Design of Metamaterial Surfaces with Broad-band Absorbance

A simple design paradigm for making broad-band ultra-thin plasmonic absorbers is introduced. The absorber's unit cell is composed of sub-units of various sizes, resulting in nearly 100% absorbance at multiple adjacent frequencies and high absorbance over a broad frequency range. A simple theoretical model for designing broad-band absorbers is presented. It uses a single-resonance model to describe the optical response of each sub-unit and employs the series circuit model to predict the overall response. Validity of the circuit model relies on short propagation lengths of the surface plasmons

Asymmetric magnetization reversal in exchange biased polycrystalline F/AF bilayers

This paper describes a model for magnetization reversal in polycrystalline Ferromagnetic/Antiferromagnetic exchange biased bilayers. We assume that the exchange energy can be expanded into cosine power series. We show that it is possible to fit experimental asymmetric shape of hysteresis loops in exchange biased bilayer for any direction of the applied field. The hysteresis asymmetry is discussed in terms of energy considerations. An angle beta is introduced to quantify the easy axis dispersion of AF grains.Comment: 15 pages, 4 figure

Magnetization reversal by injection and transfer of spin: experiments and theory

Reversing the magnetization of a ferromagnet by spin transfer from a current, rather than by applying a magnetic field, is the central idea of an extensive current research. After a review of our experiments of current-induced magnetization reversal in Co/Cu/Co trilayered pillars, we present the model we have worked out for the calculation of the current-induced torque and the interpretation of the experiments

Curvature-informed multi-task learning for graph networks

Properties of interest for crystals and molecules, such as band gap, elasticity, and solubility, are generally related to each other: they are governed by the same underlying laws of physics. However, when state-of-the-art graph neural networks attempt to predict multiple properties simultaneously (the multi-task learning (MTL) setting), they frequently underperform a suite of single property predictors. This suggests graph networks may not be fully leveraging these underlying similarities. Here we investigate a potential explanation for this phenomenon: the curvature of each property's loss surface significantly varies, leading to inefficient learning. This difference in curvature can be assessed by looking at spectral properties of the Hessians of each property's loss function, which is done in a matrix-free manner via randomized numerical linear algebra. We evaluate our hypothesis on two benchmark datasets (Materials Project (MP) and QM8) and consider how these findings can inform the training of novel multi-task learning models.Comment: Published at the ICML 2022 AI for Science workshop: https://openreview.net/forum?id=m5RYtApKFO

Evaluating the diversity and utility of materials proposed by generative models

Generative machine learning models can use data generated by scientific modeling to create large quantities of novel material structures. Here, we assess how one state-of-the-art generative model, the physics-guided crystal generation model (PGCGM), can be used as part of the inverse design process. We show that the default PGCGM's input space is not smooth with respect to parameter variation, making material optimization difficult and limited. We also demonstrate that most generated structures are predicted to be thermodynamically unstable by a separate property-prediction model, partially due to out-of-domain data challenges. Our findings suggest how generative models might be improved to enable better inverse design.Comment: 12 pages, 9 figures. Published at SynS & ML @ ICML2023: https://openreview.net/forum?id=2ZYbmYTKo

Meredys, a multi-compartment reaction-diffusion simulator using multistate realistic molecular complexes

<p>Abstract</p> <p>Background</p> <p>Most cellular signal transduction mechanisms depend on a few molecular partners whose roles depend on their position and movement in relation to the input signal. This movement can follow various rules and take place in different compartments. Additionally, the molecules can form transient complexes. Complexation and signal transduction depend on the specific states partners and complexes adopt. Several spatial simulator have been developed to date, but none are able to model reaction-diffusion of realistic multi-state transient complexes.</p> <p>Results</p> <p><it>Meredys </it>allows for the simulation of multi-component, multi-feature state molecular species in two and three dimensions. Several compartments can be defined with different diffusion and boundary properties. The software employs a Brownian dynamics engine to simulate reaction-diffusion systems at the reactive particle level, based on compartment properties, complex structure, and hydro-dynamic radii. Zeroth-, first-, and second order reactions are supported. The molecular complexes have realistic geometries. Reactive species can contain user-defined feature states which can modify reaction rates and outcome. Models are defined in a versatile NeuroML input file. The simulation volume can be split in subvolumes to speed up run-time.</p> <p>Conclusions</p> <p><it>Meredys </it>provides a powerful and versatile way to run accurate simulations of molecular and sub-cellular systems, that complement existing multi-agent simulation systems. <it>Meredys </it>is a Free Software and the source code is available at <url>http://meredys.sourceforge.net/</url>.</p

Field dependence of magnetization reversal by spin transfer

We analyse the effect of the applied field (Happl) on the current-driven magnetization reversal in pillar-shaped Co/Cu/Co trilayers, where we observe two different types of transition between the parallel (P) and antiparallel (AP) magnetic configurations of the Co layers. If Happl is weaker than a rather small threshold value, the transitions between P and AP are irreversible and relatively sharp. For Happl exceding the threshold value, the same transitions are progressive and reversible. We show that the criteria for the stability of the P and AP states and the experimentally observed behavior can be precisely accounted for by introducing the current-induced torque of the spin transfer models in a Landau-Lifschitz-Gilbert equation. This approach also provides a good description for the field dependence of the critical currents

A meta-analysis of the relation between therapeutic alliance and treatment outcome in eating disorders.

The therapeutic alliance has demonstrated an association with favorable psychotherapeutic outcomes in the treatment of eating disorders (EDs). However, questions remain about the inter-relationships between early alliance, early symptom improvement, and treatment outcome. We conducted a meta-analysis on the relations among these constructs, and possible moderators of these relations, in psychosocial treatments for EDs. Twenty studies met inclusion criteria and supplied sufficient supplementary data. Results revealed small-to-moderate effect sizes, βs = 0.13 to 0.22 (p < .05), indicating that early symptom improvement was related to subsequent alliance quality and that alliance ratings also were related to subsequent symptom reduction. The relationship between early alliance and treatment outcome was partially accounted for by early symptom improvement. With regard to moderators, early alliance showed weaker associations with outcome in therapies with a strong behavioral component relative to nonbehavioral therapies. However, alliance showed stronger relations to outcome for younger (vs. older) patients, over and above the variance shared with early symptom improvement. In sum, early symptom reduction enhances therapeutic alliance and treatment outcome in EDs, but early alliance may require specific attention for younger patients and for those receiving nonbehaviorally oriented treatments

Inhibition of Toxic Shock by Human Monoclonal Antibodies against Staphylococcal Enterotoxin B

BACKGROUND: Staphylococcus aureus is implicated in many opportunistic bacterial infections around the world. Rising antibiotic resistance and few alternative methods of treatment are just two looming problems associated with clinical management of S. aureus. Among numerous virulence factors produced by S. aureus, staphylococcal enterotoxin (SE) B is a secreted protein that binds T-cell receptor and major histocompatibility complex class II, potentially causing toxic shock mediated by pathological activation of T cells. Recombinant monoclonal antibodies that target SEB and block receptor interactions can be of therapeutic value. METHODOLOGY/PRINCIPAL FINDINGS: The inhibitory and biophysical properties of ten human monoclonal antibodies, isolated from a recombinant library by panning against SEB vaccine (STEBVax), were examined as bivalent Fabs and native full-length IgG (Mab). The best performing Fabs had binding affinities equal to polyclonal IgG, low nanomolar IC(50)s against SEB in cell culture assays, and protected mice from SEB-induced toxic shock. The orthologous staphylococcal proteins, SEC1 and SEC2, as well as streptococcal pyrogenic exotoxin C were recognized by several Fabs. Four Fabs against SEB, with the lowest IC(50)s, were converted into native full-length Mabs. Although SEB-binding kinetics were identical between each Fab and respective Mab, a 250-fold greater inhibition of SEB-induced T-cell activation was observed with two Mabs. CONCLUSIONS/SIGNIFICANCE: Results suggest that these human monoclonal antibodies possess high affinity, target specificity, and toxin neutralization qualities essential for any therapeutic agent