Search for: All records

Massif-type anorthosites, enormous and enigmatic plagioclase-rich cumulate intrusions emplaced into Earth’s crust, formed in large numbers only between 1 and 2 billion years ago. Conflicting hypotheses for massif-type anorthosite formation, including melting of upwelling mantle, lower crustal melting, and arc magmatism above subduction zones, have stymied consensus on what parental magmas crystallized the anorthosites and why the rocks are temporally restricted. Using B, O, Nd, and Sr isotope analyses, bulk chemistry, and petrogenetic modeling, we demonstrate that the magmas parental to the Marcy and Morin anorthosites, classic examples from North America’s Grenville orogen, require large input from mafic melts derived from slab-top altered oceanic crust. The anorthosites also record B isotopic signatures corresponding to other slab lithologies such as subducted abyssal serpentinite. We propose that anorthosite massifs formed underneath convergent continental margins wherein a subducted or subducting slab melted extensively and link massif-type anorthosite formation to Earth’s thermal and tectonic evolution. 

Innate immune priming increases an organism’s survival of a second infection after an initial, non-lethal infection. We usedDrosophila melanogasterand an insect-derived strain ofEnterococcus faecalisto study transcriptional control of priming. In contrast to other pathogens, the enhanced survival in primed animals does not correlate with decreasedE.faecalisload. Further analysis shows that primed organisms tolerate, rather than resist infection. Using RNA-seq of immune tissues, we found many genes were upregulated in only primed flies, suggesting a distinct transcriptional program in response to initial and secondary infections. In contrast, few genes continuously express throughout the experiment or more efficiently re-activate upon reinfection. Priming experiments in immune deficient mutants revealed Imd is largely dispensable for responding to a single infection but needed to fully prime. Together, this indicates the fly’s innate immune response is plastic—differing in immune strategy, transcriptional program, and pathway use depending on infection history. 

Abstract The use of external controls in genome-wide association study (GWAS) can significantly increase the size and diversity of the control sample, enabling high-resolution ancestry matching and enhancing the power to detect association signals. However, the aggregation of controls from multiple sources is challenging due to batch effects, difficulty in identifying genotyping errors and the use of different genotyping platforms. These obstacles have impeded the use of external controls in GWAS and can lead to spurious results if not carefully addressed. We propose a unified data harmonization pipeline that includes an iterative approach to quality control and imputation, implemented before and after merging cohorts and arrays. We apply this harmonization pipeline to aggregate 27 517 European control samples from 16 collections within dbGaP. We leverage these harmonized controls to conduct a GWAS of Crohn’s disease. We demonstrate a boost in power over using the cohort samples alone, and that our procedure results in summary statistics free of any significant batch effects. This harmonization pipeline for aggregating genotype data from multiple sources can also serve other applications where individual level genotypes, rather than summary statistics, are required. 

Code clones are fragments of code that are duplicated in the codebase of an application. They create problems with maintainability, duplicate buggy code, and increase the size of the repository. To combat these issues, there currently exists a multitude of programs to detect duplicated code segments. However, there are not many varieties of languages among the benchmarks for code clone detection tools. Without covering enough languages for modern software development, the development of code-clone detection tools remains stunted. This paper describes a novel tool that will take a seed of Python source code and generate Type 1, 2, and 3 code clones in Python. As one of the most used and rapidly-growing languages in modern software development, our testbed will provide the opportunity for Python code-clone detection tools to be developed and tested. 

Abstract Garnet is a common metamorphic and igneous mineral with extensive solid solution that can be stable to mantle depths ≥400 km. High-T and/or high-P garnet may contain oriented lamellae of other minerals, most commonly simple oxides (e.g., rutile, ilmenite), apatite, and, in ultrahigh-P cases, silicates including pyroxene and amphibole. Lamellae have classically been considered to be precipitation features preserving a record of former garnet chemistry richer in the lamellae nutrients (e.g., Ti4+). Such microtextural origins in precipitation systems (e.g., alloys) have long been studied via the crystallographic orientation relationships (COR) that form between a host and a separating phase, and by the shape-preferred orientation (SPO) of the lamellae. Recently, however, alternative hypotheses to precipitation have been suggested that require emplacement of lamellae in garnet by fluids, or co-growth, overgrowth, or inheritance mechanisms. These hypotheses posit that lamellae cannot be used to study former garnet chemistry. Moreover, they predict that lamellae phases, SPO, and COR should differ widely between localities, as lamellae formation will be controlled by various local rock-specific factors such as fluid presence, fluid chemistry, or mineral growth sequence. On the other hand, if lamellae characteristics are largely consistent between localities, it likely reflects control by precipitation energetics, rather than external factors. There have been few comparative COR studies in geologic systems, but the integrative assessment of COR, SPO, and lamellae assemblages should fingerprint lamellae growth process. To test the precipitation and alternative hypotheses, we collected large electron backscatter diffraction (EBSD) data sets for rutile, ilmenite, and apatite lamellae in garnet from the Brimfield Schist, Connecticut (≥1000 °C metamorphism; Central Maine Terrane, U.S.A.). We analyzed these data alongside published EBSD data for rutile, ilmenite, and corundum from metapegmatites metamorphosed in the eclogite facies from the Austrian Alps (Griffiths et al. 2016). The apatite data set is the first of its kind, and reveals that apatite preferentially aligns its close-packed direction parallel to that of garnet (c-axisapatite//<111>garnet). We also recognize a rutile-garnet COR related to those in meteorites with Widmanstätten patterns that are unequivocal products of exsolution. This is the first identification of direct similarities between silicate-oxide and metal-metal COR of which we are aware. Significantly, this rutile-garnet COR is found in diverse geologic settings including Connecticut and Idaho (U.S.A.), Austria, Germany, Greece, and China over a broad range of bulk-rock compositions. Results for all lamellae minerals show that COR are largely consistent between localities and, furthermore, are shared between apatite, ilmenite, and corundum. Moreover, between 70% and 95% of lamellae have COR and there is a dominant COR for each lamellae phase. Calculations show that d-spacing ratios of host-lamellae pairs can successfully predict the most commonly observed specific COR (those COR with two or more axial alignments with the host). These results, especially similarity of COR from markedly different geologic settings and a low diversity of lamellae minerals, are fully consistent with lamellae formation by precipitation (likely via exsolution). In contrast, the alternative hypotheses remain unsupported by COR results as well as by mineralogical and petrological evidence. Lamellae with similar traits as those in this work should thus be considered precipitates formed during unmixing of garnet compositions originally stable at elevated or extreme pressures and temperatures. 

Inertia from rotating masses of generators in power systems influence the instantaneous frequency change when an imbalance between electrical and mechanical power occurs. Renewable energy sources (RES), such as solar and wind power, are connected to the grid via electronic converters. RES connected through converters affect the system's inertia by decreasing it and making it time-varying. This new setting challenges the ability of current control schemes to maintain frequency stability. Proposing adequate controllers for this new paradigm is key for the performance and stability of future power grids. The contribution of this paper is a framework to learn sparse time-invariant frequency controllers in a power system network with a time-varying evolution of rotational inertia. We model power dynamics using a Switched-Affine hybrid system to consider different modes corresponding to different inertia coefficients. We design a controller that uses as features, i.e. input, the systems states. In other words, we design a control proportional to the angles and frequencies. We include virtual inertia in the controllers to ensure stability. One of our findings is that it is possible to restrict communication between the nodes by reducing the number of features in the controller (from 22 to 10 in our case study) without disrupting performance and stability. Furthermore, once communication between nodes has reached a threshold, increasing it beyond this threshold does not improve performance or stability. We find a correlation between optimal feature selection in sparse controllers and the topology of the network. 

With the increasing penetration of non-synchronous variable renewable energy sources (RES) in power grids, the system's inertia decreases and varies over time, affecting the capability of current control schemes to handle frequency regulation. Providing virtual inertia to power systems has become an interesting topic of research, since it may provide a reasonable solution to address this new issue. However, power dynamics are usually modeled as time-invariant, without including the effect of varying inertia due to the presence of RES. This paper presents a framework to design a fixed learned controller based on datasets of optimal time-varying LQR controllers. In our scheme, we model power dynamics as a hybrid system with discrete modes representing different rotational inertia regimes of the grid. We test the performance of our controller in a twelve-bus system using different fixed inertia modes. We also study our learned controller as the inertia changes over time. By adding virtual inertia we can guarantee stability of high-renewable (low-inertia) modes. The novelty of our work is to propose a design framework for a stable controller with fixed gains for time-varying power dynamics. This is relevant because it would be simpler to implement a proportional controller with fixed gains compared to a time-varying control. 

This paper studies rooftop solar photovoltaic (PV) investment decisions of households. Two cases are considered: (a) the status quo of net-metering, and (b) a new sharing economy model. Under net-metering, households can sell back their excess generation to the utility at their retail tariff subject to the prevalent constraint that they cannot be net producers of electricity on an annual basis. In our sharing economy model, households can pool their excess PV generation and trade it in a spot market among themselves, but the collective cannot sell electricity back to the utility. Our objective in studying these two cases is that net-metering programs are under threat and being phased out, which places future residential PV investment at risk. In the event of this contingency, we argue that the sharing economy model offers a pathway to preserve and even accelerate residential PV investment. We derive expressions for the optimal investment decisions in each case assuming that households are rational and wish to minimize their costs. We characterize the random clearing price in the spot market for excess PV generation under the sharing model. We show that the optimal investment decisions are determined by a simple threshold policy. Households whose PV productivity metric exceeds this threshold invest the maximum possible, while those that fall below the threshold do not invest. We offer a convergent algorithm to compute this threshold. We close with a small-scale simulation study that reveals the favorable properties of the sharing economy model for residential PV investments.