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AbstractWith the rapid development of data acquisition and storage space, massive data sets exhibited with large sample size emerge increasingly and make more advanced statistical tools urgently need. To accommodate such big volume in the analysis, a variety of methods have been proposed in the circumstances of complete or right censored survival data. However, existing development of big data methodology has not attended to interval-censored outcomes, which are ubiquitous in cross-sectional or periodical follow-up studies. In this work, we propose an easily implemented divide-and-combine approach for analyzing massive interval-censored survival data under the additive hazards model. We establish the asymptotic properties of the proposed estimator, including the consistency and asymptotic normality. In addition, the divide-and-combine estimator is shown to be asymptotically equivalent to the full-data-based estimator obtained from analyzing all data together. Simulation studies suggest that, relative to the full-data-based approach, the proposed divide-and-combine approach has desirable advantage in terms of computation time, making it more applicable to large-scale data analysis. An application to a set of interval-censored data also demonstrates the practical utility of the proposed method. [ABSTRACT FROM AUTHOR]

Based on the McCabe-Thiele (M-T) method and the conservation of mass and energy, the PandaX-4T collaboration group designs an efficient cryogenic distillation system to reduce the mole fraction of krypton in commercial xenon from 5×10-7 to 1×10-14. Since the ultra-high purity xenon cryogenic distillation system has completed the offline purification operation, it is necessary to conduct the corresponding operation analysis. Therefore, the stability and purification performance of the ultra-high purity xenon cryogenic distillation system are studied by analyzing the parameters such as temperature, pressure, flowrate, and mole faction of the product xenon in each operating stage. The PandaX-4T cryogenic distillation system has been operating stably for 1.5 m at a collection efficiency of 99% and a purification rate of 10 kg/h, and has purified 5.75 t of xenon. The experimental data show that the system is stable, safe, and reliable in all stages of operation, and the krypton concentration in product xenon is less than 7.99×10-12. The operation analysis of the PandaX-4T ultra-high purity xenon cryogenic distillation system has a theoretical research value and practical engineering significance, providing very important reference for optimization of distillation operation of the next stage.

To meet the requirements of the low background and high sensitivity of the large-scale dark-matter detector PandaX-4T, nKr/nXe≤1×10 -13 (n is the amount of substance) is required. The ultra-high purity krypton/xenon cryogenic distillation tower was used as an example in this paper. First, the distillation tower and the construction design of the distillation system were briefly introduced. Then, the operation parameters of the distillation tower were simulated and optimized by using HYSYS, and the distributions of the pressure, temperature and Kr concentration in the distillation tower were obtained. After that, the influences of those operational conditions including the feeding point position, the reflux ratio, the feeding flowrate, the heat of the reboiler, the flowrate of the off-gas, and the feeding pressure to the xenon purity of the distillation system were studied, and the optimized operation parameters were proposed. When the column pressure is set at 221 kPa and the reflux radio is set at 145, the krypton content in xenon can be purified from 5×10-7 to 4.1×10-14, which has an important guiding significance for further improvement of the purity of the xenon product of the xenon/krypton cryogenic distillation system.

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Background: Sepsis is an inflammatory disease that leads to severe mortality, highlighting the urgent need to identify new therapeutic strategies for sepsis. Proteomic research serves as a primary source for drug target identification. We employed proteome-wide Mendelian randomization (MR), genetic correlation analysis, and colocalization analysis to identify potential targets for sepsis and sepsis-related death., Methods: Genetic data for plasma proteomics were obtained from 35,559 Icelandic individuals and an initial MR analysis was conducted using 13,531 sepsis cases from the FinnGen R10 cohort to identify associations between plasma proteins and sepsis. Subsequently, significant proteins underwent genetic correlation analysis, followed by replication in 54,306 participants from the UK Biobank Pharma Proteomics Project and validation in 11,643 sepsis cases from the UK Biobank. The identified proteins were then subjected to colocalization analysis, enrichment analysis, and protein-protein interaction network analysis. Additionally, we also investigated a MR analysis using plasma proteins on 1,896 sepsis cases with 28-day mortality from the UK Biobank., Results: After FDR correction, MR analysis results showed a significant causal relationship between 113 plasma proteins and sepsis. Genetic correlation analysis revealed that only 8 proteins had genetic correlations with sepsis. In the UKB-PPP replication analysis, only 4 proteins were found to be closely associated with sepsis, while validation in the UK Biobank sepsis cases found overlaps for 21 proteins. In total, 30 proteins were identified in the aforementioned analyses, and colocalization analysis revealed that only 2 of these proteins were closely associated with sepsis. Additionally, in the 28-day mortality MR analysis of sepsis, we also found that only 2 proteins were significant., Conclusions: The identified plasma proteins and their associated metabolic pathways have enhanced our understanding of the complex relationship between proteins and sepsis. This provides new avenues for the development of drug targets and paves the way for further research in this field., 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 Zhang, Shi, Li, Huang, Chen and Yao.)

Background/aim: We employed Mendelian randomization (MR) analysis to investigate the causal relationship between the gut microbiota, acute pancreatitis, and potential inflammatory proteins., Methods: The data for gut microbiota, acute pancreatitis, and inflammatory proteins are sourced from public databases. We conducted a bidirectional MR analysis to explore the causal relationship between gut microbiota and acute pancreatitis, and employed a two-step MR analysis to identify potential mediating inflammatory proteins. IVW is the primary analysis method, heterogeneity, pleiotropy, and sensitivity analyses were also conducted simultaneously., Results: We identified five bacterial genera associated with the risk of acute pancreatitis, namely genus.Coprococcus3 , genus.Eubacterium fissicatena group , genus.Erysipelotrichaceae UCG-003, genus.Fusicatenibacter, and genus.Ruminiclostridium6 . Additionally, we have discovered three inflammatory proteins that are also associated with the occurrence of acute pancreatitis, namely interleukin-15 receptor subunit alpha (IL-15RA), monocyte chemoattractant protein-4 (CCL13), and tumor necrosis factor receptor superfamily member 9 (TNFRSF9). Following a two-step MR analysis, we ultimately identified IL-15RA as a potential intermediate factor, with a mediated effect of 0.018 (95% CI: 0.005 - 0.032)., Conclusion: Our results support the idea that genus.Coprococcus3 promotes the occurrence of acute pancreatitis through IL-15RA. Furthermore, there is a potential causal relationship between the gut microbiota, inflammatory proteins, and acute pancreatitis. These findings provide new insights for subsequent acute pancreatitis prevention., 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 Huang, Liu, Zhang and Yang.)