A novel artificial bee colony based clustering algorithm for categorical data

Funding: This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. (21127010, 61202309, http://www.nsfc.gov.cn/), China Postdoctoral Science Foundation under Grant No. 2013M530956 (http://res.chinapostdoctor.org.cn), the UK Economic & Social Research Council (ESRC): award reference: ES/M001628/1 (http://www.esrc.ac.uk/), Science and Technology Development Plan of Jilin province under Grant No. 20140520068JH (http://www.jlkjt.gov.cn), Fundamental Research Funds for the Central Universities under No. 14QNJJ028 (http://www.nenu.edu.cn), the open project program of Key Laboratory of Symbolic Computation andKnowledge Engineering of Ministry of Education, Jilin University under Grant No. 93K172014K07 (http://www.jlu.edu.cn). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Towards an Understanding of Large Language Models in Software Engineering Tasks

Large Language Models (LLMs) have drawn widespread attention and research due to their astounding performance in tasks such as text generation and reasoning. Derivative products, like ChatGPT, have been extensively deployed and highly sought after. Meanwhile, the evaluation and optimization of LLMs in software engineering tasks, such as code generation, have become a research focus. However, there is still a lack of systematic research on the application and evaluation of LLMs in the field of software engineering. Therefore, this paper is the first to comprehensively investigate and collate the research and products combining LLMs with software engineering, aiming to answer two questions: (1) What are the current integrations of LLMs with software engineering? (2) Can LLMs effectively handle software engineering tasks? To find the answers, we have collected related literature as extensively as possible from seven mainstream databases, and selected 123 papers for analysis. We have categorized these papers in detail and reviewed the current research status of LLMs from the perspective of seven major software engineering tasks, hoping this will help researchers better grasp the research trends and address the issues when applying LLMs. Meanwhile, we have also organized and presented papers with evaluation content to reveal the performance and effectiveness of LLMs in various software engineering tasks, providing guidance for researchers and developers to optimize

Role of APE1 in differentiated neuroblastoma SH-SY5Y cells in response to oxidative stress; Use of APE1 small molecule inhibitors to delineate APE1 functions

Oxidative DNA damage has been implicated in a number of central nervous system pathologies. The base excision repair (BER) pathway is one of the most important cellular protection mechanisms that respond to oxidative DNA damage. Human apurinic (apyrimidinic) endonuclease/redox effector factor (APE1/Ref-1 or APE1) is an essential enzyme in the BER pathway and is expressed in both mitotic and post-mitotic cells in humans. In neurons, a reduction of APE1 expression increases chemotherapy-induced cytotoxicity, while overexpression of APE1 protects cells against the cytotoxicity. However, given the multiple functions of APE1, knockdown of total APE1 is not completely informative of whether it is the redox or DNA repair activity, or interactions with other proteins. Therefore, the use of selective small molecules that can block each function independent of the other is of great benefit in ascertaining APE1 function in post-mitotic cells. In this study, we chose differentiated SH-SY5Y cells as our post-mitotic cell line model to investigate whether a drug-induced decrease in APE1 DNA repair or redox activity contributes to the growth and survival of post-mitotic cells under oxidative DNA damaging conditions. Here, we demonstrate that overexpression of WT-APE1 or C65-APE1 (repair competent) results in significant increase in cell viability after exposure to H2O2. However, the 177/226-APE1 (repair deficient) did not show a protective effect. This phenomenon was further confirmed by the use of methoxyamine (MX), which blocks the repair activity of APE1 that results in enhanced cell killing and apoptosis in differentiated SH-SY5Y cells and in neuronal cultures after oxidative DNA damaging treatments. Blocking APE1 redox function by a small molecule inhibitor, BQP did not decrease viability of SH-SY5Y cells or neuronal cultures following oxidative DNA damaging treatments. Our results demonstrate that the DNA repair function of APE1 contributes to the survival of nondividing post-mitotic cells following oxidative DNA damage

Demineralized Bone Matrix Combined Bone Marrow Mesenchymal Stem Cells, Bone Morphogenetic Protein-2 and Transforming Growth Factor-β3 Gene Promoted Pig Cartilage Defect Repair

Objectives To investigate whether a combination of demineralized bone matrix (DBM) and bone marrow mesenchymal stem cells (BMSCs) infected with adenovirus-mediated- bone morphogenetic protein (Ad-BMP-2) and transforming growth factor-β3 (Ad-TGF-β3) promotes the repair of the full-thickness cartilage lesions in pig model. Methods BMSCs isolated from pig were cultured and infected with Ad-BMP-2(B group), Ad-TGF-β3 (T group), Ad-BMP-2 + Ad-TGF-β3(BT group), cells infected with empty Ad served as a negative group(N group), the expression of the BMP-2 and TGF-β3 were confirmed by immunofluorescence, PCR, and ELISA, the expression of SOX-9, type II collagen(COL-2A), aggrecan (ACAN) in each group were evaluated by real-time PCR at 1w, 2w, 3w, respectively. The chondrogenic differentiation of BMSCs was evaluated by type II collagen at 21d with immunohistochemical staining. The third-passage BMSCs infected with Ad-BMP-2 and Ad-TGF-β3 were suspended and cultured with DBM for 6 days to construct a new type of tissue engineering scaffold to repair full-thickness cartilage lesions in the femur condyles of pig knee, the regenerated tissue was evaluated at 1,2 and 3 months after surgery by gross appearance, H&E, safranin O staining and O\u27driscoll score. Results Ad-BMP-2 and Ad-TGF-β3 (BT group) infected cells acquired strong type II collagen staining compared with Ad-BMP-2 (B group) and Ad-TGF-β3 (T group) along. The Ad-BMP-2 and Ad-TGF-β3 infected BMSCs adhered and propagated well in DBM and the new type of tissue engineering scaffold produced hyaline cartilage morphology containing a stronger type II collagen and safranin O staining, the O\u27driscoll score was higher than other groups. Conclusions The DBM compound with Ad-BMP-2 and Ad-TGF-β3 infected BMSCs scaffold has a good biocompatibility and could well induce cartilage regeneration to repair the defects of joint cartilage. This technology may be efficiently employed for cartilage lesions repair in vivo

When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?

With the development of blockchain technology, smart contracts have become an important component of blockchain applications. Despite their crucial role, the development of smart contracts may introduce vulnerabilities and potentially lead to severe consequences, such as financial losses. Meanwhile, large language models, represented by ChatGPT, have gained great attentions, showcasing great capabilities in code analysis tasks. In this paper, we presented an empirical study to investigate the performance of ChatGPT in identifying smart contract vulnerabilities. Initially, we evaluated ChatGPT's effectiveness using a publicly available smart contract dataset. Our findings discover that while ChatGPT achieves a high recall rate, its precision in pinpointing smart contract vulnerabilities is limited. Furthermore, ChatGPT's performance varies when detecting different vulnerability types. We delved into the root causes for the false positives generated by ChatGPT, and categorized them into four groups. Second, by comparing ChatGPT with other state-of-the-art smart contract vulnerability detection tools, we found that ChatGPT's F-score is lower than others for 3 out of the 7 vulnerabilities. In the case of the remaining 4 vulnerabilities, ChatGPT exhibits a slight advantage over these tools. Finally, we analyzed the limitation of ChatGPT in smart contract vulnerability detection, revealing that the robustness of ChatGPT in this field needs to be improved from two aspects: its uncertainty in answering questions; and the limited length of the detected code. In general, our research provides insights into the strengths and weaknesses of employing large language models, specifically ChatGPT, for the detection of smart contract vulnerabilities