Tissue-specific silencing of homoeologs in natural populations of the recent allopolyploid Tragopogon mirus

The definitive version is available at www3.interscience.wiley.com http://dx.doi.org/10.1111/j.1469-8137.2010.03205.

Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion

We introduce a technique to compute exact anelastic sensitivity kernels in the time domain using parsimonious disk storage. The method is based on a reordering of the time loop of time-domain forward/adjoint wave propagation solvers combined with the use of a memory buffer. It avoids instabilities that occur when time-reversing dissipative wave propagation simulations. The total number of required time steps is unchanged compared to usual acoustic or elastic approaches. The cost is reduced by a factor of 4/3 compared to the case in which anelasticity is partially accounted for by accommodating the effects of physical dispersion. We validate our technique by performing a test in which we compare the $K_\alpha$ sensitivity kernel to the exact kernel obtained by saving the entire forward calculation. This benchmark confirms that our approach is also exact. We illustrate the importance of including full attenuation in the calculation of sensitivity kernels by showing significant differences with physical-dispersion-only kernels

3D N = 1 SYM Chern-Simons theory on the Lattice

We present a method to implement 3-dimensional N = 1 SUSY Yang-Mills theory (a theory with two real supercharges containing gauge fields and an adjoint Majorana fermion) on the lattice, including a way to implement the Chern-Simons term present in this theory. At nonzero Chern-Simons number our implementation suffers from a sign problem which will make the numerical effort grow exponentially with volume. We also show that the theory with vanishing Chern-Simons number is anomalous; its partition function identically vanishes.Comment: v2, minor changes: expanded discussion in section III c, typos corrected, 17 pages, 9 figure

Using gamma regression for photometric redshifts of survey galaxies

Machine learning techniques offer a plethora of opportunities in tackling big data within the astronomical community. We present the set of Generalized Linear Models as a fast alternative for determining photometric redshifts of galaxies, a set of tools not commonly applied within astronomy, despite being widely used in other professions. With this technique, we achieve catastrophic outlier rates of the order of ~1%, that can be achieved in a matter of seconds on large datasets of size ~1,000,000. To make these techniques easily accessible to the astronomical community, we developed a set of libraries and tools that are publicly available.Comment: Refereed Proceeding of "The Universe of Digital Sky Surveys" conference held at the INAF - Observatory of Capodimonte, Naples, on 25th-28th November 2014, to be published in the Astrophysics and Space Science Proceedings, edited by Longo, Napolitano, Marconi, Paolillo, Iodice, 6 pages, and 1 figur

Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

BackgroundAdvanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human and veterinary medicine.Hypothesis/objectivesTo derive a model to predict the risk of cats developing chronic kidney disease (CKD) using data from electronic health records (EHRs) collected during routine veterinary practice.AnimalsA total of 106 251 cats that attended Banfield Pet Hospitals between January 1, 1995, and December 31, 2017.MethodsLongitudinal EHRs from Banfield Pet Hospitals were extracted and randomly split into 2 parts. The first 67% of the data were used to build a prediction model, which included feature selection and identification of the optimal neural network type and architecture. The remaining unseen EHRs were used to evaluate the model performance.ResultsThe final model was a recurrent neural network (RNN) with 4 features (creatinine, blood urea nitrogen, urine specific gravity, and age). When predicting CKD near the point of diagnosis, the model displayed a sensitivity of 90.7% and a specificity of 98.9%. Model sensitivity decreased when predicting the risk of CKD with a longer horizon, having 63.0% sensitivity 1 year before diagnosis and 44.2% 2 years before diagnosis, but with specificity remaining around 99%.Conclusions and clinical importanceThe use of models based on machine learning can support veterinary decision making by improving early identification of CKD

Dark matter constraints on the parameter space and particle spectra in the nonminimal SUSY standard model

We investigate the dark matter constraints for the nonminimal SUSY standard model (NMSSM). The cosmologically restricted mass spectra of the NMSSM are compared to the minimal SUSY standard model (MSSM). The differences of the two models concerning the neutralino, sfermion and Higgs sector are discussed. The dark matter condition leads to cosmologically allowed mass ranges for the SUSY particles in the NMSSM: m_{\tilde{\chi}^0_1} < 300 GeV, m_{\tilde{e}_R} < 300 GeV, 300 GeV < m_{\tilde{u}_R} < 1900 GeV, 200 GeV < m_{\tilde{t}_1} < 1500 GeV, 350 GeV < m_{\tilde{g}} < 2100 GeV and for the mass of the lightest scalar Higgs m_{S_1} < 140 GeV.Comment: revised version to appear in Phys. Lett. B, 18 pages, LaTeX, 3 figures, uses epsfig.sty and amssymb.st

Systematics of the Quadrupole-Quadrupole Interaction and Convergence Properties

Our main concern in this work is to show how higher shell admixtures affect the spectrum of a Q.Q interaction. We first review how, in the valence space, the familiar SU(3) result for the energy spectrum can be obtained using a coordinate space Q.Q interaction rather than the Elliott one which is symmetric in r and p. We then reemphasize that the Elliott spectrum goes as L(L+1) where L is the orbital angular momentum. While in many cases this is compatible with the rotational formula which involves I(I+1), where I is the total angular momentum, there are cases, e.g. odd-odd nuclei, where there is disagreement. Finally, we consider higher shell admixtures and devise a scheme so as to obtain results, with the Q.Q interaction, which converge as the model spaces are increased. We consider not only ground state rotational bands but also those that involve intruder states.Comment: 13 pages, Revtex, to appear in Annals of Physic

General health and residential proximity to the coast in Belgium : results from a cross-sectional health survey

The health risks of coastal areas have long been researched, but the potential benefits for health are only recently being explored. The present study compared the general health of Belgian citizens a) according to the EU's definition of coastal ( 50 km), and b) between eight more refined categories of residential proximity to the coast ( 250 km). Data was drawn from the Belgian Health Interview Survey (n = 60,939) and investigated using linear regression models and mediation analyses on several hypothesized mechanisms. Results indicated that populations living 50-100 km. Four commonly hypothesized mechanisms were considered but no indirect associations were found: scores for mental health, physical activity levels and social contacts were not higher at 0-5 km from the coast, and air pollution (PM ic , concentrations) was lower at 0-5 km from the coast but not statistically associated with better health. Results are controlled for typical variables such as age, sex, income, neighbourhood levels of green and freshwater blue space, etc. The spatial urban-rural-nature mosaic at the Belgian coast and alternative explanations are discussed. The positive associations between the ocean and human health observed in this study encourage policy makers to manage coastal areas sustainably to maintain associated public health benefits into the future