Improving Fisher matrix forecasts for galaxy surveys: window function, bin cross-correlation and bin redshift uncertainty

The Fisher matrix is a widely used tool to forecast the performance of future experiments and approximate the likelihood of large data sets. Most of the forecasts for cosmological parameters in galaxy clustering studies rely on the Fisher matrix approach for large-scale experiments like DES, Euclid or SKA. Here, we improve upon the standard method by taking into account three effects: the finite window function, the correlation between redshift bins and the uncertainty on the bin redshift. The first two effects are negligible only in the limit of infinite surveys. The third effect, in contrast, is negligible for infinitely small bins. Here, we show how to take into account these effects and what the impact on forecasts of a Euclid-type experiment will be. The main result of this paper is that the windowing and the bin cross-correlation induce a considerable change in the forecasted errors, of the order of 10–30 per cent for most cosmological parameters, while the redshift bin uncertainty can be neglected for bins smaller than Δz = 0.1 roughly

Changes of particle size distribution and chemical composition of a hay-based ration offered once or twice daily to dairy cows

The objective of this experiment is to evaluate the changes of particle size distribution and chemical composition of the total mixed ration (TMR) based on hay as the main forage component ("dry" TMR) and distributed once (7.00 am) or twice (7.00 am and 1.00 pm) daily to 32 lactating cows. The trial was divided in two periods of 14 days each. Diet (DM=53.7%) was formulated in order to assure the nutritional requirements of cows producing 24 kg/d of milk (crude protein=14.4% DM; NDF=40.9% DM; milk FU=0.88/kg DM) and additional amounts of concentrates were distributed using automatic feeders. Four TMR samples were collected daily (7.00 am, 10.00 am, 1.00 pm, 4.00 pm) for six days during each experiment period for a total number of 48 feed samples. Each feed sample was subjected to the estimation of the particle size distribution using the separator of Pennsylvania State University composed of two sieves (diameters of 19 and 8 mm) and a collector on the bottom, and to the determination of the chemical composition. Changes of all three particle size fractions for TMRs were observed during the day with distributions of the TMR both once and twice daily. With the once daily distribution, the large particles fraction increased linearly (P<0.001) from 19.7 to 23.4, 32.2, and 35.1%, while the finest particle fraction decreased (from 60.1 to 58.3, 50.0, 47.8%). According to particle size changes, the chemical composition varied significantly at the different times of sampling when TMR was distributed once daily. Significant variations of DM were detected for TMR with a linear (P<0.001) increase (from 54.4 to 57.9, 60.7, 61.5%). Considering once TMR distribution, the values of NDF and starch showed an opposite trend with an increase of 6.5 and a decrease of 8.3 points from 7.00 am to 4.00 pm (i.e., 9 hrs after distribution). Correlations were estimated between chemical and physical characteristics of TMRs. NDF content was positively and significantly correlated to the fraction of particles retained by a 19 mm sieve (r=0.42; P<0.001) and negatively correlated with the smaller particles (r=-0.51; P<0.001). In conclusion, when the TMRs are prepared excluding corn/hay silages, twice daily distributions of diet can avoid the selection of large feed by the cows, thereby preserving both a uniform particle size distribution and a steady chemical composition of the diet during the day. However, the cost for the extra time needed for twice daily dis- tribution should be carefully considered

A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation

We propose a novel theoretical framework that generalizes algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes. This framework defines GASP, a Generalized Algorithm for Signed graph Partitioning, and allows us to explore many combinations of different linkage criteria and cannot-link constraints. We prove the equivalence of existing clustering methods to some of those combinations, and introduce new algorithms for combinations which have not been studied. An extensive comparison is performed to evaluate properties of the clustering algorithms in the context of instance segmentation in images, including robustness to noise and efficiency. We show how one of the new algorithms proposed in our framework outperforms all previously known agglomerative methods for signed graphs, both on the competitive CREMI 2016 EM segmentation benchmark and on the CityScapes dataset.Comment: 19 pages, 8 figures, 6 table

Feeding dairy cows with full fat extruded or toasted soybean seeds as replacement of soybean meal and effects on milk yield, fatty acid profile and CLA content

The aim of this study was to evaluate the effects of the replacement of about 70% of soybean meal (SBM) with extruded(ES) or toasted (TS) full-fat soybean seeds in diets for lactating cows on milk quality, fatty acid profile, and conjugatedlinoleic acid (CLA) content. Eighteen lactating cows were assigned to 3 groups which received a basal diet, supplementedwith 1.8, 2.1 and 2.1 kg/head, respectively, of SBM, ES and TS. There was no significant effect on milk yield,calculated as the difference between daily yield during the experimental period and the mean of the last 5 days of adaptation(-1.65, -1.29 and -0.20 kg/d, respectively, for SBM, ES and TS; P>0.10) and milk quality parameters (fat, protein,urea and cheese making parameters) among treatments. In the ES group there was a decrease in the short chainFA content (from C4 to C13) in milk fat (9.2 vs 11.0 and 10.8 g/100 g lipids, respectively, for ES, SBM and TS; P<0.05).Medium chain FA (from C14 to C17) content in milk fat was lower for ES and TS groups compared with SBM (46.8 and48.0 vs 54.8 g/100 g lipids respectively; P<0.01), while long chain FA (C≥18) concentration in milk fat was lower in theSBM group compared to the others (34.3 vs 44.2 and 41.2 g/100 g lipids, respectively, for SBM, ES and TS; P<0.001).The replacement of SBM with ES enhanced oleic and linoleic acid and, particularly, CLA content. Intermediate values wereobserved for the TS group. CLA content (0.91, 0.62 and 0.56 g/100 g lipids, respectively, for ES, TS and SBM; P<0.05)increased throughout the trial in all groups. ES also reduced the proportion of SFA with respect to SBM (65.2, 68.2 and70.9 g/100 g lipids, respectively, for ES, TS and SBM; P<0.05), and increased MUFA (26.9, 24.5 and 23.1 g/100 g lipidsin the same order; P<0.05) and PUFA (7.4, 6.9 and 5.5 g/100 g lipids in the same order; P<0.05) of milk fat, thusimproving the health-quality of milk. The various soybean products did not affect either metabolic profile (protein, urea,glucose, cholesterol, NEFA, triglycerides, liver parameters and mineral serum content) or rumen parameters (pH, ammoniaand VFAs). The replacement of SBM with ES and TS permitted an improvement in the nutritional properties of milkwithout negatively affecting animal performances

Accurate and versatile 3D segmentation of plant tissues at cellular resolution

Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface