| 1. |
- Sieberts, SK, et al.
(författare)
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Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
- 2016
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 7, s. 12460-
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Tidskriftsartikel (refereegranskat)abstract
- Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.
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| 2. |
- Bellon-Maurel, V., et al.
(författare)
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Streamlining life cycle inventory data generation in agriculture using traceability data and information and communication technologies - part I: concepts and technical basis
- 2014
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Ingår i: Journal of Cleaner Production. - : Elsevier BV. - 0959-6526. ; 69, s. 60-66
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Forskningsöversikt (refereegranskat)abstract
- Quantitative environmental assessment methodologies such as life cycle assessment demand significant time and resource inputs during the data acquisition and life cycle inventory (LCI) phase. Approaches to streamline the LCI data collection process without degrading data quality are therefore required. This requirement is especially true for agricultural products, as agricultural systems are inherently 'open' and complex. We present a two-part paper on this topic. In this first part, we examine streamlined methods for LCI data collection in agriculture by using today's voluntary or compulsory farm traceability information systems and related information and communication technologies (ICTs), with the aim of later converting them into LCI data. The second part is to examine the application of these technologies in a case study. Our hypothesis is that both traceability data and ICTs could be major drivers for generating accurate, relevant and low-cost LCI data for use in quantitative environmental assessments of agricultural product performance. To that end, we identified the types of data being collected in agriculture as a part of current business practice, especially those with relevance to LCA studies. We also examined the status and current trends in ICTs in use in agriculture to identify the potential for automating LCI data generation. The review identified considerable potential to piggy-back current trends in ICTs in agriculture with the goal of simplifying LCI data collection. This study concludes that given the increasing need to collect traceability data in modern agriculture and the parallel growing adoption of information and communication technologies, it is likely that ICTs and associated information systems will represent an important potential route for the acquisition of future LCI data. (C) 2014 Elsevier Ltd. All rights reserved.
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| 3. |
- Bellon-Maurel, V., et al.
(författare)
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Streamlining life cycle inventory data generation in agriculture using traceability data and information and communication technologies - Part II: Application to viticulture
- 2015
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Ingår i: Journal of Cleaner Production. - : Elsevier BV. - 0959-6526. ; 87:1, s. 119-129
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Tidskriftsartikel (refereegranskat)abstract
- Agricultural systems are increasingly subjected to environmental life cycle assessment (LCA) but generating life cycle inventory (LCI) data in agriculture remains a challenge. In Part I, it was suggested that traceability data are a good basis for generating precise LCI with reduced effort, especially when collected by efficient information and communication technologies (ICTs). The aim of this paper is to demonstrate this for wine grape production and generate a list of data to be collected for streamlined LCI generation. The study is carried out in the South of France, on a viticultural farm implementing electronic traceability of each cultivation operation, i.e. tillage, fertilisation, crop protection, weeding, canopy management and harvesting (no irrigation is needed at this vineyard). For each operation, specific emission models which satisfy the trade-off between accuracy and need for data have been identified. Traceability data must be supplemented with data related to the plot, equipment and inputs to feed the models. The sensitivity of the LCA outputs to plot soil type and year of cultivation was studied. Consistent with previous agricultural studies, the results show that operations such as pesticide spraying and fertilising have large environmental impacts in this Mediterranean vineyard. Notable variations occur in life cycle impact assessment indicators, principally due to variations in crop yield; however, the influence of secondary factors such as soil type and agricultural practices is also evident and this contribution allows us to better characterise the variability of grape production and to show that streamlined LCI can be created using traceability data. Ultimately, this paper delivers two results. It provides simple models, and relevant data and methodology to enable viticultural LCAs to be undertaken. Additionally, it demonstrates that accurate LCIs can be built based on data already collected for traceability when supplemented with other easily collectable data (weather and farm structural data). Overall, this work paves the way for streamlined LCI in agriculture.
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| 4. |
- Chauchard, F, et al.
(författare)
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Least-squares support vector machines modelization for time-resolved spectroscopy
- 2005
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Ingår i: Applied Optics. - 2155-3165. ; 44:33, s. 7091-7097
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Tidskriftsartikel (refereegranskat)abstract
- By use of time-resolved spectroscopy it is possible to separate light scattering effects from chemical absorption effects in samples. In the study of propagation of short light pulses in turbid samples the reduced scattering coefficient and the absorption coefficient are usually obtained by fitting diffusion or Monte Carlo models to the measured data by use of numerical optimization techniques. In this study we propose a prediction model obtained with a semiparametric modeling technique: the least-squares support vector machines. The main advantage of this technique is that it uses theoretical time dispersion curves during the calibration step. Predictions can then be performed by use of data measured on different kinds of sample, such as apples.
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| 5. |
- Chauchard, F, et al.
(författare)
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MADSTRESS: A linear approach for evaluating scattering and absorption coefficients of samples measured using time-resolved spectroscopy in reflection
- 2005
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Ingår i: Applied Spectroscopy. - : SAGE Publications. - 1943-3530 .- 0003-7028. ; 59:10, s. 1229-1235
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Tidskriftsartikel (refereegranskat)abstract
- Time-resolved spectroscopy is a powerful technique permitting the separation of the scattering properties from the chemical absorption properties of a sample. The reduced scattering coefficient and the absorption coefficient are usually obtained by fitting diffusion or Monte Carlo models to the measured data using numerical optimization techniques. However, these methods do not take the spectral dimension of the data into account during the evaluation procedure, but evaluate each wavelength separately. A procedure involving multivariate methods may seem more appealing for people used to handling conventional near-infrared data. In this study we present a new method for processing TRS spectra in order to compute the absorption and reduced scattering coefficients. This approach, MADSTRESS, is based on linear regression and a twodimensional (2D) interpolation procedure. The method has allowed us to calculate absorption and scattering coefficients of apples and fructose powder. The accuracy of the method was good enough to provide the identification of fructose absorption peaks in apple absorption spectra and the construction of a calibration model predicting the sugar content of apples.
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| 6. |
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