dataesr/scientific-paragraphs-categorization huggingface dataset:
Uncertainties As with any study that involves modeling PSDs, the dominant sources of uncertainty are in the assumptions of aerosol composition and distribution parameters. Here, the particle number density and mode radius play a minor role. However, as seen in Fig. 2 , the selection of σ g has a variable impact. The statistics presented in Sect. 3. 1 were calculated using σ g = 1. 5 but are not influenced by the selection of σ g since changing the selection of σ g will shift all datasets up or down equally. On the other hand, the accuracy of the method is highly dependent on σ g. As an example, setting σ g = 1. 5 leads to a +32/ -16 % uncertainty (compared to σ = 1. 8 and σ = 1. 2, respectively) when the extinction ratio equals 6. Since > 90 % of the stratospheric extinction ratios do not exceed 6, we consider +32/ -16 % to act as a reasonable upper limit of expected uncertainty for this analysis.
6, 7 , and 8 for Table Mountain, Mauna Loa, and OHP, respectively. The spread in the β SAGE value, due to varying  results in solving Eq. (  4 ) for differing values of σ g , is represented by the black shaded time series data. It is noted that, most of the time, this shaded area is indistinguishable from the black line thickness. Error bars in Figs. 6, 7 , and 8 represent the standard error (error on mean). We observed that the datasets were in qualitatively good agreement at all altitudes, especially when the atmosphere was impacted by the Pinatubo eruption  (June 1991 (June -1998)). Statistics for the time series data are presented in Table  2. The data were broken into two time periods: (1) when the signal was perturbed by the Pinatubo eruption (labeled PE in the table, June 1991-December 1997), as defined by  Deshler et al.
Mauna Loa Similar to OHP, the MLO record did not begin until ≈ 2. 5 years into the Pinatubo recovery. Beginning in June 1995 the two datasets began to diverge at 20 km (Fig. 8 ), with the lidar record flattening out. In contrast, β SAGE continued with a quasi-exponential decay until January 1998, in agreement with the other two sites and previously published studies (e. g. ,  Deshler et al. , 2003; Thomason et al. , 2018). In January 1998 the lidar signal experienced an anomaly wherein the signal decreased by approximately an order of magnitude. After this time, β SAGE was consistently larger than β Lidar. The discrepancy from June 1995 to January 1998 at 20 km is currently not understood. However, the sudden change in January 1998 coincides with a new lidar instrument setup. The statistics in Table  2  show the MLO comparison to be the worst of the three stations (excluding the -29.
SAGE III/ISS To date, the SAGE III mission has made observations under relatively clean stratospheric conditions similar to conditions at the end of the SAGE II mission. Due to the limited data record (3 years since launch), the comparison between SAGE III and the Mauna Loa and OHP lidars will be cursory. Data from the Table  Mountain  Facility have not been released for this time period; therefore, Table  Mountain  was excluded from the current analysis. The SAGE III and lidar backscatter coefficients show similar qualitative agreement at both Mauna Loa and OHP (Figs. 9 and 10, respectively), similar to what was observed in the SAGE II comparison (vide supra). During the SAGE III mission the atmosphere has been relatively stable, with a minor increase in backscatter and aerosol extinction in late 2017 due to a significant pyrocumulonimbus (pyroCB, indicated by the vertical line in the figures) event in northwestern Canada, which was comparable to a moderate volcanic eruption  (Peterson et al.
However, further evaluation involving major volcanic eruptions is required to better understand whether this agreement is fortuitous or the EBC algorithm is actually insensitive to aerosol composition and shape. The calculated S at each site was in good agreement with values calculated by  Jäger et al. (1995). Immediately prior to the eruption S was approximately 40-45 for the lowermost altitudes (tropopause-20 km) and slightly higher (50-60) in the 25-30 km altitudes. This was followed by a quick decrease after the eruption of Pinatubo, down to values of 20 in the Jäger dataset, with our calculated value being slightly lower. Overall, the calculated S shows good agreement with the Jäger dataset in both magnitude and trend with altitude. Other studies that did not overlap with either SAGE II or SAGE III have shown similar S values to those calculated here  (Bingen et al.
g. , small differences such as 2 × 10 -10 for small numbers such as 1 × 10 -10 yield large percent differences -here, 100 %). Another consideration is that the SAGE III record, to date, is short compared to SAGE II, and the lidar coverage within the SAGE III time period is approximately 1 year, further limiting the intercomparison. As the record expands (possibly including observations of moderate to major volcanic events) we expect the comparison with the lidar data to improve. A potential application of this method is informing lidar ratio (S) selection for lidar observations. As an example, processing for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar currently assumes a static lidar ratio (50 sr) for all latitudes and all altitudes. As was recently shown by  Kar et al.
Aggregate statistics for line-of-best-fit slope and R 2 for the SAGE II and SAGE III products compared to β S(520). Values in parentheses were calculated after restricting the dataset to altitudes between 15 and 31 km. The last column was calculated using data from both missions and backscatter values calculated using both β S(385) and β S(385). The slope P 95 data show the range of slopes (centered on the mean).
0654 • a 1. 45 dg + 0. 2 (1) A similar local calibration method has provided good results with other water quality parameters in earlier studies such as  Koponen et al. (2007) ;  Attila et al. (2013). The data processing and extraction are done in a Calvalus massive parallel processing system (http://www. brockmannconsult. de/calvalus, last access: 22 September 2020). Data extraction areas are manually defined in the vicinity of the mouths of 69 rivers that represent ERGOM input locations (Fig. 1 ). The areas are designed so that islands, mixed pixels and shallow areas are excluded. All valid pixels (not masked as land or cloud by the pixel classification processor Idepix) within each area and image are collected and analyzed, and the 75th percentile value is chosen to represent the river a CDOM.
Cases in which the number of valid pixels is less than 50% of all available pixels from an area are removed from the analysis. Assumedly, these represent cases with partial cloud cover and they are discarded to keep only estimates with highest quality and low uncertainty. The arithmetic means of the 75th percentile pixel values of all valid days within each calendar month during years 2017-2019 are then computed for each extraction area. We are aiming at providing the ecosystem model ERGOM with an annual cycle of CDOM loads based on monthly data. Since optical EO methods cannot provide a CDOM estimates in darkness and throughout times with ice coverage, the values for the winter months have been interpolated. As a result, the dataset contains a CDOM value for each month for each of the areas under investigation (69 extraction areas in total).
CDOM absorption In Fig. 3a , we show the simulated CDOM absorption at the sea surface. The snapshot clearly illustrates the spatial patterns. Strong absorption is visible in the northern Baltic and the river mouths. The difference to the salinity based estimate (Eq. 3) is depicted in the right panel. Strongest differences appear in the Gulf of Bothnia and the Gulf of Finland while in the central Baltic differences are small. Strong differences are also pronounced in river estuaries. Owing to the low salinity, the salt-CDOM Both datasets were compared against in situ data collected from monitoring stations in coastal waters of Finland and from the Northern coast of Sweden. As shown in Fig. 4 , the improvement becomes obvious. With the salinity method, the correlation is low and there are some clear overestimates while most data points are underestimated.
The impact on water temperature is small (order of 0. 01K, not shown). The effect is a temperature increase in the surface layer and a lower temperature below. We demonstrate changes in the biogeochemistry with a climatology of surface nutrient concentrations at three stations in CDOM are shown in black, based on a conversion from simulated salinity in green, and red diamonds are observations. The map was created using the software package GrADS 2. 1. 1. b0 (http://cola. gmu. edu/grads/), using published bathymetry data  (Seifert et al. , 2008). Gulf of Finland at station KAS-11, the spring bloom related nutrient depletion is delayed by 2 weeks (Fig. 7a  and b ). Sufficient PAR intensity, initiating a bloom, is available later in the season. The winter nutrient concentrations are elevated compared to the salinity model version.
Code and data availability. In situ absorption observations are available from http://eo. ymparisto. fi/data/water/Baltic_SeaLaBio/. Monthly CDOM absortion data are available from http://eo. ymparisto. fi/data/water/Baltic_SeaLaBio/CDOM_input_to_ERGOM/. Model data can be accessed via https://thredds-iow. io-warnemuende. de/thredds/catalogs/projects/SeaLaBio/catalog_sealabio. html. The code of the biogeochemical model is available at www. ergom. net (last access: 22 September 2020). The ocean model "Modular Ocean Model MOM 5-1", used in this study, is available from the developers respository https://github. com/mom-ocean/MOM5 (last access: 1 December 2020). The meteorological forcing is archived at https://cera-www. dkrz. de/WDCC/ui/cerasearch/entry?acronym=coastDat-2_COSMO-CLM (last access: 1 Decenber 2020).
The version of the model code used to produce the results in this study is archived on Zenodo at https://doi. org/10. 5281/zenodo. 4299873 (last access: 1 December 2020). In addition to the source code, the archive includes initial fields and boundary conditions exept the meteorological forcing. Sample availability. Simulated CDOM data: https://wwwi4. ymparisto. fi/i4/eng/tarkka_beta/index. html?type=ERGOM_CDOM&date=2019-12-01&lang=en&zoom=5&lat=61. 46508&lon=32. 98851.
FN_BoB_13Figure 1. Figure 2. 12 Figure1. Location of model rivers (black squares). The green line is the coastline of the 3nm model. We refer to the labeled river later in the text. The map was created using the software package GrADS 2. 1. 1. b0 (http://cola. gmu. edu/grads/), using published bathymetry data (Seifert et al. , 2008).
Cells were maintained at 37°C with a heating chamber. Protein recruitment was quantified using a custom-made MATLAB (MathWorks) routine. For FRAP experiments, we bleached using a 490-nm laser, a 20 × 20 pixel square located within the area previously irradiated at 405 nm to induce DNA damage. Images were collected at a frequency of 4 frames/s. After background subtraction, the fluorescence recovery kinetics were obtained by dividing the signal within the bleached area to the one measured in the unbleached part of the damaged region. FRAP curves were fitted with the following equation: I(t) = (1 -FI) × (1 -exp(-t/)) , where  is the characteristic recovery time of the fast population and FI is the immobile fraction.
8 or a Plan-Apochromat 63×/1. 4 oil objective and controlled by ZEN 2. 3 software. Fluorescence excitation was performed using diode lasers at 405, 488, and 561 nm. Images were analyzed after generating the maximum intensity projections of the z-stacks. For Rad51 foci formation, cells were treated with olaparib (10 M) for 48 hours before fixation and staining. The percentage of cells with >5 cells per nuclei was quantified. An average of 100 cells per condition were counted.
HR and NHEJ reporter assays HeLa pGC or HeLa pEJ cells were transfected with siCtrl, siBRCA1, siKu70, or siALC1 siRNAs by DharmaFECT or mCherry-ALC1 plasmid construct by Xfect (Takara). Twenty-four hours after transfection, cells were transfected a second time with I-SceI plasmid constructs using Xfect (Takara) according to the manufacturer's instructions. Forty-eight hours after I-SceI transfection, cells were fixed with 3% PFA for 3 min and stained with Hoechst. Z-stacks of images were acquired on an LSM800 confocal setup with a Plan-Apochromat 20×/0. 8 or a Plan-Apochromat 63×/1. 4 oil objective and controlled by ZEN 2. 3 software. Fluorescence excitation was performed using diode lasers at 405, 488, and 561 nm. The raw images were analyzed in CellProfiler (58) after generating the maximum intensity projections of the z-stacks in Fiji (ImageJ)  (59).
Cell cycle analysis by flow cytometry Cells were incubated with or without 1 M olaparib for 24 hours. Cells were then collected and fixed with 70% EtOH for 30 min on ice. Cells were washed with PBS and treated with ribonuclease A solution (10 g/ml; Sigma-Aldrich) for 20 min at RT. DNA content of the cells were stained with propidium iodide solution [0. 1% Triton X-100 and propidium iodide (500 g/ml); Sigma-Aldrich] for 10 min. G 0 -G 1 , S phase, and G 2 -M were differentiated by a flow cytometer (BD FACSCalibur), using BD CellQuest Pro version 6. 0, ModFit LT software.
Visualization of the chromatin fraction of PARP1-mCherry in cells HeLa-PARP1-mCherry cells were transfected with siCtrl, siBRCA1, or siALC1 siRNAs, and then 24 hours later, cells were plated onto glass-bottom cell culture chambers (Greiner). Forty-eight hours after the transfection, cells were incubated with or without 1 M olaparib for 1 hour and then washed with pre-extraction buffer (10 mM tris-HCl, 2. 5 mM MgCl 2 , 0. 5% NP-40, and protease inhibitor cocktail 1:100) for 3 min and fixed with 3% PFA for 5 min. The nuclei were stained with Hoechst. At least 6000 cells per condition were captured using an LSM800 confocal setup with a Plan-Apochromat 20×/0. 8 or a Plan-Apochromat 63×/1. 4 oil objective and controlled by ZEN 2. 3 software. Fluorescence excitation was performed using diode lasers at 405, 488, and 561 nm.
Quantification and statistical analysis All data were derived from at least n = 3 replicates, and for each experiment, at least 40 nuclei or chromosome spreads were analyzed. A minimum of 20 cells per condition were examined in live-cell imaging experiments. P values from live-cell imaging experiments (Fig. 4  and fig. S4 ) were calculated using an unpaired Student's t test with Bonferroni correction (*P < 0. 05, **P < 0. 01, and ***P < 0. 001). Statistical analysis was performed using R statistical software (version 3. 6). Linear regression models or linear mixed effect models were fitted using lm (stats package) or lmer (lme4 package version 1. 1-23 and lmerTest package version 3. 1-2) functions, respectively. Dose-response curves were analyzed similarly as previously published  (61). Specifically linear, quadratic, or cubic regression models were fitted using orthogonal polynomials of degree 1, 2, or 3, respectively (stats package poly function).
The final model was chosen by the Akaike information criterion (bbmle package version 1. 0. 23. ) and by diagnostics of the residuals (DHARMa package version 0. 3. 1). Plots were generated by ggplot2 package (version 3. 3. 1). Asterisks represent P values, which correspond to the significance of regression coefficients (*P < 0. 05, **P < 0. 01, and ***P < 0. 001). For second-or third-degree polynomial models, the lowest P value is chosen from linear, quadratic, or cubic terms, as a significant difference in any of these coefficients indicates different curve characteristics. Model summaries are provided in table  S2. Detailed analysis code is available on GitHub: (https://github. com/tschauer/Juhasz_ etal_2020).
Acknowledgments: We thank  Microscopy Rennes Imaging Center (BIOSIT, Université Rennes 1)  for technical assistance. We also thank  F. Zhang  for the GeCKOv2 library,  D. Trono  for psPAX2, and  G. Schotta  for the pLP-eco env plasmid and the HeLa-mCAT1 cells. We specifically thank the technical assistance of  A. Hegele ,  A. Kószó , and  Z. Nacsa. We thank  S. Krebs  and the  Laboratory for Functional Genome Analysis (LAFUGA)  for sequencing. Last, we thank  W. Mansour  for the pGC and pEJ reporter cell lines and  S. Kern  for the BRCA2 cells. Funding: This work was supported by the  Hungarian Academy of Sciences  ( LP2017-11/2017  to G. T. ), the  National Research Development and Innovation Office  ( K128239  to G. T. and.
eu/), and the Multi Experiment Data & Operation Center (MEDOC; https://idoc. ias. u-psud. fr/MEDOC), as well as the space-weather team in Toulouse (Solar-Terrestrial Observations and Modelling Service; STORMS; https: //stormsweb. irap. omp. eu/). This includes funding for the data mining tools AMDA (http://amda. cdpp. eu/), CLWEB (clweb. cesr. fr/), and the propagation tool (http://propagationtool. cdpp. eu). The work of A. P. R. and N. P. was funded by the ERC SLOW SOURCE project (SLOW SOURCE -DLV-819189). A. K. also acknowledges financial support from the COROSHOCK (ANR-17-CE31-0006-01) and FP7 HELCATS project https://www. helcats-fp7. eu/. P. H. , R. H. , G. S. , and A. V. acknowledge support from the NASA PSP program office. The toroidal current of the FR is simplified to a current loop (ring) of major radius R, and both toroidal and poloidal currents are allowed to flow inside a minor radius a, just as in  Chen (1989 Chen ( , 1996)).