Detecting Anti-Semitic Hate Speech using Transformer-based Large Language Models

Academic researchers and social media entities grappling with the identification of hate speech face significant challenges, primarily due to the vast scale of data and the dynamic nature of hate speech. Given the ethical and practical limitations of large predictive models like ChatGPT in directly addressing such sensitive issues, our research has explored alternative advanced transformer-based and generative AI technologies since 2019. Specifically, we developed a new data labeling technique and established a proof of concept targeting anti-Semitic hate speech, utilizing a variety of transformer models such as BERT (arXiv:1810.04805), DistillBERT (arXiv:1910.01108), RoBERTa (arXiv:1907.11692), and LLaMA-2 (arXiv:2307.09288), complemented by the LoRA fine-tuning approach (arXiv:2106.09685). This paper delineates and evaluates the comparative efficacy of these cutting-edge methods in tackling the intricacies of hate speech detection, highlighting the need for responsible and carefully managed AI applications within sensitive contexts

A novel lysosome-related gene signature coupled with gleason score for prognosis prediction in prostate cancer

Background: Prostate cancer (PCa) is highly heterogeneous, which makes it difficult to precisely distinguish the clinical stages and histological grades of tumor lesions, thereby leading to large amounts of under- and over-treatment. Thus, we expect the development of novel prediction approaches for the prevention of inadequate therapies. The emerging evidence demonstrates the pivotal role of lysosome-related mechanisms in the prognosis of PCa. In this study, we aimed to identify a lysosome-related prognostic predictor in PCa for future therapies.Methods: The PCa samples involved in this study were gathered from The Cancer Genome Atlas database (TCGA) (n = 552) and cBioPortal database (n = 82). During screening, we categorized PCa patients into two immune groups based on median ssGSEA scores. Then, the Gleason score and lysosome-related genes were included and screened out by using a univariate Cox regression analysis and the least absolute shrinkage and selection operation (LASSO) analysis. Following further analysis, the probability of progression free interval (PFI) was modeled by using unadjusted Kaplan–Meier estimation curves and a multivariable Cox regression analysis. A receiver operating characteristic (ROC) curve, nomogram and calibration curve were used to examine the predictive value of this model in discriminating progression events from non-events. The model was trained and repeatedly validated by creating a training set (n = 400), an internal validation set (n = 100) and an external validation (n = 82) from the cohort.Results: Following grouping by ssGSEA score, the Gleason score and two LRGs—neutrophil cytosolic factor 1 (NCF1) and gamma-interferon-inducible lysosomal thiol reductase (IFI30)—were screened out to differentiate patients with or without progression (1-year AUC = 0.787; 3-year AUC = 0.798; 5-year AUC = 0.772; 10-year AUC = 0.832). Patients with a higher risk showed poorer outcomes (p < 0.0001) and a higher cumulative hazard (p < 0.0001). Besides this, our risk model combined LRGs with the Gleason score and presented a more accurate prediction of PCa prognosis than the Gleason score alone. In three validation sets, our model still achieved high prediction rates.Conclusion: In conclusion, this novel lysosome-related gene signature, coupled with the Gleason score, works well in PCa for prognosis prediction

Recent Advances in the GPR Detection of Grouting Defects behind Shield Tunnel Segments

Injecting grout into the gaps between tunnel shield segments and surrounding rocks can reduce ground subsidence and prevent ground water penetration. However, insufficient grouting and grouting defects may cause serious geological disasters. Ground penetrating radar (GPR) is widely used as a nondestructive testing (NDT) method to evaluate grouting quality and determine the existence of defects. This paper provides an overview of GPR applications for grouting defect detection behind tunnel shield segments. State-of-the-art methodologies, field cases, experimental tests and signal processing methods are discussed. The reported field cases and model test results show that GPR can detect grouting defects behind shield tunnel segments by identifying reflected waves. However, some subsequent problems still exist, including the interference of steel bars and small differences in the dielectric constants among media. Recent studies have focused on enhancing the signal-to-noise ratio and imaging methods. Advanced GPR signal processing methods, including full waveform inversion and machine learning methods, are promising for detecting imaging defects. Additionally, we conduct a preliminary experiment to investigate environmental noise, antenna configuration and coupling condition influences. Some promising topics, including multichannel configuration, rapid evaluation methods, elastic wave method scanning equipment for evaluating grout quality and comprehensive NDT methods, are recommended for future studies

Near-Surface Geological Structure Seismic Wave Imaging Using the Minimum Variance Spatial Smoothing Beamforming Method

Erecting underground structures in regions with unidentified weak layers, cavities, and faults is highly dangerous and potentially disastrous. An efficient and accurate near-surface exploration method is thus of great significance for guiding construction. In near-surface detection, imaging methods suffer from artifacts that the complex structure caused and a lack of efficiency. In order to realize a rapid, accurate, robust near-surface seismic imaging, a minimum variance spatial smoothing (MVSS) beamforming method is proposed for the seismic detection and imaging of underground geological structures under a homogeneous assumption. Algorithms such as minimum variance (MV) and spatial smoothing (SS), the coherence factor (CF) matrix, and the diagonal loading (DL) methods were used to improve imaging quality. Furthermore, it was found that a signal advance correction helped improve the focusing effect in near-surface situations. The feasibility and imaging quality of MVSS beamforming are verified in cave models, layer models, and cave-layer models by numerical simulations, confirming that the MVSS beamforming method can be adapted for seismic imaging. The performance of MVSS beamforming is evaluated in the comparison with Kirchhoff migration, the DAS beamforming method, and reverse time migration. MVSS beamforming has a high computational efficiency and a higher imaging resolution. MVSS beamforming also significantly suppresses the unnecessary components in seismic signals such as S-waves, surface waves, and white noise. Moreover, compared with basic delay and sum (DAS) beamforming, MVSS beamforming has a higher vertical resolution and adaptively suppresses interferences. The results show that the MVSS beamforming imaging method might be helpful for detecting near-surface underground structures and for guiding engineering construction

Enhancing the antioxidant capacity and quality attributes of fermented goat milk through the synergistic action of Limosilactobacillus fermentum WXZ 2-1 with a starter culture

ABSTRACT: This study evaluated 75 strains of lactic acid bacteria (LAB) isolated from traditional dairy products in western China for their probiotic properties. Among them, Limosilactobacillus fermentum WXZ 2-1, Lactiplantibacillus plantarum TXZ 2-35, Companilactobacillus crustorum QHS 9, and Companilactobacillus crustorum QHS 10 demonstrated potential probiotic characteristics. The antioxidant capacity of these 4 strains was assessed, revealing that L. fermentum WXZ 2-1 exhibited the highest antioxidant capacity. Furthermore, when cocultured with Streptococcus salivarius ssp. thermophilus and Lactobacillus delbrueckii ssp. bulgaricus, L. fermentum WXZ 2-1 demonstrated a synergistic effect in growth medium and goat milk. To explore its effect on goat milk fermentation, different amounts of L. fermentum WXZ 2-1 were added to goat milk, and its physicochemical properties, antioxidant activity, flavor substances, and metabolomics were analyzed. The study found that the incorporation of L. fermentum WXZ 2-1 in goat milk fermentation significantly improved the texture characteristics, antioxidant capacity, and flavor of fermented goat milk. These findings highlight the potential of L. fermentum WXZ 2-1 as a valuable probiotic strain for enhancing the functionality and desirability of fermented goat milk, contributing to the development of novel functional foods with improved health benefits and enhanced quality attributes

DIPAN: Detecting personalized intronic polyadenylation derived neoantigens from RNA sequencing data

Intronic polyadenylation (IPA) refers to a particular type of alternative polyadenylation where a gene makes use of a polyadenylation site located within its introns. Aberrant IPA events have been observed in various types of cancer. IPA can produce noncoding transcripts or truncated protein-coding transcripts with altered coding sequences in the resulting protein product. Therefore, IPA events hold the potential to act as a reservoir of tumor neoantigens. Here, we developed a computational method termed DIPAN, which incorporates IPA detection, protein fragmentation, and MHC binding prediction to predict IPA-derived neoantigens. Utilizing RNA-seq from breast cancer cell lines and ovarian cancer clinical samples, we demonstrated the significant contribution of IPA events to the neoantigen repertoire. Through mass spectrometry immunopeptidome analysis, we further illustrated the processing and presentation of IPA-derived neoantigens on the surface of cancer cells. While most IPA-derived neoantigens are sample-specific, shared neoantigens were identified in both cancer cell lines and clinical samples. Furthermore, we demonstrated an association between IPA-derived neoantigen burden and overall survival in cancer patients

Expanding the Potential of Neoantigen Vaccines: Harnessing Bacille Calmette–Guérin Cell-Wall-Based Nanoscale Adjuvants for Enhanced Cancer Immunotherapy

Personalized antitumor immunotherapy utilizing neoantigen vaccines holds great promise. However, the limited immunogenicity of existing recognized neoantigens and the inadequate stimulation of antitumor immune responses by conventional adjuvants pose significant challenges. To address these limitations, we developed a nanovaccine that combines a BCG bacterial cell wall skeleton (BCG-CWS) based nanoscale adjuvant (BCNA) with peptide neoantigens (M27 and M30). This integrated approach provides an efficient translational strategy for cancer immunotherapy. The BCNA nanovaccine, formulated with PLGA as an emulsifier, exhibits excellent biocompatibility and superior antigen presentation compared with conventional BCG-CWS adjuvants. Subcutaneous immunization with the BCNA-based nanovaccine effectively targets lymph nodes, eliciting robust innate and tumor-specific immune responses. Importantly, our findings demonstrate that BCNAs significantly enhance neoantigen immunogenicity while minimizing acute systemic toxicity. Furthermore, when combined with a mouse PD-L1 antibody, our strategy achieves complete tumor elimination in 60% of cases and prevents 25% of tumor growth in a melanoma mouse model. In conclusion, our BCNA-based nanovaccine represents a promising avenue for advancing personalized therapeutic neoantigen vaccines and holds significant implications for enhancing personalized immunotherapy and improving patient outcomes in the field of cancer treatment

Reconnaissance de mots isoles a travers le reseau telephonique

SIGLECNRS TD Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc