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- Salami, B., et al.
(författare)
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LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing
- 2020
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Ingår i: PROCEEDINGS OF THE 2020 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2020). - 1530-1591. - 9783981926347 ; , s. 169-174
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Konferensbidrag (refereegranskat)abstract
- The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC, balanced with the security and resilience challenges. LEGaTO is an ongoing three-year EU H2020 project started in December 2017.
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- Meisgen, S, et al.
(författare)
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Auxilin is a novel susceptibility gene for congenital heart block which directly impacts fetal heart function
- 2022
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Ingår i: Annals of the rheumatic diseases. - : BMJ. - 1468-2060 .- 0003-4967. ; 81:8, s. 1151-1161
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Tidskriftsartikel (refereegranskat)abstract
- Neonatal lupus erythematosus (NLE) may develop after transplacental transfer of maternal autoantibodies with cardiac manifestations (congenital heart block, CHB) including atrioventricular block, atrial and ventricular arrhythmias, and cardiomyopathies. The association with anti-Ro/SSA antibodies is well established, but a recurrence rate of only 12%–16% despite persisting maternal autoantibodies suggests that additional factors are required for CHB development. Here, we identify fetal genetic variants conferring risk of CHB and elucidate their effects on cardiac function.MethodsA genome-wide association study was performed in families with at least one case of CHB. Gene expression was analysed by microarrays, RNA sequencing and PCR and protein expression by western blot, immunohistochemistry, immunofluorescence and flow cytometry. Calcium regulation and connectivity were analysed in primary cardiomyocytes and cells induced from pleuripotent stem cells. Fetal heart performance was analysed by Doppler/echocardiography.ResultsWe identified DNAJC6 as a novel fetal susceptibility gene, with decreased cardiac expression of DNAJC6 associated with the disease risk genotype. We further demonstrate that fetal cardiomyocytes deficient in auxilin, the protein encoded by DNAJC6, have abnormal connectivity and Ca2+ homoeostasis in culture, as well as decreased cell surface expression of the Cav1.3 calcium channel. Doppler echocardiography of auxilin-deficient fetal mice revealed cardiac NLE abnormalities in utero, including abnormal heart rhythm with atrial and ventricular ectopias, as well as a prolonged atrioventricular time intervals.ConclusionsOur study identifies auxilin as the first genetic susceptibility factor in NLE modulating cardiac function, opening new avenues for the development of screening and therapeutic strategies in CHB.
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| 5. |
- Mellström, Dan, 1945, et al.
(författare)
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Proportion and Characteristics of Patients in Sweden Remaining at High Risk of Fracture Despite Prior Treatment
- 2016
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Ingår i: Clinical Therapeutics. - : Elsevier BV. - 0149-2918. ; 38:7, s. 1686-1695
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Fragility fractures axe a clinical consequence of osteoporosis (OP). Evidence suggests however, current OP treatments may be inadequate in reducing fracture risk. The purpose of this study was to estimate the proportion and characteristics of Swedish patients who remain at high risk of fracture after 2 years of treatment, as evidenced by osteoporotic bone mineral density (BMD), a decrease in BMD, or the occurrence of new fractures. Methods: This was a retrospective, descriptive analysis of a subset of participants obtained from a Swedish osteoporosis patient registry from 1991 to 2009. Patients were required to be osteoporotic, to be treatment naive at baseline, to have returned for at least 1 follow-up visit, and to have reported osteoporosis treatment use for >= 2 years after the baseline visit with a BMD T score. Two overlapping cohorts remaining at high risk of fracture were defined using the BMD T score measured after 2 years of treatment from baseline. The osteoporosis cohort comprised patients who remained osteoporotic, whereas the BMD decrease cohort included patients whose total hip or lumbar spine T score decreased by >= 3%. Findings: A total of 3292 osteoporotic patients were identified in the registry, of whom 392 met the study inclusion criteria. The mean (SD) patient age was 68.3 (8.5) years, with most patients being female (92.3%). Among all patients, 297 (75.8%) remained osteoporotic after at least 2 years of treatment, 90 (23.0%) experienced a BMD decrease of >= 3%, and 23 (5.9%) reported an incident fracture between the baseline and first follow-up visit. More than three-quarters (76.8%) of all patients reported taking bisphosphonates, whereas only 72.4% and 47.8% reported this in the osteoporosis and BMD decrease cohorts, respectively. Raloxifene was the only nonbisphosphonate used, with 24.2% of all patients reportedly taking it. Implications: This study highlighted that despite 2 years of osteoporosis treatment, a high percentage of patients remain at high risk of fracture. There is a need for improved treatment strategies that reduce fracture risk and improve patient outcomes in the real-world setting.
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| 10. |
- Kaiser, M., et al.
(författare)
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VEDLIoT: Very Efficient Deep Learning in IoT
- 2022
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Ingår i: Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022. - : IEEE. - 9783981926361
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Konferensbidrag (refereegranskat)abstract
- The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.
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