Heat conductivity of DNA double helix

Thermal conductivity of isolated single molecule DNA fragments is of importance for nanotechnology, but has not yet been measured experimentally. Theoretical estimates based on simplified (1D) models predict anomalously high thermal conductivity. To investigate thermal properties of single molecule DNA we have developed a 3D coarse-grained (CG) model that retains the realism of the full all-atom description, but is significantly more efficient. Within the proposed model each nucleotide is represented by 6 particles or grains; the grains interact via effective potentials inferred from classical molecular dynamics (MD) trajectories based on a well-established all-atom potential function. Comparisons of 10 ns long MD trajectories between the CG and the corresponding all-atom model show similar root-mean-square deviations from the canonical B-form DNA, and similar structural fluctuations. At the same time, the CG model is 10 to 100 times faster depending on the length of the DNA fragment in the simulation. Analysis of dispersion curves derived from the CG model yields longitudinal sound velocity and torsional stiffness in close agreement with existing experiments. The computational efficiency of the CG model makes it possible to calculate thermal conductivity of a single DNA molecule not yet available experimentally. For a uniform (polyG-polyC) DNA, the estimated conductivity coefficient is 0.3 W/mK which is half the value of thermal conductivity for water. This result is in stark contrast with estimates of thermal conductivity for simplified, effectively 1D chains ("beads on a spring") that predict anomalous (infinite) thermal conductivity. Thus, full 3D character of DNA double-helix retained in the proposed model appears to be essential for describing its thermal properties at a single molecule level.Comment: 16 pages, 12 figure

Enhancement of Rabi Splitting in a Microcavity with an Embedded Superlattice

We have observed a large coupling between the excitonic and photonic modes of an AlAs/AlGaAs microcavity filled with an 84-({\rm {\AA}})/20({\rm {\AA}}) GaAs/AlGaAs superlattice. Reflectivity measurements on the coupled cavity-superlattice system in the presence of a moderate electric field yielded a Rabi splitting of 9.5 meV at T = 238 K. This splitting is almost 50% larger than that found in comparable microcavities with quantum wells placed at the antinodes only. We explain the enhancement by the larger density of optical absorbers in the superlattice, combined with the quasi-two-dimensional binding energy of field-localized excitons.Comment: 5 pages, 4 figures, submitted to PR

Do Invariances in Deep Neural Networks Align with Human Perception?

An evaluation criterion for safe and trustworthy deep learning is how well the invariances captured by representations of deep neural networks (DNNs) are shared with humans. We identify challenges in measuring these invariances. Prior works used gradient-based methods to generate identically represented inputs (IRIs), i.e., inputs which have identical representations (on a given layer) of a neural network, and thus capture invariances of a given network. One necessary criterion for a network's invariances to align with human perception is for its IRIs look “similar” to humans. Prior works, however, have mixed takeaways; some argue that later layers of DNNs do not learn human-like invariances yet others seem to indicate otherwise. We argue that the loss function used to generate IRIs can heavily affect takeaways about invariances of the network and is the primary reason for these conflicting findings. We propose an adversarial regularizer on the IRI-generation loss that finds IRIs that make any model appear to have very little shared invariance with humans. Based on this evidence, we argue that there is scope for improving models to have human-like invariances, and further, to have meaningful comparisons between models one should use IRIs generated using the regularizer-free loss. We then conduct an in-depth investigation of how different components (e.g. architectures, training losses, data augmentations) of the deep learning pipeline contribute to learning models that have good alignment with humans. We find that architectures with residual connections trained using a (self-supervised) contrastive loss with `p ball adversarial data augmentation tend to learn invariances that are most aligned with humans. Code: github.com/nvedant07/Human-NN-Alignment. We strongly recommend reading the arxiv version of this paper: https://arxiv.org/abs/2111.14726

Myocardial ischemia in the absence of epicardial coronary artery disease in Friedreich's ataxia

We present the first in vivo detection of microvascular abnormality in a patient with Friedreich's ataxia (FA) without epicardial coronary artery disease using cardiac magnetic resonance (CMR). The patient had exertional chest pain and dyspnea prompting referral for cardiac evaluation. These symptoms were reproduced during intravenous adenosine infusion, and simultaneous first-pass perfusion imaging showed a significant subendocardial defect; both symptoms and perfusion deficit were absent at rest. Epicardial coronaries were free of disease by invasive angiography; together, these findings support the notion of impaired myocardial perfusion reserve in FA

SO2 Emissions and Lifetimes: Estimates from Inverse Modeling Using In Situ and Global, Space-Based (SCIAMACHY and OMI) Observations

Top-down constraints on global sulfur dioxide (SO2) emissions are inferred through inverse modeling using SO2 column observations from two satellite instruments (SCIAMACHY and OMI). We first evaluated the S02 column observations with surface SO2 measurements by applying local scaling factors from a global chemical transport model (GEOS-Chem) to SO2 columns retrieved from the satellite instruments. The resulting annual mean surface SO2 mixing ratios for 2006 exhibit a significant spatial correlation (r=0.86, slope=0.91 for SCIAMACHY and r=0.80, slope = 0.79 for OMI) with coincident in situ measurements from monitoring networks throughout the United States and Canada. We evaluate the GEOS-Chem simulation of the SO2 lifetime with that inferred from in situ measurements to verity the applicability of GEOS-Chem for inversion of SO2 columns to emissions. The seasonal mean SO2 lifetime calculated with the GEOS-Chem model over the eastern United States is 13 h in summer and 48 h in winter, compared to lifetimes inferred from in situ measurements of 19 +/- 7 h in summer and 58 +/- 20 h in winter. We apply SO2 columns from SCIAMACHY and OMI to derive a top-down anthropogenic SO2 emission inventory over land by using the local GEOS-Chem relationship between SO2 columns and emissions. There is little seasonal variation in the top-down emissions (<15%) over most major industrial regions providing some confidence in the method. Our global estimate for annual land surface anthropogenic SO2 emissions (52.4 Tg S/yr from SCIAMACHY and 49.9 Tg S / yr from OMI) closely agrees with the bottom-up emissions (54.6 Tg S/yr) in the GEOS-Chem model and exhibits consistency in global distributions with the bottom-up emissions (r = 0.78 for SCIAMACHY, and r = 0.77 for OMI). However, there are significant regional differences

Modeling DNA Structure, Elasticity and Deformations at the Base-pair Level

We present a generic model for DNA at the base-pair level. We use a variant of the Gay-Berne potential to represent the stacking energy between neighboring base-pairs. The sugar-phosphate backbones are taken into account by semi-rigid harmonic springs with a non-zero spring length. The competition of these two interactions and the introduction of a simple geometrical constraint leads to a stacked right-handed B-DNA-like conformation. The mapping of the presented model to the Marko-Siggia and the Stack-of-Plates model enables us to optimize the free model parameters so as to reproduce the experimentally known observables such as persistence lengths, mean and mean squared base-pair step parameters. For the optimized model parameters we measured the critical force where the transition from B- to S-DNA occurs to be approximately $140{pN}$. We observe an overstretched S-DNA conformation with highly inclined bases that partially preserves the stacking of successive base-pairs.Comment: 15 pages, 25 figures. submitted to PR

Visualization of Frequent Itemsets with Nested Circular Layout and Bundling Algorithm

International audienceFrequent itemset mining is one of the major data mining issues. Once generated by algorithms, the itemsets can be automatically processed, for instance to extract association rules. They can also be explored with visual tools, in order to analyze the emerging patterns. Graphical itemsets representation is a convenient way to obtain an overview of the global interaction structure. However, when the complexity of the database increases, the network may become unreadable. In this paper, we propose to display itemsets on concentric circles, each one being organized to lower the intricacy of the graph through an optimization process. Thanks to a graph bundling algorithm, we finally obtain a compact representation of a large set of itemsets that is easier to exploit. Colors accumulation and interaction operators facilitate the exploration of the new bundle graph and to illustrate how much an itemset is supported by the data

Protein dynamics with off-lattice Monte Carlo moves

A Monte Carlo method for dynamics simulation of all-atom protein models is introduced, to reach long times not accessible to conventional molecular dynamics. The considered degrees of freedom are the dihedrals at C$_\alpha$-atoms. Two Monte Carlo moves are used: single rotations about torsion axes, and cooperative rotations in windows of amide planes, changing the conformation globally and locally, respectively. For local moves Jacobians are used to obtain an unbiased distribution of dihedrals. A molecular dynamics energy function adapted to the protein model is employed. A polypeptide is folded into native-like structures by local but not by global moves.Comment: 10 pages, 4 Postscript figures, uses epsf.sty and a4.sty; scheduled tentatively for Phys.Rev.E issue of 1 March 199

Economic crisis and the construction of a neo-liberal regulatory regime in Korea

A consistent theme of the literature on the ontology of the 1997 South Korean crisis is the key role played by regulatory failures and the growing weakness of the state. This paper seeks to briefly highlight both the insights and the limitations of this approach to understanding the crisis. Having done so, we shall set out the argument that the crisis created an opportunity for reformist Korean élites to advance their longstanding, but previously frustrated, project to create a comprehensive unambiguously neo-liberal regulatory regime. This paper will also seek to highlight the implications of our reading of the development of the Korean political economy for broader debates on economic liberalisation, crisis and the future of the developmental state

Wringing out DNA

The chiral nature of DNA plays a crucial role in cellular processes. Here we use magnetic tweezers to explore one of the signatures of this chirality, the coupling between stretch and twist deformations. We show that the extension of a stretched DNA molecule increases linearly by 0.42 nm per excess turn applied to the double helix. This result contradicts the intuition that DNA should lengthen as it is unwound and get shorter with overwinding. We then present numerical results of energy minimizations of torsionally restrained DNA that display a behaviour similar to the experimental data and shed light on the molecular details of this surprising effect.Comment: 4 pages revtex4, 4 figure