Nano-photocatalysts in microfluidics, energy conversion and environmental applications

Extensive studies have been carried out on photocatalytic materials in recent years as photocatalytic reactions offer a promising solution for solar energy conversion and environmental remediation. Currently available commercial photocatalysts still lack efficiency and thus are economically not viable for replacing traditional sources of energy. This article focuses on recent developments in novel nano-photocatalyst materials to enhance photocatalytic activity. Recent reports on optofluidic systems, new synthesis of photocatalytic composite materials and motile photocatalysts are discussed in this article.Postprint (published version

Best-Response Dynamics, Playing Sequences, and Convergence to Equilibrium in Random Games

We analyze the performance of the best-response dynamic across all normal-form games using a random games approach. The playing sequence -- the order in which players update their actions -- is essentially irrelevant in determining whether the dynamic converges to a Nash equilibrium in certain classes of games (e.g. in potential games) but, when evaluated across all possible games, convergence to equilibrium depends on the playing sequence in an extreme way. Our main asymptotic result shows that the best-response dynamic converges to a pure Nash equilibrium in a vanishingly small fraction of all (large) games when players take turns according to a fixed cyclic order. By contrast, when the playing sequence is random, the dynamic converges to a pure Nash equilibrium if one exists in almost all (large) games.Comment: JEL codes: C62, C72, C73, D83 Keywords: Best-response dynamics, equilibrium convergence, random games, learning models in game

A Method Based on Multi-Sensor Data Fusion for Fault Detection of Planetary Gearboxes

Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults

Protein Name Tagging Guidelines: Lessons Learned

Interest in information extraction from the biomedical literature is motivated by the need to speed up the creation of structured databases representing the latest scientific knowledge about specific objects, such as proteins and genes. This paper addresses the issue of a lack of standard definition of the problem of protein name tagging. We describe the lessons learned in developing a set of guidelines and present the first set of inter-coder results, viewed as an upper bound on system performance. Problems coders face include: (a) the ambiguity of names that can refer to either genes or proteins; (b) the difficulty of getting the exact extents of long protein names; and (c) the complexity of the guidelines. These problems have been addressed in two ways: (a) defining the tagging targets as protein named entities used in the literature to describe proteins or protein-associated or -related objects, such as domains, pathways, expression or genes, and (b) using two types of tags, protein tags and long-form tags, with the latter being used to optionally extend the boundaries of the protein tag when the name boundary is difficult to determine. Inter-coder consistency across three annotators on protein tags on 300 MEDLINE abstracts is 0.868 F-measure. The guidelines and annotated datasets, along with automatic tools, are available for research use

Express yourself? Ease to express one’s identity mediates the relationship between national belonging and mental health

A number of studies have reported a positive relationship between levels of national identification and well-being. Although this link is clear, the relationship is likely influenced by a number of other variables. In the current study, we examine two such variables: age and the ease with which people feel they can express their identity in the national context. Participants were drawn from three waves (2008–12) of the biannual New Zealand General Social Survey (NZGSS). The NZGSS consists of a number of questions related to well-being. The current study utilised the questions related to one’s sense of belonging to New Zealand, ease to express one’s identity in New Zealand, and mental health. When controlling for physical health, standard of living, and several demographic control variables, there was a clear relationship between one’s sense of belonging to New Zealand and mental health. Further, this relationship was stronger for older than younger participants. Finally, the ease with which participants felt they could express their identity in New Zealand partially mediated the relationship. Future research should elucidate which specific aspects of their identity people feel is constrained in the national context.A number of studies have reported a positive relationship between levels of national identification and well-being. Although this link is clear, the relationship is likely influenced by a number of other variables. In the current study, we examine two such variables: age and the ease with which people feel they can express their identity in the national context. Participants were drawn from three waves (2008-12) of the biannual New Zealand General Social Survey (NZGSS). The NZGSS consists of a number of questions related to well-being. The current study utilised the questions related to one's sense of belonging to New Zealand, ease to express one?s identity in New Zealand, and mental health. When controlling for physical health, standard of living, and several demographic control variables, there was a clear relationship between one?s sense of belonging to New Zealand and mental health. Further, this relationship was stronger for older than younger participants. Finally, the ease with which participants felt they could express their identity in New Zealand partially mediated the relationship. Future research should elucidate which specific aspects of their identity people feel is constrained in the national context.Publisher PDFPeer reviewe

Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge

Motivation: Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large. Results: We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach.Peer reviewe

All-d-Enantiomer of β-Amyloid Peptide Forms Ion Channels in Lipid Bilayers

Alzheimer’s disease (AD) is the most common type of senile dementia in aging populations. Amyloid β (Aβ)-mediated dysregulation of ionic homeostasis is the prevailing underlying mechanism leading to synaptic degeneration and neuronal death. Aβ-dependent ionic dysregulation most likely occurs either directly via unregulated ionic transport through the membrane or indirectly via Aβ binding to cell membrane receptors and subsequent opening of existing ion channels or transporters. Receptor binding is expected to involve a high degree of stereospecificity. Here, we investigated whether an Aβ peptide enantiomer, whose entire sequence consists of d-amino acids, can form ion-conducting channels; these channels can directly mediate Aβ effects even in the absence of receptor–peptide interactions. Using complementary approaches of planar lipid bilayer (PLB) electrophysiological recordings and molecular dynamics (MD) simulations, we show that the d-Aβ isomer exhibits ion conductance behavior in the bilayer indistinguishable from that described earlier for the l-Aβ isomer. The d isomer forms channel-like pores with heterogeneous ionic conductance similar to the l-Aβ isomer channels, and the d-isomer channel conductance is blocked by Zn2+, a known blocker of l-Aβ isomer channels. MD simulations further verify formation of β-barrel-like Aβ channels with d- and l-isomers, illustrating that both d- and l-Aβ barrels can conduct cations. The calculated values of the single-channel conductance are approximately in the range of the experimental values. These findings are in agreement with amyloids forming Ca2+ leaking, unregulated channels in AD, and suggest that Aβ toxicity is mediated through a receptor-independent, nonstereoselective mechanism

Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models