Impact of the Ohmic Electrode on the Endurance of Oxide-Based Resistive Switching Memory

As one of the key aspects in the reliability of redox-based resistive switching memories (ReRAMs), maximizing their endurance is of high relevance for industrial applications. The major limitation regarding endurance is considered the excessive generation of oxygen vacancies during cycling, which eventually leads to irreversible RESET failures. Thus, the endurance could be increased by using combinations of switching oxide and ohmic electrode (OE) metal that provides a high barrier for the generation of oxygen vacancies [defect formation energy (DFE)]. In this work, we present a sophisticated programming algorithm that aims to maximize the endurance within reasonable measurement time. Using this algorithm, we compare ReRAM devices with four different OE metals and confirm the theoretically predicted trend. Thus, our work provides valuable information for device engineering toward higher endurance

Mitochondrial misreading in skeletal muscle accelerates metabolic aging and confers lipid accumulation and increased inflammation

We have recently reported on an experimental model of mitochondrial mistranslation conferred by amino acid exchange V338Y in the mitochondrial ribosomal protein MrpS5. Here we used a combination of RNA-Seq and metabolic profiling of homozygous transgenic MrpS5V338Y/V338Y mice to analyze the changes associated with the V338Y mutation in post-mitotic skeletal muscle. Metabolic profiling demonstrated age-dependent metabolic changes in the mutant V338Y animals, which included enhanced levels of age-associated metabolites and which were accompanied by increased glycolysis, lipid desaturation and eicosanoid biosynthesis, and alterations of the pentose phosphate pathway. In addition, transcriptome signatures of aged V338Y mutant muscle pointed to elevated inflammation, likely reflecting the increased levels of bioactive lipids. Our findings indicate that mistranslation-mediated chronic impairment of mitochondrial function affects specific bioenergetic processes in muscle in an age-dependent manner

MiR-17/20/93/106 promote hematopoietic cell expansion by targeting sequestosome 1-regulated pathways in mice

MicroRNAs (miRNAs) are pivotal for regulation of hematopoiesis but their critical targets remain largely unknown. Here, we show that ectopic expression of miR-17, -20,-93 and -106, all AAAGUGC seed-containing miRNAs, increases proliferation, colony outgrowth and replating capacity of myeloid progenitors and results in enhanced P-ERK levels. We found that these miRNAs are endogenously and abundantly expressed in myeloid progenitors and down-regulated in mature neutrophils. Quantitative proteomics identified sequestosome 1 (SQSTM1), an ubiquitin-binding protein and regulator of autophagy-mediated protein degradation, as a major target for these miRNAs in myeloid progenitors. In addition, we found increased expression of Sqstm1 transcripts during CSF3-induced neutrophil differentiation of 32D-CSF3R cells and an inverse correlation of SQSTM1 protein levels and miR-106 expression in AML samples. ShRNA-mediated silencing of Sqstm1 phenocopied the effects of ectopic miR-17/20/93/106 expression in hematopoietic progenitors in vitro and in mice. Further, SQSTM1 binds to the ligand-activated colony-stimulating factor 3 receptor (CSF3R) mainly in the late endosomal compartment, but not in LC3 positive autophagosomes. SQSTM1 regulates CSF3R stability and ligand-induced mitogen-activated protein kinase signaling. We demonstrate that AAAGUGC seed-containing miRNAs promote cell expansion, replating capacity and signaling in hematopoietic cells by interference with SQSTM1-regulated pathways

ALICE HLT High Speed Tracking on GPU

The on-line event reconstruction in ALICE is performed by the High Level Trigger, which should process up to 2000 events per second in proton-proton collisions and up to 300 central events per second in heavy-ion collisions, corresponding to an inp ut data stream of 30 GB/s. In order to fulfill the time requirements, a fast on-line tracker has been developed. The algorithm combines a Cellular Automaton method being used for a fast pattern recognition and the Kalman Filter method for fitting of found trajectories and for the final track selection. The tracker was adapted to run on Graphics Processing Units (GPU) using the NVIDIA Compute Unified Device Architecture (CUDA) framework. The implementation of the algorithm had to be adjusted at many points to allow for an efficient usage of the graphics cards. In particular, achieving a good overall workload for many processor cores, efficient transfer to and from the GPU, as well as optimized utilization of the different memories the GPU offers turned out to be critical. To cope with these problems a dynamic scheduler was introduced, which redistributes the workload among the processor cores. Additionally a pipeline was implemented so that the tracking on the GPU, the initialization and the output process ed by the CPU, as well as the DMA transfer can overlap. The GPU tracking algorithm significantly outperforms the CPU version for large events while it entirely maintains its efficiency