Notes

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Many existing models that predict landslide hazards utilize ground-based sources of precipitation data. In locations where ground-based precipitation observations are limited (i.e., a vast majority of the globe), or for landslide hazard models that assess regional or global domains, satellite multisensor precipitation products offer a promising near-real-time alternative to ground-based data. NASA's global Landslide Hazard Assessment for Situational Awareness (LHASA) model uses the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) product to issue hazard "nowcasts" in near-real time for areas that are currently at risk for landsliding. Satellite-based precipitation estimates, however, can contain considerable systematic bias and random error, especially over mountainous terrain and during extreme rainfall events. This study combines a precipitation error modeling framework with a probabilistic adaptation of LHASA. Compared with the routine version of LHASA, this probabilistic version correctly predicts more of the observed landslides in the study region with fewer false alarms by high hazard nowcasts. PF-04965842 purchase This study demonstrates that improvements in landslide hazard prediction can be achieved regardless of whether the IMERG error model is trained using abundant ground-based precipitation observations or using far fewer and more scattered observations, suggesting that the approach is viable in data-limited regions. Results emphasize the importance of accounting for both random error and systematic satellite precipitation bias. The approach provides an example of how environmental prediction models can incorporate satellite precipitation uncertainty. Other applications such as flood and drought monitoring and forecasting could likely benefit from consideration of precipitation uncertainty.The launch of NOAA's latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to SCaMPR. However, the analysis indicated that that the major portion of error in SCaMPR is propagated from the MWCOMB calibration data. The major challenge starts with poor detection from MWCOMB, which propagates in SCaMPR. In particular, MWCOMB misses 90% of cool stratiform precipitation and the overall detection score is around 40%. The ability of the algorithms to quantify precipitation amounts for the Warm Stratiform, Cool Stratiform, and Tropical/Stratiform Mix categories is poor compared to the Convective and Tropical/Convective Mix categories with additional challenges in complex terrain regions. Further analysis showed strong similarities in systematic and random error models with both products. This suggests that the potential of high-resolution GOES-R observations remains underutilized in SCaMPR due to the errors from the calibrator MWCOMB.The Comparative Panel File (CPF) harmonizes the world's largest and longest-running household panel surveys from seven countries Australia (HILDA), Germany (SOEP), United Kingdom (BHPS and UKHLS), South Korea (KLIPS), Russia (RLMS), Switzerland (SHP), and the United States (PSID). The project aims to support the social science community in the analysis of comparative life course data. The CPF builds on the Cross-National Equivalent File but offers a larger range of variables, larger and more recent samples, an easier and more flexible workflow, and an open science platform for development. The CPF is not a data product but an open-source code that integrates individual and household panel data from all seven surveys into a harmonized three-level data structure. The CPF allows analysing individual trajectories, time trends, contextual effects, and country differences. The project is organized as an open science platform. The CPF version 1.0 contains 2.7 million observations from 360,000 respondents, covering the period from 1968 to 2019 and up to 40 panel waves per respondent. In this data brief, we present the background, design, and content of the CPF.
To study airway pathophysiology and the role of dendritic cells (DCs) and IL-17 receptor (IL-17R) signals in a mouse model for CBD.

Here, we present a CBD mouse model in which mice were exposed to beryllium during three weeks. We also exposed IL-17R-deficient mice and mice in which DCs were depleted.

Eight weeks after the initial beryllium exposure, an inflammatory response was detected in the lungs. Mice displayed inflammation of the lower airways that included focal dense infiltrates, granuloma-like foci, and tertiary lymphoid structure (TLS) containing T cells, B cells, and germinal centers. Alveolar cell analysis showed significantly increased numbers of CD4
T cells expressing IFN
, IL-17, or both cytokines. The pathogenic role of IL-17R signals was demonstrated in IL-17R-deficient mice, which had strongly reduced lung inflammation and TLS development following beryllium exposure. In CBD mice, pulmonary DC subsets including CD103
conventional DCs (cDCs), CD11b
cDCs, and monocyte-derived DCs (moDCs) were also prominently increased. We used diphtheria toxin receptor-mediated targeted cell ablation to conditionally deplete DCs and found that DCs are essential for the maintenance of TLS in CBD. Furthermore, the presence of antinuclear autoantibodies in the serum of CBD mice showed that CBD had characteristics of autoimmune disease.

We generated a translational model of sarcoidosis driven by beryllium and show that DCs and IL-17R signals play a pathophysiological role in CBD development as well as in established CBD in vivo.
We generated a translational model of sarcoidosis driven by beryllium and show that DCs and IL-17R signals play a pathophysiological role in CBD development as well as in established CBD in vivo.
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