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In the summer of 2016, a robotic sun photometer called the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Photometer Revision for Incident Surface Measurements (SeaPRISM), was deployed at a Coast Guard channel marker in western Lake Erie, measuring atmospheric properties and spectral water-leaving radiance. The instrument was deployed by the National Oceanic and Atmospheric Administration (NOAA) to support remote sensing validation and harmful algal bloom (HAB) satellite products. The Lake Erie SeaPRISM is also part of the international federated AERONET program maintained by the National Aeronautics and Space Administration (NASA), and more specifically is part of the AERONET Ocean Color (AERNOET-OC) network. The main purpose of this component of AERONET is specific to calibration/validation efforts for ocean color. The AERONET-OC network currently consists of 23 field radiometers at aquatic sites around the world. The Lake Erie site is the second freshwater lake location world-wide after the Palgrunden site in Sweden. During its operating period from mid-July to early September 2016, various environmental conditions were observed including a cyanobacteria bloom. Water-leaving radiance observations were generated on 43 out of 51 days, and varied by a factor of five. The variability in the above-water radiometry tracked that of in-water measurements made by a nearby buoy. During this brief operating window, satellite matchups were generated for several satellites. We highlight the first year's observations in relation to remote sensing validation and report on observations of cyanobacteria blooms from hourly to weekly time scales.

The vertical distributions of freshwater cyanobacteria populations are important to plankton community structure, ecology and for influencing water column optical properties relevant to remote sensing. In August of 2014, we examined the vertical structure of a cyanobacteria bloom across the western basin of Lake Erie with new technologies, including LIDAR and a digital holographic system. In addition, vertical profiles of environmental and optical properties were made. The active LIDAR penetrated the water column, and provided a detailed picture of the particle distribution for the whole water column. The holographic system provided digital images processed for particle size, count and identification of Microcystis and Planktothrix - the two main cyanobacteria genera that were present. The correlations between the LIDAR backscatter intensity and the cyanobacteria cell counts from holography averaged to 0.53 and ranged from −0.13 to 0.96 based on nearest matchups. The vertical structure of the overall cyanobacteria population was influenced by wind speed, and to a lesser degree the solar heating of surface waters. On a more detailed level, Microcystis populations were consistently nearer to the surface relative to Planktothrix. Pigments from surface samples revealed a higher degree of photoprotection for Planktothrix-dominated communities. The vertical distributions of the cyanobacteria genera were related to light intensity in the water column and known tolerances and/or preferences for each genus. Vertical profiles of optical properties supported the patterns seen in the LIDAR and holographic data, and had direct implications on the exiting light field. These combined data provide a unique view into the natural variations in spatial (vertical and horizontal) distribution patterns of cyanobacteria and resulting impacts on remote sensing detection and associated interpretations, and demonstrate the potential for these technologies to observe cyanobacteria in lake environments.

Occurrence of cyanobacterial harmful blooms (CHBs) in water has caused serious concern to environmental and health authorities because of their potential to produce and release lethal biological toxins. Among many toxins, microcystins (MCs) are of particular interest. There have been significant efforts to observe the harmful algal bloom events and cyanotoxin levels, including: (i) manual field sampling followed by lab analysis to directly measure MCs, (ii) remote sensing based on satellite image analysis to estimate cyanobacterial index (CI), and (iii) in-situ sensing of proxy parameters to cyanobacterial blooms such as phycocyanin. This study compared the observation systems in western Lake Erie to find any potential correlations among these CHB monitoring parameters based on the Pearson Product-Moment equation. We found the relationships among the parameters to be site-specific and so we compared geographical, ecological, meteorological, and analytical factors specific to the locations to explain the observed correlations and variations. The CHB observing parameters (MCs, CI, and phycocyanin) were generally well correlated because they inherently represented the same phenomenon. In particular, we found the measured biological toxin concentration (MCs) to be strongly correlated with the cyanobacterial bloom activity (CI) estimated by satellite image analysis. The phycocyanin concentration also had a strong correlation with CI, implying that measuring an easy-to-detect proxy parameter in-situ and in real-time is effective for monitoring CHBs. The results support the notion that key environmental management parameters such as CHB toxicity can be inferred from remotely-sensed ocean color through proxy variables such as CI.

Harmful algae blooms (HAB) in coastal marine environments are increasing in number and duration, pressuring local resource managers to implement mitigation solutions to protect human and ecosystem health. However, insufficient spatial and temporal observations create uninformed management decisions. In order to better detect and map blooms, as well as the environmental conditions responsible for their formation, long-term, unattended observation platforms are desired. In this article, we describe a new cost-efficient, autonomous, mobile platform capable of accepting several sensors that can be used to monitor harmful algae blooms in near real-time. The Navocean autonomous sail-powered surface vehicle is deployable by a single person from shore, capable of waypoint navigation in shallow and deep waters, and powered completely by renewable energy. We present results from three surveys of the Florida Red Tide harmful algae bloom (Karenia brevis) of 2017-2018. The vessel made significant progress towards waypoints regardless of wind conditions while underway measurements revealed patches of elevated chl. a likely attributable to the K. brevis blooms as based on ancillary measurements. Measurements of colored dissolved organic matter (CDOM) and turbidity provided an environmental context for the blooms. While the autonomous sailboat directly adds to our phytoplankton/HAB monitoring capabilities, the package may also help to ground-truth satellite measurements of HABs if careful validation measurements are performed. Finally, several other pending and future use cases for coastal and inland monitoring are discussed. To our knowledge, this is the first demonstration of a sail-driven vessel used for coastal HAB monitoring.

Digital holography was used to characterize particle/plankton distributions, biophysical interactions and ‘patchiness’ phenomena including ‘thin layers’ and harmful algal blooms in diverse aquatic environments. An autonomous underwater holographic system with long-term deployment capabilities was developed.

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a S tatistical, inherent O ptical property (IOP) -based, and mu L ti-conditional I nversion proce D ure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (Rrs), (b) retrieval of particulate backscattering (bbp) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from bbp via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m3], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms ( Miller and McKee, 2004 ; Nechad et al., 2010 ; Novoa et al., 2017 ; Ondrusek et al., 2012 ; Petus et al., 2010 ). We show that SOLID outperforms all the other models to varying degrees, i.e.,from 10 to >100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to

Environmental conditions are important drivers in regulating the distribution pattern of phytoplankton composition in the world's oceans. We constructed models that predict pico-, nano- and micro-phytoplankton size classes and assessed the impact of separately including sea surface temperature (SST) and estimates of light level in the surface mixed-layer on model skill. The empirical models were trained using size classes estimated by chemotaxonomic analysis of in situ high performance liquid chromatography (HPLC) pigments and environmental data originating from the Atlantic Ocean. As the accuracy of transforming pigment data into quantitative size classes is crucial when constructing phytoplankton size composition (PSC) models, we also quantified the resulting differences of our and several existing PSC models when using class sizes derived from HPLC pigments by two common chemotaxonomic methods, CHEMTAX and Diagnostic Pigments (DP). Addition of the environmental variables to abundance-based models using our approach improved the skill of correctly predicting PSC, reducing the root mean square difference (RMSD) by 10 to 20% in the best cases. Addition of SST yielded the highest percentage decreases, on average, for all three size classes, with greatest improvement in microplankton and nanoplankton fractions. These models performed equal to or better than several existing abundance-based models. The improvements in model predictions, however, could be obscured by the choice of pigment method used to generate the initial PSC data set. Insufficient data is available to assess whether CHEMTAX or DP is the more appropriate chemotaxonomic method to employ when estimating PSC. Further collection and analysis of additional water samples for phytoplankton taxa and size by microscopic methods - including traditional microscopic cell counts and automated methods - and HPLC pigment data are required to answer this question.

ObjectiveWe hypothesized ultrasound (US) guidance improves outcomes of corticosteroid injection of the painful shoulder.Methods30 patients with symptomatic shoulders due to osteoarthritis were randomized to glenohumeral injection with 3 milliliters of 1% lidocaine and 60 mg of triamcinolone acetonide using the anterior approach with 1) conventional anatomic landmark palpation-guidance or 2) US-guidance. Injection pain (visual analogue pain scale (VAS)), pain at outcome (2 weeks and 6 months), therapeutic duration, time-to-next-injection, and costs were determined.ResultsInjection pain was less with US (VAS: 0.3±0.6 cm) vs. landmark-guidance (VAS: 1.4±2.4 cm, 95% CI of difference: 0.5ConclusionAnatomic landmark guidance in the short-term is equally effective as US for injection of the osteoarthritic shoulder and modestly less costly, however, US may reduce the need for repetitive injections by prolonging the therapeutic effect and thus time to next injection.IRB StatementThis project was in compliance with the Helsinki Declaration, was approved by the Institutional Review Board (IRB) as ultrasound subset of a syringe safety trial (Human Research Review Committee approval 04-347), and was registered at ClinicalTrials.gov (Clinical Trial Identifier NCT00651625). The subjects gave informed consent to participate prior to all studies and interventions. Patient confidentiality was protected according to the U.S. Health Insurance Portability and Accountability Act (HIPAA) and all data was de-identified.

An optical class-based approach was adapted and applied to satellite imagery of aquatic color radiometry over Lake Superior. Lake Superior exhibits an optically complex environment whose physical and biogeochemical variability is unknown for much of the year. We characterized optical classes or optical water types (OWTs) from remotely sensed data and determined the temporal and spatial distribution of the OWTs. OWT distributions were interpreted through their inherent optical properties (IOPs) and physical drivers. Five identified OWTs based on spectrally normalized remote sensing reflectance imagery from the MODIS-Aqua mission revealed a gradient between class-specific IOPs for colored dissolved organic matter absorption (a CDOM ) and particulate backscattering (b bp ). The clearest OWT displayed widespread prominence over the lake and within and across years, indicating strongly stable physical dynamics including stratification and permanent circulation patterns. The most optically complex OWT (highest a CDOM and b bp ) followed more stochastic dynamics coinciding with localized runoff and mixing events. OWTs with intermediate optical complexity delineated river outflow plumes, upwelling, and annual lake wide turnover events while revealing annual and biannual harmonic patterns. OWTs verified both previously observed and unobserved dynamics in Lake Superior, demonstrating a valuable utility to characterize spatio-temporal, optical, and biogeochemical variability.

An integrated nutrient observatory is being developed within the Northeastern Regional Association of Coastal Ocean Observing Systems (NERACOOS), capable of monitoring nutrient dynamics year-round at temporal and spatial scales necessary to address critical needs of stakeholders throughout the Northeast region. Nutrient levels and fluxes drive total biological productivity throughout the region, from phytoplankton to commercially exploited fish stocks. Nitrate sensors (Satlantic SUNAs) are being installed on existing mooring assets in western Long Island Sound, Narragansett Bay (Prudence Island), Great Bay in New Hampshire, Massachusetts Bay, three sites along the coastal shelf of the Gulf of Maine (GOM), at five depths in Jordan Basin in the interior GOM, and at two depths in the GOM Northeast Channel. Phosphate and ammonium sensors (WET Labs Cycle-PO4 and Cycle-NH4) are also being deployed at the three nearshore sites. The measurements from these sensors will extend the current sparse, long-term records of nutrients from discretely collected samples in the Northeast region and will dramatically improve temporal resolution and continuity of the data for use in studying potential impacts of climate change. Nearshore measurements will be used by NERACOOS stakeholders to help assess, regulate, and mitigate the adverse impacts on water quality associated with excessive pollutant loadings. Measurements throughout the GOM will be used to assess basin-wide nutrient variability and to initialize harmful algal bloom (Alexandrium fundyense) forecast models.

Aquatic color radiometry remote sensing of coastal and inland water bodies is of great interest to a wide variety of research, management, and commercial entities as well as the general public. However, most current satellite radiometers were primarily designed for observing the global ocean and not necessarily for observing coastal and inland waters. Therefore, deriving coastal and inland aquatic applications from existing sensors is challenging. We describe the current and desired state of the science and highlight unresolved issues in four fundamental elements of aquatic satellite remote sensing namely, mission capability, in situ observations, algorithm development, and operational capacity. We discuss solutions, future plans, and recommendations that directly affect the science and societal impact of future missions with capability for observing coastal and inland aquatic systems.

Uncertainties in the spectral remote-sensing reflectance for SeaWiFS and MODIS-Aqua are characterized based on globally distributed data sets of in situ/satellite matchups. The in situ data sets were derived from fixed mooring sites and ship data using a variety of field radiometers. An optical classification procedure was applied to the satellite reflectance from each matchup pair to derive its fuzzy membership to eight previously characterized, optical water types. The memberships were then used as weights to derive uncertainty measures for the optical water types. The resulting uncertainty measures are shown to vary by optical water type for both sensors. Overall, the root-mean-square difference (RMSD) for open ocean waters is low and diminishes with increasing wavelength. The RMSD increases with optical complexity and is the highest in coastal waters associated with highly absorbing or highly scattering conditions. Although it plays a small role in RMSD, a spectral bias with a distinct zigzag pattern was detected across all water types. The most sensitive measure associated with optical water types is the relative uncertainty, which is shown to be near the desired 5% threshold for much of the open ocean for both SeaWiFS and MODIS-Aqua, but exceeds 50% in waters where the reflectance signal is low. This approach provides an objective method for characterizing uncertainty in water environments with distinct optical properties, and the results can be used in the processing of future satellite data to derive their associated uncertainty fields.

We hypothesized that constant compression of the knee would mobilize residual synovial fluid and promote successful arthrocentesis. Two hundred and ten knees with grade II–III osteoarthritis were included in this paired design study: (1) conventional arthrocentesis was performed with manual compression and success and volume (milliliters) determined; and (2) the intra-articular needle was left in place, and a circumferential elastomeric brace was tightened on the knee to provide constant compression. Arthrocentesis was attempted again and additional fluid volume was determined. Diagnostic procedural cost-effectiveness was determined using 2017 US Medicare costs. No serious adverse events were noted in 210 subjects. In the 158 noneffusive (dry) knees, sufficient synovial fluid for diagnostic purposes (≥ 2 ml) was obtained in 5.0% (8/158) without compression and 22.8% (36/158) with compression (p = 0.0001, z for 95% CI = 1.96), and the absolute volume of arthrocentesis fluid obtained without compression was 0.28 ± 0.79 versus 1.10 ± 1.81 ml with compression (293% increase, p = 0.0001). In the 52 effusive knees, diagnostic synovial fluid (≥ 2 ml) was obtained in 75% (39/52) without compression and 100% (52/52) with compression (p = 0.0001, z for 95% CI = 1.96), and the absolute volume of arthrocentesis without compression was 14.7 ± 13.8 versus 25.3 ± 15.5 ml with compression (72.1% increase, p = 0.0002). Diagnostic procedural cost-effectiveness was $655/sample without compression and $387/sample with compression. The new technique of constant compression via circumferential mechanical compression mobilizes residual synovial fluid beyond manual compression improving the success, cost-effectiveness, and yield of diagnostic and therapeutic arthrocentesis in both the effusive and noneffusive knee.

Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral remote sensing reflectance spectra collected by partners and advisors of the European Union 7th Framework Programme (FP7) Global Lakes Sentinel Services (GLaSS) project. The method has been integrated in the Envisat-BEAM software and the Sentinel Application Platform (SNAP) and generates maps of water types from image data. Two variations of water type classification are provided: one based on area-normalized spectral reflectance focusing on spectral shape (6CN, six-class normalized) and one that retains magnitude with no modification to the reflectance signal (6C). This resulted in a protocol, or processing scheme, that can also be applied or adapted for Sentinel-3 Ocean and Land Colour Imager (OLCI) datasets. We apply both treatments to MERIS imagery of a variety of European lakes to demonstrate its applicability. The studied target lakes cover a range of biophysical types, from shallow turbid to deep and clear, as well as eutrophic and dark absorbing waters, rich in colored dissolved organic matter (CDOM). In shallow, high-reflecting Dutch and Estonian lakes with high sediment load, 6C performed better, while in deep, low-reflecting clear Italian and Swedish lakes, 6CN performed better. The 6CN classification of in situ data is promising for very dark, high CDOM, absorbing lakes, but we show that our atmospheric correction of the imagery was insufficient to corroborate this. We anticipate that the application of the protocol to other lakes with unknown in-water characterization, but with comparable biophysical properties will suggest similar atmospheric correction (AC) and in-water retrieval algorithms for global lakes.

A 27-year-old female with Trisomy 9 mosaicism presented to Children's Hospital Colorado for outpatient dental surgery under general anesthesia. The patient's past medical history was also significant for premature birth, gastroesophageal reflux, scoliosis and kyphosis, obesity, and developmental delay. Per her mother's report, the patient had no cardiac issues. She had undergone multiple previous general anesthetics, some of which documented respiratory complications such as laryngospasm, bronchospasm, and possible aspiration. During this anesthetic, the patient became hypotensive on induction, with sluggish response to intravenous fluids, glycopyrrolate, and ephedrine. Her electrocardiogram demonstrated what appeared to be left bundle branch block at baseline, with possible ST segment changes after induction. Due to her abnormal reaction to the induction and subsequent treatment for hypotension, an echocardiogram was performed. The patient was found to have an ejection fraction of 25%–30%. The anesthetic was uneventful for the remainder of the procedure, and following recovery, the patient was admitted by the heart failure team for further care.

Estimating primary production at large spatial scales is key to our understanding of the global carbon cycle. Algorithms to estimate primary production are well established and have been used in many studies with success. One of the key parameters in these algorithms is the chlorophyll-normalised production rate under light saturation (referred to as the light saturation parameter or the assimilation number). It is known to depend on temperature, light history and nutrient conditions, but assigning a magnitude to it at particular space-time points is difficult. In this paper, we explore two models to estimate the assimilation number at the global scale from remotely-sensed data that combine methods to estimate the carbon-to-chlorophyll ratio and the maximum growth rate of phytoplankton. The inputs to the algorithms are the surface concentration of chlorophyll, sea-surface temperature, photosynthetically-active radiation at the surface of the sea, sea surface nutrient concentration and mixed-layer depth. A large database of in situ estimates of the assimilation number is used to develop the models and provide elements of validation. The comparisons with in situ observations are promising and global maps of assimilation number are produced.

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS), and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales, their role in marine biogeochemistry, the global carbon cycle, the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean, and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration, since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency, the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA), and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Global chlorophyll products derived from NASA's ocean color satellite programs have a nominal uncertainty of ± 35%. This metric has been hard to assess, in part because the data sets for evaluating performance do not reflect the true distribution of chlorophyll in the global ocean. A new technique is introduced that characterizes the chlorophyll uncertainty associated with distinct optical water types, and shows that for much of the open ocean the relative error is under 35%. This technique is based on a fuzzy classification of remote sensing reflectance into eight optical water types for which error statistics have been calculated. The error statistics are based on a data set of coincident MODIS Aqua satellite radiances and in situ chlorophyll measurements. The chlorophyll uncertainty is then mapped dynamically based on fuzzy memberships to the optical water types. The uncertainty maps are thus a separate, companion product to the standard MODIS chlorophyll product.

The relationship between surface production and benthic standing stock is fundamental to understanding biogeography in the deep sea. While much has been learned about the complex oceanographic processes involved in energy transfer to the benthos on local scales, the correspondence of overhead production to benthic community structure on regional scales remains poorly characterized. We compiled a database on the biomass and abundance of deep-sea macrobenthos in the western North Atlantic collected from 1961 to 1985. Using SeaWiFS satellite color imagery, we calculated POC from surface chlorophyll a concentrations (from 1997 to 2001), and estimated POC flux to the seafloor by using the empirically derived Pace et al. [1987. Primary production, new production and vertical flux in the eastern Pacific Ocean. Nature 325, 803–804] algorithm. The standing stock and surface production data are not concurrent, but their basic geographic trends at these very large spatial scales appear to be relatively stable over the time scales of measurement. Estimated POC flux at depth accounts for 62–67% of the variance ðPo0:0001Þ in benthic standing stock, suggesting that macroecological studies of the relationship between satellite-derived surface production and deep-sea community structure may be possible. r 2007 Elsevier Ltd. All rights reserved.

Surface-based measurements of aerosol optical depth at a rural site in southern New Hampshire (43.11°N, 70.95°W) are compared to retrievals of the same parameter by the Moderate Resolution Imaging Spectrometer (MODIS) during April–August, 2001. Hourly averages of aerosol optical depth (AOD) were derived using a multi-filter rotating shadowband radiometer (MFRSR) at the time of NASA's Terra satellite overpass. The MODIS Level 2 aerosol product at a wavelength of 550 nm was directly compared to the MFRSR interpolated AOD at 550 nm. We were able to compare the two AOD measurement platforms on 46 days (out of a possible 128 days) and observed a good agreement between the two methods ( R =0.81; slope=0.95±0.10). However, there were 11 days during this study period when MODIS measured AOD at the site, but the MFRSR did not due to excessive cloud cover. There were also 7 days when clear skies prevailed at the site during the time of MODIS overpass, but there was no AOD retrieved by MODIS. Surface measurements of fine particle (PM 2.5 ) mass, chemical composition, and optical properties were also performed during summer 2001. A good correlation ( R =0.87) between fine particle mass and AOD measured by the MFRSR was observed. A comparison between fine particle light extinction at the surface and MFRSR AOD (at the same wavelength) also showed good agreement ( R =0.80). Aerosol chemical analysis revealed that ammonium sulfate was the main aerosol component during times of very high turbidity, while organic carbon dominated during times of below-average turbidity.

Sexually transmitted diseases (STDs), HIV, and AIDS disproportionately affect the African American community. In 1999, the rates of gonorrhea and primary and secondary syphilis among African Americans in the United States were approximately 30 times greater than those rates in Whites. Although African Americans represent only 12% of the population nationwide, they constitute 37% of the cumulative AIDS cases. In North Carolina’s Durham County, African Americans accounted for 88% (553) of the HIV cases reported as of December 2000. There remains a demand for prevention efforts that are culturally relevant, incorporating the social norms and values of the African American community. Through the Barber and Beautician STD/HIV Peer Educator Program of the Durham County Health Department’s Project StraighTalk (PS), local barbers and beauticians provide condoms, educational materials, and education to their clients about STDs/HIV. In collaboration with PS, Lewis and Shain performed a needs assessment of the program, including interviews with stylists and clients, to inform program enhancement and materials development. This article describes the needs assessment process, with a specific focus on the challenges of working closely with a community and the lessons learned.

Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll-a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll-a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll-a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll-a algorithms—the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands—with RMS error of 0.416 and 0.437 for each in log chlorophyll-a units, respectively. However, it is clear that each algorithm performs better at different chlorophyll-a ranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll-a product compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie.

The purpose of our study was to use the 7.5-year coastal zone color scanner (CZCS) image time series (Oct. 1978 to July, 1986) to study general patterns in near-surface phytoplankton chlorophyll concentrations in ocean margin waters off the US East Coast. We defined 21 relatively large study areas (>100 km 2 ) within the MAB and SAB to set boundaries for averaging and subsequent analyses. Our objective was to partition the observed CZCS-derived chlorophyll concentration (CSAT, mg m −3 ) variability of these 21 study areas within three general categories based on time scale: daily (i.e. day–week), seasonal and interannual. An additional objective was to determine relations between the temporal patterns in the 21 study areas. All available CZCS imagery (more than 3500 scenes of Level 1 imagery, i.e. top-of-the-atmosphere radiance in satellite swath coordinates) covering some or all of our area of interest (northwest Atlantic off the US East Coast) were obtained at full resolution, processed to Level 2 (water-leaving radiance, chlorophyll concentration and other derived products in satellite swath coordinates) and mapped to two different study regions located off the southeast and northeast coasts of the US. Satellite-derived estimates of near-surface chlorophyll concentrations (CSAT) were extracted on a pixel-by-pixel basis from each of the 21 study areas (chosen based on oceanographic criteria) from each of the daily composite CSAT images. For each image and when satellite coverage permitted, CSAT values were averaged to yield a time series of daily mean values for each of the 21 study areas. We used three basic approaches to quantify temporal and spatial patterns in the 21 time series: (1) multiple linear correlation, (2) structure functions (semi-variance calculations) and (3) empirical orthogonal functions (EOF). Our results show: (1) a simple annual CSAT cycle common to all ocean margin waters along the entire US East Coast, consisting of a broad peak in CSAT concentration during winter and minimum concentrations during the summer; (2) relatively subtle across- and along-shelf changes to the timing and relative magnitude of the winter CSAT maxima and summer minima, as well as the presence of secondary seasonal peaks in some regions; (3) high variability at time scales of days to weeks superimposed on the seasonal pattern; (4) high spatial coherence of the seasonal component between all 21 study areas; (5) high coherence of the days-to-weeks component between adjacent study areas, but generally low or no coherence for study areas not adjacent or near each other; and (6) detectable, but low interannual variability.

An approach for selecting and blending bio-optical algorithms is demonstrated using an ocean color satellite image of the northwest Atlantic shelf. This approach is based on a fuzzy logic classification scheme applied to the satellite-derived water-leaving radiance data, and it is used to select and blend class-specific algorithms. Local in situ bio-optical data were used to characterize optically-distinct water classes a priori and to parameterize algorithms for each class. Although the algorithms can be of any type (empirical or analytical), this demonstration involves class-specific semi-analytic algorithms, which are the inverse of a radiance model. The semi-analytic algorithms retrieve three variables related to the concentrations of optically active constituents. When applied to a satellite image, the fuzzy logic approach involves three steps. First, a membership function is computed for each pixel and each class. This membership function expresses the likelihood that the measured radiance belongs to a class, with a known reflectance distribution. Thus, for each pixel, class memberships are assigned to the predetermined classes on the basis of the derived membership functions. Second, three variables are retrieved from each of the class-specific algorithms for which the pixel has membership. Third, the class memberships are used to weight the class specific retrievals to obtain a final blended retrieval for each pixel. This approach allows for graded transitions between water types, and blends separately tuned algorithms for different water masses without suffering from the "patchwork quilt" effect associated with hard-classification schemes.

Midline carcinomas associated with the nuclear protein in testis (NUT) gene rearrangement are rare, aggressive tumors that have been diagnosed most commonly in the head, neck, mediastinum, and upper aerodigestive tract. The ultrastructural features associated with this tumor have thus far received only brief comment and have never been illustrated. The authors provide a more extensive description and illustrate the electron microscopic findings in a typical case of NUT midline carcinoma, confirmed by cytogenetic and fluorescence in situ hybridization studies. This tumor was composed of cells displaying large, irregularly shaped nuclei with prominent compact nucleoli and abundant cytoplasm containing prominent bundles of tonofilaments, occasional clusters of pleomorphic granules, small numbers of lipid inclusions, and rare glycogen deposits. The cells exhibited stubby microvillous projections, were intermittently enveloped by basal lamina, and were interjoined by numerous well-formed desmosomal-type junctions and occasional junctional complexes. The authors propose that this constellation of ultrastructural features can prove helpful in discriminating NUT midline carcinoma from similar appearing entities.

A generalized coccolithophore bloom classifier has been developed for use with ocean color imagery. The bloom classifier was developed using extracted satellite reflectance data from SeaWiFS images screened by default bloom detection mask. In the current application, we extend the optical water type (OWT) classification scheme by adding a new coccolithophore bloom class formed from these extracted reflectances. Based on an in situ coccolithophore data set from the North Atlantic, the detection levels with the new scheme were between 1,500 and 1,800 coccolithophore cells/mL and 43,000 and 78,000 liths/mL. The detected bloom area using the OWT method was an average of 1.75 times greater than the default bloom detector based on a collection of SeaWiFS 1 km imagery. The versatility of the scheme is shown with SeaWiFS, MODIS Aqua, CZCS and MERIS imagery at the 1 km scale. The OWT scheme was applied to the daily global SeaWiFS imagery mission data set (years 1997–2010). Based on our results, average annual coccolithophore bloom area was more than two times greater in the southern hemisphere compared to the northern hemisphere with values of 2.00 × 106 km2 and 0.75 × 106 km2, respectively. The new algorithm detects larger bloom areas in the Southern Ocean compared to the default algorithm, and our revised global annual average of 2.75 × 106 km2 is dominated by contributions from the Southern Ocean., JRC.H.5-Land Resources Management

Regional differences in the Sea-viewing Wide Field-of-view Sensor chlorophyll algorithm uncertainty were observed in a large global data set containing coincident in situ measurements of chlorophyll a concentration (Chla) and spectral radiometry. The uncertainty was found to be systematic when the data were sorted by ocean: Atlantic, Pacific, Southern, and Indian Oceans. Artifacts associated with different instrumentation and analytical methods had been previously ruled out. Given these oceanic biases in the chlorophyll algorithm, we hypothesized that the oceans may be optically different, and their optical differences may be intrinsically related to regional differences in phytoplankton community structure or biogeochemical processes. The oceanic biases, originally observed using radiometric measurements, were independently verified using total absorption measurements in a subset of the data. Moreover, they were explained through oceanic differences in the absorption of colored detrital matter (CDM) and phytoplankton. Both effects were considered together in explaining the ocean biases through a stepwise linear regression analysis. Significant oceanic differences in the amount of CDM and in phytoplankton cell sizes and pigmentation would give rise to optical differences, but we raise a concern for the spatial coverage of the data. We do not suggest the application of ocean-based algorithms but rather emphasize the importance of consolidating regional data sets before reaching this conclusion.

This study presents an approach for optimally parameterizing a reflectance model. A parameterization scheme is realized based on a comprehensive bio-optical data set, including subsurface downwelling and upwelling irradiance spectra, absorption spectra of particle and dissolved substances, as well as chlorophyll and total suspended matter concentrations at 45 stations near Tokyo Bay between 1982 and 1984. The irradiance reflectance model is implemented with three-component inherent optical property submodels. In this parameterization scheme, an unsupervised classification was applied in the hyper-spectral space of reflectance, leading to three spectrally distinct optical water types. The reflectance model was parameterized for the entire data set, and then parameterized for each of the water types. The three sets of type-specific model parameters, which define corresponding IOP submodels, are believed to accommodate differences in the optical properties of the in-water constituents. The parameterized reflectance model was evaluated by both reconstructing measured reflectance spectra and solving for the nonlinear inverse problem to retrieve in-water constituent concentrations. The model accuracy was significantly improved in the forward direction for classified waters over that of non-classified waters, but no significant improvement was achieved in the retrieval accuracy (inverse direction). A larger data set with greater resolution of constituent inherent optical properties would likely improve the modeling results.

The present study considers, through the implementation of an optical river typology, the effect of the optical characteristics of different river plumes on estimates of Photosynthetically Available and Usable Radiation (PAR and PUR). The global distribution of waters influenced by the input of rivers is determined through the use of an Index of Fluvial Influence (IFI), which uses climatological salinity fields and hydrograph data for the world's 60 largest rivers. This IFI map is used as a template to extract spectral radiance data from ocean colour sensors. The radiance data is then analyzed using a fuzzy logic based classification scheme where the resulting classes are intended to represent the optical water types of globally distributed rivers. Each dataset of spectra clustered through the above classification, are inverted based on a nonlinear optimization routine and a multicomponent reflectance model including bidirectional effects. A second nonlinear optimization method using a published reflectance model was also included for comparison. The retrieved variables (backscattering coefficient at 555 nm, absorption of detrital material at 443 nm and the concentration of chlorophyll) provide the "typical" optical characteristics of the different water types to be used in the subsequent analysis. Typical Inherent Optical Properties (TOP) distributions and representative surface PAR distribution for subpolar, temperate and tropical regimes are used to estimate euphotic zone integrated PAR and PUR for the differing river optical water types. The effects of the TOPs in the differing water types affect two main aspects of PAR and PUR determination. The first influence is through the diffuse attenuation coefficient and thus the estimated euphotic depth itself, the second is the effect the relative proportions of different lOPs have on the conversion of downwelling irradiance to scalar irradiance (the relevant quantity for photosynthesis). Under certain, demonstrated, condition these two influences actually can counteract each other. Examples of high-resolution imagery, processed to retrieve derived PAR and PUR for three specific river plumes (Mississippi, Yangtze and Po), are also presented. Finally based on the above described sensitivity analysis, and using generic formulations for photosynthetic parameters, the impact of erroneously not accounting for the optical properties of river plumes in global calculations of primary production are estimated.

Historically ocean surface feature tracking analyses have been based on data from a single orbital sensor collected over the revisit interval of a lone low-Earth orbital satellite. Today ocean surface layer currents are being derived by performing feature tracking using data from a number of sensors on different satellites. Satellite ocean color data provide important insight into the marine biosphere by quantifying certain fundamental properties—for example, phytoplankton pigment concentration and marine primary production—on a global scale. In addition, satellite ocean color data can also be used as a tracer for measuring ocean surface layer currents, because the ocean color signal comprises information from a deeper water depth, from 10 to 30 m, than surface signatures such as sea surface temperature.

Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral remote sensing reflectance spectra collected by partners and advisors of the European Union 7th Framework Programme (FP7) Global Lakes Sentinel Services (GLaSS) project. The method has been integrated in the Envisat-BEAM software and the Sentinel Application Platform (SNAP) and generates maps of water types from image data. Two variations of water type classification are provided: one based on area-normalized spectral reflectance focusing on spectral shape (6CN, six-class normalized) and one that retains magnitude with no modification to the reflectance signal (6C). This resulted in a protocol, or processing scheme, that can also be applied or adapted for Sentinel-3 Ocean and Land Colour Imager (OLCI) datasets. We apply both treatments to MERIS imagery of a variety of European lakes to demonstrate its applicability. The studied target lakes cover a range of biophysical types, from shallow turbid to deep and clear, as well as eutrophic and dark absorbing waters, rich in colored dissolved organic matter (CDOM). In shallow, high-reflecting Dutch and Estonian lakes with high sediment load, 6C performed better, while in deep, low-reflecting clear Italian and Swedish lakes, 6CN performed better. The 6CN classification of in situ data is promising for very dark, high CDOM, absorbing lakes, but we show that our atmospheric correction of the imagery was insufficient to corroborate this. We anticipate that the application of the protocol to other lakes with unknown in-water characterization, but with comparable biophysical properties will suggest similar atmospheric correction (AC) and in-water retrieval algorithms for global lakes.

Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral remote sensing reflectance spectra collected by partners and advisors of the European Union 7th Framework Programme (FP7) Global Lakes Sentinel Services (GLaSS) project. The method has been integrated in the Envisat-BEAM software and the Sentinel Application Platform (SNAP) and generates maps of water types from image data. Two variations of water type classification are provided: one based on area-normalized spectral reflectance focusing on spectral shape (6CN, six-class normalized) and one that retains magnitude with no modification to the reflectance signal (6C). This resulted in a protocol, or processing scheme, that can also be applied or adapted for Sentinel-3 Ocean and Land Colour Imager (OLCI) datasets. We apply both treatments to MERIS imagery of a variety of European lakes to demonstrate its applicability. The studied target lakes cover a range of biophysical types, from shallow turbid to deep and clear, as well as eutrophic and dark absorbing waters, rich in colored dissolved organic matter (CDOM). In shallow, high-reflecting Dutch and Estonian lakes with high sediment load, 6C performed better, while in deep, low-reflecting clear Italian and Swedish lakes, 6CN performed better. The 6CN classification of in situ data is promising for very dark, high CDOM, absorbing lakes, but we show that our atmospheric correction of the imagery was insufficient to corroborate this. We anticipate that the application of the protocol to other lakes with unknown in-water characterization, but with comparable biophysical properties will suggest similar atmospheric correction (AC) and in-water retrieval algorithms for global lakes.