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Flower color is one of the most important traits in horticulture, and is one of the characteristics recorded to describe new varieties. In this paper, we examine four large real-world databases of roses and gerberas containing both images and color descriptions, and use state-of-the-art methods to automatically extract color descriptions from the images. Both Deep Learning and methods based on color histograms lead to success rates of approximately 85%. Deep learning has the advantage that no preprocessing is necessary—the more traditional methods lead to additional insight in the final color classification. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. 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. [ABSTRACT FROM AUTHOR]

For those chemical compounds absorbing in the UV-Vis region and not readily applicable to routine mass spectrometry ionisation methods, liquid chromatography coupled to diode array detection is a convenient platform to perform metabolite profiling. Data processing by hand is labour-intensive and error prone. In the present study a strategy based on multivariate curve resolution, and its implementation in an R package called alsace are described. The final result of an analysis is a table containing peak heights or peak areas for all features of the individual injections. The capabilities of the software, providing elements such as splitting the data into separate, possibly overlapping time windows, merging the results of the individual time windows, and parametric time warping to align features, are illustrated using a cassava-derived data set. [ABSTRACT FROM AUTHOR]

We present automated data analysis of high-throughput high-performance liquid chromatography with diode array detection (HPLC-DAD) data using multivariate curve resolution. This technique provides spectra and elution profiles of all UV-Vis active compounds present in the mixture. The specifics of using this method in noninteractive fashion are discussed. A case study on the stability of isoprenoids in grape extracts under two different experimental regimes serves to illustrate the potential of the method: quantitative results clearly show that the addition of triethylamine is beneficial in that carotenoid, chlorophyll, and tocopherol compounds are much more stable and in this way can be kept up to at least 30 days without any sign of degradation. [ABSTRACT FROM AUTHOR]

Optimal solvent conditions for grape sample preparation were investigated for the purpose of metabolite profiling studies, with the aim of obtaining as many features as possible with the best analytical repeatability. Mixtures of water, methanol and chloroform in different combinations were studied as solvents for the extraction of ground grapes. The experimental design used a two stage study to find the optimum extraction medium. The extracts obtained were further purified using solid phase extraction and analysed using a UPLC full scan TOF MS with both reversed phase and hydrophilic interaction chromatography. The data obtained were processed using data extraction algorithms and advanced statistical software for data mining. The results obtained indicated that a fairly broad optimal area for solvent composition could be identified, containing approximately equal amounts of methanol and chloroform and up to 20% water. Since the water content of the samples was variable, the robustness of the optimal conditions suggests these are appropriate for large scale profiling studies for characterisation of the grape metabolome. [ABSTRACT FROM AUTHOR]

Our current knowledge of the general factor requirement in transcription by the three mammalian RNA polymerases is based on a small number of model promoters. Here, we present a comprehensive chromatin immunoprecipitation (ChIP)-on-chip analysis for 28 transcription factors on a large set of known and novel TATA-binding protein (TBP)-binding sites experimentally identified via ChIP cloning. A large fraction of identified TBP-binding sites is located in introns or lacks a gene/mRNA annotation and is found to direct transcription. Integrated analysis of the ChIP-on-chip data and functional studies revealed that TAF12 hitherto regarded as RNA polymerase II (RNAP II)-specific was found to be also involved in RNAP I transcription. Distinct profiles for general transcription factors and TAF-containing complexes were uncovered for RNAP II promoters located in CpG and non-CpG islands suggesting distinct transcription initiation pathways. Our study broadens the spectrum of general transcription factor function and uncovers a plethora of novel, functional TBP-binding sites in the human genome. [ABSTRACT FROM AUTHOR]

The rapid increase in Ihe size of data sets makes clustering all the more important to capture and summarize the information, at the same time making clustering more difficult to accomplish. If model-based clustering is applied directly to a large data set, it can be too slow for practical application. A simple and common approach is to first cluster a random sample of moderate size, and then use the clustering model found in this way to classify the remainder of the objects. We show that, in its simplest form, this method may lead to unstable results. Our experiments suggest that a stable method with better performance can be obtained with two straightforward modifications to the simple sampling method: several tentative models are identified from the sample instead of just one, and several HM sleps are used rather than just one E step to classify the full data set. We find that there are significant gains from increasing the size of the sample up to about 2,000, but not from further increases. These conclusions are based on the application of several alternative strategies to the segmentation of three different multispectral images, and to several simulated data sets. [ABSTRACT FROM AUTHOR]

Floodplain soils along the river Rhine in the Netherlands show a large spatial variability in pollutant concentrations. For an accurate ecological risk characterization of the river floodplains, this heterogeneity has to be included into the ecological risk assessment. In this paper a procedure is presented that incorporates spatial components of exposure into the risk assessment by linking geographical information systems (GIS) with models that estimate exposure for the most sensitive species of a floodplain. The procedure uses readily available site-specific data and is applicable to a wide range of locations and floodplain management scenarios. The procedure is applied to estimate exposure risks to metals for a typical foodweb in the Afferdensche and Deestsche Waarden floodplain along the river Waal, the main branch of the Rhine in the Netherlands. Spatial variability of pollutants is quantified by overlaying appropriate topographic and soil maps resulting in the definition of homogeneous pollution units. Next to that, GIS is used to include foraging behavior of the exposed terrestrial organisms. Risk estimates from a probabilistic exposure model were used to construct site-specific risk maps for the floodplain. Based on these maps, recommendations for future management of the floodplain can be made that aim at both ecological rehabilitation and an optimal flood defense. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]