A systematic comparison of supervised classifiers

Pattern recognition techniques have been employed in a myriad of industrial, medical, commercial and academic applications. To tackle such a diversity of data, many techniques have been devised. However, despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, the consideration of as many as possible techniques presents itself as an fundamental practice in applications aiming at high accuracy. Typical works comparing methods either emphasize the performance of a given algorithm in validation tests or systematically compare various algorithms, assuming that the practical use of these methods is done by experts. In many occasions, however, researchers have to deal with their practical classification tasks without an in-depth knowledge about the underlying mechanisms behind parameters. Actually, the adequate choice of classifiers and parameters alike in such practical circumstances constitutes a long-standing problem and is the subject of the current paper. We carried out a study on the performance of nine well-known classifiers implemented by the Weka framework and compared the dependence of the accuracy with their configuration parameter configurations. The analysis of performance with default parameters revealed that the k-nearest neighbors method exceeds by a large margin the other methods when high dimensional datasets are considered. When other configuration of parameters were allowed, we found that it is possible to improve the quality of SVM in more than 20% even if parameters are set randomly. Taken together, the investigation conducted in this paper suggests that, apart from the SVM implementation, Weka's default configuration of parameters provides an performance close the one achieved with the optimal configuration

Adenocarcinoma classification: patterns and prognosis

Lung cancer is the most frequent human malignancy and the principal cause of cancer-related death worldwide. Adenocarcinoma is now the main histologic type, accounting for almost half of all the cases. The 2015 World Health Organization has adopted the classification recently developed by the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society. This new adenocarcinoma classification has incorporated up-to-date advances in radiological, molecular and oncological knowledge, providing univocal diagnostic criteria and terminology. For resection specimens, new entities have been defined such as adenocarcinoma in situ and minimally invasive adenocarcinoma to designate adenocarcinomas, mostly nonmucinous and ≤ 3 cm in size, with either pure lepidic growth or predominant lepidic growth with ≤ 5 mm invasion, respectively. For invasive adenocarcinoma, the new classification has introduced histological subtyping according to the predominant pattern of growth of the neoplastic cells: lepidic (formerly non mucinous brochioloalveolar adenocarcinoma), acinar, papillary, micropapillary, and solid. Of note, micropapillary pattern is a brand new histologic subtype. In addition, four variants of invasive adenocarcinoma are recognized, namely invasive mucinous (formerly mucinous brochioloalveolar adenocarcinoma), colloid, fetal, and enteric. Importantly, three variants that were considered in the previous classification have been eliminated, specifically mucinous cystadenocarcinoma, signet ring cell, and clear cell adenocarcinoma. This review presents the changes introduced by the current histological classification of lung adenocarcinoma and its prognostic implications

Modular Nucleic Acid Assembled p/MHC Microarrays for Multiplexed Sorting of Antigen-Specific T Cells

The human immune system consists of a large number of T cells capable of recognizing and responding to antigens derived from various sources. The development of peptide-major histocompatibility (p/MHC) tetrameric complexes has enabled the direct detection of these antigen-specific T cells. With the goal of increasing throughput and multiplexing of T cell detection, protein microarrays spotted with defined p/MHC complexes have been reported, but studies have been limited due to the inherent instability and reproducibility of arrays produced via conventional spotted methods. Herein, we report on a platform for the detection of antigen-specific T cells on glass substrates that offers significant advantages over existing surface-bound schemes. In this approach, called “Nucleic Acid Cell Sorting (NACS)”, single-stranded DNA oligomers conjugated site-specifically to p/MHC tetramers are employed to immobilize p/MHC tetramers via hybridization to a complementary-printed substrate. Fully assembled p/MHC arrays are used to detect and enumerate T cells captured from cellular suspensions, including primary human T cells collected from cancer patients. NACS arrays outperform conventional spotted arrays assessed in key criteria such as repeatability and homogeneity. The versatility of employing DNA sequences for cell sorting is exploited to enable the programmed, selective release of target populations of immobilized T cells with restriction endonucleases for downstream analysis. Because of the performance, facile and modular assembly of p/MHC tetramer arrays, NACS holds promise as a versatile platform for multiplexed T cell detection

Determining the Surface-To-Bulk Progression in the Normal-State Electronic Structure of Sr2RuO4 by Angle-Resolved Photoemission and Density Functional Theory

In search of the potential realization of novel normal-state phases on the surface of Sr2RuO4 - those stemming from either topological bulk properties or the interplay between spin-orbit coupling (SO) and the broken symmetry of the surface - we revisit the electronic structure of the top-most layers by ARPES with improved data quality as well as ab-initio LDA slab calculations. We find that the current model of a single surface layer (\surd2x\surd2)R45{\deg} reconstruction does not explain all detected features. The observed depth-dependent signal degradation, together with the close quantitative agreement with LDA+SO slab calculations based on the LEED-determined surface crystal structure, reveal that (at a minimum) the sub-surface layer also undergoes a similar although weaker reconstruction. This points to a surface-to-bulk progression of the electronic states driven by structural instabilities, with no evidence for Dirac and Rashba-type states or surface magnetism.Comment: 4 pages, 4 figures, 1 table. Further information and PDF available at: http://www.phas.ubc.ca/~quantmat/ARPES/PUBLICATIONS/articles.htm

Postnatal and postweaning endocrine setting in dairy calves through hair cortisol, dehydroepiandrosterone and dehydroepiandrosterone sulphate

Importance of the work: The care of calves on dairy farms between birth and weaning can improve their long-term development and growth. In fact, a poor newborn health status and a high allostatic load may adversely affect development in dairy cows. To determine cortisol, dehydroepiandrosterone (DHEA) and dehydroepiandrosterone sulphate (DHEA-S) individually is useful for an understanding of the individual state, being biomarkers of hypothalamic-pituitary-adrenal (HPA) axis activity. Objectives: As a preliminary study, to investigate the hair concentrations of cortisol, DHEA, DHEA-S and their ratios in dairy calves in two key periods of their growth characterized by considerable environmental changes. Materials & Methods: Hair sampling was conducted on clinically healthy dairy calves during the postnatal period at age 64.8±0.65 d (POP; mean±standard error; n = 73) and during the postweaning period at age 155.3±0.85 d (PWP, n = 62). The hair hormone concentrations were measured using a radioimmunoassay. Results: Hair cortisol concentrations were higher in the POP than in the PWP. Furthermore, the cortisol:DHEA and cortisol:DHEA-S ratios were higher in the first period of evaluation, showing a higher animal allostatic load at birth. Main finding: Identification was achieved non-invasively of calves with a high allostatic load through biomarkers of HPA axis activity. The evaluation of this activity is very important given its influence on many biological processes, such as energy balance, development of the reproductive system and immune response

Na2IrO3 as a spin-orbit-assisted antiferromagnetic insulator with a 340 meV gap

We study Na2IrO3 by ARPES, optics, and band structure calculations in the local-density approximation (LDA). The weak dispersion of the Ir 5d-t2g manifold highlights the importance of structural distortions and spin-orbit coupling (SO) in driving the system closer to a Mott transition. We detect an insulating gap {\Delta}_gap = 340 meV which, at variance with a Slater-type description, is already open at 300 K and does not show significant temperature dependence even across T_N ~ 15 K. An LDA analysis with the inclusion of SO and Coulomb repulsion U reveals that, while the prodromes of an underlying insulating state are already found in LDA+SO, the correct gap magnitude can only be reproduced by LDA+SO+U, with U = 3 eV. This establishes Na2IrO3 as a novel type of Mott-like correlated insulator in which Coulomb and relativistic effects have to be treated on an equal footing.Comment: Accepted in Physical Review Letters. Auxiliary and related material can be found at: http://www.phas.ubc.ca/~quantmat/ARPES/PUBLICATIONS/articles.htm