Variations of protein profiles and calcium and phospholipase A2 concentrations in thawed bovine semen and their relation to acrosome reaction

Just as calcium plays an integral role in acrosome capacitation and reaction, several spermatozoon proteins have been reported as binding to the ovum at fertilization. We examined the relationship between thawed bovine semen protein profiles, seminal plasma calcium ion concentration, spermatozoon phospholipase A2 (PLA2) activity and acrosome reaction. Electrophoretic profile analysis of spermatozoa and bovine seminal plasma proteins (total and membrane) revealed qualitative and quantitative differences among bulls. Variations in PLA2 and seminal plasma calcium concentration indicated genetic diversity among individuals. A 15.7-kDa membrane protein was significantly correlated (r = 0.71) with acrosome reaction, which in turn has been associated with in vivo fertility

Wine making by-products

Approximately, 67.1 million tons of grapes were utilized in wine production in 2013 (FAO 2014). This generates a considerable amount of waste because as much as 20% of the weight of processed grapes is not found in the final product (Mazza and Miniati 1993). There is growing interest in the utilization of this waste including its conversion into biofuels and use as nutrient supplements, food ingredients, and animal feeds.The authors acknowledge the funding received from the New Zealand Ministry for Environment (Community Environment Fund & Waste Minimisation Fund, Deed Number 20398) and the Sustainable Farm Fund (Project Number 09/099). This work is part of the New Zealand Grape and Wine Research Program, a joint investment by the Plant and Food Research and NZ Winegrowers

Measurement of single-diffractive dijet production in proton–proton collisions at √s=8Te with the CMS and TOTEM experiments

Measurements are presented of the single-diffractive dijet cross section and the diffractive cross section as a function of the proton fractional momentum loss ξ and the four-momentum transfer squared t. Both processes pp→pX and pp→Xp, i.e. with the proton scattering to either side of the interaction point, are measured, where X includes at least two jets; the results of the two processes are averaged. The analyses are based on data collected simultaneously with the CMS and TOTEM detectors at the LHC in proton–proton collisions at s=8Te during a dedicated run with β∗=90m at low instantaneous luminosity and correspond to an integrated luminosity of 37.5nb-1. The single-diffractive dijet cross section σjjpX, in the kinematic region ξ< 0.1 , 0.03<|t|<1Ge2, with at least two jets with transverse momentum pT>40Ge, and pseudorapidity | η| < 4.4 , is 21.7±0.9(stat)-3.3+3.0(syst)±0.9(lumi)nb. The ratio of the single-diffractive to inclusive dijet yields, normalised per unit of ξ, is presented as a function of x, the longitudinal momentum fraction of the proton carried by the struck parton. The ratio in the kinematic region defined above, for x values in the range - 2.9 ≤ log 10x≤ - 1.6 , is R=(σjjpX/Δξ)/σjj=0.025±0.001(stat)±0.003(syst), where σjjpX and σjj are the single-diffractive and inclusive dijet cross sections, respectively. The results are compared with predictions from models of diffractive and nondiffractive interactions. Monte Carlo predictions based on the HERA diffractive parton distribution functions agree well with the data when corrected for the effect of soft rescattering between the spectator partons. © 2020, CERN for the benefit of the CMS and TOTEM collaborations

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton–proton collisions at an energy of s=13TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb-1. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯. © 2020, The Author(s)