Ancient genome-wide analyses infer kinship structure in an Early Medieval Alemannic graveyard

From historical and archeological records, it is posited that the European medieval household was a combination of close relatives and recruits. However, this kinship structure has not yet been directly tested at a genomic level on medieval burials. The early 7th century CE burial at Niederstotzingen, discovered in 1962, is the most complete and richest example of Alemannic funerary practice in Germany. Excavations found 13 individuals who were buried with an array of inscribed bridle gear, jewelry, armor, and swords. These artifacts support the view that the individuals had contact with France, northern Italy, and Byzantium. This study analyzed genome-wide sequences recovered from the remains, in tandem with analysis of the archeological context, to reconstruct kinship and the extent of outside contact. Eleven individuals had sufficient DNA preservation to genetically sex them as male and identify nine unique mitochondrial haplotypes and two distinct Y chromosome lineages. Genome-wide analyses were performed on eight individuals to estimate genetic affiliation to modern west Eurasians and genetic kinship at the burial. Five individuals were direct relatives. Three other individuals were not detectably related; two of these showed genomic affinity to southern Europeans. The genetic makeup of the individuals shares no observable pattern with their orientation in the burial or the cultural association of their grave goods, with the five related individuals buried with grave goods associated with three diverse cultural origins. These findings support the idea that not only were kinship and fellowship held in equal regard: Diverse cultural appropriation was practiced among closely related individuals as well.© 2018 The Author

Strategies to improve the explanatory power of a dynamic slope stability model by enhancing land cover parameterisation and model complexity

Despite the importance of land cover on landscape hydrology and slope stability, the representation of land cover dynamics in physically based models and their associated ecohydrological effects on slope stability is rather scarce. In this study, we assess the impact of different levels of complexity in land cover parameterisation on the explanatory power of a dynamic and process‐based spatial slope stability model. Firstly, we present available and collected data sets and account for the stepwise parameterisation of the model. Secondly, we present approaches to simulate land cover: 1) a grassland landscape without forest coverage; 2) spatially static forest conditions, in which we assume limited knowledge about forest composition; 3) more detailed information of forested areas based on the computation of leaf area development and the implementation of vegetation‐related processes; 4) similar to the third approach but with the additional consideration of the spatial expansion and vertical growth of vegetation. Lastly, the model is calibrated based on meteorological data sets and groundwater measurements. The model results are quantitatively validated for two landslide‐triggering events that occurred in Western Austria. Predictive performances are estimated using the Area Under the receiver operating characteristic Curve (AUC). Our findings indicate that the performance of the slope stability model was strongly determined by model complexity and land cover parameterisation. The implementation of leaf area development and land cover dynamics further yield an acceptable predictive performance (AUC ~0.71‐0.75) and a better conservativeness of the predicted unstable areas (FoC ~0.71). The consideration of dynamic land cover expansion provided better performances than the solely consideration of leaf area development. The results of this study highlight that an increase of effort in the land cover parameterisation of a dynamic slope stability model can increase the explanatory power of the model.© 2018 The Author

Local Climate Mitigation and Local Climate Adaptation Plans of European Urban Audit Cities

This database shows the availability of local climate mitigation plans, local climate adaptation plans, municipal energy plans, heat wave plans and flood risk plans of all 885 Urban Audit Core Cities of the 28 countries of the European Union. Additionally, it lists per Urban Audit Core City the membership in the largest climate networks [Old Covenant of Mayors Member (2020 goal); New Covenant of Mayors Member (2030 goal); Status (1-2-3); Mayors Adapt Commitment; Compact of Mayors Member (yes/no); Compact of Mayors Stage (Badge)]. Furthermore, based on a typology of plans developed in Reckien et al., 2018 and Reckien et al., 2019, the database categorizes the plans into types, and specifies whether adaptation and mitigation aspects are jointly addressed in one plan ('joint plan')

PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Abstract Background Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors. Methods We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models. Results The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56–0.74) versus 0.63 (95%PI 0.54–0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34–2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging

Additional file 2 of PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Additional file 2: Table S1. Description of the studies included in the analyses

Additional file 3 of PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Additional file 1: Table S3. Patient and primary breast cancer characteristics per study

Additional file 2 of PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Additional file 2: Table S1. Description of the studies included in the analyses