dataesr/scientific-paragraphs-categorization huggingface dataset:
Bathymetric lidar provides a fundamental spatial dataset to support subsequent investigations, such as living marine resource distribution, abundance, and biogeographical linkages; evaluating instantaneous and time series impacts of natural and anthropogenic stressors; considering regulatory or coastal development situations; and supporting resiliency and restoration efforts. The need for spatial information is typically amplified within nearshore waters, where traditionally there has been a paucity of data. Airborne lidar and, in particular, the Experimental Advanced Airborne Research Lidar, version B (EAARL-B), which is one of the few sensors to have a broad enough depth range to be truly useful across the seascape, provides the capability to satisfy the desired data requirement. Each laser pulse is divided into three spatially separated beams for two reasons: 1) to reduce the energy density below American National Standards Institute (ANSI) eye safe limits, and 2) to increase the spatial point density by a factor of three.
Integration with a Kalman filter enables the complementary error characteristics of GNSS and INS to be leveraged to reduce uncertainties in the blended navigation solution, with tightly coupled implementations often being advantageous in kinematic surveying  (Grewal, Andrews, and Bartone, 2013; Hutton et al. , 2008). Additional component uncertainties in airborne lidar include range-measurement uncertainty, and scan angle uncertainty. For bathymetric measurement, additional uncertainty comes from the detection of the water surface  (Guenther, LaRocque, and Lillycrop, 1994; Guenther, Thomas, and LaRocque, 1996). In this study, the spatial accuracy of an EAARL-B lidar point is assessed empirically through comparison against independent, "reference" datasets of higher accuracy, expressed at the 95% confidence level, and examined in relation to maximum permissible total vertical uncertainty (TVU) and total horizontal uncertainty (THU), as specified for different hydrographic survey orders in  IHO S-44-5E (International Hydrographic Organization, 2008).
Reference Datasets EAARL-B bathymetric data were compared with three different reference datasets in two different geographic locations. The calibration and primary dataset consisted of a lidar dataset collected 9 years earlier at the SFTF site southeast of the Ft. Lauderdale International Airport (FLL) and ranges in depth from near zero to 40 m. The second was a multibeam sonar dataset collected concurrently with EAARL-B data in St. Croix, U. S. Virgin Islands (STX) and ranges in depth from 10 to 100 m. The final dataset was single beam sonar collected by jet ski within a few days of the EAARL-B survey. There were no unsettling weather events between the jet ski survey and the EAARL-B survey. The jet ski data covered a depth range from 0. 9 to 35 m; however, only depths between 0. 9 and 10 m were determined to be usable for validation.
The JALBTCX Primary Reference Dataset The JALBTCX bathymetric lidar reference dataset collected at the SFTF site was selected for the calibration. The U. S. Army Corps of Engineers (USACE) collected the reference dataset in 2005 using their Optech SHOALS-3000 3 kHz lidar  (Irish and Lillycrop, 1999; Long, Cottin, and Collin, 2007) , which uses a hemispherical constant angle-of-incidence scan pattern. The USACE cross-calibrated and validated the reference dataset against multibeam and other measurements from the SFTF before providing it to the USGS. Although the reference dataset was 9 years old at the time of the calibration flights, the bottom topography appears to be stable where there is hardbottom. The USACE provided the reference dataset to the USGS in the North American Datum of 1983 (NAD83) epoch 2010.
00 coordinate system as a laser file format (LAS) point-cloud dataset. The JALBTCX reference dataset was processed in ITRF-2005 and converted to NAD83 (2011) using the NOAA National Geodetic Survey's (NGS) VDatum tool  (Hess, Jeong, and White, 2013; NOAA 2012). The data are the result of more than a dozen flights between June 23, 2005, and July 6, 2005, covering 56 km 2 and 51 million samples with greater than 200% coverage of more than 85% of the entire test area. The data were used to produce a 2-m mean depth surface for validating Coastal Zone Mapping and Imaging Lidar (CZMIL) sensors. Sandy areas were readily apparent and easily discernible by visual inspection of the bathymetric digital elevation map (DEM) when analyzing the difference between the reference and the EAARL-B data, and were not used for EAARL-B calibration.
The unstable sandy areas appeared to be limited to depths shallower than 6. 5 m along the beach. One of the primary reasons for selecting the SFTF site is the clarity of the water. This area features near-optimal water properties, that is, clear water, relatively free of suspended sediment and dissolved organic material. This enables the EAARL-B to receive bottomreturn signals that cover as much as possible of its operational depth range of 0 to 44 m. During the survey, the EAARL-B was able to capture data between 0 and 34 m depth.
The NOAA Multibeam Sonar Dataset The second reference dataset consists of multibeam sonar data collected by NOAA National Centers for Coastal Ocean Science between March 12 and April 2, 2014, over Lang Bank, St. Croix, U. S. Virgin Islands, coincident with EAARL-B surveys. The NOAA ship Nancy Foster conducted the hydrographic survey using a Reson SeaBat 7125-SV2 dual frequency sonar capable of operating at 200 or 400 kHz and an integrated Reson SVP 71 sound velocity sensor. The 7125-SV2 multibeam is mounted on the hull and is located port of the keel and forward of the reference point of the vessel. The 7125-SV2 was operated at 400 kHz for the duration of the cruise. The 7125-SV2 produced a 128-degree swath of 512 equiangular beams with an along-track beam width of 1. 1 degrees and across-track beam width of 0.
5 degree. A multibeam patch test was performed for the Reson 7125-SV2 sonar system on March 21, 2014 (DN080), to calibrate the sonar system, and a total vertical uncertainty quality check (TVU QC) was performed to ensure that data acquired met the required IHO Order 1 specifications  (Battista and Stecher, 2014). The dataset was delivered referenced to the NAD83 (2011) datum, with depths relative to the NOAA chart datum, Mean Lower Low Water (MLLW).
The USGS Single Beam Sonar Dataset The third reference dataset consists of single beam sonar data collected at the SFTF site by the USGS within 1 day of the EAARL-B surveys. The near-concurrent jet ski data were used to evaluate the EAARL-B performance over the depth range from 0. 9 to 10 m. The more recent jet ski data were desired because the primary reference dataset was 9 years old, and the areas shallower than 6. 5 m are dominated by shifting sand. The data were collected using two jet-ski-mounted System for Accurate Nearshore Depth Surveying (SANDS) systems  (Hansen, 2008)  along two lines perpendicular to and extending 1. 75 km from the beach. Data were collected along each line on _________________________________________________________________________________________________ Journal of Coastal Research, Special Issue No.
76, 2016 both the outbound and inbound journeys, and a diagonal line, which intersected the offshore lines, was then run in order to check the consistency of depth measurements at intersection points. The dataset was delivered referenced to the World Geodetic System 1984 (WGS84), realization G1674, and contained ellipsoid heights. While the jet ski data were collected over the depth range from zero to 35 m, the accuracy was degraded in the deeper portions because the jet skis are not equipped with attitude and orientation systems. Pitch and roll due to sea state causes a substantial angle between the slant range and the vertical water depth. The resulting uncorrectable measurement error becomes too large to be useful in water deeper than about 10 m.
Subaerial Calibration Before attempting to calibrate the EAARL-B for depth, it was first calibrated using a ground-based topographic dataset of the runway complex at the Salisbury Wicomico Ocean City Airport in Salisbury, Maryland. The runway ground-survey data were collected in November 2008 using two Ashtech Z-EuroCard Global Positioning System (GPS) dual-frequency survey receivers and Ashtech choke ring antennas, model 701945-01, rigidly mounted to a wheeled cart towed behind a vehicle. The data from the ground survey were processed to point data in the WGS84 (G1150) coordinate reference system. The EAARL-B was flown over each of the two runways in opposing directions and the resulting EAARL-B point-cloud points were matched with the ground-survey point cloud and the differences determined.
Calibration Mission Flights Two calibration-survey flights were flown on April 21 and April 22, 2014. The EAARL-B survey aircraft operated from the Albert Whitted (KSPG) airport located in St. Petersburg, Florida, and transited to and from the FLL/SFTF survey area, without landing, on both survey days. While in the survey area, the ZMA1 Continuously Operating Reference Station (CORS) GPS station was used as the kinematic base station. ZMA1 is nominally 25 km southwest of the calibration site and provides 1 Hz dual-frequency GPS data. The EAARL-B GPS data were processed using precision GPS ephemeral data in the Novatel Inertial Explorer software program, referenced to the NAD 83 coordinate system. The aircraft was flown at a nominal altitude of 1,100 feet (~335 m) on parallel and zigzag flight lines along the coastline and within the JALBTCX site polygon.
Selection of Calibration Regions The objective of this study was to calibrate the depth measurements made by the EAARL-B and not to conduct an overall accuracy assessment of lidar performance in terms of horizontal accuracy, or accuracy where there is substantial topographic relief, slope, or complex small-scale bottom structure. To facilitate optimal conditions for determining vertical error and depth-dependent offsets, the data were analyzed and polygons were delineated at 23 distinct depth ranges from 6. 3 to 34 m. Each polygon was examined and analyzed for average depth, roughness, topographic complexity, levelness, flatness, and for overlap between the JALBTCX and EAARL-B surveys. Figure  4  shows the selected comparison regions. References to named polygons throughout this paper correspond to those defined in this figure.
Areas with minimal sand were selected. Additionally, areas with spur-and-groove reefs, coral rubble, seagrass, and other complex small-scale bottom topography could erroneously influence the depth-calibration measurements or cause range noise in either the EAARL-B or the JALBTCX reference dataset, so those areas were avoided. Although there are plenty of data from both sensors between 0 and 6. 5 m, all appeared to be sandy bottom and exhibited evidence of substantial geomorphologic change, so those areas were excluded from the calibrations.
Random Consensus Filtering The built-in ALPS multi-gridded Random Consensus Filter (RCF)  (Nagle and Wright, 2016)  was used to automatically remove outliers and noise in both the topobathy and deep-_________________________________________________________________________________________________ Journal of Coastal Research, Special Issue No. 76, 2016 channel data. The RCF algorithm works by detecting where the highest concentration of points in each volume is located. These points are deemed a "consensus," and any points within the volume but outside a user-defined buffer distance from the consensus points are removed from the dataset. The number of points required to form a consensus, the dimensions of the volume, the maximum buffer distance, and the percent overlap are all user-defined parameters of the filter.
Point-to-Point Comparison and Difference The filtered and manually edited data were processed to find the nearest reference data points within 1-m horizontal. For each EAARL-B point within a designated polygon, the elevation difference between the EAARL-B point and the mean of the reference points within the 1-m circle was computed, and the mean and standard deviation were calculated on the resulting differences. The mean difference of a polygon is defined by Equation  2. This process was repeated for each of the selected depth polygons defined in Figure  4. The resulting statistics are tabulated in Table  3  and depicted graphically in Figure  6. Mean difference ∑ (2) where n is the number of reference dataset points in a 1-m circle around an EAARL-B data point, z j is the elevation of a reference dataset point, z i is the elevation of an EAARL-B data point, and N is the number of EAARL-B points in the polygon.
Derivation of the Calibration Constants Only the JALBTCX reference dataset was used in the derivation of the calibration constants. Kernel Density Estimation  (Silverman, 1986)  was used to smooth the vertical error distributions of each selected depth polygon. Figure  6  shows a scatterplot of the raw EAARL-B depth versus the combined mean difference of all four channels for each polygon defined in Figure  4. A simple linear regression analysis was performed on these data yielding slope and intercept coefficients of 0. 98103 and -0. 00068, respectively. The blue line in Figure  6  is the least squares fit. For a given EAARL-B raw depth, a corrected depth is computed by (3) Figure  5. Components of the bathy processing algorithm for (A) shallow water, using an exponential decay model, and (B) deep water, using a log-normal decay model.
Testing the Calibration Constants In order to test the validity of the calibration constants, they were applied to both the SFTF and STX EAARL-B surveys, and the same procedure of defining test regions and calculating mean differences was performed. The calibration regions for the  SFTF survey were reused, and 30 regions with approximately 1m (vertical) depth difference were selected for the St. Croix (STX) dataset within areas common to both the EAARL-B and NOAA MBES datasets. The polygons were selected to minimize depth variation and to exclude coral heads and other bottom topography that could contaminate the results by allowing small horizontal errors to be inappropriately translated into vertical errors. The selected polygons for STX are shown in Figure  7.
Applying the Calibration Results After deriving the calibration constants, they were applied to the entire SFTF dataset and the same selected depth polygons were reexamined. Validation of the constants was performed by comparing the calibrated EAARL-B data to the single beam jet ski data at SFTF and the NOAA MBES data at STX, the latter having been collected a month earlier, at a location more than 1,600 km away.
Calibrating the SFTF EAARL-B Data Figure  8  shows the results of the comparisons between calibrated EAARL-B and both the JALBTCX and single beam reference datasets at the SFTF site. Circles represent comparison with the JALBTCX dataset, and triangles represent comparison with the single beam jet ski data. Blue points are the mean differences between EAARL-B and the reference, and red points represent the vertical accuracy at 95% confidence. The green and dark red lines represent the IHO minimum requirements for vertical accuracy at the Special Order and Order 1 levels, respectively. Compliance with the standard is determined by the location of the red points relative to the IHO lines. If the red points fall below the green line, they satisfy the Special Order standard, whereas if they are above the green line but below the dark red line, then they only comply with the less stringent Order 1 standard.
The results show that the relative vertical accuracy of the EAARL-B (to the JALBTCX reference data) is within the IHO Special Order down to approximately 20-m depth and Order 1 specification for the entire depth range collected during the SFTF survey. Vertical accuracy relative to the single beam jet ski data is mostly within the IHO Special Order spec down to approximately 13 m, after which vertical accuracy decreases rapidly and nonlinearly. This is where the lack of an attitude and orientation system aboard the jet skis begins to make a significant difference. For this reason, single beam reference data at depths greater than 10 m should be viewed with suspicion.
Validating the Calibration Using the St. Croix Dataset The depth calibration constants developed from the SFTF EAARL-B survey and JALBTCX reference data were applied to an EAARL-B dataset captured in March 2014 over Lang Bank near St. Croix, U. S. Virgin Islands. The calibrated EAARL-B data were then compared to the second reference dataset, the multibeam echosounder dataset captured by the Nancy Foster. Figure  9  shows the results of this analysis. As before, each blue point represents the mean difference of all the points in a single selected polygon and has a corresponding red point representing the vertical accuracy at 95%. The results indicate that more than 90% of the selected depth regions exceed the IHO Special Order requirements for vertical accuracy, with an absolute deviation from the reference of less than ±12.
CONCLUSIONS This extensive, empirical study provided answers to the six central questions that formed the starting point for the work. Two calibration constants were found that can be used in a simple linear equation to correct the EAARL-B depth data to agree with the reference datasets. The constants are a scale factor of 0. 98103 and an offset of -0. 00068 m, and were found to hold well in comparisons using different reference datasets and geographic locations. Additionally, the topobathy channels were found to track the deep channel over the entire operational depth range of the topobathy channels. This finding is important because the shallow channels operate very differently from the deep channel. The deep channel is viewing backscatter from an area that is much larger than each of the topobathy channel areas.
FUTURE WORK Future work will involve determining whether the calibration is stable over time, or, if not, at what temporal frequency the EAARL-B system needs to be recalibrated. Further investigation is also planned to derive calibration constants for non-clearwater conditions. EAARL-B data will be collected over a suitable test area during times of differing water clarity along with concurrent sea truth-field measurement of seafloor elevation, water column optical properties, and bottom reflectivity would be required. For each survey mission conducted, the procedure described herein will be performed. Ultimately, the goal is to have ALPS automatically determine the depth calibration coefficients based on the calculated optical properties of the water column.
show the same _________________________________________________________________________________________________ Journal of Coastal Research, Special Issue No. 76, 2016 distributions for deep-channel data after calibration. If the EAARL-B depth measurements match the reference data, the means of the density estimate curves would all be approximately zero, as is the case in the post-calibrated data (red). The pre-calibration curves (blue and grey) indicate that uncalibrated EAARL-B data exhibit a deep bias because their means are positive and the deviation from zero increases as depth increases. Blue curves appear on only the first five plots because the shallow channels produced returns down to approximately 15 m, whereas the deep channel (red and grey) produced returns to roughly 34 m. The curves for polygons A-I also indicate excellent alignment between the topobathy channels and the deep channel.
ACKNOWLEDGMENTS The work by  C. Wayne Wright  was done while serving as a Scientist Emeritus with the  U. S. Geological Survey. The work performed by  Christine Kranenburg of Cherokee Nation Technology Solutions ,  St. Petersburg , Florida, was done under contract to the  U. S. Geological Survey. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
Image analysis MetaMorph and ImageJ (National Institutes of Health) software were used for all data analysis. Metaphase plate positions were measured 30 s before anaphase onset. In Fig. 1  C, positions are shown relative to the A-P axis with the anterior pole being 0% and the posterior pole being 100%. In Fig. S2 (A  and C ), the distance between the posterior pole and the metaphase plate was subtracted from the distance between the anterior pole and the metaphase plate. Positions reflect the displacement of the metaphase plate from the cell center (0 µm). Contractile ring diameter was measured at the cell equator from anaphase onset until, in the case of cytokinesis completion, anaphase of the subsequent cell cycle or, in the case of cytokinesis failure, the contractile ring had completely regressed and the next cell cycle began in the aneuploid cell.
Online supplemental material Fig. S1  shows that ts mutants allow tuning of specific protein functions that are necessary for cytokinesis. Fig. S2  shows successful RNAi-mediated knockdown of PAR proteins as confirmed by classic hallmarks of the loss of polarity. Fig. S3  shows that cortical PAR proteins regulate cytokinesis and sequester septin and anillin in the cell anterior. Table  S1  contains the strain names and genotypes used in the study. Table  S2  details the feeding RNAi constructs used, as well as relevant information for their construction when needed. Online supplemental material is available at http ://www. jcb. org /cgi /content /full /jcb. 201510063 /DC1. Additional data are available in the JCB DataViewer at http ://dx. doi. org /10. 1083 /jcb. 201510063. dv.
Acknowledgments We thank all members of the  Canman ,  Shirasu-Hiza , and  Dumont laboratories  for support;  Gregg Gundersen ,  Clare Waterman ,  Sriramkumar Sundaramoorthy , and  Andy Kummel  for critical reading;  Natalia Spica ,  Isaiah Thomas , and  Vandana Chand  for laboratory assistance;  Jennifer Waters  for microscopy consultation; and  Amy Maddox ,  Michael Glotzer , and the  Caenorhabditis Genetics Center  for worm strains. This work is supported by grants  ANR-09-RPD OC-005-01  ( Agence Nationale de la Recherche ) and  FRM-AJE201112  ( Fondation pour la Recherche Médicale)  and the  Mairie de Paris Emergence Program  to  J. Dumont ; grants  NIH-R01-GM105775  and  NIH-R01-AG045842  ( National Institutes of Health)  to  M. Shirasu-Hiza ; and grant  NIH-DP2-OD008773  ( National Institutes of Health)  to  J.
Hybridised microarray slides were then washed according to manufacturer's instructions and scanned using an Agilent Microarray Scanner incorporating the Agilent Feature Extraction Software (Agilent Technologies). The microarray data is available from the Gene Expression Omnibus (GEO, http://www. ncbi. nlm. nih. gov/geo/) under the series accession number GSE67836. Quantification files derived from the Agilent Feature Extraction Software were analyzed using the AgiND package (http://tagc. univmrs. fr/AgiND). We also used the AgiND R package for data quality control and normalization. Quantile methods and a background correction were used for data normalization. Gene expression data was then imported into XLStat (Addinsoft®) for filtering and statistical analyses. Because the primary objective was identifying genes suitable for being subjected to further bioinformatics analysis, all gene names containing the words «expressed sequence», «RIKEN», « PREDICTED», «cDNA sequence», or "similar to" were filtered out.