An Analytical Approach to Evaluate the Reliability of Offshore Wind Power Plants Considering Environmental Impact

The accurate quantitative reliability evaluation of off-shore wind power plants (OWPPs) is an important part in planning and helps to obtain economic optimization. However, loop structures in collector systems and large quantities of components with correlated failures caused by shared ambient influences are significant challenges in the reliability evaluation. This paper proposes an ana-lytical approach to evaluate the reliability of OWPPs considering environmental impact on failures and solve the challenges by protection zone models, equivalent power unit models and common cause failure (CCF) analysis. Based on investigation of the characteristics of OWPP and related failures mechanisms, the components are divided into three CCF subsets. With the aid of the protection zone model and equivalent power unit model merged with CCF, the faulty collector system state eval-uation is applied to reduce the computational burden. The case studies present the necessity and improved per-formance of merging CCF analysis into modeling via the comparison with other two simplified methods. A sensi-tivity analysis is also carried out to account for inaccu-racy of failure data. The results show that the assump-tion of independent failures in the conventional method might lead to over-optimistic or over-pessimistic evalua-tion depending on the CCF style

Use of Antipsychotic Medications and Cholinesterase Inhibitors and the Risk of Falls and Fractures: self-controlled case series

Objective: To evaluate the association between the use of antipsychotic medications and cholinesterase inhibitors, and the risk of falls and fractures in elderly patients with major neurocognitive disorders. / Design: Self-controlled case series / Setting: Taiwan’s National Health Insurance Database / Participants: 15,278 patients who were aged 65 or older, were newly prescribed antipsychotic medications and cholinesterase inhibitors, and suffered an incident fall or fracture between 2006 and 2017. Prescription records of cholinesterase inhibitors were used to confirm the diagnosis of major neurocognitive disorders since all use of cholinesterase inhibitors was subject to review by experts based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and patients’ scores of Mini-Mental State Examination. We excluded those with schizophrenia and bipolar disorder before the first prescription of cholinesterase inhibitors to ensure that antipsychotic medications were used for neuropsychiatric symptoms of major neurocognitive disorders. / Main outcome measures: We used conditional Poisson regression to derive the incidence rate ratio and the 95% confidence interval for evaluating the association between the risk of falls and fractures and different exposure periods, including cholinesterase inhibitors alone, antipsychotic medications alone, and combination, as compared with the non-exposure period for the same individual. Moreover, we defined a 14-day pre-exposure period before study drug initiation over concerns about confounding by indication. / Results: Compared with the non-exposure period (incidence rate per 100 person-years; 95% confidence interval: 8.30; 8.14 to 8.46), the highest risk of falls and fractures occurred during the pre-exposure period (52.35; 48.46 to 56.47), followed by combination (10.55; 9.98 to 11.14), antipsychotic medications alone (10.34; 9.80 to 10.89), and cholinesterase inhibitors alone (9.41; 8.98 to 9.86). Conclusions: The incidence of falls and fractures was especially high in the pre-exposure period, suggesting that factors other than the study medications, such as underlying diseases, should be taken into consideration when evaluating the association between the risk of falls and fractures, and the use of cholinesterase inhibitors and antipsychotic medications. The exposure periods were also associated with a higher risk of falls and fractures, compared with the non-exposure period, although the magnitude was much lower than during the pre-exposure period. Prevention strategies and close monitoring of the risk of falls are still necessary until there is evidence that patients have regained a steady status

Context based mixture model for cell phase identification in automated fluorescence microscopy

BACKGROUND: Automated identification of cell cycle phases of individual live cells in a large population captured via automated fluorescence microscopy technique is important for cancer drug discovery and cell cycle studies. Time-lapse fluorescence microscopy images provide an important method to study the cell cycle process under different conditions of perturbation. Existing methods are limited in dealing with such time-lapse data sets while manual analysis is not feasible. This paper presents statistical data analysis and statistical pattern recognition to perform this task. RESULTS: The data is generated from Hela H2B GFP cells imaged during a 2-day period with images acquired 15 minutes apart using an automated time-lapse fluorescence microscopy. The patterns are described with four kinds of features, including twelve general features, Haralick texture features, Zernike moment features, and wavelet features. To generate a new set of features with more discriminate power, the commonly used feature reduction techniques are used, which include Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Maximum Margin Criterion (MMC), Stepwise Discriminate Analysis based Feature Selection (SDAFS), and Genetic Algorithm based Feature Selection (GAFS). Then, we propose a Context Based Mixture Model (CBMM) for dealing with the time-series cell sequence information and compare it to other traditional classifiers: Support Vector Machine (SVM), Neural Network (NN), and K-Nearest Neighbor (KNN). Being a standard practice in machine learning, we systematically compare the performance of a number of common feature reduction techniques and classifiers to select an optimal combination of a feature reduction technique and a classifier. A cellular database containing 100 manually labelled subsequence is built for evaluating the performance of the classifiers. The generalization error is estimated using the cross validation technique. The experimental results show that CBMM outperforms all other classifies in identifying prophase and has the best overall performance. CONCLUSION: The application of feature reduction techniques can improve the prediction accuracy significantly. CBMM can effectively utilize the contextual information and has the best overall performance when combined with any of the previously mentioned feature reduction techniques

Chinese Medicines as an Adjuvant Therapy for Unresectable Hepatocellular Carcinoma during Transarterial Chemoembolization: A Meta-Analysis of Randomized Controlled Trials

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Ultrafast phase-change logic device driven by melting processes.

The ultrahigh demand for faster computers is currently tackled by traditional methods such as size scaling (for increasing the number of devices), but this is rapidly becoming almost impossible, due to physical and lithographic limitations. To boost the speed of computers without increasing the number of logic devices, one of the most feasible solutions is to increase the number of operations performed by a device, which is largely impossible to achieve using current silicon-based logic devices. Multiple operations in phase-change-based logic devices have been achieved using crystallization; however, they can achieve mostly speeds of several hundreds of nanoseconds. A difficulty also arises from the trade-off between the speed of crystallization and long-term stability of the amorphous phase. We here instead control the process of melting through premelting disordering effects, while maintaining the superior advantage of phase-change-based logic devices over silicon-based logic devices. A melting speed of just 900 ps was achieved to perform multiple Boolean algebraic operations (e.g., NOR and NOT). Ab initio molecular-dynamics simulations and in situ electrical characterization revealed the origin (i.e., bond buckling of atoms) and kinetics (e.g., discontinuouslike behavior) of melting through premelting disordering, which were key to increasing the melting speeds. By a subtle investigation of the well-characterized phase-transition behavior, this simple method provides an elegant solution to boost significantly the speed of phase-change-based in-memory logic devices, thus paving the way for achieving computers that can perform computations approaching terahertz processing rates.This is the author's accepted manuscript. The final version is published by PNAS here: http://www.pnas.org/content/early/2014/08/27/1407633111.full.pdf+html?with-ds=yes

Impacts of household sources on air pollution at village and regional scales in India

Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM2:5 and O3) using state-of-thescience emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM2:5 speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O3 and PM2:5 levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions

Co3O4 Nanocrystals on Graphene as a Synergistic Catalyst for Oxygen Reduction Reaction

Catalysts for oxygen reduction and evolution reactions are at the heart of key renewable energy technologies including fuel cells and water splitting. Despite tremendous efforts, developing oxygen electrode catalysts with high activity at low costs remains a grand challenge. Here, we report a hybrid material of Co3O4 nanocrystals grown on reduced graphene oxide (GO) as a high-performance bi-functional catalyst for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). While Co3O4 or graphene oxide alone has little catalytic activity, their hybrid exhibits an unexpected, surprisingly high ORR activity that is further enhanced by nitrogen-doping of graphene. The Co3O4/N-doped graphene hybrid exhibits similar catalytic activity but superior stability to Pt in alkaline solutions. The same hybrid is also highly active for OER, making it a high performance non-precious metal based bi-catalyst for both ORR and OER. The unusual catalytic activity arises from synergetic chemical coupling effects between Co3O4 and graphene.Comment: published in Nature Material

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas

Error, reproducibility and sensitivity : a pipeline for data processing of Agilent oligonucleotide expression arrays

Background Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples. Results We describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that intrarray variability is small (only around 2% of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log2 units ( 6% of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators. Conclusions This study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells

Serotonin tranporter methylation and response to cognitive behaviour therapy in children with anxiety disorders

Anxiety disorders that are the most commonly occurring psychiatric disorders in childhood, are associated with a range of social and educational impairments and often continue into adulthood. Cognitive behaviour therapy (CBT) is an effective treatment option for the majority of cases, although up to 35-45% of children do not achieve remission. Recent research suggests that some genetic variants may be associated with a more beneficial response to psychological therapy. Epigenetic mechanisms such as DNA methylation work at the interface between genetic and environmental influences. Furthermore, epigenetic alterations at the serotonin transporter (SERT) promoter region have been associated with environmental influences such as stressful life experiences. In this study, we measured DNA methylation upstream of SERT in 116 children with an anxiety disorder, before and after receiving CBT. Change during treatment in percentage DNA methylation was significantly different in treatment responders vs nonresponders. This effect was driven by one CpG site in particular, at which responders increased in methylation, whereas nonresponders showed a decrease in DNA methylation. This is the first study to demonstrate differences in SERT methylation change in association with response to a purely psychological therapy. These findings confirm that biological changes occur alongside changes in symptomatology following a psychological therapy such as CBT