dataesr/scientific-paragraphs-categorization huggingface dataset:
In the case of weak pesticide signals, extracted ion chromatograms were generated with base peak ions to confirm the presence of chemicals (Paraoxon (I) and Parathion ethyl (II) m/z 109; Malathion (V) and Fenitrothion (VIII) m/z 125; Diazinon (VII) m/z 137; Coumaphos (X) m/z 97; Fensulfothion (IX) m/z 293; and Chlorpyrifos (VI) m/z 197). Selected Ion Recording using base peaks ions was applied in order to specifically monitor pesticides and collect peak areas for kinetics. All samples were analyzed over short periods of time to avoid signal drift. All data were processed using Turbomass 6. 1 (Perkin Elmer). Crystallization. Crystallization assays were performed as previously described  42, 46. Crystallization was performed using the hanging drop vapor diffusion method in 96-well plates (Greiner Microplate, 96 well, PS, F-bottom) on ViewDrop II seals (TPP Labtech).
Equal volumes (0. 5 µL) of protein and reservoir solutions were mixed, and the resulting drops were equilibrated against a 150 µL reservoir solution containing 20-30% (w/v) PEG 8000 and 50 mM Tris-HCl buffer (pH 8). Crystals appeared after few days at 4 °C. Data collection and structure refinement. Crystals were first transferred to a cryoprotectant solution consisting of the reservoir solution and 20% (v/v) glycerol. Crystals were then flash-cooled in liquid nitrogen. X-ray diffraction data were collected at 100 K using synchrotron radiation at the ID23-1 beam line (ESRF, Grenoble, France) and using an ADSC Q315r detector. X-ray diffraction data were integrated and scaled with the XDS package (Table  1 )  61. The phases were obtained using the native structure of SsoPox (PDB code 2vc5) as a starting model, performing a molecular replacement with MOLREP or PHASER  62, 63.
Acknowledgements This work was granted by  DGA, France  ( REI. 2009 34 0045 ). G. G. and P. J. are PhD students granted by  DGA. Financial support was also obtained from the  MnDrive Initiative (M. E. )  to support this work. This work was supported by " Investissements d'avenir" program  (Méditerranée Infection  10-IAHU-03 ) of the  French Agence Nationale de la Recherche (ANR).
Study design: flow charts and patients In Rouen University Hospital transplantation unit, 58 patients had a CNI to belatacept switch between October 2013 and July 2016. Belatacept indication was systematically decided during a medical meeting, after discussion of potential therapeutic alternatives. All the patients were EBV seropositive before the switch. Transplant biopsies were obtained before conversion and scored according to the Banff classification (9,10) by a single experienced pathologist. The patients who fulfilled the following eligibility criteria were included in the "CNI to belatacept switch group" of the present study: 1 A CNI to belatacept switch performed after 6 months following kidney transplantation 2 Patients who underwent a kidney graft biopsy before the switch 3 Histological features associating: a A chronic vascular score (CV) ≥ CV2 b And the sum of interstitial inflammation (i) and tubulitis (t) scores (i + t) ≤1 c And the sum of glomerulitis (g) and peritubular capillaritis (ptc) score (g + ptc) ≤2 and chronic glomerulopathy (cg) ≤1 4 A follow-up >6 months under belatacept and after kidney graft biopsy This group was compared to a historical cohort including kidney transplant recipients who continued after the graft biopsy ("control group") and fulfilled the following eligibility criteria (this group was built with our local kidney allograft biopsies database [Figure  1] ):.
Statistical methods Quantitative data were presented as mean (SD), or median (range) when data were not normally distributed. Qualitative data were presented as percentages. The non-parametric Wilcoxon (quantitative data) and Mann-Whitney test (qualitative data) were used to compare baseline characteristics and mean eGFR observed during the follow up in the two groups. All analyses were performed using StatView version 5. 0 (SAS Institute, Cary, NC).
, 2014). Indeed, a subset of human G3 MBs overexpress both MYC and GFI1 but retain wild type TP53, implying that GFI1 may act to subvert p53dependent tumor suppression. RNA-Seq analysis revealed that expression of several hundred genes was differentially regulated in Myc + MBs generated from 18 primary Trp53 -/-GNPs versus MBs arising from four Trp53 +/+ GNPs overexpressing Gfi1 (Figure  4J ). However, there were no significant differences in expression of canonical p53-responsive genes (including Cdkn1a (p21), Cdkn2a (Ink4a/Arf), Mdm2, or Bcl2 family members) between these cohorts (see data accession numbers in Experimental Procedures), providing no mechanistic data directly linking Gfi1 with the p53 pathway. In summary, Ezh2 and Gfi1 play both common and distinctly different cross-regulatory roles in MB tumor development with Ezh2 loss and Gfi1 overexpression contributing to enhanced tumorigenesis.
6 (St Louis MO, USA) and STATA MP/11. 2 (College Station TX, USA). A two factor ANOVA of the cells lacking Ezh2 was performed, and false discovery rates calculated for a model of genotype, tumor of origin and the interaction of the two factors. A two class comparison of Ezh2-deficient versus Ezh2 +/+ control tumors was examined by GSEA, and the 100 most differentially expressed genes were analyzed for GO category enrichment using DAVID. Heat maps were produced using Z transforms of log2 FPKM data in Spotfire Decision Site 9. 1. 2 from TIBCO (Boston MA, USA).
Statistical analysis The Kaplan-Meier method was used to calculate the significance of mouse survival. Statistical analyses were performed in the GraphPad Prism software version 6. 0. P-values were calculated by an unpaired two-tailed t-test from 3 independent experiments. for cranial implants. We thank Jose Grenet for making high titer retroviruses, Dana Farmer and Frederique Zindy for tumor cell purification and fixation of cells for ChIP-Seq analysis, and Kimberly Mercer for PDX amplification. This work was funded in part by the National Institutes of Health grants CA-096832 and CA-21765 to M. F. R. , Sununu Endowed Fellowship to B. T. V. and the American Lebanese Syrian Charities of St. Jude Children's Research Hospital. C. J. S. is an investigator of the Howard Hughes Medical Institute.
To parameterize the constant, an exponential approximation between W and T 0 was used. A LST retrieval algorithm was proposed. The error for the proposed algorithm was RMSE = 0. 70 K. Sensitivity analysis results showed that under the consideration of NE∆T = 0. 2 K, 20% uncertainty in W and 1% uncertainties in the channel mean emissivity and the channel emissivity difference, the RMSE was 1. 29 K. Compared with AST 08 product, the proposed algorithm underestimated LST by about 0. 8 K for both study areas when ASTER L1B data was used as a proxy of Gaofen-5 (GF-5) satellite data. The GF-5 satellite is scheduled to be launched in 2017.
One of its missions is to collect land information at high spatial resolution from visible to thermal infrared (TIR) spectral range for performing disaster monitoring. The multiple spectral-imager (MSI) is a payload onboard this satellite and has a 40-m spatial resolution for TIR channels. Development of a suitable algorithm for retrieving LST from the GF-5 data is urgently needed. Remote sensing is a more effective method to map LST at large temporal and spatial scales than traditional measurement. However, accurate determination of the LST from satellite data is a difficult task because it is necessary to correct the remote sensing measurements for atmospheric absorption and emission and for the effect of surface emissivity, the latter generally differing from unity and being channel dependent  [13].
Efforts have been devoted to estimating LST from satellite measurements  [14] [15] [16] [17] [18] [19]. A complete review of different LST retrieval methods can be found in the literature  [20]. The selection of the LST algorithm for GF-5 data is based on a literature review and the characteristics of the GF-5 satellite data. The single-window method requires high-quality atmospheric profile and is sensitive to uncertainties in the atmospheric corrections. Considering the simultaneous retrieval of the LST and the land surface emissivity (LSE), the temperature and emissivity separation (TES) method may be a candidate. However, significant errors in the LST and LSE for the surfaces with low spectral contrast emissivity (e. g. , water, snow, vegetation) can be caused by the TES  [21, 22]. Since the GF-5 satellite observes the land almost at nadir, the dual-angle algorithm was discarded.
This study aims to improve the quadratic SW method and develop a LST algorithm for GF-5 data. The variation in the channel emissivity will be also considered, which was not checked in the study by  François and  Ottlé. The paper is organized as follows: Section 2 describes the data used in this study. The algorithm development is documented in Section 3. Section 4 presents the results using the simulated data and the sensitivity of the developed algorithm to the uncertainties of the input parameters is also analysed in this Section. The operational application of the algorithm to the satellite data is given in Section 5. Section 6 discusses the developed algorithm. Finally, Section 7 describes the conclusions.
Chinese GF-5 Data GF-5 is the fifth satellite of a series of China High-resolution Earth Observation System (CHEOS) satellites of the China National Space Administration (CNSA) and is scheduled to be launched in 2017. One sensor onboard the GF-5 satellite is the MSI, which includes 13 channels covering the spectral range from visible to TIR. Two TIR channels suitable for the SW method are designed to be centred at 10. 8 µm and 11. 95 µm (CH 10. 8 and CH 11. 95 , respectively) with a resolution of 40 m. The spectral response functions of these two channels are displayed in Figure  1. The MSI has a narrow swath (60 km) and observes the land almost at nadir.
ASTER Data The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a sensor on NASA Earth Observing System (EOS) Terra satellite. ASTER is capable of creating detailed land information, with four 15-m visible near-infrared (NIR) bands, including stereo, six 30-m shortwave infrared (SWIR) bands and five 90-m TIR bands. The central wavelengths of two TIR bands, which are suitable for SW method, are 10. 657 μm and 11. 318 μm. The spectral response functions for these two SW channels are shown in Figure  1. The noise equivalent differential temperature (NEΔT) at 300 K is ≤ 0. 3 K. ASTER L1B is the registered radiance data. AST 05 and AST 08 are the standard emissivity and temperature products, generated using the TES algorithm. The numerical simulation and some field experiments has evaluated the accuracy of AST 05 and AST 08 to be within ±0.
MOD 05 Data The Moderate-resolution Imaging Spectroradiometer (MODIS) is another instrument on Terra satellite. MOD 05 Level 2 is a precipitable water product generated by the NIR algorithm during the day at 1-km spatial resolution and by infrared algorithm both day and night at 5-km resolution. The infrared column water vapor parameter was derived by integrating the moisture profile through the atmospheric column or by SW method. The data quality of MOD 05 is guaranteed by performing comparisons of specific validation sites across as many different climatic and geographic regions as possible. The MOD 05 Level 2 was used as the source of water vapour input for the LST retrieval algorithm.
ASTER Data The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a sensor on NASA Earth Observing System (EOS) Terra satellite. ASTER is capable of creating detailed land information, with four 15-m visible near-infrared (NIR) bands, including stereo, six 30-m shortwave infrared (SWIR) bands and five 90-m TIR bands. The central wavelengths of two TIR bands, which are suitable for SW method, are 10. 657 µm and 11. 318 µm. The spectral response functions for these two SW channels are shown in Figure  1. The noise equivalent differential temperature (NE∆T) at 300 K is ≤ 0. 3 K. ASTER L1B is the registered radiance data. AST 05 and AST 08 are the standard emissivity and temperature products, generated using the TES algorithm. The numerical simulation and some field experiments has evaluated the accuracy of AST 05 and AST 08 to be within ±0.
Atmospheric Profile Data The Thermodynamic Initial Guess Retrieval (TIGR) database has been widely used for development of LST retrieval algorithms  [28]. In this work, we chose the TIGR database TIGR2002_v1. 1 as the atmospheric data to execute the radiative transfer simulation procedure (see Section 2. 5). However, it was found that TIGR2002_v1. 1 is centred at low W values of less than 1. 0 g/cm 2. To obtain robust statistics, it is necessary to select different atmospheric profiles to make W uniformly distributed from dry to moist. Figure  2  shows the selected 126 atmospheric profiles in which the atmospheric temperature (T 0 ) in the lowest layer varies from 232. 25 K to 311. 95 K and W varies from 0. 09 g/cm 2 to 6. 15 g/cm 2 , which represent worldwide atmospheric conditions with a moderate sample number and ensure that there is a nearly uniform distribution for W.
Generation of the Simulation Database The radiative transfer equation (RTE), which is the basis for determining LST, gives the radiance measured from the sensor in channel i for the cloud-free atmosphere: _ _ ( ) ( ) (1 ) i i i i s i atm i i atm i i B T B T R R ε τ ε τ ↑ ↓ = + + - (2) where Bi is the Planck function, εi is the channel emissivity, τi is the total atmospheric transmittance along the target-to-sensor path, R ↑ atm_i is the thermal-path atmospheric upwelling radiance and R ↓ atm_i is the atmospheric downwelling radiance from the entire hemisphere divided by π. To obtain an appropriate simulation database for developing the LST retrieval algorithm for GF-5 TIR data, the atmospheric radiative transfer model  MODTRAN 4 [29]  was used to simulate the spectral atmospheric parameters of spectral transmittance τλ, spectral atmospheric upwelling radiance R ↑ atm_λ and spectral atmospheric downwelling radiance R ↓ atm_λ.
Generation of the Simulation Database The radiative transfer equation (RTE), which is the basis for determining LST, gives the radiance measured from the sensor in channel i for the cloud-free atmosphere: B i (T i ) = ε i B i (T s )τ i + R ↑ atm_i + (1 -ε i )R ↓ atm_i τ i (2) where B i is the Planck function, ε i is the channel emissivity, τ i is the total atmospheric transmittance along the target-to-sensor path, R ↑ atm_i is the thermal-path atmospheric upwelling radiance and R ↓ atm_i is the atmospheric downwelling radiance from the entire hemisphere divided by π. To obtain an appropriate simulation database for developing the LST retrieval algorithm for GF-5 TIR data, the atmospheric radiative transfer model  MODTRAN 4 [29]  was used to simulate the spectral atmospheric parameters of spectral transmittance τ λ , spectral atmospheric upwelling radiance R ↑ atm_λ and spectral atmospheric downwelling radiance R ↓ atm_λ.
Data Processing The satellite imagery used for study area A was recorded on 10 October, 2016 and for study area B recorded on 20 June, 2015. Considering that the developed algorithm is not sensitive to the determination error of W, MOD 05 precipitable water product was resampled to 90-m resolution using the nearest-neighbor interpolation method. After the data quality checking for all data, the geographic coordinate matching and temporal matching between ASTER and MOD 05, the images with 613 × 485 pixels for study area A and 619 × 625 pixels for study area B were used. Considering the difference of the spectral response functions between the GF-5 and ASTER data, the coefficients in Equations (  11 ) and (  12 ), given in Table  6 , were recalculated using the spectral response function of ASTER TIR data, with the RMSE of 0.
Acknowledgments: We thank the anonymous reviewers for their constructive suggestions and comments, from which this paper has greatly benefited. The author  Yuanyuan Chen  would like to thank the  China Scholarship Council  for the financial support of her stay in ICube ( UMR7357 ), France. This work was funded by  National High -resolution Earth Observation Project ( 11-Y20A32-9001-15/17 ) and the  National Natural Science Foundation of China  ( 41501406  and  41231170 ).
Eliminating the Encoding The mechanical ornamentation both creates intermediate hybrid data structures and includes extra abstractions and applications. Fortunately, these additional computations can be avoided, which not only removes sources of inefficiencies, but also helps generate code with fewer indirections that is more similar to hand-written code. We first perform meta-reduction of append 2 , which removes all helper functions: let rec append 3 m n = match (match m with Nil → Z' | Cons (x, m') → S' m') witÂŕh | Z' → n | S' m' → b where b is match S'(append 3 m' n) with | Z' -> Nil | S' r' → Cons ((match m with Cons(x, _) → x), r') (The grayed out branch is inaccessible). Still, append 3 computes two pattern matchings that do not appear in the manually written version append. Interestingly, both of them can be eliminated.
META ML As explained above (ğ3), we elaborate programs into a larger meta-language mML that extends ML with dependent types and separate meta-abstractions and meta-applications. We extend ML in two steps: we first enrich the language with equality constraints in typing judgments, obtaining an intermediate language eML. We then add meta-operations to obtain mML. Our design is carefully crafted so that terms that have an mML typing containing only eML types can be meta-reduced to eML (Theorem 5. 5). Then, in an environment without equalities, they can be simplified into ML terms (ğ5. 3). For space reasons, we omit the definitions that are not essential for understanding the elaboration. We refer the reader to  Williams and Rémy [2017]  for a complete presentation. Notation. We write (Q i ) i ∈I for a tuple (Q 1 ,.
Adding Meta-abstractions The language mML is eML extended with meta-abstractions and meta-applications, with two goals in mind: first, we need to abstract over all the elements that appear in a context so that they can be passed to patches; second, we need a form of stratification so that a well-typed mML term whose type and typing context are in eML can always be reduced to a term that can be typed in eML, i. e. without any meta-operations. The program can still be read and understood as if eML and mML reduction were interleaved, i. e. as if the encoding and decodings of ornaments were called at runtime, but they happen at ornamentation time. The syntax of mML is described in Figure  5. Terms are extended with meta-abstractions and the corresponding meta-applications on types, equalities, and non-expansive terms, while types are extended with meta-abstractions and meta-applications on types and non-expansive terms.
ENCODING ORNAMENTS We now consider how ornaments are described and represented inside the system. This section bridges the gap between mML, a language for meta-programming that does not have any notion of ornament, and the interface presented to the user for ornamentation. We define both the datatype ornaments and the higher-order functional ornaments that can be built from them. As a running example, we reuse the ornament natlist α from natural numbers to lists: type ornament natlist α : nat → list α with Z → Nil | S w → Cons (_, w) when w : natlist α The ornament natlist α defines, for all types α, a relation between values of its base type nat, which we write (natlist α) -, and its lifted type list α, written (natlist α) + : the first clause says that Z is related to Nil; the second clause says that if w -is related to w + , then S w -is related to Cons (v, w + ) for any value v.
Future Work Programming with generalized algebraic datatypes (GADT) requires writing multiple definitions of the same type holding different invariants. GADT definitions that only add constraints could be considered ornaments of regular types, which was one of the main motivations for introducing ornaments in the first place  [Dagand and McBride 2014]. It would then be useful to automatically derive, whenever possible, copies of the functions on the original type that preserve the new invariants. Extending our results to the case of GADTs is certainly useful but still challenging future work. Besides issues with type inference, GADTs also make the analysis of dead branches more difficult. The meta-language mML must fulfill two conflicting goals: be sufficiently expressive to make the generic lifting well-typed, but also restrictive enough so that elaborated programs can be reduced and simplified back to ML.
Hence, many extensions of ML will require changing the language eML as well, and it is not certain that the balance will be preserved. The languages eML and mML have only been used as intermediate languages and are not exposed to the programmer. We wonder whether they would have other useful applications for other program transformations or for providing the user with some meta-programming capabilities. For example, one might consider exposing mML to the user to let her write generic patches that could be instantiated as needed at many program points. Ornaments can be composed in multiple ways. Applying the effects of two ornaments consecutively (lifting one type to another one, and lifting the lifted type again to a third type) can be done by re-lifting an already lifted definition. An interesting direction is to combine the information added by two ornaments into a datatype representing the base type and both ornamentations  [Dagand and McBride 2013; Ko and Gibbons 2013].
They allow several interchangeable representations for the same data, using isomorphism to switch between views, but, again, the isomorphisms persist at runtime. Lenses [Foster et al. 2007 ] also focus on switching representations at runtime. Our mML language is equipped with rudimentary meta-programming facilities, by separating a meta-abstraction from the ML abstraction. Its main distinguishing features from usual approaches to meta-programming in ML (such as  Kiselyov [2014] ) is its ability to embed eML and transform equalities, allowing simplification of partial pattern matchings. Ornamentation is a form of code refactoring on which there is a lot of literature, but based on quite different techniques and rarely supported by a formal treatment. It has however not been much explored in the context of ML-like languages.
We estimated that the mean value of the EC/OC ratio in BB aerosol observed in summer 2012 was 0. 036 (±0. 009), which is less than the corresponding value (0. 061) predicted in our simulations with a chemistry transport model using the emission factors from the Global Fire Emissions Database 4 (GFED4) fire emission inventory. Based on results of our analysis, we propose a parameterization that allows constraining the EC/OC ratio in BB aerosol with available satellite observations of the absorption and extinction aerosol optical depths.
For consistency, the former term (EC) is mostly used throughout this paper, unless otherwise necessary in a given context). The partitioning between EC and OC in models simulating BB aerosol composition is usually based on data of fire emission inventories, such as, e. g. , the Global Fire Emissions Database (GFED)  [22] , the Fire INventory from NCAR (FINN)  [23]  or the Global Fire Assimilation System (GFAS)  [24] , which employ available in situ measurements of the emission factors to calculate emissions of individual species on regional and global scales. However, due to variations in combustion conditions and in fuel origin, moisture and geometry, different measurements yield a wide range of emission factor values, even when they are made in similar climate zones and types of environment  [25].