Publications

Over the last few years, extraordinary advances in experimental and theoretical tools have allowed us to monitor and control matter at short time and atomic scales with a high degree of precision. An appealing and challenging route toward engineering materials with tailored properties is to find ways to design or selectively manipulate materials, especially at the quantum level. To this end, having a state-of-the-art ab initio computer simulation tool that enables a reliable and accurate simulation of light-induced changes in the physical and chemical properties of complex systems is of utmost importance. The first principles real-space-based Octopus project was born with that idea in mind, i.e., to provide a unique framework that allows us to describe non-equilibrium phenomena in molecular complexes, low dimensional materials, and extended systems by accounting for electronic, ionic, and photon quantum mechanical effects within a generalized time-dependent density functional theory. This article aims to present the new features that have been implemented over the last few years, including technical developments related to performance and massive parallelism. We also describe the major theoretical developments to address ultrafast light-driven processes, such as the new theoretical framework of quantum electrodynamics density-functional formalism for the description of novel light–matter hybrid states. Those advances, and others being released soon as part of the Octopus package, will allow the scientific community to simulate and characterize spatial and time-resolved spectroscopies, ultrafast phenomena in molecules and materials, and new emergent states of matter (quantum electrodynamical-materials).

In the course of doing astronomy, one often encounters plots of densities, for example probability densities, flux densities, and mass functions. Quite frequently the ordinate of these diagrams is plotted logarithmically to accommodate a large dynamic range. In this situation, I argue that it is critical to adjust the density appropriately, rather than simply setting the x-scale to `log' in your favorite plotting code. I will demonstrate the basic issue with a pedagogical example, then mention a few common plots where this may arise, and finally some possible exceptions to the rule.

The occurrence of coiled or helical morphologies is common in nature, from plant roots to DNA packaging into viral capsids, as well as in applications such as oil drilling processes. In many examples, chiral structures result from the buckling of a straight fibre either with intrinsic twist or to which end moments have been applied in addition to compression forces. Here, we elucidate a generic way to form regular helicoidal shapes from achiral straight filaments transported in viscous flows with free ends. Through a combination of experiments using fluorescently labelled actin filaments in microfluidic divergent flows and two distinct sets of numerical simulations, we demonstrate the robustness of helix formation. A nonlinear stability analysis is performed, and explains the emergence of such chiral structures from the nonlinear interaction of perpendicular planar buckling modes, an effect that solely requires a strong compressional flow, independent of the exact nature of the fibre or type of flow field. The fundamental mechanism for the uncovered morphological transition and characterization of the emerging conformations advance our understanding of several biological and industrial processes and can also be exploited for the controlled microfabrication of chiral objects.

It is imperative that useful quantum computers be very difficult to simulate classically; otherwise classical computers could be used for the applications envisioned for the quantum ones. Perfect quantum computers are unarguably exponentially difficult to simulate: the classical resources required grow exponentially with the number of qubits N or the depth D of the circuit. Real quantum computing devices, however, are characterized by an exponentially decaying fidelity F∼(1−ϵ)ND with an error rate ϵ per operation as small as ~1% for current devices. In this work, we demonstrate that real quantum computers can be simulated at a tiny fraction of the cost that would be needed for a perfect quantum computer. Our algorithms compress the representations of quantum wavefunctions using matrix product states (MPS), which capture states with low to moderate entanglement very accurately. This compression introduces a finite error rate ϵ so that the algorithms closely mimic the behavior of real quantum computing devices. The computing time of our algorithm increases only linearly with N and D. We illustrate our algorithms with simulations of random circuits for qubits connected in both one and two dimensional lattices. We find that ϵ can be decreased at a polynomial cost in computing power down to a minimum error ϵ∞. Getting below ϵ∞ requires computing resources that increase exponentially with ϵ∞/ϵ. For a two dimensional array of N=54 qubits and a circuit with Control-Z gates, error rates better than state-of-the-art devices can be obtained on a laptop in a few hours. For more complex gates such as a swap gate followed by a controlled rotation, the error rate increases by a factor three for similar computing time.

More than 80 loss-of-function (LOF) mutations in the SLC6A8 creatine transporter (hCRT1) are responsible for cerebral creatine deficiency syndrome (CCDS), which gives rise to a spectrum of neurological defects, including intellectual disability, epilepsy, and autism spectrum disorder. To gain insight into the nature of the molecular defects caused by these mutations, we quantitatively profiled the cellular processing, trafficking, expression, and function of eight pathogenic CCDS variants in relation to the wild type (WT) and one neutral isoform. All eight CCDS variants exhibit measurable proteostatic deficiencies that likely contribute to the observed LOF. However, the magnitudes of their specific effects on the expression and trafficking of hCRT1 vary considerably, and we find that the LOF associated with two of these variants primarily arises from the disruption of the substrate-binding pocket. In conjunction with an analysis of structural models of the transporter, we use these data to suggest mechanistic classifications for these variants. To evaluate potential avenues for therapeutic intervention, we assessed the sensitivity of these variants to temperature and measured their response to the proteostasis regulator 4-phenylbutyrate (4-PBA). Only one of the tested variants (G132V) is sensitive to temperature, though its response to 4-PBA is negligible. Nevertheless, 4-PBA significantly enhances the activity of WT hCRT1 in HEK293T cells, which suggests it may be worth evaluating as a therapeutic for female intellectual disability patients carrying a single CCDS mutation. Together, these findings reveal that pathogenic SLC6A8 mutations cause a spectrum of molecular defects that should be taken into consideration in future efforts to develop CCDS therapeutics.

Optogenetic perturbations, live imaging, and time-resolved ChIP-seq assays in Drosophila embryos were used to dissect the ERK-dependent control of the HMG-box repressor Capicua (Cic), which plays critical roles in development and is deregulated in human spinocerebellar ataxia and cancers. We established that Cic target genes are activated before significant downregulation of nuclear localization of Cic and demonstrated that their activation is preceded by fast dissociation of Cic from the regulatory DNA. We discovered that both Cic-DNA binding and repression are rapidly reinstated in the absence of ERK activation, revealing that inductive signaling must be sufficiently sustained to ensure robust transcriptional response. Our work provides a quantitative framework for the mechanistic analysis of dynamics and control of transcriptional repression in development.

Background
The abundance and diversity of antibiotic resistance genes (ARGs) in the human respiratory microbiome remain poorly characterized. In the context of influenza virus infection, interactions between the virus, the host, and resident bacteria with pathogenic potential are known to complicate and worsen disease, resulting in coinfection and increased morbidity and mortality of infected individuals. When pathogenic bacteria acquire antibiotic resistance, they are more difficult to treat and of global health concern. Characterization of ARG expression in the upper respiratory tract could help better understand the role antibiotic resistance plays in the pathogenesis of influenza-associated bacterial secondary infection.

Results
Thirty-seven individuals participating in the Household Influenza Transmission Study (HITS) in Managua, Nicaragua, were selected for this study. We performed metatranscriptomics and 16S rRNA gene sequencing analyses on nasal and throat swab samples, and host transcriptome profiling on blood samples. Individuals clustered into two groups based on their microbial gene expression profiles, with several microbial pathways enriched with genes differentially expressed between groups. We also analyzed antibiotic resistance gene expression and determined that approximately 25% of the sequence reads that corresponded to antibiotic resistance genes mapped to Streptococcus pneumoniae and Staphylococcus aureus. Following construction of an integrated network of ARG expression with host gene co-expression, we identified several host key regulators involved in the host response to influenza virus and bacterial infections, and host gene pathways associated with specific antibiotic resistance genes.

Conclusions
This study indicates the host response to influenza infection could indirectly affect antibiotic resistance gene expression in the respiratory tract by impacting the microbial community structure and overall microbial gene expression. Interactions between the host systemic responses to influenza infection and antibiotic resistance gene expression highlight the importance of viral-bacterial co-infection in acute respiratory infections like influenza.

Although a wide variety of quantum computers are currently being developed, actual computational results have been largely restricted to contrived, artificial tasks. Finding ways to apply quantum computers to useful, real-world computational tasks remains an active research area. Here we describe our mapping of the protein design problem to the D-Wave quantum annealer. We present a system whereby Rosetta, a state-of-the-art protein design software suite, interfaces with the D-Wave quantum processing unit to find amino acid side chain identities and conformations to stabilize a fixed protein backbone. Our approach, which we call the QPacker, uses a large side-chain rotamer library and the full Rosetta energy function, and in no way reduces the design task to a simpler format. We demonstrate that quantum annealer-based design can be applied to complex real-world design tasks, producing designed molecules comparable to those produced by widely adopted classical design approaches. We also show through large-scale classical folding simulations that the results produced on the quantum annealer can inform wet-lab experiments. For design tasks that scale exponentially on classical computers, the QPacker achieves nearly constant runtime performance over the range of problem sizes that could be tested. We anticipate better than classical performance scaling as quantum computers mature.

We introduce a sparse scattering deep convolutional neural network, which provides a simple model to analyze properties of deep representation learning for classification. Learning a single dictionary matrix with a classifier yields a higher classification accuracy than AlexNet over the ImageNet 2012 dataset. The network first applies a scattering transform that linearizes variabilities due to geometric transformations such as translations and small deformations. A sparse $\ell^1$ dictionary coding reduces intra-class variability while preserving class separation through projections over unions of linear spaces. It is implemented in a deep convolutional network with a homotopy algorithm having an exponential convergence. A convergence proof is given in a general framework that includes ALISTA. Classification results are analyzed on ImageNet.