Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks

As an alternative to classical numerical solvers for partial differential equations (PDEs) subject to boundary value constraints, there has been a surge of interest in investigating neural networks that can solve such problems efficiently. In this work, we design a general solution operator for two different time-independent PDEs using graph neural networks (GNNs) and spectral graph convolutions. We train the networks on simulated data from a finite elements solver on a variety of shapes and inhomogeneities. In contrast to previous works, we focus on the ability of the trained operator to generalize to previously unseen scenarios. Specifically, we test generalization to meshes with different shapes and superposition of solutions for a different number of inhomogeneities. We find that training on a diverse dataset with lots of variation in the finite element meshes is a key ingredient for achieving good generalization results in all cases. With this, we believe that GNNs can be used to learn solution operators that generalize over a range of properties and produce solutions much faster than a generic solver. Our dataset, which we make publicly available, can be used and extended to verify the robustness of these models under varying conditions

Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks

Self-Organized Criticality (SOC) is a ubiquitous dynamical phenomenon believed to be responsible for the emergence of universal scale-invariant behavior in many, seemingly unrelated systems, such as forest fires, virus spreading or atomic excitation dynamics. SOC describes the buildup of large-scale and long-range spatio-temporal correlations as a result of only local interactions and dissipation. The simulation of SOC dynamics is typically based on Monte-Carlo (MC) methods, which are however numerically expensive and do not scale beyond certain system sizes. We investigate the use of Graph Neural Networks (GNNs) as an effective surrogate model to learn the dynamics operator for a paradigmatic SOC system, inspired by an experimentally accessible physics example: driven Rydberg atoms. To this end, we generalize existing GNN simulation approaches to predict dynamics for the internal state of the node. We show that we can accurately reproduce the MC dynamics as well as generalize along the two important axes of particle number and particle density. This paves the way to model much larger systems beyond the limits of traditional MC methods. While the exact system is inspired by the dynamics of Rydberg atoms, the approach is quite general and can readily be applied to other systems

Catchment-scale patterns of geomorphic activity and vegetation distribution in an alpine glacier foreland (Kaunertal Valley, Austria)

The interaction between geomorphological and ecological processes plays a significant role in determining landscape patterns in glacier forelands. However, the spatial organization of this biogeomorphic mosaic remains unclear due to limited catchment-scale data. To address this gap, we used a multi-proxy analysis to map potential geomorphic activity related to surface changes induced by sediment transport on drift-mantled slopes and a glaciofluvial plain. High-resolution vegetation data were used to generate a catchment-scale map delineating vegetation cover and stability thresholds. The two maps were integrated, and an exploratory regression analysis was conducted to investigate the influence of geomorphic activity on vegetation colonization. The multi-proxy analysis resulted in an accurate mapping of catchment-wide geomorphic activity, with a validation accuracy ranging from 75.3% through field mapping to 85.9% through plot sampling. Through vegetation cover mapping, we identified biogeomorphic stability thresholds, revealing a mosaic of vegetation distribution. Distinct colonization patterns emerged across different geomorphic process groups, influenced by process magnitude and the time since the last disturbance event. The exploratory regression analysis showed that vegetation distribution is significantly affected by geomorphic processes. Based on the overlay of results regarding geomorphic activity and vegetation distribution, we suggest an age-independent framework that indicates four potential situations of biogeomorphic succession

Limitations and potentials of current motif discovery algorithms

Computational methods for de novo identification of gene regulation elements, such as transcription factor binding sites, have proved to be useful for deciphering genetic regulatory networks. However, despite the availability of a large number of algorithms, their strengths and weaknesses are not sufficiently understood. Here, we designed a comprehensive set of performance measures and benchmarked five modern sequence-based motif discovery algorithms using large datasets generated from Escherichia coli RegulonDB. Factors that affect the prediction accuracy, scalability and reliability are characterized. It is revealed that the nucleotide and the binding site level accuracy are very low, while the motif level accuracy is relatively high, which indicates that the algorithms can usually capture at least one correct motif in an input sequence. To exploit diverse predictions from multiple runs of one or more algorithms, a consensus ensemble algorithm has been developed, which achieved 6–45% improvement over the base algorithms by increasing both the sensitivity and specificity. Our study illustrates limitations and potentials of existing sequence-based motif discovery algorithms. Taking advantage of the revealed potentials, several promising directions for further improvements are discussed. Since the sequence-based algorithms are the baseline of most of the modern motif discovery algorithms, this paper suggests substantial improvements would be possible for them

The MOF-containing NSL complex associates globally with housekeeping genes, but activates only a defined subset

The MOF (males absent on the first)-containing NSL (non-specific lethal) complex binds to a subset of active promoters in Drosophila melanogaster and is thought to contribute to proper gene expression. The determinants that target NSL to specific promoters and the circumstances in which the complex engages in regulating transcription are currently unknown. Here, we show that the NSL complex primarily targets active promoters and in particular housekeeping genes, at which it colocalizes with the chromatin remodeler NURF (nucleosome remodeling factor) and the histone methyltransferase Trithorax. However, only a subset of housekeeping genes associated with NSL are actually activated by it. Our analyses reveal that these NSL-activated promoters are depleted of certain insulator binding proteins and are enriched for the core promoter motif ‘Ohler 5’. Based on these results, it is possible to predict whether the NSL complex is likely to regulate a particular promoter. We conclude that the regulatory capacity of the NSL complex is highly context-dependent. Activation by the NSL complex requires a particular promoter architecture defined by combinations of chromatin regulators and core promoter motifs

Abnormal Dosage Compensation of Reporter Genes Driven by the Drosophila Glass Multiple Reporter (GMR) Enhancer-Promoter

In Drosophila melanogaster the male specific lethal (MSL) complex is required for upregulation of expression of most X-linked genes in males, thereby achieving X chromosome dosage compensation. The MSL complex is highly enriched across most active X-linked genes with a bias towards the 3′ end. Previous studies have shown that gene transcription facilitates MSL complex binding but the type of promoter did not appear to be important. We have made the surprising observation that genes driven by the glass multiple reporter (GMR) enhancer-promoter are not dosage compensated at X-linked sites. The GMR promoter is active in all cells in, and posterior to, the morphogenetic furrow of the developing eye disc. Using phiC31 integrase-mediated targeted integration, we measured expression of lacZ reporter genes driven by either the GMR or armadillo (arm) promoters at each of three X-linked sites. At all sites, the arm-lacZ reporter gene was dosage compensated but GMR-lacZ was not. We have investigated why GMR-driven genes are not dosage compensated. Earlier or constitutive expression of GMR-lacZ did not affect the level of compensation. Neither did proximity to a strong MSL binding site. However, replacement of the hsp70 minimal promoter with a minimal promoter from the X-linked 6-Phosphogluconate dehydrogenase gene did restore partial dosage compensation. Similarly, insertion of binding sites for the GAGA and DREF factors upstream of the GMR promoter led to significantly higher lacZ expression in males than females. GAGA and DREF have been implicated to play a role in dosage compensation. We conclude that the gene promoter can affect MSL complex-mediated upregulation and dosage compensation. Further, it appears that the nature of the basal promoter and the presence of binding sites for specific factors influence the ability of a gene promoter to respond to the MSL complex

Chemical–Genetic Profiling of Imidazo[1,2-a]pyridines and -Pyrimidines Reveals Target Pathways Conserved between Yeast and Human Cells

Small molecules have been shown to be potent and selective probes to understand cell physiology. Here, we show that imidazo[1,2-a]pyridines and imidazo[1,2-a]pyrimidines compose a class of compounds that target essential, conserved cellular processes. Using validated chemogenomic assays in Saccharomyces cerevisiae, we discovered that two closely related compounds, an imidazo[1,2-a]pyridine and -pyrimidine that differ by a single atom, have distinctly different mechanisms of action in vivo. 2-phenyl-3-nitroso-imidazo[1,2-a]pyridine was toxic to yeast strains with defects in electron transport and mitochondrial functions and caused mitochondrial fragmentation, suggesting that compound 13 acts by disrupting mitochondria. By contrast, 2-phenyl-3-nitroso-imidazo[1,2-a]pyrimidine acted as a DNA poison, causing damage to the nuclear DNA and inducing mutagenesis. We compared compound 15 to known chemotherapeutics and found resistance required intact DNA repair pathways. Thus, subtle changes in the structure of imidazo-pyridines and -pyrimidines dramatically alter both the intracellular targeting of these compounds and their effects in vivo. Of particular interest, these different modes of action were evident in experiments on human cells, suggesting that chemical–genetic profiles obtained in yeast are recapitulated in cultured cells, indicating that our observations in yeast can: (1) be leveraged to determine mechanism of action in mammalian cells and (2) suggest novel structure–activity relationships

Measurement of the cross-section for producing a W boson in association with a single top quark in pp collisions at √s = 13 TeV with ATLAS

The inclusive cross-section for the associated production of a W boson and top quark is measured using data from proton-proton collisions at √ s = 13 TeV. The dataset corresponds to an integrated luminosity of 3.2 fb−1 , and was collected in 2015 by the ATLAS detector at the Large Hadron Collider at CERN. Events are selected requiring two opposite sign isolated leptons and at least one jet; they are separated into signal and control regions based on their jet multiplicity and the number of jets that are identified as containing b hadrons. The W t signal is then separated from the tt¯ background using boosted decision tree discriminants in two regions. The cross-section is extracted by fitting templates to the data distributions, and is measured to be σW t = 94±10 (stat.) +28 −22 (syst.)±2 (lumi.) pb. The measured value is in good agreement with the SM prediction of σtheory = 71.7±1.8 (scale)± 3.4 (PDF) pb [1]

Toward a polycentric low-carbon transition: the roles of community-based energy initiatives in enhancing the resilience of energy systems

An understanding of the resilience of energy systems is critical in order to tackle forthcoming challenges. This chapter proposes that the polycentric governance perspective, developed by Vincent and Elinor Ostrom, may be highly relevant in formulating policies to enhance the resilience of future energy systems. Polycentric governance systems involve the coexistence of many self-organized centers of decision making at multiple levels that are formally independent of each other, but operate under an overarching set of rules. Given this polycentric approach, this chapter studies the roles of community-based energy initiatives and, in particular, of renewable energy cooperatives, in enhancing the institutional resilience of energy systems. In this perspective, the chapter identifies three major socio-institutional obstacles, which undermine this resilience capacity: the collective action problem arising from the diffusion of sustainable energy technologies and practices, the lack of public trust in established energy actors and the existence of strong vested interests in favor of the status quo. Then, it shows why the development of community-based energy initiatives and renewable energy cooperatives may offer effective responses to these obstacles, relying on many empirical illustrations. More specifically, it is argued that community-based energy initiatives present institutional features encouraging the activation of social norms and a high trust capital, therefore enabling them to offer effective solutions to avoid free riding and enhance trust in energy institutions and organizations. The creation of federated polycentric structures may also offer a partial response to the existence of vested interests in favor of the status quo. Finally, some recommendations for policymakers are derived from this analysis