{"type": "FeatureCollection", "features": [{"id": "10.1007/s10994-020-05918-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:15:09Z", "type": "Journal Article", "created": "2020-10-28", "title": "Incremental predictive clustering trees for online semi-supervised multi-target regression", "description": "Abstract

In many application settings, labeling data examples is a costly endeavor, while unlabeled examples are abundant and cheap to produce. Labeling examples can be particularly problematic in an online setting, where there can be arbitrarily many examples that arrive at high frequencies. It is also problematic when we need to predict complex values (e.g., multiple real values), a task that has started receiving considerable attention, but mostly in the batch setting. In this paper, we propose a method for online semi-supervised multi-target regression. It is based on incremental trees for multi-target regression and the predictive clustering framework. Furthermore, it utilizes unlabeled examples to improve its predictive performance as compared to using just the labeled examples. We compare the proposed iSOUP-PCT method with supervised tree methods, which do not use unlabeled examples, and to an oracle method, which uses unlabeled examples as though they were labeled. Additionally, we compare the proposed method to the available state-of-the-art methods. The method achieves good predictive performance on account of increased consumption of computational resources as compared to its supervised variant. The proposed method also beats the state-of-the-art in the case of very few labeled examples in terms of performance, while achieving comparable performance when the labeled examples are more common.Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000\uffe2\uff80\uff931700\uffc2\uffa0nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390\uffe2\uff80\uff931420\uffc2\uffa0nm contributes most to the model\uffe2\uff80\uff99s final decision.Recent observations of changes in some tundra ecosystems appear to be responses to a warming climate. Several experimental studies have shown that tundra plants and ecosystems can respond strongly to environmental change, including warming; however, most studies were limited to a single location and were of short duration and based on a variety of experimental designs. In addition, comparisons among studies are difficult because a variety of techniques have been used to achieve experimental warming and different measurements have been used to assess responses. We used metaanalysis on plant community measurements from standardized warming experiments at 11 locations across the tundra biome involved in the International Tundra Experiment. The passive warming treatment increased plant-level air temperature by 1-3\uffc2\uffb0C, which is in the range of predicted and observed warming for tundra regions. Responses were rapid and detected in whole plant communities after only two growing seasons. Overall, warming increased height and cover of deciduous shrubs and graminoids, decreased cover of mosses and lichens, and decreased species diversity and evenness. These results predict that warming will cause a decline in biodiversity across a wide variety of tundra, at least in the short term. They also provide rigorous experimental evidence that recently observed increases in shrub cover in many tundra regions are in response to climate warming. These changes have important implications for processes and interactions within tundra ecosystems and between tundra and the atmosphere.

", "keywords": ["Greenhouse Effect", "0106 biological sciences", "570", "Conservation of Natural Resources", "Hot Temperature", "Climate", "Environment", "01 natural sciences", "333", "Climatic changes Environmental aspects", "Effects of global warming on", "Climate change", "Biomass", "Ecosystem", "Plant Physiological Phenomena", "Arctic and alpine ecosystems", "Arctic Regions", "Temperature", "500", "Genetic Variation", "Biodiversity", "Models", " Theoretical", "Plants", "15. Life on land", "0503 (four-digit-FOR)", "Tundra ecology", "13. Climate action", "Vegetation change", "Plants", " Effects of global warming on", "Software", "Environmental Monitoring"]}, "links": [{"href": "http://dspace.stir.ac.uk/bitstream/1893/884/1/ITEX_PNAS%20%282006%29%20hi%20res.pdf"}, {"href": "https://doi.org/10.1073/pnas.0503198103"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%20National%20Academy%20of%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1073/pnas.0503198103", "name": "item", "description": "10.1073/pnas.0503198103", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1073/pnas.0503198103"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2006-01-20T00:00:00Z"}}, {"id": "10.1093/bioinformatics/btac037", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:24Z", "type": "Journal Article", "created": "2022-01-26", "title": "GenomeTornadoPlot: a novel R package for CNV visualization and focality analysis", "description": "Abstract Motivation

Analysis of focal copy number variations (CNVs) is highly relevant for cancer research, as they pinpoint driver genes. More specifically, due to selective pressure oncogenes and tumor suppressor genes are more often affected by these events than neighboring passengers. In cases where multiple candidates co-reside in a genomic locus, careful comparison is required to either identify multigenic minimally deleted regions of synergistic co-mutations, or the true single driver gene. The study of focal CNVs in large cancer genome cohorts requires specialized visualization and statistical analysis.

Results

We developed the GenomeTornadoPlot R-package which generates gene-centric visualizations of CNV types, locations and lengths from cohortwise NGS data. Furthermore, the software enables the pairwise comparison of proximate genes to identify co-mutation patterns or driver-passenger hierarchies. The visual examination provided by GenomeTornadoPlot is further supported by adaptable local and global focality scoring. Integrated into the GenomeTornadoPlot R-Package is the comprehensive PCAWG database of CNVs, comprising 2976 cancer genome entities from 46 cohorts of the Pan-cancer Analysis of Whole Genomes project. The GenomeTornadoPlot R-package can be used to perform exploratory or hypothesis-driven analyses on the basis of the PCAWG data or in combination with data provided by the user.

Availability and implementation

GenomeTornadoPlot is written in R script and released via github: <https://github.com/chenhong-dkfz/GenomeTornadoPlot/>. The package is under the license of GPL-3.0.

", "keywords": ["570", "DNA Copy Number Variations", "ddc-570", "Genomics", "Oncogenes", "004 Data processing Computer science", "Software", "ddc-004", "570 Life sciences", "004", "3. Good health"]}, "links": [{"href": "https://archiv.ub.uni-heidelberg.de/volltextserver/34483/1/btac037.pdf"}, {"href": "https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/34483/1/btac037.pdf"}, {"href": "https://academic.oup.com/bioinformatics/article-pdf/38/7/2036/49009547/btac037.pdf"}, {"href": "https://doi.org/10.1093/bioinformatics/btac037"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Bioinformatics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1093/bioinformatics/btac037", "name": "item", "description": "10.1093/bioinformatics/btac037", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1093/bioinformatics/btac037"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-31T00:00:00Z"}}, {"id": "10.1093/jxb/erab174", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:26Z", "type": "Journal Article", "created": "2020-12-03", "title": "Digging roots is easier with AI", "description": "Abstract

The scale of root quantification in research is often limited by the time required for sampling, measurement and processing samples. Recent developments in Convolutional Neural Networks (CNN) have made faster and more accurate plant image analysis possible which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of Machine Learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN-software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model, i.e., learning from labeled examples, can effectively exclude the debris by comparing the end-results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training and the derived measurements were compared with manual measurements. After 200 minutes of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76) and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.

", "keywords": ["0301 basic medicine", "root phenotyping", "profile wall", "root washing", "segmentation", "deep learning", "Convolutional neural network", "04 agricultural and veterinary sciences", "15. Life on land", "Soil", "03 medical and health sciences", "core-break", "monolith", "soil coring", "Image Processing", " Computer-Assisted", "0401 agriculture", " forestry", " and fisheries", "Neural Networks", " Computer", "Software"]}, "links": [{"href": "http://academic.oup.com/jxb/article-pdf/72/13/4680/38807872/erab174.pdf"}, {"href": "https://doi.org/10.1093/jxb/erab174"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Experimental%20Botany", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1093/jxb/erab174", "name": "item", "description": "10.1093/jxb/erab174", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1093/jxb/erab174"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-02T00:00:00Z"}}, {"id": "10.1105/tpc.20.00318", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:34Z", "type": "Journal Article", "created": "2020-10-10", "title": "ARADEEPOPSIS, an Automated Workflow for Top-View Plant Phenomics using Semantic Segmentation of Leaf States", "description": "Linking plant phenotype to genotype is a common goal to both plant breeders and geneticists. However, collecting phenotypic data for large numbers of plants remain a bottleneck. Plant phenotyping is mostly image based and therefore requires rapid and robust extraction of phenotypic measurements from image data. However, because segmentation tools usually rely on color information, they are sensitive to background or plant color deviations. We have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner. ARADEEPOPSIS (https://github.com/Gregor-Mendel-Institute/aradeepopsis) uses semantic segmentation of top-view images to classify leaf tissue into three categories: healthy, anthocyanin rich, and senescent. This makes it particularly powerful at quantitative phenotyping of different developmental stages, mutants with aberrant leaf color and/or phenotype, and plants growing in stressful conditions. On a panel of 210 natural Arabidopsis (Arabidopsis thaliana) accessions, we were able to not only accurately segment images of phenotypically diverse genotypes but also to identify known loci related to anthocyanin production and early necrosis in genome-wide association analyses. Our pipeline accurately processed images of diverse origin, quality, and background composition, and of a distantly related Brassicaceae. ARADEEPOPSIS is deployable on most operating systems and high-performance computing environments and can be used independently of bioinformatics expertise and resources.", "keywords": ["0301 basic medicine", "0303 health sciences", "Genotype", "Large-Scale Biology Articles", "Arabidopsis", "Computational Biology", "Semantics", "Workflow", "Plant Leaves", "03 medical and health sciences", "Phenotype", "Image Processing", " Computer-Assisted", "Phenomics", "Software", "Genome-Wide Association Study"]}, "links": [{"href": "https://doi.org/10.1105/tpc.20.00318"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20Plant%20Cell", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1105/tpc.20.00318", "name": "item", "description": "10.1105/tpc.20.00318", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1105/tpc.20.00318"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-09T00:00:00Z"}}, {"id": "10.1371/journal.pone.0200979", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:34Z", "type": "Journal Article", "created": "2019-04-11", "title": "Quantitative and qualitative evaluation of the impact of the G2 enhancer, bead sizes and lysing tubes on the bacterial community composition during DNA extraction from recalcitrant soil core samples based on community sequencing and qPCR", "description": "Abstract

Soil DNA extraction encounters numerous challenges that can affect both yield and purity of the recovered DNA. Clay particles lead to reduced DNA extraction efficiency, and PCR inhibitors from the soil matrix can negatively affect downstream analyses when applying DNA sequencing. Further, these effects impede molecular analysis of bacterial community compositions in lower biomass samples, as often observed in deeper soil layers. Many studies avoid these complications by using indirect DNA extraction with prior separation of the cells from the matrix, but such methods introduce other biases that influence the resulting microbial community composition.

To address these issues, a direct DNA extraction method was applied in combination with the use of a commercial product, the G2 DNA/RNA Enhancer\uffc2\uffae, marketed as being capable of improving the amount of DNA recovered after the lysis step. The results showed that application of G2 increased DNA yields from the studied clayey soils from layers between 1.00 and 2.20 m below ground level.

Importantly, the use of G2 did not introduce bias, as it did not result in any significant differences in the biodiversity of the bacterial community measured in terms of alpha and beta diversity and taxonomical composition.

Finally, this study considered a set of customised lysing tubes for evaluating possible influences on the DNA yield. Tubes customization included different bead sizes and amounts, along with lysing tubes coming from two suppliers. Results showed that the lysing tubes with mixed beads allowed greater DNA recovery compared to the use of either 0.1 or 1.4 mm beads, irrespective of the tube supplier.

These outcomes may help to improve commercial products in DNA/RNA extraction kits, besides raising awareness about the optimal choice of additives, offering opportunities for acquiring a better understanding of topics such as vertical microbial characterisation and environmental DNA recovery in low biomass samples.

", "keywords": ["DNA", " Bacterial", "0301 basic medicine", "Science", "Microbial Consortia", "DIVERSITY", "SOFTWARE", "Real-Time Polymerase Chain Reaction", "BACILLUS-SUBTILIS", "BIOMASS", "03 medical and health sciences", "BIOAUGMENTATION", "DNA", " Bacterial/chemistry", "MICROBIAL COMMUNITIES", "Soil Microbiology", "2. Zero hunger", "0303 health sciences", "16S RIBOSOMAL-RNA", "Q", "R", "PROFILES", "ACIDS", "TRANSFORMATION", "6. Clean water", "Microbial Consortia/genetics", "Enhancer Elements", " Genetic", "13. Climate action", "Medicine", "Research Article"]}, "links": [{"href": "https://www.biorxiv.org/content/10.1101/365395v1.full.pdf"}, {"href": "https://doi.org/10.1371/journal.pone.0200979"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLOS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1371/journal.pone.0200979", "name": "item", "description": "10.1371/journal.pone.0200979", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1371/journal.pone.0200979"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-09T00:00:00Z"}}, {"id": "10.1117/12.2576171", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:19:09Z", "type": "Journal Article", "created": "2020-09-18", "title": "An integrated service-based solution addressing the modernised common agriculture policy regulations and environmental perspectives", "description": "The EU-funded DIONE project (grant agreement No. 870378) offers an innovative close-to-market (TRL7) solution\u00a0seeking to improve the traditional methods of agricultural monitoring. The project introduces a cloud-based Software as\u00a0a Service (SaaS) system architecture, building on a fusion of novel technologies that will support the forthcoming needs\u00a0of the modernized Common Agriculture Policy (CAP) and the \u201cGreening\u201d perspectives, with an automated area-based\u00a0monitoring system. In particular, an interoperable and harmonized system is designed, connecting large volumes of Earth\u00a0Observation data (Satellite, UAV, and in-situ) and user-generated highly precise geolocated data (geo-tagged photos, soil\u00a0measurements, etc.). DIONE\u2019s system architecture encompasses customized and third-party frameworks, where\u00a0heterogeneous and multi-source data are stored, processed and managed using Artificial Intelligence (AI) algorithms.\u00a0These harmonized, curated and open accessed data are then provided as Open Geospatial Consortium (OGC)-compliant,\u00a0web-service layers (WMS, WFS, and WCS). Furthermore, the proposed solution formulates a scalable, flexible,\u00a0interoperable, and semantically enriched environment, taking advantage of a Spatial Data Infrastructure (SDI)\u00a0framework capabilities, whilst allowing an interactive connection among different tools and components through\u00a0 RESTful APIs. Our approach establishes a novel, cloud-based, accurate and inexpensive agriculture monitoring solution,\u00a0enabling the real-time provision of multi-source data to relevant stakeholders such as Paying Agencies, Policy Officers\u00a0and Control &\u00a0 Certification Bodies, and other domain experts. The system architecture was formulated exploiting a co-design\u00a0methodology, aiming to ensure a long-term and sustainable solution. Two large-scale demonstrations will take\u00a0place in Lithuania and Cyprus, evaluating the system capabilities in real-life and operational conditions.", "keywords": ["Spatial Data Infrastructure", "2. Zero hunger", "OGC services", "RESTful API", "13. Climate action", "11. Sustainability", "Common Agriculture Policy", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Earth Observation", " Software as a Service (SaaS) platform", "12. Responsible consumption"]}, "links": [{"href": "https://doi.org/10.1117/12.2576171"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20for%20Agriculture%2C%20Ecosystems%2C%20and%20Hydrology%20XXII", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1117/12.2576171", "name": "item", "description": "10.1117/12.2576171", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1117/12.2576171"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-09-20T00:00:00Z"}}, {"id": "10.18419/opus-2935", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:19:59Z", "type": "Report", "title": "Datenmanagementpatterns in multi-skalaren Simulationsworkflows", "description": "In den vergangenen Jahren haben sich im unternehmerischen Umfeld Workflows zur Beschreibung und Ausf\u00fchrung von (Gesch\u00e4fts-)Prozessen durchgesetzt. Seit kurzem wird diese Technologie auch in der Wissenschaft eingesetzt. Z.B. werden Simulationsabl\u00e4ufe als Workflows modelliert. Charakteristisch f\u00fcr solche Simulationen bzw. Simulationsabl\u00e4ufe sind komplexe mathematische Berechnungen sowie verschiedene Aufgaben im Bereich der Datenverwaltung und Datenbereitstellung. Oftmals m\u00fcssen gro\u00dfe Datenmengen, die in propriet\u00e4ren Formaten vorliegen, aus verschiedenen Quellen verarbeitet werden. Damit diese Daten durch einen Simulationsworkflow und den von ihm eingebundenen Programmen und Diensten verarbeitet werden k\u00f6nnen, m\u00fcssen sie in passende Eingabeformate transformiert werden. Gerade bei umfangreichen Simulationen, die eine Vielzahl an Datenquellen ben\u00f6tigen, f\u00fchrt dies aufgrund der enormen Komplexit\u00e4t zu Problemen. Um diese Probleme zu l\u00f6sen, wurde das SIMPL-Rahmenwerk (SimTech - Information Management, Processes and Languages) entwickelt. Das SIMPL-Rahmenwerk ist in ein Scientifc Workflow Management System eingebettet und schafft eine Abstraktionsebene f\u00fcr die Defnition des Datenmanagements. SIMPL bietet einheitliche Zugriffsmethoden, um, aus einem Simulationsworkflow heraus, auf beliebige Datenquellen zuzugreifen. Ein weiterer Bestandteil des SIMPL-Rahmenwerks sind Datenmanagementpatterns. Dabei handelt es sich um vorgefertigte Datenmanagement-Operationen, die nur noch parametrisiert werden m\u00fcssen. Auf diese Weise wird eine neue Abstraktionsebene geschaffen. In einer vorherigen Arbeit wurden bereits erste Datenmanagementpatterns erarbeitet. So k\u00f6nnen z.B. Daten zwischen zwei Datenressourcen ausgetauscht werden. Des Weiteren wurde ein Konzept erarbeitet, um Datenmanagementpatterns auf ausf\u00fchrbare Workflow-Fragmente abzubilden. Dieses Konzept nutzt Transformationsregeln sowie gespeicherte Metadaten \u00fcber beteiligte Ressourcen als Basis. Im Rahmen dieser Diplomarbeit wird das bereits entwickelte Konzept erweitert und wenn n\u00f6tig angepasst, um auf multi-skalare Simulationen angewendet werden zu k\u00f6nnen. Dar\u00fcber hinaus wird die prototypische Umsetzung des SIMPL-Rahmenwerks um Datenmanagementpatterns erweitert.", "keywords": ["000", "Heterogeneous Databases (CR H.2.5)", "Datenmanagementpatterns", "Software Engineering Software Architectures (CR D.2.11)", "wissenschaftliche Workflows", "Office Automation (CR H.4.1)", "Datenmanagement", "Simulationsworkflows", "Simulation Support Systems (CR I.6.7)", "Datenbereitstellung", "004"], "contacts": [{"organization": "Pietranek, Henrik Andreas", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.18419/opus-2935"}, {"rel": "self", "type": "application/geo+json", "title": "10.18419/opus-2935", "name": "item", "description": "10.18419/opus-2935", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.18419/opus-2935"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-01-01T00:00:00Z"}}, {"id": "10.22004/ag.econ.320304", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:20:31Z", "type": "Journal Article", "created": "2021-12-16", "title": "INSPIRE Hackathons and SmartAfriHub \u2013 Roadmap for Addressing the Agriculture Data Challenges in Africa", "description": "

Digital farming holds enormous potential for agricultural development, and giving farmers the tools to boost productivity and profitability. Although the benefits of digitalization are numerous, farmers feel they are not the ones benefiting from the value of data collected on their farms. Several issues were identified as factors restricting farmers from benefiting from data-driven agriculture. From the farmers\u2019 perspective, there is a distinct lack of awareness of the issues surrounding farm data, and the complexity of these issues. This feeds into the imbalance that exists between individual farmers and larger agribusinesses wherein the former lack enough resources to address and analyse the significance of data, and so cannot take advantage of the value in it. There is also limited legislation for the generation, flow, exchange and use of data; where legislation does exist, it is not well understood by farmer organisations. From a policy perspective, moreover, there is very little guidance as to which agricultural data can be considered personal data, and therefore protected by privacy laws. This paper analyses the interactions and effects of the 5 Concepts: Open Agricultural Data, Open-Source Software, Citizen Science, privacy and legal and ethical issues that are assumed to advance the digitalization of African Food System (AFS and the enabling Digital Innovation Hub (DIH) - SmartAfriHub (https://www.smartafrihub.com/home).

", "keywords": ["2. Zero hunger", "Research and Development/Tech Change/Emerging Technologies", "Citizen Science", "Agricultural and Food Policy", "Open Data", "Africa Smart Agriculture", "1. No poverty", "15. Life on land", "Open-Source Software"]}, "links": [{"href": "https://doi.org/10.22004/ag.econ.320304"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agris%20on-line%20Papers%20in%20Economics%20and%20Informatics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.22004/ag.econ.320304", "name": "item", "description": "10.22004/ag.econ.320304", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.22004/ag.econ.320304"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-30T00:00:00Z"}}, {"id": "10.3390/ijms25105216", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:00Z", "type": "Journal Article", "created": "2024-05-14", "title": "Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists", "description": "

A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPAR\u03b4) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPAR\u03b4 agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure\u2013activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPAR\u03b4 agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform\u2019s graphical user interface (GUI).

", "keywords": ["0301 basic medicine", "570", "610", "Quantitative Structure-Activity Relationship", "molecular docking", "01 natural sciences", "Isalos Analytics Platform", "in silico modelling", "Article", "0104 chemical sciences", "Molecular Docking Simulation", "Machine Learning", "03 medical and health sciences", "machine learning", "PPAR\u03b4 agonist", "Humans", "PPAR delta", "Software"]}, "links": [{"href": "https://www.mdpi.com/1422-0067/25/10/5216/pdf"}, {"href": "https://doi.org/10.3390/ijms25105216"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Molecular%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/ijms25105216", "name": "item", "description": "10.3390/ijms25105216", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/ijms25105216"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-05-10T00:00:00Z"}}, {"id": "10.5281/zenodo.13805742", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:22:26Z", "type": "Software", "title": "EOM4SOIL - Exogenous organic matter physicochemical characteristics visualisation tool - RShiny script", "description": "This repository contains the R and Rshiny scripts needed to run the EOM4SOIL visualisation tool. This user-friendly software allows for the dissemination of EOM references at the national and European levels to public authorities and EOM sector partners. This interactive tool enables users to explore and analyze the diverse physicochemical properties of EOM types contained in the 'Physico-chemical Characteristics of External Organic Matters' database (datapaper in progress). The software allows for dynamic filtering and visualization of data, providing users with customized graphical representations of EOM. By offering an intuitive interface, the application supports policymakers, scientists, and other stakeholders in making informed decisions by visualizing complex datasets in a clear and accessible manner.", "keywords": ["rshiny", "FOS: Agricultural sciences", "visualisation tool", "Sustainable architecture", "Software", "Agricultural sciences", "organic matter"], "contacts": [{"organization": "Caradec, Lucille, Tampio, Elina, Laakso, Johanna, Michaud, Aurelia,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13805742"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13805742", "name": "item", "description": "10.5281/zenodo.13805742", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13805742"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-09-20T00:00:00Z"}}, {"id": "10.5281/zenodo.14733168", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:22:47Z", "type": "Dataset", "title": "A collaborative effort to collect Soil datasets in Europe - EJP SOIL metadata catalogue", "description": "This dataset is a dump of a metadata catalogue of 576 European, national and EJP SOIL project datasets on soil properties and soil management around the topic of Soil Health which are collected in a collaborative effort within the EJP Soil programme (2020-2025). The metadata is imported from various repositories and, if needed, harmonised to the ISO19139:2007 schema. This is a dump of key fields in the postgres database used by the [pycsw software](https://pycsw.org) used in this project. The metadataset and its collection are described in a short document in https://doi.org/10.18174/686612. \u00a0 Sources: - MENSMEU - A study by ESDAC on the availability of soil data under the INSPIRE directive - ESDAC - A collection of soil datasets maintained by ESDAC - EJP Soil - Stocktake at national Soil institutes - EJP Soil - Research datasets from OpenAire\u00a0 \u00a0 Fields: - identifier:\u00a0 A uuid or similar identifier - title: Title of the resource - organization: Organization mentioned as owner of the recource - schema: iso19139:2007 (indicated as http://www.isotc211.org/2005/gmd)\u00a0 - xml: The actual imported (or harmonised content) The EJP Soil programme received funding from the European Union\u2019s HORIZON 2020 research and innovation programme under grant agreement No. [862695](https://cordis.europa.eu/project/id/862695).", "keywords": ["Europe", "Soil sciences", "Metadata", "soil health", "soil properties", "Life Science", "Cataloging/classification", "Soil quality", "Software"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14733168"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14733168", "name": "item", "description": "10.5281/zenodo.14733168", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14733168"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14733169", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:22:47Z", "type": "Dataset", "title": "A collaborative effort to collect Soil datasets in Europe - EJP SOIL metadata catalogue", "description": "This dataset is a dump of a metadata catalogue of 576 European, national and EJP SOIL project datasets on soil properties and soil management around the topic of Soil Health which are collected in a collaborative effort within the EJP Soil programme (2020-2025). The metadata is imported from various repositories and, if needed, harmonised to the ISO19139:2007 schema. This is a dump of key fields in the postgres database used by the [pycsw software](https://pycsw.org) used in this project. The metadataset and its collection are described in a short document in https://doi.org/10.18174/686612. \u00a0 Sources: - MENSMEU - A study by ESDAC on the availability of soil data under the INSPIRE directive - ESDAC - A collection of soil datasets maintained by ESDAC - EJP Soil - Stocktake at national Soil institutes - EJP Soil - Research datasets from OpenAire\u00a0 \u00a0 Fields: - identifier:\u00a0 A uuid or similar identifier - title: Title of the resource - organization: Organization mentioned as owner of the recource - schema: iso19139:2007 (indicated as http://www.isotc211.org/2005/gmd)\u00a0 - xml: The actual imported (or harmonised content) The EJP Soil programme received funding from the European Union\u2019s HORIZON 2020 research and innovation programme under grant agreement No. [862695](https://cordis.europa.eu/project/id/862695).", "keywords": ["Europe", "Soil sciences", "Metadata", "soil health", "soil properties", "Life Science", "Cataloging/classification", "Soil quality", "Software"], "contacts": [{"organization": "van Egmond, Fenny, van Genuchten, Paul, Collazos Cortes, Diana Fernanda,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14733169"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14733169", "name": "item", "description": "10.5281/zenodo.14733169", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14733169"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-01T00:00:00Z"}}, {"id": "10.7160/aol.2021.130404", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:24:29Z", "type": "Journal Article", "created": "2021-12-16", "title": "INSPIRE Hackathons and SmartAfriHub \u2013 Roadmap for Addressing the Agriculture Data Challenges in Africa", "description": "

Digital farming holds enormous potential for agricultural development, and giving farmers the tools to boost productivity and profitability. Although the benefits of digitalization are numerous, farmers feel they are not the ones benefiting from the value of data collected on their farms. Several issues were identified as factors restricting farmers from benefiting from data-driven agriculture. From the farmers\u2019 perspective, there is a distinct lack of awareness of the issues surrounding farm data, and the complexity of these issues. This feeds into the imbalance that exists between individual farmers and larger agribusinesses wherein the former lack enough resources to address and analyse the significance of data, and so cannot take advantage of the value in it. There is also limited legislation for the generation, flow, exchange and use of data; where legislation does exist, it is not well understood by farmer organisations. From a policy perspective, moreover, there is very little guidance as to which agricultural data can be considered personal data, and therefore protected by privacy laws. This paper analyses the interactions and effects of the 5 Concepts: Open Agricultural Data, Open-Source Software, Citizen Science, privacy and legal and ethical issues that are assumed to advance the digitalization of African Food System (AFS and the enabling Digital Innovation Hub (DIH) - SmartAfriHub (https://www.smartafrihub.com/home).

", "keywords": ["2. Zero hunger", "Research and Development/Tech Change/Emerging Technologies", "Citizen Science", "Agricultural and Food Policy", "Open Data", "Africa Smart Agriculture", "1. No poverty", "15. Life on land", "Open-Source Software"]}, "links": [{"href": "https://doi.org/10.7160/aol.2021.130404"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agris%20on-line%20Papers%20in%20Economics%20and%20Informatics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.7160/aol.2021.130404", "name": "item", "description": "10.7160/aol.2021.130404", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7160/aol.2021.130404"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-30T00:00:00Z"}}, {"id": "10400.22/4544", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:24:48Z", "type": "Report", "title": "Implementa\u00e7\u00e3o de um software de gest\u00e3o de log\u00edstica no Hospital Agostinho Ribeiro", "description": "O presente relat\u00f3rio possui o prop\u00f3sito de apresentar o est\u00e1gio curricular desenvolvido na empresa \u201cSanta Casa da Miseric\u00f3rdia de Felgueiras\u201d, iniciado a 1 de maio de 2013, englobando horas de aprendizagem e consolida\u00e7\u00e3o de conhecimentos adquiridos no percurso acad\u00e9mico. A realiza\u00e7\u00e3o do presente est\u00e1gio est\u00e1 enquadrada no protocolo celebrado entre a \u201cSanta Casa da Miseric\u00f3rdia de Felgueiras\u201d e a Associa\u00e7\u00e3o de Polit\u00e9cnicos do Norte, cumprindo desta forma o art.\u00ba 2 e 3 do Regulamento de Est\u00e1gios Curriculares da Associa\u00e7\u00e3o de Polit\u00e9cnicos do Norte (APNOR). A realiza\u00e7\u00e3o do est\u00e1gio, bem como a realiza\u00e7\u00e3o deste relat\u00f3rio, \u00e9 o produto final de meses de experi\u00eancia laboral, pesquisa bibliogr\u00e1fica e de reflex\u00e3o, de um ano curricular. Com a realiza\u00e7\u00e3o deste est\u00e1gio e do relat\u00f3rio permitiu-me concluir acerca de como \u00e9 elaborada a gest\u00e3o hospitalar e a sua import\u00e2ncia no contexto empresarial. A gest\u00e3o contribui com o fornecimento de informa\u00e7\u00f5es econ\u00f3micas nos diferentes usu\u00e1rios como: Gestores, Governo, Sindicato, Funcion\u00e1rios.", "keywords": ["Gest\u00e3o", "Supervisi\u00f3n", "Log\u00edstica", "4. Education", "Supervis\u00e3o", "Supervision", "Logistics", "Gesti\u00f3n", "Software", "Management"], "contacts": [{"organization": "Sousa, Vitor Manuel Rocha", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10400.22/4544"}, {"rel": "self", "type": "application/geo+json", "title": "10400.22/4544", "name": "item", "description": "10400.22/4544", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10400.22/4544"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-01-01T00:00:00Z"}}, {"id": "11579/182202", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:25:05Z", "type": "Journal Article", "created": "2024-05-14", "title": "Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists", "description": "

A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPAR\u03b4) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPAR\u03b4 agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure\u2013activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPAR\u03b4 agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform\u2019s graphical user interface (GUI).

", "keywords": ["0301 basic medicine", "570", "610", "Quantitative Structure-Activity Relationship", "molecular docking", "01 natural sciences", "Isalos Analytics Platform", "in silico modelling", "Article", "0104 chemical sciences", "Molecular Docking Simulation", "Machine Learning", "03 medical and health sciences", "machine learning", "PPAR\u03b4 agonist", "Humans", "PPAR delta", "Software"]}, "links": [{"href": "https://www.mdpi.com/1422-0067/25/10/5216/pdf"}, {"href": "https://doi.org/11579/182202"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Molecular%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11579/182202", "name": "item", "description": "11579/182202", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11579/182202"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-05-10T00:00:00Z"}}, {"id": "2117/418477", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:25:40Z", "type": "Journal Article", "created": "2024-08-30", "title": "ONCOLINER: A new solution for monitoring, improving, and harmonizing somatic variant calling across genomic oncology centers", "description": "The characterization of somatic genomic variation associated with the biology of tumors is fundamental for cancer research and personalized medicine, as it guides the reliability and impact of cancer studies and genomic-based decisions in clinical oncology. However, the quality and scope of tumor genome analysis across cancer research centers and hospitals are currently highly heterogeneous, limiting the consistency of tumor diagnoses across hospitals and the possibilities of data sharing and data integration across studies. With the aim of providing users with actionable and personalized recommendations for the overall enhancement and harmonization of somatic variant identification across research and clinical environments, we have developed ONCOLINER. Using specifically designed mosaic and tumorized genomes for the analysis of recall and precision across somatic SNVs, insertions or deletions (indels), and structural variants (SVs), we demonstrate that ONCOLINER is capable of improving and harmonizing genome analysis across three state-of-the-art variant discovery pipelines in genomic oncology.", "keywords": ["330", "Bioinformatics", "Genome", " Human", "610", "Genomics", "Medical Oncology", "Somatic variant calling", "Polymorphism", " Single Nucleotide", "Article", "Benchmarking", "Oncology", "INDEL Mutation", "\u00c0rees tem\u00e0tiques de la UPC::Inform\u00e0tica::Aplicacions de la inform\u00e0tica::Bioinform\u00e0tica", "Neoplasms", "Cancer genomics", "Humans", "Benchmarking data", "Precision Medicine", "Software"]}, "links": [{"href": "https://doi.org/2117/418477"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Cell%20Genomics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/418477", "name": "item", "description": "2117/418477", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/418477"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-09-01T00:00:00Z"}}, {"id": "3095352776", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:16Z", "type": "Journal Article", "created": "2020-10-28", "title": "Incremental predictive clustering trees for online semi-supervised multi-target regression", "description": "Abstract

In many application settings, labeling data examples is a costly endeavor, while unlabeled examples are abundant and cheap to produce. Labeling examples can be particularly problematic in an online setting, where there can be arbitrarily many examples that arrive at high frequencies. It is also problematic when we need to predict complex values (e.g., multiple real values), a task that has started receiving considerable attention, but mostly in the batch setting. In this paper, we propose a method for online semi-supervised multi-target regression. It is based on incremental trees for multi-target regression and the predictive clustering framework. Furthermore, it utilizes unlabeled examples to improve its predictive performance as compared to using just the labeled examples. We compare the proposed iSOUP-PCT method with supervised tree methods, which do not use unlabeled examples, and to an oracle method, which uses unlabeled examples as though they were labeled. Additionally, we compare the proposed method to the available state-of-the-art methods. The method achieves good predictive performance on account of increased consumption of computational resources as compared to its supervised variant. The proposed method also beats the state-of-the-art in the case of very few labeled examples in terms of performance, while achieving comparable performance when the labeled examples are more common.Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000\uffe2\uff80\uff931700\uffc2\uffa0nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390\uffe2\uff80\uff931420\uffc2\uffa0nm contributes most to the model\uffe2\uff80\uff99s final decision.1 MDa) macromolecular entity. The reduced rate of reorientational motion in the bound state that precludes characterization by traditional NMR methods permits the observation of DEST. (15)N-DEST profiles are measured on a sample comprising the dark state in exchange with an NMR-visible species; in addition, the difference (\u0394R(2)) in (15)N transverse relaxation rates between this sample and a control sample comprising only the NMR-visible species is also obtained. The (15)N-DEST and \u0394R(2) data for all residues are then fitted simultaneously to the McConnell equations for various exchange models describing the residue-specific dynamics in the bound state(s) and the interconversion rate constants. Although the length of the experiments depends strongly on sample conditions, approximately 1 week of NMR spectrometer time was sufficient for full characterization of samples of amyloid-\u03b2 (A\u03b2) at concentrations of ~100 \u03bcM.", "keywords": ["Molecular Weight", "0301 basic medicine", "Kinetics", "0303 health sciences", "03 medical and health sciences", "Amyloid beta-Peptides", "Time Factors", "Nitrogen Isotopes", "Macromolecular Substances", "Nuclear Magnetic Resonance", " Biomolecular", "Software"]}, "links": [{"href": "https://doi.org/PMC3500623"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature%20Protocols", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC3500623", "name": "item", "description": "PMC3500623", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC3500623"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-07-19T00:00:00Z"}}, {"id": "PMC5679399", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:28:10Z", "type": "Journal Article", "created": "2017-11-01", "title": "NDEx 2.0: A Clearinghouse for Research on Cancer Pathways", "description": "Abstract

We present NDEx 2.0, the latest release of the Network Data Exchange (NDEx) online data commons (www.ndexbio.org) and the ways in which it can be used to (i) improve the quality and abundance of biological networks relevant to the cancer research community; (ii) provide a medium for collaboration involving networks; and (iii) facilitate the review and dissemination of networks. We describe innovations addressing the challenges of an online data commons: scalability, data integration, data standardization, control of content and format by authors, and decentralized mechanisms for review. The practical use of NDEx is presented in the context of a novel strategy to foster network-oriented communities of interest in cancer research by adapting methods from academic publishing and social media. Cancer Res; 77(21); e58\uffe2\uff80\uff9361. \uffc2\uffa92017 AACR.Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000\uffe2\uff80\uff931700\uffc2\uffa0nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390\uffe2\uff80\uff931420\uffc2\uffa0nm contributes most to the model\uffe2\uff80\uff99s final decision.Analysis of focal copy number variations (CNVs) is highly relevant for cancer research, as they pinpoint driver genes. More specifically, due to selective pressure oncogenes and tumor suppressor genes are more often affected by these events than neighboring passengers. In cases where multiple candidates co-reside in a genomic locus, careful comparison is required to either identify multigenic minimally deleted regions of synergistic co-mutations, or the true single driver gene. The study of focal CNVs in large cancer genome cohorts requires specialized visualization and statistical analysis.

Results

We developed the GenomeTornadoPlot R-package which generates gene-centric visualizations of CNV types, locations and lengths from cohortwise NGS data. Furthermore, the software enables the pairwise comparison of proximate genes to identify co-mutation patterns or driver-passenger hierarchies. The visual examination provided by GenomeTornadoPlot is further supported by adaptable local and global focality scoring. Integrated into the GenomeTornadoPlot R-Package is the comprehensive PCAWG database of CNVs, comprising 2976 cancer genome entities from 46 cohorts of the Pan-cancer Analysis of Whole Genomes project. The GenomeTornadoPlot R-package can be used to perform exploratory or hypothesis-driven analyses on the basis of the PCAWG data or in combination with data provided by the user.

Availability and implementation

GenomeTornadoPlot is written in R script and released via github: <https://github.com/chenhong-dkfz/GenomeTornadoPlot/>. The package is under the license of GPL-3.0.

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