Families of Quintic Calabi-Yau 3-Folds with Discrete Symmetries

At special loci in their moduli spaces, Calabi-Yau manifolds are endowed with discrete symmetries. Over the years, such spaces have been intensely studied and have found a variety of important applications. As string compactifications they are phenomenologically favored, and considerably simplify many important calculations. Mathematically, they provided the framework for the first construction of mirror manifolds, and the resulting rational curve counts. Thus, it is of significant interest to investigate such manifolds further. In this paper, we consider several unexplored loci within familiar families of Calabi-Yau hypersurfaces that have large but unexpected discrete symmetry groups. By deriving, correcting, and generalizing a technique similar to that of Candelas, de la Ossa and Rodriguez-Villegas, we find a calculationally tractable means of finding the Picard-Fuchs equations satisfied by the periods of all 3-forms in these families. To provide a modest point of comparison, we then briefly investigate the relation between the size of the symmetry group along these loci and the number of nonzero Yukawa couplings. We include an introductory exposition of the mathematics involved, intended to be accessible to physicists, in order to make the discussion self-contained.Comment: 54 pages, 3 figure

Exceeding the nonlinear-shannon limit using Raman laser based amplification and optical phase conjugation

We demonstrate that a combination of Raman laser based amplification and optical phase conjugation enables transmission beyond the nonlinear-Shannon limit. We show nonlinear compensation of 7x114Gbit/s DP-QPSK channels, increasing system reach by 30%

Breast MRI segmentation for density estimation:Do different methods give the same results and how much do differences matter?

PURPOSE: To compare two methods of automatic breast segmentation with each other and with manual segmentation in a large subject cohort. To discuss the factors involved in selecting the most appropriate algorithm for automatic segmentation and, in particular, to investigate the appropriateness of overlap measures (e.g., Dice and Jaccard coefficients) as the primary determinant in algorithm selection. METHODS: Two methods of breast segmentation were applied to the task of calculating MRI breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children, a large cohort study with an MRI component. A semiautomated, bias-corrected, fuzzy C-means (BC-FCM) method was combined with morphological operations to segment the overall breast volume from in-phase Dixon images. The method makes use of novel, problem-specific insights. The resulting segmentation mask was then applied to the corresponding Dixon water and fat images, which were combined to give Dixon MRI density values. Contemporaneously acquired T1 - and T2 -weighted image datasets were analyzed using a novel and fully automated algorithm involving image filtering, landmark identification, and explicit location of the pectoral muscle boundary. Within the region found, fat-water discrimination was performed using an Expectation Maximization-Markov Random Field technique, yielding a second independent estimate of MRI density. RESULTS: Images are presented for two individual women, demonstrating how the difficulty of the problem is highly subject-specific. Dice and Jaccard coefficients comparing the semiautomated BC-FCM method, operating on Dixon source data, with expert manual segmentation are presented. The corresponding results for the method based on T1 - and T2 -weighted data are slightly lower in the individual cases shown, but scatter plots and interclass correlations for the cohort as a whole show that both methods do an excellent job in segmenting and classifying breast tissue. CONCLUSIONS: Epidemiological results demonstrate that both methods of automated segmentation are suitable for the chosen application and that it is important to consider a range of factors when choosing a segmentation algorithm, rather than focus narrowly on a single metric such as the Dice coefficient

Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study

BACKGROUND: The aim of this work is to evaluate the performance of radiomics predictions for a range of molecular, genomic and clinical targets in patients with clear cell renal cell carcinoma (ccRCC) and demonstrate the impact of novel feature selection strategies and sub-segmentations on model interpretability. METHODS: Contrast-enhanced CT scans from the first 101 patients recruited to the TRACERx Renal Cancer study (NCT03226886) were used to derive radiomics classification models to predict 20 molecular, histopathology and clinical target variables. Manual 3D segmentation was used in conjunction with automatic sub-segmentation to generate radiomics features from the core, rim, high and low enhancing sub-regions, and the whole tumour. Comparisons were made between two classification model pipelines: a Conventional pipeline reflecting common radiomics practice, and a Proposed pipeline including two novel feature selection steps designed to improve model interpretability. For both pipelines nested cross-validation was used to estimate prediction performance and tune model hyper-parameters, and permutation testing was used to evaluate the statistical significance of the estimated performance measures. Further model robustness assessments were conducted by evaluating model variability across the cross-validation folds. RESULTS: Classification performance was significant (p  0.1. Five of these targets (necrosis on histology, presence of renal vein invasion, overall histological stage, linear evolutionary subtype and loss of 9p21.3 somatic alteration marker) had AUROC > 0.8. Models derived using the Proposed pipeline contained fewer feature groups than the Conventional pipeline, leading to more straightforward model interpretations without loss of performance. Sub-segmentations lead to improved performance and/or improved interpretability when predicting the presence of sarcomatoid differentiation and tumour stage. CONCLUSIONS: Use of the Proposed pipeline, which includes the novel feature selection methods, leads to more interpretable models without compromising prediction performance. TRIAL REGISTRATION: NCT03226886 (TRACERx Renal

Growth Trajectories, Breast Size, and Breast-Tissue Composition in a British Prebirth Cohort of Young Women.

Mammographic percent density, the proportion of fibroglandular tissue in the breast, is a strong risk factor for breast cancer, but its determinants in young women are unknown. We examined associations of magnetic resonance imaging (MRI) breast-tissue composition at age 21 years with prospectively collected measurements of body size and composition from birth to early adulthood and markers of puberty (all standardized) in a sample of 500 nulliparous women from a prebirth cohort of children born in Avon, United Kingdom, in 1991-1992 and followed up to 2011-2014. Linear models were fitted to estimate relative change in MRI percent water, which is equivalent to mammographic percent density, associated with a 1-standard-deviation increase in the exposure of interest. In mutually adjusted analyses, MRI percent water was positively associated with birth weight (relative change (RC) = 1.03, 95% confidence interval (CI): 1.00, 1.06) and pubertal height growth (RC = 1.07, 95% CI: 1.02, 1.13) but inversely associated with pubertal weight growth (RC = 0.86, 95% CI: 0.84, 0.89) and changes in dual-energy x-ray absorptiometry percent body fat mass (e.g., for change between ages 11 years and 13.5 years, RC = 0.96, 95% CI: 0.93, 0.99). Ages at thelarche and menarche were positively associated with MRI percent water, but these associations did not persist upon adjustment for height and weight growth. These findings support the hypothesis that growth trajectories influence breast-tissue composition in young women, whereas puberty plays no independent role

Risk of infections in bronchiectasis during disease-modifying treatment and biologics for rheumatic diseases

<p>Abstract</p> <p>Background</p> <p>Bronchiectasis is frequently associated (up to 30%) with chronic inflammatory rheumatic diseases and leads to lower respiratory tract infections. Data are lacking on the risk of lower respiratory tract infections in patients treated with biologic agents.</p> <p>Methods</p> <p>Monocenter, retrospective systematic study of all patients with a chronic inflammatory rheumatic disease and concomitant bronchiectasis, seen between 2000 and 2009. Univariate and multivariate analyses were performed to evidence predictive factors of the number of infectious respiratory events.</p> <p>Results</p> <p>47 patients were included (mean age 64.1 ± 9.1 years, 33 (70.2%) women), with a mean follow-up per patient of 4.3 ± 3.1 years. Rheumatoid arthritis was the main rheumatic disease (90.1%). The mean number of infectious events was 0.8 ± 1.0 event per patient-year. The factors predicting infections were the type of treatment (biologic vs. non biologic disease-modifying treatments), with an odds ratio of 8.7 (95% confidence interval: 1.7-43.4) and sputum colonization by any bacteria (odds ratio 7.4, 2.0-26.8). In multivariate analysis, both factors were independently predictive of infections.</p> <p>Conclusion</p> <p>Lower respiratory tract infectious events are frequent among patients receiving biologics for chronic inflammatory rheumatic disease associated with bronchiectasis. Biologic treatment and pre-existing sputum colonization are independent risk factors of infection occurrence.</p

Incentive payments are not related to expected health gain in the pay for performance scheme for UK primary care: cross-sectional analysis

Background: The General Medical Services primary care contract for the United Kingdom financially rewards performance in 19 clinical areas, through the Quality and Outcomes Framework. Little is known about how best to determine the size of financial incentives in pay for performance schemes. Our aim was to test the hypothesis that performance indicators with larger population health benefits receive larger financial incentives. Methods: We performed cross sectional analyses to quantify associations between the size of financial incentives and expected health gain in the 2004 and 2006 versions of the Quality and Outcomes Framework. We used non-parametric two-sided Spearman rank correlation tests. Health gain was measured in expected lives saved in one year and in quality adjusted life years. For each quality indicator in an average sized general practice we tested for associations first, between the marginal increase in payment and the health gain resulting from a one percent point improvement in performance and second, between total payment and the health gain at the performance threshold for maximum payment. Results: Evidence for lives saved or quality adjusted life years gained was found for 28 indicators accounting for 41% of the total incentive payments. No statistically significant associations were found between the expected health gain and incentive gained from a marginal 1% increase in performance in either the 2004 or 2006 version of the Quality and Outcomes Framework. In addition no associations were found between the size of financial payment for achievement of an indicator and the expected health gain at the performance threshold for maximum payment measured in lives saved or quality adjusted life years. Conclusions: In this subgroup of indicators the financial incentives were not aligned to maximise health gain. This disconnection between incentive and expected health gain risks supporting clinical activities that are only marginally effective, at the expense of more effective activities receiving lower incentives. When designing pay for performance programmes decisions about the size of the financial incentive attached to an indicator should be informed by information on the health gain to be expected from that indicator

The VLT-FLAMES Tarantula Survey: XXX. Red stragglers in the clusters Hodge 301 and SL 639

Aims: We estimate physical parameters for the late-type massive stars observed as part of the VLT-FLAMES Tarantula Survey (VFTS) in the 30 Doradus region of the Large Magellanic Cloud (LMC). Methods: The observational sample comprises 20 candidate red supergiants (RSGs) which are the reddest ((B − V) > 1 mag) and brightest (V < 16 mag) objects in the VFTS. We use optical and near-infrared (near-IR) photometry to estimate their temperatures and luminosities, and introduce the luminosity–age diagram to estimate their ages. Results: We derive physical parameters for our targets, including temperatures from a new calibration of (J − Ks)0 colour for luminous cool stars in the LMC, luminosities from their J-band magnitudes (thence radii), and ages from comparisons with current evolutionary models. We show that interstellar extinction is a significant factor for our targets, highlighting the need to take it into account in the analysis of the physical parameters of RSGs. We find that some of the candidate RSGs could be massive AGB stars. The apparent ages of the RSGs in the Hodge 301 and SL 639 clusters show a significant spread (12–24 Myr). We also apply our approach to the RSG population of the relatively nearby NGC 2100 cluster, finding a similarly large spread. Conclusions We argue that the effects of mass transfer in binaries may lead to more massive and luminous RSGs (which we call “red stragglers”) than expected from single-star evolution, and that the true cluster ages correspond to the upper limit of the estimated RSG ages. In this way, the RSGs can serve as a new and potentially reliable age tracer in young star clusters. The corresponding analysis yields ages of 24-3+5 Myr for Hodge 301, 22-5+6 Myr for SL 639, and 23-2+4 Myr for NGC 2100

Ensemble-marginalized Kalman filter for linear time-dependent PDEs with noisy boundary conditions: application to heat transfer in building walls

In this work, we present the ensemble-marginalized Kalman filter (EnMKF), a sequential algorithm analo- gous to our previously proposed approach [1, 2], for estimating the state and parameters of linear parabolic partial differential equations in initial-boundary value problems when the boundary data are noisy. We apply EnMKF to infer the thermal properties of building walls and to estimate the corresponding heat flux from real and synthetic data. Compared with a modified Ensemble Kalman Filter (EnKF) that is not marginalized, EnMKF reduces the bias error, avoids the collapse of the ensemble without needing to add in- flation, and converges to the mean field posterior using 50% or less of the ensemble size required by EnKF. According to our results, the marginalization technique in EnMKF is key to performance improvement with smaller ensembles at any fixed time