dataesr/scientific-paragraphs-categorization huggingface dataset:
Next, cells were permeabilized by adding 350 μl of 0. 05% Triton X-100 diluted in PBS (PBSX). After washing twice with PBS, cells were stained using 300 μl of staining solution (50 μg/ml propidium iodide, 20 μg/ml RNase A in PBS) for 30 min in the dark. Fixed and stained cells were analyzed by flow cytometry using the BD FACS Canto II (BD Biosciences) and the FlowJo data analysis software (Tree Star Inc). Cell cycle ELISA. Levels of cell cycle marker protein CDK2 protein phosphorylated TYR15 (CDK2-pTYR15) were determined using the Abcam Cell Cycle In-Cell ELISA Kit (ab140363) and performed according to manufacturer instructions. Proteasome activity. Proteasome activity was determined as previously described  66. Briefly, cells were grown to 80% confluency and lysed using Passive Lysis Buffer (Promega).
The XBP1-splicing assay was performed as previously described  23. Caspase 3/7-assays and TUNEL-assays. Caspase 3/7 activity was measured using the ApoLive-Glo™ Multiplex Assay according to manufacturer instructions. The in situ BrdU-Red DNA Fragmentation (TUNEL) assay kit (Abcam, ab66110) was used to detect DNA fragmentation by flow cytometry and was performed according to manufacturer instructions. Drug-treatments (STA, 10 µM, 6 h; RITA, 1 µM, 12 h) for specific experiments were performed prior to paraformaldehyde fixing. Mitochondria staining, mtDNA:nDNA ratio determination. To estimate mitochondrial mass, Mitotracker™ DeepRed FM (M22426, Invitrogen) and TMRE (ab113852, Abcam) staining were performed according to manufacturer instructions and analyzed by flow cytometry using the BD FACS Canto II and the FlowJo software.
Next, DMEM was aspirated and replaced with serum-free DMEM for overnight incubation. Prior to the experiment, the plate was incubated for 30 min at 37 °C in a CO2-free incubator. Cells were subsequently washed with Mitochondrial Assay Solution (MAS; 70 mM sucrose, 220 mM mannitol, 10 mM KH 2 PO, 4. 5 mM MgCl 2 , 2 mM HEPES, 1 mM EGTA, 0. 2% (w/v) fatty acid-free BSA, pH 7. 2 at 37 °C) according to manufacturer instructions (Seahorse Bioscience). Measurements were performed in MAS, supplemented with 10 mM pyruvate, 10 mM succinate, 2 mM malate, and 0. 2 nM Plasma Membrane Permeabilizer (PMP) reagent (Seahorse Bioscience). The oxygen consumption rate (OCR) in a coupled state (state 2, basal respiration) was measured. Data was extracted from the Seahorse XF-24 software and the bioenergetic parameters were calculated according to the manufacturer instructions.
ATP and ROS measurements. ATP levels in HEK293 cell lines were determined using the ViaLight TM plus kit (Lonza), which is based on luciferase activity. Five microliter Alamar blue (Life Technologies) were added to each well and incubated for 3 h. Alamar blue was used as an internal standard, followed by measurement of ATP content as per manufacturer instructions. To detect mitochondria-specific superoxide in HEK293 cell lines, MitoSOX™ Red staining (Life Technologies) was performed in live cells. Cells were grown to ~80% confluency and stained with MitoSOX™ Red (5 μM) in FACS buffer (1× PBS, 2% FBS) for 30 min at 37 °C. Cells were detached using Accutase and resuspended in FACS buffer. Fluorescence was analyzed by flow cytometry using the BD FACS Canto II and the FlowJo data analysis software.
Twenty-four and 48 h post transfection GFP and mCherry fluorescence intensities were assessed by flow cytometry using FACS LSR Fortessa (BD Biosciences) or by wide-field fluorescent microscopy using InCellAnalyser 2500HS (GE Life Sciences). For quantification of the FACS data, FlowJo analysis software was used. Expression levels of Fluc WT and Fluc DM were normalized by western blot (Fluc WT versus Fluc DM) or by luciferase activity (untreated versus geneticin treatment). GFP/mCherry ratio relative to Fluc expression level was calculated and used as a measure of Fluc mistargeted to mitochondria. Quantification of wide-field fluorescent microscopy data. Paraformaldehyde fixed cells on 12 mm cover slips were imaged on GE InCell Analyser 2500 DS spinning disc microscope using DAPI, AlexaFluor 488 (for GFP) and RFP (for mCherry) channels.
For each sample 100 images were acquired covering in total around 2 × 10 4 cells. After global threshold segmentation, cellular outlines were acquired and integrated intensities of GFP and mCherry inside individual cells were calculated (using CellProfiler 3. 0 software)  69. Further analysis was done in Microsoft Excel software: cell measurements were imported as individual data rows; manual threshold was applied for the filtering out extremely autofluorescent apoptotic cells and the background signal from untreated cells was subtracted. The results were presented as the ratio between integrated intensities of GFP and mCherry relative to luciferase activity. Each sample was independently reproduced and analyzed 3 times. Statistics and reproducibility. Several independent clones of each pRps2-WT and pRps2-A226Y transfected cells were used for the experiments; the number of independent clones used in each experiment is specified in the corresponding figure legend.
Statistical analysis was performed with Prism 5. 0 software. For all assays, except quantification of wide-field fluorescent microscopy, unpaired Student's t-test was used to determine significant difference between analyzed samples. Significance of wide-field fluorescent microscopy quantification was calculated using Mann-Whitney test. RNA sequencing. For transcriptome analysis we used 4 independent clones of RPS2-WT transfected cells and 3 independent clones of RPS2-A226Y-transfected cells. RNA sequencing (RNAseq) was performed at the UZH/ETH Functional Genomics Center Zurich (FGCZ [http://www. fgcz. ch/]) according to the Illumina RNA sequencing protocol. Differential gene expression analysis between sample groups of interest was performed using the R/bioconductor package edgeR  70  To analyze enrichment of genes in specific cellular functions, differential expressed genes were mapped to process networks using MetaCore (GeneGo, Thomson Reuters) and to known biological ontologies based on the GO project (http://www.
Tbet controls the expression of cell migration mediators in bone marrow NK cells Having confirmed that the accumulation in the bone marrow of Tbx21 -/-mice is caused by a migration defect, we sought to define the mediators involved. We therefore sorted iNK and mNK1 cells from the bone marrow of wild type and Tbx21 -/- mice, and mNK2 (which are not present in Tbx21 -/-; Fig. 2ad ) from wild type mice and analyzed them by RNAseq. Raw RNAseq data and differentially expressed gene lists are avail-able from the National Center for Biotechnology Information Gene Expression Omnibus under accession no. GSE122874. Consistent with previous reports  (Jenne et al, 2009) , we found S1pr5 mRNA underexpressed by a factor of approximately seven-fold in both iNK (Fig. 3a, c ) and mNK1 (Fig. 3b, c ) cells in Tbx21 -/-mice compared to wild type.
In Figures  1 2 3 4 , the lineage cocktail used to identify iNK, mNK1 and mNK2 consisted of anti-CD3-, CD8α-, CD19and Gr-1-FITC (Lin1). The lineage cocktail used for the identification of LMPP, CLP, preNKP and rNKP in Figure  1  consisted of anti-CD3-, CD8α-, CD19-, Gr-1-and NK1. 1-FITC. Lineage cocktail for Figure  5  consisted of anti-CD3-, CD8α-, CD19-, TCRαβ-, TCRγδ-, GR1-and TER119-biotin, stained with streptavidin-PE-Cyanine5. Dead cells were excluded using fixable viability dye eFluor 450 (eBioscience), fixable viability dye eFluor 455 ultraviolet (eBioscience), Propidium iodide (Sigma Aldrich) or DAPI (4',6-Diamidino-2-Phenylindole, Dilactate; Biolegend). Intracellular staining was carried out using Human FoxP3 Buffer (BD Biosciences) according to the manufacturer's instructions. Data were acquired on an LSRFortessa II (BD Biosciences) and analyzed using FlowJo (Tree Star, Ashland, OR).
Asian dust is a seasonal meteorological phenomenon which affects East Asia, and has severe consequences on the air quality of China, North and South Korea and Japan. Despite the continental extent, the prediction of severe episodes and the anticipation of their consequences is challenging. Three one-year experiments were run to assess the skill of the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) in monitoring Asian dust and understand its relative contribution to the aerosol load over China. Data used were the MODIS Dark Target and the Deep Blue Aerosol Optical Depth. In particular the experiments aimed at understanding the added value of data assimilation runs over a model run without any aerosol data. The year 2013 was chosen as representative for the availability of independent Aerosol Optical Depth (AOD) data from two established ground-based networks (AERONET and CARSNET), which could be used to evaluate experiments.
Particulate Matter (PM) data from the China Environmental Protection Agency were also used in the evaluation. Results show that the assimilation of satellite AOD data is beneficial to predict the extent and magnitude of desert-dust events and to improve the short-range forecast of such events. The availability of observations from the MODIS Deep Blue algorithm over bright surfaces is an asset, allowing for a better localization of the sources and definition of the dust events. In general both experiments constrained by data assimilation perform better that the unconstrained experiment, generally showing smaller mean normalized bias and fractional gross error with respect to the independent verification datasets. The impact of the assimilated satellite observations is larger at analysis time, but lasts into the forecast up to 48 hours.
e. Liu et al. (2011) ) rests in the use of a fully operational 4D-Var system in which the model dynamics serve as a constraint during the assimilation. For dust storms of synoptic extent, this constraint might imply a longer-lasting impact of the assimilated data into the forecast range. The verification of the analysis and forecast benefits from the availability of data from the Aerosol Robotic Network (AERONET,  Holben et al. (1998 Holben et al. ( , 2001)) ), as well from the China Aerosol Remote Sensing Network (CARSNET,  Che et al. (2015) ). Additionally, PM data from the China Environmental Protection Agency (CEPA) are also used to assess the value of AOD assimilation in surface PM forecasts. 2 Description of the ECMWF/CAMS system Desert dust forecasts are provided by the ECMWF/CAMS system which is a comprehensive modeling and assimilation system based on the IFS developed at ECMWF with the extended capabilities of including atmospheric composition tracers in the transport model and in the analysis  (Flemming et al.
(2017). During the minimization, the tabulated optical properties of the individual species are retrieved and used in the calculation of the total extinction. The vertical integral of the extinction profile provided the total AOD, which is the observed quantity, at every observation location. The vertical profile of the aerosol mixing ratio is not modified by the assimilation as only AOD is used as observation. Thus, the vertical profile is largely dictated by the model and the distribution of the increments in the vertical is due to the vertical structure function in the background error. AOD data at 550nm from the Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors on board of the Terra and Aqua satellites have been assimilated in the ECMWF'4D-Var system since 2008  (Benedetti et al.
, 2009). Initially, only the Dark Target product was used  (Levy et al. , 2010). Since 2015, both MODIS Dark Target and Deep Blue  (Hsu et al. , 2013)  data are used. Deep Blue offers the advantage to provide Aerosol Optical Depth(AOD) retrievals over bright surfaces, which is of particular interest for getting insight on dust sources by means of data assimilation. It can be assumed that over deserts the biggest contribution to AOD comes from dust aerosols, even if the analysis uses total AOD and the other aerosol species are also included. Recently, MODIS Collection 6 has become available  (Levy et al. , 2013; Sayer et al. , 2013 Sayer et al. , , 2014)). This is the collection that has been used in this study. The operational ECMWF/CAMS systems also uses AOD data from the Polar Multi-Sensor Aerosol Optical Properties -Metop (PMAP) product, operationally provided by EUMETSAT, but this product was not available for the experiments outlined in this paper.
To investigate the impact of the assimilation on the dust prediction, three experiments were set-up for the whole of 2013: one AERONET data is similarly averaged, with each data value receiving a weight proportional to the time difference between the data values before and after it, up to a maximum of three hours. The CARSNET data used was already in the form of daily averages and no further averaging was done. While the entire year was considered, we focus in this paper on the spring season when Asian dust is at its peak due to the seasonal increased frequency of cyclonic weather systems with associated high surface wind speeds. At this time of the year the contrast between warm air masses in the south and cold air masses from Siberia and Mongolia is maximum. The high north-south thermal gradient can give rise to situations characterized by a high baroclinic instability, with the development of fast developing and moving lows associated with high winds over the dust emitting regions of the Taklimakan and Gobi deserts and inner Mongolia.
Within a certain margin of noise, it is possible to see that the performance of both experiments including assimilated MODIS data is more skillful than that of the control experiment. The figure also shows that including the Deep Blue data, which provide Aerosol Optical Depth information over bright surfaces, helps to further reduce the bias with respect to the ground-based observations. Figure  5  shows maps of FGE and confirms that the error with respect to the independent observations is reduced in the runs with assimilated satellite data. Time-series of FB and FGE average over the same area are shown in figure  6. Again, the improvement in bias and error in the assimilation experiments is visible. The model has generally a similar bias when compared to the CARSNET observations and AERONET stations over this region.
The bias is particularly large for the CONTROL experiment, except for some days. Yet again the experiments with assimilated satellite data perform visibly better than the CONTROL. If we focus on the FGE, its maximum value during the study period reached values of 1. Focusing for example on April 10, the error in the experiment CONTROL is close to 1. 1 with respect to the AERONET stations (solid red line) and 0. 7 with respect to the CARSNET station (dotted red line). However, the FGE for the DT and DTDB experiments is around 0. 2 for AERONET stations (green and blue solid lines) and 0. 4 for CASRNET stations (green and blue dotted lines) with a 50-80% reduction in error due to assimilation of satellite data. However, on other dates, the improvement is less marked. The dust storm case of March 9th is quite interesting.
The FGE is 0. 9 for all experiments with respect to the CARSNET data (red, green and blue dotted lines), but is reduced to 0. 3 the following day with a faster decrease for the DTDB experiment (blue dotted line) than for the other experiments. This indicates the additional benefits of including observational satellite information over source areas. Curiously on March 10th one can observe a similar behavior but this time with respect to the AERONET observations with FGE going from 0. 9 in the CONTROL (red solid line) to 0. 4 in experiments DT and DTDB (green and blue solid lines) for an improved agreement with the AOD at the AERONET stations close to 60%. In this case, the satellite data have improved the analysis observations downwind of the storm (see figure  1 ). Specific sites in China have also been selected in order to understand the model skill in both remote and urbanized regions that are particularly affected by Asian dust plumes.
Impact of the assimilation on the short-range AOD forecasts In the previous subsection, results have been presented that show how assimilation of AOD satellite data from the MODIS retrievals brings beneficial improvements to the simulation of AOD within the ECMWF/CAMS system. The impact on the analysis is clearly shown in the improvement in the FB and FGE. The question that might arise at this point is whether the impact of the assimilated satellite data is only felt at the analysis time or whether it carries on to the subsequent forecast. To answer this question, the spatial data relative to FB shown in figure  4  are summarized in figure  8  using an histogram which shows the FB as a function of the number of AERONET and CARSNET sites. The upper panels show the histogram for the analysis time, whereas the lower panels show the same for the 48h forecast.
Moreover, experiment DTDB, which included both MODIS Dark Target and Deep Blue data, shows a slightly lower fractional gross error in the comparison with CARSNET data than the DT experiment, indicating an additional beneficial 5 impact of observations close to the dust sources. The model captures the front passage which is visible in the increase of PM10. However, the timing of the PM maximum in the model is earlt by approximately 3 hours. If we consider the average value of the PM10 observation peak, then the model underestimates it by ≈100 mg/m 3. Outside the peak, the values of PM in the models are too large, indicating a broader spatial extent of the aerosol plume in the model with respect to what was observed. It is important to notice that the coarse model resolution does not allow to resolve the fine details of the local pollution, particularly in regions in which the topography is varied.
5 Several concluding points can be learned from these experiments. Results confirm that assimilation of advanced satellite data is effective for improving the representation of desert dust distributions over Asia and for monitoring and forecasting dust episodes (in the short-range). In particular, MODIS Deep Blue data are beneficial both in source regions (i. e. Tazhong) as well as downwind of desert areas (i. e. Xilinhot and Shangri-La). The fractional bias and the gross error with respect to independent AOD observations from the AERONET and CARSNET ground-based networks are lower in the assimilation experiments than 10 in the control forecast with no assimilated aerosol data. Timeseries of FGE averaged over central/northern China, reveal that in some extreme cases the FGE is reduced by as much as 60-80% in the experiment which includes all MODIS data (DTDB) with respect to the experiment which has no aerosol data (CONTROL).
The impact of the assimilated data is mainly felt at the analysis time but remains visible in the 48h forecast. Comparisons with PM10 data from the CEPA network for March 2013 in the Beijing area show that the model has skill in predicting the passage of a dust front. However there is a mismatch in PM10 values with large biases (up to an order of magnitude in some cases) displayed by the model, even for the assimilation experiments. The assimilation of satellite AOD in this case can only make small adjustments to PM10 but is unable to improve fundamentally the quality of forecast. Despite this, the passage of the storm is captured, indicating that the global model is somewhat able to capture the regional pollution flow even at coarse resolution. This indicates that the global model analyses may be used as boundary conditions for regional air quality models at higher resolution, enhancing their performance in situations when part of the pollution may have originated via synoptic flow mechanisms.
However, the skill of the global model for PM10 is poor, due to model biases, coarse resolution, unresolved topography, lack of resolved local emissions and lack of observations to constrain the aerosol speciation and vertical structure. It is worth mentioning that AOD is less affected by these factors as it is a column-integrated variable. Also, spatial patterns of AOD do not always match with those of surface PM due to variability in aerosol hygroscopicity and relative humidity which can affect AOD greatly but leave PM10 completely unchanged. These results highlight the crucial importance of the verifying datasets to assess model skill and assimilation impact. CARSNET data offer unprecedented coverage over remote Asian dust regions, which are otherwise poorly observed. From the regional distribution maps of figure 4 it is possible to see that CARSNET and AERONET stations over Asia are largely com-plementary.
Data availability in these regions both for assimilation and model evaluation and development is key to progress in further addressing the challenges of Asian dust prediction. As part of CAMS, ECMWF is providing twice-daily forecasts of atmospheric composition (including desert dust) up to 5 days ahead, which are publicly available. These forecasts rely on assimilation of MODIS Collection 6. 1 AOD (both Dark Target and Deep Blue data) as well as PMAP data. They have been shown to provide useful information and reliable boundary 5 conditions for local air quality applications in Europe, China and East Asia, provided they are used in conjunction with highresolution regional air quality models.
4 Outcomes of the investigation 4. 1 Comparisons with independent AOD datasets Results from the three experiments (DT, DTDB and CONTROL) are presented in what follows. Comparisons were made with independent datasets from AERONET and CARSNET. AERONET data are routinely used for the verification of the ECMWF/CAMS forecasts, but this is the first time that the CARSNET dataset has been used in this context. Model data was bilinearly interpolated to the AERONET/CARSNET site locations and then averaged over 24 hour periods (from T+3 to T+24).
Acknowledgements. This work has been initiated and partly funded by the  PANDA European Project  ( FP7 , n. 606719). The work has also been supported by the  Copernicus Atmosphere Monitoring Service , which is part of the  European Union 's flagship space programme Copernicus. CAMS data are freely available from http://atmosphere. copernicus. eu. The data providers contributing to the AERONET 10 and CARSNET networks are also gratefully acknowledged. We acknowledge the use of imagery from the  NASA  Worldview application (https://worldview. earthdata. nasa. gov/)operated by the  NASA/Goddard Space Flight Center Earth Science Data and Information System (ESDIS)  project. PM10 data were courtesy of the  China Environmental Protection Agency network (China).
Data Analysis All NPI terms were searched in the compiled database of 264,249 posts. These queries were made from the ontology of NPIs provided by the academic and collaborative Plateforme CEPS (Figure  1 )  [1, 25]. Each query considered singular/plural, abbreviations and misspellings, and words with and without dashes (eg, non-pharmacological and non pharmacological) as equivalent to the ontology's featured word. The method consists of identifying and counting the NPI terms mentioned in the social network posts. We conducted 2 successive descriptive frequency analyses: (1) an analysis of the occurrences of NPI categories and their synonyms (Figure  1 ) and (  2 ) a subcategory analysis with NPI terms, their synonyms, and their related terms (eg, ingredient, technique, method, and profession).
Nonpharmacological Interventions' Categories and Synonyms Within our dataset, patients referred to an NPI category 14,185 times, 84. 51% (11962/14195) from the Impatientes forum, 8. 57% (1217/14195) from the Breast cancer forum, and 6. 92 % (986/14195) from Facebook groups and pages (Table  1 ). The study population mainly referred to physical and nutritional interventions and others in similar proportions between forums and Facebook groups/pages. The term NPI (abbreviated or not) was rarely used by patients (20 occurrences in total), whereas the term CAM was never used. Regarding other health interventions, the most popular was herbal medicine in forums and Facebook solutions (Table  5 ).