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Soil degradation by water is a serious environmental problem worldwide, with specific climatic factors being the major causes. We investigated the relationships between synoptic atmospheric patterns (i.e. weather types, WTs) and runoff, erosion and sediment yield throughout the Mediterranean basin by analyzing a large database of natural rainfall events at 68 research sites in 9 countries. Principal Component Analysis (PCA) was used to identify spatial relationships of the different WTs including three hydro-sedimentary variables: rainfall, runoff, and sediment yield (SY, used to refer to both soil erosion measured at plot scale and sediment yield registered at catchment scale). The results indicated 4 spatial classes of rainfall and runoff: (a) northern sites dependent on North (N) and North West (NW) flows; (b) eastern sites dependent on E and NE flows; (c) southern sites dependent on S and SE flows; and, finally, (d) western sites dependent on W and SW flows. Conversely, three spatial classes are identified for SY characterized by: (a) N and NE flows in northern sites (b) E flows in eastern sites, and (c) W and SW flows in western sites. Most of the rainfall, runoff and SY occurred during a small number of daily events, and just a few WTs accounted for large percentages of the total. Our results confirm that characterization by WT improves understanding of the general conditions under which runoff and SY occur, and provides useful information for understanding the spatial variability of runoff, and SY throughout the Mediterranean basin. The approach used here could be useful to aid of the design of regional water management and soil conservation measures.
Data integration has become a useful strategy for uncovering new insights into complex biological networks. We studied whether this approach can help to delineate the signal transducer and activator of transcription 6 (STAT6)-mediated transcriptional network driving T helper (Th) 2 cell fate decisions. To this end, we performed an integrative analysis of publicly available RNA-seq data of Stat6-knockout mouse studies together with STAT6 ChIP-seq data and our own gene expression time series data during Th2 cell differentiation. We focused on transcription factors (TFs), cytokines, and cytokine receptors and delineated 59 positively and 41 negatively STAT6-regulated genes, which were used to construct a transcriptional network around STAT6. The network illustrates that important and well-known TFs for Th2 cell differentiation are positively regulated by STAT6 and act either as activators for Th2 cells (e.g., Gata3, Atf3, Satb1, Nfil3, Maf, and Pparg) or as suppressors for other Th cell subpopulations such as Th1 (e.g., Ar), Th17 (e.g., Etv6), or iTreg (e.g., Stat3 and Hifla) cells. Moreover, our approach reveals 11 TFs (e.g., Atf5, Creb3l2, and Asb2) with unknown functions in Th cell differentiation. This fact together with the observed enrichment of asthma risk genes among those regulated by STAT6 underlines the potential value of the data integration strategy used here. Thus, our results clearly support the opinion that data integration is a useful tool to delineate complex physiological processes.