Sparse random Fourier features based interatomic potentials for high entropy alloys

Computational modeling of high entropy alloys (HEA) is challenging given the scalability issues of Density functional theory (DFT) and the non-availability of Interatomic potentials (IP) for molecular dynamics simulations (MD). This work presents a computationally efficient IP for modeling complex elemental interactions present in HEAs. The proposed random features-based IP can accurately model melting behaviour along with various process-related defects. The disordering of atoms during the melting process was simulated. Predicted atomic forces are within 0.08 eV/\unicode{xC5} of corresponding DFT forces. MD simulations predictions of mechanical and thermal properties are within 7$\%$ of the DFT values. High-temperature self-diffusion in the alloy system was investigated using the IP. A novel sparse model is also proposed which reduces the computational cost by 94$\%$ without compromising on the force prediction accuracy

Resistance Status of the Malaria Vector Mosquitoes, Anopheles stephensi and Anopheles subpictus Towards Adulticides and Larvicides in Arid and Semi-Arid Areas of India

Susceptibility studies of malaria vectors Anopheles stephensi Liston (Diptera: Culicidae) and An. subpictus Grassi collected during 2004–2007 from various locations of Arid and Semi-Arid Zone of India were conducted by adulticide bioassay of DDT, malathion, deltamethrin and larvicide bioassay of fenthion, temephos, chlorpyriphos and malathion using diagnostic doses. Both species from all locations exhibited variable resistance to DDT and malathion from majority of location. Adults of both the species were susceptible to Deltamethrin. Larvae of both the Anopheline species showed some evidence of resistance to chlorpyriphos followed by fenthion whereas susceptible to temephos and malathion

Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery

<p>Abstract</p> <p>Background</p> <p>Although high-throughput microarray based molecular diagnostic technologies show a great promise in cancer diagnosis, it is still far from a clinical application due to its low and instable sensitivities and specificities in cancer molecular pattern recognition. In fact, high-dimensional and heterogeneous tumor profiles challenge current machine learning methodologies for its small number of samples and large or even huge number of variables (genes). This naturally calls for the use of an effective feature selection in microarray data classification.</p> <p>Methods</p> <p>We propose a novel feature selection method: multi-resolution independent component analysis (MICA) for large-scale gene expression data. This method overcomes the weak points of the widely used transform-based feature selection methods such as principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF) by avoiding their global feature-selection mechanism. In addition to demonstrating the effectiveness of the multi-resolution independent component analysis in meaningful biomarker discovery, we present a multi-resolution independent component analysis based support vector machines (MICA-SVM) and linear discriminant analysis (MICA-LDA) to attain high-performance classifications in low-dimensional spaces.</p> <p>Results</p> <p>We have demonstrated the superiority and stability of our algorithms by performing comprehensive experimental comparisons with nine state-of-the-art algorithms on six high-dimensional heterogeneous profiles under cross validations. Our classification algorithms, especially, MICA-SVM, not only accomplish clinical or near-clinical level sensitivities and specificities, but also show strong performance stability over its peers in classification. Software that implements the major algorithm and data sets on which this paper focuses are freely available at <url>https://sites.google.com/site/heyaumapbc2011/</url>.</p> <p>Conclusions</p> <p>This work suggests a new direction to accelerate microarray technologies into a clinical routine through building a high-performance classifier to attain clinical-level sensitivities and specificities by treating an input profile as a ‘profile-biomarker’. The multi-resolution data analysis based redundant global feature suppressing and effective local feature extraction also have a positive impact on large scale ‘omics’ data mining.</p

On reliable discovery of molecular signatures

<p>Abstract</p> <p>Background</p> <p>Molecular signatures are sets of genes, proteins, genetic variants or other variables that can be used as markers for a particular phenotype. Reliable signature discovery methods could yield valuable insight into cell biology and mechanisms of human disease. However, it is currently not clear how to control error rates such as the false discovery rate (FDR) in signature discovery. Moreover, signatures for cancer gene expression have been shown to be unstable, that is, difficult to replicate in independent studies, casting doubts on their reliability.</p> <p>Results</p> <p>We demonstrate that with modern prediction methods, signatures that yield accurate predictions may still have a high FDR. Further, we show that even signatures with low FDR may fail to replicate in independent studies due to limited statistical power. Thus, neither stability nor predictive accuracy are relevant when FDR control is the primary goal. We therefore develop a general statistical hypothesis testing framework that for the first time provides FDR control for signature discovery. Our method is demonstrated to be correct in simulation studies. When applied to five cancer data sets, the method was able to discover molecular signatures with 5% FDR in three cases, while two data sets yielded no significant findings.</p> <p>Conclusion</p> <p>Our approach enables reliable discovery of molecular signatures from genome-wide data with current sample sizes. The statistical framework developed herein is potentially applicable to a wide range of prediction problems in bioinformatics.</p

Using the information embedded in the testing sample to break the limits caused by the small sample size in microarray-based classification

<p>Abstract</p> <p>Background</p> <p>Microarray-based tumor classification is characterized by a very large number of features (genes) and small number of samples. In such cases, statistical techniques cannot determine which genes are correlated to each tumor type. A popular solution is the use of a subset of pre-specified genes. However, molecular variations are generally correlated to a large number of genes. A gene that is not correlated to some disease may, by combination with other genes, express itself.</p> <p>Results</p> <p>In this paper, we propose a new classiification strategy that can reduce the effect of over-fitting without the need to pre-select a small subset of genes. Our solution works by taking advantage of the information embedded in the testing samples. We note that a well-defined classification algorithm works best when the data is properly labeled. Hence, our classification algorithm will discriminate all samples best when the testing sample is assumed to belong to the correct class. We compare our solution with several well-known alternatives for tumor classification on a variety of publicly available data-sets. Our approach consistently leads to better classification results.</p> <p>Conclusion</p> <p>Studies indicate that thousands of samples may be required to extract useful statistical information from microarray data. Herein, it is shown that this problem can be circumvented by using the information embedded in the testing samples.</p

Exfoliation mechanisms of 2D materials and their applications

Due to the strong in-plane but weak out-of-plane bonding, it is relatively easy to separate nanosheets of two-dimensional (2D) materials from their respective bulk crystals. This exfoliation of 2D materials can yield large 2D nanosheets, hundreds of micrometers wide, that can be as thin as one or a few atomic layers thick. However, the underlying physical mechanisms unique to each exfoliation technique can produce a wide distribution of defects, yields, functionalization, lateral sizes, and thicknesses, which can be appropriate for specific end applications. The five most commonly used exfoliation techniques include micromechanical cleavage, ultrasonication, shear exfoliation, ball milling, and electrochemical exfoliation. In this review, we present an overview of the field of 2D material exfoliation and the underlying physical mechanisms with emphasis on progress over the last decade. The beneficial characteristics and shortcomings of each exfoliation process are discussed in the context of their functional properties to guide the selection of the best technique for a given application. Furthermore, an analysis of standard applications of exfoliated 2D nanosheets is presented including their use in energy storage, electronics, lubrication, composite, and structural applications. By providing detailed insight into the underlying exfoliation mechanisms along with the advantages and disadvantages of each technique, this review intends to guide the reader toward the appropriate batch-scale exfoliation techniques for a wide variety of industrial applications

Comparison of gene expression profiles in core biopsies and corresponding surgical breast cancer samples

INTRODUCTION: Gene expression profiling has been successfully used to classify breast cancer into clinically distinct subtypes, and to predict the risk of recurrence and treatment response. The aim of this study was to investigate whether the gene expression profile (GEP) detected in a core biopsy (CB) is representative for the entire tumor, since CB is an important tool in breast cancer diagnosis. Moreover, we investigated whether performing CBs prior to the surgical excision could influence the GEP of the respective tumor. METHODS: We quantified the RNA expression of 60 relevant genes by quantitative real-time PCR in paired CBs and surgical specimens from 22 untreated primary breast cancer patients. Subsequently, expression data were compared with independent GEPs obtained from tumors of 317 patients without preceding CB. RESULTS: In 82% of the cases the GEP detected in the CB correlated very well with the corresponding profile in the surgical sample (r(s )≥ 0.95, p < 0.001). Gene-by-gene analysis revealed four genes significantly elevated in the surgical sample compared to the CB; these comprised genes mainly involved in inflammation and the wound repair process as well as in tumor invasion and metastasis. CONCLUSION: A GEP detected in a CB are representative for the entire tumor and is, therefore, of clinical relevance. The observed alterations of individual genes after performance of CB deserve attention since they might impact the clinical interpretation with respect to prognosis and therapy prediction of the GEP as detected in the surgical specimen following CB performance

The transitioning experiences of internationally-educated nurses into a Canadian health care system: A focused ethnography

<p>Abstract</p> <p>Background</p> <p>Beyond well-documented credentialing issues, internationally-educated nurses (IENs) may need considerable support in transitioning into new social and health care environments. This study was undertaken to gain an understanding of transitioning experiences of IENs upon relocation to Canada, while creating policy and practice recommendations applicable globally for improving the quality of transitioning and the retention of IENs.</p> <p>Methods</p> <p>A focused ethnography of newly-recruited IENs was conducted, using individual semi-structured interviews at both one-to-three months (Phase 1) and nine-to-twelve months post-relocation (Phase 2). A purposive sample of IENs was recruited during their orientation at a local college, to a health authority within western Canada which had recruited them for employment throughout the region. The interviews were recorded and transcribed, and data was managed using qualitative analytical software. Data analysis was informed by Roper and Shapira's framework for focused ethnography.</p> <p>Results</p> <p>Twenty three IENs consented to participate in 31 interviews. All IENs which indicated interest during their orientation sessions consented to the interviews, yet 14 did not complete the Phase 2 interview due to reorganization of health services and relocation. The ethno-culturally diverse group had an average age of 36.4 years, were primarily educated to first degree level or higher, and were largely (under) employed as "Graduate Nurses". Many IENs reported negative experiences related to their work contract and overall support upon arrival. There were striking differences in nursing practice and some experiences of perceived discrimination. The primary area of discontentment was the apparent communication breakdown at the recruitment stage with subsequent discrepancy in expected professional role and financial reimbursement.</p> <p>Conclusions</p> <p>Explicit and clear communication is needed between employers and recruitment agencies to avoid employment contract misunderstandings and to enable clear interpretation of the credentialing processes. Pre-arrival orientation of IENs including health care communications should be encouraged and supported by the recruiting institution. Moreover, employers should provide more structured and comprehensive workplace orientation to IENs with consistent preceptorship. Similar to findings of many other studies, diversity should be valued and incorporated into the professional culture by nurse managers.</p