Evaluation and Intercomparison of Topographic Correction Methods Based on Landsat Images and Simulated Data

Topographic effects in medium and high spatial resolution remote sensing images greatly limit the application of quantitative parameter retrieval and analysis in mountainous areas. Many topographic correction methods have been proposed to reduce such effects. Comparative analyses on topographic correction algorithms have been carried out, some of which drew different or even contradictory conclusions. Performances of these algorithms over different terrain and surface cover conditions remain largely unknown. In this paper, we intercompared ten widely used topographic correction algorithms by adopting multi-criteria evaluation methods using Landsat images under various terrain and surface cover conditions as well as images simulated by a 3D radiative transfer model. Based on comprehensive analysis, we found that the Teillet regression-based models had the overall best performance in terms of topographic effects’ reduction and overcorrection; however, correction bias may be introduced by Teillet regression models when surface reflectance in the uncorrected images do not follow a normal distribution. We recommend including more simulated images for a more in-depth evaluation. We also recommend that the pros and cons of topographic correction methods reported in this paper should be carefully considered for surface parameters retrieval and applications in mountain regions

Additional file 1 of Validation of the Meet-URO score in metastatic clear cell renal cell carcinoma patients receiving second or third-line tyrosine kinase inhibitors-immune checkpoint inhibitors combination therapy

Additional file 1: Table S1. Summary of application of Meet-URO score in mRCC patients receiving systemic therapies

Crisis communication in the WHO COVID-19 press conferences: A retrospective analysis.

ObjectivesThe objective of this study is to investigate, from a longitudinal perspective, how WHO communicated COVID-19 related information to the public through its press conferences during the first two years of the pandemic.MethodsThe transcripts of 195 WHO COVID-19 press conferences held between January 22, 2020 and February 23, 2022 were collected. All transcripts were syntactically parsed to extract highly frequent noun chunks that were potential topics of the press conferences. First-order autoregression models were fit to identify "hot" and "cold" topics. In addition, sentiments and emotions expressed in the transcripts were analyzed using lexicon-based sentiment/emotion analyses. Mann-Kendall tests were performed to capture the possible trends of sentiments and emotions over time.ResultsFirst, eleven "hot" topics were identified. These topics were pertinent to anti-pandemic measures, disease surveillance and development, and vaccine-related issues. Second, no significant trend was captured in sentiments. Last, significant downward trends were found in anticipation, surprise, anger, disgust, and fear. However, no significant trends were found in joy, trust, and sadness.ConclusionsThis retrospective study provided new empirical evidence on how WHO communicated issues pertaining to COVID-19 to the general public through its press conferences. With the help of the study, members of the general public, health organizations, and other stake-holders will be able to better understand the way in which WHO has responded to various critical events during the first two years of the pandemic

Improved modeling and analysis of the patch size–frequency distribution of forest disturbances in China based on a Landsat forest cover change product

Forest disturbances have been altering the ecological properties of ecosystems; meanwhile, disturbance events of varying sizes create different structures and functions for a forest landscape. Therefore, size and frequency are important attributes of disturbances, and their relationship should be studied. We present a hierarchical method through the modeling of the overall trend of the size–frequency distribution and the characterization of the non-constant variances of disturbance sizes at each frequency level. This method was demonstrated to accurately model the sizes as well as the corresponding frequencies; thus, the total disturbed area and number of disturbance patches were both accurately estimated. By applying the method to 13 provinces in China, consistent patterns were revealed by the modeling results and remote-sensing-based product, showing that between 2000 and 2005, forests in most provinces were dominated by moderate disturbances (10 ha 1000 ha) occurred in the northeastern and northwestern provinces. This study concludes that the proposed method can improve the representation of the size–frequency distribution of forest disturbances

Additional file 1 of Diagnostic performance of metagenomic next-generation sequencing for the detection of pathogens in cerebrospinal fluid in pediatric patients with central nervous system infection: a systematic review and meta-analysis

Additional file 1: Supplementary file. The command used in Stata Software. Supplementary Figure 1. The certainty of evidence measure by GRADE score system. Supplementary Figure 2. Forest plot for the positive likelihood ratio (PLR) of mNGS for the diagnosis of pediatric CNSI. Supplementary Figure 3. Forest plot for the negative likelihood ratio (NLR) of mNGS for the diagnosis of pediatric CNSI. Supplementary Figure 4. Forest plot for the Diagnostic Odd’s Ratio (DOR) Supplementary Table 1. Leave-one-out analysis depicting the pooled sensitivity and specificity