Pheromone-induced polarization is dependent on the Fus3p MAPK acting through the formin Bni1p

During mating, budding yeast cells reorient growth toward the highest concentration of pheromone. Bni1p, a formin homologue, is required for this polarized growth by facilitating cortical actin cable assembly. Fus3p, a pheromone-activated MAP kinase, is required for pheromone signaling and cell fusion. We show that Fus3p phosphorylates Bni1p in vitro, and phosphorylation of Bni1p in vivo during the pheromone response is dependent on Fus3p. fus3 mutants exhibited multiple phenotypes similar to bni1 mutants, including defects in actin and cell polarization, as well as Kar9p and cytoplasmic microtubule localization. Disruption of the interaction between Fus3p and the receptor-associated Gα subunit caused similar mutant phenotypes. After pheromone treatment, Bni1p-GFP and Spa2p failed to localize to the cortex of fus3 mutants, and cell wall growth became completely unpolarized. Bni1p overexpression suppressed the actin assembly, cell polarization, and cell fusion defects. These data suggest a model wherein activated Fus3p is recruited back to the cortex, where it activates Bni1p to promote polarization and cell fusion.</jats:p

Learning to Classify from Impure Samples with High-Dimensional Data

A persistent challenge in practical classification tasks is that labeled training sets are not always available. In particle physics, this challenge is surmounted by the use of simulations. These simulations accurately reproduce most features of data, but cannot be trusted to capture all of the complex correlations exploitable by modern machine learning methods. Recent work in weakly supervised learning has shown that simple, low-dimensional classifiers can be trained using only the impure mixtures present in data. Here, we demonstrate that complex, high-dimensional classifiers can also be trained on impure mixtures using weak supervision techniques, with performance comparable to what could be achieved with pure samples. Using weak supervision will therefore allow us to avoid relying exclusively on simulations for high-dimensional classification. This work opens the door to a new regime whereby complex models are trained directly on data, providing direct access to probe the underlying physics.Comment: 6 pages, 2 tables, 2 figures. v2: updated to match PRD versio

Pileup Mitigation with Machine Learning (PUMML)

Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone. The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables. We test the robustness of the algorithm in a number of ways and discuss how the network can be trained directly on data.Comment: 20 pages, 8 figures, 2 tables. Updated to JHEP versio

Deep learning in color: towards automated quark/gluon jet discrimination

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.Massachusetts Institute of Technology. Department of Physic

EffEct of brEEding sEason on thE kinEmatic paramEtErs and morphology of ram&apos; spErm from synthEtic population bulgarian milk shEEp brEEd

abstract AbADjievA, D., M. Chervenkov, r. StefAnov, n. MetoDiev, e. kiStAnovA, D. kAChevA and e. rAyChevA, 2014. effect of breeding season on the kinematic parameters and morphology of ram&apos; sperm from synthetic population bulgarian milk sheep breed. Bulg. J. Agric. Sci., the aim of this study was the investigation of the breeding season effect on the kinematic and main spermatological parameters of the rams from Synthetic Population Bulgarian Milk sheep breed (SPBM), new Bulgarian breed certificated in 2005. the experiment was carried out with seven rams. Two consecutive ejaculates from each ram were obtained by artificial vagina before and during the breeding campaign (n=28). overall sperm motility and the individual kinematic parameters of motile spermatozoa were assessed by the computer-aided sperm analysis system Sperm Class Analyzer (SCA). the sperm morphology was estimated after sperm blue stain and calculated as a percent of abnormal cells among 100 sperm cells from several fields on the slide. it was found that the ejaculates obtained from SPbM rams during the breeding season had better features of sperm motion kinetics. the values of the velocity parameters (P&lt; 0.05), motility (P&lt; 0.05), and percentages of spermatozoa with rapid (P&lt; 0.01) and medium (P&lt; 0.001) speed were higher than those from the ejaculates collected before the breeding season. Minor and not significant changes in the kinematic parameters of motile spermatozoa in consecutive ejaculates were observed. No significant differences were established in morphological status of spermatozoa in nonbreeding and breeding season. It seems that the better sperm motility kinematic parameters during the breeding season ensure the higher sperm fertility and success on the future insemination

The effect of salt-free - salt diet on the reproductive performance of Ile de France ewes

The aim of the present study was to establish the effect of salt-free - salt diet (SFSD) on the size of antral follicles during salt consumption, the duration of controlled breeding campaign and the ferti¬lities of ewes from the Ile de France breed. Тhe experiment was carried out with 57 ewes, which were divided into 3 groups (19 ewes in each) depending on whether they were subjected to SFSD and their contacts with rams during the first 8 days of the beginning of breeding: Group I – SFSD + ram con-tact; Group II – only SFSD; Group III – no SFSD, only ram contacts. The first day of the salt diet coincided with the first day of ram contacts. Transrectal ultrasound examinations of the ovaries were done on days 1, 2, 3, 5, 6 and 7. The time of manifestation of estrus (in days), pregnancy rate and fecundity were studied. A significant effect of time (P<0.05) and diet (P<0.01) on the size of follicles was established. The onset of the first estrus, the shortest terms of breeding campaign, the fertility and the fecundity results gave us reason to favour the scheme applied to Group II. A stimulatory effect of the diet in that study was demonstrated, but the pattern was different from our previous studies

The mating-specific Gα interacts with a kinesin-14 and regulates pheromone-induced nuclear migration in budding yeast

As a budding yeast cell elongates toward its mating partner, cytoplasmic microtubules connect the nucleus to the cell cortex at the growth tip. The Kar3 kinesin-like motor protein is then thought to stimulate plus-end depolymerization of these microtubules, thus drawing the nucleus closer to the site where cell fusion and karyogamy will occur. Here, we show that pheromone stimulates a microtubule-independent interaction between Kar3 and the mating-specific Gα protein Gpa1 and that Gpa1 affects both microtubule orientation and cortical contact. The membrane localization of Gpa1 was found to polarize early in the mating response, at about the same time that the microtubules begin to attach to the incipient growth site. In the absence of Gpa1, microtubules lose contact with the cortex upon shrinking and Kar3 is improperly localized, suggesting that Gpa1 is a cortical anchor for Kar3. We infer that Gpa1 serves as a positional determinant for Kar3-bound microtubule plus ends during mating. © 2009 by The American Society for Cell Biology

The Machine Learning Landscape of Top Taggers

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.Comment: Yet another tagger included

Proteome Profiling of Breast Tumors by Gel Electrophoresis and Nanoscale Electrospray Ionization Mass Spectrometry

We have conducted proteome-wide analysis of fresh surgery specimens derived from breast cancer patients, using an approach that integrates size-based intact protein fractionation, nanoscale liquid separation of peptides, electrospray ion trap mass spectrometry, and bioinformatics. Through this approach, we have acquired a large amount of peptide fragmentation spectra from size-resolved fractions of the proteomes of several breast tumors, tissue peripheral to the tumor, and samples from patients undergoing noncancer surgery. Label-free quantitation was used to generate protein abundance maps for each proteome and perform comparative analyses. The mass spectrometry data revealed distinct qualitative and quantitative patterns distinguishing the tumors from healthy tissue as well as differences between metastatic and non-metastatic human breast cancers including many established and potential novel candidate protein biomarkers. Selected proteins were evaluated by Western blotting using tumors grouped according to histological grade, size, and receptor expression but differing in nodal status. Immunohistochemical analysis of a wide panel of breast tumors was conducted to assess expression in different types of breast cancers and the cellular distribution of the candidate proteins. These experiments provided further insights and an independent validation of the data obtained by mass spectrometry and revealed the potential of this approach for establishing multimodal markers for early metastasis, therapy outcomes, prognosis, and diagnosis in the future. © 2008 American Chemical Society