Leptin induces inflammation-related genes in RINm5F insulinoma cells

<p>Abstract</p> <p>Background</p> <p>Leptin acts not only on hypothalamic centers to control food intake but has additional functions in peripheral tissues, <it>e.g</it>. inhibition of insulin secretion from pancreatic islets. The leptin receptor (LEPRb) is a class I cytokine receptor that mediates activation of STAT transcription factors. In this study, we characterise the regulation of inflammation-related genes by leptin in insulinoma cells and compare the effect of transcriptional regulation by leptin with that of other cytokines.</p> <p>Results</p> <p>We have used RINm5F insulinoma cells as a model system for a peripheral target cell of leptin. Six transcripts encoding inflammation-related proteins were found to be upregulated by activation of LEPRb, namely lipocalin-2, pancreatitis-associated protein, preprotachykinin-1, fibrinogen-β, tissue-type plasminogen activator (tPA) and manganese-dependent superoxide dismutase (MnSOD). Four of these transcripts (fibrinogen-β, lipocalin-2, tPA, MnSOD) were also induced by the proinflammatory cytokine interleukin-1β (IL-1β). Interferon-γ alone had no effect on the leptin-induced transcripts but enhanced the upregulation by IL-1β of lipocalin-2, tPA and MnSOD mRNA levels. Experiments with LEPRb point mutants revealed that the upregulation of the inflammation-related genes depended on the presence of tyrosine-1138 which mediates the activation of the transcription factors STAT1 and STAT3. Reporter gene assays showed that leptin induced the expression of preprotachykinin-1 and lipocalin-2 on the level of promoter regulation. Finally, leptin treatment increased caspase 3-like proteolytic activity in RINm5F cells.</p> <p>Conclusion</p> <p>The present data show that leptin induces a cytokine-like transcriptional response in RINm5F cells, consistent with the proposed function of leptin as a modulator of immune and inflammatory responses.</p

Low-Power Methodologies for High-Performance and Yield-Enhanced 3D Interconnects

A set of physically precise high-level models for the 3D-interconnect (TSV-based) power consumption and performance are presented in this thesis. Based on these models, a low-power technique is contributed, which can reduce the interconnect power consumption in modern 3D ICs by over 40 %. Despite its drastic power savings, the method results in negligible implementation costs. Additionally, optimization techniques are presented, which improve the TSV power consumption and performance simultaneously. The methods improve the performance by up to 65% while providing power savings of 17%, and this at lower costs than the best previous technique. Moreover, a low-power technique is presented, which also improves the manufacturing yield of TSVs. The method reduces the TSV-related defect rate by a factor of 17×, while additionally providing an improvement in the interconnect power consumption by over 30%

Breaking the habit: On the highly habitualized nature of meat consumption and implementation intentions as one effective way of reducing it

Rees J, Bamberg S, Jaeger A, Victor L, Bergmeyer M, Friese M. Breaking the habit: On the highly habitualized nature of meat consumption and implementation intentions as one effective way of reducing it. Basic and Applied Social Psychology. 2018;40(3):136-147.Reducing meat consumption is an important element of an effective climate protection strategy, but meat consumption is highly habitualized and therefore difficult to change. This article uses an extended version of the theory of planned behavior with habit strength as additional predictor. In one longitudinal (N=227) and one prospective correlational study (N=212), attitudes toward and perceived ease of meat consumption reduction explained about 60% of variance of meat consumption reduction intentions, with habit strength being the strongest correlate of actual self-reported meat consumption. A third experimental study (N=192) demonstrated that implementation intentions can be an effective strategy for realizing reduction aims. We discuss the central role of habits for meat consumption

ARTS:An adaptive regularization training schedule for activation sparsity exploration

Brain-inspired event-based processors have attracted considerable attention for edge deployment because of their ability to efficiently process Convolutional Neural Networks (CNNs) by exploiting sparsity. On such processors, one critical feature is that the speed and energy consumption of CNN inference are approximately proportional to the number of non-zero values in the activation maps. Thus, to achieve top performance, an efficient training algorithm is required to largely suppress the activations in CNNs. We propose a novel training method, called Adaptive-Regularization Training Schedule (ARTS), which dramatically decreases the non-zero activations in a model by adaptively altering the regularization coefficient through training. We evaluate our method across an extensive range of computer vision applications, including image classification, object recognition, depth estimation, and semantic segmentation. The results show that our technique can achieve 1.41 × to 6.00 × more activation suppression on top of ReLU activation across various networks and applications, and outperforms the state-of-the-art methods in terms of training time, activation suppression gains, and accuracy. A case study for a commercially-available event-based processor, Neuronflow, shows that the activation suppression achieved by ARTS effectively reduces CNN inference latency by up to 8.4 × and energy consumption by up to 14.1 ×.</p