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To identify the potential anabolic properties of a dairy-plant protein blend as compared to single plant-based and single dairy protein, the postprandial amino acid (AA) response of pea protein, milk protein, micellar casein, and a casein–pea protein blend was investigated in healthy older adults (age 72.3 ± 3.4 years, BMI 25.3 ± 2.9 kg/m2). Plasma AA levels were measured, before and up to 5 h after ingestion of each 20 g protein. Blending casein–pea in a 60/40 mixture resulted in improved plasma AA availability, i.e. area under the curve (AUC) and peak height, of total (essential) AA and of key AAs methionine and leucine compared to pea only, while preserving the higher availability of arginine. The casein/pea blend clearly showed an AA response that was in between that of its single constituents, indicating that blending could be a solution to improve a lower quality (plant) protein, which could be of relevance for older adults.

Pea protein is an interesting alternative for animal-based proteins due to its good availability, low cost, and relatively balanced amino acid (AA) profile. Its digestibility may be affected by heat treatment and food texture. Our aim was to study in vivo AA absorption kinetics and gastric behavior of pea protein products differing in heat treatment and texture and compare this with in vitro digestion. We included fourteen males in this randomized crossover trial with three iso-caloric and iso-volumetric treatments: a 420-mL heated drink, 420-mL unheated drink and 105-g heated gel (semi-solid) consumed with 315 mL water, all containing 20 g pea protein. Gastric MRI scans were made until 90 min post-prandial. Blood samples were collected at baseline and up to 5 h. All treatments were tested with an in vitro digestion model (INFOGEST). Heat treatment did not alter AA absorption kinetics and gastric emptying. Time to maximum peak was delayed for the gel treatment (total AAs: 66.9 versus 48.0 min for both drinks, essential AAs: 75.4 versus 50.0 and 46.6 min for the drinks). For the gel treatment initial emptying was faster due to the rapid passage of water. In vitro, the degree of hydrolysis was highest for the unheated drink in the gastric phase and for the gel treatment in the intestinal phase. In conclusion, heat treating pea protein products does not affect digestion. In contrast, texture of pea protein products can be altered to influence the rate of gastric emptying and AA absorption without affecting total AA absorption.

Nowadays, a healthier and more sustainable lifestyle is the subject of much research. One example is the use of crossover trials to investigate the uptake of proteins, usually from alternatives to animal-based sources, by healthy volunteers. The data analysis is complex and requires many decisions on the part of the scientists involved. Such a process can be streamlined and made more objective and reproducible through bespoke software. This paper describes such a software package, aaresponse, for the R environment, available as open source. It features ample visualization functions, supports consistent curation strategies, and compares amino acid uptake of different protein meals (interventions) through the use of mixed models analysing parameters of interest like the area under the curve (AUC). The defining feature is the use of parametric curves to fit the amino acid levels over time, increasing the robustness of the approach and allowing for more strict quality control strategies.

Valorization and utilization of brewers’ spent grain (BSG) are of great interest in terms of reducing food waste and promoting more sustainable food systems. In this study, we aimed to evaluate the nutritional value of upcycled barley/rice proteins (BRP) extracted from BSG and compare this with pea proteins (PP). A randomized, cross-over, double-blind controlled trial was conducted with twelve participants (age: 24 ± 2.8 years, BMI: 23.3 ± 3.0 kg/m2). During three separate visits with a one-week washout period between visits, participants received 20 g BRP, PP, or the benchmark protein whey (WP). Blood-free amino acids (AA) were measured to determine postprandial AA uptake kinetics. The estimated total AA (TAA) uptake of BRP was 69% when compared to WP and 87% when compared to PP. The time to reach the maximum values was similar between the three protein sources. When comparing individual essential AA responses between BRP and PP, we observed higher responses in methionine and tryptophane and lower responses in lysine, histidine, and isoleucine for BRP compared to PP. This study demonstrates that BRP exhibits comparable postprandial TAA uptake profiles to PP. The findings highlight the complementarity of BRP and PP, which may offer the potential for blending approaches to optimize protein quality for overall health.

Key message: We evaluate self-organizing maps (SOM) to identify adaptation zones and visualize multi-environment genotypic responses. We apply SOM to multiple traits and crop growth model output of large-scale European sunflower data. Abstract: Genotype-by-environment interactions (G × E) complicate the selection of well-adapted varieties. A possible solution is to group trial locations into adaptation zones with G × E occurring mainly between zones. By selecting for good performance inside those zones, response to selection is increased. In this paper, we present a two-step procedure to identify adaptation zones that starts from a self-organizing map (SOM). In the SOM, trials across locations and years are assigned to groups, called units, that are organized on a two-dimensional grid. Units that are further apart contain more distinct trials. In an iterative process of reweighting trial contributions to units, the grid configuration is learnt simultaneously with the trial assignment to units. An aggregation of the units in the SOM by hierarchical clustering then produces environment types, i.e. trials with similar growing conditions. Adaptation zones can subsequently be identified by grouping trial locations with similar distributions of environment types across years. For the construction of SOMs, multiple data types can be combined. We compared environment types and adaptation zones obtained for European sunflower from quantitative traits like yield, oil content, phenology and disease scores with those obtained from environmental indices calculated with the crop growth model Sunflo. We also show how results are affected by input data organization and user-defined weights for genotypes and traits. Adaptation zones for European sunflower as identified by our SOM-based strategy captured substantial genotype-by-location interaction and pointed to trials in Spain, Turkey and South Bulgaria as inducing different genotypic responses.

A human intervention trial was conducted to study amino acid uptake of the novel Lemna protein concentrate (LPC) in comparison to whey (WPC). The study was a cross-over, double-blind, controlled trial in which 12 healthy participants received 20 grams of LPC and WPC in randomised order. The LPC consumption resulted in a significant lower postprandial increase in almost all individual amino acids, total amino acid (TAA) and total essential amino acids (TEAA) compared to WPC based on area under the curve (AUC) calculations. When the AUC after WPC consumption was set at 100%, LPC showed a relative AUC of 60.4% for TAA and 66.3% for the TEAA. Interindividual variation for LPC was high with an uptake of TEAA of LPC compared to WPC ranging from 18.2 to 94.2%. Human intervention trials can partly replace animal trials as they fully reflect the human situation and provide estimates on individual variations.

Three main types of uncertainty relevant for digital twins are identified: (1) Model uncertainty, (2) State uncertainty and (3) Run time uncertainty. For balancing these different uncertainties three methods are discussed: (1) Kalman filtering, (2) sensitivity analysis, (3) experimental perturbance. The Digital Twin for decision making can be deployed in various ways. The advice is to use this as a tool within reactive policy problems and to include statistical uncertainty information in decision making. Uncertainty information can be used for preventive policy problems to indicate where knowledge and information is missing. For transformative policy problems, the DT will mainly serve as a means of communication in which the judgments of partners can be included., Drie hoofdtypen onzekerheid worden geïdentificeerd die relevant zijn voor Digital Twins: (1) modelonzekerheid, (2) onzekerheid in de toestanden en (3) onzekerheid in de run time. Om grip te krijgen op deze onzekerheden, worden in dit rapport drie methoden besproken: (1) Kalman- filtering, (2) gevoeligheidsanalyse, (3) experimentele verstoring. Voor het inzetten van Digital Twins wordt geadviseerd om binnen reactieve beleidsproblemen deze te gebruiken als hulpmiddel en daarbij statistische onzekerheidsinformatie mee te nemen in besluitvorming. Voor preventieve beleidsproblemen kan onzekerheidsinformatie gebruikt worden om aan te geven waar kennis en informatie mist. Voor transformatieve beleidsproblemen zal de DT vooral dienen als een communicatiemiddel waarbij de waardeoordelen van partners mee kunnen worden genomen.

High-throughput phenotyping is playing an increasingly important role in many areas of agriculture. Breeders will use it to obtain values for the traits of interest so that they can estimate genetic value and select promising varieties, growers may be interested in having predictions of yield well in advance of the actual harvest. In most phenotyping applications, image analysis plays an important role, drastically reducing the dependence on manual labor while being non-destructive. An automatic phenotyping system combines a reliable acquisition system, a high-performance segmentation algorithm for detecting fruits in individual images, and a registration algorithm that brings the images (and the corresponding detected plants or plant components) into a coherent spatial reference frame. Recently, significant advances have been made in the fields of robotics, image registration, and especially image segmentation, which each individually have improved the prospect of developing a fully integrated automatic phenotyping system. However, so far no complete phenotyping systems have been reported for routine use in a production environment. This work catalogs the outstanding issues that remain to be resolved by describing a prototype phenotyping system for a production tomato greenhouse, for many reasons a challenging environment.

Dataset of RGBD images of tomato plants in a production greenhouse (Enza Zaden BV) obtained using Realsense cameras, and object instance level ground truth annotations of the fruit for using object detectors such as MaskRCNN or YOLACT. These data were used to obtain the results reported in our paper Tomato Fruit Detection and Counting in Greenhouses using Deep Learning, Frontiers in Plant Science, 2020.

The increased usage of alternative Ayurvedic treatments as potential health-beneficial therapies emphasizes the importance of studying its efficacy in sound placebo-controlled intervention trials. An example of such a traditional Ayurvedic herbal preparation is Mohana Choorna, a mixture composed of 20 different herbs and used to prevent and treat type 2-diabetes (T2D). We studied the efficacy of “Mohana Choorna” on T2D-related parameters in subjects with impaired glucose tol-erance. In a double blind, placebo-controlled cross-over trial, 19 overweight (BMI > 27 kg/m2) subjects aged 50–70 years with an impaired glucose tolerance received two four-week interven-tions, i.e., herbal or placebo with a four-week wash-out between interventions. HbA1c, glucose, insulin, triglycerides, cholesterol, blood pressure and augmentation index were measured before and after both interventions at fasting and during a glucose tolerance test. After both interventions, urine was collected to measure treatment exposure using LCMS-based metabolomics and whole genome gene-expression in adipose tissue of 13 subjects. The herbal intervention did not affect plasma glucose triglycerides, cholesterol, blood pressure or the augmentation index but showed a trend towards an increased insulin, HOMA-IR and postprandial insulin levels (p = 0.054, p = 0.056 and p = 0.095 respectively). An increase in expression of inflammation-related gene sets in adipose tissue was observed after the herbal intervention compared to placebo. Urine metabolomic analysis did not reveal a correlation of the presence of specific plant metabolites with “health markers”. Our findings suggest that there is no substantiating evidence to claim that four weeks’ use of the Ayurvedic herbal supplement Mohana Choorna beneficially affects glucose homeo-stasis.

In plants, secondary metabolites provide a unique opportunity to explore seasonal variation and responses to the environment. These include both abiotic and biotic factors. In field experiments, such stress factors occur in combination. This variation alters the plant metabolic profiles in yet uninvestigated ways. This data set contains trait and mass spectrometry data of thirteen grassland species collected at four time points in the growing season in 2017. We collected above-ground vegetative material of seven grass and six herb species that were grown in plant communities with different levels of diversity in the Jena Experiment. For each sample, we recorded visible traits and acquired shoot metabolic profiles on a UPLC-ESI-Qq-TOF-MS. We performed the feature detection, grouping, and annotation in Galaxy-W4M and prepared the data for statistical analysis in R by applying missing data imputation, batch correction, and validity checks on the features. Together with this comprehensive data set, we also provide a detailed tutorial for the processing steps in R.

Accurately detecting and counting fruits during plant growth using imaging and computer vision is of importance not only from the point of view of reducing labor intensive manual measurements of phenotypic information, but also because it is a critical step toward automating processes such as harvesting. Deep learning based methods have emerged as the state-of-the-art techniques in many problems in image segmentation and classification, and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. This paper reports results on the detection of tomatoes in images taken in a greenhouse, using the MaskRCNN algorithm, which detects objects and also the pixels corresponding to each object. Our experimental results on the detection of tomatoes from images taken in greenhouses using a RealSense camera are comparable to or better than the metrics reported by earlier work, even though those were obtained in laboratory conditions or using higher resolution images. Our results also show that MaskRCNN can implicitly learn object depth, which is necessary for background elimination.

Nederland is een van de grootste producenten van champignons in de wereld. Er zijn echter de laatste decennia slechts weinig nieuwe champignon-gebaseerde producten op de markt gekomen. Dit project beoogt een aanzet te leveren tot de ontwikkeling van champignonrassen die zich onderscheiden in smaak en die zich daarnaast zichtbaar onderscheiden van de huidige commercieel verkrijgbare champignons. Als eerste stap voor dergelijk toekomstig veredelingsonderzoek moeten metabolieten in champignons worden geïdentificeerd die voorspellende waarde hebben voor de smaak. Als dergelijke merker-moleculen betrouwbaar zijn geïdentificeerd, kan in een vervolgproject daadwerkelijk een veredelingstraject worden opgestart. Uit een collectie van ongeveer 250 wildisolaten van de champignons is een selectie gemaakt van 43 isolaten die de gehele genetische variatie van de collectie min of meer omvatten. Deze isolaten zijn opgekweekt in 6 verschillende teeltproeven en aangeboden aan een sensorisch panel dat verschillende aspecten van de smaak heeft beoordeeld. Statistische analyse van de resultaten laat zien dat hoewel voor de meeste isolaten de smaak erg lijkt op de commercieel beschikbare witte champignons, er een aantal isolaten zijn die anders smaken. Dezelfde champignons die zijn aangeboden aan het sensorisch panel zijn daarnaast onderzocht op metaboliet samenstelling. Het is nog niet mogelijk gebleken om de verschillen in smaak te koppelen aan specifieke metaboliet samenstellingen in de champignons. Een van de oorzaken is de grote variatie in beoordeling van smaakaspecten door het sensorisch panel. Daarnaast bleek bij vergelijking van de verschillende batches van controle ras A15, ook batch tot batch variatie op te treden in metabolietgehalten. Vervolgens is geprobeerd om de variatie in beoordeling door het sensorisch smaakpanel te verminderen door: • Veel minder rassen aanbieden aan het sensorisch panel • Rassen met grootst mogelijk verschil in smaak aanbieden • Interne herhalingen

Turnip (Brassica rapa spp. rapa) is an important vegetable species, with a unique physiology. Several plant parts, including both the turnip tubers and leaves, are important for human consumption. During the development of turnip plants, the leaves function as metabolic source tissues, while the tuber first functions as a sink, while later the tuber turns into a source for development of flowers and seeds. In the present study, chemical changes were determined for two genotypes with different genetic background, and included seedling, young leaves, mature leaves, tuber surface, tuber core, stalk, flower and seed tissues, at seven different time points during plant development. As a basis for understanding changes in glucosinolates during plant development, the profile of glucosinolates was analysed using liquid chromatography (LC) coupled to mass spectrometry (MS). This analysis was complemented by a gene expression analysis, focussed on GLS biosynthesis, which could explain part of the observed variation, pointing to important roles of specific gene orthologues for defining the chemical differences. Substantial differences in glucosinolate profiles were observed between above-ground tissues and turnip tuber, reflecting the differences in physiological role. In addition, differences between the two genotypes and between tissues that were harvested early or late during the plant lifecycle. The importance of the observed differences in glucosinolate profile for the ecophysiology of the turnip and for breeding turnips with optimal chemical profiles is discussed.

Introduction: Lettuce (Lactuca sativa L.) is generally not specifically acknowledged for its taste and nutritional value, while its cultivation suffers from limited resistance against several pests and diseases. Such key traits are known to be largely dependent on the ability of varieties to produce specific phytochemicals. Objectives: We aimed to identify promising genetic resources for the improvement of phytochemical composition of lettuce varieties. Methods: Phytochemical variation was investigated using 150 Lactuca genebank accessions, comprising a core set of the lettuce gene pool, and resulting data were related to available phenotypic information. Results: A hierarchical cluster analysis of the variation in relative abundance of 2026 phytochemicals, revealed by untargeted metabolic profiling, strongly resembled the known lettuce gene pool structure, indicating that the observed variation was to a large extent genetically determined. Many phytochemicals appeared species-specific, of which several are generally related to traits that are associated with plant health or nutritional value. For a large number of phytochemicals the relative abundance was either positively or negatively correlated with available phenotypic data on resistances against pests and diseases, indicating their potential role in plant resistance. Particularly the more primitive lettuces and the closely related wild relatives showed high levels of (poly)phenols and vitamin C, thus representing potential genetic resources for improving nutritional traits in modern crop types. Conclusion: Our large-scale analysis of phytochemical variation is unprecedented in lettuce and demonstrated the ample availability of suitable genetic resources for the development of improved lettuce varieties with higher nutritional quality and more sustainable production.

Reverse-transfected cell arrays in microfluidic systems have great potential to perform large-scale parallel screening of G protein-coupled receptor (GPCR) activation. Here, we report the preparation of a novel platform using reverse transfection of HEK293 cells, imaging by stereo-fluorescence microscopy in a flowcell format, real-time monitoring of cytosolic calcium ion fluctuations using the fluorescent protein Cameleon and analysis of GPCR responses to sequential sample exposures. To determine the relationship between DNA concentration and gene expression, we analyzed cell arrays made with variable concentrations of plasmid DNA encoding fluorescent proteins and the Neurokinin 1 (NK1) receptor. We observed pronounced effects on gene expression of both the specific and total DNA concentration. Reverse transfected spots with NK1 plasmid DNA at 1% of total DNA still resulted in detectable NK1 activation when exposed to its ligand. By varying the GPCR DNA concentration in reverse transfection, the sensitivity and robustness of the receptor response for sequential sample exposures was optimized. An injection series is shown for an array containing the NK1 receptor, bitter receptor TAS2R8 and controls. Both receptors were exposed 14 times to alternating samples of two ligands. Specific responses remained reproducible. This platform introduces new opportunities for high throughput screening of GPCR libraries.

The aim of the study was to develop an approach to discriminate whether cheese is produced from raw or heat-treated milk. The method was based on multivariate discrimination of volatile organic compounds in the cheeses’ headspace. Although the method was developed to detect issues with food authenticity for a Dutch traditional speciality guaranteed-protected cheese, this principle is likely to be applicable to detect heat-treatment in milk in other raw-milk cheese types. The multivariate classification method was aimed to be robust, employing an ensemble classification, based on six independent classification algorithms that require little or no tuning. The method was validated using a recently developed validation protocol designed for multivariate classification methods with a large validation set that was gathered separately from the training set. Based on the method's “worst-case”-classification performance, an overall 88% correct classification is expected.

Self-organizing maps (SOMs) are popular tools for grouping and visualizing data in many areas of science. This paper describes recent changes in package kohonen, implementing several different forms of SOMs. These changes are primarily focused on making the package more useable for large data sets. Memory consumption has decreased dramatically, amongst others, by replacing the old interface to the underlying compiled code by a new one relying on Rcpp. The batch SOM algorithm for training has been added in both sequential and parallel forms. A final important extension of the package’s repertoire is the possibility to define and use data-dependent distance functions, extremely useful in cases where standard distances like the Euclidean distance are not appropriate. Several examples of possible applications are presented.

Introduction: Batch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account. Objectives: This paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects. Methods: Batch correction methods usually consist of regression models, possibly also accounting for trends within batches. To fit these models quality control samples (QCs), injected at regular intervals, can be used. Also study samples can be used, provided that the injection order is properly randomized. Normalization methods, not using information on batch labels or injection order, can correct for batch effects as well. Introducing two easy-to-use quality criteria, we assess the merits of these batch correction strategies using three large LC–MS and GC–MS data sets of samples from Arabidopsis thaliana. Results: The three data sets have very different characteristics, leading to clearly distinct behaviour of the batch correction strategies studied. Explicit inclusion of information on batch and injection order in general leads to very good corrections; when enough QCs are available, also general normalization approaches perform well. Several approaches are shown to be able to handle non-detects—replacing them with very small numbers such as zero seems the worst of the approaches considered. Conclusion: The use of quality control samples for batch correction leads to good results when enough QCs are available. If an experiment is properly set up, batch correction using the study samples usually leads to a similar high-quality correction, bu

Annotation of metabolites is an essential, yet problematic, aspect of mass spectrometry (MS)-based metabolomics assays. The current repertoire of definitive annotations of metabolite spectra in public MS databases is limited and suffers from lack of chemical and taxonomic diversity. Furthermore, the heterogeneity of the data prevents the development of universally applicable metabolite annotation tools. Here we present a combined experimental and computational platform to advance this key issue in metabolomics. WEIZMASS is a unique reference metabolite spectral library developed from high-resolution MS data acquired from a structurally diverse set of 3,540 plant metabolites. We also present MatchWeiz, a multi-module strategy using a probabilistic approach to match library and experimental data. This strategy allows efficient and high-confidence identification of dozens of metabolites in model and exotic plants, including metabolites not previously reported in plants or found in few plant species to date.

Likelihood estimates of the Dirichlet distribution parameters can be obtained only through numerical algorithms. Such algorithms can provide estimates outside the correct range for the parameters and/or can require a large amount of iterations to reach convergence. These problems can be aggravated if good starting values are not provided. In this paper we discuss several approaches that can partially avoid these problems providing a good trade-off between efficiency and stability. The performances of these approaches are compared on high-dimensional real and simulated data., Peer Reviewed