BACKGROUND The Islet Autoantibody Standardization Program (IASP) aims to improve the performance of immunoassays measuring type 1 diabetes (T1D)-associated autoantibodies and the concordance of results among laboratories. IASP organizes international interlaboratory assay comparison studies in which blinded serum samples are distributed to participating laboratories, followed by centralized collection and analysis of results, providing participants with an unbiased comparative assessment. In this report, we describe the results of glutamic acid decarboxylase autoantibody (GADA) assays presented in the IASP 2018 workshop. METHODS In May 2018, IASP distributed to participants uniquely coded sera from 43 new-onset T1D patients, 7 multiple autoantibody-positive nondiabetic individuals, and 90 blood donors. Results were analyzed for the following metrics: sensitivity, specificity, accuracy, area under the ROC curve (ROC-AUC), partial ROC-AUC at 95% specificity (pAUC95), and concordance of qualitative and quantitative results. RESULTS Thirty-seven laboratories submitted results from a total of 48 different GADA assays adopting 9 different formats. The median ROC-AUC and pAUC95 of all assays were 0.87 [interquartile range (IQR), 0.83–0.89] and 0.036 (IQR, 0.032–0.039), respectively. Large differences in pAUC95 (range, 0.001–0.0411) were observed across assays. Of formats widely adopted, bridge ELISAs showed the best median pAUC95 (0.039; range, 0.036–0.041). CONCLUSIONS Several novel assay formats submitted to this study showed heterogeneous performance. In 2018, the majority of the best performing GADA immunoassays consisted of novel or established nonradioactive tests that proved on a par or superior to the radiobinding assay, the previous gold standard assay format for GADA measurement.

The impact of grazing activity on terrestrial carbon (C) sequestration has been noticed and studied worldwide. Recent efforts have been made to incorporate the disturbance into process-based land models. However, the performance of grazing models has not been well investigated at large scales. In this study, we performed a spatially explicit model uncertainty assessment in the world's largest pasture ecosystem, the temperate Eurasian Steppe. Five grazing models were explicitly incorporated into a single terrestrial biogeochemical model to simulate regional C consumption from grazing activity (C graze ). First, we summarized the underlying mechanisms and explicitly compared the general functions used to describe the processes in different models. Then, the models (five models with 12 simulations) were run in parallel using the same forcing data and livestock distribution map in 2006. Results indicated that the modeled regional C graze varied from 0.1-16.1 gC m −2 for the year. The corresponding ratios of C graze to aboveground net primary productivity ANPP and net primary productivity (NPP) ranged from 0.08%-24.6% and 0.028%-11.2%, respectively. Parameter sensitivity was further analyzed. Model outputs are highly sensitive to the intake rate (i.e. feeding rate of livestock per day), half maximum intake rate, and initial livestock weight. Our results indicate that great uncertainty exists in simulating C graze . We ascribed the major uncertainty to the different process description and poor parameterization. This study calls for more efforts to the comprehensive synthesis of usable dataset, the foundation of a standard observation system and the observe-based inter-comparison to evaluate models, which would facilitate more accurate assessment of C sequestration by pasture ecosystems and lead to better representation in earth system models.

Background People are at a high risk of gastric cancer if their first-degree relatives suffered from atrophic gastritis (AG), intestinal metaplasia (IM), intraepithelial neoplasia (IEN), dysplasia (DYS), or gastric cancer (GC). This study was performed to analyse the association between FDR-GC and GC precursors. Methods A cross-sectional study was performed to screen the prevalence of GC precursors from November 2016 to September 2019. A total of 1329 participants with FDR-GC, 193 participants with a family history of non-gastric cancer in FDRs (FDR-nGC), and 860 participants without a family history of cancer in FDRs (FDR-nC) were recruited in this study. The logistic regression model was used in this study. Results The prevalence of normal, Non-AG, AG/IM, IEN/DYS, and GC was 31.91, 44.21, 13.81, 8.73, and 1.34%, respectively. The prevalence of IEN/DYS was higher in people with FDR-GC and FDR-nGC (FDR-GC: odds ratio (OR) = 1.655; 95%CI, 1.153–2.376; FDR-nGC: OR = 1.984; 95%CI, 1.122–3.506) than those with FDR-nC. The younger the age at which FDRs were diagnosed with GC, the more likely the participants were to develop AG/IM (Ptrend = 0.019). The risk of precursors to GC was higher in participants whose FDR-GC was the mother than in those whose FDR-GC was the father or sibling (OR, non-AG: 1.312 vs. 1.007, 1.274; AG/IM: 1.430 vs. 1.296, 1.378; IEN/DYS: 1.988 vs. 1.573, 1.542). There was no statistically significant difference in non-AG (OR = 1.700; 95%CI, 0.940–3.074), AG/IM (OR = 1.291; 95%CI, 0.579–2.877), and IEN/DYS (OR = 1.265; 95%CI, 0.517–3.096) between participants with one or more FDR-GC. Conclusion People with FDR-GC and FDR-nGC are at a high risk of IEN/DYS. When an FDR was diagnosed at a younger age, the risk of AG/IM was higher. The risk of GC precursors was higher in people whose FDR-GC was the mother.

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