Big Data in Agriculture – From FOODIE towards data bio

What’s the role of Big Data in the farming ecosystem? Farmers need to measure and understand the impact of a huge amount and variety of data which drive overall quality and yield of their fields. Among those are local weather data, GPS data, ortophotos, satellite imagery, soil specifics, soil conductivity, seed, fertilizer and crop protectant specifications and many more. Being able to leverage this data for running long and short term simulations in response to “events” like changed weather, market need or other parameters is indispensable for farmers in terms of maximizing their profits. IoT (Internet of Technology) including field sensors and machinery monitoring. The experimentation in FarmTelemetry project demonstrates that one average Czech farm (i.e. around 1’000 hectares) could generate daily 20 MegaBytes of data. This could be only for Czech Republic something between 30 and 50 GB per one day. We may easily reach Terabytes of data a day from agricultural basic monitoring by sensors in Europe. Together with satellite data agriculture will need to manage extremely large amount of data. On one side there is growing whole ecosystem with a strong need to secure Big Data from different repositories and heterogeneous sources. In some cases, sharing of data could be common interest, but on other side, there are also different interests and data could help to one part of value chain to take bigger part of profit. From this reason Big data are sensitive topics and trusting of producers about data security is essential. The producers of seeds and chemicals want to maximize their business with farmers. Our team stated implementation of Big Data technologies in frame of European 7FP project FOODIE. This work currently the work continue as part of DataBio project

MicroRNAs-1299, -126-3p and -30e-3p as Potential Diagnostic Biomarkers for Prediabetes

This cross-sectional study investigated the association of miR-1299, -126-3p and -30e-3p with and their diagnostic capability for dysglycaemia in 1273 (men, n = 345) South Africans, aged >20 years. Glycaemic status was assessed by oral glucose tolerance test (OGTT). Whole blood microRNA (miRNA) expressions were assessed using TaqMan-based reverse transcription quantitative-PCR (RT-qPCR). Receiver operating characteristic (ROC) curves assessed the ability of each miRNA to discriminate dysglycaemia, while multivariable logistic regression analyses linked expression with dysglycaemia. In all, 207 (16.2%) and 94 (7.4%) participants had prediabetes and type 2 diabetes mellitus (T2DM), respectively. All three miRNAs were significantly highly expressed in individuals with prediabetes compared to normotolerant patients, p < 0.001. miR-30e-3p and miR-126-3p were also significantly more expressed in T2DM versus normotolerant patients, p < 0.001. In multivariable logistic regressions, the three miRNAs were consistently and continuously associated with prediabetes, while only miR-126-3p was associated with T2DM. The ROC analysis indicated all three miRNAs had a significant overall predictive ability to diagnose prediabetes, diabetes and the combination of both (dysglycaemia), with the area under the receiver operating characteristic curve (AUC) being significantly higher for miR-126-3p in prediabetes. For prediabetes diagnosis, miR-126-3p (AUC = 0.760) outperformed HbA1c (AUC = 0.695), p = 0.042. These results suggest that miR-1299, -126-3p and -30e-3p are associated with prediabetes, and measuring miR-126-3p could potentially contribute to diabetes risk screening strategies

Exercise-induced increases in the expression and activity of cardiac sarcoplasmic reticulum calcium-ATPase 2 (SERCA2) is attenuated in AMPKα2 kinase-dead mice

Exercise enhances cardiac sarcoplasmic reticulum Ca2+-ATPase 2a (SERCA2a) function through unknown mechanisms. The present study tested the hypothesis that the positive effects of exercise on SERCA2a expression and function in the left ventricle is dependent on adenosine-monophosphate-activated protein kinase (AMPK) α2 function. AMPKα2 kinase-dead (KD) transgenic mice, which overexpress inactivated AMPKα2 subunit, and wild-type C57Bl/6 (WT) mice were randomized into sedentary groups or groups with access to running wheels. After 5 months, exercised KD mice exhibited shortened deceleration time compared with sedentary KD mice. In left ventricular tissue, the ratio of phosphorylated AMPKαThr172:total AMPKα was 65% lower (P < 0.05) in KD mice compared with WT mice. The left ventricle of KD mice had 37% lower levels of SERCA2a compared with WT mice. Although exercise increased SERCA2a protein levels in WT mice by 53%, this response of exercise was abolished in exercised KD mice. Exercise training reduced total phospholamban protein content by 23% in both the WT and KD mice but remained 20% higher overall in KD mice. Collectively, these data suggest that AMPKα influences SERCA2a and phospholamban protein content in the sedentary and exercised heart, and that exercise-induced changes in SERCA2a protein are dependent on AMPKα function.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author