Towards synthesis for nitrogen fertilisation using a decision support system

Nitrogen (N) fertilisation in crops can be made more efficient by moving from uniform application to meeting variable crop requirements within fields. Within field variable rate N fertilisation of winter wheat (Triticum aestivum L.) is practically feasible using information from web-based decision support systems (DSS). Data from different source platforms, such as satellite, unmanned aerial vehicle (UAV) or weather stations can be used for fertilisation planning. System output offers information that can be used  to instruct variable rate fertilizer spreaders to increase or decrease fertilizer application rate on-the-go. In Sweden, satellite-based variable rate N fertilisation was available for winter wheat via a DSS, however, the existing module could be improved in different ways. In this thesis work, a new N-uptake model was estimated and opportunities using UAV-based modelling of grain quality were tested. Transferability of UAV-based models to a satellite data scale improved understanding of the complexity of data transfer from UAV-scale to a satellite scale for use in a DSS. Furthermore, it was possible to model crop phenology from historical data, which can improve accuracy of current implemented models, by taking timing of field operations in to account

Upscaling proximal sensor N-uptake predictions in winter wheat (Triticum aestivum L.) with Sentinel-2 satellite data for use in a decision support system

Total nitrogen (N) content in aboveground biomass (N-uptake) in winter wheat (Triticum aestivum L.) as measured in a national monitoring programme was scaled up to full spatial coverage using Sentinel-2 satellite data and implemented in a decision support system (DSS) for precision agriculture. Weekly field measurements of N-uptake had been carried out using a proximal canopy reflectance sensor (handheld Yara N-Sensor) during 2017 and 2018. Sentinel-2 satellite data from two processing levels (top-of-atmosphere reflectance, L1C, and bottom-of-atmosphere reflectance, L2A) were extracted and related to the proximal sensor data (n = 251). The utility of five vegetation indices for estimation of N-uptake was compared. A linear model based on the red-edge chlorophyll index (CI) provided the best N-uptake prediction (L1C data: r(2) = 0.74, mean absolute error; MAE = 14 kg ha(-1)) when models were applied on independent sites and dates. Use of L2A data, rather than L1C, did not improve the prediction models. The CI-based prediction model was applied on all fields in an area with intensive winter wheat production. Statistics on N-uptake at the end of the stem elongation growth stage were calculated for 4169 winter wheat fields > 5 ha. Within-field variation in predicted N-uptake was > 30 kg N ha(-1) in 62% of these fields. Predicted N-uptake was compared against N-uptake maps derived from tractor-borne Yara N-Sensor measurements in 13 fields (1.7-30 ha in size). The model based on satellite data generated similar information as the tractor-borne sensing data (r(2) = 0.81; MAE = 7 kg ha(-1)), and can therefore be valuable in a DSS for variable-rate N application

Predicting grain protein concentration in winter wheat (Triticum aestivum L.) based on unpiloted aerial vehicle multispectral optical remote sensing

Prediction models for crude protein concentration (CP) in winter wheat (Triticum aestivum L.) based on multispectral reflectance data from field trials in 2019 and 2020 in southern Sweden were developed and evaluated for independent trial sites. Reflectance data were collected using an unpiloted aerial vehicle (UAV)-borne camera with nine spectral bands having similar specification to nine bands of Sentinel-2 satellite data. Models were tested for application on near-real time Sentinel-2 imagery, on the prospect that CP prediction models can be made available in satellite-based decision support systems (DSS) for precision agriculture. Two different prediction methods were tested: linear regression and multivariate adaptive regression splines (MARS). Linear regression based on the best-performing vegetation index (the chlorophyll index) was found to be approximately as accurate as the best performing MARS model with multiple predictor variables in leave-one-trial-out cross-validation (R-2 = 0.71, R-2 = 0.70 and mean absolute error 0.64%, 0.60% CP respectively). Models applied on satellite data explained to a small degree between-field variations in CP (R-2 = 0.36), however did not reproduce within-field variation accurately. The results of the different methods presented here show the differences between methods used and their potential for application in a DSS

Industry solutions on Smart Farming Technology

Smart AKIS project aims at examining the suitability and use of Smart Farming Technologies (SFT) in the EU Agriculture involving farmers, the agricultural machinery industry, academia, research centers, agricultural engineering and public bodies. The purpose of this document is to present the report on methodology, standards and current findings within the Smart-AKIS project. The report provides a selection guide, detailing the issues that have to be taken into account in order to ensure the collection of data in a homogeneous way, and avoid misconceptions. This document is an update on the progress made in the data assessment that is currently ongoing on captuing industrial products related to SFTs that have not yet reached mainstreaming agriculture. This report is organized in three chapters. The first chapter will introduce current work on the Smart-Akis project as well as the objective of this document in the overall smart-akis framework. The second chapter will present the methodological approach that has taken to reach the industrial partners, the specific questions and the analysis procedure, wjhile the last chapter will present the interim results. The last chapter summarizes conclusion

Connections between Exoproteome Heterogeneity and Virulence in the Oral Pathogen Aggregatibacter actinomycetemcomitans

Aggregatibacter actinomycetemcomitans is a Gram-negative bacterial pathogen associated with severe periodontitis and nonoral diseases. Clinical isolates of A. actinomycetemcomitans display a rough (R) colony phenotype with strong adherent properties. Upon prolonged culturing, nonadherent strains with a smooth (S) colony phenotype emerge. To date, most virulence studies on A. actinomycetemcomitans have been performed with S strains of A. actinomycetemcomitans, whereas the virulence of clinical R isolates has received relatively little attention. Since the extracellular proteome is the main bacterial reservoir of virulence factors, the present study was aimed at a comparative analysis of this subproteome fraction for a collection of R isolates and derivative S strains, in order to link particular proteins to the virulence of A. actinomycetemcomitans with serotype b. To assess the bacterial virulence, we applied different infection models based on larvae of the greater wax moth Galleria mellonella, a human salivary gland-derived epithelial cell line, and freshly isolated neutrophils from healthy human volunteers. A total number of 351 extracellular A. actinomycetemcomitans proteins was identified by mass spectrometry, with the S strains consistently showing more extracellular proteins than their parental R isolates. A total of 50 known extracellular virulence factors was identified, of which 15 were expressed by all investigated bacteria. Importantly, the comparison of differences in exoproteome composition and virulence highlights critical roles of 10 extracellular proteins in the different infection models. Together, our findings provide novel clues for understanding the virulence of A. actinomycetemcomitans and for development of potential preventive or therapeutic avenues to neutralize this important oral pathogen. IMPORTANCE Periodontitis is one of the most common inflammatory diseases worldwide, causing high morbidity and decreasing the quality of life of millions of people. The bacterial pathogen Aggregatibacter actinomycetemcomitans is strongly associated with aggressive forms of periodontitis. Moreover, it has been implicated in serious nonoral infections, including endocarditis and brain abscesses. Therefore, it is important to investigate how A. actinomycetemcomitans can cause disease. In the present study, we applied a mass spectrometry approach to make an inventory of the virulence factors secreted by different clinical A. actinomycetemcomitans isolates and derivative strains that emerged upon culturing. We subsequently correlated the secreted virulence factors to the pathogenicity of the investigated bacteria in different infection models. The results show that a limited number of extracellular virulence factors of A. actinomycetemcomitans have central roles in pathogenesis, indicating that they could be druggable targets to prevent or treat oral disease

A compilation of charged-particle induced thermonuclear reaction rates

Low-energy cross section data for 86 charged-particle induced reactions involving light (1 less than or equal to Z less than or equal to 14), mostly stable, nuclei are compiled. The corresponding Maxwellian-averaged thermonuclear reaction rates of relevance in astrophysical plasmas at temperatures in the range from 10(6) K to 10(10) K are calculated. These evaluations assume either that the target nuclei are in their ground state, or that the target states are thermally populated following a Maxwell-Boltzmann distribution, except in some cases involving isomeric states. Adopted values complemented with lower and upper limits of the rates are presented in tabular form. Analytical approximations to the adopted rates, as well as to the inverse/direct rate ratios, are provided. (C) 1999 Elsevier Science B.V. All rights reserved

One- and two-dimensional photonic crystal micro-cavities in single crystal diamond

The development of solid-state photonic quantum technologies is of great interest for fundamental studies of light-matter interactions and quantum information science. Diamond has turned out to be an attractive material for integrated quantum information processing due to the extraordinary properties of its colour centres enabling e.g. bright single photon emission and spin quantum bits. To control emitted photons and to interconnect distant quantum bits, micro-cavities directly fabricated in the diamond material are desired. However, the production of photonic devices in high-quality diamond has been a challenge so far. Here we present a method to fabricate one- and two-dimensional photonic crystal micro-cavities in single-crystal diamond, yielding quality factors up to 700. Using a post-processing etching technique, we tune the cavity modes into resonance with the zero phonon line of an ensemble of silicon-vacancy centres and measure an intensity enhancement by a factor of 2.8. The controlled coupling to small mode volume photonic crystal cavities paves the way to larger scale photonic quantum devices based on single-crystal diamond

Association of MicroRNA-618 Expression With Altered Frequency and Activation of Plasmacytoid Dendritic Cells in Patients With Systemic Sclerosis

Objective. Plasmacytoid dendritic cells (PDCs) are a critical source of type I interferons (IFNs) that can contribute to the onset and maintenance of autoimmunity. Molecular mechanisms leading to PDC dysregulation and a persistent type I IFN signature are largely unexplored, especially in patients with systemic sclerosis (SSc), a disease in which PDCs infiltrate fibrotic skin lesions and produce higher levels of IFN alpha than those in healthy controls. This study was undertaken to investigate potential microRNA (miRNA)-mediated epigenetic mechanisms underlying PDC dysregulation and type I IFN production in SSc.Methods. We performed miRNA expression profiling and validation in highly purified PDCs obtained from the peripheral blood of 3 independent cohorts of healthy controls and SSc patients. Possible functions of miRNA-618 (miR-618) on PDC biology were identified by overexpression in healthy PDCs.Results. Expression of miR-618 was up-regulated in PDCs from SSc patients, including those with early disease who did not present with skin fibrosis. IFN regulatory factor 8, a crucial transcription factor for PDC development and activation, was identified as a target of miR-618. Overexpression of miR-618 reduced the development of PDCs from CD34+ cells in vitro and enhanced their ability to secrete IFN alpha, mimicking the PDC phenotype observed in SSc patients.Conclusion. Up-regulation of miR-618 suppresses the development of PDCs and increases their ability to secrete IFN alpha, potentially contributing to the type I IFN signature observed in SSc patients. Considering the importance of PDCs in the pathogenesis of SSc and other diseases characterized by a type I IFN signature, miR-618 potentially represents an important epigenetic target to regulate immune system homeostasis in these conditions