dataesr/scientific-paragraphs-categorization huggingface dataset:
10D  and E ). These results coincide with the M9/M4 night re (day 3) and the make-up M4 class (day 4), again suggesting that exposure to M4 ring emissions results in a stronger biological response than exposure to M9 ring emissions. This trend was not observed at WPAFB (Fig. 10A  and B ). However, because the plots are represented with the same axis scales, it is easy to appreciate that the exposure at WPAFB was much less than observed at JBC. In fact, the exposure at WPAFB, at least for LDSA, seems to be on par with the control levels seen at JBC (Fig. 10B  v Fig. 10E ). The same trend was observed at JBC with Cu exposure, although, Cu was not detectable in the control samples which is why only line instructor data is represented (Fig. 10F ).
Acknowledgements: We would like to thank the Combat Arms instructors and control subjects of the 88th SFS at WPAFB and the 628th SFS at  JBC  for participating as subjects in this study. We would also like to thank the  SFS Commander Lt. Col Aaron Rittgers  and  SFS   Chief CMSgt Joangela Porter at WPAFB  and  SFS Commander Lt. Col Speck  and  SMSgt Rhude  at  JBC  for supporting this study. We would like to thank  Capt. Bryanna Spangler  and  MSgt Christopher Fuquay  for organizing recruitment of control subjects at WPAFB and  Loretta Fisher , 628 th Medical Group JBC, for organizing subject recruitment at  JBC. We would like to thank  Sgt. McCall  with the 628 th  Medical Lab  for providing dry ice and biological sample storage. We would like to acknowledge  Sgt. Barret Curlis ,  Tsgt Hoffhines , and  MSgt Huss  of the 88 th SFS at WPAFB for coordinating logistics and providing space for sample preparation and class observation.
| Subjects Participants were recruited randomly through a faculty internal recruitment system available to all undergraduate business students at the Rotterdam School of Management. As an incentive for taking part, participants were awarded with course credits. On arrival at the laboratory, a maximum of four students completed the procedure in the same room. A total of 124 students took part in the study. For two subjects, a program failure occurred during data collection. One student re-contacted us, asking to be excluded from the study because he had not answered faithfully. Therefore, a total of three cases were excluded from the study. The final sample size was N = 121 (51. 2% female). The average age of participants was 20. 1 years (SD = 1. 44). n = 19 participants reported a physical health condition (16.
| Analysis Data analysis was performed in R. We used the number of choices (out of 3) that are compatible with a given risk trait as our measurement of the strength of multi-and univariate risk preferences. In our analysis, a subject is classified according to a risk trait if the majority of her choices is consistent with that particular trait. Thus, for example, an individual is classified as being risk averse (seeking) if most of her choices are compatible with risk aversion (seeking). For each of these traits, we investigated whether people show a given risk preference or behave at random based on a chi-square test. At the aggregate level, we report the average percentage of choices over tasks compatible with each trait. We use Fisher exact tests to compare the classifications obtained for each trait.
Human breast cancer sample collection The tumors from 440 patients of the Centre Léon Bérard (CLB) with invasive non-metastatic BC, whose clinical and biological data were available from the regularly updated institutional database, were analyzed. Written informed consent was obtained from each patient. The study protocol was approved by the institutional ethics committee. Patient characteristics are presented in the additional material (Additional file 2, Table  S1 ). In our study, tumors exhibiting less than 10% of ERαpositive cells were considered to be ERα-negative tumors.
RT-QPCR analysis RNA extraction was performed as previously described  [26, 27]. Quantitative values were obtained from the number of the cycle (Ct value) at which the increase in the fluorescent signal associated with the exponential growth of PCR products was initially detected by the laser detector of the ABI Prism 7900 sequence detection system (Perkin-Elmer Applied Biosystems, Foster City, CA), using the PE biosystems analysis software according to the manufacturer's manuals. For gene normalization, we used the human TATA box-binding protein (TBP, GenBank accession no. NM_ 003194). We used protocols for cDNA synthesis and PCR amplification described in detail elsewhere  [28]. The results, expressed as N-fold differences in target gene expression relative to the TBP gene and termed "Ntarget," were determined as Ntarget = 2 ΔCtsample , where the ΔCt value of the sample is obtained by subtracting the average Ct value of the target gene from the average Ct value of TBP gene.
Acknowledgements We would like to thank  B. Manship  for proofreading the manuscript. We also thank  Amelie Colombe ,  Laetitia Odeyer , and  Nicolas Gadot  for the technical help. Authors' contributions MLR and EM designed the experiments and wrote the manuscript. JJ and CP performed the experiments and the analyses. LK performed the statistical analyses. OT and IT participated in the discussion of the results and the writing of the manuscript. AD, LDP, EM, LS, and LM established the PDX models and performed the in vivo experiments. REB, SV, and IB performed the RNA extractions and RT-PCR experiments. SCJ embedded the tumor tissues in paraffin blocks and generated the tissue slides for IHC staining. All authors reviewed and approved the final draft of the manuscript.
The selected storms were induced by coronal mass ejections (CMEs), that are defined as large expulsions of plasma and magnetic field from the Sun's corona. CMEs expand in size as they propagate away from the Sun, and larger ones can reach a size comprising nearly a quarter of the space between Earth and the Sun by the time they reach our planet  [19]. For these three storms we analyzed the B2 parameter behavior obtained from profiles derived from ionogram data using the B2 NeQuick analytical formulation; the maximum density of the F2 layer, NmF2, and VTEC derived from GNSS measurements for different longitude sectors. The paper is organized as follows: The second section describes the datasets and data processing techniques, and the third section presents an overview of the geomagnetic storms analyzed.
Materials and Methods The main parameters used to analyze the characteristics of the selected geomagnetic storms are given in Table  1. The Sun observations of Sunspot number and F10. 7 cm radio flux give the solar regular activity during the periods of the selected storms. The SOHO satellite data allow the determination of the solar event at the origin of the geomagnetic storm. As mentioned above, in the present study, CMEs are at the origin of the storms analyzed. Magnetic indices are used to define the characteristics of the storm in the magnetosphere (Dst for the ring current), in the high-latitude ionosphere (AE and the Polar cap magnetic indices, North and South, give information on the energy transmitted to the ionosphere). The polar cap indices are proxy of the merging field (Em),  [20].
VTEC Data Maps and from Individual Stations The relevant VTEC data used are global ionospheric maps (GIMs) produced by the Center for Orbit Determination in Europe (CODE). These VTEC maps are generated at CODE with a 1 h time interval using data from more than 200 GNSS sites of the International GNSS Service (IGS) and other networks. In order to analyze the daily variability of VTEC in different sectors, namely Asian, African and American, VTEC data with 1 h time resolution were extracted from GIMs at the corresponding longitudes. To obtain VTEC at individual stations, RINEX files from the IGS, UNAVCO, CORS, SONEL and EUREF GNSS networks were processed using the arc-by-arc procedure suggested in  [21]. Figure  1  shows the locations of the ionosondes and GPS stations used in this study.
Ionogram Data The experimental values of the maximum of the electron density derivative with respect to height, (dN/dh)max, were obtained from digisondes' ionograms from several locations (Figure  1 ). The ionograms were manually edited using the interactive ionogram analysis tool SAO Explorer  [22]. The SAO files were processed with the THTABLE software, a data utility which tabulates and outputs electron density vs. height  [23]. The (dN/dh)max value was extracted starting from NmF2 down the profile. Then, B2 values were calculated by applying Equation (1) which is described in detail in  [11] : B2 = (0. 385 NmF2)/(dN/dh)max (1) Figure  2  presents a bottomside profile of the height derivative dN/dh (left) obtained from a diurnal electron density profile at Ebre station (in blue at the right).
Acknowledgments: The authors would like to thank the following institutions/networks: the OMNIWEB for providing interplanetary data; the  World Data Center (WDC)  for providing magnetic indices; the  WDC-SILSO Royal Observatory of Belgium, Brussels  and the  Solar and Heliospheric Observatory (SOHO)  for providing solar data; the  University of Massachusetts Lowell, Center for Atmospheric Research, USA , for DIDBase and Sao Explorer software access; the  CODE Analysis Center of the IGS at the Astronomisches Institut Universitaet Bern, Switzerland  for providing the GIM of VTEC and the  IGS, UNAVCO, CORS , SONEL and EUREF networks for the RINEX files from GNSS stations. The authors also thank the editor and three anonymous reviewers for revising the manuscript.
Discussion Here we describe a proximity interactome map of human macroautophagy. Our dataset confirms many known interactions, validating its utility, and reports novel protein-protein proximity interactions, including those acting at membrane contact sites. Of note, while we performed BioID under normal growth conditions associated with "basal" (or "constitutive") macroautophagy, we identified many factors required for macroautophagy responses to specific cellular stresses (i. e. "induced" macroautophagy). We also identified factors involved in different selective macroautophagy events. For example, TEX264  [44, 45]  and OSBPL1A  [43]  were previously shown to be required for autophagy of cellular cargo. We propose that basal macroautophagy involves a diversity of cellular cargoes and specific recognition signals that operate to maintain cellular homeostasis and keep the macroautophagy system primed for large-scale induction.
Further studies will be required to examine which factors in our dataset control different selective macroautophagy events. We also identify OSBPL family members that are new regulators of LC3B recruitment to Salmonella, an important cellular defense mechanism against microbial infection and a powerful model to visualize selective autophagy. Prior observations have highlighted the importance of membrane contact sites in macroautophagy  [8, 40, 46]. Indeed, 3-dimensional electron microscopy reveals close proximity of the ER, mitochondria, vesicles, and lysosomes with the nascent phagophore  [47]. Membrane contact sites are known to be sites of lipid transport  [48] [49] [50]  and in the case of macroautophagy, ATG2 was recently shown to promote phospholipid transport from the ER to the growing autophagosome  [29] [30] [31] [32].
The OSBPL9 and OSBPL10 inserts were cloned from OSBPL9 and OSBPL10 cDNA derived from the MGC library at SPARC (SickKids, Toronto) and the backbone vector used for both constructs was pEGFP-C2 (Clontech, 6083-1). All constructs were verified by DNA sequencing (TCAG, Toronto, Canada). The UHRF1BP1 cDNA was derived from the MGC clone (SPARC, SickKids, Toronto). Macroautophagy bait CDSs have been amplified from MGC clones when available, or from HEK293 cDNAs (Genbank references are listed in Table  S5 ). All unique/stable reagents generated in this study are available from the Lead Contact with a completed Materials Transfer Agreement.
Fragment ions were detected in the LTQ. Dynamic exclusion was activated such that MS/MS of the same m/z (within a range of 15 ppm; exclusion list size = 500) detected twice within 15 s were excluded from analysis for 30 s. For protein identification, Thermo. RAW files were converted to the. mzXML format using Proteowizard, then searched using iProphet against the human (Human RefSeq Version 45) database. X!Tandem search parameters were: 15 ppm parent mass error; 0. 4 Da fragment mass error; complete modifications, none; cysteine modifications, none; potential modifications, +16@M and W, +32@M and W, +42@N-terminus, +1@N and Q. Each of the two biological replicates of FlagBirA*-Salmonella effector samples was analyzed using two technical replicates. Data were analyzed using the transproteomic pipeline (TPP), via the ProHits software suite.
Real-time quantitative polymerase chain reaction (RT-qPCR) RNA was extracted from cells using the RNeasy kit (Qiagen, 74104) and cDNA was synthesized using the BioRad iScript cDNA Synthesis kit with 1 µg of RNA as the template. All quantitative PCR was performed using a QuantStudio 3 Real-Time PCR machine (Thermo Fisher). Data were analyzed using the QuantStudio 3 software (Thermo Fisher). The relative expression of the target genes was normalized to GAPDH transcript levels for each condition and then relative to expression in the non-targeting siRNA-treated sample. Primer sequences can be found in Table  1. No-template and noreverse transcriptase controls were run for each primer pair to confirm the lack of primer-dimer formation/DNA contamination and genomic DNA contamination, respectively.
Acknowledgments J. H. B. holds the  Pitblado Chair in Cell Biology. Infrastructure for the Brumell Laboratory  was provided by a  John Evans Leadership Fund grant  from the  Canadian Foundation for Innovation  and the  Ontario Innovation Trust. N. M. was supported by a fellowship from the  Research Training Centre at the Hospital for Sick Children. This work was supported by operating grants the  Canadian Institutes of Health Research  (MOP# 97756 ; PJT# 148668 ;  FDN154329 ).
Vegetation optical depth (VOD), as a microwave-based vegetation index for vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. Currently, VOD is mainly retrieved from passive microwave data and few studies focused on VOD retrievals from active microwave data. The Advanced SCATterometer (ASCAT) provides long-term C-band backscatter data at Vertical-Vertical (VV) polarization. In this study, a new ASCAT INRAE Bordeaux (IB) VOD (hereafter, IB VOD), was developed based on the Water Cloud Model (WCM) coupled with the Ulaby linear model for soil backscattering. The main features of IB VOD are that (i) the ERA5-Land soil moisture (SM) dataset was used as an auxiliary SM dataset in the retrievals, (ii) pixel-based soil model parameters were mapped using Random Forest (RF), and (iii) the vegetation model parameter was calibrated for each day.
, 2018) , the crop yields  (Chaparro et al. , 2018; Patton and Hornbuckle, 2013)  and asymmetry patterns in inter-annual productivity  (Al-Yaari et al. , 2020). Several VOD datasets used in the above-mentioned studies are mainly derived from passive microwave sensors operating at different frequencies  (Frappart et al. , 2020). Those datasets include the highfrequencies (C-/X-/Ku-band) VOD  (Du et al. , 2017; Karthikeyan et al. , 2019 Karthikeyan et al. , , 2020;; Owe et al. , 2008; Wang et al. , 2021)  from AMSR-E (the Advanced Microwave Scanning Radiometer, July 2002-2011)  (Koike et al. , 2004)  and its successor, AMSR2 (the Advanced Microwave Scanning Radiometer 2, 2012-present)  (Imaoka et al. , 2012)  and the low frequency VOD at L-band (L-VOD)  (Feldman et al. , 2018; Fernandez-Moran et al.
, 2017; Konings et al. , 2016; Wigneron et al. , 2017 Wigneron et al. , , 2021) )  from the Soil Moisture and Ocean Salinity (SMOS)  (Kerr et al. , 2010)  and Soil Moisture Active Passive (SMAP)  (Entekhabi et al. , 2010)  satellites. In addition, a long-term VOD product merging the different high frequency datasets was also released  (Liu et al. , 2011; Moesinger et al. , 2020). Although different passive VOD products have been widely used in different applications, they still have some deficiencies. For instance, the time period of each product is rather short in terms of years (the longest acquisition period of the different sensors is ~11 years for SMOS), and the spatial resolution of the VOD data is coarse (~25 km). Moreover, the data quality from passive sensors (especially at low frequency (L-band)) are more likely to be affected by radio frequency interference (RFI)  (Li et al.
To our knowledge, there are only preliminary results obtained from Sentinel-1 in southern France  (El Hajj et al. , 2019)  and one global active VOD dataset (hereafter, ASCAT V16 VOD) developed by  Vreugdenhil et al. (2016)  from ASCAT observations. Two parameters were used in the retrieval of ASCAT V16 VOD. The first one is the maximum range of the backscatter values over bare soils that are related to SM changes. The setting of this parameter was based on the Koppen-Geiger climate classification map  (Kottek et al. , 2006)  and the parameter was set to a constant value over most regions except for the desert climate zone. The second parameter is the difference between the wet and dry references that are obtained from the historically wettest and driest backscatter measurements. This product is not yet public, and it has not been evaluated in detail in the literature  (Vreugdenhil et al.
Following  Li et al. , (2021) , several vegetation parameters and indices (Aboveground Biomass (AGB), Lidar tree height (TH), the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and Enhanced Vegetation Index (EVI) and leaf area index (LAI)) were used to evaluate the performance of IB VOD in space and time. In addition, to understand the performance of IB VOD, we made a comparison between IB VOD and three other VOD products at C-band. This study is organized as follows: Section 2 introduces the datasets. Section 3 presents the models including WCM, Ulaby linear model and the method used for model calibration. In Section 4, we present the results of the calibrated model and the evaluation of IB VOD. Discussion and conclusion are provided in Sections 5 and 6.
The L2 data are normalized by using a second-order polynomial function describing the relationship between incidence angle and backscatter  (Wagner et al. , 1999a) , and are included in the soil moisture datasets. The data are stored in a discrete global grid (Swath Grid format); the grid spacing of the 50 km data is 25 km, and 12. 5 km for the 25 km data. Five years (2015-2019) L2 MetOp-A backscatter (25 km × 25 km) data normalized at an incidence of 40 degrees were used in the present study. As there is usually a lower vegetation water stress in the morning making descending VOD data more suitable to monitor biomass  (Frappart et al. , 2020) , only observations from the descending orbits were used. 'Low quality' data were masked through the quality flags (snow cover, frozen soil, topography and wetland probability)  (Lievens et al.
ERA5-Land Soil Moisture data The ERA5-Land Soil Moisture (SM) dataset from the topsoil layer (layer 1, 0-7 cm) was used in this study. ERA5-Land SM is a reanalysis dataset modelled by the European Centre for Medium-Range Weather Forecasts (ECMWF) surface model  (Berrisford et al. , 2011)  with an enhanced resolution compared to ERA5 SM. The data are hourly and have a spatial resolution of 0. 1 degree × 0. 1 degree (around 10 km × 10 km). An evaluation of ERA5-Land SM by using the International Soil Moisture Network (ISMN,  Dorigo et al. , 2021)  in situ measurements at the global scale suggested that it has an overall good performance (R = 0. 72-0. 76, ubRMSE = 0. 05 m 3 /m 3 )  (Beck et al. , 2021; Chen et al. , 2021). ERA5-Land SM modelled at the local time of 09:00 am, which is close to the time of the ASCAT observation, were inputs to the water cloud model (WCM) used to retrieve IB VOD.
Soil and terrain data The soil and terrain data have an important impact on the soil moisture and the signal acquired from the microwave observations (Guio  Blanco et al. , 2018; Ma et al. , 2015). Therefore, we considered using those two kinds of data in the calibration of the soil model parameters. The soil data include the soil property and the soil temperature (ST) data. The applied soil property data were obtained from the Soil-Grid250m dataset  (Hengl et al. , 2017). This dataset was generated by the machine learning method at 250 m spatial resolution and seven standard depths. In this study, we used the average value of the first (0 cm, corresponding to the surface) and second depth (5 cm) of each soil property. More details about the dataset and the method used to compute average data from different depths were described in  Hengl et al.
(2017). Fifteen SoilGird250 m parameters were used in the present study and are described in Appendix Table  1. The ST data were obtained from ERA5-Land, for the same soil layer and at the same time as ERA5-Land SM. The terrain parameters were obtained from a digital elevation model (DEM). The DEM data used here is the Global Multi-resolution Terrain Elevation Data (GMTED) 2010 at a spatial resolution of 1 km  (Danielson and Gesch, 2011). The SAGA (System for Automated Geoscientific Analyses) GIS software  (Conrad et al. , 2015)  was used to calculate six terrain parameters: Slope, Terrain Surface Convexity (TSC), Terrain Surface Texture (TST), Terrain Ruggedness Index (TRI), Plan Curvature (PlanCur) and Profile Curvature (ProfCur). All terrain parameters used in this study are given in Appendix Table  2.
Vegetation variables used for validation As there is no large-scale in situ dataset that can be used for the validation of VOD  (Li et al. , 2020 (Li et al. , , 2021)) , AGB (Saatchi and CCI AGB), Lidar tree height (TH) (Simard and Potapov tree height) and vegetation indices (MODIS NDVI, EVI and LAI) were used as the benchmark to assess the performance of IB VOD  (Fernandez-Moran et al. , 2017; Li et al. , 2021). The rationale for using these different vegetation parameters is given in the following. VOD is related to the vegetation water content which is determined by the quantity of vegetation (parameterized by biomass) and the vegetation water status (parameterized by the vegetation moisture content). VOD can thus provide information on AGB and on the vegetation water status and stress of the vegetation canopy  (Frappart et al.
C-band VOD products used for inter-comparison To evaluate the quality of the IB VOD product, we compared it with other VOD products retrieved at the same frequency (C-band). In this comparison, we used three datasets: two public passive VOD datasets and one active VOD dataset  (Frappart et al. , 2020). The two passive VOD datasets are AMSR2 VOD  (Owe et al. , 2008)  and Vegetation Optical Depth Climate Archive (VODCA) VOD  (Moesinger et al. , 2020). The retrieval algorithm of the two products is the Land Parameter Retrieval Model (LPRM) but a different version (version 5 (V5)) was used for AMSR2, while Version 6 (V6) was used for VODCA  (Li et al. , 2021). VODCA LPRM V6 VOD was rescaled via cumulative distribution function matching using AMSR-E VOD as the reference. AMSR2 LPRM V5 VOD is available at the Goddard Earth Sciences Data and Information Services Center (GES DISC) website.
VODCA LPRM V6 is available at doi:https://doi. org/10. 5281/zenodo. 2575599. The active VOD dataset is the ASCAT V16 VOD  (Vreugdenhil et al. , 2016). As there is no available website to download the dataset, the ASCAT V16 VOD dataset was produced by the authors of this study based on the published algorithm  (Vreugdenhil et al. , 2016).