dataesr/scientific-paragraphs-categorization huggingface dataset:
Applicability of the Algorithm to Other Lakes To examine its wider applicability, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm was applied to Lake Chaohu, which is the fifth largest freshwater lake in China and is a typical eutrophic lake  [63, 64]. A total of 23 synchronous satellite and ground data points for Lake Chaohu were obtained by following the synchronous satellite and ground data matching criteria. The estimation accuracy of the dual band ratio algorithm was found to be low when it was used directly with the Lake Chaohu data (Figure  11a ; R 2 = 0. 60, RMSE = 1. 38 m -1 and MAPE = 21. 85%), and the intersection points between the estimated and measured values of K d (490) are generally below the 1:1 line. This is because the coefficient of the algorithm was obtained by empirical regression based on the Lake Taihu data.
Based on synchronous satellite and ground datasets, a novel dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm was found to accurately estimate Kd(490) for turbid inland waters. The development of new algorithms that take advantage of the improved radiometric and spatial resolution of OLCI data will increase in the coming years. Compared to available algorithms, the algorithm developed in this study has a higher accuracy and can reduce the effects of atmospheric and environmental factors. Satellite imagery improves our understanding of the characteristics of the photoecological environments in water and thereby supports the evaluation and management of lake water quality. In future work, data acquired by multiple satellite sensors, such as MERIS, MODIS, and OLCI, can be used to determine the historical evolution of Kd(490) for inland turbid waters.
Considering the highly dynamic variations in the waters of Lake Taihu, high temporal-resolution Geostationary Ocean Color Imager (South Korea) and GaoFen-4 (China) satellite data and meteorological and hydrological data can be used to further understand the evolution of spatial patterns inthe photoecological environment over short time scales. In summary, we believe that the addition of OLCI data will provide more possibilities for monitoring, evaluating, and managing lake water quality.
Based on synchronous satellite and ground datasets, a novel dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm was found to accurately estimate K d (490) for turbid inland waters. The development of new algorithms that take advantage of the improved radiometric and spatial resolution of OLCI data will increase in the coming years. Compared to available algorithms, the algorithm developed in this study has a higher accuracy and can reduce the effects of atmospheric and environmental factors. Satellite imagery improves our understanding of the characteristics of the photoecological environments in water and thereby supports the evaluation and management of lake water quality. In future work, data acquired by multiple satellite sensors, such as MERIS, MODIS, and OLCI, can be used to determine the historical evolution of K d (490) for inland turbid waters.
Acknowledgments: The authors would like to thank all of the study participants and volunteer contributors ( Jing Li ,  Dian Wang ,  Qichun Liang ,  Yixuan Zhang , and  Tianci Qi  [ Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences: NIGLAS ]). We also thank  ESA  and  NASA  for providing the satellite data and processing software. Financial support was provided by the  Provincial Natural Science Foundation of Jiangsu, China  ( BK20160049 ), the  National Natural Science Foundation of China  ( 41671358 ,  41431176 ), the  Youth Innovation Promotion Association of CAS  (2012238) and Dragon 4.
The three types of tests for this analysis are a simplified nuclear core model with flow in the low Mach regime (boiling channel), tests in the compressible regime exhibiting shock waves and others in the compressible regime but with no shock waves. First, we present the four balance equations which are solved with the FLICA4 code ( § 1). Then, time and space discretisation of these equations using finite volume approach on staggered grids is detailed in section 2. The time discretisation of the equations is based on a semi-implicit scheme. Since the equations are not linear, the solution at each time step is obtained by a Newton-Raphson iterative method. The new numerical method is described in section 3. It leads to a linear system of equations for the increments of the principal variables.
The pressure based-solver solutions are equivalent to the analytical ones. These results are very promising since the new solver improves FLICA4 code robustness without degrading its accuracy. However, the equations are in a non-conservative form, it is expected that it might not be as accurate in the compressible regime. Based on two test-cases, we demonstrate that the pressure-based solver is fairly accurate as long as the meshing remains relatevely coarse. Because of the domain of application of the FLICA4 code, coarse meshing and low Mach regime, the new pressure-based solver described in this paper is a real alternative to the current solver. The new solver should greatly improve the use of the code in an industrial context for nuclear reactor safety analysis.
Although it would be almost impossible to measure the relative effects of these miRNAs on the expression of each of these putative target genes, the above observation suggests that the effects of upregulated miRNAs as a whole might be greater than those of downregulated miRNAs, leading to reduced levels of expression of their respective target gene and, as a consequence, reduced fatty acid oxidation. 1  and 2. The sign "x" indicates the number of putative target sequence for a given miRNA. Target transcripts were identified using the Targetscan software (www. targetscan. com). Numbers in front of miRNAs indicate that more than one consensus target site for this miRNA is present in the 3 -untranslated region of the transcript. For instance, SIRT1 3-untranslated region contains two consensus target sequences for miR-449b.
Acknowledgments: This work was supported by  ANR  (grant  ANR-09-GENO-024-01 ). We thank  Jean-Jacques Michaille  from  Université de Bourgogne-Franche Comté, Laboratoire Bio-PeroxIL, Dijon, France , for his review and advice. Author Contributions: N. L. and D. D. conceived and designed the experiments; V. A. performed the experiments; V. A. , D. D. , M. C. -M. , and N. L. analyzed the data; V. A. , F. D. , and J. B. contributed reagents/materials/analysis tools; N. L. wrote the paper.
When using inhibitors, those were added to the starving media 30 min prior the stimulation and present while treating with the growth factor. EGFR inhibitors (Iressa and CI1033) and Akt inhibitor MK2206 were purchased from Selleckchem (Houston, TX) whereas MEK inhibitor U0126 was from Promega (Madison, WI). Where indicated, 10 g/ml cycloheximide (Sigma-Aldrich, St. Louis, MO) was used to block the de novo protein synthesis. RNAi-based Silencing and Rescue Expression of Wild Type and Y15F Mutant of CYLD-DNA constructs for RNAi silencing of CYLD and generation of stable cell lines were performed using a lentiviral system as described before  (33). We used the following targeting sequences for RNAi: 5Ј-GCAATATGACGAGTTAGTA-3Ј for shControl and 5Ј-GGGTAGAACCTTTGCTAAA-3Ј for shCYLD. Depletion of CYLD was confirmed by real-time PCR and immunoblotting (see Supplemental Data for details).
For immunofluorescence, HeLa cells were transfected with GFP-Rab7 plasmid and lipofectamine as described previously  (34). Antibody sc-120 from Santa Cruz was used for detection of EGFR (see supplemental Data for details). StUbEx and Peptide Pull-down Analyses-For the enrichment of ubiquitinated proteins, shControl and shCYLD HeLa cells containing the StUbEx construct  (33)  were grown in the presence of doxycycline 60 h prior the experiment. Cells were subjected to serum starvation and stimulated with 150 ng/ml of EGF as indicated. To study the four conditions of interest, two paralleled triple SILAC experiments were performed per replica (we carried out two biological replicas, thus corresponding to a total of four triple SILAC experiments; the experimental design of one replica is exemplified in Fig.
An empty-beads pull-down was carried out in the light condition to discriminate unspecific background proteins (see supplemental Data for details). NanoLC Tandem Mass Spectrometry (LC-MS/MS) and Data Analysis-Peptides were separated by reversed-phase in an EASY-nLC 1000 (Thermo Scientific) coupled to a mass spectrometer equipped with a nanoelectrospray ion source. The samples were analyzed in either an LTQ Orbitrap Velos or a Q Exactive (Thermo Scientific). For the chromatographic separation, solvent A was 0. 5% acetic acid and solvent B was 80% acetonitrile in 0. 5% acetic acid. The peptides were eluted from an analytical in-house packed column of ReproSil Pur C 18 -AQ, 3 m resin (Dr Maisch GmbH), at a flow rate of 250 nL/min. The mass spectrometers were operated in positive ionization mode, in a top 12 data-dependent manner, at a resolution of 70,000 (at m/z 400) and AGC target of 1e6 for the MS survey, scanning from 300 to 1750 m/z.
For the MS/MS analysis, resolution was set to 35,000 (at m/z 400), AGC target to 1e5, minimum intensity to 4e4 and isolation window to 2. 0 m/z. To minimize the repeated fragmentation of ions, an exclusion time of 45 s was programmed. The maximum injection time values for survey and MS/MS scans were 120ms and 124ms, respectively. Raw data files were analyzed with MaxQuant (36) version 1. 3. 0. 5, which includes the Andromeda search engine. Peak lists were searched against human UniProt database version 2014. 01 (88479 sequence entries). Three SILAC labeling channels were set, corresponding to Lys0/Arg0, Lys4/Arg6 and Lys8/Arg10 for the light, medium and heavy conditions, respectively. Variable modifications for the searches included N-terminal protein acetylation, methionine oxidation, deamidation of asparagine and glutamine.
Parallel Reaction Monitoring (PRM) Analysis-All PRM analyses were performed on a Q Exactive HF (Thermo Scientific) using the same LC-MS/MS system previously described with the following method modifications. Precursor m/z of proteotypic target peptides for EGFR, Cbl-b, CYLD, and FLNA (supplemental Table  S8 ) have been selected from previous top 12 data-dependent runs and added to the instrument inclusion list. MS/MS spectra of target peptides were acquired in unscheduled PRM mode using a resolution of 60,000 (at m/z 200) and an AGC target of 5e5 with a maximum injection time of 150 -200ms  (37, 38). Fragmentation was performed in HCD mode with normalized collision energy of 28. PRM data analysis was carried out on Skyline™ 3. 6. 0 software  (39). Spectral libraries were built using MaxQuant ms/ms search files.
Two biological replicates were performed for peptide pull-down experiments per experiment, swapping medium and heavy SILAC conditions to avoid any technical bias. A control condition, in which the light SILAC protein extract was incubated with empty beads, was included in both pulldown experiments, helping to discriminate background and contaminant signals. In the case of CYLD immunoprecipitation, two biological replicates we performed as well. The visualization of overlapping protein identifications was performed using the BioVenn web application (http://www. cmbi. ru. nl/ cdd/biovenn/), whereas the significance of the changes in SILAC protein ratios were based on significance B calculations inbuilt in the MaxQuant/Perseus software and visualized with GProX  (40). The analysis of functional groups and GO term categories were carried out in MetaCore (Thomson Reuters), which relies on Fisher statistical test with an FDR adjusted p values.
The array contains ~21,000 of the best-defined human gene regions. Hybridization and microarray procedures were performed in accordance with the manufacturer's instructions. Each microarray analysis was duplicated for one tumor and matched normal tissue samples. A High Resolution Microarray Scanner (Agilent Technologies) was utilized for scanning fluorescent intensities. Microarray Data Analysis. ChIP-on-chip data analyses were carried out using the package R/Bioconductor Ringo software 1. 26. 1. The selection criteria for the enriched regions or genes were: the region require a certain 250 bp probe spacing; contain at least 3 probes/region and smoothed intensities of probes mapped to this region need to exceed a threshold defined at 1. 5. Network Analysis. The network associated with genes regulated by epigenetic modifications among different subtypes was constructed using Cytoscape 14 (version 3.
Data collection Each participant was followed up for 12 months during which he/she had 4 programmed medical visits: at enrolment and at months 3, 6 and 12 (M0, M3, M6 and M12, respectively). At each visit, a medical questionnaire and a short self-administered questionnaire were completed. A phone interview (Computer-Assisted Telephone Interview: CATI) was conducted after each visit. Pain intensity, interference with daily functioning and pain treatments. We assessed pain intensity and interference (i. e. the repercussions of pain on daily functioning) using the self-administered BPI-Short Form  [21]  at enrolment (before methadone initiation), at M6 and at the end of the study (M12). Pain intensity was measured using an 11-point visual analogue scale from 0 (no pain) to 10 (worst possible pain).
We recorded anxiolytic/hypnotic agent use as positive when at least one of the following data sources indicated their use: medical questionnaire, self-report and urine test. Alcohol consumption was assessed at M0 and M12 during the CATI using the AUDIT questionnaire, with a cut-off point higher than 13 identifying alcohol dependence  [24]. Binge drinking was defined as drinking 6 glasses or more of alcohol on one occasion at least once a month. At M0 and M6, nicotine dependence was screened for using the Fagerstro ¨m test for nicotine dependence, a score !6 indicating high/very high dependence  [25]. Daily prescribed methadone doses were recorded at M6 and M12. During each medical visit, physicians collected data on withdrawal symptoms using the Objective Opioid Withdrawal Scale (OOWS) which comprises a list of 13 withdrawal symptoms  [26].
Variables associated with pain intensity and pain interference severity with a p-value lower than 0. 20 in the univariable analysis for at least one category of the outcome were considered eligible to enter multivariable models. A stepwise procedure was used to identify the best model by removing variables one at a time based on a p-value of >0. 05 for at least one of the outcome's categories. When coefficient estimates for different categories of pain and interference were similar in the multivariable analysis, the categories were collapsed together to obtain a single estimate. All analyses were performed using Intercooled Stata 12 (StataCorpLP, College Station, TX) software packages. The GLAMM Stata procedure was used to create a multinomial distribution.
entire study area and the entire time series, the gravel to sand ratio fluctuated, but was overall stable. Nonetheless, when only the biodiversity hotspots were considered, net losses and a gradual trend, indicative of potential smothering, was captured by ensemble and post-classification approaches respectively. Additionally, a two-dimensional morphological analysis, based on the bathymetric data, suggested a loss of profile complexity from 2004 to 2015. Causal relationships with natural and anthropogenic stressors are yet to be established. The methodologies presented and discussed are repeatable and can be applied to broadscale geographical extents given that broad-scale time series datasets become available.
This paper addresses the application of change detection methods to capture seafloor substrate changes over a period of 10 years based on a time series of seven datasets of MBES depth and backscatter data  (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015). It relates to assessing good environmental status of gravel beds in the Belgian part of the North Sea (BPNS) for which the Belgian State specified two indicators on seafloor integrity (MSFD descriptor 6) and for which multibeam technology was put forward as monitoring tool (Belgian State 2012): 1. The areal extent and distribution of the European Nature Information System (EUNIS) level 3 Habitats (sandy mud to mud; muddy sand to sand and coarse sediments), as well as of the gravel beds, remain within the margin of uncertainty of the sediment distribution with reference to the Initial Assessment.
Acquisition The MBES data were acquired by Ghent University in 2004, and later by the Operational Directorate of Natural Environment of the Royal Belgian Institute of Natural   (Van Lancker et al. 2016). Of the eight acoustic surveys undertaken between 2004 and 2015, seven were kept for this investigation. Surveys exhibiting a significant amount of navigation artefacts (mostly due to failure in vessel-motion related compensation during rough-sea conditions), were considered unsuitable for the analysis and were discarded. The first survey used a 100-kHz Kongsberg EM1002S, and the remaining six surveys operated a 300-kHz Kongsberg EM3002D (Dual-head system). Both systems were installed on Belgian oceanographic vessel R/V Belgica. The hydrographic quality of the EM3002D dataset is consistent with the IHO S44 Special Order, whereas with the former EM1002S only the Order 1A  (Wells and Monahan 2002)  was attained.
Under these standards, the total vertical uncertainty with ±95% confidence levels of the depth measurements result in ±0. 63 and 0. 33 m vertical error for the EM1002S and EM3002D, respectively, for a depth of 30 m (Tables  1, 2 ). These intervals encompass all sources of errors originating from the suite of instrumentation used during acquisition. Pitch, roll, heave and yaw were automatically compensated for during acquisition and a sound velocimeter constantly monitored the sound velocity at the transducers. Survey lines were spaced to reach a good compromise between survey time/costs and quality of the data resulting in a minimum of 20% across-swath overlap between adjacent lines. Throughout the timespan of acquisition (inter-and intra-survey), the MBES settings controlled by the on-board software (i.
e. SIS: Kongsberg native acquisition platform) remained unchanged (i. e. pulse length, beam aperture, beam spacing). The state of the antenna transducers was thoroughly checked and maintained for biofouling and deterioration of its components (either by divers or during regular dry-dock operations). Similarly, across all surveys, track lines were sailed in a SW-NE direction. Maintaining operational parameters stable and checking the physical state of the instrument ensured that instrumental drift was kept to the minimum. Regarding sound absorption throughout the water column, the α coefficient (see  Francois and Garrison 1982)  was computed according to the local seawater properties at the surface which were fed into the acquisition system every half an hour. The necessary water medium environmental parameters were obtained from the On board Data Acquisition System (ODAS), which logs these data at 1-s intervals.
To allow consistency in the last phases of the data processing (i. e. mosaic production) and hence enable their subsequent inter-comparability (in terms of relative dB values expressing a reflectivity scale according to a common reference), the EM3002D data were subject to a standardised processing procedure following the BSWG recommendations (see  Lurton and Lamarche 2015). Fledermaus Geocoder (FMGT, v7. 4. 5. b) and QPS QIMERA (v1. 2. 4. 429a) software suites were used to process the MBES raw data. Initially, tide-corrected bathymetry was produced and exported as 1-m horizontal resolution raster (32-bit float files) and as sound density files for integration in FMGT. The bathymetric surfaces are used to correctly allocate the backscatter snippet traces from single pings to their true seabed position.
Each survey was normalised by applying a flat angle varied gain (AVG) filter with a window size of 300. In order to weight nadir pixels and reduce their banding effect, the "No Nadir if Possible 2" algorithm and "50% line blending" FMGT options were applied. As such, the final dataset consisted of (1) relative (standardised to a common reference surface area) backscatter reflectivity (in dB), and (2) bathymetric surfaces (m) at 1 m horizontal resolution. The EM1002S data did not prove to be comparable in terms of backscatter levels with those from the EM3002D system, due to the differing intrinsic properties of the sensors (i. e. electronics and hardware) and to the absence of a cross-calibration of both sensors. Consequently, the first campaign was not included in the pre-and ensemble classification analyses (in "Pre-classification" and "Ensemble approach classification").
Ground-truth data The ground-truth data used in this study were acquired in complement to the T 7 survey. Collection of ground truth is necessary to validate the assumptions developed during the observation of acoustic data and ultimately to derive confidence metrics expressing the validity of the map produced. Ten samples were collected using a Van Veen grab, each with three replicas to ensure the spatial consistency of the acoustic theme being sampled. Video samples were acquired by means of a dropframe, equipped with underwater lights and a camera with a 1 × 1 m field of view. Video-frame data with poor visibility (i. e. due to turbidity or too strong current) were discarded. Visual sampling was very useful to acquire data in the gravel areas where conventional gears failed (i. e. box core and Van Veen).
Morphological evolution At first, an assessment of the spatio-temporal morphological evolution is carried out to determine whether changes in substrate are due to morphological evolution (i. e. migrating dunes), to an actual reconfiguration of the substrate delineations or to a combination of both. Regions of interest (ROI) encompassing the main morphological and substrate features of the study area were selected to extract 2D profiles from the time series (see Fig. 4  for profile locations). Simple yes/no and quantitative metrics of change with information about the directionality (i. e. ebb or flood dominated bedforms) of the migration can be derived from here. For ease of interpretation, data from 2004 to 2015 were used only (T 1 and T 7 , Table  2 ).
Supervised classification The second phase of the analysis makes use of the most recent (T 7 ) acoustic survey for which complementary ground-truth data are available. In order to efficiently combine the two datasets, a supervised classification algorithm is used. Unlike an unsupervised method, where no a priori information about the class labels is provided to the algorithm (i. e. clustering procedures), supervised classification uses ground-truth information to train and test the classification results. The Random Forest (RF; Breiman 2001) algorithm was used for classification. RF has high predictive accuracy in studies focusing on the comparison of supervised classifications of MBES data  (Diesing et al. 2014; Diesing and Stephens 2015)  and have proven highly successful in data mining research  (Li et al.