An autosomal recessive leucoencephalopathy with ischemic stroke, dysmorphic syndrome and retinitis pigmentosa maps to chromosome 17q24.2-25.3

Background Single-gene disorders related to ischemic stroke seem to be an important cause of stroke in young patients without known risk factors. To identify new genes responsible of such diseases, we studied a consanguineous Moroccan family with three affected individuals displaying hereditary leucoencephalopathy with ischemic stroke, dysmorphic syndrome and retinitis pigmentosa that appears to segregate in autosomal recessive pattern. Methods All family members underwent neurological and radiological examinations. A genome wide search was conducted in this family using the ABI PRISM linkage mapping set version 2.5 from Applied Biosystems. Six candidate genes within the region linked to the disease were screened for mutations by direct sequencing. Results Evidence of linkage was obtained on chromosome 17q24.2-25.3. Analysis of recombination events and LOD score calculation suggests linkage of the responsible gene in a genetic interval of 11 Mb located between D17S789 and D17S1806 with a maximal multipoint LOD score of 2.90. Sequencing of seven candidate genes in this locus, ATP5H, FDXR, SLC25A19, MCT8, CYGB, KCNJ16 and GRIN2C, identified three missense mutations in the FDXR gene which were also found in a homozygous state in three healthy controls, suggesting that these variants are not disease-causing mutations in the family. Conclusion A novel locus for leucoencephalopathy with ischemic stroke, dysmorphic syndrome and retinitis pigmentosa has been mapped to chromosome 17q24.2-25.3 in a consanguineous Moroccan family

Protein Hydrolysates as Resistance Inducers for Controlling Green Mould of Citrus Fruits.

The effectiveness of eight protein hydrolysates of natural origin (soybean, lupin, pea, yeast, casein, and malt hydrolysate) for controlling Penicillium digitatum, the main postharvest pathogen of citrus fruit, was evaluated by both in vitro and in vivo trials. The in vitro screening showed no significant effect of the different compounds on pathogen radial growth, whereas the application of the different protein hydrolysates (1.6 mg/ml) to wounded fruits proved their ability to induce resistance against P. digitatum by reducing significantly disease incidence and severity. The best activity was observed on fruits treated with soybean, lupin, and casein hydrolysates. Although further studies are needed, the results obtained are encouraging and potential application against green mould on citrus fruit is foreseen

Protein hydrolysates as resistance inducers for controlling green mould of citrus fruit

The effectiveness of eight protein hydrolysates of natural origin (soybean, lupin, pea, yeast, casein, and malt hydrolysate) for controlling Penicillium digitatum, the main postharvest pathogen of citrus fruit, was evaluated by both in vitro and in vivo trials. The in vitro screening showed no significant effect of the different compounds on pathogen radial growth, whereas the application of the different protein hydrolysates (1.6 mg/ml) to wounded fruits proved their ability to induce resistance against P. digitatum by reducing significantly disease incidence and severity. The best activity was observed on fruits treated with soybean, lupin, and casein hydrolysates. Although further studies are needed, the results obtained are encouraging and potential application against green mould on citrus fruit is foreseen

Towards a context-driven platform using IoT and big data technologies for energy efficient buildings

International audienceContext-awareness is crucial for leveraging energy-efficient buildings by developing intelligent control approaches in which sensing and actuation tasks are performed according to the contextual changes. This could be done by including the users' actions and behaviours in up-to-date context taking into account the complex interlinked elements, situations, processes, and their dynamics. In this paper, we introduce a holistic platform that integrates recent sensing/actuating and Big data technologies for monitoring and data processing. The main aim is to develop context-driven control approaches whereby energy consumption, production, and storage could be controlled according to actual situations (e.g., occupancy, occupant behaviour patterns, energy production patterns, and weather data). A platform prototype was deployed in our university test site. Experiments have been conducted and preliminary results show the usefulness of this holistic platform for monitoring and data processing in energy efficient buildings