Aprendizaje Basado en Tareas y Suficiencia de Inglés en una Universidad de Negocios

This project adds to the growing body of empirical research focusing on the effects of task-based learning (TBL) on second language acquisition. Through the design and implementation of two business English case studies, in which learning was scaffolded through a sequence of tasks, the authors argue that a TBL approach to language teaching more effectively engages students and promotes greater oral language proficiency than traditional approaches. The authors argue that guiding students to utilise and combine their existing knowledge and skills with vocabulary and structures presented in class to solve case study problems has the potential to result in greater student confidence and, subsequently, greater language proficiency. Nevertheless, various factors can significantly reduce the successfulness of TBL in this context, including disparate learner profiles in cultural and business knowledge, life experiences, motivation, as well as constraints in terms of time and institutional assessment.Este proyecto se suma a un significativo número de investigaciones empíricas, centrándose en los efectos del aprendizaje basado en tareas (TBL) en el proceso de adquisición de una segunda lengua. Mediante el diseño e implementación de dos estudios de caso en las clases de inglés de negocios, en las que el aprendizaje fue escalonado a través de una secuencia de tareas. Las autoras sostienen que el enfoque de aprendizaje basado en tareas involucra de manera efectiva a los estudiantes en el aprendizaje del idioma y promueve una mayor competencia del lenguaje oral que los enfoques tradicionales. Las autoras afirman que el guiar a los estudiantes en el uso y combinación de sus habilidades y conocimiento existente con el vocabulario y las estructuras presentadas en clase para resolver problemas de estudio de caso contribuyen a que el estudiante tenga mayor confianza y posteriormente un mayor dominio del idioma. Sin embargo, diferentes factores pueden reducir significativamente el éxito del aprendizaje basado en tareas en este contexto, incluyendo los diversos perfiles de los estudiantes en relación al conocimiento cultural y empresarial, experiencias, motivaciones, así como las limitaciones en términos de tiempo y evaluación institucional

Bayesian joint inversion of controlled source electromagnetic and magnetotelluric data to image freshwater aquifer offshore New Jersey

Author Posting. © The Authors, 2019. This article is posted here by permission of The Royal Astronomical Society for personal use, not for redistribution. The definitive version was published in Geophysical Journal International 218(3), (2019): 1822-1837, doi: 10.1093/gji/ggz253.Joint inversion of multiple electromagnetic data sets, such as controlled source electromagnetic and magnetotelluric data, has the potential to significantly reduce uncertainty in the inverted electrical resistivity when the two data sets contain complementary information about the subsurface. However, evaluating quantitatively the model uncertainty reduction is made difficult by the fact that conventional inversion methods—using gradients and model regularization—typically produce just one model, with no associated estimate of model parameter uncertainty. Bayesian inverse methods can provide quantitative estimates of inverted model parameter uncertainty by generating an ensemble of models, sampled proportional to data fit. The resulting posterior distribution represents a combination of a priori assumptions about the model parameters and information contained in field data. Bayesian inversion is therefore able to quantify the impact of jointly inverting multiple data sets by using the statistical information contained in the posterior distribution. We illustrate, for synthetic data generated from a simple 1-D model, the shape of parameter space compatible with controlled source electromagnetic and magnetotelluric data, separately and jointly. We also demonstrate that when data sets contain complementary information about the model, the region of parameter space compatible with the joint data set is less than or equal to the intersection of the regions compatible with the individual data sets. We adapt a trans-dimensional Markov chain Monte Carlo algorithm for jointly inverting multiple electromagnetic data sets for 1-D earth models and apply it to surface-towed controlled source electromagnetic and magnetotelluric data collected offshore New Jersey, USA, to evaluate the extent of a low salinity aquifer within the continental shelf. Our inversion results identify a region of high resistivity of varying depth and thickness in the upper 500 m of the continental shelf, corroborating results from a previous study that used regularized, gradient-based inversion methods. We evaluate the joint model parameter uncertainty in comparison to the uncertainty obtained from the individual data sets and demonstrate quantitatively that joint inversion offers reduced uncertainty. In addition, we show how the Bayesian model ensemble can subsequently be used to derive uncertainty estimates of pore water salinity within the low salinity aquifer.We gratefully acknowledge funding support from National Science Foundation grants 1458392 and 1459035. We thank the captain and crew of the R.V. Marcus G. Langseth for a successful cruise and the Marine EM Lab at Scripps Institution of Oceanography for providing the instrumentation. We also thank Chris Armerding, Marah Dahn, John Desanto, Jimmy Elsenbeck, Matt Folsom, Keiichi Ishizu, Jeff Pepin, Charlotte Wiman and Georgie Zelenak for participating in the cruise. We gratefully acknowledge Alberto Malinverno for the idea to use a Monte Carlo scheme to estimate the distribution of pore fluid salinity, and William Menke for many constructive conversations and suggestions

Niches for Species, a multi-species model to guide woodland management: An example based on Scotland's native woodlands

Designating and managing areas with the aim of protecting biodiversity requires information on species distributions and habitat associations, but a lack of reliable occurrence records for rare and threatened species precludes robust empirical modelling. Managers of Scotland’s native woodlands are obliged to consider 208 protected species, which each have their own, narrow niche requirements. To support decision-making, we developed Niches for Species (N4S), a model that uses expert knowledge to predict the potential occurrence of 179 woodland protected species representing a range of taxa: mammals, birds, invertebrates, fungi, bryophytes, lichens and vascular plants. Few existing knowledge-based models have attempted to include so many species. We collated knowledge to define each species’ suitable habitat according to a hierarchical habitat classification: woodland type, stand structure and microhabitat. Various spatial environmental datasets were used singly or in combination to classify and map Scotland’s native woodlands accordingly, thus allowing predictive mapping of each species’ potential niche. We illustrate how the outputs can inform individual species management, or can be summarised across species and regions to provide an indicator of woodland biodiversity potential for landscape scale decisions. We tested the model for ten species using available occurrence records. Although concordance between predicted and observed distributions was indicated for nine of these species, this relationship was statistically significant in only five cases. We discuss the difficulties in reliably testing predictions when the records available for rare species are typically low in number, patchy and biased, and suggest future model improvements. Finally, we demonstrate how using N4S to synthesise complex, multi-species information into an easily digestible format can help policy makers and practitioners consider large numbers of species and their conservation needs

Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications

Plasma p-tau181, neurofilament light chain and association with cognition in Parkinson's disease

Early identification of cognitive impairment in Parkinson’s disease (PD) has important clinical and research implications. The aim of our study was to investigate the role of plasma tau phosphorylated at amino acid 181 (p-tau181) and plasma neurofilament light chain (NfL) as biomarkers of cognition in PD. Baseline concentrations of plasma p-tau181 and NfL were measured in a cohort of 136 patients with PD and 63 healthy controls (HC). Forty-seven PD patients were followed up for up to 2 years. Cross-sectional and longitudinal associations between baseline plasma biomarkers and cognitive progression were investigated using linear regression and linear mixed effects models. At baseline, plasma p-tau181 concentration was significantly higher in PD subjects compared with HC (p = 0.026). In PD patients, higher plasma NfL was associated with lower MMSE score at baseline, after adjusting for age, sex and education (p = 0.027). Baseline plasma NfL also predicted MMSE decline over time in the PD group (p = 0.020). No significant association between plasma p-tau181 concentration and baseline or longitudinal cognitive performance was found. While the role of p-tau181 as a diagnostic biomarker for PD and its relationship with cognition need further elucidation, plasma NfL may serve as a feasible, non-invasive biomarker of cognitive progression in PD

PINT: A Modern Software Package for Pulsar Timing

Over the past few decades, the measurement precision of some pulsar-timing experiments has advanced from ~10 us to ~10 ns, revealing many subtle phenomena. Such high precision demands both careful data handling and sophisticated timing models to avoid systematic error. To achieve these goals, we present PINT (PINT Is Not Tempo3), a high-precision Python pulsar timing data analysis package, which is hosted on GitHub and available on Python Package Index (PyPI) as pint-pulsar. PINT is well-tested, validated, object-oriented, and modular, enabling interactive data analysis and providing an extensible and flexible development platform for timing applications. It utilizes well-debugged public Python packages (e.g., the NumPy and Astropy libraries) and modern software development schemes (e.g., version control and efficient development with git and GitHub) and a continually expanding test suite for improved reliability, accuracy, and reproducibility. PINT is developed and implemented without referring to, copying, or transcribing the code from other traditional pulsar timing software packages (e.g., TEMPO and TEMPO2) and therefore provides a robust tool for cross-checking timing analyses and simulating pulse arrival times. In this paper, we describe the design, usage, and validation of PINT, and we compare timing results between it and TEMPO and TEMPO2.Comment: Re-submitted to the Astrophysical Journal at December 31st, 202

Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE).

PURPOSE: Gene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. EXPERIMENTAL DESIGN: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. RESULTS: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations. CONCLUSIONS: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications.See related commentary by McMullen et al., p. 5271.Core funding for this project was provided by the National Institutes of Health (R01-CA172404, PI: S.J. Ramus; and R01-CA168758, PIs: J.A. Doherty and M.A.Rossing), the Canadian Institutes for Health Research (Proof-of-Principle I program, PIs: D.G.Huntsman and M.S. Anglesio), the United States Department of Defense Ovarian Cancer Research Program (OC110433, PI: D.D. Bowtell). A. Talhouk is funded through a Michael Smith Foundation for Health Research Scholar Award. M.S. Anglesio is funded through a Michael Smith Foundation for Health Research Scholar Award and the Janet D. Cottrelle Foundation Scholars program managed by the BC Cancer Foundation. J. George was partially supported by the NIH/National Cancer Institute award number P30CA034196. C. Wang was a Career Enhancement Awardee of the Mayo Clinic SPORE in Ovarian Cancer (P50 CA136393). D.G. Huntsman receives support from the Dr. Chew Wei Memorial Professorship in Gynecologic Oncology, and the Canada Research Chairs program (Research Chair in Molecular and Genomic Pathology). M. Widschwendter receives funding from the European Union’s Horizon 2020 European Research Council Programme, H2020 BRCA-ERC under Grant Agreement No. 742432 as well as the charity, The Eve Appeal (https://eveappeal.org.uk/), and support of the National Institute for Health Research (NIHR) and the University College London Hospitals (UCLH) Biomedical Research Centre. G.E. Konecny is supported by the Miriam and Sheldon Adelson Medical Research Foundation. B.Y. Karlan is funded by the American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124. H.R. Harris is 20 supported by the NIH/National Cancer Institute award number K22 CA193860. OVCARE (including the VAN study) receives support through the BC Cancer Foundation and The VGH+UBC Hospital Foundation (authors AT, BG, DGH, and MSA). The AOV study is supported by the Canadian Institutes of Health Research (MOP86727). The Gynaecological Oncology Biobank at Westmead, a member of the Australasian Biospecimen Network-Oncology group, was funded by the National Health and Medical Research Council Enabling Grants ID 310670 & ID 628903 and the Cancer Institute NSW Grants ID 12/RIG/1-17 & 15/RIG/1-16. The Australian Ovarian Cancer Study Group was supported by the U.S. Army Medical Research and Materiel Command under DAMD17-01-1-0729, The Cancer Council Victoria, Queensland Cancer Fund, The Cancer Council New South Wales, The Cancer Council South Australia, The Cancer Council Tasmania and The Cancer Foundation of Western Australia (Multi-State Applications 191, 211 and 182) and the National Health and Medical Research Council of Australia (NHMRC; ID199600; ID400413 and ID400281). BriTROC-1 was funded by Ovarian Cancer Action (to IAM and JDB, grant number 006) and supported by Cancer Research UK (grant numbers A15973, A15601, A18072, A17197, A19274 and A19694) and the National Institute for Health Research Cambridge and Imperial Biomedical Research Centres. Samples from the Mayo Clinic were collected and provided with support of P50 CA136393 (E.L.G., G.L.K, S.H.K, M.E.S.)

Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016