Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment

Pretrained language models (PLMs) based knowledge-grounded dialogue systems are prone to generate responses that are factually inconsistent with the provided knowledge source. In such inconsistent responses, the dialogue models fail to accurately express the external knowledge they rely upon. Inspired by previous work which identified that feed-forward networks (FFNs) within Transformers are responsible for factual knowledge expressions, we investigate two methods to efficiently improve the factual expression capability {of FFNs} by knowledge enhancement and alignment respectively. We first propose \textsc{K-Dial}, which {explicitly} introduces {extended FFNs in Transformers to enhance factual knowledge expressions} given the specific patterns of knowledge-grounded dialogue inputs. Additionally, we apply the reinforcement learning for factual consistency (RLFC) method to implicitly adjust FFNs' expressions in responses by aligning with gold knowledge for the factual consistency preference. To comprehensively assess the factual consistency and dialogue quality of responses, we employ extensive automatic measures and human evaluations including sophisticated fine-grained NLI-based metrics. Experimental results on WoW and CMU\_DoG datasets demonstrate that our methods efficiently enhance the ability of the FFN module to convey factual knowledge, validating the efficacy of improving factual consistency for knowledge-grounded dialogue systems.Comment: EMNLP2023 Finding

Enhanced Acetone-Sensing Properties of PEI Thin Film by GO-NH2 Functional Groups Modification at Room Temperature

The functional groups of organic gas-sensing materials play a crucial role in adsorbing specific gas molecules, which is significant to the sensing performances of gas sensor. In this work, amido-graphene oxide (GO-NH2) loaded poly(ethyleneimine) (PEI) composite thin film (PEI/GO-NH2) with abundant amino functional groups -NH2 was successfully prepared on quartz crystal microbalance (QCM) by a combined spraying and drop coating method for acetone detection at room temperature (25°C). The morphological, spectrographic and acetone-sensing properties of composite film were investigated. The results demonstrated that a wrinkled surface morphology was formed and the ratio of nucleophilic -NH2 was increased for PEI/GO-NH2 composite film. Meanwhile, the composite film sensor possessed excellent acetone-sensing performances, and its sensitivity was about 4.2 times higher than that of pure PEI one owing to the increased -NH2 groups. This study reveals the important role of absorbing favorable functional groups and provides a novel method for the rational design and construction of acetone-sensing materials

Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms

Background: Almost all patients treated with androgen deprivation therapy (ADT) eventually develop castration-resistant prostate cancer (CRPC). Our research aims to elucidate the potential biomarkers and molecular mechanisms that underlie the transformation of primary prostate cancer into CRPC.Methods: We collected three microarray datasets (GSE32269, GSE74367, and GSE66187) from the Gene Expression Omnibus (GEO) database for CRPC. Differentially expressed genes (DEGs) in CRPC were identified for further analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. The diagnostic efficiency of the selected biomarkers was evaluated based on gene expression level and receiver operating characteristic (ROC) curve analyses. We conducted virtual screening of drugs using AutoDock Vina. In vitro experiments were performed using the Cell Counting Kit-8 (CCK-8) assay to evaluate the inhibitory effects of the drugs on CRPC cell viability. Scratch and transwell invasion assays were employed to assess the effects of the drugs on the migration and invasion abilities of prostate cancer cells.Results: Overall, a total of 719 DEGs, consisting of 513 upregulated and 206 downregulated genes, were identified. The biological functional enrichment analysis indicated that DEGs were mainly enriched in pathways related to the cell cycle and metabolism. CCNA2 and CKS2 were identified as promising biomarkers using a combination of WGCNA, LASSO logistic regression, SVM-RFE, and Venn diagram analyses. These potential biomarkers were further validated and exhibited a strong predictive ability. The results of the virtual screening revealed Aprepitant and Dolutegravir as the optimal targeted drugs for CCNA2 and CKS2, respectively. In vitro experiments demonstrated that both Aprepitant and Dolutegravir exerted significant inhibitory effects on CRPC cells (p < 0.05), with Aprepitant displaying a superior inhibitory effect compared to Dolutegravir.Discussion: The expression of CCNA2 and CKS2 increases with the progression of prostate cancer, which may be one of the driving factors for the progression of prostate cancer and can serve as diagnostic biomarkers and therapeutic targets for CRPC. Additionally, Aprepitant and Dolutegravir show potential as anti-tumor drugs for CRPC

AREA: An adaptive reference-set based evolutionary algorithm for multiobjective optimisation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Population-based evolutionary algorithms have great potential to handle multiobjective optimisation problems. However, the performance of these algorithms depends largely on problem characteristics. There is a need to improve these algorithms for wide applicability. References, often specified by the decision maker’s preference in different forms, are very effective to boost the performance of algorithms. This paper proposes a novel framework for effective use of references to strengthen algorithms. This framework considers references as search targets which can be adjusted based on the information collected during the search. The proposed framework is combined with new strategies, such as reference adaptation and adaptive local mating, to solve different types of problems. The proposed algorithm is compared with state-of-the-arts on a wide range of problems with diverse characteristics. The comparison and extensive sensitivity analysis demonstrate that the proposed algorithm is competitive and robust across different types of problems studied in this paper

Pica in a girl with non-suicidal self-injury: a case report

Non-suicidal self-injury (NSSI) is on the rise globally, posing a significant societal challenge. Pica, an eating disorder, presents difficulties in treatment due to the absence of effective medications. In this report, we discuss a complex case involving the co-occurrence of pica and non-suicidal self-injury. A 13-year-old girl was admitted to our hospital due to ingesting two batteries. She features a persistent, intense appetite along with sudden and compulsive behaviors such as consuming inedible items or self-inflicted cutting. After receiving a combination of pharmacological treatments (quetiapine, lithium and sertraline), cognitive behavioral therapy (CBT) and modified electroconvulsive therapy (MECT) for 25 days, she was discharged with relief from her clinical symptoms

Metabolic engineering of Escherichia coli for the biosynthesis of alpha-pinene

Background: alpha-Pinene is an important natural product that is widely used in flavorings, fragrances, medicines, fine chemicals and high-density renewable fuels. Currently, alpha-Pinene used in industry is mainly produced either by tapping trees (gum turpentine) or as a byproduct of paper pulping (crude sulfate turpentine, CST). However, the extraction of it from trees is tedious and inefficient and requires substantial expenditure of natural resources. Therefore, it is necessary to seek sustainable technologies for alpha-pinene production

Dynamic Ochratoxin A Production by Strains of <em>Aspergillus niger</em> Intended Used in Food Industry of China

Thirty strains of Aspergillus niger, including 27 intended used in the food industry of China, were studied for their ochratoxin A (OTA) production on the three natural substrates—corn, rice, and wheat bran—at different time intervals by high-performance liquid chromatography. It was found that the frequencies of OTA for the studied 27 industrial strains ranged from 14.8% (4/27) at day 28 to 25.9% (7/27) at day 7 on corn, 14.8% (4/27) at day 7 to 33.3% (9/27) at day 21 on rice, and 22.2% (6/27) at day 7, 14, and 28 to 44.4% (12/27) at day 21 on wheat bran, respectively. The average concentrations of OTA produced by the studied 27 industrial strains ranged from 5.1 μg/kg at day 28 to 8.7 μg/kg at day 21 on corn, 4.2 μg/kg at day 7 to 17.9 μg/kg at day 14 on rice, and 4.5 μg/kg at day 7 to 7.2 μg/kg at day 21 on wheat bran, respectively. Furthermore, the OTA production in the studied 27 industrial strains of A.niger was strongly associated with their function (or application), culture substrate, and time. The saccharifying enzyme producers produced higher levels of OTA, compared with the organic acid producers, the tannase producers, and the β-galactosidase producer, while concentration differences were also observed in OTA production among strains of A.niger with the same application. In a word, some strains of A.niger intended used in the Chinese food industry indeed have the capability of producing OTA, elevating the risks to food safety associated with their use