A central role for C1q/TNF-related protein 13 (CTRP13) in modulating food intake and body weight.

C1q/TNF-related protein 13 (CTRP13), a hormone secreted by adipose tissue (adipokines), helps regulate glucose metabolism in peripheral tissues. We previously reported that CTRP13 expression is increased in obese and hyperphagic leptin-deficient mice, suggesting that it may modulate food intake and body weight. CTRP13 is also expressed in the brain, although its role in modulating whole-body energy balance remains unknown. Here, we show that CTRP13 is a novel anorexigenic factor in the mouse brain. Quantitative PCR demonstrated that food restriction downregulates Ctrp13 expression in mouse hypothalamus, while high-fat feeding upregulates expression. Central administration of recombinant CTRP13 suppressed food intake and reduced body weight in mice. Further, CTRP13 and the orexigenic neuropeptide agouti-related protein (AgRP) reciprocally regulate each other's expression in the hypothalamus: central delivery of CTRP13 suppressed Agrp expression, while delivery of AgRP increased Ctrp13 expression. Food restriction alone reduced Ctrp13 and increased orexigenic neuropeptide gene (Npy and Agrp) expression in the hypothalamus; in contrast, when food restriction was coupled to enhanced physical activity in an activity-based anorexia (ABA) mouse model, hypothalamic expression of both Ctrp13 and Agrp were upregulated. Taken together, these results suggest that CTRP13 and AgRP form a hypothalamic feedback loop to modulate food intake and that this neural circuit may be disrupted in an anorexic-like condition

Vehicle trajectory clustering based on dynamic representation learning of internet of vehicles

With the widely used Internet of Things, 5G, and smart city technologies, we are able to acquire a variety of vehicle trajectory data. These trajectory data are of great significance which can be used to extract relevant information in order to, for instance, calculate the optimal path from one position to another, detect abnormal behavior, monitor the traffic flow in a city, and predict the next position of an object. One of the key technology is to cluster vehicle trajectory. However, existing methods mainly rely on manually designed metrics which may lead to biased results. Meanwhile, the large scale of vehicle trajectory data has become a challenge because calculating these manually designed metrics will cost more time and space. To address these challenges, we propose to employ network representation learning to achieve accurate vehicle trajectory clustering. Specifically, we first construct the k-nearest neighbor-based internet of vehicles in a dynamic manner. Then we learn the low-dimensional representations of vehicles by performing dynamic network representation learning on the constructed network. Finally, using the learned vehicle vectors, vehicle trajectories are clustered with machine learning methods. Experimental results on the real-word dataset show that our method achieves the best performance compared against baseline methods. © 2000-2011 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record*

Sim-T: Simplify the Transformer Network by Multiplexing Technique for Speech Recognition

In recent years, a great deal of attention has been paid to the Transformer network for speech recognition tasks due to its excellent model performance. However, the Transformer network always involves heavy computation and large number of parameters, causing serious deployment problems in devices with limited computation sources or storage memory. In this paper, a new lightweight model called Sim-T has been proposed to expand the generality of the Transformer model. Under the help of the newly developed multiplexing technique, the Sim-T can efficiently compress the model with negligible sacrifice on its performance. To be more precise, the proposed technique includes two parts, that are, module weight multiplexing and attention score multiplexing. Moreover, a novel decoder structure has been proposed to facilitate the attention score multiplexing. Extensive experiments have been conducted to validate the effectiveness of Sim-T. In Aishell-1 dataset, when the proposed Sim-T is 48% parameter less than the baseline Transformer, 0.4% CER improvement can be obtained. Alternatively, 69% parameter reduction can be achieved if the Sim-T gives the same performance as the baseline Transformer. With regard to the HKUST and WSJ eval92 datasets, CER and WER will be improved by 0.3% and 0.2%, respectively, when parameters in Sim-T are 40% less than the baseline Transformer

SODIUM AESCINATE INJECTION FOR SKIN FLAP TRANSPLANTATION OF HAND OR FOOT IN CHILDREN

Background: The purpose of this study is to evaluate the efficiency and safety of sodium aescinate injection for treating children suffering transplanted flap in children hand or foot. Materials and Methods: Ninety children patients with transplanted cutaneous nerve nutrition vascular flaps in hand or foot were selected and divided into “treatment” and “control” groups randomly by computer. The treatment group was prescribed intravenous sodium aescine injection and conventional therapy. The control group was only offered conventional therapy. Seven days following treatment, cumulative wound drainage, swelling of flap and adverse reactions were recorded. One month subsequent to treatment, two-point discrimination of flap was recorded. At the outset of treatment (0-d) and 1, 3, 7-days following treatment, the concentrations of C-reactive protein (CRP) and malondialdehyde(MDA) in venous blood were tested. Results: At the outset of treatment, there was no statistically significant difference between the two groups in terms of CRP and MDA (P>0.05). At 3 and 7 day intervals following treatment, the concentrations of CRP and MDA in treatment group were lower than those in control group (

Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters

Resource management of virtualized servers in data-centres has become a critical task, since it enables costeffective consolidation of server applications. Resource management is an important and challenging task, especially for multi-tier applications with unpredictable time-varying workloads. Work in resource management using control theory has shown clear benefits of dynamically adjusting resource allocations to match fluctuating workloads. However, little work has been done towards adaptive controllers for unknown workload types. This work presents a new resource management scheme that incorporates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. We present a set of controllers that continuously detect and self-adapt to unforeseen workload changes. Furthermore, our most advanced controller also self-configures itself without any a priori information and with a small 4.8% performance penalty in the case of high intensity workload changes. In addition, our controllers are enhanced to deal with multi-tier server applications: by using the pair-wise resource coupling between tiers, they improve server response to large workload increases as compared to controllers with no such resource-coupling mechanism. Our approaches are evaluated and their performance is illustrated on a 3-tier Rubis benchmark web-site deployed on a prototype Xen-virtualized cluster

Deep high-temperature hydrothermal circulation in a detachment faulting system on the ultra-slow spreading ridge

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Tao, C., Seyfried, W. E., Jr., Lowell, R. P., Liu, Y., Liang, J., Guo, Z., Ding, K., Zhang, H., Liu, J., Qiu, L., Egorov, I., Liao, S., Zhao, M., Zhou, J., Deng, X., Li, H., Wang, H., Cai, W., Zhang, G., Zhou, H., Lin, J., & Li, W. Deep high-temperature hydrothermal circulation in a detachment faulting system on the ultra-slow spreading ridge. Nature Communications, 11(1), (2020): 1300, doi:10.1038/s41467-020-15062-w.Coupled magmatic and tectonic activity plays an important role in high-temperature hydrothermal circulation at mid-ocean ridges. The circulation patterns for such systems have been elucidated by microearthquakes and geochemical data over a broad spectrum of spreading rates, but such data have not been generally available for ultra-slow spreading ridges. Here we report new geophysical and fluid geochemical data for high-temperature active hydrothermal venting at Dragon Horn area (49.7°E) on the Southwest Indian Ridge. Twin detachment faults penetrating to the depth of 13 ± 2 km below the seafloor were identified based on the microearthquakes. The geochemical composition of the hydrothermal fluids suggests a long reaction path involving both mafic and ultramafic lithologies. Combined with numerical simulations, our results demonstrate that these hydrothermal fluids could circulate ~ 6 km deeper than the Moho boundary and to much greater depths than those at Trans-Atlantic Geotraverse and Logachev-1 hydrothermal fields on the Mid-Atlantic Ridge.This work was supported by National Key R&D Program of China under contract no. 2018YFC0309901, 2017YFC0306603, 2017YFC0306803, and 2017YFC0306203, COMRA Major Project under contract No. DY135-S1-01-01 and No. DY135-S1-01-06

Adipokines in Sleep Disturbance and Metabolic Dysfunction: Insights from Network Analysis

Adipokines are a growing group of secreted proteins that play important roles in obesity, sleep disturbance, and metabolic derangements. Due to the complex interplay between adipokines, sleep, and metabolic regulation, an integrated approach is required to better understand the significance of adipokines in these processes. In the present study, we created and analyzed a network of six adipokines and their molecular partners involved in sleep disturbance and metabolic dysregulation. This network represents information flow from regulatory factors, adipokines, and physiologic pathways to disease processes in metabolic dysfunction. Analyses using network metrics revealed that obesity and obstructive sleep apnea were major drivers for the sleep associated metabolic dysregulation. Two adipokines, leptin and adiponectin, were found to have higher degrees than other adipokines, indicating their central roles in the network. These adipokines signal through major metabolic pathways such as insulin signaling, inflammation, food intake, and energy expenditure, and exert their functions in cardiovascular, reproductive, and autoimmune diseases. Leptin, AMP activated protein kinase (AMPK), and fatty acid oxidation were found to have global influence in the network and represent potentially important interventional targets for metabolic and sleep disorders. These findings underscore the great potential of using network based approaches to identify new insights and pharmaceutical targets in metabolic and sleep disorders

Sleep Disturbance and Metabolic Dysfunction: The Roles of Adipokines

Adipokines are a growing group of peptide or protein hormones that play important roles in whole body metabolism and metabolic diseases. Sleep is an integral component of energy metabolism, and sleep disturbance has been implicated in a wide range of metabolic disorders. Accumulating evidence suggests that adipokines may play a role in mediating the close association between sleep disorders and systemic metabolic derangements. In this review, we briefly summarize a group of selected adipokines and their identified function in metabolism. Moreover, we provide a balanced overview of these adipokines and their roles in sleep physiology and sleep disorders from recent human and animal studies. These studies collectively demonstrate that the functions of adipokine in sleep physiology and disorders could be largely twofold: (1) adipokines have multifaceted roles in sleep physiology and sleep disorders, and (2) sleep disturbance can in turn affect adipokine functions that likely contribute to systemic metabolic derangements