Paraoxonase 1 gene (PON1) polymorphisms and dietary vegetable and fruit intake are both established determinants of ischemic stroke (IS). However, little is known about whether these factors jointly influence the risk of IS. We analyzed the main effects of PON1, as well as the interactions between PON1 and dietary vegetable or fruit intake with the risk of total IS and its subtypes in a family-based case-control study conducted among 2158 Chinese participants (1007 IS cases and 1151 IS-free controls) from 918 families. Conditional logistic regression models, with each family as a stratum, were used to examine the association between rs662 and IS. Gene-diet interactions were tested by including a cross-product term of dietary vegetable or fruit intake by rs662_G allele count in the models. Each copy of the PON1 rs662_G allele was associated with 28% higher risk of total IS (p = 0.008) and 32% higher risk of large artery atherosclerosis subtype (LAA) (p = 0.01). We observed an interaction between rs662 and vegetable intake for both total IS (p = 0.006) and LAA (p = 0.02) after adjustment for covariates. Individuals who carry the rs662_A allele may benefit to a greater extent from intake of vegetables and thus be more effectively protected from ischemic stroke, whereas carriers of the G allele may still remain at greater risk for ischemic stroke due to their genetic backgrounds even when they consume a high level of vegetables. More studies are needed to replicate our findings among other populations.
This paper presents new insights into constructing a cyber-infrastructure system, called J-Park Simulator, for the resource and energy management of eco-industrial parks. The concept of Industry 4.0 is applied to develop the system. Advanced information modeling and managing technology – ontology technology is introduced to build the knowledge base for J-Park Simulator, which delivers a decentralized information managing system. The system allows information of the individual entities been managed locally by the entity themselves, in the meanwhile facilitates information sharing via the Internet and/or intranet. A bottom-up object-oriented approach is adopted to partition and organize information of the complex eco-industrial park. The proposed work addresses key issues in computer-aided design and operation of eco-industrial parks in the context of realizing Industry 4.0.
This work reports a kilovolt and low current collapse normally-off GaN MOSHEMT on silicon substrate. The device with a drift length of 3 mum features a threshold voltage of 1.7 V and an output current of 430 mA/mm at 8 V gate bias. The off-state breakdown voltage (BV) is as high as 1021 V (800 V) defined at a drain leakage criterion of 10 muA/mm with floating (grounded) substrate. The corresponding breakdown electric field is 3.4 MV/cm and the Baliga's figure-of-merit (BFOM) is 1.6 GW/cm2. A small degradation of the dynamic on-resistance (Ron, d) about 30% is observed with a short pulse width of 500 ns and a quiescent drain bias of 60 V. The record value is supposed to benefit from the intrinsic step-graded field plate, high quality LPCVD Si3N4 passivation and material optimization of 4.5 mum thick epitaxial layer.
An ontology-based approach for Eco-Industrial Park (EIP) knowledge management is proposed in this paper. The designed ontology in this study is formalized conceptualization of EIP. Based on such an ontological representation, a Knowledge-Based System (KBS) for EIP energy management named J-Park Simulator (JPS) is developed. By applying JPS to the solution of EIP waste heat utilization problem, the results of this study show that ontology is a powerful tool for knowledge management of complex systems such as EIP. The ontology-based approach can increase knowledge interoperability between different companies in EIP. The ontology-based approach can also allow intelligent decision making by using disparate data from remote databases, which implies the possibility of self-optimization without human intervention scenario of Internet of Things (IoT). It is shown through this study that KBS can bridge the communication gaps between different companies in EIP, sequentially more potential Industrial Symbiosis (IS) links can be established to improve the overall energy efficiency of the whole EIP.
In this paper, we introduce a complementary relationship between consumption and labor hours by revising the household's period utility function in Liu et al. (2013). The revision concomitantly allows for a finite Frisch elasticity of labor supply and a stronger consumption smoothing motive. We find that, in general, the estimation of Liu et al. (2013) is quite robust. In addition, the propagation mechanism of the credit constraint triggered by a housing demand shock still persists. However, the amplification effect of the credit constraint triggered by the housing demand shock on key macroeconomic variables is greatly muted. We also find that, except for land price fluctuations, the housing demand shock cannot act as the primary force to drive the fluctuations in other macroeconomic variables.
The repeatability of laser proton accelerator is mainly limited by laser plasma interaction, laser target coupling and laser parameter variation. In our recent experiments performed on the Compact Laser Plasma Accelerator at Peking University, gain of proton beams with improved repeatability is demonstrated. In order to control the laser plasma interaction in pre-plasma, cross polarized-wave generation technique is employed to provide a laser pulse with an ultrahigh contrast of 10 −10 A semi-automatic laser and target alignment system with a sensitivity of few microns is employed. The repetition rate of the laser pro-ton accelerator is at the level of 0.1 Hz which is beneficial to decrease laser parameter variation. The shot-to-shot variation of proton energies is about 9% for a level of confidence of 0.95.
Methanesulfonic acid (MSA) has been widely used as a proxy for marine biogenic sources, but it is still a challenge to provide an accurate MSA mass concentration with high time resolution. This study offers an improved MSA quantification method using high resolution time of-flight aerosol mass spectrometer (HR-ToF-AMS). Particularly, the method was validated based on an excellent agreement with parallel offline measurements (slope = 0.88, R-2 = 0.89). This comparison is much better than those using previously reported methods, resulting in underestimations of 31-54% of MSA concentration. With this new method, MSA mass concentrations were obtained during 4 North/South Atlantic cruises in spring and autumn of 2011 and 2012. The seasonal and spatial variation of the particulate MSA mass concentration as well as the MSA to non-sea-salt sulfate ratio (MSA:nssSO(4)) over the North/South Atlantic Ocean were determined for the first time. Seasonal variation of the MSA mass concentration was observed, with higher values in spring (0.03 mu g m(-3)) than in autumn (0.01 mu g m(-3)). The investigation of MSA:nssSO(4) suggests a ubiquitous and significant influence of anthropogenic sources on aerosols in the marine boundary layer.
Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task. As one of the key models for sequence learning, recurrent neural network (RNN) and its variants such as long short term memory (LSTM) and gated recurrent unit (GRU) are still not powerful enough in practice. One possible reason is that they have only feedforward connections, which is different from the biological neural system that is typically composed of both feedforward and feedback connections. To address this problem, this paper proposes a biologically-inspired deep network, called shuttleNet. Technologically, the shuttleNet consists of several processors, each of which is a GRU while associated with multiple groups of hidden states. Unlike traditional RNNs, all processors inside shuttleNet are loop connected to mimic the brain's feedforward and feedback connections, in which they are shared across multiple pathways in the loop connection. Attention mechanism is then employed to select the best information flow pathway. Extensive experiments conducted on two benchmark datasets (i.e UCF101 and HMDB51) show that we can beat state-of-the-art methods by simply embedding shuttleNet into a CNN-RNN framework.