Elektromagnetische velden veroorzaken stressreacties bij planten: Wat wordt de impact van 5G in de kas?

Planten hebben zich aangepast aan de natuurlijke omgevingsfactoren. Maar hoe gaat het als er een factor bijkomt, die door de mens gecreëerd wordt? Mobiele telefoons, wifi en draadloze apparatuur rukken op in de kas en daarmee hun elektromagnetische velden. En door de komst van 5G zal dat nog flink toenemen

Identifying novel elements and regulators in auxin-dependent gene expression

Auxin is a key phytohormone which controls plant growth and development and many other related processes. Hormones are signalling molecules which regulate different processes via influencing the expression of thousands of genes, auxin is not an exception. The auxin signalling pathway was studied in detail and the main involved proteins-interplayers are already known, however, this knowledge is not sufficient to explain the diversity and specificity of auxin-mediated plant’s reactions. In our study we developed a bioinformatics tool to identify another potentially involved in auxin proteins. With that tool we predicted a number of potential interplayers and performed experimental verification. Appeared to be that specificity is provided by the involvement of different proteins in the control of different processes

A deterministic equation to predict the accuracy of multi-population genomic prediction with multiple genomic relationship matrices

BACKGROUND: A multi-population genomic prediction (GP) model in which important pre-selected single nucleotide polymorphisms (SNPs) are differentially weighted (MPMG) has been shown to result in better prediction accuracy than a multi-population, single genomic relationship matrix ([Formula: see text]) GP model (MPSG) in which all SNPs are weighted equally. Our objective was to underpin theoretically the advantages and limits of the MPMG model over the MPSG model, by deriving and validating a deterministic prediction equation for its accuracy. METHODS: Using selection index theory, we derived an equation to predict the accuracy of estimated total genomic values of selection candidates from population [Formula: see text] ([Formula: see text]), when individuals from two populations, [Formula: see text] and [Formula: see text], are combined in the training population and two [Formula: see text], made respectively from pre-selected and remaining SNPs, are fitted simultaneously in MPMG. We used simulations to validate the prediction equation in scenarios that differed in the level of genetic correlation between populations, heritability, and proportion of genetic variance explained by the pre-selected SNPs. Empirical accuracy of the MPMG model in each scenario was calculated and compared to the predicted accuracy from the equation. RESULTS: In general, the derived prediction equation resulted in accurate predictions of [Formula: see text] for the scenarios evaluated. Using the prediction equation, we showed that an important advantage of the MPMG model over the MPSG model is its ability to benefit from the small number of independent chromosome segments ([Formula: see text]) due to the pre-selected SNPs, both within and across populations, whereas for the MPSG model, there is only a single value for [Formula: see text], calculated based on all SNPs, which is very large. However, this advantage is dependent on the pre-selected SNPs that explain some proportion of the total genetic variance for the trait. CONCLUSIONS: We developed an equation that gives insight into why, and under which conditions the MPMG outperforms the MPSG model for GP. The equation can be used as a deterministic tool to assess the potential benefit of combining information from different populations, e.g., different breeds or lines for GP in livestock or plants, or different groups of people based on their ethnic background for prediction of disease risk scores.</p

Soils in lakes : the impact of inundation and storage on surface water quality

The large-scale storage and inundation of contaminated soils and sediments in deep waterlogged former sand pits or in lakes have become a fairly common practice in recent years. Decreasing water depth potentially promotes aquatic biodiversity, but it also poses a risk to water quality as was shown in a previous study on the impact on groundwater. To provide in the urgent need for practical and robust risk indicators for the storage of terrestrial soils in surface waters, the redistribution of metals and nutrients was studied in long-term mesocosm experiments. For a range of surface water turbidity (suspended matter concentrations ranging from 0 to 3000 mg/L), both chemical partitioning and toxicity of pollutants were tested for five distinctly different soils. Increasing turbidity in surface water showed only marginal response on concentrations of heavy metals, phosphorus (P) and nitrogen (N). Toxicity testing with bioluminescent bacteria, and biotic ligand modelling (BLM), indicated no or only minor risk of metals in the aerobic surface water during aerobic mixing under turbid conditions. Subsequent sedimentation of the suspended matter revealed the chemical speciation and transport of heavy metals and nutrients over the aerobic and anaerobic interface. Although negative fluxes occur for Cd and Cu, most soils show release of pollutants from sediment to surface waters. Large differences in fluxes occur for PO4, SO4, B, Cr, Fe, Li, Mn and Mo between soils. For an indicator of aerobic chemical availability, dilute nitric acid extraction (0.43 M HNO3; Aqua nitrosa) performed better than the conventional Aqua regia destruction. Both the equilibrium concentrations in surface waters, and fluxes from sediment, were adequately (r2 = 0.81) estimated by a 1 mM CaCl2 soil extraction procedure. This study has shown that the combination of 0.43 M HNO3 and 1 mM CaCl2 extraction procedures can be used to adequately estimate emissions from sediment to surface waters, and assess potential water quality changes, when former sand pits are being filled with soil materials.</p

Evidence on effects of plant pests on IPPC strategic objectives and monitoring and evaluation mechanisms by the SPS community : Report based on literature review and interviews with SPS organisations

This report is commissioned by the International Plant Protection Commission (IPPC) and is the result of a literature review on the effects of plant pests on the IP strategic objectives, as well as interviews and document review on the monitoring and evaluation (M&E) mechanisms of the SPS community. The review shows that there is evidence that the prevention of pests contributes to IPPC’s strategic objectives: enhancing global food security and sustainable agriculture productivity; protecting the environment; and facilitating safe trade, development, and economic growth. However, the context is very important and requires context specific interventions. In particular low-income countries struggle to reduce plant pests and need support in this to help them to also contribute to these overarching objectives. The review also shows that the different SPS organisations have different mechanisms in place for monitoring and evaluation, and these are generally not embedded in a formal monitoring and evaluation system. There is need for more attention to monitoring and evaluation in support of adaptive management of the SPS organisations, that work in often complex environments

Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty

Food production is a complex process where uncertainty is very relevant (e.g. stochastic yield and demand, variability in raw materials and ingredients…), resulting in differences between planned production and actual output. These discrepancies have an economic cost for the company (e.g. waste disposal), as well as an environmental impact (food waste and increased carbon footprint). This research aims to develop tools based on data analytics to predict the magnitude of these discrepancies, improving enterprise profitability while, at the same time, reducing environmental impact aiding food waste management. A food company that produces liquid products based on fruits and vegetables was analyzed. Data was gathered on 1,795 batches, including the characteristics of the product (recipe, components used…) and the difference between the input and the output weight. Machine Learning (ML) algorithms were used to predict deviations in production, reducing uncertainties related to the amount of waste produced. The ML models had greater predictive capacity than a linear model with stepwise parameter selection. Then, uncertainty is included in the predictions using a normal distribution based on the residuals of the model. Furthermore, we also demonstrate that ML models can be used as a tool to identify possible production anomalies. This research shows innovative ways to deal with uncertainty in production planning using modern methods in the field of operation research. These tools improve classical methods and provide production managers with valuable information to assess the economic benefits of improved machinery or process controls. As a consequence, accurate predictive models can potentially improve the profitability of food companies, also reducing their environmental impact.</p

Involvement of lactate and pyruvate in the anti-inflammatory effects exerted by voluntary activation of the sympathetic nervous system

We recently demonstrated that the sympathetic nervous system can be voluntarily activated following a training program consisting of cold exposure, breathing exercises, and meditation. This resulted in profound attenuation of the systemic inflammatory response elicited by lipopolysaccharide (LPS) administration. Herein, we assessed whether this training program affects the plasma metabolome and if these changes are linked to the immunomodulatory effects observed. A total of 224 metabolites were identified in plasma obtained from 24 healthy male volunteers at six timepoints, of which 98 were significantly altered following LPS administration. Effects of the training program were most prominent shortly after initiation of the acquired breathing exercises but prior to LPS administration, and point towards increased activation of the Cori cycle. Elevated concentrations of lactate and pyruvate in trained individuals correlated with enhanced levels of anti-inflammatory interleukin (IL)-10. In vitro validation experiments revealed that co-incubation with lactate and pyruvate enhances IL-10 production and attenuates the release of pro-inflammatory IL-1β and IL-6 by LPS-stimulated leukocytes. Our results demonstrate that practicing the breathing exercises acquired during the training program results in increased activity of the Cori cycle. Furthermore, this work uncovers an important role of lactate and pyruvate in the anti-inflammatory phenotype observed in trained subjects.</p