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Cancer vaccines represent a promising form of immunotherapy employed in the treatment of cancer. However, their efficiency in eliciting immune responses is limited, and satisfactory results have yet to be achieved. Optimizing adjuvants and antigens is an important approach to promoting the anti‐tumor efficacy of cancer vaccines. Here, a novel nanoadjuvant (LNP/siRNA) designed to silence T‐cell immunoglobulin and mucin‐domain containing‐3 (TIM3) and activate Toll‐like receptors (TLRs) is presented. The LNP/siRNA demonstrates significant potential in promoting dendritic cell (DC) maturation and enhancing the anti‐tumor response. Furthermore, desialylated cancer cell membrane is utilized as antigens, providing a variety of tumor antigens for DCs and enhancing their function. Additionally, they are integrated to create a core‐shell structured nanovaccine (dClip‐LNP/siRNA) through coextrusion, which collectively enhances the cross‐presentation ability of DCs, thus achieving a dual enhancement strategy. The dClip‐LNP/siRNA significantly silences TIM3 expression in DCs and promotes antigen presentation by DCs. Besides, dClip‐LNP/siRNA significantly promotes the activation of T cells in lymph nodes and induces robust and durable anti‐tumor immunity in tumor sites to eliminate established B16‐OVA tumors, prevent tumor occurrence, and suppress tumor lung metastasis. The dClip‐LNP/siRNA is also suitable for combination with adoptive OT‐I cell therapy to enhance cancer immunotherapy. The dClip‐LNP/siRNA represents a robust vaccine platform for personalized cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Context: Cross-project defect prediction (CPDP), due to the potential of adaption by industry in realistic scenarios, had gained significant attention from the research community. Currently, existing CPDP studies use static statistical features designed by experts, which might not capture the semantic and structural aspects of software, resulting in low accuracy in defect prediction. Meanwhile, they tend to overlook the valuable iterative information brought about by version updates in mature software projects. Objective: This paper introduces DETECTOR, a novel CPDP approach based on coDE semanTic and cross-vErsion struCTural infORmation to leverage cross-versions features of the software and improve the performance of CPDP. Methods: DETECTOR parses source code to exploit Abstract Syntax Trees (ASTs) and cross-version software network (Cross-SN) that consists of internal class dependency network and cross-version class dependency edges. It utilizes Attention-based Bi-LSTM and simplified graph convolutional neural networks to automatically extract software features from ASTs and Cross-SN. The extracted features are fused using gate(⋅) to generate more effective cross-version features. Finally the source project is selected to carry out the data used to train the classifier to predict the defects. Results: Empirical studies on seven open-source Java projects, the experiment results show that: (1) DETECTOR outperforms the state-of-the-art models in CPDP; (2) our proposed cross-version dependent edges positively contribute to DETECTOR performance; (3) gate(⋅) outperforms existing strategies in fusion features; (4) more multi-versions information enhance DETECTOR's performance. Conclusion: DETECTOR can predict more defects in CPDP and improve the accuracy and effectiveness of prediction. [ABSTRACT FROM AUTHOR]

Global human‐induced warming has intensified water circulation in the atmospheric environment and altered the streamflow generation regime. The VIC hydrological model approach for impact assessment of climate change and human activities mainly focuses on variations in streamflow, but ignores other critical flooding characteristics induced by extreme streamflow, especially bivariate flooding characteristics. In this work, the copula functions are employed to structure the flooding risk under the shared socioeconomic pathway (SSP) across the Yellow River basin (YRB). This is based on the multi‐model ensemble (MME) and Delta downscaling outputs (Delta‐MME) of the CMIP6 global climate models (GCMs), as well as the flooding characteristics simulated by VIC hydrological model. Compared to the reference period (1995–2014), Delta‐MME reveals a significant warming and humidifying trend under three SSPs over the YRB. Despite uncertainties originating from climate variables and hydrological model, multiple findings underscore the substantial influence of climate change on the flooding generation regime in YRB. This includes: (a) an increase in the streamflow under all SSPs; (b) a larger flooding peak (Q) and volume (W) under SSP585, with Q and W at the Huayuankou hydrologic station (HYK) increasing by 52.7% and 44.8%, respectively; (c) an advancement in the bivariate flooding risk, particularly in SSP585 where flooding co‐occurrence return period at HYK may be more than 50 times earlier. This study underscores that the urgent need to enhance social resilience to climate change in the YRB. Plain Language Summary: The YRB, as the second‐longest river in China, is confronted with the threat of catastrophic flooding hazards. The CMIP6, representing the latest climate prediction products, was developed by Earth System Grid Federation. This research aims to propose a novel perspective for predicting future flooding risks along the YRB under all SSPs. The findings of this research could hold great potential for informing mitigation and adaptation policies regarding flooding hazards in the YRB. Key Points: The VIC hydrologic model is employed to simulate flooding magnitude to climate change over the Yellow River basinFlooding magnitude over the Yellow River basin become more fluctuating under three SSPsFuture return period levels for flooding peak and volume have reduced significantly from the historical period, with the co‐occurrence return period being more prominent than the joint return period [ABSTRACT FROM AUTHOR]

The correction practice of scientific publications is usually used to correct publication errors by issuing correction notices, and it is less explored compared to retraction practice. The aim of this study is to present an overview of correction practice and to explore the citation situation of correction notices in library and information science (LIS) journals, using 720 correction notices in the Web of Science from 2001–2020. Through bibliometrics and content analysis, we found the correcting rate of LIS was relatively low. The main types of corrected errors occurred in authorship, figure or table, references, etc. Most corrected errors were trivial or minor. The citation situation of correction notices was more complex than expected and could be classified into five types. It was relatively rare to cite both the corrected paper and correction notice in a standardized manner. The remaining four types of citation were unreasonable, which could influence citation practice and reduce the citations of corrected papers. We concluded that the appearance of the correction notice had affected the citation of the corrected papers to some extent, and researchers and databases needed to pay attention to this problem. We also provided some suggestions for improving correction practice. [ABSTRACT FROM AUTHOR]

Melanoma is one of the most aggressive types of cancer and is prone to metastasis, making current clinical treatment quite difficult. The usage of the first-line medication dacarbazine (DTIC) for melanoma is limited due to harsh side effects, limited water solubility, and a short half-life. To tackle these disadvantages, polylactic acid-hydroxyacetic acid copolymer nanoparticles (NPs) loaded with dacarbazine and ursolic acid (NPs) were fabricated, which were further encapsulated with a red blood cell membrane (RNPs). MTT, apoptosis assay, wound healing assay, colony formation assay, and immunohistochemistry were used to assess the antitumor effect of NPs and RNPs. Ferroptosis evaluation was implemented using GSH detection and the malondialdehyde assay. We found that RNPs exhibited stability and biosafety in vitro and in vivo and achieved superior anticancer ability against xenograft tumors compared with single agents and NPs, which indicated the synergistic and biomimetic efficacy. Furthermore, ferroptotic activity was observed in RNPs-treated tumor cells, and ferroptosis inhibition could partially rescue melanoma cells from RNPs-induced cell death. Collectively, this study evaluated the potential of RNPs as a novel biomimetic nanomedicine for synergistic melanoma therapy by eliciting ferroptosis in tumor cells with both anticancer activity and biosafety., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)