Spin-order-dependent magneto-elastic coupling in two dimensional antiferromagnetic MnPSe3_3 observed through Raman spectroscopy

Layered antiferromagnetic materials have emerged as a novel subset of the two-dimensional family providing a highly accessible regime with prospects for layer-number-dependent magnetism. Furthermore, transition metal phosphorous trichalcogenides, MPX3 (M = transition metal; X = chalcogen) provide a platform for investigating fundamental interactions between magnetic and lattice degrees of freedom providing new insights for developing fields of spintronics and magnonics. Here, we use a combination of temperature dependent Raman spectroscopy and density functional theory to explore magnetic-ordering-dependent interactions between the manganese spin degree of freedom and lattice vibrations of the non-magnetic sub-lattice via a Kramers-Anderson super-exchange pathway in both bulk, and few-layer, manganese phosphorous triselenide (MnPSe$_3$). We observe a nonlinear temperature dependent shift of phonon modes predominantly associated with the non-magnetic sub-lattice, revealing their non-trivial spin-phonon coupling below the N{\'e}el temperature at 74 K, allowing us to extract mode-specific spin-phonon coupling constants.Comment: 20 pages, 4 figures, Submitted to ACS Nano Letter

Understanding the impact of heavy ions and tailoring the optical properties of large-area Monolayer WS2 using Focused Ion Beam

Focused ion beam (FIB) has been used as an effective tool for precise nanoscale fabrication. It has recently been employed to tailor defect engineering in functional nanomaterials such as two-dimensional transition metal dichalcogenides (TMDCs), providing desirable properties in TMDC-based optoelectronic devices. However, the damage caused by the FIB irradiation and milling process to these delicate atomically thin materials, especially in the extended area, has not yet been elaboratively characterised. Understanding the correlation between lateral ion beam effects and optical properties of 2D TMDCs is crucial in designing and fabricating high-performance optoelectronic devices. In this work, we investigate lateral damage in large-area monolayer WS2 caused by the gallium focused ion beam milling process. Three distinct zones away from the milling location are identified and characterised via steady-state photoluminescence (PL) and Raman spectroscopy. An unexpected bright ring-shaped emission around the milled location has been revealed by time-resolved PL spectroscopy with high spatial resolution. Our finding opens new avenues for tailoring the optical properties of TMDCs by charge and defect engineering via focused ion beam lithography. Furthermore, our study provides evidence that while some localised damage is inevitable, distant destruction can be eliminated by reducing the ion beam current. It paves the way for the use of FIB to create nanostructures in 2D TMDCs, as well as the design and realisation of optoelectrical devices on a wafer scale

Comparative regulomics supports pervasive selection on gene dosage following whole genome duplication

publishedVersio

Resonant band hybridization in alloyed transition metal dichalcogenide heterobilayers

Bandstructure engineering using alloying is widely utilised for achieving optimised performance in modern semiconductor devices. While alloying has been studied in monolayer transition metal dichalcogenides, its application in van der Waals heterostructures built from atomically thin layers is largely unexplored. Here, we fabricate heterobilayers made from monolayers of WSe$_2$ (or MoSe$_2$) and Mo$_x$W$_{1-x}$Se$_2$ alloy and observe nontrivial tuning of the resultant bandstructure as a function of concentration $x$. We monitor this evolution by measuring the energy of photoluminescence (PL) of the interlayer exciton (IX) composed of an electron and hole residing in different monolayers. In Mo$_x$W$_{1-x}$Se$_2$/WSe$_2$, we observe a strong IX energy shift of $\approx$100 meV for $x$ varied from 1 to 0.6. However, for $x<0.6$ this shift saturates and the IX PL energy asymptotically approaches that of the indirect bandgap in bilayer WSe$_2$. We theoretically interpret this observation as the strong variation of the conduction band K valley for $x>0.6$, with IX PL arising from the K-K transition, while for $x<0.6$, the bandstructure hybridization becomes prevalent leading to the dominating momentum-indirect K-Q transition. This bandstructure hybridization is accompanied with strong modification of IX PL dynamics and nonlinear exciton properties. Our work provides foundation for bandstructure engineering in van der Waals heterostructures highlighting the importance of hybridization effects and opening a way to devices with accurately tailored electronic properties.Comment: Supporting Information can be found downloading and extracting the gzipped tar source file listed under "Other formats

The ESCAPE project : Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) and ALADIN (Aire Limitee Adaptation Dynamique Developpement International); and COSMO-EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU-GPU arrangements

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression

Cellular Islet Autoimmunity Associates with Clinical Outcome of Islet Cell Transplantation

Islet cell transplantation can cure type 1 diabetes (T1D), but only a minority of recipients remains insulin-independent in the following years. We tested the hypothesis that allograft rejection and recurrent autoimmunity contribute to this progressive loss of islet allograft function.Twenty-one T1D patients received cultured islet cell grafts prepared from multiple donors and transplanted under anti-thymocyte globulin (ATG) induction and tacrolimus plus mycophenolate mofetil (MMF) maintenance immunosuppression. Immunity against auto- and alloantigens was measured before and during one year after transplantation. Cellular auto- and alloreactivity was assessed by lymphocyte stimulation tests against autoantigens and cytotoxic T lymphocyte precursor assays, respectively. Humoral reactivity was measured by auto- and alloantibodies. Clinical outcome parameters--including time until insulin independence, insulin independence at one year, and C-peptide levels over one year--remained blinded until their correlation with immunological parameters. All patients showed significant improvement of metabolic control and 13 out of 21 became insulin-independent. Multivariate analyses showed that presence of cellular autoimmunity before and after transplantation is associated with delayed insulin-independence (p = 0.001 and p = 0.01, respectively) and lower circulating C-peptide levels during the first year after transplantation (p = 0.002 and p = 0.02, respectively). Seven out of eight patients without pre-existent T-cell autoreactivity became insulin-independent, versus none of the four patients reactive to both islet autoantigens GAD and IA-2 before transplantation. Autoantibody levels and cellular alloreactivity had no significant association with outcome.In this cohort study, cellular islet-specific autoimmunity associates with clinical outcome of islet cell transplantation under ATG-tacrolimus-MMF immunosuppression. Tailored immunotherapy targeting cellular islet autoreactivity may be required. Monitoring cellular immune reactivity can be useful to identify factors influencing graft survival and to assess efficacy of immunosuppression.Clinicaltrials.gov NCT00623610

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Abstract. In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International); and COSMO–EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU–GPU arrangements