Autonomous marine hyperspectral radiometers for determining solar irradiances and aerosol optical properties

We have developed two hyperspectral radiometer systems which require no moving parts, shade rings or motorised tracking, making them ideally suited for autonomous use in the inhospitable remote marine environment. Both systems are able to measure direct and diffuse hyperspectral irradiance in the wavelength range 350–1050 nm at 6 nm (Spectrometer 1) or 3.5 nm (Spectrometer 2) resolution. Marine field trials along a 100� transect (between 50� N and 50� S) of the Atlantic Ocean resulted in close agreement with existing commercially available instruments in measuring (1) photosynthetically available radiation (PAR), with both spectrometers giving regression slopes close to unity (Spectrometer 1: 0.960; Spectrometer 2: 1.006) and R2 �0.96; (2) irradiant energy, with R2�0.98 and a regression slope of 0.75 which can be accounted for by the difference in wavelength integration range; and (3) hyperspectral irradiance where the agreement on average was between 2 and 5 %. Two long duration land-based field campaigns of up to 18 months allowed both spectrometers to be well calibrated. This was also invaluable for empirically correcting for the wider field of view (FOV) of the spectrometers in comparison with the current generation of sun photometers (�7.5� compared with �1�). The need for this correction was also confirmed and independently quantified by atmospheric radiative transfer modelling and found to be a function of aerosol optical depth (AOD) and solar zenith angle. Once Spectrometer 2 was well calibrated and the FOV effect corrected for, the RMSE in retrievals of AOD when compared with a CIMEL sun photometer were reduced to �0.02–0.03 with R2 > 0.95 at wavelengths 440, 500, 670 and 870 nm. Corrections for the FOV as well as ship motion were applied to the data from the marine field trials. This resulted in AOD500 nm ranging between 0.05 in the clear background marine aerosol regions and �0.5 within the Saharan dust plume. The RMSE between the handheld Microtops sun photometer and Spectrometer 2 was between 0.047 and 0.057 with R2 > 0.94

Bayesian model choice in cumulative link ordinal regression models

The use of the proportional odds (PO) model for ordinal regression is ubiquitous in the literature. If the assumption of parallel lines does not hold for the data, then an alternative is to specify a non-proportional odds (NPO) model, where the regression parameters are allowed to vary depending on the level of the response. However, it is often difficult to fit these models, and challenges regarding model choice and fitting are further compounded if there are a large number of explanatory variables. We make two contributions towards tackling these issues: firstly, we develop a Bayesian method for fitting these models, that ensures the stochastic ordering conditions hold for an arbitrary finite range of the explanatory variables, allowing NPO models to be fitted to any observed data set. Secondly, we use reversible-jump Markov chain Monte Carlo to allow the model to choose between PO and NPO structures for each explanatory variable, and show how variable selection can be incorporated. These methods can be adapted for any monotonic increasing link functions. We illustrate the utility of these approaches on novel data from a longitudinal study of individual-level risk factors affecting body condition score in a dog population in Zenzele, South Africa.TJM is supported by Biotechnology and Biological Sciences Research Council grant number BB/I012192/1. MM is supported by a grant from the International Fund for Animal Welfare (IFAW) and the World Society for the Protection of Animals (WSPA), with additional support from the Charles Slater Fund and the Jowett Fund. JW is supported by the Alborada Trust and the RAPIDD program of the Science and Technology Directorate, Department of Homeland Security and the Fogarty International Centre.This is the final version of the article. It was first available from International Society for Bayesian Analysis via http://dx.doi.org/10.1214/14-BA88

Taking Fact-Checks Literally But Not Seriously? The Effects of Journalistic Fact-Checking on Factual Beliefs and Candidate Favorability

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.Are citizens willing to accept journalistic fact-checks of misleading claims from candidates they support and to update their attitudes about those candidates? Previous studies have reached conflicting conclusions about the effects of exposure to counter-attitudinal information. As fact-checking has become more prominent, it is therefore worth examining how respondents respond to fact-checks of politicians—a question with important implications for understanding the effects of this journalistic format on elections. We present results to two experiments conducted during the 2016 campaign that test the effects of exposure to realistic journalistic fact-checks of claims made by Donald Trump during his convention speech and a general election debate. These messages improved the accuracy of respondents’ factual beliefs, even among his supporters, but had no measurable effect on attitudes toward Trump. These results suggest that journalistic fact-checks can reduce misperceptions but often have minimal effects on candidate evaluations or vote choice

Protein kinase C is essential for viability of the rice blast fungus Magnaporthe oryzae

This is the final version of the article. Available from Wiley via the DOI in this record.Protein kinase C constitutes a family of serine-threonine kinases found in all eukaryotes and implicated in a wide range of cellular functions, including regulation of cell growth, cellular differentiation and immunity. Here, we present three independent lines of evidence which indicate that protein kinase C is essential for viability of Magnaporthe oryzae. First, all attempts to generate a target deletion of PKC1, the single copy protein kinase C-encoding gene, proved unsuccessful. Secondly, conditional gene silencing of PKC1 by RNA interference led to severely reduced growth of the fungus, which was reversed by targeted deletion of the Dicer2-encoding gene, MDL2. Finally, selective kinase inhibition of protein kinase C by targeted allelic replacement with an analogue-sensitive PKC1(AS) allele led to specific loss of fungal viability in the presence of the PP1 inhibitor. Global transcriptional profiling following selective PKC inhibition identified significant changes in gene expression associated with cell wall re-modelling, autophagy, signal transduction and secondary metabolism. When considered together, these results suggest protein kinase C is essential for growth and development of M. oryzae with extensive downstream targets in addition to the cell integrity pathway. Targeting protein kinase C signalling may therefore prove an effective means of controlling rice blast disease.This work was funded by a BBSRC CASE PhD studentship to TJP with support from Syngenta and a European Research Council, Advanced Investigator Award to NJT under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 294702 GENBLAST

Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information

Background: Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM2.5 ground networks to cover a much larger area. Objectives: In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM2.5 concentrations. Methods: We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM2.5 concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. Results: The AOD model has a higher predicting power judged by adjusted R2 (0.79) than does the non-AOD model (0.48). The predicted PM2.5 concentrations by the AOD model are, on average, 0.8–0.9 μg/m3 higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM2.5, meteorologic parameters are major contributors to the better performance of the AOD model. Conclusions: GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM2.5 concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM2.5 spatial patterns related to AOD availability

Network Analysis of Host-Virus Communities in Bats and Rodents Reveals Determinants of Cross-Species Transmission

Bats are natural reservoirs of several important emerging viruses. Cross-species transmission appears to be quite common among bats, which may contribute to their unique reservoir potential. Therefore, understanding the importance of bats as reservoirs requires examining them in a community context rather than concentrating on individual species. Here, we use a network approach to identify ecological and biological correlates of cross-species virus transmission in bats and rodents, another important host group. We show that given our current knowledge the bat viral sharing network is more connected than the rodent network, suggesting viruses may pass more easily between bat species. We identify host traits associated with important reservoir species: gregarious bats are more likely to share more viruses and bats which migrate regionally are important for spreading viruses through the network. We identify multiple communities of viral sharing within bats and rodents and highlight potential species traits that can help guide studies of novel pathogen emergence

Toward Chirality‐Encoded Domain Wall Logic

Nonvolatile logic networks based on spintronic and nanomagnetic technologies have the potential to create high‐speed, ultralow power computational architectures. This article explores the feasibility of “chirality‐encoded domain wall logic,” a nanomagnetic logic architecture where data are encoded by the chiral structures of mobile domain walls in networks of ferromagnetic nanowires and processed by the chiral structures' interactions with geometric features of the networks. High‐resolution magnetic imaging is used to test two critical functionalities: the inversion of domain wall chirality at tailored artificial defect sites (logical NOT gates) and the chirality‐selective output of domain walls from 2‐in‐1‐out nanowire junctions (common operation to AND/NAND/OR/NOR gates). The measurements demonstrate both operations can be performed to a good degree of fidelity even in the presence of complex magnetization dynamics that would normally be expected to destroy chirality‐encoded information. Together, these results represent a strong indication of the feasibility of devices where chiral magnetization textures are used to directly carry, rather than merely delineate, data

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Assessment of higher order cognitive skills in undergraduate education: modified essay or multiple choice questions? Research paper

Background: Reliable and valid written tests of higher cognitive function are difficult to produce, particularly for the assessment of clinical problem solving. Modified Essay Questions (MEQs) are often used to assess these higher order abilities in preference to other forms of assessment, including multiple-choice questions (MCQs). MEQs often form a vital component of end-of-course assessments in higher education. It is not clear how effectively these questions assess higher order cognitive skills. This study was designed to assess the effectiveness of the MEQ to measure higher-order cognitive skills in an undergraduate institution. Methods: An analysis of multiple-choice questions and modified essay questions (MEQs) used for summative assessment in a clinical undergraduate curriculum was undertaken. A total of 50 MCQs and 139 stages of MEQs were examined, which came from three exams run over two years. The effectiveness of the questions was determined by two assessors and was defined by the questions ability to measure higher cognitive skills, as determined by a modification of Bloom's taxonomy, and its quality as determined by the presence of item writing flaws. Results: Over 50% of all of the MEQs tested factual recall. This was similar to the percentage of MCQs testing factual recall. The modified essay question failed in its role of consistently assessing higher cognitive skills whereas the MCQ frequently tested more than mere recall of knowledge. Conclusion: Construction of MEQs, which will assess higher order cognitive skills cannot be assumed to be a simple task. Well-constructed MCQs should be considered a satisfactory replacement for MEQs if the MEQs cannot be designed to adequately test higher order skills. Such MCQs are capable of withstanding the intellectual and statistical scrutiny imposed by a high stakes exit examination.Edward J Palmer, Peter G Devit