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0001), but also a statistically significant difference was achieved when comparing AS patients at a mild degree stage with control individuals. We found a strong relationship of GDF-15 levels regarding AS severity degree (p less then 0.0001). VEGF-A, FGF-2 and FGF-21 levels were significantly higher in AS patients than in controls, but relationships regarding the AS severity degree were weaker (p less then 0.02). ROC analysis of the study growth factors showed that GDF-15 might serve as a specific and sensitive biomarker of AS stenosis (AUC = 0.75, p = 0.0002). FGF-21 correlated with GDF-15, Ang-2, and FGF-2, but it did not reach the level to serve as a clinically relevant biomarker of AS stenosis. Conclusions AS is associated with significantly increased GDF-15, VEGF-A, FGF-2, and FGF-21 levels in plasma, but only GDF-15 shows a pronounced relationship regarding AS severity degree, and GDF-15 might serve as a specific and sensitive biomarker of AS stenosis.Bacteria currently included in Rhizobium leguminosarum are too diverse to be considered a single species, so we can refer to this as a species complex (the Rlc). We have found 429 publicly available genome sequences that fall within the Rlc and these show that the Rlc is a distinct entity, well separated from other species in the genus. Its sister taxon is R. anhuiense. We constructed a phylogeny based on concatenated sequences of 120 universal (core) genes, and calculated pairwise average nucleotide identity (ANI) between all genomes. From these analyses, we concluded that the Rlc includes 18 distinct genospecies, plus 7 unique strains that are not placed in these genospecies. Each genospecies is separated by a distinct gap in ANI values, usually at approximately 96% ANI, implying that it is a 'natural' unit. Five of the genospecies include the type strains of named species R. laguerreae, R. sophorae, R. ruizarguesonis, "R. indicum" and R. leguminosarum itself. The 16S ribosomal RNA sequence is remarkably diverse within the Rlc, but does not distinguish the genospecies. Partial sequences of housekeeping genes, which have frequently been used to characterize isolate collections, can mostly be assigned unambiguously to a genospecies, but alleles within a genospecies do not always form a clade, so single genes are not a reliable guide to the true phylogeny of the strains. We conclude that access to a large number of genome sequences is a powerful tool for characterizing the diversity of bacteria, and that taxonomic conclusions should be based on all available genome sequences, not just those of type strains.Polyphosphates (polyP) are polymers of orthophosphate residues linked by high-energy phosphoanhydride bonds that are important in all domains of life and function in many different processes, including biofilm development. To study the effect of polyP in archaeal biofilm formation, our previously described Sa. solfataricus polyP (-) strain and a new polyP (-) S. acidocaldarius strain generated in this report were used. These two strains lack the polymer due to the overexpression of their respective exopolyphosphatase gene (ppx). Both strains showed a reduction in biofilm formation, decreased motility on semi-solid plates and a diminished adherence to glass surfaces as seen by DAPI (4',6-diamidino-2-phenylindole) staining using fluorescence microscopy. Even though arlB (encoding the archaellum subunit) was highly upregulated in S. acidocardarius polyP (-), no archaellated cells were observed. These results suggest that polyP might be involved in the regulation of the expression of archaellum components and their assembly, possibly by affecting energy availability, phosphorylation or other phenomena. Artenimol This is the first evidence indicating polyP affects biofilm formation and other related processes in archaea.d-aspartate oxidase (DDO) catalyzes the oxidative deamination of acidic d-amino acids, and its production is induced by d-Asp in several eukaryotes. The yeast Cryptococcus humicola strain UJ1 produces large amounts of DDO (ChDDO) only in the presence of d-Asp. In this study, we analyzed the relationship between d-Asp uptake by an amino acid permease (Aap) and the inducible expression of ChDDO. We identified two acidic Aap homologs, named "ChAap4 and ChAap5," in the yeast genome sequence. ChAAP4 deletion resulted in partial growth defects on d-Asp as well as l-Asp, l-Glu, and l-Phe at pH 7, whereas ChAAP5 deletion caused partial growth defects on l-Phe and l-Lys, suggesting that ChAap4 might participate in d-Asp uptake as an acidic Aap. Interestingly, the growth of the Chaap4 strain on d- or l-Asp was completely abolished at pH 10, suggesting that ChAap4 is the only Aap responsible for d- and l-Asp uptake under high alkaline conditions. In addition, ChAAP4 deletion significantly decreased the induction of DDO activity and ChDDO transcription in the presence of d-Asp. This study revealed that d-Asp uptake by ChAap4 might be involved in the induction of ChDDO expression by d-Asp.Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical physics, have enabled probabilistic modeling to overcome these limitations (i) Approximate probabilistic inference is now possible over a broad class of probabilistic models containing a large number of parameters, and (ii) scalable inference methods based on stochastic gradient descent and distributed computing engines allow probabilistic modeling to be applied to massive data sets. One important practical consequence of these advances is the possibility to include deep neural networks within probabilistic models, thereby capturing complex non-linear stochastic relationships between the random variables. These advances, in conjunction with the release of novel probabilistic modeling toolboxes, have greatly expanded the scope of applications of probabilistic models, and allowed the models to take advantage of the recent strides made by the deep learning community. In this paper, we provide an overview of the main concepts, methods, and tools needed to use deep neural networks within a probabilistic modeling framework.