Characterizations of Lamellar Interfaces and Segregations in a PST-TiAl Intermetallic Alloy by an Analytical Scanning Transmission Electron Microscope

Polysynthetically-twinned titanium aluminide (PST-TiAl), a fully lamellar γ-TiAl+α2- Ti3Al dual-phase alloy, is under evaluation for applications in rotary components in aircraft and automobile industries due to its high specific strength, and a high strength-retention capability at elevated-temperatures. However, the low ductility at room- to mid-high temperatures of the material hinders its application. Additions of certain tertiary elements to the binary TiAl system appear to improve the ductility at room- to mid-high temperatures, thus a balance among strength, ductility, and fracture toughness can be expected. In this article, segregation of tertiary elements to the lamellar interfaces is investigated. Single crystals of a TiAl with 0.6% atomic percentage tertiary additions are grown by an optical float-zone method. Segregation to the lamellar interfaces and the microstructure of the interfaces are investigated. Structures of the lamellar interfaces are characterized, and microchemistry and distribution habits of these elements along the γ+α2 lamellar boundaries as well as the γ-γ lamellar and domain boundaries are analyzed

Epigenetics in ovarian cancer: premise, properties, and perspectives.

Malignant ovarian tumors bear the highest mortality rate among all gynecological cancers. Both late tumor diagnosis and tolerance to available chemical therapy increase patient mortality. Therefore, it is both urgent and important to identify biomarkers facilitating early identification and novel agents preventing recurrence. Accumulating evidence demonstrates that epigenetic aberrations (particularly histone modifications) are crucial in tumor initiation and development. Histone acetylation and methylation are respectively regulated by acetyltransferases-deacetylases and methyltransferases-demethylases, both of which are implicated in ovarian cancer pathogenesis. In this review, we summarize the most recent discoveries pertaining to ovarian cancer development arising from the imbalance of histone acetylation and methylation, and provide insight into novel therapeutic interventions for the treatment of ovarian carcinoma

Not All Metrics Are Guilty: Improving NLG Evaluation with LLM Paraphrasing

Most research about natural language generation (NLG) relies on evaluation benchmarks with limited references for a sample, which may result in poor correlations with human judgements. The underlying reason is that one semantic meaning can actually be expressed in different forms, and the evaluation with a single or few references may not accurately reflect the quality of the model's hypotheses. To address this issue, this paper presents a novel method, named Para-Ref, to enhance existing evaluation benchmarks by enriching the number of references. We leverage large language models (LLMs) to paraphrase a single reference into multiple high-quality ones in diverse expressions. Experimental results on representative NLG tasks of machine translation, text summarization, and image caption demonstrate that our method can effectively improve the correlation with human evaluation for sixteen automatic evaluation metrics by +7.82% in ratio. We release the code and data at https://github.com/RUCAIBox/Para-Ref

Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning

Chain-of-thought prompting~(CoT) and tool augmentation have been validated in recent work as effective practices for improving large language models~(LLMs) to perform step-by-step reasoning on complex math-related tasks. However, most existing math reasoning datasets may be not able to fully evaluate and analyze the ability of LLMs in manipulating tools and performing reasoning, as they may only require very few invocations of tools or miss annotations for evaluating intermediate reasoning steps. To address the issue, we construct \textbf{CARP}, a new Chinese dataset consisting of 4,886 computation-intensive algebra problems with formulated annotations on intermediate steps. In CARP, we test four LLMs with CoT prompting, and find that they are all prone to make mistakes at the early steps of the solution, leading to wrong answers. Based on this finding, we propose a new approach that can deliberate the reasoning steps with tool interfaces, namely \textbf{DELI}. In DELI, we first initialize a step-by-step solution based on retrieved exemplars, then iterate two deliberation procedures that check and refine the intermediate steps of the generated solution, from the perspectives of tool manipulation and natural language reasoning, until obtaining converged solutions or reaching the maximum turn. Experimental results on CARP and six other datasets show that the proposed DELI mostly outperforms competitive baselines, and can further boost the performance of existing CoT methods. Our data and code are available in \url{https://github.com/RUCAIBox/CARP}.Comment: 17 pages, working in progres

Cardiac-derived CTRP9 protects against myocardial ischemia/reperfusion injury via calreticulin-dependent inhibition of apoptosis.

Cardiokines play an essential role in maintaining normal cardiac functions and responding to acute myocardial injury. Studies have demonstrated the heart itself is a significant source of C1q/TNF-related protein 9 (CTRP9). However, the biological role of cardiac-derived CTRP9 remains unclear. We hypothesize cardiac-derived CTRP9 responds to acute myocardial ischemia/reperfusion (MI/R) injury as a cardiokine. We explored the role of cardiac-derived CTRP9 in MI/R injury via genetic manipulation and a CTRP9-knockout (CTRP9-KO) animal model. Inhibition of cardiac CTRP9 exacerbated, whereas its overexpression ameliorated, left ventricular dysfunction and myocardial apoptosis. Endothelial CTRP9 expression was unchanged while cardiomyocyte CTRP9 levels decreased after simulated ischemia/`reperfusion (SI/R) in vitro. Cardiomyocyte CTRP9 overexpression inhibited SI/R-induced apoptosis, an effect abrogated by CTRP9 antibody. Mechanistically, cardiac-derived CTRP9 activated anti-apoptotic signaling pathways and inhibited endoplasmic reticulum (ER) stress-related apoptosis in MI/R injury. Notably, CTRP9 interacted with the ER molecular chaperone calreticulin (CRT) located on the cell surface and in the cytoplasm of cardiomyocytes. The CTRP9-CRT interaction activated the protein kinase A-cAMP response element binding protein (PKA-CREB) signaling pathway, blocked by functional neutralization of the autocrine CTRP9. Inhibition of either CRT or PKA blunted cardiac-derived CTRP9\u27s anti-apoptotic actions against MI/R injury. We further confirmed these findings in CTRP9-KO rats. Together, these results demonstrate that autocrine CTRP9 of cardiomyocyte origin protects against MI/R injury via CRT association, activation of the PKA-CREB pathway, ultimately inhibiting cardiomyocyte apoptosis

Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network

Personalized review generation (PRG) aims to automatically produce review text reflecting user preference, which is a challenging natural language generation task. Most of previous studies do not explicitly model factual description of products, tending to generate uninformative content. Moreover, they mainly focus on word-level generation, but cannot accurately reflect more abstractive user preference in multiple aspects. To address the above issues, we propose a novel knowledge-enhanced PRG model based on capsule graph neural network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG) for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules for encoding underlying characteristics from the HKG. Our generation process contains two major steps, namely aspect sequence generation and sentence generation. First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence. Then, conditioned on the inferred aspect label, we design a graph-based copy mechanism to generate sentences by incorporating related entities or words from HKG. To our knowledge, we are the first to utilize knowledge graph for the PRG task. The incorporated KG information is able to enhance user preference at both aspect and word levels. Extensive experiments on three real-world datasets have demonstrated the effectiveness of our model on the PRG task.Comment: Accepted by CIKM 2020 (Long Paper

LncRNAs: the bridge linking RNA and colorectal cancer.

Long noncoding RNAs (lncRNAs) are transcribed by genomic regions (exceeding 200 nucleotides in length) that do not encode proteins. While the exquisite regulation of lncRNA transcription can provide signals of malignant transformation, lncRNAs control pleiotropic cancer phenotypes through interactions with other cellular molecules including DNA, protein, and RNA. Recent studies have demonstrated that dysregulation of lncRNAs is influential in proliferation, angiogenesis, metastasis, invasion, apoptosis, stemness, and genome instability in colorectal cancer (CRC), with consequent clinical implications. In this review, we explicate the roles of different lncRNAs in CRC, and the potential implications for their clinical application

Epigenetic repression of PDZ-LIM domain-containing protein 2 promotes ovarian cancer via NOS2-derived nitric oxide signaling.

Ovarian cancer constitutes one of the most lethal gynaecological malignancies worldwide and currently no satisfactory therapeutic approaches have been established. Therefore, elucidation of molecular mechanisms to develop targeted therapy of ovarian cancer is crucial. PDLIM2 is critical to promote ubiquitination of nuclear p65 and thus its role in inflammation has been highlighted recently. We demonstrate that PDLIM2 is decreased in both ovarian high-grade serous carcinoma and in various human ovarian cancer cell lines compared with normal ovary tissues and human ovarian surface epithelial cells (HOSE). Further functional analysis revealed that PDLIM2 is epigenetically repressed in ovarian cancer development and inhibition of PDLIM2 promoted ovarian cancer growth both in vivo and in vitro via NOS2-derived nitric oxide signaling, leading to recruitment of M2 type macrophages. These results suggest that PDLIM2 might be involved in ovarian cancer pathogenesis, which could serve as a promising therapeutic target for ovarian cancer patients

A Survey on Large Language Model based Autonomous Agents

Autonomous agents have long been a prominent research topic in the academic community. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from the human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating autonomous agents based on LLMs. To harness the full potential of LLMs, researchers have devised diverse agent architectures tailored to different applications. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of autonomous agents from a holistic perspective. More specifically, our focus lies in the construction of LLM-based agents, for which we propose a unified framework that encompasses a majority of the previous work. Additionally, we provide a summary of the various applications of LLM-based AI agents in the domains of social science, natural science, and engineering. Lastly, we discuss the commonly employed evaluation strategies for LLM-based AI agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository for the related references at https://github.com/Paitesanshi/LLM-Agent-Survey.Comment: 32 pages, 3 figure

Deleted in Colorectal Cancer Is a Putative Conditional Tumor-Suppressor Gene Inactivated by Promoter Hypermethylation in Head and Neck Squamous Cell Carcinoma

Deleted in colorectal cancer (DCC) is a candidate tumor-suppressor gene located at chromosome 18q21. However, DCC gene was found to have few somatic mutations and the heterozygous mice (DCC+/−) showed a similar frequency of tumor formation compared with the wild-type mice (DCC+/+). Recently, DCC came back to the spotlight as a better understating of its function and relationship with its ligand (netrin-1) had shown that DCC may act as a conditional tumor-suppressor gene. We evaluated hypermethylation as a mechanism for DCC inactivation in head and neck squamous cell carcinoma (HNSCC). DCC promoter region hypermethylation was found in 75% of primary HNSCC. There was a significant correlation between DCC promoter region hypermethylation and DCC expression (assessed by immunohistochemistry; P = 0.021). DCC nonexpressing HNSCC cell lines JHU-O12 and JHU-O19 with baseline hypermethylation of the DCC promoter were treated with 5-aza-2′-deoxycytidine (a demethylating agent) and reexpression of DCC was noted. Transfection of DCC into DCC-negative HNSCC cell lines resulted in complete abrogation of growth in all cell lines, whereas additional cotransfection of netrin-1 resulted in rescue of DCC-mediated growth inhibition. These results suggest that DCC is a putative conditional tumor-suppressor gene that is epigenetically inactivated by promoter hypermethylation in a majority of HNSCC. (Cancer Res 2006; 66(19): 9401-07