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Programmed cell death (PCD) is a process that eliminates infected, damaged, or possibly neoplastic cells to sustain homeostatic multicellular organisms. Although long noncoding RNAs (lncRNAs) are involved in various types of PCD and regulate tumor growth, invasion, and migration, the role of PCD-related lncRNAs in bladder cancer still lacks systematic exploration. In this research, we integrated multiple types of PCD as pan-PCD and identified eight pan-PCD-related lncRNAs (LINC00174, HCP5, HCG27, UCA1, SNHG15, GHRLOS, CYB561D2, and AGAP11). Then, we generated a pan-PCD-related lncRNA prognostic signature (PPlncPS) with excellent predictive power and reliability, which performed equally well in the E-MTAB-4321 cohort. In comparison with the low-PPlncPS score group, the high-PPlncPS score group had remarkably higher levels of angiogenesis, matrix, cancer-associated fibroblasts, myeloid cell traffic, and protumor cytokine signatures. In addition, the low-PPlncPS score group was positively correlated with relatively abundant immune cell infiltration, upregulated expression levels of immune checkpoints, and high tumor mutation burden (TMB). Immunogenomic profiles revealed that patients with both low PPlncPS scores and high TMB had the best prognosis and may benefit from immune checkpoint inhibitors. Furthermore, for patients with high PPlncPS scores, docetaxel, staurosporine, and luminespib were screened as potential therapeutic candidates. In conclusion, we generated a pan-PCD-related lncRNA signature, providing precise and individualized prediction for clinical prognosis and some new insights into chemotherapy and immune checkpoint inhibitor therapy for bladder cancer. [ABSTRACT FROM AUTHOR]

Fungal diseases of plants are one of the key factors causing global crop losses. In this study, we isolated a Bacillus velezensis strain VC3, which was found to have a broad-spectrum inhibitory effect on a variety of phytopathogenic fungi through in vitro and in planta experiments, especially on Magnaporthe oryzae and Colletotrichum gloeosporioides. Further genomic and transcriptomic analyses revealed that the B. velezensis VC3 has multiple functional gene clusters encoding for the synthesis of a variety of antifungal secondary metabolites, including antimicrobial peptides (AMPs) and lipopeptides (LPs). In addition, AMPs and LPs were isolated and purified from B. velezensis VC3 fermentation broth and their antifungal activities were verified in this study. AMPs and LPs significantly inhibited spore germination, appressorium formation, and disease development, and AMPs have a better potential for controlling M. oryzae and C. gloeosporioides than LPs. These findings open new avenues for utilizing B. velezensis VC3 as biocontrol agents, providing potential sustainable solutions for agricultural production. [ABSTRACT FROM AUTHOR]

Background: Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic infectious disease commonly found in Asia and Europe, characterized by fever, hemorrhage, shock, and renal failure. China is the most severely affected region, necessitating an analysis of the temporal incidence patterns in the country., Methods: We employed Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Nonlinear AutoRegressive with eXogenous inputs (NARX), and a hybrid CNN-LSTM model to model and forecast time series data spanning from January 2009 to November 2023 in the mainland China. By comparing the simulated performance of these models on training and testing sets, we determined the most suitable model., Results: Overall, the CNN-LSTM model demonstrated optimal fitting performance (with Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) of 93.77/270.66, 7.59%/38.96%, and 64.37/189.73 for the training and testing sets, respectively, lower than those of individual CNN or LSTM models)., Conclusion: The hybrid CNN-LSTM model seamlessly integrates CNN's data feature extraction and LSTM's recurrent prediction capabilities, rendering it theoretically applicable for simulating diverse distributed time series data. We recommend that the CNN-LSTM model be considered as a valuable time series analysis tool for disease prediction by policy-makers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Yang, Li, Wu, Xu and Feng.)

Electrochemical CO 2 reduction reaction (eCO 2 RR) is a viable approach to achieve carbon neutrality. Bismuth-based electrocatalysts demonstrate exceptional selectivity in CO 2 -to-formate conversion, but their reconstruction mechanisms during the eCO 2 RR remain elusive. Herein, the reconstruction processes of bismuth molybdate (Bi 2 MoO 6 ) nanoplates are elucidated during the eCO 2 RR. Operando and ex situ measurements reveal the in situ partial reduction of Bi 2 MoO 6 to Bi metal, forming Bi@Bi 2 MoO 6 at negative potentials. Meanwhile, CO 3 2- ions in the electrolyte spontaneously exchange with MoO 4 2- in Bi 2 MoO 6 . The obtained Bi@Bi 2 MoO 6 /Bi 2 O 2 CO 3 delivers a formate Faradaic efficiency (FE) of 95.2% at -1.0 V. Notably, high formate FEs (>90%) are maintained within a wide 500 mV window. Although computational calculations indicate a higher energy barrier for *OCHO formation on Bi 2 O 2 CO 3 , the prevention of excessive reduction to metal Bi significantly enhances long-term stability. Furthermore, the CO 3 2- ion exchange process occurs in various 2D Bi-containing precatalysts, which should be emphasized in further studies.