dataesr/scientific-paragraphs-categorization huggingface dataset:
Literature review The literature on penny auctions is relatively recent. Augenblick (2016) proposes a tractable penny auction model, for which he obtains a Markov perfect equilibrium. He uses auction-level and bidlevel data from Swoopo to show that the sunk cost fallacy (as bidders spend more money on bids, they become more reluctant to leave the auction although the bidding costs are sunk) explains the persistence of auction participation above and beyond the prediction of the normative model. In turn this provides a rationale for the observed high and positive (51%) average pro…t margins in Swoopo auctions. Bidders apparently learn how to use more e¤ective bidding strategies (including aggressive bidding strategies), but such learning takes place at a very slow rate and requires a large number of submitted bids (and consequently high losses).
In total, we received bid level information for all 414 auctions in the auction database, totaling 956,742 bids placed by 38,733 unique bidders. A closer inspection of the two databases revealed a 'coverage' problem, which has led us to exclude 11 auctions from the analysis and focus on 403 auctions, for which we have 940,968 bids placed by 38,000 unique bidders in the bid level database. 9 ble 6 (Appendix A) presents the descriptive statistics of the auction-level database. On average, the …nal auction price was e24. 01 and bid costs yielded e1,200. 50 in revenue, which means that, using as a reference the average retail price of e369. 97, each auction yielded a net pro…t of e854. 77 -a pro…t margin of 219%. 10  Figure  1 (Appendix A ) shows that some auctions present negative (and large, in absolute value) pro…t margins, but others yield extremely high pro…t margins.
There is also some variability across auction weeks and between item types. On average, there are 6. 88 auctions active at the same time (simultaneous auctions) and, in each auction, 2,401 bids were placed by 439 unique bidders. Table  7  (Appendix A) presents the descriptive statistics of the bid-level database, which contains 176,908 unique bidder-auction combinations. 9 Note that the auction-level database, by registering the …nal bid, allows us to infer the total number of bids placed in each auction because each bid increment is …xed and equal to e0. 01. Comparing the total number of bids in each auction both in the auction-level as well as in the bid-level database, we noted that the latter typically contained fewer bids than suggested by the former. This problem was particularly acute for 11 auctions in the original sample.
In 10 out of these 11 auctions, the coverage was below 30%. We suspect that this may have been due to an error in the bid-registering software (the operator could not explain this), as all these 11 excluded auctions occurred on a given day (14th December 2011) and the missing bid-level observations related to bids placed after 7pm. The average coverage of these 403 auctions was 97. 2%. The coverage problem was widespread and a¤ected 76% of these 403 auctions -that is, the two databases only coincided fully for 24% of all auctions. But in most cases, the di¤erences were relatively small -basically a few missing bids from the bid level database for each auction (the median is 8 missing bids per auction), which could be explained by small errors in the operator's registering procedure: 44. 5% of auctions were missing less than 1% of bids; 57% were missing less than 2%; and 66.
5% were missing less than 5%. Therefore, only 9. 5% of auctions had missing bids in excess of 5%. 1 0 The average pro…t margin in our dataset (219%) is clearly higher than in Augenblick (2016) (51%). This could be associated with the type of good most frequently auctioned in our dataset: mobile phones account for 85% of total auctions. Augenblick (  2016   2 ) present evidence of signi…cantly higher deviations (reported in standard deviation units) between observed and theoretical average revenue for videogames-related items: the distribution is clearly right-tailed, with 30% (approx. 50%) of items having average revenue greater than one (one half) standard deviation above the model's prediction. Byers et al. (2010, Figure  3 ) present evidence of an average pro…t margin of 86%, with the item that is more frequently auctioned obtaining an average pro…t margin of 365% (Wii Play with Wii Remote).
Goodman (2012, Table  3 ) provides a breakdown of average pro…t margins by bid increment and by good value: although the average (overall) pro…t margin is 67%, for some bid increments (2 cents and 6 cents) it is higher than that -126% and 132% respectively -, and, for those same increments and for goods whose value is in the $25-$50 range, it is even higher (477% and 240% respectively). Therefore, although average pro…t margins in our dataset's penny auctions are higher than in Swoopo auctions, there are several instances in which the latter yield comparable or even higher pro…t margins.
Auction level analysis Using data from the auction-level database, we wish to understand the main drivers of …nal prices in penny auctions. The auction price is, in e¤ect, a duration-type variable: higher auction prices indicate, according to the bidding rules, longer durations and, because we have normalized auction prices by dividing them by the retail price of the auctioned item (as suggested by Augenblick, 2016), we can compare the durations of auctions of di¤erent items. 11 ;13 As potential explanatory variables we have included: (i) the average number of simultaneous auctions; (ii) the number of normalized (divided by the retail price) …rst hour bids;  14  (iii) the auction week in question; (iv) the type and (v) brand of the auctioned item; (vi) the number of unique bidders; and (vii) a Her…ndahl-Hirschman concentration index of unique bidders' bids.
Bid level analysis 4. 2. 1 Decision to participate in an auction We have used the bid-level database to construct a balanced panel with 15,314,000 observations, which includes all unique bidders (38,000) in all auctions (403). In that panel, the dummy variable 'participate'takes on the value of 1 if bidder i participated in auction j and 0 otherwise. Our purpose is to look deeper into the motivation of each bidder to participate in an auction. In addition to the average number of simultaneous auctions, the auction week in question, the type and brand of the auctioned item, we also consider three additional variables: the retail price, the number of bids previously submitted by each bidder (in other auctions) and the number of auctions previously won by each bidder. The number of previously submitted bids is of particular interest for two reasons: on the one hand, because it entails a cost, bidders who have already submitted many bids prior to auction j may be (relatively more) …nancially constrained and hence decide not to participate in that auction; on the other hand, it is clearly a proxy for bidder experience (as in Augenblick, 2016, or Goodman, 2012).
25 With this framework in mind, we use the bid level database and calculate, for each bidder and for each auction day in which she was active, bidder-speci…c and auction day-speci…c variables (in total, 80,349 active bidder-auction day observations). 26 r general approach was the following: does a bidder's choice of (i) how many bids to place or (ii) the total number of auctions in which she participates depend on other bidder-speci…c or auction day-speci…c variables, including the previous day's total …nancial expenditure (a proxy for the possible existence of a budget constraint)? To answer this question, we have implemented two speci…cations. In a …rst speci…cation, we have expanded our dataset to include all 'inactive' auction days which immediately follow an 'active' auction day because, if a bidder is …nancially constrained, it may be that exhausting the available funds in day t makes her not bid in day t + 1: This yields a total number of 131,089 bidder-auction day observations.
Discussion and conclusion This paper contributes to the emerging empirical evidence on penny auctions, an auction format which typically raises signi…cantly higher revenue than the underlying market value of the auctioned item. Our particular interest is the e¤ect of learning over time and across auctions as bidders become more experienced. In the dataset we have used, possibly because it is related to a relatively more homogeneous set of products than that used by earlier literature, pro…t margins are very high: on average, 219%. We …nd that, using auction-level data, the standard supply and demand variables have the expected e¤ects on …nal auction prices: increased supply (through a higher number of penny auctions occurring simultaneously) reduces …nal auction prices, whilst increased demand (through a higher number of unique bidders) increases them.
We thank a telecommunications operator (which has requested its identity not to be disclosed) for providing us the bid-level database for the auctions analyzed in this paper. Financial support from  Fundação para a Ciência e Tecnologia  (through project  PEst-OE/EGE/UI0731/2011 ) is gratefully acknowledged. We would like to thank  Ricardo Ribeiro  and participants in a seminar at  Universidade Católica Portuguesa , as well as in the 7th Meeting of the Portuguese Economic Journal (2013, Covilhã, Portugal) and in the 40th Annual Conference of the European Association for Research in Industrial Economics.
For analysis of ERK phosphorylation in transfected PBT, 10 6 cells were fixed with 4% paraformaldehyde for 10 min, permeabilized with methanol at -20°C for 1 hour, and incubated with anti-ERK P antibody (# 4370, Cell Signaling Technology) in PBS 0. 2% BSA for 30 min at room temperature and then with F(ab')2-Goat anti-Rabbit IgG (H+L) secondary antibody (#A-21246, Thermofisher). Cytometry data were obtained using a BD-LSR Fortessa flow cytometer (BD Bioscience). Data files were analyzed using FlowJo software (TreeStar).
Fluorescence microscopy Time-lapse microscopy was performed on HeLa cells grown on coverslips in a Zeiss Axiovert 200 microscope at 37°C with a 20× objective (LD Plan Neofluar). The camera CoolSnap HQ (Photometrics) was used to acquire images. Images were processed using ImageJ software. biochemistry Primary T cells were stimulated using anti-CD3/ CD28 -coated beads (1 bead per 5 cells, Dynabeads, Life technologies) into 96-well round-bottom microtiter plates (Falcon; 2 × 10 5 cells per well). Collected cells were lysed in lysis buffer [20 mM Bicine, pH7. 6; 0. 6% CHAPS complete protease and phosphatase inhibitor cocktail (Roche)] for 30 min on ice. Lysates were collected after centrifugation at 14,000 rpm for 10 min. Protein concentration in supernatants was quantified with the Bradford assay.
AcknowledgMents We thank  E. Gussoni  for providing reagents. C. Randriamampita  and  C. Charvet  for discussions and  N. Delon  for editing the manuscript. This work was supported by  Inserm ,  CNRS  and  Ligue Nationale Contre le Cancer. Cytometry data acquisition, cell sorting and time-lapse experiments were performed in the Immunobiology and  Imaging Cochin  facilities, respectively.
TGF-β1 and TGF-β2 expression in cirrhotic and HCC patients Taken together, the analysis of HCC cell lines revealed significant TGF-β2 expression and secretion of HCC cells and mouse data suggest significant upregulation of TGF-β2 within liver tumor tissue (and not only of TGF-β1). Based on that knowledge, we investigated the occurrence of TGF-β isoforms in human HCC patients. The Oncomine® database was searched in order to analyze TGF-β expression in different HCC patient cohorts. 4 of 7 studies reported TGF-β2 upregulation, whereas only one described TGF-β2 downregulation. TGF-β2 was one of the top 5-10% upregulated genes, while detected downregulation was less prominent (within 25% of downregulated genes). In the same cohorts, TGF-β1 was reported to be upregulated in two studies (within 25% of the most upregulated genes) and downregulated in two other studies (within 25% of the top downregulated genes) (Table  1  and Supplementary Figure  3A ).
Analyzing cirrhotic patient cohorts via Oncomine ® , we detected a continuous increase of TGF-β1 in line with disease progression from healthy via cirrhosis to HCC. In contrast, TGF-β2 expression was higher at the cirrhosis stage than in normal liver and even higher than in HCC (Table  2 , Supplementary Figure  3B-3C ), suggesting a different regulation mechanism of the two isoforms during disease progression (Supplementary Figure  3D ). We then decided to take a closer look at six further HCC cohorts. First, we selected those cohorts that exhibited significant p-values (p < 0. 05) regarding TGF-β1 and/or TGF-β2 regulation in the respective studies (896 patients in total, summaries in Tables  3, 4, 5 ). It is worth noting that the p-value for expression changes provided was neither significant for TGF-β1 in the GSE5975 EpCAM positive and the GSE14520 cohorts, nor for TGF-β2 in the GSE4024/GSE1898 and GSE14520 cohorts.
Thus, they were excluded from this analysis. We then applied the selection criteria < -0. 5 and > 0. 5 Log2 (fold change) to the analysis of individual patients´ expression data (single dots). Summarizing the results, TGF-β1 and -β2 were mainly upregulated compared to normal liver; however, a downregulation of TGF-β2 was obvious compared to surrounding tissue of the tumors (Figure  7 ). This implies that tumor-surrounding tissue itself is significantly altered as compared to normal liver and already displays changed TGF-β signaling signatures. Due to these and other specific changes of the tumor environment, which are known to impact tumorigenesis, expression changes need to be carefully investigated dependent on the context. Interestingly, analyzing patients with defined TGF-β2 regulation in GSE1898/4024, we found a correlation between high TGF-β2 expression and a poorer survival rate (p < 0.
, who described more prominent expression of TGF-β2 than TGF-β1 in bile duct epithelial cells, and particularly high TGF-β2 levels in proliferating bile ducts of fibrotic livers  [53]. While a small study including eight twins (E-MTAB-2347) did not reveal TGF-β2 deregulation in primary biliary cirrhosis (PBC), Shakel et al. [54]  reported on upregulation of TGF-β2 in PBC, thus providing a further link between TGF-β2 and biliaryderived liver diseases. In a study including 87 ICC patients, TGF-β2 expression was linked to bad survival and its overexpression in the ICC stroma was significantly associated with ICC classification, microvascular invasion, and the presence of hilar lymph nodes  [33] , indicating TGF-β2 as a possible prognostic marker for the clinical outcome of ICC and HCC. Recently, a brief report by Fan et al.
Non-significant p-values are given in brackets. NA = Not announced. Only those cohorts with significant p-values for the respective genes are used for this analysis, thus excluding TGF-β1 regulation in the GSE5975 EpCAM positive and GSE 14520 cohort as well as TGF-β2 in the GSE4024/GSE1898 and GSE 14520 cohort (see  Table 4). that distinct and independent as well as parallel roles exist for the two TGF-β isoforms in liver regeneration and the development of CLD. The significance and the mechanistic details of TGF-β2 regulation and effects are possibly etiology-dependent and have to be delineated. Specific multivariate analyses need to be performed and evaluated in order to make TGF-β2 a promising new candidate and target for the development of liver therapeutics.
Patient collectives Publicly available databases (iCOD omics. tmd. ac. jp/icod_pub_eng/; Oncomine https://www. oncomine. org/; GEO, http://www. ncbi. nlm. nih. gov/gds; and Arrayexpress, http://www. ebi. ac. uk/arrayexpress/), as well as data generated within the former SFB/TRR77 on Liver Cancer (GSE50579; GEO ID) and those collected from our own collaborative projects, were used in order to analyze expression of TGF-β1 and TGF-β2. The tools Bioconductor (R) 2. 13 (3. 0. 1) and GraphPad Prism 6 were used to retrieve and analyze data from six HCC sample cohorts (GSE5975, GSE1898, GSE50579, iCOD, GSE4024/GSE1898, GSE14520) comprising a total number of 896 HCC patients, presented from different etiologies. The expression values were matched to normal liver samples and/or to each patient´s surrounding non-cancer tissue.
ACKNOWLEDGMENTS We thank the following cooperation partners for providing cell lines and support: the  SFB/TTR77  funded by the  Deutsche Forschungsgemeinschaft (DFG)  for providing HepG2,  Hep3B ,  PLC/PRF/5 , HLE, and  HLF  purchased from ATCC and HuH7 purchased from  JCRB (Japanese Collection of Research Bioresources Cell Bank) ;  Prof. Michael Kern ,  Institute of Pathology, Cologne, Germany , for FLC-4 and HuH6 cells; and  Prof. Scott Friedman ,  Mount Sinai Hospital, Icahn Medical Institute New York, USA , for LX-2. The authors thank  S. S. Thorgeirsson  and  E. A. Conner  ( Laboratory of Experimental Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA ) for kindly providing c-myc and TGFα transgenic mice. Thanks to  Alexandra Müller  ( Medical Faculty Mannheim/UMM ) and  Igor Liebermann  ( IKP Stuttgart ) for technical help.
Another constraint for testing the links between plant traits and EFPs is that so far, measurements of plant functional traits have not yet been carried out systematically at FLUXNET sites. Consequently, a number of studies linking plant traits and EFPs using a wide range of ecosystems are few (e. g. ,  Kergoat, Lafont, Arneth, Le Dantec, & Saugier, 2008). Although plant trait data from FLUXNET sites are currently limited, the global database of plant traits-TRY  (Kattge et al. , 2011) -facilitates the identification of many different traits for most of the plant species present at FLUXNET sites, which could potentially help testing such relationships. However, the use of trait values derived from such broadscale databases may suffer from inaccuracies, when trait values for a particular site deviate from those reported in databases, which may hamper deducing the patterns of plant traits influences on EFPs.
Hence, it is important to test the potentials and limitations of using plant functional traits derived from a global database (e. g. , TRY) versus in situ measurements obtained from the sites to infer the impact of plant traits on ecosystem processes derived from EC flux data. We still do not know how temporal and spatial variations in both EFPs and plant functional traits are linked. Likewise, the uncertainties of the relationship between EFPs to plant functional traits related to the temporal dynamics of both ecosystem functioning and traits have not been evaluated before. This is the first time to our knowledge that the relationship between an EFP (here ecosystem photosynthetic capacity) derived from EC CO 2 fluxes and plant traits and the associated uncertainties have been systematically investigated for spatiotemporal variation and the relevance of synchronized observations.
| Eddy covariance flux measurements The analysis used data from the FLUXNET La Thuile database  (Baldocchi, 2008) , referred hereafter as "La Thuile. " Very dry sites and forest site-years with disturbances (i. e. , forest thinning, harvesting, and planting) were removed opting for optimal conditions to avoid To characterize ecosystem photosynthetic capacity, we used halfhourly values of GPP (μmol CO 2 m -2 s -1 ) and the corresponding PAR (μmol m -2 s -1 ). The GPP values were computed using the commonly used algorithm for flux partitioning, which is based on the extrapolation of nighttime net ecosystem exchange measurements using an ecosystem respiration model based on air temperature  (Reichstein et al. , 2005). As PAR was not always available at the selected sites, we derived PAR by multiplying global incoming shortwave radiation (Rg, Wm -2 ) by 2.
11  (Britton & Dodd, 1976). Only GPP data derived from measured net ecosystem exchange were used for the analysis and gap-filled values were omitted. In addition, only daytime GPP data were used (Rg > 10 Wm -2 ). For each siteyear, we estimated the number of days with more than 80% gaps in half-hourly net ecosystem exchange measurements during the period from April to September. Site-years with more than 25% of such days were excluded.
| Plant functional trait collection-vegetation characteristics Plant traits known to be relevant for photosynthesis at ecosystem scale, specifically leaf nutrient content and stoichiometry of the nutrients, were determined  (Sardans & Penuelas, 2012) : leaf nitrogen content per dry mass (N mass or per 100 g leaf dry mass-N%), leaf nitrogen content per leaf area (N area , g/m 2 ), leaf phosphorus content per leaf dry mass (P mass , mg/g) and per leaf area (P area , g/m 2 ), leaf carbon content per leaf dry mass (C, mg/g), leaf C/N ratio (C/N, g/g), leaf stable isotope concentration (δ 13 C), and specific leaf area, (SLA, mm/mg). In situ leaf samples from the selected sites were collected in the period 2011-2012 (except for two sites in 2003 and in 2004). The leaf sampling protocol was based on "Protocols for Vegetation Sampling and Data Submission" of the terrestrial carbon observations panel of the global terrestrial observing system  (Law et al.
δ 13 C was determined by an elemental analyzer (NC2500, ThemoQuest Italia, Rodana, Italy) coupled online to a stable isotope ratio mass spectrometer (Deltaplus, ThermoFinnigan, Bremen, Germany). Leaf area was calculated with the ImageJ freeware (http://rsb. info. nih. gov/ij/). Species abundance information was collected for each site, or if not available (e. g. , for one tropical forest site), all species were considered equally abundant. Abundance information for each species was used to calculate the community weighted means (CWM,  Garnier et al. , 2004)  of the different plant traits considered in the analysis: foliar N, P, and C concentration of leaves, SLA, and δ 13 C. Plant trait data were also extracted from the TRY global database  (Kattge et al. , 2011). Species mean values were calculated from the observed plant trait values included in TRY, which were subsequently used to compute CWM trait values at each site.
g. , LAI) as it stands for the amount of light that is absorbed by the leaves of the ecosystem. The ecosystem photosynthetic capacity values were estimated using a 5-day moving window. The parameters of the light response curves were estimated and attributed to the day at the center of the window (Figure  1a ). The parameters were estimated by minimizing the model observation residual sum of square with the quasi-Newton (1) GPP = 1 2θ αQ + A max - √ αQ + A max 2 -4αA max θQ optimization method that allows box constraints  (Byrd, Lu, Nocedal, & Zhu, 1995). To this purpose, we used the optim function implemented in R (http://CRAN. R-project. org/). For comparison, a Michaelis-Menten-based light response curve  (Hollinger et al. , 2004)  was used. Results were comparable with the nonrectangular hyperbolic light response curve (data not shown).
structure : as GPP sat but using APAR as radiation until light saturation (Figure  1a , Table  1 ). A time series of daily values of A max , A max. structure , GPP sat , GPP sat. structure , GPP cum , and GPP cum. structure was then derived for each year. In Figure  1b , GPP sat is shown as an example. Daily parameters were retained for further analysis only if the R 2 of the fit of light response curve was higher than 0. 6. In this way, we first retain parameters estimated when the performance of the fitting is good, and second, we retain data only in the active growing season as the R 2 of the model fit was typically higher than 0. 6 only within the growing season (Fig. S1 ). To extract the corresponding annual ecosystem photosynthetic capacity for each site-year, maximum and different percentiles (90th to 60th) of the time series of the estimated parameters were computed.