Proximity superconductivity in atom-by-atom crafted quantum dots.

Gapless materials in electronic contact with superconductors acquire proximity-induced superconductivity in a region near the interface1,2. Numerous proposals build on this addition of electron pairing to originally non-superconducting systems and predict intriguing phases of matter, including topological3-7, odd-frequency8, nodal-point9 or Fulde-Ferrell-Larkin-Ovchinnikov10 superconductivity. Here we investigate the most miniature example of the proximity effect on only a single spin-degenerate quantum level of a surface state confined in a quantum corral11 on a superconducting substrate, built atom by atom by a scanning tunnelling microscope. Whenever an eigenmode of the corral is pitched close to the Fermi energy by adjusting the size of the corral, a pair of particle-hole symmetric states enters the gap of the superconductor. We identify these as spin-degenerate Andreev bound states theoretically predicted 50 years ago by Machida and Shibata12, which had-so far-eluded detection by tunnel spectroscopy but were recently shown to be relevant for transmon qubit devices13,14. We further find that the observed anticrossings of the in-gap states are a measure of proximity-induced pairing in the eigenmodes of the quantum corral. Our results have direct consequences on the interpretation of impurity-induced in-gap states in superconductors, corroborate concepts to induce superconductivity into surface states and further pave the way towards superconducting artificial lattices.

3-Dimensional Reconstruction From Histopathological Sections: A Systematic Review.

Although pathological tissue analysis is typically performed on single 2-dimensional (2D) histologic reference slides, 3-dimensional (3D) reconstruction from a sequence of histologic sections could provide novel opportunities for spatial analysis of the extracted tissue. In this review, we analyze recent works published after 2018 and report information on the extracted tissue types, the section thickness, and the number of sections used for reconstruction. By analyzing the technological requirements for 3D reconstruction, we observe that software tools exist, both free and commercial, which include the functionality to perform 3D reconstruction from a sequence of histologic images. Through the analysis of the most recent works, we provide an overview of the workflows and tools that are currently used for 3D reconstruction from histologic sections and address points for future work, such as a missing common file format or computer-aided analysis of the reconstructed model.

Characterisation of evolutionarily conserved key players affecting eukaryotic flagellar motility and fertility using a moss model.

Defects in flagella/cilia are often associated with infertility and disease. Motile male gametes (sperm cells) are an ancestral eukaryotic trait that has been lost in several lineages like flowering plants. Here, we made use of a phenotypic male fertility difference between two moss (Physcomitrella patens) ecotypes to explore spermatozoid function. We compare genetic and epigenetic variation as well as expression profiles between the Gransden and Reute ecotype to identify a set of candidate genes associated with moss male infertility. We generated a loss-of-function mutant of a coiled-coil domain containing 39 (ccdc39) gene that is part of the flagellar hydin network. Defects in mammal and algal homologues of this gene coincide with a loss of fertility, demonstrating the evolutionary conservation of flagellar function related to male fertility across kingdoms. The Ppccdc39 mutant resembles the Gransden phenotype in terms of male fertility. Potentially, several somatic (epi-)mutations occurred during prolonged vegetative propagation of Gransden, causing regulatory differences of for example the homeodomain transcription factor BELL1. Probably these somatic changes are causative for the observed male fertility defect. We propose that moss spermatozoids might be employed as an easily accessible system to study male infertility of humans and animals in terms of flagellar structure and movement.

Zika virus infection studies with CD34+ hematopoietic and megakaryocyte-erythroid progenitors, red blood cells and platelets.

BACKGROUND: To date, several cases of transfusion-transmitted ZIKV infections have been confirmed. Multiple studies detected prolonged occurrence of ZIKV viral RNA in whole blood as compared to plasma samples indicating potential ZIKV interaction with hematopoietic cells. Also, infection of cells from the granulocyte/macrophage lineage has been demonstrated. Patients may develop severe thrombocytopenia, microcytic anemia, and a fatal course of disease occurred in a patient with sickle cell anemia suggesting additional interference of ZIKV with erythroid and megakaryocytic cells. Therefore, we analyzed whether ZIKV propagates in or compartmentalizes with hematopoietic progenitor, erythroid, and megakaryocytic cells. METHODS: ZIKV RNA replication, protein translation and infectious particle formation in hematopoietic cell lines as well as primary CD34+ HSPCs and ex vivo differentiated erythroid and megakaryocytic cells was monitored using qRT-PCR, FACS, immunofluorescence analysis and infectivity assays. Distribution of ZIKV RNA and infectious particles in spiked red blood cell (RBC) units or platelet concentrates (PCs) was evaluated. RESULTS: While subsets of K562 and KU812Ep6EPO cells supported ZIKV propagation, primary CD34+ HSPCs, MEP cells, RBCs, and platelets were non-permissive for ZIKV infection. In spiking studies, ZIKV RNA was detectable for 7 days in all fractions of RBC units and PCs, however, ZIKV infectious particles were not associated with erythrocytes or platelets. CONCLUSION: Viral particles from plasma or contaminating leukocytes, rather than purified CD34+ HSPCs or the cellular component of RBC units or PCs, present the greatest risk for transfusion-transmitted ZIKV infections.

The Physcomitrella patens chromosome-scale assembly reveals moss genome structure and evolution.

The draft genome of the moss model, Physcomitrella patens, comprised approximately 2000 unordered scaffolds. In order to enable analyses of genome structure and evolution we generated a chromosome-scale genome assembly using genetic linkage as well as (end) sequencing of long DNA fragments. We find that 57% of the genome comprises transposable elements (TEs), some of which may be actively transposing during the life cycle. Unlike in flowering plant genomes, gene- and TE-rich regions show an overall even distribution along the chromosomes. However, the chromosomes are mono-centric with peaks of a class of Copia elements potentially coinciding with centromeres. Gene body methylation is evident in 5.7% of the protein-coding genes, typically coinciding with low GC and low expression. Some giant virus insertions are transcriptionally active and might protect gametes from viral infection via siRNA mediated silencing. Structure-based detection methods show that the genome evolved via two rounds of whole genome duplications (WGDs), apparently common in mosses but not in liverworts and hornworts. Several hundred genes are present in colinear regions conserved since the last common ancestor of plants. These syntenic regions are enriched for functions related to plant-specific cell growth and tissue organization. The P. patens genome lacks the TE-rich pericentromeric and gene-rich distal regions typical for most flowering plant genomes. More non-seed plant genomes are needed to unravel how plant genomes evolve, and to understand whether the P. patens genome structure is typical for mosses or bryophytes.

Sexual reproduction, sporophyte development and molecular variation in the model moss Physcomitrella patens: introducing the ecotype Reute.

Rich ecotype collections are used for several plant models to unravel the molecular causes of phenotypic differences, and to investigate the effects of environmental adaption and acclimation. For the model moss Physcomitrella patens collections of accessions are available, and have been used for phylogenetic and taxonomic studies, for example, but few have been investigated further for phenotypic differences. Here, we focus on the Reute accession and provide expression profiling and comparative developmental data for several stages of sporophyte development, as well as information on genetic variation via genomic sequencing. We analysed cross-technology and cross-laboratory data to define a confident set of 15 mature sporophyte-specific genes. We find that the standard laboratory strain Gransden produces fewer sporophytes than Reute or Villersexel, although gametangia develop with the same time course and do not show evident morphological differences. Reute exhibits less genetic variation relative to Gransden than Villersexel, yet we found variation between Gransden and Reute in the expression profiles of several genes, as well as variation hot spots and genes that appear to evolve under positive Darwinian selection. We analyzed expression differences between the ecotypes for selected candidate genes in the GRAS transcription factor family, the chalcone synthase family and in genes involved in cell wall modification that are potentially related to phenotypic differences. We confirm that Reute is a P. patens ecotype, and suggest its use for reverse-genetics studies that involve progression through the life cycle and multiple generations.

Protein arginine methyltransferase 6 controls erythroid gene expression and differentiation of human CD34+ progenitor cells.

Hematopoietic differentiation is driven by transcription factors, which orchestrate a finely tuned transcriptional network. At bipotential branching points lineage decisions are made, where key transcription factors initiate cell type-specific gene expression programs. These programs are stabilized by the epigenetic activity of recruited chromatin-modifying cofactors. An example is the association of the transcription factor RUNX1 with protein arginine methyltransferase 6 (PRMT6) at the megakaryocytic/erythroid bifurcation. However, little is known about the specific influence of PRMT6 on this important branching point. Here, we show that PRMT6 inhibits erythroid gene expression during megakaryopoiesis of primary human CD34+ progenitor cells. PRMT6 is recruited to erythroid genes, such as glycophorin A Consequently, a repressive histone modification pattern with high H3R2me2a and low H3K4me3 is established. Importantly, inhibition of PRMT6 by shRNA or small molecule inhibitors leads to upregulation of erythroid genes and promotes erythropoiesis. Our data reveal that PRMT6 plays a role in the control of erythroid/megakaryocytic differentiation and open up the possibility that manipulation of PRMT6 activity could facilitate enhanced erythropoiesis for therapeutic use.

Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study.

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine.

Probing the topologically trivial nature of end states in antiferromagnetic atomic chains on superconductors.

Spin chains proximitized by s-wave superconductors are predicted to enter a mini-gapped phase with topologically protected Majorana modes (MMs) localized at their ends. However, the presence of non-topological end states mimicking MM properties can hinder their unambiguous observation. Here, we report on a direct method to exclude the non-local nature of end states via scanning tunneling spectroscopy by introducing a locally perturbing defect on one of the chain's ends. We apply this method to particular end states observed in antiferromagnetic spin chains within a large minigap, thereby proving their topologically trivial character. A minimal model shows that, while wide trivial minigaps hosting end states are easily achieved in antiferromagnetic spin chains, unrealistically large spin-orbit coupling is required to drive the system into a topologically gapped phase with MMs. The methodology of perturbing candidate topological edge modes in future experiments is a powerful tool to probe their stability against local disorder.

Multimodal integration of image, epigenetic and clinical data to predict BRAF mutation status in melanoma.

BACKGROUND: In machine learning, multimodal classifiers can provide more generalised performance than unimodal classifiers. In clinical practice, physicians usually also rely on a range of information from different examinations for diagnosis. In this study, we used BRAF mutation status prediction in melanoma as a model system to analyse the contribution of different data types in a combined classifier because BRAF status can be determined accurately by sequencing as the current gold standard, thus nearly eliminating label noise. METHODS: We trained a deep learning-based classifier by combining individually trained random forests of image, clinical and methylation data to predict BRAF-V600 mutation status in primary and metastatic melanomas of The Cancer Genome Atlas cohort. RESULTS: With our multimodal approach, we achieved an area under the receiver operating characteristic curve of 0.80, whereas the individual classifiers yielded areas under the receiver operating characteristic curve of 0.63 (histopathologic image data), 0.66 (clinical data) and 0.66 (methylation data) on an independent data set. CONCLUSIONS: Our combined approach can predict BRAF status to some extent by identifying BRAF-V600 specific patterns at the histologic, clinical and epigenetic levels. The multimodal classifiers have improved generalisability in predicting BRAF mutation status.

A 3-dimensional histology computer model of malignant melanoma and its implications for digital pathology.

BACKGROUND: Historically, cancer diagnoses have been made by pathologists using two-dimensional histological slides. However, with the advent of digital pathology and artificial intelligence, slides are being digitised, providing new opportunities to integrate their information. Since nature is 3-dimensional (3D), it seems intuitive to digitally reassemble the 3D structure for diagnosis. OBJECTIVE: To develop the first human-3D-melanoma-histology-model with full data and code availability. Further, to evaluate the 3D-simulation together with experienced pathologists in the field and discuss the implications of digital 3D-models for the future of digital pathology. METHODS: A malignant melanoma of the skin was digitised via 3 µm cuts by a slide scanner; an open-source software was then leveraged to construct the 3D model. A total of nine pathologists from four different countries with at least 10 years of experience in the histologic diagnosis of melanoma tested the model and discussed their experiences as well as implications for future pathology. RESULTS: We successfully constructed and tested the first 3D-model of human melanoma. Based on testing, 88.9% of pathologists believe that the technology is likely to enter routine pathology within the next 10 years; advantages include a better reflectance of anatomy, 3D assessment of symmetry and the opportunity to simultaneously evaluate different tissue levels at the same time; limitations include the high consumption of tissue and a yet inferior resolution due to computational limitations. CONCLUSIONS: 3D-histology-models are promising for digital pathology of cancer and melanoma specifically, however, there are yet limitations which need to be carefully addressed.

Can arrows change the subjective perception of space? Exploring symbolic attention repulsion.

The attention repulsion effect (ARE) refers to distortions in the perception of space for areas near the focus of attention. For instance, when attending to the right-hand side of the visual field, objects in central vision may appear as though they are shifted to the left. The phenomenon is likely caused by changes in visual cell functioning. To date, research on the ARE has almost exclusively used exogenous manipulations of attention. In contrast, research exploring endogenous attention repulsion has been mixed, and no research has explored the effects of nonpredictive arrow cues on this phenomenon. This gap in the literature is unexpected, as symbolic attention appears to be a unique form of attentional orienting compared with endogenous and exogenous attention. Therefore, this study explored the effects of symbolic orienting on spatial repulsion and compared it with an exogenously generated ARE. Across four experiments, both exogenous and symbolic orienting resulted in AREs; however, the magnitude of the symbolic ARE was smaller than the exogenous ARE. This difference in magnitude persisted, even after testing both phenomena using stimulus timing parameters known to produce optimal effects in traditional attentional cueing paradigms. Therefore, compared with symbolic attention, it appears that exogenous manipulations may tightly constrict attention resources on the cued location, in turn, potentially influencing the functioning of visual cells for enhanced perceptual processing.

Precursors of Majorana modes and their length-dependent energy oscillations probed at both ends of atomic Shiba chains.

Isolated Majorana modes (MMs) are highly non-local quantum states with non-Abelian exchange statistics, which localize at the two ends of finite-size 1D topological superconductors of sufficient length. Experimental evidence for MMs is so far based on the detection of several key signatures: for example, a conductance peak pinned to the Fermi energy or an oscillatory peak splitting in short 1D systems when the MMs overlap. However, most of these key signatures were probed only on one of the ends of the 1D system, and firm evidence for an MM requires the simultaneous detection of all the key signatures on both ends. Here we construct short atomic spin chains on a superconductor-also known as Shiba chains-up to a chain length of 45 atoms using tip-assisted atom manipulation in scanning tunnelling microscopy experiments. We observe zero-energy conductance peaks localized at both ends of the chain that simultaneously split off from the Fermi energy in an oscillatory fashion after altering the chain length. By fitting the parameters of a low-energy model to the data, we find that the peaks are consistent with precursors of MMs that evolve into isolated MMs protected by an estimated topological gap of 50 µeV in chains of at least 35 nm length, corresponding to 70 atoms.

Correlation of Magnetism and Disordered Shiba Bands in Fe Monolayer Islands on Nb(110).

Two-dimensional (2D) magnet-superconductor hybrid systems are intensively studied due to their potential for the realization of 2D topological superconductors with Majorana edge modes. It is theoretically predicted that this quantum state is ubiquitous in spin-orbit-coupled ferromagnetic or skyrmionic 2D spin-lattices in proximity to an s-wave superconductor. However, recent examples suggest that the requirements for topological superconductivity are complicated by the multiorbital nature of the magnetic components and disorder effects. Here, we investigate Fe monolayer islands grown on a surface of the s-wave superconductor with the largest gap of all elemental superconductors, Nb, with respect to magnetism and superconductivity using spin-resolved scanning tunneling spectroscopy. We find three types of islands which differ by their reconstruction inducing disorder, the magnetism and the subgap electronic states. All three types are ferromagnetic with different coercive fields, indicating diverse exchange and anisotropy energies. On all three islands, there is finite spectral weight throughout the substrate's energy gap at the expense of the coherence peak intensity, indicating the formation of Shiba bands overlapping with the Fermi energy. A strong lateral variation of the spectral weight of the Shiba bands signifies substantial disorder on the order of the substrate's pairing energy with a length scale of the period of the three different reconstructions. There are neither signs of topological gaps within these bands nor of any kind of edge modes. Our work illustrates that a reconstructed growth mode of magnetic layers on superconducting surfaces is detrimental for the formation of 2D topological superconductivity.

Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review.

BACKGROUND: Over the past decade, the development of molecular high-throughput methods (omics) increased rapidly and provided new insights for cancer research. In parallel, deep learning approaches revealed the enormous potential for medical image analysis, especially in digital pathology. Combining image and omics data with deep learning tools may enable the discovery of new cancer biomarkers and a more precise prediction of patient prognosis. This systematic review addresses different multimodal fusion methods of convolutional neural network-based image analyses with omics data, focussing on the impact of data combination on the classification performance. METHODS: PubMed was screened for peer-reviewed articles published in English between January 2015 and June 2021 by two independent researchers. Search terms related to deep learning, digital pathology, omics, and multimodal fusion were combined. RESULTS: We identified a total of 11 studies meeting the inclusion criteria, namely studies that used convolutional neural networks for haematoxylin and eosin image analysis of patients with cancer in combination with integrated omics data. Publications were categorised according to their endpoints: 7 studies focused on survival analysis and 4 studies on prediction of cancer subtypes, malignancy or microsatellite instability with spatial analysis. CONCLUSIONS: Image-based classifiers already show high performances in prognostic and predictive cancer diagnostics. The integration of omics data led to improved performance in all studies described here. However, these are very early studies that still require external validation to demonstrate their generalisability and robustness. Further and more comprehensive studies with larger sample sizes are needed to evaluate performance and determine clinical benefits.

Atomic-scale spin-polarization maps using functionalized superconducting probes.

A scanning tunneling microscope (STM) with a magnetic tip that has a sufficiently strong spin polarization can be used to map the sample's spin structure down to the atomic scale but usually lacks the possibility to absolutely determine the value of the sample's spin polarization. Magnetic impurities in superconducting materials give rise to pairs of perfectly, i.e., 100%, spin-polarized subgap resonances. In this work, we functionalize the apex of a superconducting Nb STM tip with such impurity states by attaching Fe atoms to probe the spin polarization of atom-manipulated Mn nanomagnets on a Nb(110) surface. By comparison with spin-polarized STM measurements of the same nanomagnets using Cr bulk tips, we demonstrate an extraordinary spin sensitivity and the possibility to measure the sample's spin-polarization values close to the Fermi level quantitatively with our new functionalized probes.

Spin-orbit coupling induced splitting of Yu-Shiba-Rusinov states in antiferromagnetic dimers.

Magnetic atoms coupled to the Cooper pairs of a superconductor induce Yu-Shiba-Rusinov states (in short Shiba states). In the presence of sufficiently strong spin-orbit coupling, the bands formed by hybridization of the Shiba states in ensembles of such atoms can support low-dimensional topological superconductivity with Majorana bound states localized on the ensembles' edges. Yet, the role of spin-orbit coupling for the hybridization of Shiba states in dimers of magnetic atoms, the building blocks for such systems, is largely unexplored. Here, we reveal the evolution of hybridized multi-orbital Shiba states from a single Mn adatom to artificially constructed ferromagnetically and antiferromagnetically coupled Mn dimers placed on a Nb(110) surface. Upon dimer formation, the atomic Shiba orbitals split for both types of magnetic alignment. Our theoretical calculations attribute the unexpected splitting in antiferromagnetic dimers to spin-orbit coupling and broken inversion symmetry at the surface. Our observations point out the relevance of previously unconsidered factors on the formation of Shiba bands and their topological classification.

PRMT6 activates cyclin D1 expression in conjunction with the transcription factor LEF1.

The establishment of cell type specific gene expression by transcription factors and their epigenetic cofactors is central for cell fate decisions. Protein arginine methyltransferase 6 (PRMT6) is an epigenetic regulator of gene expression mainly through methylating arginines at histone H3. This way it influences cellular differentiation and proliferation. PRMT6 lacks DNA-binding capability but is recruited by transcription factors to regulate gene expression. However, currently only a limited number of transcription factors have been identified, which facilitate recruitment of PRMT6 to key cell cycle related target genes. Here, we show that LEF1 contributes to the recruitment of PRMT6 to the central cell cycle regulator CCND1 (Cyclin D1). We identified LEF1 as an interaction partner of PRMT6. Knockdown of LEF1 or PRMT6 reduces CCND1 expression. This is in line with our observation that knockdown of PRMT6 increases the number of cells in G1 phase of the cell cycle and decreases proliferation. These results improve the understanding of PRMT6 activity in cell cycle regulation. We expect that these insights will foster the rational development and usage of specific PRMT6 inhibitors for cancer therapy.

Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries.

This paper forecasts the future spread of COVID-19 by exploiting the identified lead-lag effects between different countries. Specifically, we first determine the past relation among nations with the aid of dynamic time warping. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences. Afterwards, the established framework utilizes information about the leading country to predict the Coronavirus spread of the following nation. The presented methodology is applied to confirmed Coronavirus cases from 1 January 2020 to 28 March 2020. Our results show that China leads all other countries in the range of 29 days for South Korea and 44 days for the United States. Finally, we predict a future collapse of the healthcare systems of the United Kingdom and Switzerland in case of our explosion scenario.

Controlling in-gap end states by linking nonmagnetic atoms and artificially-constructed spin chains on superconductors.

Chains of magnetic atoms with either strong spin-orbit coupling or spiral magnetic order which are proximity-coupled to superconducting substrates can host topologically non-trivial Majorana bound states. The experimental signature of these states consists of spectral weight at the Fermi energy which is spatially localized near the ends of the chain. However, topologically trivial Yu-Shiba-Rusinov in-gap states localized near the ends of the chain can lead to similar spectra. Here, we explore a protocol to disentangle these contributions by artificially augmenting a candidate Majorana spin chain with orbitally-compatible nonmagnetic atoms. Combining scanning tunneling spectroscopy with ab-initio and tight-binding calculations, we realize a sharp spatial transition between the proximity-coupled spiral magnetic order and the non-magnetic superconducting wire termination, with persistent zero-energy spectral weight localized at either end of the magnetic spiral. Our findings open a new path towards the control of the spatial position of in-gap end states, trivial or Majorana, via different chain terminations, and the realization of designer Majorana chain networks for demonstrating topological quantum computation.