A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning

Logical reasoning has been an ongoing pursuit in the field of AI. Despite significant advancements made by large language models (LLMs), they still struggle with complex logical reasoning problems. To enhance reasoning performance, one promising direction is scalable oversight, which requires LLMs to identify their own errors and then improve by themselves. Various self-verification methods have been proposed in pursuit of this goal. Nevertheless, whether existing models understand their own errors well is still under investigation. In this paper, we take a closer look at the self-verification abilities of LLMs in the context of logical reasoning, focusing on their ability to identify logical fallacies accurately. We introduce a dataset, FALLACIES, containing 232 types of reasoning fallacies categorized in a hierarchical taxonomy. By conducting exhaustive experiments on FALLACIES, we obtain comprehensive and detailed analyses of a series of models on their verification abilities. Our main findings suggest that existing LLMs could struggle to identify fallacious reasoning steps accurately and may fall short of guaranteeing the validity of self-verification methods. Drawing from these observations, we offer suggestions for future research and practical applications of self-verification methods.Comment: work in progres

Prognostic value of various immune cells and Immunoscore in triple-negative breast cancer

BackgroundThis study aimed to evaluate the expression status and prognostic role of various immunoregulatory cells and test in triple-negative breast cancer (TNBC).MethodsThe expression of five markers (CD3/CD4/CD8/CD19/CD163) of tumor immune cells was evaluated retrospectively in tumor sections from 68 consecutive cases of TNBC by immunohistochemistry. Computational image analysis was used to quantify the density and distribution of each immune marker within the tumor region, tumor invasive margin, and expression hotspots. Immunoscores were calculated using an automated approach. Other clinical characteristics were also analyzed.ResultsFor all patients, Kaplan–Meier survival analysis showed that high CD3+ signals in the tumor region (disease-free survival (DFS), P=0.0014; overall survival (OS), P=0.0031) and total region (DFS, P=0.0014; OS, P=0.0031) were significantly associated with better survival. High CD4+ levels in the tumor region and total regions were significantly associated with better survival (P<0.05). For Hotspot analysis, CD3+ was associated with significantly better survival for all Top1, Top2, and Top3 densities (DFS and OS, P<0.05). High CD4+ levels were significantly associated with better prognosis for Top1 and Top3 densities (DFS and OS, P<0.05). For stage IIB and IIIC patients, CD3+ in the tumor region and all Top hotspots was found to be significantly correlated with survival (DFS and OS, P<0.05). CD4+ cells were significantly associated with survival in the tumor region, total region, and Top3 density (DFS, P=0.0213; OS, P=0.0728). CD8+ cells were significantly associated with survival in the invasive margin, Top2 density, and Top3 density. Spatial parameter analysis showed that high colocalization of tumor cells and immune cells (CD3+, CD4+, or CD8+) was significantly associated with patient survival.ConclusionComputational image analysis is a reliable tool for evaluating the density and distribution of immune regulatory cells and for calculating the Immunoscore in TNBC. The Immunoscore retains its prognostic significance in TNBC later than IIB stage breast cancer. Future studies are required to confirm its potential to predict tumor responses to chemotherapy and immune therapy

Expression of neuroendocrine markers predicts increased survival in triple-negative breast cancer patients

BackgroundThe significance of neuroendocrine (NE) markers in triple-negative breast cancer (TNBC) patients has not been investigated. This study aims to clarify the incidence and prognostic significance of NE marker expression in TNBC, determine its association with other clinicopathological parameters, and further explore the pathological features and potential treatment options for TNBC patients expressing NE markers.MethodsClinicopathological data were collected from 396 TNBC patients undergoing radical breast cancer surgery at Peking Union Medical College Hospital from January 2002 to December 2014, with a final follow-up in July 2019. Immunohistochemistry (IHC) staining was performed for NE markers including chromogranin A (CgA) and synaptophysin (Syn). For TNBC patients with positive NE marker expression, IHC staining was then performed for alpha-thalassemia/mental retardation X-linked (ATRX), O(6)-methylguanine-methyltransferase (MGMT), somatostatin receptor 2 (SSTR2), and programmed death receptor-ligand 1 (PD-L1). The chi-square or Fisher exact test was used to evaluate the correlations between NE marker expression and other parameters. Survival curves were plotted using the Kaplan-Meier (K-M) method to assess the prognostic significance of NE markers in TNBC.ResultsNE marker-positive staining was observed in 7.6% (30/396) of all TNBC cases. Only 0.5% (2/396) cases had ≥ 90% neoplastic cells expressing NE markers. Positive NE marker expression was associated with negative basal-like marker expression. K-M survival analysis showed that the NE marker-positive TNBC patients had higher disease-free survival (DFS) rates than the NE marker-negative patients at the same stage. Among the 30 NE marker-positive TNBC cases, 13.3% and 26.7% showed negative IHC staining for ATRX and MGMT, respectively, while 13.3% had a 3+ score for SSTR2 IHC staining. For PD-L1 IHC staining, 13.3% of the 30 TNBC cases were higher than 10 scores in Combined Positive Score (CPS), and 10.0% were higher than 10% in Tumor Cell Proportion Score (TPS).ConclusionThere was a small proportion of TNBC patients expressing NE markers. TNBC patients with positive NE marker expression had a better prognosis than the negative group at the same stage. TNBC cases with positive NE marker expression may potentially benefit from immunotherapy or somatostatin analogue treatment

Pandemic (H1N1) 2009 among Quarantined Close Contacts, Beijing, People’s Republic of China

The attack rate was low, and having contact with an ill household member and younger age were the major risk factors

Causal relationships between COVID-19 and osteoporosis: a two-sample Mendelian randomization study in European population

IntroductionThe causal relationship between Coronavirus disease 2019 (COVID-19) and osteoporosis (OP) remains uncertain. We aimed to assess the effect of COVID-19 severity (severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19 hospitalization, and severe COVID-19) on OP by a two-sample Mendelian randomization (MR) study.MethodsWe conducted a two-sample MR analysis using publicly available genome-wide association study (GWAS) data. Inverse variance weighting (IVW) was used as the main analysis method. Four complementary methods were used for our MR analysis, which included the MR–Egger regression method, the weighted median method, the simple mode method, and the weighted mode method. We utilized the MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) global test to identify the presence of horizontal pleiotropy. Cochran’s Q statistics were employed to assess the existence of instrument heterogeneity. We conducted a sensitivity analysis using the leave-one-out method.ResultsThe primary results of IVW showed that COVID-19 severity was not statistically related to OP (SARS-CoV-2 infection: OR (95% CI) = 0.998 (0.995 ~ 1.001), p = 0.201403; COVID-19 hospitalization: OR (95% CI) =1.001 (0.999 ~ 1.003), p = 0.504735; severe COVID-19: OR (95% CI) = 1.000 (0.998 ~ 1.001), p = 0.965383). In addition, the MR-Egger regression, weighted median, simple mode and weighted mode methods showed consistent results. The results were robust under all sensitivity analyses.ConclusionThe results of the MR analysis provide preliminary evidence that a genetic causal link between the severity of COVID-19 and OP may be absent

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

An Optimization Grey Bernoulli Model and Its Application in Forecasting Oil Consumption

Energy consumption in the world is mainly dependent on fossil energy, and oil is one of the main energy sources. Accurate prediction of oil consumption can provide an important basis for national energy security, which can provide reference and early warning for the implementation of the environmental strategy developed by the government. According to the nonlinearity of the energy system, this paper uses the principle of the grey nonlinear prediction model NGBM(1,1) to improve the background value of the model, and by the simulated annealing algorithm, we put forward the optimized grey nonlinear model ONGBM(1,1). At the same time, the model is applied to the oil consumption of China, Chile, Mexico, and Japan. Based on the validity analysis of the existing data of the four countries, the model ONGBM(1,1) is basically superior to the other six grey forecast models. Finally, ONGBM(1,1) is used to predict the oil consumption of the four countries in the next five years, which can provide effective information for energy economic policy