Objective: Fructose bisphosphate aldolase (ALDOB) is a glycolytic metabolic enzyme, which is considered to be a therapeutic target for many cancers. However, ALDOB expression level and its regulatory mechanism in renal clear cell carcinoma is not clear. To explore ALDOB expression level and its regulatory mechanism in renal clear cell carcinoma we downloaded gene expression data sets and analyzed them by bioinformatics. Methods: The gene expression data sets of GSE53757, GSE40435 and GSE105261 about human renal clear cell carcinoma were downloaded from the GEO database and analyzed by using the Venn diagram. We analyzed and screened out the relationship network of the interested target genes through GeneMANIA and STRING online software. These 6 target genes obtained were analyzed by Kaplan-Meier curve. GO enrichment analysis of the target gene ALDOB was performed by DAVID, and the relationship between the expression of ALDOB and immune infiltration in clear cell renal cell carcinoma was analyzed by means of TIMER and TISIDB databases. Finally, a prognostic nomogram was constructed to predict the individual’s 3-year and 5-year survival probabilities. Results: ALDOB gene is positively correlated to the survival and prognosis of patients with renal clear cell carcinoma. Furthermore, the overexpression of ALDOB can prolong the survival time of ccRCC patients. In addition, ALDOB can affect the ratio of CD4+T/CD8+T cells to influence renal clear cell carcinoma. Finally, the main mechanism of its overexpression prolonging the survival time of renal clear cell carcinoma may be involved in glycolysis. Conclusions: These data showed that ALDOB gene could be a biomarker and therapeutic target for renal clear cell carcinoma

Achieving human-like communication with machines remains a classic, challenging topic in the field of Knowledge Representation and Reasoning and Natural Language Processing. While Large Language Models (LLMs) have shown promise in generating human-like sentences for tasks such as question answering, paragraph summarization, and translation, they rely on pattern-matching rather than a true understanding of the semantic meaning of a sentence. As a result, they may generate incorrect responses. To generate an assuredly correct response, one has to "understand" the semantics of a sentence, so that the missing information can be further requested and the correct response computed. To achieve this "understanding", logic-based (commonsense) reasoning methods such as Answer Set Programming (ASP) are arguably needed. In this paper, we describe the AutoConcierge system that leverages LLMs and ASP to develop a conversational agent that can truly "understand" human dialogs, at least in restricted domains. AutoConcierge is focused on a specific domain-advising users about restaurants in their local area based on their preferences. AutoConcierge will interactively understand a user's utterances, identify the missing information in them, and request the user via a natural language sentence to provide it. Once AutoConcierge has determined that all the information has been received, it computes a restaurant recommendation based on the user-preferences it has acquired from the human user. AutoConcierge is based on our STAR framework developed earlier, which uses GPT-3 to convert human dialogs into predicates that capture the deep structure of the dialog's sentence. These predicates are then input into the goal-directed s(CASP) ASP system for performing commonsense reasoning. To the best of our knowledge, AutoConcierge is the first automated conversational agent that can realistically converse like a human and provide help to humans based on truly understanding human utterances.

Background：Bladder urothelial carcinoma (BLCA) is a malignant tumor occurring in the bladder and derived from urothelial cells. It is one of the ten most common cancers in the world. There has been no significant progress in the treatment of patients with recurrence of BLCA in the past decades. With the progress of systemic treatment, the overall survival period (OS) of patients has improved, but the prognosis is still poor, which needs improvement clinically. The development of immunotherapy has changed the status initially. Natural killer (NK) cells, as an important type of innate immunomodulatory cells, have proved their potential in the treatment of several malignant tumors, so which received widespread attention in recent years. Objective: The clinical data in TCGA and GEO was used to provide ideas for the diagnosis, treatment and evaluation of prognosis indicators of BLCA. And the relationship between BLCA and the expression of NRGs was investigated. Methods: In this study, the normal and tumor samples of TCGA-BLCA and GSE3167 were analyzed to find out differential expression genes based on the "limma" R package. The genes and the natural killer cell-related genes (NRGs) were intersected to obtain NRGs with differential expression. We used univariate COX regression analysis, LASSO and multifactor COX analysis to obtain the risk score and build the prognosis gene model. GSE31684 queue has been verified externally. In order to further confirm the reliability of genes, we analyzed their mechanism of action, divided them into groups by the risk score, and performed immune analysis on subgroups to evaluate the immune treatment response. Results: There are differences in the expression of LRP1 and INHBB, which are independent risk factors for the prognosis of BLCA. The nomogram was established based on the characteristics of 2-NRGs and clinicopathological characteristics. The low risk group has more abundant immune infiltration, suggesting a better immune response and better prognosis, which verifies the reliability of grouping. The sensitivity of the hig-risk and low-risk group to the two target sites, PDL1 and FGFR3, is significantly different. And the risk score is closely related to the relevant chemotherapy drugs. These suggested that the risk score can screen the patients well at the drug level, and also providing a meaningful reference for the patients in drug selection. Conclusion: NK cell core genes, LRP1 and INHBB, may play a crucial role in the occurrence and progression of BLCA. We constructed nomogram according to the NK core genes and verified its feasibility, which can provide a reference for clinical prognosis.

Humans understand language by extracting information (meaning) from sentences, combining it with existing commonsense knowledge, and then performing reasoning to draw conclusions. While large language models (LLMs) such as GPT-3 and ChatGPT are able to leverage patterns in the text to solve a variety of NLP tasks, they fall short in problems that require reasoning. They also cannot reliably explain the answers generated for a given question. In order to emulate humans better, we propose STAR, a framework that combines LLMs with Answer Set Programming (ASP). We show how LLMs can be used to effectively extract knowledge-represented as predicates-from language. Goal-directed ASP is then employed to reliably reason over this knowledge. We apply the STAR framework to three different NLU tasks requiring reasoning: qualitative reasoning, mathematical reasoning, and goal-directed conversation. Our experiments reveal that STAR is able to bridge the gap of reasoning in NLU tasks, leading to significant performance improvements, especially for smaller LLMs, i.e., LLMs with a smaller number of parameters. NLU applications developed using the STAR framework are also explainable: along with the predicates generated, a justification in the form of a proof tree can be produced for a given output.

No abstract