Measurements of hybrid fertility and a test of mate preference for two house mouse races with massive chromosomal divergence

Abstract Background Western house mice Mus musculus domesticus are among the most important mammalian model species for chromosomal speciation. Hybrids between chromosomal races of M. m. domesticus suffer various degrees of fertility reduction between full fertility and complete sterility, depending on the complexity of the chromosomal differences between the races. This complexity presents itself in hybrids as meiotic configurations of chromosome chains and rings, with longer configurations having a stronger impact on fertility. While hybrids with short configurations have been intensively studied, less work has been done on hybrids with very long configurations. In this study, we investigated laboratory-reared wild mice from two chromosomally very different races in Switzerland found in close proximity. Hybrids between these races form a meiotic chain of fifteen chromosomes. We performed a detailed analysis of male and female hybrid fertility, including three generations of female backcrosses to one of the parental races. We also tested for possible divergence of mate preference in females. Results While all male F1 hybrids were sterile with sperm counts of zero, 48% of female F1 hybrids produced offspring. Their litter sizes ranged from one to three which is significantly lower than the litter size of parental race females. When hybrid females were backcrossed to a parental race, half of the offspring resembled the parental race in karyotype and fertility, while the other half resembled the F1 hybrids. In the preference test, females of both races indicated a lack of a preference for males of their own karyotype. Conclusions Although the fertility of the F1 hybrids was extremely low because of the complexity of the chromosomal differences between the races, reproductive isolation was not complete. As we did not find assortative female preferences, we expect that contact between these races would lead to the production of hybrids and that gene flow would occur eventually, as fertility can be restored fully after one backcross generation

An output-sensitive algorithm for the minimization of 2-dimensional String Covers

String covers are a powerful tool for analyzing the quasi-periodicity of 1-dimensional data and find applications in automata theory, computational biology, coding and the analysis of transactional data. A \emph{cover} of a string $T$ is a string $C$ for which every letter of $T$ lies within some occurrence of $C$. String covers have been generalized in many ways, leading to \emph{k-covers}, \emph{$\lambda$-covers}, \emph{approximate covers} and were studied in different contexts such as \emph{indeterminate strings}. In this paper we generalize string covers to the context of 2-dimensional data, such as images. We show how they can be used for the extraction of textures from images and identification of primitive cells in lattice data. This has interesting applications in image compression, procedural terrain generation and crystallography

The age of data-driven proteomics : how machine learning enables novel workflows

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. In this viewpoint we therefore point out highly promising recent machine learning developments in proteomics, alongside some of the remaining challenges

Low Temperature Magnetic Properties of the Double Exchange Model

We study the {\it ferromagnetic} (FM) Kondo lattice model in the strong coupling limit (double exchange (DE) model). The DE mechanism proposed by Zener to explain ferromagnetism has unexpected properties when there is more than one itinerant electron. We find that, in general, the many-body ground state of the DE model is {\it not} globally FM ordered (except for special filled-shell cases). Also, the low energy excitations of this model are distinct from spin wave excitations in usual Heisenberg ferromagnets, which will result in unusual dynamic magnetic properties.Comment: 5 pages, RevTeX, 5 Postscript figures include

Orientation and symmetries of Alexandrov spaces with applications in positive curvature

We develop two new tools for use in Alexandrov geometry: a theory of ramified orientable double covers and a particularly useful version of the Slice Theorem for actions of compact Lie groups. These tools are applied to the classification of compact, positively curved Alexandrov spaces with maximal symmetry rank.Comment: 34 pages. Simplified proofs throughout and a new proof of the Slice Theorem, correcting omissions in the previous versio

Simulation of High Conversion Efficiency and Open-circuit Voltages Of {\alpha}-si/poly-silicon Solar Cell

The P+ {\alpha}-Si /N+ polycrystalline solar cell is molded using the AMPS-1D device simulator to explore the new high efficiency thin film poly-silicon solar cell. In order to analyze the characteristics of this device and the thickness of N+ poly-silicon, we consider the impurity concentration in the N+ poly-silicon layer and the work function of transparent conductive oxide (TCO) in front contact in the calculation. The thickness of N+ poly-silicon has little impact on the device when the thickness varies from 20 {\mu}m to 300 {\mu}m. The effects of impurity concentration in polycrystalline are analyzed. The conclusion is drawn that the open-circuit voltage (Voc) of P+ {\alpha}-Si /N+ polycrystalline solar cell is very high, reaching 752 mV, and the conversion efficiency reaches 9.44%. Therefore, based on the above optimum parameters the study on the device formed by P+ {\alpha}-Si/N+ poly-silicon is significant in exploring the high efficiency poly-silicon solar cell.Comment: 8 pages 6figures, 1 table

Structure, magnetic and transport properties of Ti-substituted La0.7Sr0.3MnO3

Ti-substituted perovskites, La0.7Sr0.3Mn1-xTixO3, with x between 0 to 0.20, were investigated by neutron diffraction, magnetization, electric resistivity, and magnetoresistance (MR) measurements. All samples show a rhombohedral structure (space group R3c) from 10 K to room temperature. At room temperature, the cell parameters a, c and the unit cell volume increase with increasing Ti content. However, at 10 K, the cell parameter a has a maximum value for x = 0.10, and decreases for x greater than 0.10, while the unit cell volume remains nearly constant for x greater than 0.10. The average (Mn,Ti)-O bond length increases up to x=0.15, and the (Mn,Ti)-O-(Mn,Ti) bond angle decreases with increasing Ti content to its minimum value at x=0.15 at room temperature. Below the Curie temperature T_C, the resistance exhibits metallic behavior for the x _ 0.05 samples. A metal (semiconductor) to insulator transition is observed for the x_ 0.10 samples. A peak in resistivity appears below T_C for all samples, and shifts to a lower temperature as x increases. The substitution of Mn by Ti decreases the 2p-3d hybridization between O and Mn ions, reduces the bandwidth W, and increases the electron-phonon coupling. Therefore, the TC shifts to a lower temperature and the resistivity increases with increasing Ti content. A field-induced shift of the resistivity maximum occurs at x less than or equal to 0.10. The maximum MR effect is about 70% for La0.7Sr0.3Mn0.8Ti0.2O3. The separation of TC and the resistivity maximum temperature Tmax enhances the MR effect in these compounds due to the weak coupling between the magnetic ordering and the resistivity as compared with La0.7Sr0.3MnO3.Comment: zip fil

Bayesian detection of unmodeled bursts of gravitational waves

The data analysis problem of coherently searching for unmodeled gravitational-wave bursts in the data generated by a global network of gravitational-wave observatories has been at the center of research for almost two decades. As data from these detectors is starting to be analyzed, a renewed interest in this problem has been sparked. A Bayesian approach to the problem of coherently searching for gravitational wave bursts with a network of ground-based interferometers is here presented. We demonstrate how to systematically incorporate prior information on the burst signal and its source into the analysis. This information may range from the very minimal, such as best-guess durations, bandwidths, or polarization content, to complete prior knowledge of the signal waveforms and the distribution of sources through spacetime. We show that this comprehensive Bayesian formulation contains several previously proposed detection statistics as special limiting cases, and demonstrate that it outperforms them.Comment: 18 pages, 3 figures, revisions based on referee comment