The Biodiversity and Climate Change Virtual Laboratory: Where ecology meets big data

Advances in computing power and infrastructure, increases in the number and size of ecological and environmental datasets, and the number and type of data collection methods, are revolutionizing the field of Ecology. To integrate these advances, virtual laboratories offer a unique tool to facilitate, expedite, and accelerate research into the impacts of climate change on biodiversity. We introduce the uniquely cloud-based Biodiversity and Climate Change Virtual Laboratory (BCCVL), which provides access to numerous species distribution modelling tools; a large and growing collection of biological, climate, and other environmental datasets; and a variety of experiment types to conduct research into the impact of climate change on biodiversity. Users can upload and share datasets, potentially increasing collaboration, cross-fertilisation of ideas, and innovation among the user community. Feedback confirms that the BCCVL's goals of lowering the technical requirements for species distribution modelling, and reducing time spent on such research, are being met

Four new T dwarfs identified in PanSTARRS 1 commissioning data

A complete well-defined sample of ultracool dwarfs is one of the key science programs of the Pan-STARRS 1 optical survey telescope (PS1). Here we combine PS1 commissioning data with 2MASS to conduct a proper motion search (0.1--2.0\arcsec/yr) for nearby T dwarfs, using optical+near-IR colors to select objects for spectroscopic followup. The addition of sensitive far-red optical imaging from PS1 enables discovery of nearby ultracool dwarfs that cannot be identified from 2MASS data alone. We have searched 3700 sq. deg. of PS1 y-band (0.95--1.03 um) data to y$\approx$19.5 mag (AB) and J$\approx$16.5 mag (Vega) and discovered four previously unknown bright T dwarfs. Three of the objects (with spectral types T1.5, T2 and T3.5) have photometric distances within 25 pc and were missed by previous 2MASS searches due to more restrictive color selection criteria. The fourth object (spectral type T4.5) is more distant than 25 pc and is only a single-band detection in 2MASS. We also examine the potential for completing the census of nearby ultracool objects with the PS1 3$\pi$ survey.Comment: 25 pages, 8 figures, 5 table, AJ accepted, updated to comply with Pan-STARRS1 naming conventio

The effect of adherence to spectacle wear on early developing literacy: a longitudinal study based in a large multi-ethnic city, Bradford, UK

YesObjectives: To determine the impact of adherence to spectacle wear on visual acuity (VA) and developing literacy following vision screening at age 4–5 years. Design: Longitudinal study nested within the Born in Bradford birth cohort. Setting and participants: Observation of 944 children: 432 had failed vision screening and were referred (treatment group) and 512 randomly selected (comparison group) who had passed (<0.20 logarithm of the minimum angle of resolution (logMAR) in both eyes). Spectacle wear was observed in school for 2 years following screening and classified as adherent (wearing spectacles at each assessment) or non-adherent. Main outcome measures: Annual measures of VA using a crowded logMAR test. Literacy was measured by Woodcock Reading Mastery Tests-Revised subtest: letter identification. Results: The VA of all children improved with increasing age, −0.009 log units per month (95% CI −0.011 to −0.007) (worse eye). The VA of the adherent group improved significantly more than the comparison group, by an additional −0.008 log units per month (95% CI −0.009 to −0.007) (worse eye) and −0.004 log units per month (95% CI −0.005 to −0.003) in the better eye. Literacy was associated with the VA, letter identification (ID) reduced by −0.9 (95% CI −1.15 to −0.64) for every one line (0.10 logMAR) fall in VA (better eye). This association remained after adjustment for socioeconomic and demographic factors (−0.33, 95% CI −0.54 to −0.12). The adherent group consistently demonstrated higher letter-ID scores compared with the non-adherent group, with the greatest effect size (0.11) in year 3. Conclusions: Early literacy is associated with the level of VA; children who adhere to spectacle wear improve their VA and also have the potential to improve literacy. Our results suggest failure to adhere to spectacle wear has implications for the child’s vision and education.AB is funded by a National Institute for Health Research Post- Doctoral Fellowship Award (PDF-2013-06-050). The Born in Bradford study presents independent research commissioned by the National Institute for Health Research Collaboration for Applied Health Research and Care (NIHR CLAHRC) and the Programme Grants for Applied Research funding scheme (RP-PG-0407-10044)

HIP 38939B: A New Benchmark T Dwarf in the Galactic Plane Discovered with Pan-STARRS1

We report the discovery of a wide brown dwarf companion to the mildly metal-poor ([Fe/H]=-0.24), low galactic latitude (b = 1.88 deg) K4V star HIP 38939. The companion was discovered by its common proper motion with the primary and its red optical (Pan-STARRS1) and blue infrared (2MASS) colors. It has a projected separation of 1630 AU and a near-infrared spectral type of T4.5. As such it is one of only three known companions to a main sequence star which have early/mid-T spectral types (the others being HN Peg B and eps Indi B). Using chromospheric activity we estimate an age for the primary of 900{+1900,-600} Myr. This value is also in agreement with the age derived from the star's weak ROSAT detection. Comparison with evolutionary models for this age range indicates that HIP 38939B falls in the mass range 38+/-20 Mjup with an effective temperature range of 1090+/-60 K. Fitting our spectrum with atmospheric models gives a best fitting temperature of 1100 K. We include our object in an analysis of the population of benchmark T dwarfs and find that while older atmospheric models appeared to over-predict the temperature of the coolest objects compared to evolutionary models, more recent atmospheric models provide better agreement.Comment: ApJ, in press. Tiny changes incorporated into final version: added analysis of likelihood of companionship, clarified the fitting proceedure, and updated the benchmark analysis to highlight when the quoted evolutionary models use the atmospheric model they are being compared to as a boundary conditio

Author Correction: A consensus-based transparency checklist.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

The Biodiversity and Climate Change Virtual Laboratory: How Ecology and Big Data can be utilised in the fight against vector-borne diseases

Advances in computing power and infrastructure, increases in the number and size of ecological and environmental datasets, and the number and type of data collection methods, are revolutionizing the field of Ecology. To integrate these advances, virtual laboratories offer a unique tool to facilitate, expedite, and accelerate research into the impacts of climate change on biodiversity. We introduce the uniquely cloud-based Biodiversity and Climate Change Virtual Laboratory (BCCVL), which provides access to numerous species distribution modelling tools; a large and growing collection of biological, climate, and other environmental datasets, as well as a variety of experiment types to conduct research into the impact of climate change on biodiversity. Users can upload and share datasets, potentially increasing collaboration and cross-fertilisation of ideas and innovation among the user community. Feedback confirms that the BCCVL's goals of lowering the technical requirements for species distribution modelling, and reducing time spent on such research, are being met. We present a case study that illustrates the utility of the BCCVL as a research tool that can be applied to the problem of vector borne diseases and the likelihood of climate change altering their future distribution across Australia. This case study presents the preliminary results of an ensemble modelling experiment which employs multiple (15) different species distribution modelling algorithms to model the distribution of one of the main mosquito vectors of the most common vector borne disease in Australia: Ross River Virus (RRV). We use the BCCVL to do future projection of these models with future climates based on two extreme emissions scenarios, for multiple years. Our results show a large range in both the modelled current distribution, and projected future distribution, of the mosquito species studied. Most models (that were built using different algorithms) show somewhat similar current distributions of the species however there are three models that are obvious outliers. The projected models show a similar range in the distribution of the species, with some models indicating a fewer areas (and also areas with a lower probability of occurrence in specific areas) where the species is likely to be found under a climate change scenario. However, a majority of models show an expanded distribution, with some areas that have a greater probability of the occurrence of this species; this will provide a more robust indication of future distribution for policy makers and planners, than if just one or a few models had been employed. The economic and human health impact of vector borne diseases underline the importance of scientifically sound projections of the future spread of common disease vectors such as mosquitos under various climate change scenarios. This is because such information is essential for policy–makers to identify vulnerable communities and to better manage outbreaks and potential epidemics of such diseases. The BCCVL has provided the means to effectively and robustly bracket multiple sources of uncertainty in the future spread of RRV: this study focuses on two of these - the future distribution of a primary mosquito vector of the disease under two extreme scenarios of climate change. Research is underway to expand our analysis to take into account more sources of uncertainty: more vector and amplifying host species, emissions scenarios, and future climate projections from a range of different global climate model

NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.

Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in MR have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes