dataesr/scientific-paragraphs-categorization huggingface dataset:
Advanced Microwave Scanning Radiometer 2 (AMSR-2) satellite sensors provide the T B observations at 11, 19, and 37 GHz. The data assimilation scheme (DA) is a Sequential Importance Resampling particle filter (referred to as PF-SIR)  (Van Leeuwen, 2009 , 2014). The PMW emission from the snowpack is computed by using the Crocus snowpack model  (Brun et al. , 1989)  coupled to a microwave snow emission model, the Dense Media Radiative Transfer -Multi-Layer model (DMRT-ML)  (Picard et al. , 2013). This scheme is further referred as the Crocus/DMRT-ML chain and was previously calibrated over Quebec  (Larue et al. , 2018). As a first step, the previous study of  Larue et al. (2018)  tested the feasibility of the DA scheme in a controlled environment by using synthetic T Bsnow observations, obtained by running the Crocus/DMRT-ML chain with perturbed meteorological forcings.
, 2016). The specific objectives of this paper were thus to (1) calibrate the soil and the vegetation radiative transfer models coupled with the Crocus/DMRT-ML chain to simulate T B TOA over several years (2012 to 2016); and (2) evaluate the performance of the assimilation of PMW data in Crocus using SWE measurements obtained over 12 reference nivometric stations from 2012 to 2016 (43 winters). This paper opens the way to a functional spatialized method for improving SWE estimates over large and remote areas without using surface-based data. Figure  1. SWE measurement stations with the "GMON" SWE sensors (yellow squares; see Table  1  for details) in the province of Quebec. The red circles are the snow depth sensors ("SR50") used by Hydro-Québec for hydrological purposes, overlaid on a relief map (from blue -low -to brown -higher altitudes) and watershed contours (black lines).
Study area and evaluation database Figure  1  shows the region of interest located in the province of Quebec, eastern Canada (46-56 • N). This area includes the La Grande (LG) watershed, in north-central Quebec (below 56 • N), and the Outaouais and Saint-Maurice watersheds, in southwestern and south-central Quebec, respectively (46-48 • N), which are equipped with SWE and snow depth sensors for hydrological purposes. Quebec is characterized by different ecoclimatic conditions, a high percentage of forested area (dense boreal forests and mixed coniferous and deciduous), and a flat topography. To evaluate SWE simulations, SWE measurements were acquired from 2012 to 2016 by 12 nivometric stations (see numbered stations on Fig. 1 ), located through a north-south gradient in Quebec. This SWE database (coordinates, sensors, operating period, etc.
General setup Figure  2  shows the general methodology developed to simulate and to assimilate AMSR-2 satellite observations into the snowpack model. To simulate the signal measured by satellite sensors at the top of the atmosphere (T B TOA ), a chain of models was implemented and calibrated over eastern Canada. The 3hourly continuous atmospheric forcing database provided by the Global Environmental Multiscale weather prediction model (referred to as "GEM";  Coté et al. , 1998)  was used to drive the multilayer Crocus snowpack model (described in Sect. 3. 2. 1). Each GEM grid cell has a spatial resolution of 10 × 10 km 2 , which is on the same order as the observation scale. The Crocus model updates the snowpack every 15 min by interpolating meteorological inputs, but in this study we used daily Crocus outputs (SWE, snow depth, density, etc.
Sherb: University of Sherbrooke, U. Laval: University of Laval and vegetation parameters were performed over the summer period to avoid bias due to the presence of the snowpack. The brightness temperatures (T Bs ) measured by AMSR-2 satellite sensors were assimilated in a DA scheme (see Sect. 3. 4). Raleigh et al. (2015)  have shown that meteorological forcing data are the major sources of errors in snow model simulations. Hence, we assume here that the uncertainties of GEM meteorological forcing data are the only sources of errors in the T B modeling. It is very difficult to quantify modeling errors due to physical simplifications inside the model due to the spatial scale of the observations. Further studies are needed to estimate these errors over the study area and to take them into account in the DA experiment.
The observation error was assumed to be known and the modeling errors were estimated by perturbing selected meteorological forcing variables. An ensemble of 150 T B simulations was obtained and the distribution of these prior estimates represents the modeling error in response to GEM uncertainties. A particle filter with an SIR algorithm was used to update the simulated T B TOA over the winter by adjusting meteorological forcing data and snowpack states (posterior estimates) when an observation was available (Fig. 2 ). Several configurations of the DA scheme were tested over three evaluation sites representing different environmental conditions. The best configuration was evaluated over the validation reference sites from 2012 to 2016 (for 43 winters; Sect. 3. 4). Comparing data simulated at the station against model cells involves uncertainty due to spatial variations of the snowpack and land cover.
This is a well-known problem for model validation studies and we assume here that the high number of sites (12 SWE stations or 43 snowpack simulations) provides a useful assessment of simulations. It is also known that the spatial localization of measurements can lead to some biases  (Molotch and Bales, 2005). To diversify its measurements, Hydro-Québec has installed two SWE sensors in the forest, and not in a clearing as is the usual practice for ease of maintenance. 3 Materials and methods 3. 1 Data 3. 1. 1 AMSR-2 observations AMSR-2 satellite sensors  (Imaoka et al. , 2010)  provide PMW satellite observations on the 11 (10. 7), 19, and 37 GHz channels at V-pol and H-pol. Images produced by AMSR-2 are freely available on the Japan Aerospace Exploration Agency (JAXA) website. This study used the Level 3 Version 2 product, which provides daily T Bs normalized on a North Hemisphere polar stereographic projection with a spatial resolution of 10×10 km 2 (see https://gportal.
DA and inflation technique The snowpack prior state x t at time t is computed with the updated past state of snowpack simulations at time t -1 (posterior state x t-1 ) and the prior perturbed meteorological forcing data F t from time t -1 to t (see Fig. 2 ). The predicted observation is computed with y i t = h x i t , (10) where y i t is T B TOA predicted from particle i (i = 0. N , with N the ensemble size). The observation operator h is the τ snow -calibrated DMRT-ML model and the calibrated radiative transfer models estimating soil, atmosphere, and vegetation contributions. In the analysis step, the new posterior distribution is updated by weighting each particle x i t according to the distance between y i t and the AMSR-2 T B observation (with the weight we i t ). With the SIR algorithm, the pdf is resampled by duplicating particles with high weights (i.
2 kg m -2 by assimilating T Bs over the whole winter (dry and wet snow conditions). Here we used the LWC simulated by Crocus to detect wet and dry snow. This variable is subject to model errors and is linked to the original atmospheric forcing data and to their uncertainties. Further studies are needed to automatically detect wet snow events by using direct satellite observations. Previous studies have shown the potential to use the gradient ratio (GR = T B 37 -T B 19 /T B 37 + T B 19 ) to detect rain-onsnow events in arctic areas  (Langlois et al. , 2017; Dolant et al. , 2016) , and this approach should be investigated for boreal forest areas in further work. The use of active microwave observations is also a promising approach with a good spatial resolution  (Roy et al. , 2010).
Summary and conclusion An ensemble data assimilation (DA) scheme was implemented in a calibrated chain of models (Crocus/DMRT-ML,    2 ). Summer and winter periods are defined as periods when snowpack is observed or not. soil, vegetation, and atmosphere radiative transfer models) to improve SWE estimates by updating forcing data and snowpack states with the AMSR-2 satellite observations. The developed approach does not use any surface-based data and was tested over a boreal forest area in Quebec (eastern Canada). The proposed DA scheme is a particle filter with a resampled SIR algorithm, using an inflation technique of the R matrix to avoid degeneracy problems. The multilayer snowpack model, Crocus, coupled to the surface land model ISBA, was used to simulate the evolution of the snowpack.
In TUT the course is given as a web based course. Different universities around Finland participate in the project. Each week a different university is responsible for that week's topic. The main coordination is done by TUT. Students form groups in each university and work on the given tasks as teams. Modern lecture technology is used in TUT: e-Learning, with a hint of blended learning, Moodle, MATLAB or similar software, online lecture videos. Moodle is used for file sharing, course information, peer assessment of tasks and MATLAB for solving the exercises. Video lectures online, weekly exercises are done in groups, posted online and then reviewed and commented on by other groups. Students are awarded points for good answers and good comments. At the end of the course a final project is given to the students to undertake.
The final project work is assessed by the other students and by the course staff, and it is presented in a video conference. ATSU will prepare new syllabi for modernized courses that are more in line with European university courses. This will be done in order to better prepare the Master students of ATSU for their future careers. The Georgian educational system teaches less mathematics on the high school level, and thus ATSU has to design courses that upgrade students' knowledge in these topics as well. As the general level of the students is quite low, ATSU considers that the increase of the credits in mathematics on the Bachelor level is necessary. Also, the use of different software packages to support learning (Math-Bridge, GeoGebra, etc. ) will increase the quality of knowledge of our students.
Moreover, in MM patient cells with elevated E2F expression, we observed increased promoter chromatin accessibility of the top E2F promoter gene regulatory axis as measured by ATAC sequencing (ATAC-seq) (Figures  4D, S3A,  and S4E ). Although we observed robust differences in promoter ATAC-seq occupancy at top E2F genes, we also detected changes at super-enhancer regions  (Figures 4E and S4F). We observed similar standard quality-control metrics (Figures  S4G  and S4H ) and equivalent total aligned read GC% between low and high E2F ATAC-seq datasets with the distributions of reads being statistically similar (Figures  S4I-S4L ). We also observed similar QC metrics between ATAC-seq samples and E2F1-DP1 ChIP-seq datasets (Table  S3 ). We next applied two approaches specifically controlling for batch variability.
After 48 hours in culture, cells were stained with anti-CD138-PE (PharMingen), FITC-conjugated annexin V (BioVision) and DAPI, and cell viability was assessed by flow cytometry. Exvitech® automated flow cytometry platform (Vivia biotech, Madrid, Spain) was used to evaluate activity of blocking peptide against primary myeloma cells in their microenvironment, as previously described. Statistical significance was determined by Student's t test. Isobologram analysis was performed using the CalcuSyn software program (Biosoft, Ferguson, MO, and Cambridge, United Kingdom). A combination index (CI) less than 1. 0 indicates synergistic activity. E2F disrupting peptide-The 19-aa sequence is the H2 fragment derived from the DEF box region in DP1  (Bandara et al. , 1997) : R-R-R-V-Y-D-A-L-N-V-L-M-A-M-N-I-I-S-K Peptide was purified by HPLC.
ChIP-Seq, ATAC-Seq, and RNA-Seq Data analysis Genomic coordinates and gene annotation: All coordinates in this study were based on human reference genome assembly hg19, GRCh37 (https://www. ncbi. nlm. nih. gov/assembly/ 2758/). Gene annotations were based on Refseq annotation release 19 (https:// www. ncbi. nlm. nih. gov/refseq/).
Calculating read density: We calculated the normalized read density of a ChIP-Seq dataset in any genomic region using the Bamliquidator (version 1. 0) read density calculator (https:// github. com/BradnerLab/pipeline/wiki/bamliquidator). Briefly, ChIP-Seq reads aligning to the region were extended by 200bp and the density of reads per base pair (bp) was calculated. The density of reads in each region was normalized to the total number of million mapped reads producing read density in units of reads per million mapped reads per bp (rpm/bp). Identifying ChIP-Seq enriched regions: We used the MACS version 1. 4. 2 (Model based analysis of ChIP-Seq)  (Zhang et al. , 2008)  peak finding algorithm to identify regions of ChIP-Seq enrichment over background. A p value threshold of enrichment of 1e-9 was used for all datasets.
See Table  S4  for the GEO accession number and background used for each dataset. Identifying actively transcribed genes: In MM1. S, U266 and LY1, transcriptionally active genes were defined as those with a RNA Pol II enriched region within ± 1kb of the transcription start site (TSS). Mapping typical enhancers and super-enhancers: Super-enhancers (SEs) and typical enhancers (TEs) in MM1. S, U266, and LY1 samples were mapped using the ROSE2 software package available at https://github. com/BradnerLab/pipeline and originally described in  Brown et al. (2014). For MM1. S and U266, enhancers were defined in MM1. S using MED1 for Figure  3  to exactly match the original enhancer landscape published in  Lovén et al. (2013). We and others have demonstrated that enhancers and super-enhancers can be mapped using MED1, BRD4, and H3K27ac  (Brown et al.
ChIP-seq and ATAC profiles summary metric: The BAM files were read in R environment by readGAlignments function of GenomicAlignments Bioconductor package. The uniquely mapping reads in the BAM files were filtered by the XS tags. The counts were determined by the summarizeOverlaps function of GenomicAlignments Bioconductor package with mode as IntersectionNotEmpty. BSgenome. Hsapiens. UCSC. hg19 package was used for determining the GC content of the peak region.
S) using enhancers defined by H3K27ac in either MM1. S or U266 (Figures  S4A  and S4B ). Identifying enriched cancer hallmark gene sets of BRD4 SE associated genes or Top E2F genes: We defined BRD4SE associated genes as those with an SE within 50kb of the TSS. Top E2F genes were defined as the top 500 genes ranked by E2F signal in MM1S. These gene sets (BRD4 SE associated genes, n = 506 &Top E2F genes, n = 500) were queried using the MSigDB (http://software. broadinstitute. org/gsea/msigdb) to identify statistically enriched cancer hallmark gene sets. The top 4 enriched gene sets are shown in Figures  4C  and 4D.
Quantifying changes in expression for BRD4 SE associated genes or Top E2F genes: To quantify changes upon JQ1 or E2F perturbation, we accessed JQ1 perturbation expression data from GSE44931 at 6 hours and 24 hours post 500nM treatment collected on Affymetrix Primeview 3′ UTR arrays (GPL16043). For E2F perturbation, we utilized data collected on Affymetrix exon arrays. Probe values were collapsed to common gene name and log 2 expression values versus DMSO (for JQ1) or empty vector (for E2F) were utilized. Boxplots of gene expression values are compared in Figures  4E  and S4F. The statistical significance of changes in distributions was assessed using a Welch's two tailed t test. Quantifying changes in ATAC-Seq promoter accessibility in patient MM with high and low E2F levels: Using ATAC-Seq datasets for patient MM samples with either high or low E2F expression (n of 2 each), we quantified promoter accessibility at Top E2F genes in the ± 1kb TSS region (Figure  S4E ).
The statistical significance of changes in distributions was assessed using a Welch's two tailed t test. Quantifying changes in ATAC-Seq occupancy at individual loci: We first determined high-confidence ATAC-Seq peaks as those present in at least 2 of 4 samples. This resulted in 41,735 peaks in the analysis. Of these, 389 were proximal (+/-1kb of the TSS) to a MM top E2F as determined by the intersection of the top 500 E2F genes in MM1. S and U266. A generalized linear model was used to normalize dataset variability and a fisher's exact test was used to determine the statistical enrichment of top E2F genes in the set considered gained in E2F high patients (> log 2 fold change and adjusted p value < 0. 1) (Figure  4F ). Determining leading edge enrichment of top E2F genes: High confidence ATAC-Seq peaks associated with top E2F genes were determine as above.
DATA AND SOFTWARE AVAILABILITY Patient multiple myeloma data-Exon-array data from 172 multiple myeloma patient samples used in Figures 2A and 2B can be found in the NCBI GEO repository https:// www. ncbi. nlm. nih. gov/geo/ under accession number GSE39754. An extended cohort of RNA-Seq data used in Figure  S4D  is currently under preparation  (Cleynen et al. , 2017). E2F perturbation gene expression array data-Gene expression data after E2F depletion can be found in the Harvard Dataverse database using following link https:// dataverse. harvard. edu/dataset. xhtml?persistentId=doi:10. 7910/DVN/E7MAKN. (E) Genetic depletion of E2F1 was achieved using 4 different tetracycline-inducible pTRIPz-Turbo-RFP vectors (Thermo Scientific, Pittsburgh, PA) containing the target sequence or scramble control.
Transfected MM1. S cells were plated in growth medium in the absence or presence of 2. 5 μg mL -1 doxycycline. Western blot (WB) analysis (top) was performed at day 3 confirming decreased E2F1 protein expression in cells expressing inducible E2F1 shRNAs. Cellular proliferation (bottom) was evaluated by 3 (H)thymidine uptake (day 3) and presented as percentage of cell proliferation compared to untreated cells. In the medium containing doxycycline, reduced expression of E2F1 is accompanied by a reduction of cell growth rate compared to control cells. Data are shown as the mean values ± SD of triplicates. (F) In vivo mouse xenograft studies were performed with MM cells harboring doxycyclineinducible shRNAs targeting E2F1. In the early intervention model, a cohort of mice was treated with irradiated 0.
0625% doxycycline diet continuously starting 3 days after injection and monitored for tumor progression. Tumors were measured in two perpendicular dimensions using caliper measurements at the indicated time. Each dot in the graph represent a single mouse. (G) In the late-intervention model, mice were treated with irradiated 0. 0625% doxycycline diet continuously (1-6 mg of doxycycline per mouse per day) after tumor appearance and monitored for tumor progression using caliper measurements. Data are presented as mean values ± SD (n = 5/group); Student's t test. (H) Survival was evaluated from the first day of tumor appearance until death using the GraphPad analysis software. Survival curves (Kaplan-Meier) show prolongation of survival in mice with E2F1 depletion. Median survival for Scrambled, pTRIPZ93, pTRIPZ98, and pTRIPZ46 was 17, 22, 28, and 32 days, respectively.
See also Figure  S1. (E) Primary CD138 + MM cells from 3 MM patients and NPC from healthy donor were cultured with bone marrow stromal cells (BMSC) with and without E2F-DP1 blocking peptide. Cell proliferation was assessed by ( 3 H)thymidine uptake and expressed as cpm (counts per minute). (F) Bone marrow from 2 myeloma patients was diluted with RPMI to seed 400-8,000 live cells per well into 96-well plates previously prepared with increasing concentration of peptide and were incubated for 48 hr. After red cell lysis, cells were stained with annexin V and CD138 mAb to identify viable myeloma and normal cells. EC 50 analysis was performed using GraphPad analysis software. See also Figure  S2. S3  and S4. (B) RNA-seq expression analysis log2 fold changes between treatment and control in MM1.
Baseline values before treatment were not significantly different among groups. Significance between groups was evaluated using one-way ANOVA test. (D) MM1. S and U266 MM cell lines as well as PBMC from healthy donors were cultured in the presence of different doses of blocking peptide and JQ1 alone or in combination for 72 hr. Cell viability was assessed by CellTiter-Glo assay. Data are mean values ± SD of triplicates, Student's t test. (E and F) BM cells from MM patients were cultured with or without E2F-DP1 blocking peptide (14 μM) and/or JQ1 (10 nM) for 48 hr and analyzed using multichannel flow cytometry. Viability of CD138 + MM cells and CD138 -normal BM stromal cells was determined by Annexin V and DAPI staining. A representative experiment (E) as well as effects of these agents alone or in combination on MM cells from 2 different patients (F) are shown.
ChIP-Seq-All ChIP-Seq data generated in this publication can be found in the NCBI GEO repository https://www. ncbi. nlm. nih. gov/geo/ under accession number GSE80661. Patient multiple myeloma ATAC-Seq-ATAC-Seq data from patients multiple myeloma samples is submitted to the Harvard Dataverse https://dataverse. harvard. edu/ dataset. xhtml?persistentId=doi:10. 7910/DVN/E7MAKN.
ACKNOWLEDGMENTS We thank  Jun Qi  for providing JQ1 and  Jennifer Perry  for critical review of the manuscript. This work was supported by  Department of Veterans Affairs Merit Review   Award1 I01BX001584  (N. C. M. ),  NIH  grants  P01155258-07  (N. C. M. , H. A. -L. , R. A. Y. , C. Y. L. , M. F. , and K. C. A. ),  P01-CA066996  (J. E. B. ), and  1R01CA215452-01  (C. Y. L. ),  National Cancer Institute Spore   5P50CA100707-13  (M. F. , C. Y. L. , J. E. B. , N. C. M. , H. A. -L. , and K. C. A. ),  Department of Defense   CDMRP CA120184  (C. Y. L. ), and  Cancer Prevention Research Institute of Texas   RR150093  (C. Y. L. ).
Interpreting volcanic unrest is a highly challenging and non-unique problem at calderas, since large hydrothermal systems may either hide or amplify the dynamics of buried magma(s). Here we use the exceptional ground displacement and geochemical datasets from the actively degassing Campi Flegrei caldera (Southern Italy) to show that ambiguities disappear when the thermal evolution of the deep hydrothermal system is accurately tracked. By using temperatures from the CO 2 -CH 4 exchange of 13 C and thermodynamic analysis of gas ascending in the crust, we demonstrate that after the last 1982-84 crisis the deep hydrothermal system evolved through supercritical conditions under the continuous isenthalpic inflow of hot CO 2 -rich gases released from the deep (~8 km) magma reservoir of regional size.
Data availability. Solfatara fumaroles data are fully available from refs  15, [22] [23] [24] [25] 53. Sup lementary data file reports the full listing of composition of the Pisciarelli fumarole, sampled since 1999. For this fumarole, data until September 2009 can also be found in ref. 53. In all cases, concentrations are expressed in μm/mol and analytical methods are reported in ref. 22. Seismological data (magnitudo and hypocenters depth can be found on the Plinio seismic data repository (http://www. ov. ingv. it/ov/en/banche-dati. html) or from INGV-OV bulletins (http://www. ov. ingv. it/ov/en/campi-flegrei/275. html). Al  data, computations and time series are available on request to the corresponding author.