Elliptic Flow of Electrons from Beauty-Hadron Decays in Pb-Pb Collisions at sqrt[s_{NN}]=5.02 TeV

The elliptic flow of electrons from beauty hadron decays at midrapidity (|y|<0.8) is measured in Pb-Pb collisions at sqrt[s_{NN}]=5.02  TeV with the ALICE detector at the LHC. The azimuthal distribution of the particles produced in the collisions can be parametrized with a Fourier expansion, in which the second harmonic coefficient represents the elliptic flow, v_{2}. The v_{2} coefficient of electrons from beauty hadron decays is measured for the first time in the transverse momentum (p_{T}) range 1.3-6  GeV/c in the centrality class 30%-50%. The measurement of electrons from beauty-hadron decays exploits their larger mean proper decay length cτ≈500  μm compared to that of charm hadrons and most of the other background sources. The v_{2} of electrons from beauty hadron decays at midrapidity is found to be positive with a significance of 3.75  σ. The results provide insights into the degree of thermalization of beauty quarks in the medium. A model assuming full thermalization of beauty quarks is strongly disfavored by the measurement at high p_{T}, but is in agreement with the results at low p_{T}. Transport models including substantial interactions of beauty quarks with an expanding strongly interacting medium describe the measurement within uncertainties.

LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction

Deep learning approaches for tomographic image reconstruction have become very effective and have been demonstrated to be competitive in the field. Comparing these approaches is a challenging task as they rely to a great extent on the data and setup used for training. With the Low-Dose Parallel Beam (LoDoPaB)-CT dataset, we provide a comprehensive, open-access database of computed tomography images and simulated low photon count measurements. It is suitable for training and comparing deep learning methods as well as classical reconstruction approaches. The dataset contains over 40000 scan slices from around 800 patients selected from the LIDC/IDRI database. The data selection and simulation setup are described in detail, and the generating script is publicly accessible. In addition, we provide a Python library for simplified access to the dataset and an online reconstruction challenge. Furthermore, the dataset can also be used for transfer learning as well as sparse and limited-angle reconstruction scenarios.

Measurement(s)	Low Dose Computed Tomography of the Chest • feature extraction objective
Technology Type(s)	digital curation • image processing technique
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13526360

Outcomes of diagnosed COVID-19 cancer patients: concerning results of a systematic review

Since the outbreak of coronavirus disease 2019 (COVID-19), malignancy was shown to be prevalent in COVID-19 patients. This systematic review's searches were conducted in MEDLINE(R), Embase, Web of Science, and Scopos. Considered for inclusion were all reports on outcomes of cancer patients diagnosed with COVID-19. A total of 1099 references were identified through database searching and manual search. Finally, 17 references comprising 88 cancer patients, diagnosed with COVID-19, were included. Prevalence of cancer patients with COVID-19 was shown to range from 0.9% to 3%. The evidence suggested a severe clinical course of 50.6% in COVID-19 diagnosed cancer patients and a mortality rate of 34.5%. Subgroup analysis according to recent anti-cancer treatment showed a similar pattern, with the most concerning results in patients receiving recent immunotherapy/immunosuppressive treatment. COVID-19 morbidity and mortality among cancer patients should be reduced by consideration of testing asymptomatic COVID-19 cancer patients, reduction of hospital visits, and consideration of anti-cancer treatment.

Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications

The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint. We focus on two applications of CT, namely, low-dose CT and sparse-angle CT. This enables us to fairly compare different methods using standardized settings. As a general result, we observe that the deep learning-based methods are able to improve the reconstruction quality metrics in both CT applications while the top performing methods show only minor differences in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). We further discuss a number of other important criteria that should be taken into account when selecting a method, such as the availability of training data, the knowledge of the physical measurement model and the reconstruction speed.

Thermally driven state in a spin-1 model with competing interactions

We study a recently proposed spin-1 model with competing antiferromagnetic first-neighbor interaction and a third-neighbor coupling mediated by nonmagnetic states, which reproduces topological features of the phase diagrams of high-T_{c} superconductors [S. A. Cannas and D. A. Stariolo, Phys. Rev. E 99, 042137 (2019)2470-004510.1103/PhysRevE.99.042137]. We employ a cluster mean-field approach to investigate effects of crystal field anisotropy on the phase transitions hosted by this model. At low temperatures, the temperature-crystal field phase diagram exhibits superantiferromagnetic (SAF), antiferromagnetic (AF), and paramagnetic (PM) phases. In addition, we found a thermally driven state between SAF and PM phases. This thermally driven state and the SAF phase appears in the phase diagram as a domelike structure. Our calculations indicate that only second-order phase transitions occur in the PM-AF phase boundary, as suggested by previous Monte Carlo simulations.

Single-molecule conductance of dibenzopentalenes: antiaromaticity and quantum interference

The effects of antiaromaticity and destructive quantum interference (DQI) are investigated on the charge transport through dibenzo-[a,e]pentalene (DBP). 5,10-Connectivity gives high single-molecule conductance whereas 2,7 gives low conductance due to DQI. Comparison of the 5,10-DBP with phenyl and anthracene analogues yields the trend GDBP ≈ GAnth > GPh, despite the aromatic anthracene having a larger HOMO-LUMO gap than 5,10-DBP. This is explained by unfavourable level alignment for 5,10-DBP.

A comprehensive study of the sorption mechanism and thermodynamics of f-element sorption onto K-feldspar

The mobility of heavy metal contaminants and radionuclides in the environment is directly controlled by their interactions with charged mineral surfaces, hence an assessment of their potential toxicity, e.g. in the context of radioactive waste disposal sites, requires understanding of sorption processes on the molecular level. Here, we investigate the sorption of a variety of rare earth elements (REE) and trivalent actinides (Am, Cm) on K-feldspar using batch sorption, time-resolved laser-induced fluorescence spectroscopy (TRLFS), and a surface complexation model. Initially, a reliable pK_a for K-feldspar's surface deprotonation reaction was determined as 2.5 ± 0.02 by column titration experiments, in excellent agreement with a measured pH_IEP of 2.8. Batch sorption experiments over a broad range of experimental conditions in terms of mineral grain size, pH, [M³⁺], ionic radius, solid/liquid ratio, ionic strength, and equilibration procedures were carried out to quantify macroscopic retention. The trivalent d-block element Y, early, mid, and late lanthanides (La, Eu, Nd, Lu), as well as two minor actinides (Am, Cm) were used for batch sorption experiments and showed similar pH dependent uptake behavior, underlining their chemical analogy. In parallel, spectroscopic investigations provided insight into surface speciation. Cm TRLFS spectra indicate the formation of three inner-sphere sorption complexes with increasing hydrolysis. Additionally, a ternary K-feldspar/Cm/silicate complex was found for pH > 10, and batch and spectroscopic data at low pH (<4) point to small amounts of outer sphere sorption complexes. Based on TRLFS data, batch sorption, and titration data, a generic geochemical sorption model was developed, that describes sorption edges for all investigated M³⁺/K-feldspar systems satisfactorily. The derived stability constants for the binary sorption complexes (logK_1-4 = -3.6, -7.7, -11.5, and -17.4, respectively) could successfully be used to reproduce literature data. The stability constants obtained for the surface complexes were included into the database for the Smart K_d-concept, which will further improve the safety assessment of potential repositories for radioactive waste.

Measurement of the Low-Energy Antideuteron Inelastic Cross Section

In this Letter, we report the first measurement of the inelastic cross section for antideuteron-nucleus interactions at low particle momenta, covering a range of 0.3≤p<4  GeV/c. The measurement is carried out using p-Pb collisions at a center-of-mass energy per nucleon-nucleon pair of sqrt[s_{NN}]=5.02  TeV, recorded with the ALICE detector at the CERN LHC and utilizing the detector material as an absorber for antideuterons and antiprotons. The extracted raw primary antiparticle-to-particle ratios are compared to the results from detailed ALICE simulations based on the geant4 toolkit for the propagation of (anti)particles through the detector material. The analysis of the raw primary (anti)proton spectra serves as a benchmark for this study, since their hadronic interaction cross sections are well constrained experimentally. The first measurement of the inelastic cross section for antideuteron-nucleus interactions averaged over the ALICE detector material with atomic mass numbers ⟨A⟩=17.4 and 31.8 is obtained. The measured inelastic cross section points to a possible excess with respect to the Glauber model parametrization used in geant4 in the lowest momentum interval of 0.3≤p<0.47  GeV/c up to a factor 2.1. This result is relevant for the understanding of antimatter propagation and the contributions to antinuclei production from cosmic ray interactions within the interstellar medium. In addition, the momentum range covered by this measurement is of particular importance to evaluate signal predictions for indirect dark-matter searches.

Transverse field effects on the competition between antiferromagnetic and cluster spin-glass phases

We investigate a disordered cluster Ising antiferromagnet in the presence of a transverse field. By adopting a replica cluster mean-field framework, we analyze the role of quantum fluctuations in a model with competing short-range antiferromagnetic and intercluster disordered interactions. The model exhibits paramagnetic (PM), antiferromagnetic (AF), and cluster spin-glass (CSG) phases, which are separated by thermal and quantum phase transitions. A scenario of strong competition between AF and CSG unveils a number of interesting phenomena induced by quantum fluctuations, including a quantum PM state and quantum driven criticality. The latter occurs when the thermally driven PM-AF discontinuous phase transition becomes continuous at strong transverse fields. Analogous phenomena have been reported in a number of systems, but a description of underlying mechanisms is still required. Our results indicate that quantum driven criticality can be found in a highly competitive regime of disordered antiferromagnets, which is in consonance with recent findings in spin models with competing interactions.

SILEX: a fast and inexpensive high-quality DNA extraction method suitable for multiple sequencing platforms and recalcitrant plant species

BACKGROUND

The use of sequencing and genotyping platforms has undergone dramatic improvements, enabling the generation of a wealth of genomic information. Despite this progress, the availability of high-quality genomic DNA (gDNA) in sufficient concentrations is often a main limitation, especially for third-generation sequencing platforms. A variety of DNA extraction methods and commercial kits are available. However, many of these are costly and frequently give either low yield or low-quality DNA, inappropriate for next generation sequencing (NGS) platforms. Here, we describe a fast and inexpensive DNA extraction method (SILEX) applicable to a wide range of plant species and tissues.

RESULTS

SILEX is a high-throughput DNA extraction protocol, based on the standard CTAB method with a DNA silica matrix recovery, which allows obtaining NGS-quality high molecular weight genomic plant DNA free of inhibitory compounds. SILEX was compared with a standard CTAB extraction protocol and a common commercial extraction kit in a variety of species, including recalcitrant ones, from different families. In comparison with the other methods, SILEX yielded DNA in higher concentrations and of higher quality. Manual extraction of 48 samples can be done in 96 min by one person at a cost of 0.12 €/sample of reagents and consumables. Hundreds of tomato gDNA samples obtained with either SILEX or the commercial kit were successfully genotyped with Single Primer Enrichment Technology (SPET) with the Illumina HiSeq 2500 platform. Furthermore, DNA extracted from Solanum elaeagnifolium using this protocol was assessed by Pulsed-field gel electrophoresis (PFGE), obtaining a suitable size ranges for most sequencing platforms that required high-molecular-weight DNA such as Nanopore or PacBio.

CONCLUSIONS

A high-throughput, fast and inexpensive DNA extraction protocol was developed and validated for a wide variety of plants and tissues. SILEX offers an easy, scalable, efficient and inexpensive way to extract DNA for various next-generation sequencing applications including SPET and Nanopore among others.