What we talk about when we talk about "global mindset": managerial cognition in multinational corporations

Recent developments in the global economy and in multinational corporations have placed significant emphasis on the cognitive orientations of managers, giving rise to a number of concepts such as “global mindset” that are presumed to be associated with the effective management of multinational corporations (MNCs). This paper reviews the literature on global mindset and clarifies some of the conceptual confusion surrounding the construct. We identify common themes across writers, suggesting that the majority of studies fall into one of three research perspectives: cultural, strategic, and multidimensional. We also identify two constructs from the social sciences that underlie the perspectives found in the literature: cosmopolitanism and cognitive complexity and use these two constructs to develop an integrative theoretical framework of global mindset. We then provide a critical assessment of the field of global mindset and suggest directions for future theoretical and empirical research

The DEEP2 Galaxy Redshift Survey: Design, Observations, Data Reduction, and Redshifts

We describe the design and data sample from the DEEP2 Galaxy Redshift Survey, the densest and largest precision-redshift survey of galaxies at z ~ 1 completed to date. The survey has conducted a comprehensive census of massive galaxies, their properties, environments, and large-scale structure down to absolute magnitude M_B = -20 at z ~ 1 via ~90 nights of observation on the DEIMOS spectrograph at Keck Observatory. DEEP2 covers an area of 2.8 deg^2 divided into four separate fields, observed to a limiting apparent magnitude of R_AB=24.1. Objects with z < 0.7 are rejected based on BRI photometry in three of the four DEEP2 fields, allowing galaxies with z > 0.7 to be targeted ~2.5 times more efficiently than in a purely magnitude-limited sample. Approximately sixty percent of eligible targets are chosen for spectroscopy, yielding nearly 53,000 spectra and more than 38,000 reliable redshift measurements. Most of the targets which fail to yield secure redshifts are blue objects that lie beyond z ~ 1.45. The DEIMOS 1200-line/mm grating used for the survey delivers high spectral resolution (R~6000), accurate and secure redshifts, and unique internal kinematic information. Extensive ancillary data are available in the DEEP2 fields, particularly in the Extended Groth Strip, which has evolved into one of the richest multiwavelength regions on the sky. DEEP2 surpasses other deep precision-redshift surveys at z ~ 1 in terms of galaxy numbers, redshift accuracy, sample number density, and amount of spectral information. We also provide an overview of the scientific highlights of the DEEP2 survey thus far. This paper is intended as a handbook for users of the DEEP2 Data Release 4, which includes all DEEP2 spectra and redshifts, as well as for the publicly-available DEEP2 DEIMOS data reduction pipelines. [Abridged]Comment: submitted to ApJS; data products available for download at http://deep.berkeley.edu/DR4

Use of structure-activity landscape index curves and curve integrals to evaluate the performance of multiple machine learning prediction models

<p>Abstract</p> <p>Background</p> <p>Standard approaches to address the performance of predictive models that used common statistical measurements for the entire data set provide an overview of the average performance of the models across the entire predictive space, but give little insight into applicability of the model across the prediction space. Guha and Van Drie recently proposed the use of structure-activity landscape index (SALI) curves via the SALI curve integral (SCI) as a means to map the predictive power of computational models within the predictive space. This approach evaluates model performance by assessing the accuracy of pairwise predictions, comparing compound pairs in a manner similar to that done by medicinal chemists.</p> <p>Results</p> <p>The SALI approach was used to evaluate the performance of continuous prediction models for MDR1-MDCK <it>in vitro </it>efflux potential. Efflux models were built with ADMET Predictor neural net, support vector machine, kernel partial least squares, and multiple linear regression engines, as well as SIMCA-P+ partial least squares, and random forest from Pipeline Pilot as implemented by AstraZeneca, using molecular descriptors from <it>SimulationsPlus </it>and AstraZeneca.</p> <p>Conclusion</p> <p>The results indicate that the choice of training sets used to build the prediction models is of great importance in the resulting model quality and that the SCI values calculated for these models were very similar to their Kendall τ values, leading to our suggestion of an approach to use this SALI/SCI paradigm to evaluate predictive model performance that will allow more informed decisions regarding model utility. The use of SALI graphs and curves provides an additional level of quality assessment for predictive models.</p

Smart-aggregation imaging for single molecule localisation with SPAD cameras

Single molecule localisation microscopy (SMLM) has become an essential part of the super-resolution toolbox for probing cellular structure and function. The rapid evolution of these techniques has outstripped detector development and faster, more sensitive cameras are required to further improve localisation certainty. Single-photon avalanche photodiode (SPAD) array cameras offer single-photon sensitivity, very high frame rates and zero readout noise, making them a potentially ideal detector for ultra-fast imaging and SMLM experiments. However, performance traditionally falls behind that of emCCD and sCMOS devices due to lower photon detection efficiency. Here we demonstrate, both experimentally and through simulations, that the sensitivity of a binary SPAD camera in SMLM experiments can be improved significantly by aggregating only frames containing signal, and that this leads to smaller datasets and competitive performance with that of existing detectors. The simulations also indicate that with predicted future advances in SPAD camera technology, SPAD devices will outperform existing scientific cameras when capturing fast temporal dynamics

The effectiveness of home versus community-based weight control programmes initiated soon after breast cancer diagnosis:a randomised controlled trial

Background: Breast cancer diagnosis may be a teachable moment for lifestyle behaviour change and to prevent adjuvant therapy associated weight gain. We assessed the acceptability and effectiveness of two weight control programmes initiated soon after breast cancer diagnosis to reduce weight amongst overweight or obese women and prevent gains in normal-weight women. Methods: Overweight or obese (n = 243) and normal weight (n = 166) women were randomised to a three-month unsupervised home (home), a supervised community weight control programme (community) or to standard written advice (control). Primary end points were change in weight and body fat at 12 months. Secondary end points included change in insulin, cardiovascular risk markers, quality of life and cost-effectiveness of the programmes. Results: Forty-three percent of eligible women were recruited. Both programmes reduced weight and body fat: home vs. control mean (95% CI); weight −2.3 (−3.5, −1.0) kg, body fat −1.6 (−2.6, −0.7) kg, community vs. control; weight −2.4 (−3.6, −1.1) kg, body fat −1.4 (−2.4, −0.5) kg (all p &lt; 0.001). The community group increased physical activity, reduced insulin, cardiovascular disease risk markers, increased QOL and was cost-effective. Conclusions: The programmes were equally effective for weight control, but the community programme had additional benefits. Clinical trial registration: ISRCTN68576140.</p

Understanding atmospheric organic aerosols via factor analysis of aerosol mass spectrometry: a review

Organic species are an important but poorly characterized constituent of airborne particulate matter. A quantitative understanding of the organic fraction of particles (organic aerosol, OA) is necessary to reduce some of the largest uncertainties that confound the assessment of the radiative forcing of climate and air quality management policies. In recent years, aerosol mass spectrometry has been increasingly relied upon for highly time-resolved characterization of OA chemistry and for elucidation of aerosol sources and lifecycle processes. Aerodyne aerosol mass spectrometers (AMS) are particularly widely used, because of their ability to quantitatively characterize the size-resolved composition of submicron particles (PM1). AMS report the bulk composition and temporal variations of OA in the form of ensemble mass spectra (MS) acquired over short time intervals. Because each MS represents the linear superposition of the spectra of individual components weighed by their concentrations, multivariate factor analysis of the MS matrix has proved effective at retrieving OA factors that offer a quantitative and simplified description of the thousands of individual organic species. The sum of the factors accounts for nearly 100% of the OA mass and each individual factor typically corresponds to a large group of OA constituents with similar chemical composition and temporal behavior that are characteristic of different sources and/or atmospheric processes. The application of this technique in aerosol mass spectrometry has grown rapidly in the last six years. Here we review multivariate factor analysis techniques applied to AMS and other aerosol mass spectrometers, and summarize key findings from field observations. Results that provide valuable information about aerosol sources and, in particular, secondary OA evolution on regional and global scales are highlighted. Advanced methods, for example a-priori constraints on factor mass spectra and the application of factor analysis to combined aerosol and gas phase data are discussed. Integrated analysis of worldwide OA factors is used to present a holistic regional and global description of OA. Finally, different ways in which OA factors can constrain global and regional models are discussed

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe