Towards annotating the plant epigenome: the Arabidopsis thaliana small RNA locus map.

Based on 98 public and internal small RNA high throughput sequencing libraries, we mapped small RNAs to the genome of the model organism Arabidopsis thaliana and defined loci based on their expression using an empirical Bayesian approach. The resulting loci were subsequently classified based on their genetic and epigenetic context as well as their expression properties. We present the results of this classification, which broadly conforms to previously reported divisions between transcriptional and post-transcriptional gene silencing small RNAs, and to PolIV and PolV dependencies. However, we are able to demonstrate the existence of further subdivisions in the small RNA population of functional significance. Moreover, we present a framework for similar analyses of small RNA populations in all species

The small RNA locus map for Chlamydomonas reinhardtii.

Small (s)RNAs play crucial roles in the regulation of gene expression and genome stability across eukaryotes where they direct epigenetic modifications, post-transcriptional gene silencing, and defense against both endogenous and exogenous viruses. It is known that Chlamydomonas reinhardtii, a well-studied unicellular green algae species, possesses sRNA-based mechanisms that are distinct from those of land plants. However, definition of sRNA loci and further systematic classification is not yet available for this or any other algae. Here, using data-driven machine learning approaches including Multiple Correspondence Analysis (MCA) and clustering, we have generated a comprehensively annotated and classified sRNA locus map for C. reinhardtii. This map shows some common characteristics with higher plants and animals, but it also reveals distinct features. These results are consistent with the idea that there was diversification in sRNA mechanisms after the evolutionary divergence of algae from higher plant lineages

Extensive recombination challenges the utility of Sugarcane mosaic virus phylogeny and strain typing

Abstract: Sugarcane mosaic virus (SCMV) is distributed worldwide and infects three major crops: sugarcane, maize, and sorghum. The impact of SCMV is increased by its interaction with Maize chlorotic mottle virus which causes the synergistic maize disease maize lethal necrosis. Here, we characterised maize lethal necrosis-infected maize from multiple sites in East Africa, and found that SCMV was present in all thirty samples. This distribution pattern indicates that SCMV is a major partner virus in the East African maize lethal necrosis outbreak. Consistent with previous studies, our SCMV isolates were highly variable with several statistically supported recombination hot- and cold-spots across the SCMV genome. The recombination events generate conflicting phylogenetic signals from different fragments of the SCMV genome, so it is not appropriate to group SCMV genomes by simple similarity

Extensive recombination challenges the utility of Sugarcane mosaic virus phylogeny and strain typing

Abstract: Sugarcane mosaic virus (SCMV) is distributed worldwide and infects three major crops: sugarcane, maize, and sorghum. The impact of SCMV is increased by its interaction with Maize chlorotic mottle virus which causes the synergistic maize disease maize lethal necrosis. Here, we characterised maize lethal necrosis-infected maize from multiple sites in East Africa, and found that SCMV was present in all thirty samples. This distribution pattern indicates that SCMV is a major partner virus in the East African maize lethal necrosis outbreak. Consistent with previous studies, our SCMV isolates were highly variable with several statistically supported recombination hot- and cold-spots across the SCMV genome. The recombination events generate conflicting phylogenetic signals from different fragments of the SCMV genome, so it is not appropriate to group SCMV genomes by simple similarity

Quantum-Dense Metrology

Quantum metrology utilizes entanglement for improving the sensitivity of measurements. Up to now the focus has been on the measurement of just one out of two non-commuting observables. Here we demonstrate a laser interferometer that provides information about two non-commuting observables, with uncertainties below that of the meter's quantum ground state. Our experiment is a proof-of-principle of quantum dense metrology, and uses the additional information to distinguish between the actual phase signal and a parasitic signal due to scattered and frequency shifted photons. Our approach can be readily applied to improve squeezed-light enhanced gravitational-wave detectors at non-quantum noise limited detection frequencies in terms of a sub shot-noise veto-channel.Comment: 5 pages, 3 figures; includes supplementary material

A dust-parallax distance of 19 megaparsecs to the supermassive black hole in NGC 4151

The active galaxy NGC 4151 has a crucial role as one of only two active galactic nuclei for which black hole mass measurements based on emission line reverberation mapping can be calibrated against other dynamical methods. Unfortunately, effective calibration requires an accurate distance to NGC 4151, which is currently not available. Recently reported distances range from 4 to 29 megaparsecs (Mpc). Strong peculiar motions make a redshift-based distance very uncertain, and the geometry of the galaxy and its nucleus prohibit accurate measurements using other techniques. Here we report a dust-parallax distance to NGC 4151 of $D_A = 19.0^{+2.4}_{-2.6}$ Mpc. The measurement is based on an adaptation of a geometric method proposed previously using the emission line regions of active galaxies. Since this region is too small for current imaging capabilities, we use instead the ratio of the physical-to-angular sizes of the more extended hot dust emission as determined from time-delays and infrared interferometry. This new distance leads to an approximately 1.4-fold increase in the dynamical black hole mass, implying a corresponding correction to emission line reverberation masses of black holes if they are calibrated against the two objects with additional dynamical masses.Comment: Authors' version of a letter published in Nature (27 November 2014); 8 pages, 5 figures, 1 tabl

The n-level spectral correlations for chaotic systems

We study the $n$-level spectral correlation functions of classically chaotic quantum systems without time-reversal symmetry. According to Bohigas, Giannoni and Schmit's universality conjecture, it is expected that the correlation functions are in agreement with the prediction of the Circular Unitary Ensemble (CUE) of random matrices. A semiclassical resummation formalism allows us to express the correlation functions as sums over pseudo-orbits. Using an extended version of the diagonal approximation on the pseudo-orbit sums, we derive the $n$-level correlation functions identical to the $n \times n$ determinantal correlation functions of the CUE.Comment: 20 pages, no figure, minor corrections mad

Canakinumab in patients with COVID-19 and type 2 diabetes - A multicentre, randomised, double-blind, placebo-controlled trial

BACKGROUND: Patients with type 2 diabetes and obesity have chronic activation of the innate immune system possibly contributing to the higher risk of hyperinflammatory response to SARS-CoV2 and severe COVID-19 observed in this population. We tested whether interleukin-1β (IL-1β) blockade using canakinumab improves clinical outcome. METHODS: CanCovDia was a multicenter, randomised, double-blind, placebo-controlled trial to assess the efficacy of canakinumab plus standard-of-care compared with placebo plus standard-of-care in patients with type 2 diabetes and a BMI > 25 kg/m$^{2}$ hospitalised with SARS-CoV2 infection in seven tertiary-hospitals in Switzerland. Patients were randomly assigned 1:1 to a single intravenous dose of canakinumab (body weight adapted dose of 450-750 mg) or placebo. Canakinumab and placebo were compared based on an unmatched win-ratio approach based on length of survival, ventilation, ICU stay and hospitalization at day 29. This study is registered with ClinicalTrials.gov, NCT04510493. FINDINGS: Between October 17, 2020, and May 12, 2021, 116 patients were randomly assigned with 58 in each group. One participant dropped out in each group for the primary analysis. At the time of randomization, 85 patients (74·6 %) were treated with dexamethasone. The win-ratio of canakinumab vs placebo was 1·08 (95 % CI 0·69-1·69; p = 0·72). During four weeks, in the canakinumab vs placebo group 4 (7·0%) vs 7 (12·3%) participants died, 11 (20·0 %) vs 16 (28·1%) patients were on ICU, 12 (23·5 %) vs 11 (21·6%) were hospitalised for more than 3 weeks, respectively. Median ventilation time at four weeks in the canakinumab vs placebo group was 10 [IQR 6.0, 16.5] and 16 days [IQR 14.0, 23.0], respectively. There was no statistically significant difference in HbA1c after four weeks despite a lower number of anti-diabetes drug administered in patients treated with canakinumab. Finally, high-sensitive CRP and IL-6 was lowered by canakinumab. Serious adverse events were reported in 13 patients (11·4%) in each group. INTERPRETATION: In patients with type 2 diabetes who were hospitalised with COVID-19, treatment with canakinumab in addition to standard-of-care did not result in a statistically significant improvement of the primary composite outcome. Patients treated with canakinumab required significantly less anti-diabetes drugs to achieve similar glycaemic control. Canakinumab was associated with a prolonged reduction of systemic inflammation. FUNDING: Swiss National Science Foundation grant #198415 and University of Basel. Novartis supplied study medication

CT Image Segmentation Using FEM with Optimized Boundary Condition

The authors propose a CT image segmentation method using structural analysis that is useful for objects with structural dynamic characteristics. Motivation of our research is from the area of genetic activity. In order to reveal the roles of genes, it is necessary to create mutant mice and measure differences among them by scanning their skeletons with an X-ray CT scanner. The CT image needs to be manually segmented into pieces of the bones. It is a very time consuming to manually segment many mutant mouse models in order to reveal the roles of genes. It is desirable to make this segmentation procedure automatic. Although numerous papers in the past have proposed segmentation techniques, no general segmentation method for skeletons of living creatures has been established. Against this background, the authors propose a segmentation method based on the concept of destruction analogy. To realize this concept, structural analysis is performed using the finite element method (FEM), as structurally weak areas can be expected to break under conditions of stress. The contribution of the method is its novelty, as no studies have so far used structural analysis for image segmentation. The method's implementation involves three steps. First, finite elements are created directly from the pixels of a CT image, and then candidates are also selected in areas where segmentation is thought to be appropriate. The second step involves destruction analogy to find a single candidate with high strain chosen as the segmentation target. The boundary conditions for FEM are also set automatically. Then, destruction analogy is implemented by replacing pixels with high strain as background ones, and this process is iterated until object is decomposed into two parts. Here, CT image segmentation is demonstrated using various types of CT imagery