Prethermalization and Persistent Order in the Absence of a Thermal Phase Transition

We numerically study the dynamics after a parameter quench in the one-dimensional transverse-field Ising model with long-range interactions ($\propto 1/r^\alpha$ with distance $r$), for finite chains and also directly in the thermodynamic limit. In nonequilibrium, i.e., before the system settles into a thermal state, we find a long-lived regime that is characterized by a prethermal value of the magnetization, which in general differs from its thermal value. We find that the ferromagnetic phase is stabilized dynamically: as a function of the quench parameter, the prethermal magnetization shows a transition between a symmetry-broken and a symmetric phase, even for those values of $\alpha$ for which no finite-temperature transition occurs in equilibrium. The dynamical critical point is shifted with respect to the equilibrium one, and the shift is found to depend on $\alpha$ as well as on the quench parameters.Comment: 6 pages, 4 figure

Nitrogen doping of TiO2 photocatalyst forms a second eg state in the Oxygen (1s) NEXAFS pre-edge

Close inspection of the pre-edge in oxygen near-edge x-ray absorption fine structure spectra of single step, gas phase synthesized titanium oxynitride photocatalysts with 20 nm particle size reveals an additional eg resonance in the VB that went unnoticed in previous TiO2 anion doping studies. The relative spectral weight of this Ti(3d)-O(2p) hybridized state with respect to and located between the readily established t2g and eg resonances scales qualitatively with the photocatalytic decomposition power, suggesting that this extra resonance bears co-responsibility for the photocatalytic performance of titanium oxynitrides at visible light wavelengths

Bubble divergences: sorting out topology from cell structure

We conclude our analysis of bubble divergences in the flat spinfoam model. In [arXiv:1008.1476] we showed that the divergence degree of an arbitrary two-complex Gamma can be evaluated exactly by means of twisted cohomology. Here, we specialize this result to the case where Gamma is the two-skeleton of the cell decomposition of a pseudomanifold, and sharpen it with a careful analysis of the cellular and topological structures involved. Moreover, we explain in detail how this approach reproduces all the previous powercounting results for the Boulatov-Ooguri (colored) tensor models, and sheds light on algebraic-topological aspects of Gurau's 1/N expansion.Comment: 19 page

The anti-depressant and anxiolytic properties of the lyophilized aqueous leaf extract of Mimosa pudica L. (Fabaceae)

Introduction: This study seeks to determine the antidepressant and anxiolytic properties of the lyophilized aqueous leaf extract of Mimosa pudica (LAL-MP) in mice. Methods: LAL-MP was administered orally to mice at 50 – 500 mg/kg, daily for 14 days, after which mice were individually subjected to the forced swimming (FST) and tail suspension (TST) tests, the elevated plus maze (EPM) model and locomotor activity count. Results: Generally, LAL-MP from 100 to 500 mg/kg exhibit antidepressant and anxiolytic properties in all 4 models of depression and dose levels at 400 and 500 mg/kg were found to be equipotent with the standard drug fluoxetine. Conclusions: This is the first report on the combined antidepressant and anxiolytic properties of LAL-MP

In-Plane Hydrogen Bonds and Out-of-Plane Dipolar Interactions in Self-Assembled Melem Networks

Melem(2,6,10-triamino-s-heptazine) is the building block of melon,a carbon nitride (CN) polymer that is proven to produce H2 from water under visible illumination. With the aim of bringing additional insight into the electronic structure of CN materials, we performed a spectroscopic characterization of gas-phase melem and of a melem-based self-assembled 2D H-bonded layer on Au(111) by means of ultraviolet and X-ray photoemission spectroscopy (UPS, XPS) and near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. In parallel, we performed density functional theory (DFT) simulations of the same systems to unravel the molecular charge density redistribution caused by the in-plane H-bonds. Comparing the experimental results with the spectroscopic DFT simulations, we can correlate the induced charge accumulation on the N-amino atoms to the red-shift of the corresponding N 1s binding energy (BE) and of the N-amino 1s -> LUMO+n transitions. Moreover, when introducing a supporting Au(111) surface in the computational simulations, we observe a molecule-substrate interaction that almost exclusively involves the out-of-plane molecular orbitals, leaving those engaged in the in-plane H-bonded network rather unperturbed

The TOSCA Registry for Tuberous Sclerosis-Lessons Learnt for Future Registry Development in Rare and Complex Diseases.

Introduction: The TuberOus SClerosis registry to increase disease Awareness (TOSCA) is an international disease registry designed to provide insights into the clinical characteristics of patients with Tuberous Sclerosis Complex (TSC). The aims of this study were to identify issues that arose during the design, execution, and publication phases of TOSCA, and to reflect on lessons learnt that may guide future registries in rare and complex diseases. Methods: A questionnaire was designed to identify the strengths, weaknesses, and issues that arose at any stage of development and implementation of the TOSCA registry. The questionnaire contained 225 questions distributed in 7 sections (identification of issues during registry planning, during the operation of the registry, during data analysis, during the publication of the results, other issues, assessment of lessons learnt, and additional comments), and was sent by e-mail to 511 people involved in the registry, including 28 members of the Scientific Advisory Board (SAB), 162 principal investigators (PIs), and 321 employees of the sponsor belonging to the medical department or that were clinical research associate (CRA). Questionnaires received within the 2 months from the initial mailing were included in the analysis. Results: A total of 53 (10.4%) questionnaires were received (64.3% for SAB members, 12.3% for PIs and 4.7% for employees of the sponsor), and the overall completeness rate for closed questions was 87.6%. The most common issues identified were the limited duration of the registry (38%) and issues related to handling of missing data (32%). In addition, 25% of the respondents commented that biases might have compromised the validity of the results. More than 80% of the respondents reported that the registry improved the knowledge on the natural history and manifestations of TSC, increased disease awareness and helped to identify relevant information for clinical research in TSC. Conclusions: This analysis shows the importance of registries as a powerful tool to increase disease awareness, to produce real-world evidence, and to generate questions for future research. However, there is a need to implement strategies to ensure patient retention and long-term sustainability of patient registries, to improve data quality, and to reduce biases

Developing a Victorious Strategy to the Second Strong Gravitational Lensing Data Challenge

Strong Lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with Deep Learning have become a popular approach due to these astronomical objects' rarity and image complexity. Next-generation surveys will provide more opportunities to derive science from these objects and an increasing data volume to be analyzed. However, finding strong lenses is challenging, as their number densities are orders of magnitude below those of galaxies. Therefore, specific Strong Lensing search algorithms are required to discover the highest number of systems possible with high purity and low false alarm rate. The need for better algorithms has prompted the development of an open community data science competition named Strong Gravitational Lensing Challenge (SGLC). This work presents the Deep Learning strategies and methodology used to design the highest-scoring algorithm in the II SGLC. We discuss the approach used for this dataset, the choice for a suitable architecture, particularly the use of a network with two branches to work with images in different resolutions, and its optimization. We also discuss the detectability limit, the lessons learned, and prospects for defining a tailor-made architecture in a survey in contrast to a general one. Finally, we release the models and discuss the best choice to easily adapt the model to a dataset representing a survey with a different instrument. This work helps to take a step towards efficient, adaptable and accurate analyses of strong lenses with deep learning frameworks.Comment: 14 pages, 12 figure

OC152: Prevalence of cancer and optimal cut‐off levels for mathematical models to distinguish between benign and malignant adnexal masses

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