Wall-thickness-dependent strength of nanotubular ZnO

We fabricate nanotubular ZnO with wall thickness of 45, 92, 123 nm using nanoporous gold (np-Au) with ligament diameter at necks of 1.43 mu m as sacrificial template. Through micro-tensile and micro-compressive testing of nanotubular ZnO structures, we find that the exponent m in (sigma) over bar proportional to (rho) over bar (m), where (sigma) over bar is the relative strength and (rho) over bar is the relative density, for tension is 1.09 and for compression is 0.63. Both exponents are lower than the value of 1.5 in the Gibson-Ashby model that describes the relation between relative strength and relative density where the strength of constituent material is independent of external size, which indicates that strength of constituent ZnO increases as wall thickness decreases. We find, based on hole-nanoindentation and glazing incidence X-ray diffraction, that this wall-thickness-dependent strength of nanotubular ZnO is not caused by strengthening of constituent ZnO by size reduction at the nanoscale. Finite element analysis suggests that the wall-thickness-dependent strength of nanotubular ZnO originates from nanotubular structures formed on ligaments of np-Au

Spatiotemporal cluster patterns of hand, foot, and mouth disease at the county level in Mainland China, 2008-2012

Background: Hand, foot, and mouth disease (HFMD) is known to be a highly contagious childhood illness. In recent years, the number of reported cases of HFMD has significantly increased in mainland China. This study aims at the epidemiological features, spatiotemporal patterns of HMFD at the county/district level in mainland China. Methods: Data on reported HFMD cases for each county from 1 January 2008 to 31 December 2012 were obtained from the Chinese Center for Disease Control and Prevention. Cluster analysis, spatial autocorrelation, and retrospective scan methods were used to explore the spatiotemporal patterns of the disease. Results: The annual incidences varied greatly among the counties, ranging from 0 to 74.31‰with the median of 5.42‰ (interquartile range: 1.54‰–13.55‰) during 2008–2012 in mainland China. Counties close to provincial capital cities generally had higher incidences than rural counties. A seasonal distribution was observed between the northern and southern China, of which dual epidemic were shown in southern China and usually only one in northern China. Based on the global and local spatial autocorrelation analysis, we found that the spatial distribution of HFMD was presented a significant clustering pattern for each year (P \u3c 0.001), and hotspots of the disease were mostly distributed in coastal provinces of China. The retrospective scan statistic further identified the dynamics of spatiotemporal clustering areas of the disease, which were mainly distributed in the counties of eastern and southern China, as well as provincial capitals and their surrounding counties. Conclusions: The spatiotemporal clustering areas of the disease identified in this way were relatively stable, and imminent public health planning and resource allocation should be focused within those areas

Observation of a ppb mass threshoud enhancement in \psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p}) decay

The decay channel $\psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p})$ is studied using a sample of $1.06\times 10^8$ $\psi^\prime$ events collected by the BESIII experiment at BEPCII. A strong enhancement at threshold is observed in the $p\bar{p}$ invariant mass spectrum. The enhancement can be fit with an $S$-wave Breit-Wigner resonance function with a resulting peak mass of $M=1861^{+6}_{-13} {\rm (stat)}^{+7}_{-26} {\rm (syst)} {\rm MeV/}c^2$ and a narrow width that is $\Gamma<38 {\rm MeV/}c^2$ at the 90% confidence level. These results are consistent with published BESII results. These mass and width values do not match with those of any known meson resonance.Comment: 5 pages, 3 figures, submitted to Chinese Physics

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

<p>Abstract</p> <p>Background</p> <p>This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes.</p> <p>Results</p> <p>Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task.</p> <p>Conclusions</p> <p>Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR.</p

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers' connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers’ connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Radial Corrugations of Multi-Walled Carbon Nanotubes Driven by Inter-Wall Nonbonding Interactions

We perform large-scale quasi-continuum simulations to determine the stable cross-sectional configurations of free-standing multi-walled carbon nanotubes (MWCNTs). We show that at an inter-wall spacing larger than the equilibrium distance set by the inter-wall van der Waals (vdW) interactions, the initial circular cross-sections of the MWCNTs are transformed into symmetric polygonal shapes or asymmetric water-drop-like shapes. Our simulations also show that removing several innermost walls causes even more drastic cross-sectional polygonization of the MWCNTs. The predicted cross-sectional configurations agree with prior experimental observations. We attribute the radial corrugations to the compressive stresses induced by the excessive inter-wall vdW energy release of the MWCNTs. The stable cross-sectional configurations provide fundamental guidance to the design of single MWCNT-based devices and shed lights on the mechanical control of electrical properties

The prevalence of hyperuricemia in China: a meta-analysis

<p>Abstract</p> <p>Background</p> <p>The prevalence of hyperuricemia varied in different populations and it appeared to be increasing in the past decades. Recent studies suggest that hyperuricemia is an independent risk factor for cardiovascular disease. However, there has not yet been a systematic analysis of the prevalence of hyperuricemia in China.</p> <p>Methods</p> <p>Epidemiological investigations on hyperuricemia in China published in journals were identified manually and on-line by using CBMDISC, Chongqing VIP database and CNKI database. Those Reported in English journals were identified using MEDLINE database. Selected studies had to describe an original study defined by strict screening and diagnostic criteria. The fixed effects model or random effects model was employed according to statistical test for homogeneity.</p> <p>Results</p> <p>Fifty-nine studies were selected, the statistical information of which was collected for systematic analysis. The results showed that the pooled prevalence of hyperuricemia in male was 21.6% (95%CI: 18.9%-24.6%), but it was only 8.6% (95%CI: 8.2%-10.2%) in female. It was found that thirty years was the risk point age in male and it was fifty years in female.</p> <p>Conclusions</p> <p>The prevalence of hyperuricemia is different as the period of age and it increases after 30 years in male and 50 in female. Interventions are necessary to change the risk factors before the key age which is 30 years in male and 50 in female.</p

MEKK1-MKK4-JNK-AP1 Pathway Negatively Regulates Rgs4 Expression in Colonic Smooth Muscle Cells

Background: Regulator of G-protein Signaling 4 (RGS4) plays an important role in regulating smooth muscle contraction, cardiac development, neural plasticity and psychiatric disorder. However, the underlying regulatory mechanisms remain elusive. Our recent studies have shown that upregulation of Rgs4 by interleukin (IL)-1b is mediated by the activation of NFkB signaling and modulated by extracellular signal-regulated kinases, p38 mitogen-activated protein kinase, and phosphoinositide-3 kinase. Here we investigate the effect of the c-Jun N-terminal kinase (JNK) pathway on Rgs4 expression in rabbit colonic smooth muscle cells. Methodology/Principal Findings: Cultured cells at first passage were treated with or without IL-1b (10 ng/ml) in the presence or absence of the selective JNK inhibitor (SP600125) or JNK small hairpin RNA (shRNA). The expression levels of Rgs4 mRNA and protein were determined by real-time RT-PCR and Western blot respectively. SP600125 or JNK shRNA increased Rgs4 expression in the absence or presence of IL-1b stimulation. Overexpression of MEKK1, the key upstream kinase of JNK, inhibited Rgs4 expression, which was reversed by co-expression of JNK shRNA or dominant-negative mutants for MKK4 or JNK. Both constitutive and inducible upregulation of Rgs4 expression by SP600125 was significantly inhibited by pretreatment with the transcription inhibitor, actinomycin D. Dual reporter assay showed that pretreatment with SP600125 sensitized the promoter activity of Rgs4 in response to IL-1b. Mutation of the AP1-binding site within Rgs