ICT and farmers : lessons learned and future developments

Information and Communication Technologies (ICT) evolution is well advancing Moore?s Law prediction of geometric progression of computer performance indexes. Indeed, these technologies are not only fast developed but, in addition, are giving birth to newer ones nicely branching existing “old fashion” ICT systems and tools. These innovations of ICT are not only regenerating traditional sciences, like Agriculture, and practices, like farming, but also, awake well neglected human sensitiveness and indifference for poverty, environmental protection, climatic deterioration issues and the future of our planet as a whole. To refer to a few examples of these innovations affecting Agriculture and Environmental Sciences: Cloud Computing provides equality in resources management and exploitability to small budget farms against the big ones. Web2 browser allows, as a platform, effective runtime environment and considerably easy access to applications by farmers lacking proper education and training. Parallel Computing brings exponentially increased core processing to low-end computers facilitating the use of huge computer power by small agricultural research units. Never the less agricultural and farming communities, in their majority, do not adopt new ICT tools and systems to the degree required for substantial agricultural development. In this paper, experience gained over the years is used to evaluate and reason poor performance in the area of applicability of ICT innovations and tools by the vast majority of farmers throughout the world.</jats:p

Statistical Methods for Modelling Complex Longitudinal Data with Applications in Cancer Pharmacogenetics and Ageing

Technological and scientific advancements have promoted data gathering across multiple disciplines emphasizing the necessity for the development of rigorous statistical methods to draw conclusions. Longitudinal data is a key tool to study temporal changes, however, with the increasing data complexity, existing methodologies are often unable to capture non-linear or non-stationary trends. Additionally, irregularly collected, non-continuous or high-dimensional data make statistical analysis even more challenging. Through this work, we develop three statistical models to analyse complex longitudinal data from two real-world databases, the Genomics of Drug Sensitivity in Cancer and the English Longitudinal Study of Ageing. The first part of this work is motivated by the Genomics of Drug Sensitivity in Cancer project and focuses on the prediction and detection of biomarkers associated with anti-cancer drug dose-response. Here, the longitudinal data available are characterised by complete observed trajectories of drug response over multiple drug dosages which are potentially associated with high-dimensional covariates (these include expression profiles of tens of thousands of genes) in a non-stationary manner. These trends are not easily amenable to analysis by classic parametric or semi-parametric mixed models, especially if high dimensionality is present. We built a dose-varying regression model combined with a two-stage variable selection algorithm (variable screening followed by penalised regression) to identify genetic factors associated with drug response and estimate their effect over the varying dosages. The second part of this work is motivated by the English Longitudinal Study of Ageing data set. The longitudinal data available in this study are characterised by irregularly collected and, often, incomplete trajectories and many response variables of ordinal type which measure only a small number of ageing domains (data are derived from multiple questionnaires measuring multiple aspects of older peoples' life). The ultimate aim is to understand the ageing dynamics and study the interrelationships between factors associated with it. To do so, we first explore the theoretical foundations of ageing and the data set itself. Next, we adopt and extend the methodological framework of~\cite{dawson2018} to estimate the quantile dynamics and derive predictions for a common surrogate of ageing, frailty, addressing the problem of incomplete individual responses over the age interval of interest. Finally, we develop a bivariate Gaussian process framework for ordinal and potentially irregularly sampled data which allows the available questionnaire responses to be modelled directly. Here, the unobserved ageing domains are assumed to be smooth functions of age. This method allows the assessment of the interrelationships between several ageing domains after adjusting for individual variation across the observed longitudinal trajectories

FTIR Measurements of Greenhouse Gases over Thessaloniki, Greece in the Framework of COCCON and Comparison with S5P/TROPOMI Observations

In this work, column-averaged dry-air mole fractions of carbon dioxide (XCO2), methane (XCH4) and carbon monoxide (XCO) are presented for the first time at a mid-latitude urban station, Thessaloniki, Greece, using the Bruker EM27/SUN ground-based low-resolution Fourier Transform spectrometer operated according to the requirements of the Collaborative Carbon Column Observing Network (COCCON). Two years of measurements are presented and examined for seasonal variability. The observed XCO2 levels show the expected seasonal cycle (spring maximum, late summer minimum) with a peak-to-peak amplitude of 12 ppm, with maximum values reported for winter 2021 exceeding 416 ppm. The XCH4 values are shown to increase in the second half of the year, with autumn showing the highest mean value of 1.878 ± 0.01 ppm. The XCO levels, following anthropogenic sources, show high winter and low summer values, exhibiting a rise again in August and September with a maximum value of 114 ± 3 ppb and a minimum in summer 2020 of 76 ± 3 ppb. Additionally, methane and carbon monoxide products obtained from the TROPOspheric Monitoring Instrument (TROPOMI), Sentinel-5P space borne sensor, are compared with the ground-based measurements. We report a good agreement between products. The relative mean bias for methane and carbon monoxide are −0.073 ± 0.647% and 3.064 ± 5.566%, respectively. Furthermore, a 15-day running average is subtracted from the original daily mean values to provide ΔXCO2, ΔXCO and ΔXCH4 residuals, so as to identify local sources at a synoptic scale. ΔXCO and ΔXCO2 show the best correlation in the winter (R2 = 0.898, slope = 0.007) season due to anthropogenic emissions in this period of the year (combustion of fossil fuels or industrial activities), while in summer no correlation is found. ΔXCO and ΔXCH4 variations are similar through both years of measurements and have a very good correlation in all seasons including winter (R2 = 0.804, slope = 1.209). The investigation of the X-gases comparison is of primary importance in order to identify local sources and quantify the impact of these trace gases to the deregulation of earth-climate system balance

A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response

Cancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalized regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumorigenesis and DNA damage response

Dikili Tash (2022)

Données scientifiques produites :Bordereau de versement aux archives : V2022009Chroniques de l’EFA :Dikili Tash 2022Durant les deux premières campagnes de ce programme (2019 et 2021), nous avons travaillé dans deux secteurs, le secteur 7 au sommet du tell (zone explorée déjà en 2008-2010, dans la continuité des opérations réalisées dans le cadre du 1er programme durant les années 1970) et le secteur 9, nouveau secteur implanté sur le versant Nord du tell. Ayant atteint nos objectifs dans le ..

Global long-term monitoring of the ozone layer - a prerequisite for predictions

Although the Montreal Protocol now controls the production and emission of ozone depleting substances, the timing of ozone recovery is unclear. There are many other factors affecting the ozone layer, in particular climate change is expected to modify the speed of re-creation of the ozone layer. Therefore, long-term observations are needed to monitor the further evolution of the stratospheric ozone layer. Measurements from satellite instruments provide global coverage and are supplementary to selective ground-based observations. The combination of data derived from different space-borne instruments is needed to produce homogeneous and consistent long-term data records. They are required for robust investigations including trend analysis. For the first time global total ozone columns from three European satellite sensors GOME (ERS-2), SCIAMACHY (ENVISAT), and GOME-2 (METOP-A) are combined and added up to a continuous time series starting in June 1995. On the one hand it is important to monitor the consequences of the Montreal Protocol and its amendments; on the other hand multi-year observations provide the basis for the evaluation of numerical models describing atmospheric processes, which are also used for prognostic studies to assess the future development. This paper gives some examples of how to use satellite data products to evaluate model results with respective data derived from observations, and to disclose the abilities and deficiencies of atmospheric models. In particular, multi-year mean values derived from the Chemistry-Climate Model E39C-A are used to check climatological values and the respective standard deviations

First validation of GOME-2/MetOp Absorbing Aerosol Height using EARLINET lidar observations

he aim of this study is to investigate the potential of the Global Ozone Monitoring Experiment-2 (GOME-2) instruments, aboard the Meteorological Operational (MetOp)-A, MetOp-B and MetOp-C satellite programme platforms, to deliver accurate geometrical features of lofted aerosol layers. For this purpose, we use archived ground-based lidar data from stations available from the European Aerosol Research Lidar Network (EARLINET) database. The data are post-processed using the wavelet covariance transform (WCT) method in order to extract geometrical features such as the planetary boundary layer (PBL) height and the cloud boundaries. To obtain a significant number of collocated and coincident GOME-2 - EARLINET cases for the period between January 2007 and September 2019, 13 lidar stations, distributed over different European latitudes, contributed to this validation. For the 172 carefully screened collocations, the mean bias was found to be -0.18 ± 1.68 km, with a near-Gaussian distribution. On a station basis, and with a couple of exceptions where very few collocations were found, their mean biases fall in the ± 1 km range with an associated standard deviation between 0.5 and 1.5 km. Considering the differences, mainly due to the temporal collocation and the difference, between the satellite pixel size and the point view of the ground-based observations, these results can be quite promising and demonstrate that stable and extended aerosol layers as captured by the satellite sensors are verified by the ground-based data. We further present an in-depth analysis of a strong and long-lasting Saharan dust intrusion over the Iberian Peninsula. We show that, for this well-developed and spatially well-spread aerosol layer, most GOME-2 retrievals fall within 1 km of the exact temporally collocated lidar observation for the entire range of 0 to 150 km radii. This finding further testifies for the capabilities of the MetOp-borne instruments to sense the atmospheric aerosol layer heights.Horizon 2020 Framework Programme 654109, 87111