dataesr/scientific-paragraphs-categorization huggingface dataset:
Resistance to CDDP correlates with lack of apoptosis To gain further insight into the basis of CDDP resistance in lung cancer we used H1299 and H460 cells. First, we performed a dose-response assay to CDDP in both cell lines. As expected, H1299 showed a great resistance by crystal violet method as well as by MTT (Figure  1A  and 1B), in agreement with previous reports  [19]. Considering that apoptosis is the main mechanism for cell death associated to CDDP we evaluated it by different methods in both experimental models. While H460 cells displayed a consistent induction of apoptosis by means of flow cytometry assays (Figure  1C  and 1D), western blotting and caspase enzymatic activity (Figure  1E  and 1F), no effect was observed in H1299 cells (Figures 1C, 1D, 1E, 1F). In the case of H460, no activation on caspase 8 was observed (data not shown) thus leading us to conclude that apoptosis was triggered through the extrinsic pathway.
Tranfections H1299 cells were transfected with pEX-GFP-hLC3wt or pEX-GFP-hLC3ΔG120 previously described, by using Lipofectamine LTX from Invitrogene (#15338500) following manufacturer's instructions. 48 hours later, cells were selected by using G-418 from Sigma-Aldrich (A1720) at 800 μg/ml for at least 10 days. Then, selected pools were treated and analysed as indicated in Zeiss LSM-710 confocal microscope. Images were acquired and processed using Zen 2009 Light Edition software. Images show a representative fields out of 8. The scale bars represent 25 μm.
Data analysis Results are represented as mean ± SEM (Standard Error of the Mean) of, at least, three independent experiments performed in triplicate. Statistical analysis was performed using the Prism 5. 00 software (GraphPad) and Office Excel 2013 (Microsoft). Significance was determined using a t-test. The statistical significance of differences is indicated in Figures by asterisks as follows: *⇒ p < 0. 05; **⇒ p < 0. 01; and ***⇒ p < 0. 001.
ACKNOWLEDGMENTS We appreciate the comment and suggestions of  Drs FX Pimentel  and  E Knetch. www. impactjournals. com/oncotargetCompetitividad (grant  SAF2012-30862  to RSP and grant  CTQ2011-24434  to FAJ). RSP Research Institute , and the work carried out in his laboratory receive support from the  European Community through the regional development funding program (FEDER). JGC received funding from the  Regional Ministry of Education and Science of Castilla-La Mancha  ( FPI-JCCM ) and from  Fundación Leticia Castillejo Castillo. MCC and RSP have a contract from the  INCRECYT program.
, FAPERJ and FINEP (Brazil); NSFC (China); CNRS/IN2P3 (France); BMBF, DFG, HGF and MPG (Germany); INFN (Italy); FOM and NWO (The Netherlands); MNiSW and NCN (Poland); MEN/IFA (Romania); MinES and FANO (Russia); MinECo (Spain); SNSF and SER (Switzerland); NASU (Ukraine); STFC (United Kingdom); NSF (USA). The Tier1 computing centres are supported by IN2P3 (France), KIT and BMBF (Germany), INFN (Italy), NWO and SURF (The Netherlands), PIC (Spain), GridPP (United Kingdom). We are indebted to the communities behind the multiple open source software packages on which we depend. We are also thankful for the computing resources and the access to software R&D tools provided by Yandex LLC (Russia). Individual groups or members have received support from EPLANET, Marie Sk lodowska-Curie Actions and ERC (European Union), Conseil général de Haute-Savoie, Labex ENIGMASS and OCEVU, Région Auvergne (France), RFBR (Russia), XuntaGal and GENCAT (Spain), Royal Society and Royal Commission for the Exhibition of 1851 (United Kingdom).
, 2012)  which simulates atmospheric transmission including Lambertian reflection at the Earth surface. Figure  1  shows a typical transmission spectrum in the retrieval window for clear-sky conditions and the individual spectral contributions of the trace gases HDO, CO, H 2 O and CH 4. The forward model employs the crosssection database by  Gloudemans et al. (2009) , which comprises CO and CH 4 absorption cross-sections from the highresolution transmission molecular absorption database (HI-TRAN)  Rothman et al. (2005)  and  Predoi-Cross et al. (2006) , respectively, and H 2 O and HDO cross sections from  Jenouvrier et al. (2007). The spectral fit window is extended significantly with respect to the window used by  Gloudemans et al. (2008)  to establish a stable retrieval for the entire mission period.
For every SCIAMACHY measurement, the model data are spatially resampled to the satellite ground pixel and interpolated in time. Moreover, we account for the differences between the mean SCIAMACHY pixel elevation and the mean pixel elevation of the model data. First, we calculate the mean SCIAMACHY pixel height using the digital Shuttle Radar Topography Mission (SRTM) elevation map with a spatial resolution of 15 arcsec  (Farr et al. , 2007)  and subsequently, all model profiles are interpolated to the mean altitude of a SCIAMACHY ground pixel. One may question the relevance of the null-space error and the need for column averaging kernels for a proper validation of our data product. Generally, the correct use of Eq. (  7 ) requires measurements of the CO vertical profile. However, this hampers any validation of the SCIAMACHY CO data product because measurements of CO profiles are hardly available for the mission period.
For both cases, the null-space error is less than 1 ppb (< 1 % of a mean CO total column) and so far less than the SCIAMACHY measurement noise error that varies between 30 ppb and > 170 ppb for individual retrievals (see Figs. 6  and 7 ). SZA=20 • SZA=30 • SZA=40 • SZA=50 • SZA=60 • SZA=70 • 30 Because of its randomness the noise error can be reduced by averaging multiple SCIAMACHY CO retrievals, which is not necessarily the case for the null space. However, a nullspace error of the order of < 1 % for clear-sky SCIAMACHY CO retrievals represents a minor contribution to the overall error and is ignored in the following. So a direct comparison between ground-based measurements and SCIAMACHY retrievals is possible. Following this approach, strict cloud filtering of SCIAMACHY data is required.
Instrument calibration In this section, we consider SCIAMACHY nadir measurements for the full operational phase of the mission from January 2003 to April 2012, where we use Level 1b spectra disseminated by ESA. The measurements are corrected for memory-nonlinearity and dark current using the Netherlands SCIAMACHY Data Center (NADC) toolbox Version 1. 2 (http://www. sron. nl/~richardh/). Data recorded during SCIAMACHY's commissioning phase are not considered. The CO data processing relies on SCIAMACHY's forward scans for a solar zenith angle smaller than 80 • and with a ground pixel size of about 30 km × 120 km (alongtrack × across-track) for an integration time of 0. 5 s. At higher northern and southern latitudes the integration time is increased to 1 s, which accordingly doubles the across-track pixel size.
Despite the strict filtering, the absolute radiometric calibration of SCIAMACHY Earthshine measurements is not accurate enough to retrieve CO. Figure  3  shows the time dependence of the mean solar signal. Besides the seasonal variation of the signal due to the change of the Earth-Sun distance throughout the year, the effect of the ice layer formation on the overall instrument transmission and the signal recovery due to the instrument heating during so-called decontami- nation events (see Table  1 ) is clearly visible  (Gloudemans et al. , 2005). To mitigate effects on the CO data quality, our retrieval is based on the reflectance r i , which is the ratio of the Earth radiance measurement I i divided by the solar measurement S i by the same detector pixel i, r i = I i /S i. Here, we use SCIAMACHY's daily Sun mean reference measurements, determined from the Sun measurements via the elevation scan mirror.
Validation To validate our SCIAMACHY CO data product, we have to treat two main problems. First, the retrieved CO column suffers severely from measurement noise. The retrieval noise error for low radiance signal can exceed 100 % of the retrieved column. Therefore, any validation can only be performed on quantities averaged in space and time. Second, a direct comparison with ground-based measurements is affected by representation errors. For example, a monthly mean CO concentration derived from ground-based measurements may differ from a corresponding monthly mean of SCIAMACHY measurements due to different temporal sampling. A strict temporal co-registration criterion for both ground-based and SCIAMACHY measurements may reduce the sampling effect but at the cost of fewer SCIAMACHY samplings, which in turn enhances the noise contribution.
Ground-based Fourier transform spectrometers In this section, we validate the SCIAMACHY CO data product with the measurements of Fourier transform spectrometers used for observing CO column densities under clear-sky conditions allowing direct Sun measurements. Table 2 summarises the validation data set, which comprises measurements at various stations of the Infrared Working Group (IRWG) that is part of the Network for the Detection of Atmospheric Composition Change (NDACC, http:// www. ndsc. ncep. noaa. gov/) and of the TCCON  (Wunch et al. , 2010 (Wunch et al. , , 2011)). The IRWG provides measurements in the midinfrared with the aim of analysing the atmospheric composition of the troposphere and stratosphere. The NDACC-IRWG supplies CO total columns that we transformed to column mixing ratios by calculating air columns from the surface pressures at a station.
At several sites, the data record covers the entire SCIAMACHY lifetime, and thus makes these data very suited for the validation of the SCIAMACHY data product. The TCCON network collects measurements in the same spectral range as recorded by SCIAMACHY from space. This results in a similar vertical sensitivity of both the SCIA-MACHY and the TCCON product, which is in particular desirable for validation purposes (see e. g. Wunch et al. , 2010, Fig. 3 and Borsdorff et al. , 2014 , Fig. 2 ). In 2004, TCCON started with the first instrument at Park Falls, WI, USA, and since then the network has grown gradually to 19 observation sites worldwide. Therefore, the TCCON data set is very well suited to validate SCIAMACHY measurements in the later years of the mission. This study is based on the TCCON GGG2014 data set  (Deutscher et al.
The mean signal-to-noise ratio of the measurements in the fit window must be > 10. 3. The noise of the retrieved CO, CH 4 and H 2 O column must be below an upper threshold, namely CO < 1 × 10 19 , CH 4 < 6 × 10 18 , H 2 O < 2 × 10 22 molec cm -2. 4. Only SCIAMACHY measurements are used, which are classified as cloud free by the SPICI algorithm. Moreover, we selected SCIAMACHY measurements over land, which fall within a radius of 850 km around a TCCON or NDACC-IRWG station site. To derive one representative monthly value for both data sets, we interpolate the FTIR measurements to the point of the SCIAMACHY measurements at time t. For this purpose, we consider the ratio of the FTIR columns divided by the co-aligned TM5 columns at two adjacent FTIR samples, δ(t 1 ) and δ(t 2 ) at time t 1 and t 2.
The temporal interpolated FTIR column c FTIR (t) is then given by   where c TM5 (t) is the corresponding TM5 CO column and δ(t) is the linear function through the adjacent points δ(t 1 ) and δ(t 2 ). Beforehand, we applied an additive bias correction to the TM5 model values such that the overall mean of the FTIR and TM5 values are the same. This simple interpolation scheme makes use of the precise FTIR measurement where the relative temporal trend in CO due to meteorology and photo-chemistry is adopted from the TM5 model. Subsequently, we correct c FTIR (t) for differences between the surface elevation at the station site and the mean altitude of the individual satellite ground pixels using also TM5 CO profiles. c FTIR (t) = δ(t) • c TM5 (t), (14 In this manner, we obtain two co-aligned data sets, which are subsequently used to derive monthly median CO column concentrations.
The average bias trend t is calculated by an average of the individual bias trends weighted by their uncertainty, where we excluded measurements at Toronto because of a discontinuity of the NDACC-IRWG time series (see Fig. 6 ). This issue is already under investigation and does appear to be instrumental. When correcting the SCIAMACHY data for this bias trend, the bias with NDACC-IRWG becomes -8. 4 ppb and with TCCON -4. 6 ppb. Hence, a difference of 3. 8 ppb remains between the TCCON and NDACC-IRWG validation. We consider this difference to be significant due to the small mean standard error se and we conclude that it is most probably caused by   and 7. Stations with a low retrieval noise that cover the full mission period were selected. Trends are given per station in ppb/year and error bars are the standard deviation.
MOZAIC/IAGOS aircraft measurements Additionally, we validate the SCIAMACHY CO data product with CO total columns that are calculated from aircraft CO profile measurements supplied within the MOZAIC/IAGOS project. Since 1994, regular profile measurements of reactive gases by several long-distance passenger airliners were performed during ascent and descent phases (in total more than 40 000 flights). Aircraft descents and ascents are not strictly vertical profiles: the CO concentration at the surface is representative for the airports but the top of the height cruise can be far away from the take-off location. The representation error of the derived CO column for individual flight profile paths can reach up to 100 %, related to real spatiotemporal variability in CO total columns. However,  Nédélec et al.
(2003)  indicated that the aircraft CO profile measurements reach a precision of about ±5 % and de Laat et al. (2014)  showed that the errors caused by aircraft descent and ascent flight paths will average out for CO total columns when calculating the mean over longer time periods. However, biases due to highly polluted airports are still possible. Furthermore, the distance covered by the MOZAIC/IAGOS profiles used in this study is about 200-400 km and therefore is within the collocation area around the airports considered for the comparison with SCIAMACHY. Table  3  summarises the validation data set, which comprises CO profile measurement at 26 airports worldwide. At many airports the data set covers the early years of the SCIA-MACHY mission and therefore forms a complement to the TCCON data set used in the previous section.
Acknowledgements. SCIAMACHY is a joint project of the  German Space Agency DLR  and the  Dutch Space Agency NSO  with contribution of the  Belgian Space Agency. The work performed is (partly) financed by  NSO  and through the  SCIAMACHY Quality Working Group (SQWG)  by  ESA. We acknowledge the  European Commission  for the support to the  MOZAIC  project (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003) and the preparatory phase of IAGOS ( 2005IAGOS ( -2012 )), the partner institutions of the  IAGOS Research Infrastructure (FZJ, DLR ,  MPI ,  KIT in Germany ,  CNRS ,  CNES, Météo-France in France  and the  University of Manchester in UK) ,  ETHER  ( CNES-CNRS/INSU ) for hosting the database, the participating airlines ( Lufthansa, Air France, Austrian, China Airlines, Iberia, Cathay Pacific ) for the transport free of charge of the MOZAIC/IAGOS instrumentation.
• latitude). The figure also includes corresponding averages of TM5 model simulations which use the Global Fire Emissions Database (GFED) version 3 for the biomass burning input. Overall, the SCIAMACHY and TM5 fields agree well. The seasonal variation is present in both data sets including the phase shift between the northern and southern hemispheric CO concentration. In this case, the seasonal MOZAIC/IAGOS 0 10 b [ppb] 20 30 b = -6. 4 ppb σ = 5. 6 ppb 40 50 12 13 10 se = 1. 8 ppb 28 se [ppb] 6 8 13 32 2 4 19 61 37 54 22 20 28 20 16 32 21 23 69 84 34 27 14 35 28 60 26 185 0 London Frankfurt Munich Vienna Portland Montreal Toronto Boston Chicago New York Beijing Philadelphia Washington Tehran Los Angeles Atlanta Dallas Tel Aviv Cairo Houston New Delhi Kuwait City Abu Dhabi Hyderabad Caracas Windhoek Figure 10.
In this paper, we introduce a second order variation of this interpretation methodology in order to constrain the complexity of stream program computation and we obtain a characterization of uotm polynomial time computable functions. Using this characterization, we can analyze functions of this class in an easier way based on the premise that it is practically easier to write a first order functional program on streams than the corresponding Unary Oracle Turing Machine. The drawback is that the tool suffers from the same problems as polynomial interpretation: the difficulty to automatically synthesize the interpretation of a given program (see  [16] ). As a proof of versatility of this tool, we provide a partial characterization of the bff class (the full characterization remaining open), just by changing the interpretation codomain: for that purpose, we use restricted exponentials instead of polynomials in the interpretation of a stream argument.
y k , x 1 ,. , x l and we just set f = P e + 1. Proof. Let f : (N -→ N) k -→ N l -→ N be a function computed by a uotm M in time P , with P a second order polynomial. Without loss of generality, we will assume that k = l = 1. The idea of this proof is to write a program whose function symbol f 0 computes the output of M after t steps, and to use Lemma 6 to simulate the computation of P. Let f 0 be the function symbol describing the execution of M:.
Soundness In order to prove the soundness result, we need to prove that the polynomial interpretation of a program can be computed in polynomial time by a uotm in this sense: Lemma 8. If P is a second-order polynomial, then the function: F 1 ,. , F k , x 1 ,. x l → 2 P (|F1|,. ,|F k |,|x1|,. ,|x l |) -1 is computable in polynomial time by a uotm. Proof. The addition and multiplication on unary integers, and the function x → |x| are clearly computable in polynomial time. Polynomial time is also stable under composition, so we only need to prove that the size function |F | is computable in polynomial time by a uotm. This is the case since it is a max over a polynomial number of elements of polynomial length. Note that this would not be true with the size function ||. || as defined in  [3]  since is not computable in polynomial time.
Lemma 9 (Soundness). If a function symbol f :: [Bin] k -> Bin l -> Bin admits a polynomial interpretation, then it computes a type-2 function f of type (N -→ N) k -→ N l -→ N which is computable in polynomial time by a uotm. Proof. From the initial program (which uses streams), we can build a program using finite lists instead of streams as follows. For each inductive type Tau, let us define the inductive type of finite lists over Tau: data List(Tau) = Cons Tau List(Tau) | [] The type of each function symbol is changed from [Bin] k -> Bin l -> Bin to List(Bin) k -> Bin l -> Nat -> Bin (or from [Bin] k -> Bin l -> [Bin] to List(Bin) k -> Bin l -> Nat -> List(Bin)): streams are replaced by lists, and there is an extra unary argument. We also add an extra constructor Err to each type definition.
The evaluation may also terminate earlier if the error value appears at some point. The number of reduction steps is then bounded (up to a multiplicative constant) by: f ( app v 1 ,. app v k , v k +1 ,. v k +l ) Since app v i is a first order polynomial in the interpretation of v i , Lemma 1 proves that the previous expression is a first order polynomial in the interpretation of its inputs. Several results in the literature (for example  [21, 22] ) on type-1 interpretations allow us to state that this new program computes a polynomial type-1 function (in particular because it is an orthogonal term rewriting system). Let us now build a uotm which computes f. According to Lemma 8, given some inputs and oracles, we can compute f applied to their sizes and get a unary integer N in polynomial time.
Basic feasible functionals In the completeness proof (Lemma 7), the program built from the uotm deals with streams using only the !! function to simulate oracle calls. This translation can be easily adapted to implement otms with stream programs. Because of the strict inclusion between polynomial time uotm computable functions and bff (cf. Lemma 2) and the Lemma 9, it will not always be possible to provide a polynomial interpretation to this translation, but a new proof provides us with a natural class of interpretation functions. Definition 14 (exp-poly). Let exp-poly be the set of functions generated by the following grammar: EP := P | EP + EP | EP × EP | Y 2 EP The interpretation of a program is exp-poly if each symbol is interpreted by an exp-poly function. Example 10. P (Y, X) = Y 2 X 2 +1 is in exp-poly, whereas 2 X × Y 2 X is not.
Theorem 3. Every bff functional is computed by a program which admits an exp-poly interpretation. Proof. Let f be a function computed by an otm M. We can reuse the construction from the proof of lemma 7 to generate a function symbol f 0 (and its definitions) simulating the machine. Since we consider an otm and no longer a uotm, we should note that when querying, the query tape now contains a binary word, which we have to convert into unary before giving it to the !! function using an auxiliary function natofbin: natofbin :: Bin -> Nat natofbin Nil = 0 natofbin (0 x) = plus (natofbin x) (natofbin x) natofbin (1 x) = (plus (natofbin x) (natofbin x)) +1 Its interpretation should verify, using the interpretation of plus given in Example 6: natofbin (X + 1) > 3 × natofbin (X) + 1 This can be fulfilled with natofbin (X) = 2 2X.
Definition 15 (Computable real function). A function f : R -→ R will be said to be computed by an uotm if: (q n ) n∈N x ⇒ (M(q n )) n∈N f (x). (1) We will restrict to functions over the real interval [0, 1] (or any compact set). Hence a computable real function will be computed by programs of type [Q] -> [Q] in our stream language, where Q is an inductive type describing the set of rationals Q. For example, we can define the data type of rationals with a pair constructor /: data Q = Bin / Bin Only programs encoding machines verifying the implication (1) will make sense in this framework. Following  [17] , we can define polynomial complexity of real functions using polynomial time uotm computable functions. The following theorems are applications of Theorem 2 to this framework. Theorem 4 (Soundness).
For this reason, several tools dedicated to the computation of decay rates in the context of specific beyond the Standard Model theories have been developed in the past  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] , while many Monte-Carlo event generators are also able to compute partial widths on their own via a dedicated phase-space integration  [15, 16, 17, 18, 19, 20, 21, 22, 23]. The aim of this paper is to present a way to compute automatically, using the FeynRules  [24, 25]  and MadGraph5 aMC@NLO  [19, 26]  frameworks, all the partial decay widths for large classes of new physics models, in particular when specific width calculators are not publicly available. FeynRules is a Mathematica package that allows one to compute the interaction vertices of any high-energy physics model directly from its Lagrangian.
The package contains a set of translation interfaces to export the Feynman rules to various matrix element generators. The latter often require the widths of all the particles appearing inside the model to be given explicitly and to be defined as numerical input parameters. The workflow to obtain an implementation of a new physics model that can be used for phenomenological studies has so far been the following 1  : 1. obtain an implementation of the model into the matrix element generator of choice (for which an interface to FeynRules exists) where all the widths are set to some default values. 2. for each choice of the numerical input parameters, run the matrix element generator to compute the numerical values of the widths, and insert them back into the model implementation. While straightforward, it is clear that this process contains a lot of redundant workload.