Some Qualitative Properties of Solutions of Quasilinear Elliptic Equations and Applications

AbstractWe study quasilinear elliptic equations of Leray–Lions type in W1, p(Ω), maximum principles, nonexistence and existence of solutions, the control of lower (upper) bound for essential supremum (essential infimum) of solutions, sign-changing solutions, local and global oscillation of solutions, geometry of domain, generating singularities of solutions, and lower bounds on constants appearing in Schauder, Agmon, Douglis, and Nirenberg estimates

Diversity of Haloquadratum and other haloarchaea in three, geographically distant, Australian saltern crystallizer ponds

Haloquadratum walsbyi is frequently a dominant member of the microbial communities in hypersaline waters. 16S rRNA gene sequences indicate that divergence within this species is very low but relatively few sites have been examined, particularly in the southern hemisphere. The diversity of Haloquadratum was examined in three coastal, but geographically distant saltern crystallizer ponds in Australia, using both culture-independent and culture-dependent methods. Two 97%-OTU, comprising Haloquadratum- and Halorubrum-related sequences, were shared by all three sites, with the former OTU representing about 40% of the sequences recovered at each site. Sequences 99.5% identical to that of Hqr. walsbyi C23T were present at all three sites and, overall, 98% of the Haloquadratum-related sequences displayed ≤2% divergence from that of the type strain. While haloarchaeal diversity at each site was relatively low (9–16 OTUs), seven phylogroups (clones and/or isolates) and 4 different clones showed ≤90% sequence identity to classified taxa, and appear to represent novel genera. Six of these branched together in phylogenetic tree reconstructions, forming a clade (MSP8-clade) whose members were only distantly related to classified taxa. Such sequences have only rarely been previously detected but were found at all three Australian crystallizers

The performance of stochastic designs in wellbore drilling operations

© 2018, The Author(s). Wellbore drilling operations frequently entail the combination of a wide range of variables. This is underpinned by the numerous factors that must be considered in order to ensure safety and productivity. The heterogeneity and sometimes unpredictable behaviour of underground systems increases the sensitivity of drilling activities. Quite often the operating parameters are set to certify effective and efficient working processes. However, failings in the management of drilling and operating conditions sometimes result in catastrophes such as well collapse or fluid loss. This study investigates the hypothesis that optimising drilling parameters, for instance mud pressure, is crucial if the margin of safe operating conditions is to be properly defined. This was conducted via two main stages: first a deterministic analysis—where the operating conditions are predicted by conventional modelling procedures—and then a probabilistic analysis via stochastic simulations—where a window of optimised operation conditions can be obtained. The outcome of additional stochastic analyses can be used to improve results derived from deterministic models. The incorporation of stochastic techniques in the evaluation of wellbore instability indicates that margins of the safe mud weight window are adjustable and can be extended considerably beyond the limits of deterministic predictions. The safe mud window is influenced and hence can also be amended based on the degree of uncertainty and the permissible level of confidence. The refinement of results from deterministic analyses by additional stochastic simulations is vital if a more accurate and reliable representation of safe in situ and operating conditions is to be obtained during wellbore operations.Published versio

Contemporary Challenges and Solutions

CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.publishersversionpublishe

From community approaches to single-cell genomics: the discovery of ubiquitous hyperhalophilic Bacteroidetes generalists

The microbiota of multi-pond solar salterns around the world has been analyzed using a variety of culture-dependent and molecular techniques. However, studies addressing the dynamic nature of these systems are very scarce. Here we have characterized the temporal variation during 1 year of the microbiota of five ponds with increasing salinity (from 18% to >40%), by means of CARD-FISH and DGGE. Microbial community structure was statistically correlated with several environmental parameters, including ionic composition and meteorological factors, indicating that the microbial community was dynamic as specific phylotypes appeared only at certain times of the year. In addition to total salinity, microbial composition was strongly influenced by temperature and specific ionic composition. Remarkably, DGGE analyses unveiled the presence of most phylotypes previously detected in hypersaline systems using metagenomics and other molecular techniques, such as the very abundant Haloquadratum and Salinibacter representatives or the recently described low GC Actinobacteria and Nanohaloarchaeota. In addition, an uncultured group of Bacteroidetes was present along the whole range of salinity. Database searches indicated a previously unrecognized widespread distribution of this phylotype. Single-cell genome analysis of five members of this group suggested a set of metabolic characteristics that could provide competitive advantages in hypersaline environments, such as polymer degradation capabilities, the presence of retinal-binding light-activated proton pumps and arsenate reduction potential. In addition, the fairly high metagenomic fragment recruitment obtained for these single cells in both the intermediate and hypersaline ponds further confirm the DGGE data and point to the generalist lifestyle of this new Bacteroidetes group.This work was supported by the projects CGL2012-39627-C03-01 and 02 of the Spanish Ministry of Economy and Competitiveness, which were also co-financed with FEDER support from the European Union. TG group research is funded in part by a grant from the Spanish Ministry of Economy and Competitiveness (BIO2012-37161), a grant from the Qatar National Research Fund grant (NPRP 5-298-3-086) and a grant from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC (grant agreement no. ERC-2012-StG-310325)

Comparative analysis of yellow microbial communities growing on the walls of geographically distinct caves indicates a common core of microorganisms involved in their formation

Morphologically similar microbial communities that often form on the walls of geographically distinct limestone caves have not yet been comparatively studied. Here, we analysed phylotype distribution in yellow microbial community samples obtained from the walls of distinct caves located in Spain, Czech Republic and Slovenia. To infer the level of similarity in microbial community membership, we analysed inserts of 474 16S rRNA gene clones and compared those using statistical tools. The results show that the microbial communities under investigation are composed solely of Bacteria. The obtained phylotypes formed three distinct groups of operational taxonomic units (OTUs). About 60% of obtained sequences formed three core OTUs common to all three sampling sites. These were affiliated with actinobacterial Pseudonocardinae (30-50% of sequences in individual sampling site libraries), but also with gammaproteobacterial Chromatiales (6-25%) and Xanthomonadales (0.5-2.0%). Another 7% of sequences were common to two sampling sites and formed eight OTUs, while the remaining 35% were site specific and corresponded mostly to OTUs containing single sequences. The same pattern was observed when these data were compared with sequence data available from similar studies. This comparison showed that distinct limestone caves support microbial communities composed mostly of phylotypes common to all sampling sites. © 2012 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.Peer Reviewe