Sepsis-associated neuroinflammation in the spinal cord

Septic patients commonly present with central nervous system (CNS) disorders including impaired consciousness and delirium. Today, the main mechanism regulating sepsis-induced cerebral disorders is believed to be neuroinflammation. However, it is unknown how another component of the CNS, the spinal cord, is influenced during sepsis. In the present study, we intraperitoneally injected mice with lipopolysaccharide (LPS) to investigate molecular and immunohistochemical changes in the spinal cord of a sepsis model. After LPS administration in the spinal cord, pro-inflammatory cytokines including interleukin (IL)-1β, IL-6, and tumor necrosis factor alpha mRNA were rapidly and drastically induced. Twenty-four-hour after the LPS injection, severe neuronal ischemic damage spread into gray matter, especially around the anterior horns, and the anterior column had global edematous changes. Immunostaining analyses showed that spinal microglia were significantly activated and increased, but astrocytes did not show significant change. The current results indicate that sepsis induces acute neuroinflammation, including microglial activation and pro-inflammatory cytokine upregulation in the spinal cord, causing drastic neuronal ischemia and white matter edema in the spinal cord

The inhibitory effects of Orengedokuto on inducible PGE2 production in BV-2 microglial cells

[Background and aim] Reactive microglia has been associated with neuroinflammation caused by the production of proinflammatory molecules such as cytokines, nitric oxide, and prostaglandins. The overexpression of these molecules may provoke neuronal damage that can cause neurodegenerative diseases. A traditional herbal medicine, Orengedokuto (OGT), has been widely used for treating inflammation-related diseases. However, how it influences neuroinflammation remains poorly understood. [Experimental procedure] This study investigated the effects of OGT on inflammatory molecule induction in BV-2 microglial cells using real-time RT-PCR and ELISA. An in vivo confirmation of these effects was then performed in mice. [Results and conclusion] OGT showed dose-dependent inhibition of prostaglandin E2 (PGE2) production in BV-2 cells stimulated with lipopolysaccharide (LPS). To elucidate the mechanism of PGE2 inhibition, we examined cyclooxygenases (COXs) and found that OGT did not suppress COX-1 expression or inhibit LPS-induced COX-2 upregulation at either the transcriptional or translational levels. In addition, OGT did not inhibit COX enzyme activities within the concentration that inhibited PGE2 production, suggesting that the effect of OGT is COX-independent. The inhibitory effects of OGT on PGE2 production in BV-2 cells were experimentally replicated in primary cultured astrocytes and mice brains. OGT can be useful in the treatment of neuroinflammatory diseases by modulating PGE2 expression

Mixing Low-Precision Formats in Multiply-Accumulate Units for DNN Training

International audienceThe most compute-intensive stage of deep neural network (DNN) training is matrix multiplication where the multiply-accumulate (MAC) operator is key. To reduce training costs, we consider using low-precision arithmetic for MAC operations. While low-precision training has been investigated in prior work, the focus has been on reducing the number of bits in weights or activations without compromising accuracy. In contrast, the focus in this paper is on implementation details beyond weight or activation width that affect area and accuracy. In particular, we investigate the impact of fixed-versus floating-point representations, multiplier rounding, and floatingpoint exceptional value support. Results suggest that (1) lowprecision floating-point is more area-effective than fixed-point for multiplication, (2) standard IEEE-754 rules for subnormals, NaNs, and intermediate rounding serve little to no value in terms of accuracy but contribute significantly to area, (3) lowprecision MACs require an adaptive loss-scaling step during training to compensate for limited representation range, and (4) fixed-point is more area-effective for accumulation, but the cost of format conversion and downstream logic can swamp the savings. Finally, we note that future work should investigate accumulation structures beyond the MAC level to achieve further gains

Observation of micropores in hard-carbon using Xe-129 NMR porosimetry

The existence of micropores and the change of surface structure in pitch-based hard-carbon in xenon atmosphere were demonstrated using Xe-129 NMR. For high-pressure (4.0 MPa) Xe-129 NMR measurements, the hard-carbon samples in Xe gas showed three peaks at 27, 34 and 210 ppm. The last was attributed to the xenon in micropores (<1 nm) in hard-carbon particles. The NMR spectrum of a sample evacuated at 773 K and exposed to 0.1 MPa Xe gas at 773 K for 24 h showed two peaks at 29 and 128 ppm, which were attributed, respectively, to the xenon atoms adsorbed in the large pores (probably mesopores) and micropores of hard-carbon. With increasing annealing time in Xe gas at 773 K, both peaks shifted and merged into one peak at 50 ppm. The diffusion of adsorbed xenon atoms is very slow, probably because the transfer of molecules or atoms among micropores in hard-carbon does not occur readily. Many micropores are isolated from the outer surface. For that reason, xenon atoms are thought to be adsorbed only by micropores near the surface, which are easily accessible from the surrounding space.</p

Survey of Period Variations of Superhumps in SU UMa-Type Dwarf Novae. VIII: The Eighth Year (2015-2016)

Continuing the project described by Kato et al. (2009, arXiv:0905.1757), we collected times of superhump maxima for 128 SU UMa-type dwarf novae observed mainly during the 2015-2016 season and characterized these objects. The data have improved the distribution of orbital periods, the relation between the orbital period and the variation of superhumps, the relation between period variations and the rebrightening type in WZ Sge-type objects. Coupled with new measurements of mass ratios using growing stages of superhumps, we now have a clearer and statistically greatly improved evolutionary path near the terminal stage of evolution of cataclysmic variables. Three objects (V452 Cas, KK Tel, ASASSN-15cl) appear to have slowly growing superhumps, which is proposed to reflect the slow growth of the 3:1 resonance near the stability border. ASASSN-15sl, ASASSN-15ux, SDSS J074859.55+312512.6 and CRTS J200331.3-284941 are newly identified eclipsing SU UMa-type (or WZ Sge-type) dwarf novae. ASASSN-15cy has a short (~0.050 d) superhump period and appears to belong to EI Psc-type objects with compact secondaries having an evolved core. ASASSN-15gn, ASASSN-15hn, ASASSN-15kh and ASASSN-16bu are candidate period bouncers with superhump periods longer than 0.06 d. We have newly obtained superhump periods for 79 objects and 13 orbital periods, including periods from early superhumps. In order that the future observations will be more astrophysically beneficial and rewarding to observers, we propose guidelines how to organize observations of various superoutbursts.Comment: 123 pages, 162 figures, 119 tables, accepted for publication in PASJ (including supplementary information

Psychometric Properties and Correlates of Precarious Manhood Beliefs in 62 Nations

Precarious manhood beliefs portray manhood, relative to womanhood, as a social status that is hard to earn, easy to lose, and proven via public action. Here, we present cross-cultural data on a brief measure of precarious manhood beliefs (the Precarious Manhood Beliefs scale [PMB]) that covaries meaningfully with other cross-culturally validated gender ideologies and with country-level indices of gender equality and human development. Using data from university samples in 62 countries across 13 world regions (N = 33,417), we demonstrate: (1) the psychometric isomorphism of the PMB (i.e., its comparability in meaning and statistical properties across the individual and country levels); (2) the PMB’s distinctness from, and associations with, ambivalent sexism and ambivalence toward men; and (3) associations of the PMB with nation-level gender equality and human development. Findings are discussed in terms of their statistical and theoretical implications for understanding widely-held beliefs about the precariousness of the male gender role

Using mixed low-precision formats in multiply-accumulate (MAC) units for DNN training

Due to limited size, cost and power, embedded devices do not offer the same computational throughput as graphics processing units (GPUs) for training Deep Neural Networks (DNNs). The most compute-intensive stage of multilayer perceptron (MLP) and convolutional neural network (CNN) training is the general matrix multiply (GEMM) kernel which is executed three times per layer in each iteration: once for forward-propagation and twice for back-propagation. To reduce the number of operations, techniques such as distillation (to reduce model size) and pruning (to introduce sparsity) are commonly applied. This thesis considers another technique, where the computational effort of each operation is reduced using low-precision arithmetic. While the use of small data types is common in DNN inference, this is not yet common in DNN training. Previous work in the area is somewhat limited, sometimes only considering 16-bit floating-point formats or overlooking implementation details, such as the area and accuracy tradeoffs from exact digital multiplier designs. This thesis considers the use of mixed-precision operations (MPO) within the GEMM kernel for DNN training. To conduct these investigations, we have implemented a complete DNN training framework for embedded systems, Archimedes-MPO. Starting with the C++ library TinyDNN, we have abstracted each layer to use custom data types and accelerated the GEMM stage with CUDA and Vitis HLS to produce bit-accurate GPU and FPGA implementations. This framework allows us to exactly measure the impact of various multiplier and accumulator designs on area and accuracy. Compared to 32-bit floating-point multiplier, as few as 2 mantissa bits attain similar accuracy. Accuracy losses are reduced with adaptive loss scaling and the removal of hardware for rounding and not-a-number (NaN) representations. Removal of subnormals saves area as well, but hurts accuracy, so we propose a custom subnormal encoding as a compromise. For accumulation, 12-bit floating-point and 21-bit fixed-point formats work similarly. Fixed-point accumulation seems to have an area advantage, but the impact of a wider output data size can be costly on downstream logic. While precise results depend upon the model and dataset used, the observed trends and framework can help the design of future GEMM-based hardware accelerators for DNNs.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat