Reducing Energy Costs in European Union Farms: Analysis of Efficiency

Efficiency in the use of resources is one of the most adjusted approaches towards achieving sustainable development in any economic sector, including agriculture. In fact, the current challenges surrounding the global farming sector are to maintain, or even, in some circumstances, increase production in such a way that it is compatible with the increasingly desirable goals for decarbonisation. Amongst the resources which are critical for sustainability within the agricultural sector, one of the most significant is energy, considering the needs of this resource to generate numerous farming production factors and the energetic requirements for its various activities. This is particularly important in the regions and countries in the European Union, due to the variety of contexts and the framework of the European agricultural policies, where the design of adjusted policy instruments is always a great task. In this way, the main objective of this research is to analyse farming efficiency in European Union agricultural regions, over the period 2013–2018. Considering this objective, data from the European Union Farm Accountancy Data Network were considered and first analysed through factor-cluster assessment, to obtain homogenous decision-making units, and then through data envelopment analysis. For the data envelopment analysis, a model with the inverse of the energy costs as output was considered. The main findings show that the savings in energy costs in European Union farms have impacts on the output as well as on other inputs.info:eu-repo/semantics/publishedVersio

Genotypes of SD patients analyzed in this study.

<p>ND: not detectable; NA: not available. Novel mutations are indicated in bold, *RefSeq cDNA:NM_000521. For cDNA numbering +1 corresponds to the A of the first ATG translation initiation codon. RefSeq protein: NP_000512.1. **indicates parents’ consanguinity. m: month; y: years; +deceased; §Owing to the use of different assay methods and tissue samples, total Hex activity values are expressed as a percentage of average control values.</p

<i>In vitro</i> functional analysis of new missense sequence variations.

<p>Total Hex activity after immunoprecipitation with anti-myc antibody of HEXB missense mutant proteins expressed in Hek293cells. Results are expressed as the percentage of total Hex activity detected after immunoprecipitation of myc-tagged normal HEXB expressed in Hek293cells (N). The data are shown as mean±SD of three different experiments, each performed in duplicate. ^*p<0.05.</p

Location of HEXB mutations on the 3D structural model.

<p>Chains A and B are drawn as orange and blue ribbons respectively. Positions of mutations are indicated by arrows and the residues represented by spaced-filled spheres.</p

<i>In vitro</i> functional analysis of mutation c.1242+1G>A and exon 11 splicing analysis in normal fibroblasts.

<p>Panel A: RT-PCR analysis of the <i>HEXB</i> mRNA in cells transfected with normal (pcDNA3HEXBN) and a minigene containing mutation c.1242+1G>A (pcDNA3HEX1242) MW: 1 kb Plus DNA Ladder. Panel B: Schematic representation of the effect of the novel mutations on the splicing process. Sequencing analysis of RT-PCR products showed that the presence of c.1242+1G>A mutation determines the skipping of 73 nt of exon 10. In addition the presence of a 3′ cryptic splice site (present in both normal and mutant minigenes) determines the skipping of 112 nt in exon 11(denoted as red box) in cells transfected with both normal and mutant minigenes. Panel C: RT-PCR analysis of the <i>HEXB</i> mRNA in normal fibroblasts. MW: 1 kb Plus DNA Ladder.</p

<i>In silico</i> analysis of novel splicing mutations.

<p>ND: Not detected; ME: Maximum Entropy; NN: Neural Network.</p

<i>In vitro</i> functional analysis of mutations c.1082+5G>A and c.1169+5G>A.

<p>RT-PCR analysis of the <i>HEXB</i> mRNA in cells transfected with normal (pcDNA3HEXBN) and minigenes containing mutations c.1082+5G>A (pcDNA3HEX1082) and c.1169+5G>A (pcDNA3HEX1169) (panels A and C, respectively). MW: 1 kb Plus DNA Ladder. Schematic representation of the effect of the novel mutations on the splicing process (panels B and D). Sequencing analysis of RT-PCR products showed that the c.1082+5G>A mutation determines the skipping of 178 nt of exon 8 (panel B), whereas the presence of c.1169+5G>A mutation determines the retention of 87 nt of exon 9 (panel D).</p

MLPA analysis of SD1 and SD4 patients.

<p>Panels A–C: Capillary electrophoresis profile of the MLPA analysis performed in a normal control (panel A) and patients SD4 (panel B) and SD1 (panel C). Each peak corresponds to the amplification of a probe specific for each exon of <i>HEXB</i> gene (numbered 1 to 14) and for 3 different reference genes (R1, R2, R3). Arrows indicate the peaks corresponding to the <i>HEXB</i> exons deleted in SD1 and SD4 patients. Panel D) Relative quantification of <i>HEXB</i> copy number was obtained by dividing the height of each gene-specific peak by the sum of the heights of 3 reference gene peaks. This ratio was then compared to the average ratio obtained from 8 control samples having each 2 <i>HEXB</i> gene copies. A ratio between 0.75 and 1.25 corresponds to 2 <i>HEXB</i> copy number while a ratio between 0.25 and 0.75 corresponds to 1 <i>HEXB</i> copy number.</p

Planning the Human Variome Project: The Spain report.

The remarkable progress in characterizing the human genome sequence, exemplified by the Human Genome Project and the HapMap Consortium, has led to the perception that knowledge and the tools (e.g., microarrays) are sufficient for many if not most biomedical research efforts. A large amount of data from diverse studies proves this perception inaccurate at best, and at worst, an impediment for further efforts to characterize the variation in the human genome. Because variation in genotype and environment are the fundamental basis to understand phenotypic variability and heritability at the population level, identifying the range of human genetic variation is crucial to the development of personalized nutrition and medicine. The Human Variome Project (HVP; http://www.humanvariomeproject.org/) was proposed initially to systematically collect mutations that cause human disease and create a cyber infrastructure to link locus specific databases (LSDB). We report here the discussions and recommendations from the 2008 HVP planning meeting held in San Feliu de Guixols Spain, in May 2008. Hum Mutat 30, 496-510, 2009. (C) 2009 Wiley-Liss, Incclose31333