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Background: The rechallenge of immune checkpoint inhibitors (ICI) is now an optional strategy for patients who discontinued ICI due to immune-related adverse events (irAEs) or disease progression. However, little data is available for the prognosis and prognostic factors of patients receiving ICI rechallenge treatment in advanced lung cancer patients. Our study aimed to explore the efficacy, prognosis and safety of patients who received anti-programmed cell death-1/programmed cell death ligand 1 (anti-PD-1/PD-L1) inhibitor rechallenge. Methods: In our retrospective cohort study, data of advanced lung cancer patients who received anti-PD-1/PD-L1 inhibitor and discontinued due to irAEs or disease progression were collected from December 2016 to August 2021. Enrolled patients were categorized into two groups: rechallenge group (R group) and non-rechallenge group (NR group). Progression-free survival (PFS), overall survival (OS), disease control rate (DCR) and safety data were analyzed. Cox model and subgroup analysis were analyzed according to baseline characteristics, ICI type, the reason for discontinuing ICI, etc. According to Response Evaluation Criteria in Solid Tumors Version 1.1 (RECIST 1.1), evaluation was performed routinely every 6-8 weeks after initiating treatment with the PD-1/PD-L1 inhibitor. The last follow-up in the study was on September 20, 2021. Results: Eighty-one patients who met our inclusion criteria were enrolled. In the whole cohort, the R group achieved better OS than the NR group [hazard ratio (HR) =0.176; 95% confidence interval (CI): 0.065-0.477; P=0.001). In the irAEs group, the survival analyses showed a trend toward improved OS in the rechallenge subgroup (HR =0.287; 95% CI: 0.081-1.025; P=0.055), and a promising DCR of 75% after an ICI rechallenge. Additionally, the exploration of safety outcomes indicated an acceptable recurrence rate (22.5%) of irAEs and an early onset of irAEs after an ICI rechallenge. In the disease progression g

The multiscale autoregressive (MAR) framework was introduced in the last decade to process signals that exhibit multiscale features. It provides the method for identifying the multiscale structure in signals and a filtering procedure, and thus is an efficient way to solve the optimal estimation problem for many high-dimensional dynamic systems. Later, an ensemble version of this multiscale filtering procedure, the ensemble multiscale filter (EnMSF), was developed for estimation systems that rely on Monte Carlo samples, making this technique suitable for a range of applications in geosciences. Following the prototype study that introduced EnMSF, a strategy is devised here to implement the multiscale method in a hydrologic data assimilation system, which runs a land surface model. Assimilation experiments are carried out over the Arkansas–Red River basin, located in the central United States (∼645 000 km2), using the Variable Infiltration Capacity (VIC) model with a computing grid of 1062 pixels. A synthetic data assimilation experiment is performed, driven by meteorological forcing fields downscaled from the ensemble forecasts made by the NOAA/National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The classic full-rank ensemble Kalman filter is used as the benchmark to evaluate the multiscale filter performance, and comparisons are also made with a horizontally uncoupled filter. It is demonstrated that the multiscale filter is able to closely approximate the full-rank solution with a low computational cost (∼1/20 of the full-rank filter) in an experiment in which the top-layer soil moisture is assimilated, whereas the horizontally uncoupled filter fails to approximate the full-rank solution.

Skilful seasonal climate forecasts have potential to affect decision making in agriculture, health and water management. Organizations such as the National Oceanic and Atmospheric Administration (NOAA) are currently planning to move towards a climate services paradigm, which will rest heavily on skilful forecasts at seasonal (1 to 9 months) timescales from coupled atmosphere-land-ocean models. We present a careful analysis of the predictive skill of temperature and precipitation from eight seasonal climate forecast models with the joint distribution of observations and forecasts. Using the correlation coefficient, a shift in the conditional distribution of the observations given a forecast can be detected, which determines the usefulness of the forecast for applications. Results suggest there is a deficiency of skill in the forecasts beyond month-1, with precipitation having a more pronounced drop in skill than temperature. At long lead times only the equatorial Pacific Ocean exhibits significant skill. This could have an influence on the planned use of seasonal forecasts in climate services and these results may also be seen as a benchmark of current climate prediction capability using (dynamic) couple models.