Do LLMs Implicitly Exhibit User Discrimination in Recommendation? An Empirical Study

Recently, Large Language Models (LLMs) have enhanced user interaction, enabling seamless information retrieval and recommendations. However, concerns emerge as these LLMs have shown tendencies to display discrimination related to users' sensitive characteristics (such as gender), leading to explicit user unfairness. Furthermore, our analysis uncovers a more discreet variant of bias in LLMs, defined as implicit user unfairness, wherein these models demonstrate discriminatory recommendation behaviors based solely on non-sensitive user details, like usernames or email addresses. This subtle form of unfairness, while more pervasive, poses a significant threat to the ethical integrity and rights of minority user groups. To comprehensively explore implicit user unfairness, our analysis unfolds in three key steps: (1) We uncover the reasons for this implicit user unfairness: LLMs can infer users' sensitive attributes from non-sensitive attributes (e.g. user names) due to their extensive world knowledge. (2) Our findings expose that the magnitude of implicit user unfairness within LLMs surpasses the level of explicit user unfairness observed in traditional recommender models, signifying a more alarming issue of unfairness, i.e. some non-sensitive features of users like names may result in more serious discrimination phenomena. (3) We analyze the long-term effect of implicit user unfairness, identifying that it will reinforce information bubbles at an accelerated rate compared to traditional RS. We emphasize the need to identify and mitigate implicit user unfairness, aiming to avert the potential human-LLMs recommendation systems deterioration.Comment: N

Personality Understanding of Fictional Characters during Book Reading

Comprehending characters' personalities is a crucial aspect of story reading. As readers engage with a story, their understanding of a character evolves based on new events and information; and multiple fine-grained aspects of personalities can be perceived. This leads to a natural problem of situated and fine-grained personality understanding. The problem has not been studied in the NLP field, primarily due to the lack of appropriate datasets mimicking the process of book reading. We present the first labeled dataset PersoNet for this problem. Our novel annotation strategy involves annotating user notes from online reading apps as a proxy for the original books. Experiments and human studies indicate that our dataset construction is both efficient and accurate; and our task heavily relies on long-term context to achieve accurate predictions for both machines and humans. The dataset is available at https://github.com/Gorov/personet_acl23.Comment: Accepted at ACL 202

Immunogenicity in mice and rhesus monkeys vaccinated with recombinant vaccinia virus expressing bivalent E7E6 fusion proteins from human papillomavirus types 16 and 18

<p>Abstract</p> <p>Background</p> <p>Persistent infection with high-risk human papillomavirus (HPV) is a predominant cause of cervical cancer, and HPV16 and HPV18 occur in 50% and 20% of cervical cancer cases, respectively. The viral oncogenes E6 and E7 are constitutively expressed by HPV-associated tumour cells and can therefore be used as target antigens for immunotherapy. In this study, we constructed a recombinant vaccinia virus co-expressing the HPV16/18 E7E6 fusion proteins (rVVJ16/18E7E6) for use as a therapeutic vaccine for the treatment of HPV16⁺and HPV18⁺cancers.</p> <p>Methods</p> <p>We constructed a bivalent recombinant vaccinia virus expressing modified E7E6 fusion proteins of HPV type 16 and 18 (rVVJ16/18E7E6) based on the vaccinia virus Tiantan strain. We then defined the cellular immune responses to the virus in mice and rhesus monkeys and assessed antitumour efficacy of these responses in mice using the TC-1 tumour challenge model.</p> <p>Results</p> <p>Our data demonstrated that rVVJ16/18E7E6 was able to elicit varying levels of CD8⁺T cell immune responses and lysis of target cells in mice in response to peptides HPV16E7_49-57and HPV18E6_67-75. Furthermore, the virus was also able to induce anti-tumour responses in the HPV16⁺TC-1 tumour challenge model, including partial protection (30-40%) and delayed tumour appearance. In addition, the virus was able to induce immune responses in rhesus monkeys.</p> <p>Conclusions</p> <p>The recombinant vaccinia virus rVVJ16/18E7E6 can generate clear and significant cellular immunity in both mice and rhesus monkeys. These data provide a basis for the use of this recombinant virus as a potential vaccine candidate for further study.</p

COL11A1 as an novel biomarker for breast cancer with machine learning and immunohistochemistry validation

Machine learning (ML) algorithms were used to identify a novel biological target for breast cancer and explored its relationship with the tumor microenvironment (TME) and patient prognosis. The edgR package identified hub genes associated with overall survival (OS) and prognosis, which were validated using public datasets. Of 149 up-regulated genes identified in tumor tissues, three ML algorithms identified COL11A1 as a hub gene. COL11A1was highly expressed in breast cancer samples and associated with a poor prognosis, and positively correlated with a stromal score (r=0.49, p<0.001) and the ESTIMATE score (r=0.29, p<0.001) in the TME. Furthermore, COL11A1 negatively correlated with B cells, CD4 and CD8 cells, but positively associated with cancer-associated fibroblasts. Forty-three related immune-regulation genes associated with COL11A1 were identified, and a five-gene immune regulation signature was built. Compared with clinical factors, this gene signature was an independent risk factor for prognosis (HR=2.591, 95%CI 1.831–3.668, p=7.7e-08). A nomogram combining the gene signature with clinical variables, showed better predictive performance (C-index=0.776). The model correction prediction curve showed little bias from the ideal curve. COL11A1 is a potential therapeutic target in breast cancer and may be involved in the tumor immune infiltration; its high expression is strongly associated with poor prognosis

MYCBP2 expression correlated with inflammatory cell infiltration and prognosis immunotherapy in thyroid cancer patients

IntroductionImmune checkpoint inhibitors (ICIs) have shown promising results for the treatment of multiple cancers. ICIs and related therapies may also be useful for the treatment of thyroid cancer (TC). In TC, Myc binding protein 2 (MYCBP2) is correlated with inflammatory cell infiltration and cancer prognosis. However, the relationship between MYCBP2 expression and ICI efficacy in TC patients is unclear.MethodsWe downloaded data from two TC cohorts, including transcriptomic data and clinical prognosis data. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict the efficacy of ICIs in TC patients. MCPcounter, xCell, and quanTIseq were used to calculate immune cell infiltration scores. Gene set enrichment analysis (GSEA) and single sample GSEA (ssGSEA) were used to evaluate signaling pathway scores. Immunohistochemical (IHC) analysis and clinical follow up was used to identify the MYCBP2 protein expression status in patients and associated with clinical outcome.ResultsA higher proportion of MYCBP2-high TC patients were predicted ICI responders than MYCBP2-low patients. MYCBP2-high patients also had significantly increased infiltration of CD8+ T cells, cytotoxic lymphocytes (CTLs), B cells, natural killer (NK) cells and dendritic cells (DC)s. Compared with MYCBP2-low patients, MYCBP2-high patients had higher expression of genes associated with B cells, CD8+ T cells, macrophages, plasmacytoid dendritic cells (pDCs), antigen processing and presentation, inflammatory stimulation, and interferon (IFN) responses. GSEA and ssGSEA also showed that MYCBP2-high patients had significantly increased activity of inflammatory factors and signaling pathways associated with immune responses.In addiation, Patients in our local cohort with high MYCBP2 expression always had a better prognosis and greater sensitivity to therapy while compared to patients with low MYCBP2 expression after six months clinic follow up.ConclusionsIn this study, we found that MYCBP2 may be a predictive biomarker for ICI efficacy in TC patients. High MYCBP2 expression was associated with significantly enriched immune cell infiltration. MYCBP2 may also be involved in the regulation of signaling pathways associated with anti-tumor immune responses or the production of inflammatory factors

Biom Biostat Int J

CC999999/Intramural CDC HHS/United States2016-06-17T00:00:00Z27331197PMC491222

Developing a medical device-grade T2 phantom optimized for myocardial T2 mapping by cardiovascular magnetic resonance

INTRODUCTION: A long T2 relaxation time can reflect oedema, and myocardial inflammation when combined with increased plasma troponin levels. Cardiovascular magnetic resonance (CMR) T2 mapping therefore has potential to provide a key diagnostic and prognostic biomarkers. However, T2 varies by scanner, software, and sequence, highlighting the need for standardization and for a quality assurance system for T2 mapping in CMR. AIM: To fabricate and assess a phantom dedicated to the quality assurance of T2 mapping in CMR. METHOD: A T2 mapping phantom was manufactured to contain 9 T1 and T2 (T1|T2) tubes to mimic clinically relevant native and post-contrast T2 in myocardium across the health to inflammation spectrum (i.e., 43-74 ms) and across both field strengths (1.5 and 3 T). We evaluated the phantom's structural integrity, B0 and B1 uniformity using field maps, and temperature dependence. Baseline reference T1|T2 were measured using inversion recovery gradient echo and single-echo spin echo (SE) sequences respectively, both with long repetition times (10 s). Long-term reproducibility of T1|T2 was determined by repeated T1|T2 mapping of the phantom at baseline and at 12 months. RESULTS: The phantom embodies 9 internal agarose-containing T1|T2 tubes doped with nickel di-chloride (NiCl2) as the paramagnetic relaxation modifier to cover the clinically relevant spectrum of myocardial T2. The tubes are surrounded by an agarose-gel matrix which is doped with NiCl2 and packed with high-density polyethylene (HDPE) beads. All tubes at both field strengths, showed measurement errors up to ≤ 7.2 ms [< 14.7%] for estimated T2 by balanced steady-state free precession T2 mapping compared to reference SE T2 with the exception of the post-contrast tube of ultra-low T1 where the deviance was up to 16 ms [40.0%]. At 12 months, the phantom remained free of air bubbles, susceptibility, and off-resonance artifacts. The inclusion of HDPE beads effectively flattened the B0 and B1 magnetic fields in the imaged slice. Independent temperature dependency experiments over the 13-38 °C range confirmed the greater stability of shorter vs longer T1|T2 tubes. Excellent long-term (12-month) reproducibility of measured T1|T2 was demonstrated across both field strengths (all coefficients of variation < 1.38%). CONCLUSION: The T2 mapping phantom demonstrates excellent structural integrity, B0 and B1 uniformity, and reproducibility of its internal tube T1|T2 out to 1 year. This device may now be mass-produced to support the quality assurance of T2 mapping in CMR