Continental data on cave-dwelling spider communities across Europe (Arachnida: Araneae)

Background Spiders (Arachnida: Araneae) are widespread in subterranean ecosystems worldwide and represent an important component of subterranean trophic webs. Yet, global-scale diversity patterns of subterranean spiders are still mostly unknown. In the frame of the CAWEB project, a European joint network of cave arachnologists, we collected data on cave dwelling spider communities across Europe in order to explore their continental diversity patterns. Two main datasets were compiled: one listing all subterranean spider species recorded in numerous subterranean localities across Europe and another with high resolution data about the subterranean habitat in which they were collected. From these two datasets, we further generated a third dataset with individual geo-referenced occurrence records for all these species. New information Data from 475 geo-referenced subterranean localities (caves, mines and other artificial subterranean sites, interstitial habitats) are herein made available. For each subterranean locality, information about the composition of the spider community is provided, along with local geomorphological and habitat features. Altogether, these communities account for > 300 unique taxonomic entities and 2,091 unique geo-referenced occurrence records, that are made available via the Global Biodiversity Information Facility (GBIF) (Mammola and Cardoso 2019). This dataset is unique in that it covers both a large geographic extent (from 35 south to 67 degrees north) and contains high-resolution local data on geomorphological and habitat features. Given that this kind of high-resolution data are rarely associated with broad-scale datasets used in macroecology, this dataset has high potential for helping researchers in tackling a range of biogeographical and macroecological questions, not necessarily uniquely related to arachnology or subterranean biology

Why political ontology must be experimentalized, On ecoshowhomes as devices of participation

This paper contributes to debates about the ontological turn and its implications for democracy by proposing an experimental understanding of political ontology. It discusses why the shift from epistemology to ontology in STS has proved inconclusive for the study of politics and democracy: the politics of non-humans has been assumed to operate on a different level from that of politics and democracy understood as institutional and public forms. I distinguish between three different understandings of political ontology: theoretical, empirical and experimental. Each of these implies a different approach to the problem that non-humans pose for democracy. Theoretical ontology proposes to solve it by conceptual means, while empirical ontology renders it manageable by assuming a problematic analytic separation between constituting and constituted ontology. This paper makes the case for the third approach, experimental ontology, by analysing an empirical site, that of the ecoshowhome. In this setting, material entities are deliberately invested with moral and political capacities. As such, ecoshowhomes help to clarify two main features of experimental political ontology: 1) ontological work is here not so much relocated from theory to empirical practice, but distributed among actors and entities involved in them, and 2) normative variability does not just pertain to the enactment of things, but can be conceived of as internal to political objects. From these two features of experimental ontology something follows for democracy as an ontological problem. This problem does not dissolve in empirical settings, but these settings make possible its articulation by experimental means

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

Deep Learning Improves GFS Wintertime Precipitation Forecast Over Southeastern China

Abstract Wintertime precipitation, especially snowstorms, significantly impacts people's lives. However, the current forecast skill of wintertime precipitation is still low. Based on data augmentation (DA) and deep learning, we propose a DABU‐Net which improves the Global Forecast System wintertime precipitation forecast over southeastern China. We build three independent models for the forecast lead times of 24, 48, and 72 hr, respectively. After using DABU‐Net, the mean Root Mean Squared Errors (RMSEs) of the wintertime precipitation at the three lead times are reduced by 19.08%, 25.00%, and 22.37%, respectively. The threat scores (TS) are all significantly increased at the thresholds of 1, 5, 10, 15, and 20 mm day−1 for the three lead times. During heavy precipitation days, the RMSEs are decreased by 14% and TS are increased by 7% at the lead times within 48 hr. Therefore, combining DA and deep learning has great prospects in precipitation forecasting