Magnetic-Moment Fragmentation and Monopole Crystallization

The Coulomb phase, with its dipolar correlations and pinch-point-scattering patterns, is central to discussions of geometrically frustrated systems, from water ice to binary and mixed-valence alloys, as well as numerous examples of frustrated magnets. The emergent Coulomb phase of lattice-based systems has been associated with divergence-free fields and the absence of long-range order. Here, we go beyond this paradigm, demonstrating that a Coulomb phase can emerge naturally as a persistent fluctuating background in an otherwise ordered system. To explain this behavior, we introduce the concept of the fragmentation of the field of magnetic moments into two parts, one giving rise to a magnetic monopole crystal, the other a magnetic fluid with all the characteristics of an emergent Coulomb phase. Our theory is backed up by numerical simulations, and we discuss its importance with regard to the interpretation of a number of experimental results

Permeation of CO₂ and N₂ through glassy poly(dimethyl phenylene) oxide under steady- and presteady-state conditions

Glassy polymers are often used for gas separations because of their high selectivity. Although the dual‐mode permeation model correctly fits their sorption and permeation isotherms, its physical interpretation is disputed, and it does not describe permeation far from steady state, a condition expected when separations involve intermittent renewable energy sources. To develop a more comprehensive permeation model, we combine experiment, molecular dynamics, and multiscale reaction–diffusion modeling to characterize the time‐dependent permeation of N₂ and CO₂ through a glassy poly(dimethyl phenylene oxide) membrane, a model system. Simulations of experimental time‐dependent permeation data for both gases in the presteady‐state and steady‐state regimes show that both single‐ and dual‐mode reaction–diffusion models reproduce the experimental observations, and that sorbed gas concentrations lag the external pressure rise. The results point to environment‐sensitive diffusion coefficients as a vital characteristic of transport in glassy polymers

Modern Breast Cancer Detection: A Technological Review

Breast cancer is a serious threat worldwide and is the number two killer of women in the United States. The key to successful management is screening and early detection. What follows is a description of the state of the art in screening and detection for breast cancer as well as a discussion of new and emerging technologies. This paper aims to serve as a starting point for those who are not acquainted with this growing field

Development of tools to automate quantitative analysis of radiation damage in SAXS experiments

Biological small-angle X-ray scattering (SAXS) is an increasingly popular technique used to obtain nanoscale structural information on macromolecules in solution. However, radiation damage to the samples limits the amount of useful data that can be collected from a single sample. In contrast to the extensive analytical resources available for macromolecular crystallography (MX), there are relatively few tools to quantitate radiation damage for SAXS, some of which require a significant level of manual characterization, with the potential of leading to conflicting results from different studies. Here, computational tools have been developed to automate and standardize radiation damage analysis for SAXS data. RADDOSE-3D, a dose calculation software utility originally written for MX experiments, has been extended to account for the cylindrical geometry of the capillary tube, the liquid composition of the sample and the attenuation of the beam by the capillary material to allow doses to be calculated for many SAXS experiments. Furthermore, a library has been written to visualize and explore the pairwise similarity of frames. The calculated dose for the frame at which three subsequent frames are determined to be dissimilar is defined as the radiation damage onset threshold (RDOT). Analysis of RDOTs has been used to compare the efficacy of radioprotectant compounds to extend the useful lifetime of SAXS samples. Comparison of the RDOTs shows that, for radioprotectant compounds at 5 and 10 mM concentration, glycerol is the most effective compound. However, at 1 and 2 mM concentrations, di­thio­threitol (DTT) appears to be most effective. Our newly developed visualization library contains methods that highlight the unusual radiation damage results given by SAXS data collected using higher concentrations of DTT: these observations should pave the way to the development of more sophisticated frame merging strategies

Self-interest And Public Interest: The Motivations Of Political Actors

Self-Interest and Public Interest in Western Politics showed that the public, politicians, and bureaucrats are often public spirited. But this does not invalidate public-choice theory. Public-choice theory is an ideal type, not a claim that self-interest explains all political behavior. Instead, public-choice theory is useful in creating rules and institutions that guard against the worst case, which would be universal self-interestedness in politics. In contrast, the public-interest hypothesis is neither a comprehensive explanation of political behavior nor a sound basis for institutional design

A look at the anatomy educator job market: anatomists remain in short supply

In 2002, a widely publicized report projected an anatomy educator shortage based on department chairpersons' perceptions. Now, 17 years later, the question lingers: “Does an anatomy educator shortage persist and, if so, how severe is the shortage?” Trends in the number, type, and fill rate of anatomy educator job openings were explored by analyzing job posting in the United States over the past two years. A survey was distributed to leaders of anatomy‐related departments in the United States, Canada, and European Union. Most departmental leaders who responded (65% or more) from the United States/Canada (n = 81) and the European Union (n = 52) anticipate they will have “moderate” to “great” difficulty hiring anatomy educators in gross anatomy, histology, and embryology over the next five years. Within the United States, the number of anatomy educator job postings at medical schools more than doubled from at least 21 postings in 2017 to 52 postings in 2018. Twenty‐one percent of postings between 2017 and 2018 were never filled. While the number of anatomy educator openings within the United States/Canada is perceived to remain in a steady state for the next five years, the European Union estimates a five‐fold increase in the number of openings. Departmental leaders prioritize anatomy educator applicants who have teaching experience (mean ± SD = 4.64 ± 0.84 on five‐point Likert scale), versatility in teaching multiple anatomy disciplines (3.93 ± 1.07), and flexibility in implementing various teaching pedagogies (3.69 ± 1.17). Collectively, these data suggest the shortage of anatomy educators continues in the United States/Canada and the European Union

Angular clustering properties of the DESI QSO target selection using DR9 Legacy Imaging Surveys

The quasar target selection for the upcoming survey of the Dark Energy Spectroscopic Instrument (DESI) will be fixed for the next 5 yr. The aim of this work is to validate the quasar selection by studying the impact of imaging systematics as well as stellar and galactic contaminants, and to develop a procedure to mitigate them. Density fluctuations of quasar targets are found to be related to photometric properties such as seeing and depth of the Data Release 9 of the DESI Legacy Imaging Surveys. To model this complex relation, we explore machine learning algorithms (random forest and multilayer perceptron) as an alternative to the standard linear regression. Splitting the footprint of the Legacy Imaging Surveys into three regions according to photometric properties, we perform an independent analysis in each region, validating our method using extended Baryon Oscillation Spectroscopic Survey (eBOSS) EZ-mocks. The mitigation procedure is tested by comparing the angular correlation of the corrected target selection on each photometric region to the angular correlation function obtained using quasars from the Sloan Digital Sky Survey (SDSS) Data Release 16. With our procedure, we recover a similar level of correlation between DESI quasar targets and SDSS quasars in two-thirds of the total footprint and we show that the excess of correlation in the remaining area is due to a stellar contamination that should be removed with DESI spectroscopic data. We derive the Limber parameters in our three imaging regions and compare them to previous measurements from SDSS and the 2dF QSO Redshift Survey.This research is supported by the Director, Office of Science, Office of High Energy Physics of the U.S. Department of Energy under contract no. DE-AC02-05CH11231, and by the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility under the same contract; additional support for DESI is provided by the U.S. National Science Foundation, Division of Astronomical Sciences under contract no. AST-0950945 to the NSF’s National Optical–Infrared Astronomy Research Laboratory; the Science and Technology Facilities Council of the United Kingdom; the Gordon and Betty Moore Foundation; the Heising-Simons Foundation; the French Alternative Energies and Atomic Energy Commission (CEA); the National Council of Science and Technology, Mexico; the Ministry of Economy of Spain, and by the DESI Member Institutions. ADM was supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics, under Award Number DE-SC0019022

The network topology of a potential energy landscape: A static scale-free network

Here we analyze the topology of the network formed by the minima and transition states on the potential energy landscape of small clusters. We find that this network has both a small-world and scale-free character. In contrast to other scale-free networks, where the topology results from the dynamics of the network growth, the potential energy landscape is a static entity. Therefore, a fundamentally different organizing principle underlies this behaviour: The potential energy landscape is highly heterogeneous with the low-energy minima having large basins of attraction and acting as the highly-connected hubs in the network.Comment: 4 pages, 4 figures, revtex

A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia

Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra- observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed a novel artificial intelligence algorithm that can assign an Oral Malignant Transformation (OMT) risk score, based on histological patterns in the in Haematoxylin and Eosin stained whole slide images, to quantify the risk of OED progression. The algorithm is based on the detection and segmentation of nuclei within (and around) the epithelium using an in-house segmentation model. We then employed a shallow neural network fed with interpretable morphological/spatial features, emulating histological markers. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) followed by independent validation on two external cohorts (Birmingham and Belfast; n = 92 cases). The proposed OMTscore yields an AUROC = 0.74 in predicting whether an OED progresses to malignancy or not. Survival analyses showed the prognostic value of our OMTscore for predicting malignancy transformation, when compared to the manually-assigned WHO and binary grades. Analysis of the correctly predicted cases elucidated the presence of peri-epithelial and epithelium-infiltrating lymphocytes in the most predictive patches of cases that transformed (p < 0.0001). This is the first study to propose a completely automated algorithm for predicting OED transformation based on interpretable nuclear features, whilst being validated on external datasets. The algorithm shows better-than-human-level performance for prediction of OED malignant transformation and offers a promising solution to the challenges of grading OED in routine clinical practice