Series expansions in closed and open quantum many-body systems with multiple quasiparticle types

The established approach of perturbative continuous unitary transformations (pCUTs) constructs effective quantum many-body Hamiltonians as perturbative series that conserve the number of one quasiparticle type. We extend the pCUT method to similarity transformations - dubbed $\mathrm{pcst}^{\texttt{++}}$ - allowing for multiple quasiparticle types with complex-valued energies. This enlarges the field of application to closed and open quantum many-body systems with unperturbed operators corresponding to arbitrary superimposed ladder spectra. To this end, a generalized counting operator is combined with the quasiparticle generator for open quantum systems recently introduced by Schmiedinghoff and Uhrig (arXiv:2203.15532). The $\mathrm{pcst}^{\texttt{++}}$ then yields model-independent block-diagonal effective Hamiltonians and Lindbladians allowing a linked-cluster expansion in the thermodynamic limit similar to the conventional pCUT method. We illustrate the application of the $\mathrm{pcst}^{\texttt{++}}$ method by discussing representative closed, open, and non-Hermitian quantum systems

Dynamic structure factor of the antiferromagnetic Kitaev model in large magnetic fields

We investigate the dynamic structure factor of the antiferromagnetic Kitaev honeycomb model in a magnetic field by applying perturbative continuous unitary transformations about the high-field limit. One- and two-quasi-particle properties of the dressed elementary spin flip excitations of the high-field polarized phase are calculated which account for most of the spectral weight in the dynamic structure factor. We discuss the evolution of spectral features in these quasi-particle sectors in terms of one-quasi-particle dispersions, two-quasi-particle continua, the formation of anti-bound states, and quasi-particle decay. In particular, a comparably strong spectral feature above the upper edge of the upmost two-quasi-particle continuum represents three anti-bound states which form due to nearest-neighbor density-density interactions.Comment: 14 pages, 10 figure

GLAVITU:A Hybrid CNN-Transformer for Multi-Regional Glacier Mapping from Multi-Source Data

Glacier mapping is essential for studying and monitoring the impacts of climate change. However, several challenges such as debris-covered ice and highly variable landscapes across glacierized regions worldwide complicate large-scale glacier mapping in a fully-automated manner. This work presents a novel hybrid CNN-transformer model (GlaViTU) for multi-regional glacier mapping. Our model outperforms three baseline models - SETR-B/16, ResU-Net and TransU-Net - achieving a higher mean IoU of 0.875 and demonstrates better generalization ability. The proposed model is also parameter-efficient, with approximately 10 and 3 times fewer parameters than SETR-B/16 and ResU-Net, respectively. Our results provide a solid foundation for future studies on the application of deep learning methods for global glacier mapping. To facilitate reproducibility, we have shared our data set, codebase and pretrained models on GitHub at https://github.com/konstantin-a-maslov/GlaViTU-IGARSS2023.</p

Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees

Trees outside forest (TOF) can perform a variety of social, economic and ecological functions including carbon sequestration. However, detailed quantification of tree biomass is usually limited to forest areas. Taking advantage of structural information available from stereo aerial imagery and airborne laser scanning (ALS), this research models tree biomass using national forest inventory data and linear least-square regression and applies the model both inside and outside of forest to create a nationwide model for tree biomass (above ground and below ground). Validation of the tree biomass model against TOF data within settlement areas shows relatively low model performance (R 2 of 0.44) but still a considerable improvement on current biomass estimates used for greenhouse gas inventory and carbon accounting. We demonstrate an efficient and easily implementable approach to modelling tree biomass across a large heterogeneous nationwide area. The model offers significant opportunity for improved estimates on land use combination categories (CC) where tree biomass has either not been included or only roughly estimated until now. The ALS biomass model also offers the advantage of providing greater spatial resolution and greater within CC spatial variability compared to the current nationwide estimates

GLAMM: Genome-Linked Application for Metabolic Maps

The Genome-Linked Application for Metabolic Maps (GLAMM) is a unified web interface for visualizing metabolic networks, reconstructing metabolic networks from annotated genome data, visualizing experimental data in the context of metabolic networks and investigating the construction of novel, transgenic pathways. This simple, user-friendly interface is tightly integrated with the comparative genomics tools of MicrobesOnline [Dehal et al. (2010) Nucleic Acids Research, 38, D396–D400]. GLAMM is available for free to the scientific community at glamm.lbl.gov

Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks

The metabolism of organisms can be studied with comprehensive stoichiometric models of their metabolic networks. Flux balance analysis (FBA) calculates optimal metabolic performance of stoichiometric models. However, detailed biological interpretation of FBA is limited because, in general, a huge number of flux patterns give rise to the same optimal performance. The complete description of the resulting optimal solution spaces was thus far a computationally intractable problem. Here we present CoPE-FBA: Comprehensive Polyhedra Enumeration Flux Balance Analysis, a computational method that solves this problem. CoPE-FBA indicates that the thousands to millions of optimal flux patterns result from a combinatorial explosion of flux patterns in just a few metabolic sub-networks. The entire optimal solution space can now be compactly described in terms of the topology of these sub-networks. CoPE-FBA simplifies the biological interpretation of stoichiometric models of metabolism, and provides a profound understanding of metabolic flexibility in optimal states

Predicting outcomes of steady-state 13C isotope tracing experiments using Monte Carlo sampling

<p>Abstract</p> <p>Background</p> <p>Carbon-13 (¹³C) analysis is a commonly used method for estimating reaction rates in biochemical networks. The choice of carbon labeling pattern is an important consideration when designing these experiments. We present a novel Monte Carlo algorithm for finding the optimal substrate input label for a particular experimental objective (flux or flux ratio). Unlike previous work, this method does not require assumption of the flux distribution beforehand.</p> <p>Results</p> <p>Using a large <it>E. coli </it>isotopomer model, different commercially available substrate labeling patterns were tested computationally for their ability to determine reaction fluxes. The choice of optimal labeled substrate was found to be dependent upon the desired experimental objective. Many commercially available labels are predicted to be outperformed by complex labeling patterns. Based on Monte Carlo Sampling, the dimensionality of experimental data was found to be considerably less than anticipated, suggesting that effectiveness of ¹³C experiments for determining reaction fluxes across a large-scale metabolic network is less than previously believed.</p> <p>Conclusions</p> <p>While ¹³C analysis is a useful tool in systems biology, high redundancy in measurements limits the information that can be obtained from each experiment. It is however possible to compute potential limitations before an experiment is run and predict whether, and to what degree, the rate of each reaction can be resolved.</p

Labeling of mesenchymal stem cells for MRI with single-cell sensitivity

Sensitive cell detection by magnetic resonance imaging (MRI) is an important tool for the development of cell therapies. However, clinically approved contrast agents that allow single-cell detection are currently not available. Therefore, we compared very small iron oxide nanoparticles (VSOP) and new multicore carboxymethyl dextran-coated iron oxide nanoparticles (multicore particles, MCP) designed by our department for magnetic particle imaging (MPI) with discontinued Resovist® regarding their suitability for detection of single mesenchymal stem cells (MSC) by MRI. We achieved an average intracellular nanoparticle (NP) load of .10 pg Fe per cell without the use of transfection agents. NP loading did not lead to significantly different results in proliferation, colony formation, and multilineage in vitro differentiation assays in comparison to controls. MRI allowed single-cell detection using VSOP, MCP, and Resovist® in conjunction with high-resolution T2*-weighted imaging at 7 T with postprocessing of phase images in agarose cell phantoms and in vivo after delivery of 2,000 NP-labeled MSC into mouse brains via the left carotid artery. With optimized labeling conditions, a detection rate of ~45% was achieved; however, the experiments were limited by nonhomogeneous NP loading of the MSC population. Attempts should be made to achieve better cell separation for homogeneous NP loading and to thus improve NP-uptake-dependent biocompatibility studies and cell detection by MRI and future MPI. Additionally, using a 7 T MR imager equipped with a cryocoil resulted in approximately two times higher detection. In conclusion, we established labeling conditions for new high-relaxivity MCP, VSOP, and Resovist® for improved MRI of MSC with single-cell sensitivity