Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods

Although durum wheat (Triticum turgidum var. durum Desf.) is a minor cereal crop representing just 5\u20137% of the world\u2019s total wheat crop, it is a staple food in Mediterranean countries, where it is used to produce pasta, couscous, bulgur and bread. In this paper, we cover multi-trait prediction of grain yield (GY), days to heading (DH) and plant height (PH) of 270 durum wheat lines that were evaluated in 43 environments (country\u2013location\u2013year combinations) across a broad range of water regimes in the Mediterranean Basin and other locations. Multi-trait prediction analyses were performed by implementing a multi-trait deep learning model (MTDL) with a feed-forward network topology and a rectified linear unit activation function with a grid search approach for the selection of hyper-parameters. The results of the multi-trait deep learning method were also compared with univariate predictions of the genomic best linear unbiased predictor (GBLUP) method and the univariate counterpart of the multi-trait deep learning method (UDL). All models were implemented with and without the genotype 7 environment interaction term. We found that the best predictions were observed without the genotype 7 environment interaction term in the UDL and MTDL methods. However, under the GBLUP method, the best predictions were observed when the genotype 7 environment interaction term was taken into account. We also found that in general the best predictions were observed under the GBLUP model; however, the predictions of the MTDL were very similar to those of the GBLUP model. This result provides more evidence that the GBLUP model is a powerful approach for genomic prediction, but also that the deep learning method is a practical approach for predicting univariate and multivariate traits in the context of genomic selection

HPLC-DAD-ESI-QTOF-MS and HPLC-FLD-MS as valuable tools for the determination of phenolic and other polar compounds in the edible part and by-products of avocado

Avocado is a tropical fruit increasingly cultivated around the world due to global interest and rising consumption. Thus, there is also a surge in avocado by-products that needs assessment. The aim of this work is to compare the phenolic profile of avocado pulp, peel and seed when the fruit is at optimal ripeness for consumption and when overripe. Two analytical techniques were used: (1) HPLC-DAD-ESI-QTOF-MS was used for the first time to determine phenolic and other polar compounds in avocado peel and seed. Phenolic compounds quantified with these methods were in higher concentration in overripe than in pulp and seed of optimally ripe fruit. (2) HPLC-FLD-MS was used to specifically determine flavan-3-ols. Procyanidins to degree of polymerization 13 have been quantified singularly here for the first time. In addition, A- and B-type procyanidins from the degree of polymerization 2 to 6 were differentiated and quantified. The procyanidin concentration increased after ripening probably due to the release of tannins linked to cell-wall structures. Because of this situation and the presence of A-type procyanidins, avocado peel and seed from overripe fruit, the main by-products of avocado processing, hold interest for developing functional foods, nutraceuticals and cosmetics

Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks

Modeling in heterogeneous catalysis requires the extensive evaluation of the energy of molecules adsorbed on surfaces. This is done via density functional theory but for large organic molecules it requires enormous computational time, compromising the viability of the approach. Here we present GAME-Net, a graph neural network to quickly evaluate the adsorption energy. GAME-Net is trained on a well-balanced chemically diverse dataset with C1–4 molecules with functional groups including N, O, S and C6–10 aromatic rings. The model yields a mean absolute error of 0.18 eV on the test set and is 6 orders of magnitude faster than density functional theory. Applied to biomass and plastics (up to 30 heteroatoms), adsorption energies are predicted with a mean absolute error of 0.016 eV per atom. The framework represents a tool for the fast screening of catalytic materials, particularly for systems that cannot be simulated by traditional methods

Physics of ULIRGs with MUSE and ALMA: The PUMA project: II. Are local ULIRGs powered by AGN: The subkiloparsec view of the 220 GHz continuum

We analyze new high-resolution (400 pc) ∼220 GHz continuum and CO(2-1) Atacama Large Millimeter Array (ALMA) observations of a representative sample of 23 local (z < 0.165) ultra-luminous infrared systems (ULIRGs; 34 individual nuclei) as part of the "Physics of ULIRGs with MUSE and ALMA"(PUMA) project. The deconvolved half-light radii of the ∼220 GHz continuum sources, rcont, are between < 60 pc and 350 pc (median 80-100 pc). We associate these regions with the regions emitting the bulk of the infrared luminosity (LIR). The good agreement, within a factor of 2, between the observed ∼220 GHz fluxes and the extrapolation of the infrared gray-body as well as the small contributions from synchrotron and free-free emission support this assumption. The cold molecular gas emission sizes, rCO, are between 60 and 700 pc and are similar in advanced mergers and early interacting systems. On average, rCO are ∼2.5 times larger than rcont. Using these measurements, we derived the nuclear LIR and cold molecular gas surface densities (ςLIR = 1011.5-1014.3 L\ub7 kpc-2 and ςH2 = 102.9-104.2 M\ub7 pc-2, respectively). Assuming that the LIR is produced by star formation, the median ςLIR corresponds to ςSFR = 2500 M\ub7 yr-1 kpc-2. This ςSFR implies extremely short depletion times, ςH2/ςSFR < 1-15 Myr, and unphysical star formation efficiencies > 1 for 70% of the sample. Therefore, this favors the presence of an obscured active galactic nucleus (AGN) in these objects that could dominate the LIR. We also classify the ULIRG nuclei in two groups: (a) compact nuclei (rcont < 120 pc) with high mid-infrared excess emission (ΔL6-20 μm/LIR) found in optically classified AGN; and (b) nuclei following a relation with decreasing ΔL6-20 μm/LIR for decreasing rcont. The majority, 60%, of the nuclei in interacting systems lie in the low-rcont end (<120 pc) of this relation, while this is the case for only 30% of the mergers. This suggests that in the early stages of the interaction, the activity occurs in a very compact and dust-obscured region while, in more advanced merger stages, the activity is more extended, unless an optically detected AGN is present. Approximately two-thirds of the nuclei have nuclear radiation pressures above the Eddington limit. This is consistent with the ubiquitous detection of massive outflows in local ULIRGs and supports the importance of the radiation pressure in the outflow launching process

Improved calibration of a solid substrate fermentation model

Background: Calibration of dynamic models in biotechnology is challenging. Kinetic models are usually complex and differential equations are highly coupled involving a large number of parameters. In addition, available measurements are scarce and infrequent, and some key variables are often non-measurable. Therefore, effective optimization and statistical analysis methods are crucial to achieve meaningful results. In this research, we apply a metaheuristic scatter search algorithm to calibrate a solid substrate cultivation model. Results: Even though scatter search has shown to be effective for calibrating difficult nonlinear models, we show here that a posteriori analysis can significantly improve the accuracy and reliability of the estimation. Conclusions: Sensibility and correlation analysis helped us detect reliability problems and provided suggestions to improve the design of future experiments

Scientific Opinion on the safety and efficacy of Urea for ruminants

Urea supplementation to feed for ruminants provides non-protein nitrogen for microbial protein synthesis in the rumen and thus in part replaces other dietary protein sources. Urea supplementation of feed for ruminants at doses up to 1 % of complete feed DM (corresponding to 0.3 g/kg bw/day) is considered safe when given to animals with a well adapted ruminal microbiota and fed diets rich in easily digestible carbohydrates. Based on the metabolic fate of urea in ruminants, the use of urea in ruminant nutrition does not raise any concern for consumers\u2019 safety. Urea is considered to be non irritant to skin and eyes and its topical use suggests that it is not a dermal sensitiser. The risk of exposure by inhalation would be low. The substitution of protein by urea in well balanced feed for ruminants would not result in an increased environmental nitrogen load. Urea is an effective source of non-protein nitrogen substituting for dietary protein in ruminants

Synthesis of Densely Packaged, Ultrasmall Pt02Clusters within a Thioether-Functionalized MOF: Catalytic Activity in Industrial Reactions at Low Temperature

The gram\u2010scale synthesis, stabilization, and characterization of well\u2010defined ultrasmall subnanometric catalytic clusters on solids is a challenge. The chemical synthesis and X\u2010ray snapshots of Pt02 clusters, homogenously distributed and densely packaged within the channels of a metal\u2013organic framework, is presented. This hybrid material catalyzes efficiently, and even more importantly from an economic and environmental viewpoint, at low temperature (25 to 140\u2009\ub0C), energetically costly industrial reactions in the gas phase such as HCN production, CO2 methanation, and alkene hydrogenations. These results open the way for the design of precisely defined catalytically active ultrasmall metal clusters in solids for technically easier, cheaper, and dramatically less\u2010dangerous industrial reactions

VALES VI: ISM enrichment in star-forming galaxies up to z∼\sim0.2 using 12^{12}CO(1-0), 13^{13}CO(1-0) and C18^{18}O(1-0) line luminosity ratios

We present Atacama Large Millimeter/sub-millimeter Array (ALMA) observations towards 27 low-redshift ($0.02< z<0.2$) star-forming galaxies taken from the Valpara\'iso ALMA/APEX Line Emission Survey (VALES). We perform stacking analyses of the $^{12}$CO($1-0$), $^{13}$CO($1-0$) and C$^{18}$O($1-0$) emission lines to explore the $L'$ ($^{12}$CO($1-0$))/$L'$($^{13}$CO($1-0$))) (hereafter $L'$($^{12}$CO)/$L'$($^{13}$CO)) and $L'$($^{13}$CO($1-0$))/$L'$(C$^{18}$O($1-0$)) (hereafter $L'$($^{13}$CO)/$L'$(C$^{18}$O) line luminosity ratio dependence as a function of different global galaxy parameters related to the star formation activity. The sample has far-IR luminosities $10^{10.1-11.9}$L$_{\odot}$ and stellar masses of $10^{9.8-10.9}$M$_{\odot}$ corresponding to typical star-forming and starburst galaxies at these redshifts. On average we find a $L'$($^{12}$CO)/$L'$($^{13}$CO) line luminosity ratio value of 16.1$\pm$2.5. Galaxies with evidences of possible merging activity tend to show higher $L'$($^{12}$CO)/$L'$($^{13}$CO) ratios by a factor of two, while variations of this order are also found in galaxy samples with higher star formation rates or star formation efficiencies. We also find an average $L'$($^{13}$CO)/$L'$(C$^{18}$O) line luminosity ratio of 2.5$\pm$0.6, which is in good agreement with those previously reported for starburst galaxies. We find that galaxy samples with high $L_{\text{IR}}$, SFR and SFE show low $L'$($^{13}$CO)/$L'$(C$^{18}$O) line luminosity ratios with high $L'$($^{12}$CO)/$L'$($^{13}$CO) line luminosity ratios, suggesting that these trends are produced by selective enrichment of massive stars in young starbursts.Comment: 16 pages, 10 figures to be published in MNRA

Examining the independent and joint effects of genomic and exposomic liabilities for schizophrenia across the psychosis spectrum

Psychosis spectrum disorder has a complex pathoetiology characterised by interacting environmental and genetic vulnerabilities. The present study aims to investigate the role of gene-environment interaction using aggregate scores of genetic (polygenic risk score for schizophrenia (PRS-SCZ)) and environment liability for schizophrenia (exposome score for schizophrenia (ES-SCZ)) across the psychosis continuum