and used to build an ERS‐related model by Cox regression analysis. Moreover, we further screened independent prognostic elements and built a nomogram. Furthermore, enrichment analysis of genes was conducted to investigate the functions. A lncRNA–miRNA–mRNA network was built to explore mechanism of lncRNAs. Finally, qRT‐PCR was utilized to examine the expression levels of lncRNAs. Results: 30 ERS‐DELs were identified, and an ERS‐related signature was built based on AF131215.2, LINC00472, LINC01352, RP1‐78O14.1, RP11‐253E3.3, RP11‐98D18.9, and SNHG12. Gene set enrichment analysis indicated that genes in the high‐risk group were chiefly focused on the regulation of mRNA binding, and genes in the low‐risk group were significantly focused on protein localization to cilia. A lncRNA–miRNA–mRNA network, containing 7 signature lncRNAs, 23 miRNAs, and 128 mRNAs, was also established. Eventually, quantitative real‐time polymerase chain reaction was used to confirm that seven prognostic lncRNAs had a consistent expression with the analysis. Conclusions: An ERS‐related signature containing seven prognostic lncRNAs was built, which offered new thinking concerning the role of ERS‐related lncRNAs in LUAD. [ABSTRACT FROM AUTHOR]

A comparison of error rates between studies in forensic handwriting examination has been made in response to the lack of knowledge on error in this field. Fifteen indicators have been used to describe and compare error rates between studies. The results of each study have been processed to determine these indicators. Parameters related to the participants, conclusion scale, amount of time allocated to the task, and the task itself are also reported. The error rate indicators are provided for each study, and then combined across studies using values of mean and standard deviation. Experts were found to perform better than laypeople. For handwritten texts, absolute error rate for experts ranges from 0.32% to 5.85% (with a mean of 2.84 ± 2.33%), and for laypeople from 11.43% to 28.72% (with a mean of 21.40 ± 8.94%). For signatures, absolute error rate for experts ranges from 0% to 4.86% (with a mean of 2.50 ± 1.55%), and for laypeople from 10.68% to 28% (with a mean of 19.55 ± 7.05%). Overall, experts have an absolute error rate of 2.63 ± 1.73% (against 20.16 ± 7.20% for laypeople). Experts are also more likely to give inconclusive answers than laypeople. Overall, the rate of absolute inconclusive answers for experts is 21.96 ± 23.15% (against 8.13 ± 7.96% for laypeople). The comparative review of error rates presented in the present article contributes to validating the discipline by showing how research has been devoted to meeting the criteria of testing to be considered scientific. [ABSTRACT FROM AUTHOR]

Introduction: This study investigates the role of hypoxia-related genes in the neuroprotective efficacy of Yang Xue oral liquid (YXKFY) in Alzheimer's disease (AD) and Parkinson's disease (PD). Methods and results: Using differential expression and weighted gene coexpression network analysis (WGCNA), we identified 106 and 9 hypoxiaassociated genes in AD and PD, respectively, that are implicated in the transcriptomic and proteomic profiles. An artificial intelligence-driven hypoxia signature (AIDHS), comprising 17 and 3 genes for AD and PD, was developed and validated across nine independent cohorts (n = 1713), integrating 10 machine learning algorithms and 113 algorithmic combinations. Significant associations were observed between AIDHS markers and immune cells in AD and PD, including naive CD4+ T cells, macrophages, and neutrophils. Interactions with miRNAs (hsa-miR-1, hsa-miR-124) and transcription factors (USF1) were also identified. Single-cell RNA sequencing (scRNA-seq) data highlighted distinct expression patterns of AIDHS genes in various cell types, such as high expression of TGM2 in endothelial cells, PDGFRB in endothelial and mesenchymal cells, and SYK in microglia. YXKFY treatment was shown to repair cellular damage and decrease reactive oxygen species (ROS) levels. Notably, genes with previously dysfunctional expression, including FKBPL, TGM2, PPIL1, BLVRB, and PDGFRB, exhibited significant recovery after YXKFY treatment, associated with riboflavin and lysicamine. Conclusion: The above genes are suggested to be central to hypoxia and neuroinflammation responses in AD and PD, and are potential key mediators of YXKFY's neuroprotective action. [ABSTRACT FROM AUTHOR]

Major histocompatibility complex (MHC) could serve as a potential biomarker for tumor immunotherapy, however, it is not yet known whether MHC could distinguish potential beneficiaries. Single‐cell RNA sequencing datasets derived from patients with immunotherapy were collected to elucidate the association between MHC and immunotherapy response. A novel MHCsig was developed and validated using large‐scale pan‐cancer data, including The Cancer Genome Atlas and immunotherapy cohorts. The therapeutic value of MHCsig was further explored using 17 CRISPR/Cas9 datasets. MHC-related genes were associated with drug resistance and MHCsig was significantly and positively associated with immunotherapy response and total mutational burden. Remarkably, MHCsig significantly enriched 6% top‐ranked genes, which were potential therapeutic targets. Moreover, we generated Hub-MHCsig, which was associated with survival and disease-special survival of pan-cancer, especially low-grade glioma. This result was also confirmed in cell lines and in our own clinical cohort. Later low-grade glioma-related Hub-MHCsig was established and the regulatory network was constructed. We provided conclusive clinical evidence regarding the association between MHCsig and immunotherapy response. We developed MHCsig, which could effectively predict the benefits of immunotherapy for multiple tumors. Further exploration of MHCsig revealed some potential therapeutic targets and regulatory networks. [ABSTRACT FROM AUTHOR]

Background: Neuroblastoma (NB), characterized by its marked heterogeneity, is the most common extracranial solid tumor in children. The status and functionality of mitochondria are crucial in regulating NB cell behavior. While the significance of mitochondria-related genes (MRGs) in NB is still missing in key knowledge. Materials and methods: This study leverages consensus clustering and machine learning algorithms to construct and validate an MRGs-related signature in NB. Single-cell data analysis and experimental validation were employed to characterize the pivotal role of FEN1 within NB cells. Results: MRGs facilitated the classification of NB patients into 2 distinct clusters with considerable differences. The constructed MRGs-related signature and its quantitative indicators, mtScore and mtRisk, effectively characterize the MRGsrelated patient clusters. Notably, the MRGs-related signature outperformed MYCN in predicting NB patient prognosis and was adept at representing the tumor microenvironment (TME), tumor cell stemness, and sensitivity to the chemotherapeutic agents Cisplatin, Topotecan, and Irinotecan. FEN1, identified as the most contributory gene within the MRGs-related signature, was found to play a crucial role in the communication between NB cells and the TME, and in the developmental trajectory of NB cells. Experimental validations confirmed FEN1's significant influence on NB cell proliferation, apoptosis, cell cycle, and invasiveness. Conclusion: The MRGs-related signature developed in this study offers a novel predictive tool for assessing NB patient prognosis, immune infiltration, stemness, and chemotherapeutic sensitivity. Our findings unveil the critical function of FEN1 in NB, suggesting its potential as a therapeutic target. [ABSTRACT FROM AUTHOR]

Background: Anoikis is a form of programmed cell death essential for preventing cancer metastasis. In some solid cancer, anoikis resistance can facilitate tumor progression. However, this phenomenon is underexplored in clear-cell renal cell carcinoma (ccRCC). Methods: Using SVM machine learning, we identified core anoikis-related genes (ARGs) from ccRCC patient transcriptomic data. A LASSO Cox regression model stratified patients into risk groups, informing a prognostic model. GSVA and ssGSEA assessed immune infiltration, and single-cell analysis examined ARG expression across immune cells. Quantitative PCR and immunohistochemistry validated ARG expression differences between immune therapy responders and non-responders in ccRCC. Results: ARGs such as CCND1, CDKN3, PLK1, and BID were key in predicting ccRCC outcomes, linking higher risk with increased Treg infiltration and reduced M1 macrophage presence, indicating an immunosuppressive environment facilitated by anoikis resistance. Single-cell insights showed ARG enrichment in Tregs and dendritic cells, affecting immune checkpoints. Immunohistochemical analysis reveals that ARGs protein expression is markedly elevated in ccRCC tissues responsive to immunotherapy. Conclusion: This study establishes a novel anoikis resistance gene signature that predicts survival and immunotherapy response in ccRCC, suggesting that manipulating the immune environment through these ARGs could improve therapeutic strategies and prognostication in ccRCC. [ABSTRACT FROM AUTHOR]

If the right of cancellation, as a right that can be transferred through inheritance, belongs to several heirs and there is a disagreement between them regarding the use of this right, we will face this problem: how will the multiple heirs claim their rights? This problem has generally faced four main views in the course of its evolution. It has been assumed by some of the jurists as an independent all-inclusive right for those entitled to the right. A group of jurists have drawn the right so that every one of the heirs can be the owner of his own share and can use it based on his/her share. Another group has considered a fixed share for all entitled to the right, while others have considered a fixed share for the mere existence of the heir. This article, while criticizing the proposed theories and stating the problems and criticisms of them, emphasizes the theory of separation between the general title of the right of cancellation and its example. [ABSTRACT FROM AUTHOR]

This study investigates the association between audit engagement partners' narcissism (measured by the size of their signature) and audit reporting decisions and audit pricing in a private market setting. We analysed 133,267 (78,994) firm‐year observations from (financially distressed) Belgian firms audited by 795 individual engagement partners from 2008 to 2017. Our results suggest that narcissism is positively associated with the likelihood that audit partners issue going‐concern opinions to their financially distressed clients and with audit fees. An array of robustness checks corroborates these results. Additional results show that audit partner narcissism is positively associated with reporting conservatism. Interestingly, additional analyses also show that narcissism reverses the effect of gender on audit reporting decisions and audit pricing. Collectively, the evidence from this study suggests that partner narcissism is positively associated with conservative audit reporting decisions and audit pricing in a private market setting. [ABSTRACT FROM AUTHOR]

Abstract Portfolio allocation represents a significant challenge within financial markets, traditionally relying on correlation or covariance matrices to delineate relationships among stocks. However, these methodologies assume time stationarity and only capture linear relationships among stocks. In this study, we propose to substitute the conventional Pearson’s correlation or covariance matrix in portfolio optimization with a similarity matrix derived from the signature. The signature, a concept from path theory, provides a unique representation of time series data, encoding their geometric patterns and inherent properties. Furthermore, we undertake a comparative analysis of network structures derived from the correlation matrix versus those obtained from the signature-based similarity matrix. Through numerical evaluation on the Standard & Poor’s 500, we assess that portfolio allocation utilizing the signature-based similarity matrix yielded superior results in terms of cumulative log-returns and Sharpe ratio compared to the baseline network approach based on Pearson’s correlation. This assessment was conducted across various portfolio optimization strategies. This research contributes to portfolio allocation and financial network representation by proposing the use of signature-based similarity matrices over traditional correlation or covariance matrices.

Abstract Background M2-like tumor-associated macrophages (M2-like TAMs) play key roles in tumor progression and the immune response. However, the clinical significance and prognostic value of M2-like TAMs-associated regulatory genes in gastric cancer (GC) have not been clarified. Methods Herein, we identified M2-like TAM-related genes by weighted gene coexpression network analysis of TCGA-STAD and GSE84437 cohort. Lasso-Cox regression analyses were then performed to screen for signature genes, and a novel signature was constructed to quantify the risk score for each patient. Tumor mutation burden (TMB), survival outcomes, immune cells, and immune function were analyzed in the risk groups to further reveal the immune status of GC patients. A gene-drug correlation analysis and sensitivity analysis of anticancer drugs were used to identify potential therapeutic agents. Finally, we verified the mRNA expression of signature genes in patient tissues by qRT-PCR, and analyzed the expression distribution of these genes by IHC. Results A 4-gene (SERPINE1, MATN3, CD36, and CNTN1) signature was developed and validated, and the risk score was shown to be an independent prognostic factor for GC patients. Further analyses revealed that GC patients in the high-risk group had a worse prognosis than those in the low-risk group, with significant differences in TMB, clinical features, enriched pathways, TIDE score, and tumor microenvironment features. Finally, we used qRT-PCR and IHC analysis to verify mRNA and protein level expression of signature genes. Conclusion These findings highlight the importance of M2-like TAMs, provide a new perspective on individualized immunotherapy for GC patients.

Abstract Programmed cell death (PCD) is a process that eliminates infected, damaged, or possibly neoplastic cells to sustain homeostatic multicellular organisms. Although long noncoding RNAs (lncRNAs) are involved in various types of PCD and regulate tumor growth, invasion, and migration, the role of PCD-related lncRNAs in bladder cancer still lacks systematic exploration. In this research, we integrated multiple types of PCD as pan-PCD and identified eight pan-PCD-related lncRNAs (LINC00174, HCP5, HCG27, UCA1, SNHG15, GHRLOS, CYB561D2, and AGAP11). Then, we generated a pan-PCD-related lncRNA prognostic signature (PPlncPS) with excellent predictive power and reliability, which performed equally well in the E-MTAB-4321 cohort. In comparison with the low-PPlncPS score group, the high-PPlncPS score group had remarkably higher levels of angiogenesis, matrix, cancer-associated fibroblasts, myeloid cell traffic, and protumor cytokine signatures. In addition, the low-PPlncPS score group was positively correlated with relatively abundant immune cell infiltration, upregulated expression levels of immune checkpoints, and high tumor mutation burden (TMB). Immunogenomic profiles revealed that patients with both low PPlncPS scores and high TMB had the best prognosis and may benefit from immune checkpoint inhibitors. Furthermore, for patients with high PPlncPS scores, docetaxel, staurosporine, and luminespib were screened as potential therapeutic candidates. In conclusion, we generated a pan-PCD-related lncRNA signature, providing precise and individualized prediction for clinical prognosis and some new insights into chemotherapy and immune checkpoint inhibitor therapy for bladder cancer.

In this article, we consider a particular type of coherent systems, in which, when the coherent system fails, either no active component remains or the remaining active components are connected in parallel. This work studies two coherent systems of such type with two components, four coherent systems with three components, and seven coherent systems with four components as examples. Lifetime as well as system signature of each of these coherent systems is given. We investigate the restart of such a coherent system using a single cold standby component. The reliability of a system is studied through the (reliability) characteristics such as reliability function, mean time to failure, mean residual life functions of the system. We compute signature-based exact expression for the reliability function of the considered coherent system, which is equipped with a cold standby component. Also, we obtain three different mean residual life functions of the system. So far, no attempt has been made to study such coherent systems in cold standby sparing; therefore, we have provided some new results. We illustrate the theoretical results by considering three particular coherent systems as examples. [ABSTRACT FROM AUTHOR]

The article is dedicated to the handwritten legacy of Isaac Babel and examines the writer’s letters, inscriptions, and drawings introduced into scientific circulation in the last decade. Based on the analysis of these findings, the article concludes that all these “autographs of Isaac Babel” are hoaxes of varying degrees of persuasiveness. These results confirm the effectiveness of philological methods for recognizing falsified manuscripts of the classics of literature. The article pays special attention to the analysis of Isaac Babel’s gift inscription to Solomon Mikhoels, written partly in Yiddish. The research establishes that the text of the inscription testifies to the writer’s poor command of Yiddish, which in turn refutes the possibility of the authenticity of such an inscription as a whole. The issues considered in the article correlate with the general problems of modern textual criticism.

This research focuses on identifying the authenticity of signatures, which is an important part of the field of biometrics. Identification of signature authenticity has wide applications, including in document security, financial transactions, and identity verification in general. The problem to be resolved is the lack of an effective and efficient method for identifying signature authenticity. The method used is the binary extraction method and the K-nearest Neighbor feature. The main contribution of this research is to propose a new approach in identifying signature authenticity by combining binary extraction methods and K-nearest Neighbor features. This approach is expected to increase the accuracy and efficiency of the signature authenticity identification process. The results of this research are the development of a new model or algorithm for identifying the authenticity of signatures. After testing and validation, the accuracy level of the results of identifying the authenticity of this signature is 75%.

Abstract Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity leads to different molecular subtypes and survival outcomes. Recently, lipid metabolism has been identified as a critical characteristic of cancer. Therefore, our study aims to identify osteosarcoma's lipid metabolism molecular subtype and develop a signature for survival outcome prediction. Four multicenter cohorts—TARGET-OS, GSE21257, GSE39058, and GSE16091—were amalgamated into a unified Meta-Cohort. Through consensus clustering, novel molecular subtypes within Meta-Cohort patients were delineated. Subsequent feature selection processes, encompassing analyses of differentially expressed genes between subtypes, univariate Cox analysis, and StepAIC, were employed to pinpoint biomarkers related to lipid metabolism in TARGET-OS. We selected the most effective algorithm for constructing a Lipid Metabolism-Related Signature (LMRS) by utilizing four machine-learning algorithms reconfigured into ten unique combinations. This selection was based on achieving the highest concordance index (C-index) in the test cohort of GSE21257, GSE39058, and GSE16091. We identified two distinct lipid metabolism molecular subtypes in osteosarcoma patients, C1 and C2, with significantly different survival rates. C1 is characterized by increased cholesterol, fatty acid synthesis, and ketone metabolism. In contrast, C2 focuses on steroid hormone biosynthesis, arachidonic acid, and glycerolipid and linoleic acid metabolism. Feature selection in the TARGET-OS identified 12 lipid metabolism genes, leading to a model predicting osteosarcoma patient survival. The LMRS, based on the 12 identified genes, consistently accurately predicted prognosis across TARGET-OS, testing cohorts, and Meta-Cohort. Incorporating 12 published signatures, LMRS showed robust and significantly superior predictive capability. Our results offer a promising tool to enhance the clinical management of osteosarcoma, potentially leading to improved clinical outcomes.

Zhengyi Wu,&ast; Liang Sun,&ast; Yongkang Xu,&ast; He Huang, Zhipeng Wu, Bingbing Qiu, Jinlong Yan, Xiangbao Yin Hepatobiliary Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiangbao Yin; Jinlong Yan, Hepatobiliary surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No. 1, Minde Road, Nanchang, Jiangxi Province, People’s Republic of China, Email yxbefy@163.com; yjl19880608@126.comBackground: Chemokines and chemokine receptors (CCRs) are involved in a variety of anti-tumour and pro-tumour immune processes in vivo, such as angiogenesis, metastasis, proliferation and invasiveness, and influence patient prognosis and response to therapy.Methods: CCRs differentially expressed in HCC and associated with prognosis were extracted from TCGA and GEO databases, and the obtained CCRs were then used to construct signature genes, and the signature gene were selected for expression validation as well as functional experiments to explore the role of CCRs in the treatment and prognosis of HCC.Results: We constructed a prognostic model including five CCRs (CCL20, CCL23, CCR3, CCR10, and CXCR3) and validated the expression of signature genes. The model’s risk score is an independent prognostic factor for HCC. We have also developed prognostic model nomograms for clinical use. In addition, we validated that CCR3 expression is associated with poor prognosis in HCC, and the proliferation and migration ability of HCC cells was significantly inhibited after interfering with the expression of CCR3 in MHCC-LM3. We also looked at differences in pathway enrichment, immune infiltration and immune checkpoints. Finally, we found that risk scores were also correlated with drug sensitivity, the high-risk group had a better sensitivity to sorafenib.Conclusion: The CCRs-related gene signature may better assess HCC prognosis and response to immunotherapy and tyrosine kinase inhibitors such as sorafenib in HCC, providing prospective solutions for diagnosis and treatment.Keywords: chemokine, chemokine receptors, prognosis, signature, hepatocellular carcinoma

Abstract Histone deacetylases (HDACs) contribute significantly to the initiation, progression, and prognosis of colorectal adenocarcinoma (COAD). Additionally, HDACs regulate the tumor microenvironment, immune escape, and tumor stem cells, and are closely linked to COAD prognosis. We developed a prognostic model for COAD that incorporates HDACs to evaluate their specific roles. The COAD dataset containing clinical and mutation data was collected using the TCGA and GEO databases to obtain genes associated with HDAC. LASSO analysis and univariate and multivariate Cox regression analysis were used to determine the presence of prognostic genes. Multivariate Cox analysis was also used to determine risk scores for HDAC-related features. Furthermore, genomic alterations, immune infiltration, and drug response were compared between high- and low-risk groups. Cellular experiments validated the potential regulatory role of BRD3 on COAD proliferation, migration, and apoptosis. The median risk scores, calculated based on the characteristics, demonstrated a more significant prognostic improvement in patients in the low-risk group. Furthermore, HDAC-related features were identified as important independent prognostic factors for patients with COAD. Additionally, genomic mutation status, immune infiltration, and function, as well as response to immunotherapy and chemotherapy, were found to be associated with risk scores. Subgroup analyses indicate that anti-PD-1 therapy may be beneficial for patients in the low-risk group. Additionally, a decrease in risk score was associated with a decrease in immune infiltration. Finally, HCT116 and HT29 cells exhibited inhibition of BRD3 gene proliferation and migration, as well as promotion of apoptosis. In patients with COAD, HDAC-related characteristics may be useful in predicting survival and selecting treatment.

Breast cancer (BRCA) is one of the most common malignant tumors affecting women worldwide. DNA methylation modifications can influence oncogenic pathways and provide potential diagnostic and therapeutic targets for precision oncology. In this study, we used non-parametric permutation tests to identify differentially methylated positions (DMPs) between paired tumor and normal BRCA tissue samples from the Cancer Genome Atlas (TCGA) database. Then, we applied non-negative matrix factorization (NMF) to the DMPs to derive eight distinct DNA methylation signatures. Among them, signatures Hyper-S3 and Hypo-S4 signatures were associated with later tumor stages, while Hyper-S1 and Hypo-S3 exhibited higher methylation levels in earlier stages. Signature Hyper-S3 displayed an effect on overall survival. We further validated the four stage-associated signatures using an independent BRCA DNA methylation dataset from peripheral blood samples. Results demonstrated that 24 commonly hypomethylated sites in Hypo-S4 showed lower methylation in BRCA patients compared to healthy individuals, suggesting its potential as an early diagnostic biomarker. Furthermore, we found that methylation of 23 probes from four stage-related signatures exhibited predictive power for immune therapy response. Notably, methylation levels of all three probes from the Hypo-S4 and activity of the Hypo-S4 demonstrated highly positive relevance to PD-L1 gene expression, implying their significant predictive values for immunotherapy outcomes. GO and KEGG pathway enrichment analysis revealed that genes with these 23 immunotherapy-related methylation probes are mainly involved in glycan degradation or protein deglycosylation. These methylation signatures and probes may serve as novel epigenetic biomarkers for predicting tumor immunotherapy response. Our findings provide new insights into precision oncology approaches for BRCA. [ABSTRACT FROM AUTHOR]

Whole-tissue transcriptomic analyses have been helpful to characterize molecular subtypes of hepatocellular carcinoma (HCC). Metabolic subtypes of human HCC have been defined, yet whether these different metabolic classes are clinically relevant or derive in actionable cancer vulnerabilities is still an unanswered question. Publicly available gene sets or gene signatures have been used to infer functional changes through gene set enrichment methods. However, metabolism-related gene signatures are poorly co-expressed when applied to a biological context. Here, we apply a simple method to infer highly consistent signatures using graph-based statistics. Using the Cancer Genome Atlas Liver Hepatocellular cohort (LIHC), we describe the main metabolic clusters and their relationship with commonly used molecular classes, and with the presence of TP53 or CTNNB1 driver mutations. We find similar results in our validation cohort, the LIRI-JP cohort. We describe how previously described metabolic subtypes could not have therapeutic relevance due to their overall downregulation when compared to non-tumoral liver, and identify N-glycan, mevalonate and sphingolipid biosynthetic pathways as the hallmark of the oncogenic shift of the use of acetyl-coenzyme A in HCC metabolism. Finally, using DepMap data, we demonstrate metabolic vulnerabilities in HCC cell lines. [ABSTRACT FROM AUTHOR]

This paper continues the study of second-order signature properties—the characterization of the extreme 3-uniform hypergraph. Previously, bases were used to count extreme 3-uniform hypergraphs. However, the algorithm using this mechanism is extremely labor-intensive. The structure of the signature allows us to use it as a more efficient basis for the same problem. Here, we establish the nature of the mutual correspondence between the kind of second-order signature and extreme hypergraphs, and we present a new algorithm to find the power of the set of extreme 3-uniform hypergraphs through the set of their characteristic-signatures. New results obtained with the proposed tool are also presented. [ABSTRACT FROM AUTHOR]

Introduction: IgA vasculitis diagnosis relies primarily on clinical features and is confirmed by pathological findings. To date, there is no reliable noninvasive diagnostic biomarker. Objective: We aimed to explore the baseline serum metabolome of adult patients with IgA vasculitis to identify potential diagnostic biomarkers. Methods: We performed a study comparing the serum metabolome of patients with IgA vasculitis to that of patients with inflammatory condition, namely spondyloarthritis. Serum analyses were performed by high-performance liquid chromatography-mass spectrometry. Results: Fifty-five patients with IgA vasculitis and 77 controls with spondyloarthritis (age- and sex-matched) were included in this study. The median age of IgA vasculitis patients was 53 years. Two-thirds of patients were female (n = 32). At the time of vasculitis diagnosis, 100% of patients had skin involvement and 69% presented with glomerulonephritis (n = 38). Joint and digestive involvement were observed in 56% (n = 31) and 42% (n = 23) of patients. Four discriminative metabolites between the two groups were identified: 1-methyladenosine, L-glutamic acid, serotonin, and thymidine. The multivariate model built from the serum metabolomes of patients with IgA vasculitis and spondyloarthritis revealed an accuracy > 90%. As this model was significant according to the permutation test (p < 0.01), independent validation showed an excellent predictive value of the test set: sensitivity 98%; specificity 98%, positive predictive value 97% and negative predictive value 98%. Conclusion: To our knowledge, this study is the first to use the metabolomic approach for diagnostic purposes in adult IgA vasculitis, highlighting a specific diagnostic metabolome signature. [ABSTRACT FROM AUTHOR]

While there are many verification studies on signature images using deep learning algorithms in the literature, there is a lack of studies on the classification of signature images. Signatures are used as a means of identification for banking, security controls, symmetry, certificates, and contracts. In this study, the aim was to design network architectures that work very fast in areas that require only signature images. For this purpose, a new Si-CNN network architecture with existing layers was designed. Afterwards, a new loss function and layer (Si-CL), a novel architecture using Si-CL as classification layer in Si-CNN to increase the performance of this architecture, was designed. This architecture was called Si-CNN+NC (New Classification). Si-CNN and Si-CNN+NC were trained with two datasets. The first dataset which was used for training is the "C-Signatures" (Classification Signatures) dataset, which was created to test these networks. The second dataset is the "Cedar" dataset, which is a benchmark dataset. The number of classes and sample numbers in the two datasets are symmetrical with each other. To compare the performance of the trained networks, four of the most well-known pre-trained networks, GoogleNet, DenseNet201, Inceptionv3, and ResNet50, were also trained with the two datasets with transfer learning. The findings of the study showed that the proposed network models can learn features from two different handwritten signature images and achieve higher accuracy than other benchmark models. The test success of the trained networks showed that the Si-CNN+NC network outperforms the others, in terms of both accuracy and speed. Finally, Si-CNN and Si-CNN+NC networks were trained with the gold standard dataset MNIST and showed superior performance. Due to its superior performance, Si-CNN and Si-CNN+NC can be used by signature experts as an aid in a variety of applications, including criminal detection and forgery. [ABSTRACT FROM AUTHOR]

Background: Metabolic reprogramming is an emerging hallmark that influences the tumour microenvironment (TME) by regulating the behavior of cancer cells and immune cells. The relationship between metabolism and immunity remains elusive. The purpose of this study was to explore the predictive value of immune- and metabolism-related genes in hepatocellular carcinoma (HCC) and their intricate interplay with TME. Methods: We established the immune- and metabolism-related signature (IMRPS) based on the LIHC cohort from The Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier analysis, receiver operating characteristic (ROC) curve analysis and Cox regression analysis confirmed the prognostic value of IMRPS. We investigated differences in immune cell infiltration, clinical features, and therapeutic response between risk groups. The quantitative real-time PCR (qPCR) was used to confirm the expression of signature genes. Immunohistochemical staining was performed to evaluate immune infiltration features in HCC tissue samples. We conducted cell experiments including gene knockout, cell counting kit-8 (CCK-8), and flow cytometry to explore the role of the IMRPS key gene UCK2 in HCC. RNA-seq was used to further investigate the potential underlying mechanism involved. Results: The IMRPS, composed of four genes, SMS, UCK2, PFKFB4 and MAPT, exhibited significant correlations with survival, immune cell infiltration, clinical features, immune checkpoints and therapeutic response. The IMRPS was shown to be an excellent predictor of HCC prognosis. It could stratify patients appropriately and characterize the TME accurately. The high-risk HCC group exhibited an immunosuppressive microenvironment with abundant M2-like macrophage infiltration, which was confirmed by the immunohistochemistry results. The results of qPCR revealed that the expression of signature genes in 20 HCC tissues was significantly greater than that in adjacent normal tissues. After the key gene UCK2 was knocked out, the proliferation of the Huh7 cell line was significantly inhibited, and monocyte-derived macrophages polarized towards an M1-like phenotype in the coculture system. RNA-seq and GSEA suggested that the phenotypes were closely related to the negative regulation of growth and regulation of macrophage chemotaxis. Conclusions: This study established a new IMRS for the accurate prediction of patient prognosis and the TME, which is also helpful for identifying new targets for the treatment of HCC. [ABSTRACT FROM AUTHOR]

The ubiquitin–proteasome system (UPS) is an essential regulatory system for maintaining homeostasis, and its dysfunction may cause various diseases. The activity of proteasome and ubiquitin-conjugating enzymes has been found to be greatly increased in breast cancer (BC), indicating that the heterogeneity of UPS may be related to the progression of BC. Gene data was obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases and performed in multiple algorithms to construct a UPS-related signature for BC. Patients in the UPS low-risk group had greater overall and recurrence-free survival probability than those in the UPS high-risk group. This signature was closely associated with functional enrichment. Some high metabolism-related pathways were more active in the UPS high-risk group. The UPS low-risk group had more abundant anti-tumor immune cells, while in the UPS high-risk group, immunosuppressive cells were dominant. More importantly, we found that the UPS low-risk group was more sensitive to immunotherapy, while the UPS high-risk group responded better to radiotherapy. Drug sensitivity analysis identified more effective chemotherapy drugs in different UPS-related risk groups. This UPS-related signature may serve as a novel biomarker and independent prognostic factor for BC. It can effectively predict prognosis, immune infiltration, and therapy efficacy, providing new strategies for individualized treatment. [ABSTRACT FROM AUTHOR]

The present study, which is part of a broader longitudinal case study, aims to imprint the developmental course of a child learning to write his/her name during the phase of first literacy and the influence of this learning process on the emergence of the child’s writing skills. The study focuses on the in-depth analysis of the written products of a high social level boy, Andreas, examining the period since his birth until his entrance in primary school. The assessment of the results showed a satisfying delineation of his development until he learned the acceptable written form of his name and highlighted the pivotal role of Andreas' learning of writing his own name for the acquisition of alphabetic code. [ABSTRACT FROM AUTHOR]

In order to aim for diagnosis of disease and decision making, the medical imaging plays an important role in health science. Clinical pictures are portrayals of profoundly vulnerable computerized images, those can be tampered without leaving any visual clues. Hence it is challenging to keep up its credibility. However, as there are numerous ways to manipulate an image, correspondingly various strategies have also been proposed to safeguard the genuineness of medical images. This survey paper presents different techniques used for medical image authentication viz. watermarking, signature and hybrid techniques. The state-of-the-art techniques have attained promising results for authentication and tampering detection, but an efficient tampering localization and recovery have remained still as a challenge. This review article can be considered as a benchmark survey paper as it gives a complete comprehensive overview commencing from the evolution of medical image formats, types of medical imaging modalities and also provides an elaborative comparison over 40 research works in terms of prominent factors like type of medical image used, embedded region, embedded data, about the coverage of tampering localization and recovery along with the discussion on limitations and future works of each one. [ABSTRACT FROM AUTHOR]

Histone deacetylases (HDACs) contribute significantly to the initiation, progression, and prognosis of colorectal adenocarcinoma (COAD). Additionally, HDACs regulate the tumor microenvironment, immune escape, and tumor stem cells, and are closely linked to COAD prognosis. We developed a prognostic model for COAD that incorporates HDACs to evaluate their specific roles. The COAD dataset containing clinical and mutation data was collected using the TCGA and GEO databases to obtain genes associated with HDAC. LASSO analysis and univariate and multivariate Cox regression analysis were used to determine the presence of prognostic genes. Multivariate Cox analysis was also used to determine risk scores for HDAC-related features. Furthermore, genomic alterations, immune infiltration, and drug response were compared between high- and low-risk groups. Cellular experiments validated the potential regulatory role of BRD3 on COAD proliferation, migration, and apoptosis. The median risk scores, calculated based on the characteristics, demonstrated a more significant prognostic improvement in patients in the low-risk group. Furthermore, HDAC-related features were identified as important independent prognostic factors for patients with COAD. Additionally, genomic mutation status, immune infiltration, and function, as well as response to immunotherapy and chemotherapy, were found to be associated with risk scores. Subgroup analyses indicate that anti-PD-1 therapy may be beneficial for patients in the low-risk group. Additionally, a decrease in risk score was associated with a decrease in immune infiltration. Finally, HCT116 and HT29 cells exhibited inhibition of BRD3 gene proliferation and migration, as well as promotion of apoptosis. In patients with COAD, HDAC-related characteristics may be useful in predicting survival and selecting treatment. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer, yet the contribution of purine metabolism (PM) to its pathogenesis remains poorly elucidated. PM, a critical component of intracellular nucleotide synthesis and energy metabolism, is hypothesized to exert a significant influence on LUAD development. Herein, we employed single‐cell analysis to investigate the role of PM within the tumour microenvironment (TME) of LUAD. PM scoring (PMS) across distinct cell types was determined using AUCell, UCell, singscore and AddModuleScore algorithms. Subsequently, we explored communication networks among cells within high‐ and low‐PMS groups, establishing a robust PM‐associated signature (PAS) utilizing a comprehensive dataset comprising LUAD samples from TCGA and five GEO datasets. Our findings revealed that the high‐PMS group exhibited intensified cell interactions, while the PAS, constructed using PM‐related genes, demonstrated precise prognostic predictive capability. Notably, analysis across the TCGA dataset and five GEO datasets indicated that low‐PAS patients exhibited a superior prognosis. Furthermore, the low‐PAS group displayed increased immune cell infiltration and elevated CD8A expression, coupled with reduced PD‐L1 expression. Moreover, data from eight publicly available immunotherapy cohorts suggested enhanced immunotherapy outcomes in the low‐PAS group. These results underscore a close association between PAS and tumour immunity, offering predictive insights into genomic alterations, chemotherapy drug sensitivity and immunotherapy responses in LUAD. The newly established PAS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts. [ABSTRACT FROM AUTHOR]

Although morbidity has decreased in developed regions, cervical cancer (CC) continues to have the highest incidence of all gynecological malignancies. The burden of morbidity and mortality in developing regions is also rising quickly, especially for advanced CC. Along with apoptosis, iron death, and other forms of programmed cell death, pyroptosis is a significant inflammatory process. Its connection to the malignancy mechanism has been verified. The expression of genes linked to pyroptosis in CC tissue and its relationship to prognosis, however, remain poorly understood. Using The Cancer Genome Atlas, we first discovered 13 differentially expressed pyroptosis-related genes (DE-PRGs) in the study (TCGA). Based on DE-PRGs, CC patients were divided into four subtypes. The 4 parts' times showed large variations according to the K-M curve. Then, using the least absolute shrinkage and selection operator (LASSO) Cox regression method, we created a mod el for predicting CC based on pyroptosis-associated genes. There were significant differences in overall survival (OS) times involving the high-risk and low-risk groups for all CC patients in the TCGA group, who were divided into lowrisk and high-risk groups (p equals 0.0441). The risk score status as an independent prognostic factor for CC was confirmed by independent prognostic predictor validation. The analyses of single-sample gene set and enrichment analysis (ssGSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) all revealed significant differences in immune cells and immune pathways involving high- and lowrisk groups (p less than 0.001), indicating a weakened immune status in high-risk groups. In conclusion, pyroptosis-associated genes are involved in tumor immunity and can help determine a patient's prognosis for CC. As a result, we have created and validated a signature that is related to pyroptosis and predicts CC prognosis, which may aid in early diagnosis, prognostic analysis, and immunotherapy. [ABSTRACT FROM AUTHOR]

Principal Component Analysis (PCA) is the best face recognition method. This research suggests PCA for fingerprint and signature recognition. Simple image processing transforms like DCT, 2D-DCT, DWT, SWT, 2D-SWT, SVD (Singular Vector Decomposition), Entropy, and Rank can be used for feature extraction. These transforms and measures are utilized with PCA as a feature extraction module to construct uni-modal and multimodal biometric systems using face, fingerprint, and signature modalities. Most PCA biometrics systems compare stored template to claimed identification using Euclidean distance. This paper proposes matching modules using similarity and dissimilarity measures viz. Absolute Pearson's Correlation Coefficient (APCC), Absolute Uncentered Pearson's Correlation Coefficient (AUPCC), Bray Curtis Distance (BC), Canberra distance (CB), Chebyshev Distance (CBS), Chessboard Distance (CSB), City block or Manhattan distance (CTB), Cross Correlation (CC), Dot product (DP), Euclidean distance (EUC), Extended Jaccard Distance (EJ), Hamming Distance (HM), Harmonically Summed Euclidean distance (HSEUC), Kendall Correlation Coefficient (KCC), Mahalanobis Distance (MH), Minimum Coordinate Difference (MCD), Minkowiski distance (MNK), Multivariate Kurtosis Coefficient (MVK), Multivariate Skew (MVS), Normalized City Block or Manhattan distance (NCTB), Normalized Cross-correlation (NCC), Normalized Euclidean distance (NEUC), Pearson's Cosine Distance (PCOS), Pearson's Correlation Coefficient (PCC), Pearson's Absolute Value Dissimilarity (PAVD), Pearson's Linear Dissimilarity (PLDISS), Spearman Correlation Coefficient (SCC), Standardized Euclidean Distance (SEUC), Uncentered Pearson's Correlation Coefficient (UPCC), Wave-Hedges Distance (WVH). This study again discusses score level fusion of face, fingerprint, and signature using sum and max rules, z-score normalization, and decision level fusion using AND rule. [ABSTRACT FROM AUTHOR]

Objective: Spontaneous regression of tumors is an attractive phenomenon that most commonly occurs in stage 4S neuroblastoma (NB). However, the mechanism underlying this phenomenon remains unclear. Methods: Datasets correlated with NB were downloaded from online public databases, the differentially expressed genes (DEGs) between stage 4 and 4S associated with immunity were identified, and functional enrichment analysis was utilized to explore the potential functions and signaling pathways of these DEGs. In addition, based on these DEGs, a prognostic signature was constructed and validated, and differences in immune cell infiltration were analyzed. Results: A total of 13 DEGs were finally identified, and functional enrichment analysis revealed that these DEGs were primarily enriched in the positive regulation of neuron differentiation and TGF-β signaling pathway. The signature successfully stratifies patients into two risk score groups and performs well in judging prognosis and predicting overall survival time. In addition, the prognostic value of the risk score calculated by the signature was independent of clinical factors. The results of immune cell infiltration showed that patients with a high infiltration of resting CD4 + memory T cells had a better prognosis, while plasma cells had a worse prognosis. Conclusion: The results of the functional enrichment analysis of these identified DEGs suggested that these DEGs may be related to spontaneous regression of NB. In addition, the prognostic signature has the potential to create new risk stratification in patients with NB. [ABSTRACT FROM AUTHOR]