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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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- Streubel, K., et al.
(författare)
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Novel technologies for 1.55-mu m vertical cavity lasers
- 2000
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Ingår i: Optical Engineering. - : SPIE-Intl Soc Optical Eng. - 0091-3286 .- 1560-2303. ; 39:2, s. 488-497
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Tidskriftsartikel (refereegranskat)abstract
- We report on three novel vertical-cavity laser (VCL) structures for 1.55-mu m operation. Two of the structures utilize an n-type GalnAsP/InP Bragg mirror combined with an Al(Ga)As/GaAs mirror using either wafer fusion or metamorphic epitaxial growth. The third employs two wafer-fused AlGaAs/GaAs mirrors, in which lateral current confinement is obtained by localized fusion of the p mirror. Ali three VCLs use strained GalnAsP quantum welts as active material and achieve continuous-wave (cw) operation at room temperature or above. The single fused VCL operates up to 17 and 101 degrees C in continuous-wave and pulsed mode, respectively. The monolithic VCL-structure with a metamorphic GaAs/AlAs n-type mirror uses a reverse-biased tunnel junction for current injection. This laser achieves record high output power (1 mW) at room temperature and operates cw up to 45 degrees C. The double fused VCLs with a 10x10-mu m(2) active area operate cw up to 30 degrees C with threshold current as low as 2.5 mA and series resistance of 30 Omega. The emission spectra exhibit a single lasing mode polarized with 30-dB extinction ratio and a spectral linewidth of 150 MHz.
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- 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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