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

Finding the center reliably: robust patterns of developmental gene expression

We investigate a mechanism for the robust identification of the center of a developing biological system. We assume the existence of two morphogen gradients, an activator emanating from the anterior, and a co-repressor from the posterior. The co-repressor inhibits the action of the activator in switching on target genes. We apply this system to Drosophila embryos, where we predict the existence of a hitherto undetected posterior co-repressor. Using mathematical modelling, we show that a symmetric activator-co-repressor model can quantitatively explain the precise mid-embryo expression boundary of the hunchback gene, and the scaling of this pattern with embryo size.Comment: 4 pages, 3 figure

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

The influence of alfalfa-switchgrass intercropping on microbial community structure and function

The use of nitrogen fertilizer on bioenergy crops such as switchgrass results in increased costs, nitrogen leaching and emissions of N2O, a potent greenhouse gas. Intercropping with nitrogen-fixing alfalfa has been proposed as an environmentally sustainable alternative, but the effects of synthetic fertilizer versus intercropping on soil microbial community functionality remain uncharacterized. We analysed 24 metagenomes from the upper soil layer of agricultural fields from Prosser, WA over two growing seasons and representing three agricultural practices: unfertilized switchgrass (control), fertilized switchgrass and switchgrass intercropped with alfalfa. The synthetic fertilization and intercropping did not result in major shifts of microbial community taxonomic and functional composition compared with the control plots, but a few significant changes were noted. Most notably, mycorrhizal fungi, ammonia-oxidizing archaea and bacteria increased in abundance with intercropping and fertilization. However, only betaproteobacterial ammonia-oxidizing bacteria abundance in fertilized plots significantly correlated to N2O emission and companion qPCR data. Collectively, a short period of intercropping elicits minor but significant changes in the soil microbial community toward nitrogen preservation and that intercropping may be a viable alternative to synthetic fertilization

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&