Synthesis and DFT investigation of new bismuth-containing MAX phases

The M(n + 1)AX(n) phases (M = early transition metal; A = group A element and X = C and N) are materials exhibiting many important metallic and ceramic properties. In the present study powder processing experiments and density functional theory calculations are employed in parallel to examine formation of Zr(2)(Al(1−x)Bi(x))C (0 ≤ x ≤ 1). Here we show that Zr(2)(Al(1−x)Bi(x))C, and particularly with x ≈ 0.58, can be formed from powders even though the end members Zr(2)BiC and Zr(2)AlC seemingly cannot. This represents a significant extension of the MAX phase family, as this is the first report of a bismuth-based MAX phase

Setting limits on Effective Field Theories: the case of Dark Matter

The usage of Effective Field Theories (EFT) for LHC new physics searches is receiving increasing attention. It is thus important to clarify all the aspects related with the applicability of the EFT formalism in the LHC environment, where the large available energy can produce reactions that overcome the maximal range of validity, i.e. the cutoff, of the theory. We show that this does forbid to set rigorous limits on the EFT parameter space through a modified version of the ordinary binned likelihood hypothesis test, which we design and validate. Our limit-setting strategy can be carried on in its full-fledged form by the LHC experimental collaborations, or performed externally to the collaborations, through the Simplified Likelihood approach, by relying on certain approximations. We apply it to the recent CMS mono-jet analysis and derive limits on a Dark Matter (DM) EFT model. DM is selected as a case study because the limited reach on the DM production EFT Wilson coefficient and the structure of the theory suggests that the cutoff might be dangerously low, well within the LHC reach. However our strategy can also be applied to EFT's parametrising the indirect effects of heavy new physics in the Electroweak and Higgs sectors

Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation

Cine cardiac magnetic resonance (CMR) has become the gold standard for the non-invasive evaluation of cardiac function. In particular, it allows the accurate quantification of functional parameters including the chamber volumes and ejection fraction. Deep learning has shown the potential to automate the requisite cardiac structure segmentation. However, the lack of robustness of deep learning models has hindered their widespread clinical adoption. Due to differences in the data characteristics, neural networks trained on data from a specific scanner are not guaranteed to generalise well to data acquired at a different centre or with a different scanner. In this work, we propose a principled solution to the problem of this domain shift. Domain-adversarial learning is used to train a domain-invariant 2D U-Net using labelled and unlabelled data. This approach is evaluated on both seen and unseen domains from the M\&Ms challenge dataset and the domain-adversarial approach shows improved performance as compared to standard training. Additionally, we show that the domain information cannot be recovered from the learned features.Comment: Accepted at the STACOM workshop at MICCAI 202

Hybridising heuristics within an estimation distribution algorithm for examination timetabling

This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyper-heuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-level search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help to facilitate more intelligent hyper-heuristic search methods

Urinary MicroRNA Profiling in the Nephropathy of Type 1 Diabetes

Background: Patients with Type 1 Diabetes (T1D) are particularly vulnerable to development of Diabetic nephropathy (DN) leading to End Stage Renal Disease. Hence a better understanding of the factors affecting kidney disease progression in T1D is urgently needed. In recent years microRNAs have emerged as important post-transcriptional regulators of gene expression in many different health conditions. We hypothesized that urinary microRNA profile of patients will differ in the different stages of diabetic renal disease. Methods and Findings: We studied urine microRNA profiles with qPCR in 40 T1D with >20 year follow up 10 who never developed renal disease (N) matched against 10 patients who went on to develop overt nephropathy (DN), 10 patients with intermittent microalbuminuria (IMA) matched against 10 patients with persistent (PMA) microalbuminuria. A Bayesian procedure was used to normalize and convert raw signals to expression ratios. We applied formal statistical techniques to translate fold changes to profiles of microRNA targets which were then used to make inferences about biological pathways in the Gene Ontology and REACTOME structured vocabularies. A total of 27 microRNAs were found to be present at significantly different levels in different stages of untreated nephropathy. These microRNAs mapped to overlapping pathways pertaining to growth factor signaling and renal fibrosis known to be targeted in diabetic kidney disease. Conclusions: Urinary microRNA profiles differ across the different stages of diabetic nephropathy. Previous work using experimental, clinical chemistry or biopsy samples has demonstrated differential expression of many of these microRNAs in a variety of chronic renal conditions and diabetes. Combining expression ratios of microRNAs with formal inferences about their predicted mRNA targets and associated biological pathways may yield useful markers for early diagnosis and risk stratification of DN in T1D by inferring the alteration of renal molecular processes. © 2013 Argyropoulos et al

HIF-1 and SKN-1 Coordinate the Transcriptional Response to Hydrogen Sulfide in Caenorhabditis elegans

Hydrogen sulfide (H2S) has dramatic physiological effects on animals that are associated with improved survival. C. elegans grown in H2S are long-lived and thermotolerant. To identify mechanisms by which adaptation to H2S effects physiological functions, we have measured transcriptional responses to H2S exposure. Using microarray analysis we observe rapid changes in the abundance of specific mRNAs. The number and magnitude of transcriptional changes increased with the duration of H2S exposure. Functional annotation suggests that genes associated with protein homeostasis are upregulated upon prolonged exposure to H2S. Previous work has shown that the hypoxia-inducible transcription factor, HIF-1, is required for survival in H2S. In fact, we show that hif-1 is required for most, if not all, early transcriptional changes in H2S. Moreover, our data demonstrate that SKN-1, the C. elegans homologue of NRF2, also contributes to H2S-dependent changes in transcription. We show that these results are functionally important, as skn-1 is essential to survive exposure to H2S. Our results suggest a model in which HIF-1 and SKN-1 coordinate a broad transcriptional response to H2S that culminates in a global reorganization of protein homeostasis networks

An Atlas of the Speed of Copy Number Changes in Animal Gene Families and Its Implications

The notion that gene duplications generating new genes and functions is commonly accepted in evolutionary biology. However, this assumption is more speculative from theory rather than well proven in genome-wide studies. Here, we generated an atlas of the rate of copy number changes (CNCs) in all the gene families of ten animal genomes. We grouped the gene families with similar CNC dynamics into rate pattern groups (RPGs) and annotated their function using a novel bottom-up approach. By comparing CNC rate patterns, we showed that most of the species-specific CNC rates groups are formed by gene duplication rather than gene loss, and most of the changes in rates of CNCs may be the result of adaptive evolution. We also found that the functions of many RPGs match their biological significance well. Our work confirmed the role of gene duplication in generating novel phenotypes, and the results can serve as a guide for researchers to connect the phenotypic features to certain gene duplications

The RNA Chaperone Hfq Is Important for Growth and Stress Tolerance in Francisella novicida

The RNA-binding protein Hfq is recognized as an important regulatory factor in a variety of cellular processes, including stress resistance and pathogenesis. Hfq has been shown in several bacteria to interact with small regulatory RNAs and act as a post-transcriptional regulator of mRNA stability and translation. Here we examined the impact of Hfq on growth, stress tolerance, and gene expression in the intracellular pathogen Francisella novicida. We present evidence of Hfq involvement in the ability of F. novicida to tolerate several cellular stresses, including heat-shock and oxidative stresses, and alterations in hfq gene expression under these conditions. Furthermore, expression of numerous genes, including several associated with virulence, is altered in a hfq mutant strain suggesting they are regulated directly or indirectly by Hfq. Strikingly, we observed a delayed entry into stationary phase and increased biofilm formation in the hfq mutant. Together, these data demonstrate a critical role for Hfq in F. novicida growth and survival

A Chemical Screen Probing the Relationship between Mitochondrial Content and Cell Size

The cellular content of mitochondria changes dynamically during development and in response to external stimuli, but the underlying mechanisms remain obscure. To systematically identify molecular probes and pathways that control mitochondrial abundance, we developed a high-throughput imaging assay that tracks both the per cell mitochondrial content and the cell size in confluent human umbilical vein endothelial cells. We screened 28,786 small molecules and observed that hundreds of small molecules are capable of increasing or decreasing the cellular content of mitochondria in a manner proportionate to cell size, revealing stereotyped control of these parameters. However, only a handful of compounds dissociate this relationship. We focus on one such compound, BRD6897, and demonstrate through secondary assays that it increases the cellular content of mitochondria as evidenced by fluorescence microscopy, mitochondrial protein content, and respiration, even after rigorous correction for cell size, cell volume, or total protein content. BRD6897 increases uncoupled respiration 1.6-fold in two different, non-dividing cell types. Based on electron microscopy, BRD6897 does not alter the percent of cytoplasmic area occupied by mitochondria, but instead, induces a striking increase in the electron density of existing mitochondria. The mechanism is independent of known transcriptional programs and is likely to be related to a blockade in the turnover of mitochondrial proteins. At present the molecular target of BRD6897 remains to be elucidated, but if identified, could reveal an important additional mechanism that governs mitochondrial biogenesis and turnover