Activation of AMP-activated protein kinase inhibits oxidized LDL-triggered endoplasmic reticulum stress in vivo

The oxidation of LDLs is considered a key step in the development of atherosclerosis. How LDL oxidation contributes to atherosclerosis remains poorly defined. Here we report that oxidized and glycated LDL (HOG-LDL) causes aberrant endoplasmic reticulum (ER) stress and that the AMP-activated protein kinase (AMPK) suppressed HOG-LDL-triggered ER stress in vivo.

Graded/Gradient Porous Biomaterials

Biomaterials include bioceramics, biometals, biopolymers and biocomposites and they play important roles in the replacement and regeneration of human tissues. However, dense bioceramics and dense biometals pose the problem of stress shielding due to their high Young's moduli compared to those of bones. On the other hand, porous biomaterials exhibit the potential of bone ingrowth, which will depend on porous parameters such as pore size, pore interconnectivity, and porosity. Unfortunately, a highly porous biomaterial results in poor mechanical properties. To optimise the mechanical and the biological properties, porous biomaterials with graded/gradient porosity, pores size, and/or composition have been developed. Graded/gradient porous biomaterials have many advantages over graded/gradient dense biomaterials and uniform or homogenous porous biomaterials. The internal pore surfaces of graded/gradient porous biomaterials can be modified with organic, inorganic, or biological coatings and the internal pores themselves can also be filled with biocompatible and biodegradable materials or living cells. However, graded/gradient porous biomaterials are generally more difficult to fabricate than uniform or homogenous porous biomaterials. With the development of cost-effective processing techniques, graded/gradient porous biomaterials can find wide applications in bone defect filling, implant fixation, bone replacement, drug delivery, and tissue engineering.

Effect of titania addition on yttria-stabilised tetragonal zirconia ceramics sintered at high temperatures

In order to combine the mechanical properties of yttria-stabilised zirconia (ZrO2-3 mol% Y2O3; code Y-ZrO2) with the bioactivity of titania (TiO2), Y-ZrO2-TiO2, green compacts with 0-40vol.% TiO2 were sintered at 1300, 1400, and 1500degreesC for 4h, respectively. The microstructural features such as grains, pores, and phases were examined using scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy dispersive spectroscopy (EDX). The mechanical properties such as hardness and toughness were also determined using the methods of Vickers indentation and Knoop indentation. All the composites showed the major tetragonal Y-ZrO2 phase regardless of the content of the added TiO2. However, rutile TiO2 phase was obtained at 1300degreesC, whereas zirconium titanate (ZrTi04) phase was found at 1400 and 1500degreesC. The Y-ZrO2-ZrTiO4 Composites sintered at 1500degreesC showed relatively high hardness (860-1000 kg/mm(2)) and toughness (4.0-4.5 MPa m(0.5)), whereas the Y-ZrO2-TiO2 composites sintered at 1300degreesC had slightly lower hardness (720-950kg/mm(2)) and fracture toughness (3.1-3.3 MPa m(0.5)). (C) 2004 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

Being a new generation of green solvents and high-tech reaction media of the future, ionic liquids have increasingly attracted much attention. Of particular interest in this context are room temperature ionic liquids (in short as ILs in this paper). Due to the relatively high viscosity, ILs is expected to be used in the form of solvent diluted mixture with reduced viscosity in industrial application, where predicting the viscosity of IL mixture has been an important research issue. Different IL mixture and many modelling approaches have been investigated. The objective of this study is to provide an alternative model approach using soft computing technique, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of ILs [C _n-mim][NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0-328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity taking account of IL alkyl chain length, as well as temperature and compositions simultaneously, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. This illustrates the potential application of ANN in the case that the physical and thermodynamic properties are highly non-linear or too complex. © 2012 Copyright the authors.

Artificial neural network model to predict compositional viscosity over a broad range of temperatures

The objective of this study is to provide an alternative model approach, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of room temperature ionic liquids (in short as ILs) [C _n-mim] [NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity over a wide range of temperatures and more complex viscosity compositions, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. © 2010 IEEE.

Comparative functional responses predict the invasiveness and ecological impacts of alien herbivorous snails.

Understanding determinants of the invasiveness and ecological impacts of alien species is amongst the most sought-after and urgent research questions in ecology. Several studies have shown the value of comparing the functional responses (FRs) of alien and native predators towards native prey, however, the technique is under-explored with herbivorous alien species and as a predictor of invasiveness as distinct from ecological impact. Here, in China, we conducted a mesocosm experiment to compare the FRs among three herbivorous snail species: the golden apple snail, Pomacea canaliculata, a highly invasive and high impact alien listed in “100 of the World's Worst Invasive Alien Species”; Planorbarius corneus, a non-invasive, low impact alien; and the Chinese native snail, Bellamya aeruginosa, when feeding on four locally occurring plant species. Further, by using a numerical response equation, we modelled the population dynamics of the snail consumers. For standard FR parameters, we found that the invasive and damaging alien snail had the highest “attack rates” a, shortest “handling times” h and also the highest estimated maximum feeding rates, 1/hT, whereas the native species had the lowest attack rates, longest handling times and lowest maximum feeding rates. The non-invasive, low impact alien species had consistently intermediate FR parameters. The invasive alien species had higher population growth potential than the native snail species, whilst that of the non-invasive alien species was intermediate. Thus, while the comparative FR approach has been proposed as a reliable method for predicting the ecological impacts of invasive predators, our results further suggest that comparative FRs could extend to predict the invasiveness and ecological impacts of alien herbivores and should be explored in other taxa and trophic groups to determine the general utility of the approach.

Causes and five-year incidence of blindness and visual impairment in urban southern China:The Liwan Eye Study

PURPOSE. We determined the causes and five-year incidence of blindness and visual impairment (VI) in an adult, urban Chinese population. METHODS. Participants underwent a comprehensive eye examination at baseline in 2003 and then five years later. The World Health Organization (WHO) and United States (US) definitions were used to define incident blindness (WHO visual acuity [VA] < 20/400 in the better-seeing eye, US VA ≤ 20/200) and incident VI (WHO VA < 20/60-20/400, US VA < 20/40->20/200). RESULTS. Among 1405 baseline participants, 924 (75%) of 1232 survivors (87.7%) participated in the 5-year follow-up. The incidences of VI and blindness were 5.38% (95% confidence interval [CI] 3.99% ~ 7.07%) and 0.33% (95% CI 0.07% ~ 0.95%), respectively, based on the WHO definition, and 9.85% (95% CI 7.96% ~ 12.0%) and 1.42% (95% CI 0.76% ~ 2.41%), respectively, based on the US definition. Incidence of blindness and VI (WHO definition) increased significantly with older age (P < 0.001) and poorer baseline presenting VA in the worse-seeing eye (P < 0.001). The leading cause of best-corrected VI (WHO definition) was cataract (64.6%), whereas the main causes of presenting VI were refractive error (40.4%) and cataract (38.4%). CONCLUSIONS. The incidence of VI in urban Southern China is high. The major causes are unoperated cataract and undercorrected refractive error, reflecting the need for better surgical and refractive care, even in this urban setting. © 2013 The Association for Research in Vision and Ophthalmology, Inc.

Spatial variation in adult sex ratio across multiple scales in the invasive golden apple snail, Pomacea canaliculata

Adult sex ratio (ASR) has critical effects on behavior and life history and has implications for population demography, including the invasiveness of introduced species. ASR exhibits immense variation in nature, yet the scale dependence of this variation is rarely analyzed. In this study, using the generalized multilevel models, we investigated the variation in ASR across multiple nested spatial scales and analyzed the underlying causes for an invasive species, the golden apple snail Pomacea canaliculata. We partitioned the variance in ASR to describe the variations at different scales and then included the explanatory variables at the individual and group levels to analyze the potential causes driving the variation in ASR. We firstly determined there is a significant female-biased ASR for this species when accounting for the spatial and temporal autocorrelations of sampling. We found that, counter to nearly equal distributed variation at plot, habitat and region levels, ASR showed little variation at the town level. Temperature and precipitation at the region level were significantly positively associated with ASR, whereas the individual weight, the density characteristic, and sampling time were not significant factors influencing ASR. Our study suggests that offspring sex ratio of this species may shape the general pattern of ASR in the population level while the environmental variables at the region level translate the unbiased offspring sex ratio to the female-biased ASR. Future research should consider the implications of climate warming on the female-biased ASR of this invasive species and thus on invasion pattern.