Evaluating Operational AVHRR Sea Surface Temperature Data at the Coastline Using Benthic Temperature Loggers

The nearshore coastal ocean is one of the most dynamic and biologically productive regions on our planet, supporting a wide range of ecosystem services. It is also one of the most vulnerable regions, increasingly exposed to anthropogenic pressure. In the context of climate change, monitoring changes in nearshore coastal waters requires systematic and sustained observations of key essential climate variables (ECV), one of which is sea surface temperature (SST). As temperature influences physical, chemical and biological processes within coastal systems, accurate monitoring is crucial for detecting change. SST is an ECV that can be measured systematically from satellites. Yet, owing to a lack of adequate in situ data, the accuracy and precision of satellite SST at the coastline are not well known. In a prior study, we attempted to address this by taking advantage of in situ SST measurements collected by a group of surfers. Here, we make use of a three year time-series (2014–2017) of in situ water temperature measurements collected using a temperature logger (recording every 30 min) deployed within a kelp forest (∼3 m below chart datum) at a subtidal rocky reef site near Plymouth, UK. We compared the temperature measurements with three other independent in situ SST datasets in the region, from two autonomous buoys located ∼7 km and ∼33 km from the coastline, and from a group of surfers at two beaches near the kelp site. The three datasets showed good agreement, with discrepancies consistent with the spatial separation of the sites. The in situ SST measurements collected from the kelp site and the two autonomous buoys were matched with operational Advanced Very High Resolution Radiometer (AVHRR) EO SST passes, all within 1 h of the in situ data. By extracting data from the closest satellite pixel to the three sites, we observed a significant reduction in the performance of AVHRR at retrieving SST at the coastline, with root mean square differences at the kelp site over twice that observed at the two offshore buoys. Comparing the in situ water temperature data with pixels surrounding the kelp site revealed the performance of the satellite data improves when moving two to three pixels offshore and that this improvement was better when using an SST algorithm that treats each pixel independently in the retrieval process. At the three sites, we related differences between satellite and in situ SST data with a suite of atmospheric variables, collected from a nearby atmospheric observatory, and a high temporal resolution land surface temperature (LST) dataset. We found that differences between satellite and in situ SST at the coastline (kelp site) were well correlated with LST and solar zenith angle; implying contamination of the pixel by land is the principal cause of these larger differences at the coastline, as opposed to issues with atmospheric correction. This contamination could be either from land directly within the pixel, potentially impacted by errors in geo-location, or possibly through thermal adjacency effects. Our results demonstrate the value of using benthic temperature loggers for evaluating satellite SST data in coastal regions, and highlight issues with retrievals at the coastline that may inform future improvements in operational products

Marine artificial light at night:An empirical and technical guide

The increasing illumination of our world by artificial light at night (ALAN) has created a new field of global change research with impacts now being demonstrated across taxa, biological ranks and spatial scales. Following advances in terrestrial ecology, marine ALAN has become a rapidly growing research area attracting scientists from across all biomes. Technological limitations, complexities of researching many coastal and marine ecosystems and the interdisciplinary nature of ALAN research present numerous challenges. Drawing on expertise from optical oceanographers, modellers, community ecologists, experimental and molecular biologists, we share practical advice and solutions that have proven useful for marine ALAN research. Discussing lessons learnt early on can help in the effective and efficient development of a field. The guide follows a sensory ecology approach to marine light pollution and consolidates physics, ecology and biology. First, we introduce marine lightscapes highlighting how these differ from terrestrial ones and provide an overview of biological adaptations to them. Second, we discuss study design and technology to best quantify ALAN exposure of and impacts on marine and coastal organisms including molecular tools and approaches to scale-up marine ALAN research. We conclude that the growing field of marine ALAN research presents opportunities not only for improving our understanding of this globally widespread stressor, but also for advancing fundamental marine photobiology, chronobiology and night-time ecology. Interdisciplinary research will be essential to gain insights into natural marine lightscapes shaping the ecology and evolution coastal and marine ecosystems

Gene expression profiling of CD8+ T cells predicts prognosis in patients with Crohn disease and ulcerative colitis.

Crohn disease (CD) and ulcerative colitis (UC) are increasingly common, chronic forms of inflammatory bowel disease. The behavior of these diseases varies unpredictably among patients. Identification of reliable prognostic biomarkers would enable treatment to be personalized so that patients destined to experience aggressive disease could receive appropriately potent therapies from diagnosis, while those who will experience more indolent disease are not exposed to the risks and side effects of unnecessary immunosuppression. Using transcriptional profiling of circulating T cells isolated from patients with CD and UC, we identified analogous CD8+ T cell transcriptional signatures that divided patients into 2 otherwise indistinguishable subgroups. In both UC and CD, patients in these subgroups subsequently experienced very different disease courses. A substantially higher incidence of frequently relapsing disease was experienced by those patients in the subgroup defined by elevated expression of genes involved in antigen-dependent T cell responses, including signaling initiated by both IL-7 and TCR ligation - pathways previously associated with prognosis in unrelated autoimmune diseases. No equivalent correlation was observed with CD4+ T cell gene expression. This suggests that the course of otherwise distinct autoimmune and inflammatory conditions may be influenced by common pathways and identifies what we believe to be the first biomarker that can predict prognosis in both UC and CD from diagnosis, a major step toward personalized therapy

Genome-Wide Gene Expression Analysis in Response to Organophosphorus Pesticide Chlorpyrifos and Diazinon in C. elegans

Organophosphorus pesticides (OPs) were originally designed to affect the nervous system by inhibiting the enzyme acetylcholinesterase, an important regulator of the neurotransmitter acetylcholine. Over the past years evidence is mounting that these compounds affect many other processes. Little is known, however, about gene expression responses against OPs in the nematode Caenorhabditis elegans. This is surprising because C. elegans is extensively used as a model species in toxicity studies. To address this question we performed a microarray study in C. elegans which was exposed for 72 hrs to two widely used Ops, chlorpyrifos and diazinon, and a low dose mixture of these two compounds. Our analysis revealed transcriptional responses related to detoxification, stress, innate immunity, and transport and metabolism of lipids in all treatments. We found that for both compounds as well as in the mixture, these processes were regulated by different gene transcripts. Our results illustrate intense, and unexpected crosstalk between gene pathways in response to chlorpyrifos and diazinon in C. elegans

A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts

This study presents a proof-of-concept for a fully automated and adaptive observing system for coastal ocean ecosystems. Such systems present a viable future observational framework for oceanography, reducing the cost and carbon footprint of marine research. An autonomous ocean robot (an ocean glider) was deployed for 11 weeks in the western English Channel and navigated by exchanging information with operational forecasting models. It aimed to track the onset and development of the spring phytoplankton bloom in 2021. A stochastic prediction model combined the real-time glider data with forecasts from an operational numerical model, which in turn assimilated the glider observations and other environmental data, to create high-resolution probabilistic predictions of phytoplankton and its chlorophyll signature. A series of waypoints were calculated at regular time intervals, to navigate the glider to where the phytoplankton bloom was most likely to be found. The glider successfully tracked the spring bloom at unprecedented temporal resolution, and the adaptive sampling strategy was shown to be feasible in an operational context. Assimilating the real-time glider data clearly improved operational biogeochemical forecasts when validated against independent observations at a nearby time series station, with a smaller impact at a more distant neighboring station. Remaining issues to be addressed were identified, for instance relating to quality control of near-real time data, accounting for differences between remote sensing and in situ observations, and extension to larger geographic domains. Based on these, recommendations are made for the development of future smart observing systems

Large scale statistical inference of signaling pathways from RNAi and microarray data

<p>Abstract</p> <p>Background</p> <p>The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway.</p> <p>Results</p> <p>In this paper we address this challenging problem by extending previous work by Markowetz <it>et al</it>., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on <it>p</it>-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks.</p> <p>Conclusion</p> <p>Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-<it>α </it>pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability.</p> <p>The code for the module network inference method is available in the latest version of the <it>R</it>-package <it>nem</it>, which can be obtained from the Bioconductor homepage.</p

Sacred turf: the Wimbledon tennis championships and the changing politics of Englishness

© 2015 Taylor & Francis. This article is about ‘Wimbledon’, widely celebrated – not least in its own publicity material – as the world’s premier tennis tournament. It examines ‘Wimbledon’ essentially as a text (hence the inverted commas), viewed politically and historically. In this context, ‘Wimbledon’ is seen as a signifier of a certain kind of Englishness, carefully adapted to meet changing social and economic circumstance. Loose parallels are drawn between the cultural trajectory of ‘Wimbledon’ and that of the British royal family. The transmutations of ‘Wimbledon’ as a tennis championship are also seen as reflecting Britain’s decline as a world power during the twentieth century

Use of Antiviral Drugs to Reduce Household Transmission of Pandemic (H1N1) 2009, United Kingdom1

TOC Summary: Early treatment of primary case-patients and prophylaxis of household contacts provides effective protection

Genomic Prevalence of Heterochromatic H3K9me2 and Transcription Do Not Discriminate Pluripotent from Terminally Differentiated Cells

Cellular differentiation entails reprogramming of the transcriptome from a pluripotent to a unipotent fate. This process was suggested to coincide with a global increase of repressive heterochromatin, which results in a reduction of transcriptional plasticity and potential. Here we report the dynamics of the transcriptome and an abundant heterochromatic histone modification, dimethylation of histone H3 at lysine 9 (H3K9me2), during neuronal differentiation of embryonic stem cells. In contrast to the prevailing model, we find H3K9me2 to occupy over 50% of chromosomal regions already in stem cells. Marked are most genomic regions that are devoid of transcription and a subgroup of histone modifications. Importantly, no global increase occurs during differentiation, but discrete local changes of H3K9me2 particularly at genic regions can be detected. Mirroring the cell fate change, many genes show altered expression upon differentiation. Quantitative sequencing of transcripts demonstrates however that the total number of active genes is equal between stem cells and several tested differentiated cell types. Together, these findings reveal high prevalence of a heterochromatic mark in stem cells and challenge the model of low abundance of epigenetic repression and resulting global basal level transcription in stem cells. This suggests that cellular differentiation entails local rather than global changes in epigenetic repression and transcriptional activity

Evaluating operational AVHRR sea surface temperature data at the coastline using surfers

Sea surface temperature (SST) is an essential climate variable that can be measured routinely from Earth Observation (EO) with high temporal and spatial coverage. To evaluate its suitability for an application, it is critical to know the accuracy and precision (performance) of the EO SST data. This requires comparisons with co-located and concomitant in situ data. Owing to a relatively large network of in situ platforms there is a good understanding of the performance of EO SST data in the open ocean. However, at the coastline this performance is not well known, impeded by a lack of in situ data. Here, we used in situ SST measurements collected by a group of surfers over a three year period in the coastal waters of the UK and Ireland, to improve our understanding of the performance of EO SST data at the coastline. At two beaches near the city of Plymouth, UK, the in situ SST measurements collected by the surfers were compared with in situ SST collected from two autonomous buoys located ∼7 km and ∼33 km from the coastline, and showed good agreement, with discrepancies consistent with the spatial separation of the sites. The in situ SST measurements collected by the surfers around the coastline, and those collected offshore by the two autonomous buoys, were used to evaluate the performance of operational Advanced Very High Resolution Radiometer (AVHRR) EO SST data. Results indicate: (i) a significant reduction in the performance of AVHRR at retrieving SST at the coastline, with root mean square errors in the range of 1.0 to 2.0 °C depending on the temporal difference between match-ups, significantly higher than those at the two offshore stations (0.4 to 0.6 °C); (ii) a systematic negative bias in the AVHRR retrievals of approximately 1 °C at the coastline, not observed at the two offshore stations; and (iii) an increase in the root mean square error at the coastline when the temporal difference between match-ups exceeded three hours. Harnessing new solutions to improve in situ sampling coverage at the coastline, such as tagging surfers with sensors, can improve our understanding of the performance of EO SST data in coastal regions, helping inform users interested in EO SST products for coastal applications. Yet, validating EO SST products using in situ SST data at the coastline is challenged by difficulties reconciling the two measurements, which are provided at different spatial scales in a dynamic and complex environment