5632 research outputs found
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Highly efficient and rapid generation of genetic variants
No full textA new mutagenesis platform enables the fast, cost-efficient and automatable production of defined multi-site sequence variants for a wide range of applications. Demonstrations of this method included the generation of SARS-CoV-2 spike gene variants, DNA fragments for large-scale genome engineering, and adeno-associated virus 2 (AAV2) cap genes with improved packaging capacity
Rapid Seismic Waveform Modeling and Inversion With Neural Operators
No full textSeismic waveform modeling is a powerful tool for determining earth structure models and unraveling earthquake rupture processes, but it is usually computationally expensive. We introduce a scheme to vastly accelerate these calculations with a recently developed machine learning paradigm called the neural operator. Once trained, these models can simulate a full wavefield at negligible cost. We use a U-shaped neural operator to learn a general solution operator to the 2-D elastic wave equation from an ensemble of numerical simulations performed with random velocity models and source locations. We show that full-waveform modeling with neural operators is nearly two orders of magnitude faster than conventional numerical methods, and more importantly, the trained model enables accurate simulation for velocity models, source locations, and mesh discretization distinctly different from the training dataset. The method also enables convenient full-waveform inversion with automatic differentiation
Narrow Loophole for H₂-Dominated Atmospheres on Habitable Rocky Planets around M Dwarfs
No full textHabitable rocky planets around M dwarfs that have H₂-dominated atmospheres, if they exist, would permit characterizing habitable exoplanets with detailed spectroscopy using JWST, owing to their extended atmospheres and small stars. However, the H₂-dominated atmospheres that are consistent with habitable conditions cannot be too massive, and a moderate-sized H₂-dominated atmosphere will lose mass to irradiation-driven atmospheric escape on rocky planets around M dwarfs. We evaluate volcanic outgassing and serpentinization as two potential ways to supply H₂ and form a steady-state H₂-dominated atmosphere. For rocky planets of 1–7 M_⊕ and early-, mid-, and late M-type dwarfs, the expected volcanic outgassing rates from a reduced mantle fall short of the escape rates by > ∼ 1 order of magnitude, and a generous upper limit of the serpentinization rate is still less than the escape rate by a factor of a few. Special mechanisms that may sustain the steady-state H₂-dominated atmosphere include direct interaction between liquid water and mantle, heat-pipe volcanism from a reduced mantle, and hydrodynamic escape slowed down by efficient upper-atmospheric cooling. It is thus unlikely to find moderate-size, H₂-dominated atmospheres on rocky planets of M dwarfs that would support habitable environments
Late-time tails and mode coupling of linear waves on Kerr spacetimes
No full textWe provide a rigorous derivation of the precise late-time asymptotics for solutions to the scalar wave equation on subextremal Kerr backgrounds, including the asymptotics for projections to angular frequencies ℓ ≥ 1 and ℓ ≥ 2. The ℓ-dependent asymptotics on Kerr spacetimes differ significantly from the non-rotating Schwarzschild setting (“Price's law”). The main differences with Schwarzschild are slower decay rates for higher angular frequencies and oscillations along the null generators of the event horizon. We introduce a physical space-based method that resolves the following two main difficulties for establishing ℓ-dependent asymptotics in the Kerr setting: 1) the coupling of angular modes and 2) a loss of ellipticity in the ergoregion. Our mechanism identifies and exploits the existence of conserved charges along null infinity via a time invertibility theory, which in turn relies on new elliptic estimates in the full black hole exterior. This framework is suitable for addressing conflicting results on the numerology of Kerr late-time asymptotics appearing in the numerics literature and definitively determining the correct numerology
TensorCircuit: a Quantum Software Framework for the NISQ Era
No full textTensorCircuit is an open source quantum circuit simulator based on tensor network contraction, designed for speed, flexibility and code efficiency. Written purely in Python, and built on top of industry-standard machine learning frameworks, TensorCircuit supports automatic differentiation, just-in-time compilation, vectorized parallelism and hardware acceleration. These features allow TensorCircuit to simulate larger and more complex quantum circuits than existing simulators, and are especially suited to variational algorithms based on parameterized quantum circuits. TensorCircuit enables orders of magnitude speedup for various quantum simulation tasks compared to other common quantum software, and can simulate up to 600 qubits with moderate circuit depth and low-dimensional connectivity. With its time and space efficiency, flexible and extensible architecture and compact, user-friendly API, TensorCircuit has been built to facilitate the design, simulation and analysis of quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era
Hot Carrier Thermalization and Josephson Inductance Thermometry in a Graphene-Based Microwave Circuit
No full textDue to its exceptional electronic and thermal properties, graphene is a key material for bolometry, calorimetry, and photon detection. However, despite graphene’s relatively simple electronic structure, the physical processes responsible for the heat transport from the electrons to the lattice are experimentally still elusive. Here, we measure the thermal response of low-disorder graphene encapsulated in hexagonal boron nitride by integrating it within a multiterminal superconducting microwave resonator. The device geometry allows us to simultaneously apply Joule heat power to the graphene flake while performing calibrated readout of the electron temperature. We probe the thermalization rates of both electrons and holes with high precision and observe a thermalization scaling exponent not consistent with cooling through the graphene bulk and argue that instead it can be attributed to processes at the graphene – aluminum interface. Our technique provides new insights into the thermalization pathways essential for the next-generation graphene thermal detectors
Deletions of DNA in cancer and their possible uses for therapy
No full textDespite advances in treatments over the last decades, a uniformly reliable and free of side effects therapy of human cancers remains to be achieved. During chromosome replication, a premature halt of two converging DNA replication forks would cause incomplete replication and a cytotoxic chromosome nondisjunction during mitosis. In contrast to normal cells, most cancer cells bear numerous DNA deletions. A homozygous deletion permanently marks a cell and its descendants. Here, we propose an approach to cancer therapy in which a pair of sequence-specific roadblocks is placed solely at two cancer-confined deletion sites that are located ahead of two converging replication forks. We describe this method, termed “replication blocks specific for deletions” (RBSD), and another deletions-based approach as well. RBSD can be expanded by placing pairs of replication roadblocks on several different chromosomes. The resulting simultaneous nondisjunctions of these chromosomes in cancer cells would further increase the cancer-specific toxicity of RBSD
Strike slip motion and the triggering of subduction initiation
No full textPlate tectonic reconstructions of three of the best-defined Cenozoic subduction initiation (SI) events in the western Pacific, Izu-Bonin-Mariana, Vanuatu, and Puysegur subduction zones, show substantial components of strike-slip motion before and during the subduction initiation. Using computational models, we show that strike-slip motion has a large influence on the effective strength of incipient margins and the ease of subduction initiation. The parameter space associated with visco-elasto-plastic rheologies, plate weakening, and plate forces and kinematics is explored and we show that subduction initiates more easily with a higher force, a faster weakening, or greater strike-slip motion. With the analytical solution, we demonstrate that the effect of strike-slip motion can be equivalently represented by a modified weakening rate. Along transpressive margins, we show that a block of oceanic crust can become trapped between a new thrust fault and the antecedent strike-slip fault and is consistent with structural reconstructions and gravity models of the Puysegur margin. Together, models and observations suggest that subduction initiation can be triggered when margins become progressively weakened to the point that the resisting forces become smaller than the driving forces, and as the negative buoyancy builds up, the intraplate stress eventually turns from compressional into extensional. The analytical formulation of the initiation time, t_(SI), marking the moment when intraplate stress flips sign, is validated with a computational models. The analytical solution shows that t_(SI) is dominated by convergence velocity, while the plate age, strike-slip velocity, and weakening rate all have a smaller but still important effect on the time scale of subduction initiation
Geomorphic and environmental controls on microbial mat fabrics on Little Ambergris Cay, Turks and Caicos Islands
No full textTo interpret microbially influenced paleoenvironments in the sedimentary record, it is crucial to understand what processes control the development of microbial mats in modern environments. This article reports results from a multiyear study of Little Ambergris Cay, Turks and Caicos Islands, an uninhabited island floored by broad tracts of well-developed microbial mats on the wind-dominated and wave-dominated Caicos Platform. Uncrewed aerial vehicle-based imaging, differential global positioning system topographic surveys, radiocarbon data, and in situ sedimentological and microbial ecological observations were integrated to identify and quantify the environmental factors that influence the distribution and morphologies of Little Ambergris Cay microbial mats, including their response to large storm events. Based on these data, this study proposes that Little Ambergris Cay initially developed from the accretion and rapid lithification of carbonate sediment delivered by converging wave fronts in the lee of adjacent Big Ambergris Cay. Broad tracts of microbial mats developed during late Holocene time as the interior became restricted due to beach ridge development. Minor elevation differences regulate subaerial exposure time and lead to three categories of microbial mats, differentiated by surface texture and morphology: smooth mats, polygonal mats and blister mats. The surface texture and morphology of the mats is controlled by subaerial exposure time. In addition to elevation, the spatial distribution of mats is largely controlled by hydrodynamics and sediment transport during large storm events. This detailed assessment of the controls on mat formation and preservation at Little Ambergris Cay provides a framework within which to identify and understand the interactions between microbial communities and sediment transport processes in ancient high-energy carbonate depositional systems
Learning Markovian Homogenized Models in Viscoelasticity
No full textFully resolving dynamics of materials with rapidly varying features involves expensive fine-scale computations which need to be conducted on macroscopic scales. The theory of homogenization provides an approach for deriving effective macroscopic equations which eliminates the small scales by exploiting scale separation. An accurate homogenized model avoids the computationally expensive task of numerically solving the underlying balance laws at a fine scale, thereby rendering a numerical solution of the balance laws more computationally tractable. In complex settings, homogenization only defines the constitutive model implicitly, and machine learning can be used to learn the constitutive model explicitly from localized fine-scale simulations. In the case of one-dimensional viscoelasticity, the linearity of the model allows for a complete analysis. We establish that the homogenized constitutive model may be approximated by a recurrent neural network that captures the memory. The memory is encapsulated in the evolution of an appropriate finite set of hidden variables, which are discovered through the learning process and dependent on the history of the strain. Simulations are presented which validate the theory. Guidance for the learning of more complex models, such as arise in plasticity, using similar techniques, is given