Comparing Yield and Quality of Milk from Dairy Cows Fed Stockpiled Annual Ryegrass (\u3cem\u3eLolium Multiflorum\u3c/em\u3e L.) and Cereal Rye (\u3cem\u3eSecale Cereale\u3c/em\u3e L.)

Stockpiling annual ryegrass and cereal rye provides a low cost substitute to hay and creates an excellent source of feed during winter (Kallenbach et al., 2003). In addition to lowering feed costs, grazing increases the conjugated linoleic acid (CLA) content of milk compared to feeding hay. Previous research suggested that forage species might differ in their ability to alter milk CLA content during the growing season (Wu et al., 1997). However, research is needed to determine if different forage species used for winter and early spring grazing impacts the CLA content of milk. The objective of this experiment was to compare yield and quality of milk when cows graze annual ryegrass or cereal rye in late winter and early spring

Periodic pattern formation in reaction-diffusion systems -an introduction for numerical simulation

The aim of the present review is to provide a comprehensive explanation of Turing reaction–diffusion systems in sufficient detail to allow readers to perform numerical calculations themselves. The reaction–diffusion model is widely studied in the field of mathematical biology, serves as a powerful paradigm model for self-organization and is beginning to be applied to actual experimental systems in developmental biology. Despite the increase in current interest, the model is not well understood among experimental biologists, partly because appropriate introductory texts are lacking. In the present review, we provide a detailed description of the definition of the Turing reaction–diffusion model that is comprehensible without a special mathematical background, then illustrate a method for reproducing numerical calculations with Microsoft Excel. We then show some examples of the patterns generated by the model. Finally, we discuss future prospects for the interdisciplinary field of research involving mathematical approaches in developmental biology

HOLISMOKES -- IV. Efficient mass modeling of strong lenses through deep learning

Modelling the mass distributions of strong gravitational lenses is often necessary to use them as astrophysical and cosmological probes. With the high number of lens systems ($>10^5$) expected from upcoming surveys, it is timely to explore efficient modeling approaches beyond traditional MCMC techniques that are time consuming. We train a CNN on images of galaxy-scale lenses to predict the parameters of the SIE mass model ($x,y,e_x,e_y$, and $\theta_E$). To train the network, we simulate images based on real observations from the HSC Survey for the lens galaxies and from the HUDF as lensed galaxies. We tested different network architectures, the effect of different data sets, and using different input distributions of $\theta_E$. We find that the CNN performs well and obtain with the network trained with a uniform distribution of $\theta_E$ $>0.5"$ the following median values with $1\sigma$ scatter: $\Delta x=(0.00^{+0.30}_{-0.30})"$, $\Delta y=(0.00^{+0.30}_{-0.29})"$, $\Delta \theta_E=(0.07^{+0.29}_{-0.12})"$, $\Delta e_x = -0.01^{+0.08}_{-0.09}$ and $\Delta e_y = 0.00^{+0.08}_{-0.09}$. The bias in $\theta_E$ is driven by systems with small $\theta_E$. Therefore, when we further predict the multiple lensed image positions and time delays based on the network output, we apply the network to the sample limited to $\theta_E>0.8"$. In this case, the offset between the predicted and input lensed image positions is $(0.00_{-0.29}^{+0.29})"$ and $(0.00_{-0.31}^{+0.32})"$ for $x$ and $y$, respectively. For the fractional difference between the predicted and true time delay, we obtain $0.04_{-0.05}^{+0.27}$. Our CNN is able to predict the SIE parameters in fractions of a second on a single CPU and with the output we can predict the image positions and time delays in an automated way, such that we are able to process efficiently the huge amount of expected lens detections in the near future.Comment: 17 pages, 14 Figure

HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images

Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology. Especially with the large number of detections in current and upcoming surveys such as the Rubin Legacy Survey of Space and Time (LSST), it is timely to investigate in automated and fast analysis techniques beyond the traditional and time consuming Markov chain Monte Carlo sampling methods. Building upon our convolutional neural network (CNN) presented in Schuldt et al. (2021b), we present here another CNN, specifically a residual neural network (ResNet), that predicts the five mass parameters of a Singular Isothermal Ellipsoid (SIE) profile (lens center $x$ and $y$, ellipticity $e_x$ and $e_y$, Einstein radius $\theta_E$) and the external shear ($\gamma_{ext,1}$, $\gamma_{ext,2}$) from ground-based imaging data. In contrast to our CNN, this ResNet further predicts a 1$\sigma$ uncertainty for each parameter. To train our network, we use our improved pipeline from Schuldt et al. (2021b) to simulate lens images using real images of galaxies from the Hyper Suprime-Cam Survey (HSC) and from the Hubble Ultra Deep Field as lens galaxies and background sources, respectively. We find overall very good recoveries for the SIE parameters, while differences remain in predicting the external shear. From our tests, most likely the low image resolution is the limiting factor for predicting the external shear. Given the run time of milli-seconds per system, our network is perfectly suited to predict the next appearing image and time delays of lensed transients in time. Therefore, we also present the performance of the network on these quantities in comparison to our simulations. Our ResNet is able to predict the SIE and shear parameter values in fractions of a second on a single CPU such that we are able to process efficiently the huge amount of expected galaxy-scale lenses in the near future.Comment: 16 pages, including 11 figures, accepted for publication by A&

TransCom N2O model inter-comparison - Part 2:Atmospheric inversion estimates of N2O emissions

This study examines N2O emission estimates from five different atmospheric inversion frameworks based on chemistry transport models (CTMs). The five frameworks differ in the choice of CTM, meteorological data, prior uncertainties and inversion method but use the same prior emissions and observation data set. The posterior modelled atmospheric N2O mole fractions are compared to observations to assess the performance of the inversions and to help diagnose problems in the modelled transport. Additionally, the mean emissions for 2006 to 2008 are compared in terms of the spatial distribution and seasonality. Overall, there is a good agreement among the inversions for the mean global total emission, which ranges from 16.1 to 18.7 TgN yr(-1) and is consistent with previous estimates. Ocean emissions represent between 31 and 38% of the global total compared to widely varying previous estimates of 24 to 38%. Emissions from the northern mid- to high latitudes are likely to be more important, with a consistent shift in emissions from the tropics and subtropics to the mid- to high latitudes in the Northern Hemisphere; the emission ratio for 0-30A degrees N to 30-90A degrees N ranges from 1.5 to 1.9 compared with 2.9 to 3.0 in previous estimates. The largest discrepancies across inversions are seen for the regions of South and East Asia and for tropical and South America owing to the poor observational constraint for these areas and to considerable differences in the modelled transport, especially inter-hemispheric exchange rates and tropical convective mixing. Estimates of the seasonal cycle in N2O emissions are also sensitive to errors in modelled stratosphere-to-troposphere transport in the tropics and southern extratropics. Overall, the results show a convergence in the global and regional emissions compared to previous independent studies

HOLISMOKES -- II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks

We present a systematic search for wide-separation (Einstein radius >1.5"), galaxy-scale strong lenses in the 30 000 sq.deg of the Pan-STARRS 3pi survey on the Northern sky. With long time delays of a few days to weeks, such systems are particularly well suited for catching strongly lensed supernovae with spatially-resolved multiple images and open new perspectives on early-phase supernova spectroscopy and cosmography. We produce a set of realistic simulations by painting lensed COSMOS sources on Pan-STARRS image cutouts of lens luminous red galaxies with known redshift and velocity dispersion from SDSS. First of all, we compute the photometry of mock lenses in gri bands and apply a simple catalog-level neural network to identify a sample of 1050207 galaxies with similar colors and magnitudes as the mocks. Secondly, we train a convolutional neural network (CNN) on Pan-STARRS gri image cutouts to classify this sample and obtain sets of 105760 and 12382 lens candidates with scores pCNN>0.5 and >0.9, respectively. Extensive tests show that CNN performances rely heavily on the design of lens simulations and choice of negative examples for training, but little on the network architecture. Finally, we visually inspect all galaxies with pCNN>0.9 to assemble a final set of 330 high-quality newly-discovered lens candidates while recovering 23 published systems. For a subset, SDSS spectroscopy on the lens central regions proves our method correctly identifies lens LRGs at z~0.1-0.7. Five spectra also show robust signatures of high-redshift background sources and Pan-STARRS imaging confirms one of them as a quadruply-imaged red source at z_s = 1.185 strongly lensed by a foreground LRG at z_d = 0.3155. In the future, we expect that the efficient and automated two-step classification method presented in this paper will be applicable to the deeper gri stacks from the LSST with minor adjustments.Comment: 18 pages and 11 figures (plus appendix), submitted to A&

Converting genetic network oscillations into somite spatial pattern

In most vertebrate species, the body axis is generated by the formation of repeated transient structures called somites. This spatial periodicity in somitogenesis has been related to the temporally sustained oscillations in certain mRNAs and their associated gene products in the cells forming the presomatic mesoderm. The mechanism underlying these oscillations have been identified as due to the delays involved in the synthesis of mRNA and translation into protein molecules [J. Lewis, Current Biol. {\bf 13}, 1398 (2003)]. In addition, in the zebrafish embryo intercellular Notch signalling couples these oscillators and a longitudinal positional information signal in the form of an Fgf8 gradient exists that could be used to transform these coupled temporal oscillations into the observed spatial periodicity of somites. Here we consider a simple model based on this known biology and study its consequences for somitogenesis. Comparison is made with the known properties of somite formation in the zebrafish embryo . We also study the effects of localized Fgf8 perturbations on somite patterning.Comment: 7 pages, 7 figure

A Feedback Quenched Oscillator Produces Turing Patterning with One Diffuser

Efforts to engineer synthetic gene networks that spontaneously produce patterning in multicellular ensembles have focused on Turing's original model and the “activator-inhibitor” models of Meinhardt and Gierer. Systems based on this model are notoriously difficult to engineer. We present the first demonstration that Turing pattern formation can arise in a new family of oscillator-driven gene network topologies, specifically when a second feedback loop is introduced which quenches oscillations and incorporates a diffusible molecule. We provide an analysis of the system that predicts the range of kinetic parameters over which patterning should emerge and demonstrate the system's viability using stochastic simulations of a field of cells using realistic parameters. The primary goal of this paper is to provide a circuit architecture which can be implemented with relative ease by practitioners and which could serve as a model system for pattern generation in synthetic multicellular systems. Given the wide range of oscillatory circuits in natural systems, our system supports the tantalizing possibility that Turing pattern formation in natural multicellular systems can arise from oscillator-driven mechanisms

Dynamic compartmentalization of bacteria: accurate division in E. coli

Positioning of the midcell division plane within the bacterium E. coli is controlled by the min system of proteins: MinC, MinD and MinE. These proteins coherently oscillate from end to end of the bacterium. We present a reaction--diffusion model describing the diffusion of min proteins along the bacterium and their transfer between the cytoplasmic membrane and cytoplasm. Our model spontaneously generates protein oscillations in good agreement with experiments. We explore the oscillation stability, frequency and wavelength as a function of protein concentration and bacterial length.Comment: 4 pages, 4 figures, Latex2e, Revtex