PlantSpec : ett tilläggspaket för R för interaktiv analys av spektraldata för höghastighetsfenotypning

Hyperspectral phenotyping is a promising high-throughput plant phenotyping method that addresses the phenotyping bottleneck, in fields of plant science like crop improvement or study of plant diseases. However, high-throughput hyperspectral data poses a data management and analysis problem due to the sheer volume of data generated in the data collection process. The R Package presented in this paper seeks to address this problem by implementing a tool for easy and quick handling of large spectral datasets, and to provide functions for processing and analyzing of spectral data. This project also aims to implement functions in the package for calculating vegetation indices, and data visualization functions. Furthermore, this R package is extended into an online tool, that provides and interactive graphical user interface to the underlying R code

Physiological modeling of isoprene dynamics in exhaled breath

Human breath contains a myriad of endogenous volatile organic compounds (VOCs) which are reflective of ongoing metabolic or physiological processes. While research into the diagnostic potential and general medical relevance of these trace gases is conducted on a considerable scale, little focus has been given so far to a sound analysis of the quantitative relationships between breath levels and the underlying systemic concentrations. This paper is devoted to a thorough modeling study of the end-tidal breath dynamics associated with isoprene, which serves as a paradigmatic example for the class of low-soluble, blood-borne VOCs. Real-time measurements of exhaled breath under an ergometer challenge reveal characteristic changes of isoprene output in response to variations in ventilation and perfusion. Here, a valid compartmental description of these profiles is developed. By comparison with experimental data it is inferred that the major part of breath isoprene variability during exercise conditions can be attributed to an increased fractional perfusion of potential storage and production sites, leading to higher levels of mixed venous blood concentrations at the onset of physical activity. In this context, various lines of supportive evidence for an extrahepatic tissue source of isoprene are presented. Our model is a first step towards new guidelines for the breath gas analysis of isoprene and is expected to aid further investigations regarding the exhalation, storage, transport and biotransformation processes associated with this important compound.Comment: 14 page

GWAS-Assisted Genomic Prediction to Predict Resistance to Septoria Tritici Blotch in Nordic Winter Wheat at Seedling Stage

Septoria tritici blotch (STB) disease caused by Zymoseptoria tritici is one of the most damaging diseases of wheat causing significant yield losses worldwide. Identification and employment of resistant germplasm is the most cost-effective method to control STB. In this study, we characterized seedling stage resistance to STB in 175 winter wheat landraces and old cultivars of Nordic origin. The study revealed significant (p < 0.05) phenotypic differences in STB severity in the germplasm. Genome-wide association analysis (GWAS) using five different algorithms identified ten significant markers on five chromosomes. Six markers were localized within a region of 2 cM that contained seven candidate genes on chromosome 1B. Genomic prediction (GP) analysis resulted in a model with an accuracy of 0.47. To further improve the prediction efficiency, significant markers identified by GWAS were included as fixed effects in the GP model. Depending on the number of fixed effect markers, the prediction accuracy improved from 0.47 (without fixed effects) to 0.62 (all non-redundant GWAS markers as fixed effects), respectively. The resistant genotypes and single-nucleotide polymorphism (SNP) markers identified in the present study will serve as a valuable resource for future breeding for STB resistance in wheat. The results also highlight the benefits of integrating GWAS with GP to further improve the accuracy of GP

Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning

Background High-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial. Results The results show that 40-42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow rust scores using Random Forest (RF) modelling. The SVIs were selected through RF-based recursive feature elimination (RFE), and the predicted scores in the resulting models had a prediction accuracy of r(s) = 0.50-0.61 when measuring the correlation between predicted and observed scores. Some of the most important spectral features for prediction were the Plant Senescence Reflectance Index (PSRI), Photochemical Reflectance Index (PRI), Red-Green Pigment Index (RGI), and Greenness Index (GI). Conclusions The proposed HTPP method of combining SVI data from spectral sensors in RF models, has the potential to be deployed in wheat breeding trials to score yellow rust

Specalyzer—an interactive online tool to analyze spectral reflectance measurements

Low-cost phenotyping using proximal sensors is increasingly becoming popular in plant breeding. As these techniques generate a large amount of data, analysis pipelines that do not require expertise in computer programming can benefit a broader user base. In this work, a new online tool Specalyzer is presented that allows interactive analysis of the spectral reflectance data generated by proximal spectroradiometers. Specalyzer can be operated from any web browser allowing data uploading, analysis, interactive plots and exporting by point and click using a simple graphical user interface. Specalyzer is evaluated with case study data from a winter wheat fertilizer trial with two fertilizer treatments. Specalyzer can be accessed online at http://www.specalyzer.org

Application of Gas Chromatography to Determination of Total Organic Fluorine after Defluorination of Perfluorooctanoic Acid as a Model Compound

Because of the global presence of anthropogenic perfluorinated organic compounds in the environment, foods and living organisms, and their large structural variety, it can be helpful to develop a method for determination of their total content at trace level in different matrices. In the developed method, the defluorination was carried with sodium biphenyl, derivatization of released fluoride to triphenylfluorosilane and determination by gas chromatography. Three detection methods were compared: flameionization detection, electron capture detection and mass spectrometry. Among them the MS detection was found to be the most favorable one in terms of the instrumental limit of detection (LOD) , whereas the flame-ionization detection was considered to be the most favorable in terms of the method limit of detection (MDL). For the initial sample volume of 1 L and performing the whole procedure of determination, including preconcentration, the MDL value for perfluorooctanoic acid was evaluated as 0.043 ppb. (doi: 10.5562/cca1798

Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor

Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. It is, however, a challenge to perform radiometric correction of the images to create reflectance maps (orthomosaics with surface reflectance) and to compute vegetation indices with sufficient accuracy to enable comparisons between data collected at different times and locations. Studies have compared different radiometric correction methods applied to the Sequoia camera, but there is no consensus about a standard method that provides consistent results for all spectral bands and for different flight conditions. In this study, we perform experiments to assess the accuracy of the Parrot Sequoia camera and sunshine sensor to get an indication if the quality of the data collected is sufficient to create accurate reflectance maps. In addition, we study if there is an influence of the atmosphere on the images and suggest a workflow to collect and process images to create a reflectance map. The main findings are that the sensitivity of the camera is influenced by camera temperature and that the atmosphere influences the images. Hence, we suggest letting the camera warm up before image collection and capturing images of reflectance calibration panels at an elevation close to the maximum flying height to compensate for influence from the atmosphere. The results also show that there is a strong influence of the orientation of the sunshine sensor. This introduces noise and limits the use of the raw sunshine sensor data to compensate for differences in light conditions. To handle this noise, we fit smoothing functions to the sunshine sensor data before we perform irradiance normalization of the images. The developed workflow is evaluated against data from a handheld spectroradiometer, giving the highest correlation (R-2 = 0.99) for the normalized difference vegetation index (NDVI). For the individual wavelength bands, R-2 was 0.80-0.97 for the red-edge, near-infrared, and red bands

Allogeneic stem cell transplantation for patients with acute myeloid leukemia (AML) in second complete remission (CR2) transplanted from unrelated donors with post-transplant cyclophosphamide (PTCy). A study on behalf of the Acute Leukemia Working Party of the European Society for Blood and Marrow Transplantation

Post-transplant cyclophosphamide (PTCy) is being increasingly used as graft-versus-host disease (GVHD) prophylaxis post allogeneic hematopoietic stem cell transplantation (allo-HSCT) in patients with acute myeloid leukemia (AML) transplanted in first complete remission (CR1). However, results may differ in patients transplanted in CR2. We retrospectively evaluated transplant outcomes of adult AML patients transplanted between 2010–2019 from 9–10/10 human leukocyte antigen (HLA)-matched unrelated donor (UD) in CR2. In total, 127 patients were included (median age 45.5 years, 54% male). Median follow-up was 19.2 months. Conditioning was myeloablative (MAC) in 50.4% and the graft source was peripheral blood in 93.7% of the transplants. Incidence of acute (a)GVHD II-IV and III-IV was 26.2% and 9.2%. Two-year total and extensive chronic (c)GVHD were 34.3% and 13.8 %, respectively. Two-year non-relapse mortality (NRM), relapse incidence (RI), leukemia-free survival (LFS), overall survival (OS), and GVHD-free, relapse-free survival (GRFS) were 17.2%, 21.1%, 61.7, %, 65.2%, and 49.3%, respectively. Time from diagnosis to transplant (&gt;18 months) was a favorable prognostic factor for RI, LFS, OS, and GRFS while favorable risk cytogenetics was a positive prognostic factor for OS. The patient’s age was a poor prognostic factor for NRM and cGVHD. Finally, the female-to-male combination and reduced intensity conditioning (RIC) were poor and favorable prognostic factors for cGVHD, respectively. We conclude that PTCy is an effective method for GVHD prophylaxis in AML patients undergoing allo-HCT in CR2 from UD.</p

The design with intent method: A design tool for influencing user behaviour

The official published version can be found at the link below.Using product and system design to influence user behaviour offers potential for improving performance and reducing user error, yet little guidance is available at the concept generation stage for design teams briefed with influencing user behaviour. This article presents the Design with Intent Method, an innovation tool for designers working in this area, illustrated via application to an everyday human–technology interaction problem: reducing the likelihood of a customer leaving his or her card in an automatic teller machine. The example application results in a range of feasible design concepts which are comparable to existing developments in ATM design, demonstrating that the method has potential for development and application as part of a user-centred design process