dataesr/scientific-paragraphs-categorization huggingface dataset:
Weissfluhjoch site The Weissfluhjoch site (WFJ) (46. 82°N, 9. 80°E) is located at an altitude of 2540 m a. s. l. in the Swiss Alp, near Davos, Switzerland (WSL Institute for Snow and Avalanche Research SLF, 2015b). This snow experimental site is placed in an almost flat area of a south-easterly oriented slope. At WFJ the snow season generally starts in October/November and lasts until June/July  (Weveret al. , 2015). An automated weather station provides a comprehensive multiyear dataset including measurements of air temperature and relative humidity, wind speed and direction, incoming and outgoing short-and longwave radiation, snow/ice surface temperature, temperature of soil at its interface with the snowpack, snow depth and precipitation  (Schmucki et al. , 2014). Snow temperatures are measured at 50, 100 and 150 cm above the ground surface.
From September 2013 onwards, soil temperatures are measured at 50-, 30-and 10-cm depth. Additionally, snowpack runoff data are supplied by a snow lysimeter. The rain gauge and snow lysimeter measure at an interval of 10 min, whereas most other measurements are done at 30-min intervals. Lastly, an upward-looking ground-penetrating radar provides 30-min data on liquid water percolation allowing to monitor the progress of the meltwater front  (Schmid et al. , 2014). Every two weeks, generally in early and in the middle of each month depending on weather conditions, a manual full-depth snow profile is performed in order to provide measurements of snow temperature and snow density (WSL Institute for Snow and Avalanche Research SLF, 2015a). Snow density and SWE are also manually measured through snow cores.
Following the methodology proposed by  Magnusson et al. (2017) , the random perturbations are provided through a first-order autoregressive model in order to guarantee a physical consistency and a temporal correlation of the time-variant forcings. Perturbations are generated considering the error statistics evaluated at the CDP site (Table  1 )  (Magnusson et al. , 2017) , which result from the comparison between SAFRAN reanalysis data  (Vidal et al. , 2010)  and the observations supplied by the French station  (Charrois et al. , 2016). The main limitation of this procedure is the lack of correlations among the perturbed forcing variables, which does not ensure their physical consistency  (Charrois et al. , 2016). It is also noteworthy that the same error statistics specifically derived from the observations supplied by the CDP station are used for the generation of the meteorological ensembles at all the snow experimental sites.
Snow data The multivariate DA scheme has been designed to consistently update the system state by jointly assimilating ground-based observations of surface temperature, albedo, snow depth, SWE and snow density. Table  2  lists the datasets of the experimental sites. Automatic in-situ measurements of surface temperature, albedo, and snow depth are supplied on an hourly or sub-hourly basis by all the selected stations throughout the whole datasets. Even though direct SWE measurements are generally widely lacking, the snow experimental sites are one of the main sources of consistent measures of this variable. Daily automatic measures are provided at CDP since the winter season 2001/02. With a lower measurement frequency, at WFJ SWE observations are available every two weeks over the whole dataset period.
Unlike these two sites, the TGN station supplies in-situ 6-hrs automatic SWE measurements during the snow seasons 2013/14 and 2015/16. In order to be able to properly use these measures within the analysis of simulations on seasonal/annual scale, the raw observational data have been smoothed from possible inconsistent oscillations and anomalies (e. g. rain-on-snow events) through their daily average (Table  3 ). Although an exhaustive knowledge of snow density is needed to properly define the snowpack state and its dynamics, direct continuous observations of snow density are generally lacking. An exception is the TGN site, where biweekly manual measurements provide useful information throughout the whole dataset period. However, thanks to the relation among snow depth, SWE and snow density  (Jonas et al.
Evaluation metrics The results of this study are shown and discussed in terms of SWE, snow depth and surface temperature. The SWE is one of the most relevant snow-related quantities from a hydrological point of view, since its accuracy in estimate strongly impacts discharge simulations  (Zappa et al. , 2003). Along with this variable, the assessment of snow depth simulations allows to indirectly evaluate the model dynamics of snow density  (Jonas et al. , 2009). Furthermore, the impact of the filter updating on the system energy balance is analyzed by considering the simulations of the surface temperature. In order to assess the filter performance, each analysis is evaluated through the following statistical metrics by considering the whole datasets of measurements (snowless periods are neglected): Correlation coefficient: 𝑅 = 𝑐𝑜𝑣 𝑂𝑏𝑠 ,𝐸𝑥𝑝 𝜎 𝑜𝑏𝑠 •𝜎 𝑒𝑥𝑝 (11) Root Mean Square Error: 𝑅𝑀𝑆𝐸 = 1 𝑁 𝑂𝑏𝑠 𝑘 -𝐸𝑥𝑝 𝑘 2 𝑁 𝑘=1 (12) Normalized Error Reduction  (Chen et al.
Sensitivity of parameters resampling to the SWE measurement frequency The Italian case study provides evidence of the impact of the difference in SWE measurement frequency on the parameters resampling. In order to further investigate the system sensitivity, an experiment is performed at the CDP station with the aim of assessing how the reduction from daily to biweekly SWE observed data affects the 24-hours multivariate DA simulations. Obviously, a reduction in measurement frequency is expected to reduce the impact of the filter updating on the model simulations. However, when dealing with a multivariate DA scheme, the imbalance among the measurement frequency of the assimilated variables can lead to a further side-effect hindering the parameters estimate due to the largest impact of the more frequently observed variables (e.
Acknowledgements. The research has been conducted in the framework of the collaboration between the  Hydrosystems and Bioprocesses Research Unit (HBAN) of the National Research Institute of Science and Technology for Environment and agriculture (IRSTEA)  and  CIMA Research Foundation. We wish to thank both institutions for supporting and promoting these research activities. The authors would like to acknowledge  Météo-France , the  WSL Institute , and the  Environmental Protection Agency of Aosta Valley  for providing the datasets used in this study. We are grateful to all the staff working at  Col de Porte , The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-286Manuscript under review for journal The Cryosphere Discussion started: 11 January 2018 c Author(s) 2018. CC BY 4. 0 License.
Data availability The snow and meteorological data from Col de Porte site are made freely available by Météo-France both on the PANGAEA depository (doi:10. 1594/PANGAEA. 774249) and on the public ftp server ftp://ftp-cnrm. meteo. fr/pub-cencdp/. The Weissfluhjoch dataset provided by the WSL Institute for Snow and Avalanche Research SLF can be obtained via IDAWEB (https://gate. meteoswiss. ch/idaweb) as well as from the Environmental Data Portal ENVIDAT (envidat. ch/dataset/10-16904-1). The dataset of Torgnon site is available on the European Fluxes Database (www. europe-fluxdata. eu/).
The data from these tested are then collated, analysed and presented in this paper. The novelty of this paper is that it provides a bespoke set of data which show the dispersion in the viscoelastic properties of the same porous media measured with different methods and in different laboratories around the world. This paper is organised as follows: section II outlines the methodology; section III presents the results from individual laboratories and inter-laboratory data. Concluding remarks are made in section IV.
A detailed description of the measurement technique is given in ref. [5]. In this project all the participating laboratories measured the complex Young's modulus for all the materials. Not all of the 14 laboratories had the equipment and expertise to measure the Poisson's ratio of porous media. In fact, measurement of the Poisson's ratio of porous media remains a challenging and the quality of the data obtained from these tests is often controversial (e. g. ref. [4]). Therefore, the Poisson's ratio data only came out of those laboratories who had confidence in their data and techniques used to obtain them. Table  II  provides a list of the laboratories who measured the Poisson's ratio. Tables  III  and IV  give a summary of the measurement setups and procedures used in the reported experiments.
, 2010). Thus, genome association studies that leverage automated imaging analysis methodologies can be highly effective. With quantitative image analysis now routinely applied to digital pathology datasets  (Beck et al. , 2011; Khan et al. , 2017; Popovici et al. , 2016; Yu et al. , 2016; Yuan et al. , 2012) , it is clear that unbiased, continuous image features can readily be extracted for histopathological GWAS variant discovery. Nevertheless, little attention has been paid to the use of cellular imaging to obtain quantitative traits for understanding the connection between genotype and cellular phenotypes. Part of the reason for this may be that there are few available datasets for which both genomic profiling and standardized histopathology data are available for any appreciable number of samples.
Quantitative Image Analysis Image data were downloaded from the GTEx histological image archive and convolved with a Gaussian filter to smooth out pixel-level variation on a length scale smaller than that of observed regions of leukocyte invasion in HT samples. Color deconvolution to estimate the H&E channels directly was not performed to avoid the risk of introducing any biases from the deconvolution process that might negatively affect downstream QTL fits. Individual tissue pieces were segmented using adaptive thresholding (Figure  1A ), and 117 Haralick image features were extracted for each tissue piece using the Bioconductor image processing package EBImage  (Pau et al. , 2010) (Transparent Methods). To capture patterns across multiple length scales, we incorporated three Haralick scales that sampled every 1, 10, or 100 pixels.
Haralick features are widely used for the quantification of image texture, and correlations between neighboring pixels in an image are taken into account  (Haralick, 1979). Given that lymphocyte invasion has a profound effect on histological image texture, we reasoned that Haralick features could be ideal candidates for capturing HT cellular phenotype. After removing overly small tissue pieces, averaged image feature values were calculated across pieces to give a single value for each sample and feature. In preparation for downstream model fitting, features were log 2 transformed and centered and rescaled using a Z score to ensure feature comparability across samples (Figure  1B ). The mean and SD values used for the Z score were saved for use in later analyses. A fully worked through image analysis example with the applied segmentation parameters is supplied as part of a supplemental R data package (https://github.
Integrated RNA-Seq and Image Analysis: Imaging Biomarker Validation Next, we analyzed GTEx gene expression data associated with the 341 thyroid images. After the removal of lowly expressed genes, counts were log 2 transformed and quantile normalized to ensure comparability of samples (Transparent Methods). To test for associations between gene expression and image PCs, linear models were used to fit each image PC against normalized gene readouts while correcting for age, sex, sample collection site, tissue autolysis score, and RNA extraction type. To remove bias caused by unknown confounders (Figure  S3A ) we fitted 20 PEER (Probabilistic Estimation of Expression Residuals) factors  (Stegle et al. , 2012)  to the gene expression data while including the known confounders in the PEER fit; a similar approach was used by the GTEx consortium  (Aguet et al.
iQTL Identification of Variants Associated with Immune Infiltration Based on our analysis of image features and gene expression, we decided to use image PC2 as a quantitative trait capturing the cellular features of immune cell infiltration in an iQTL analysis. Since we did not have sufficient sample size to achieve high power in a genome-wide iQTL analysis that would include millions of variants, and other suitably large datasets were not available, we decided to reduce the search space by asking a more targeted question of the data.
Validation of iQTL Results Using an Independent Dataset To attempt validation of the observed associations between HDAC9 and TXNDC5 genotype, and thyroid immune cell infiltration in an independent dataset, we obtained genotype and phenotype data and clinical metadata from the Electronic Medical Records and Genomics (eMERGE) network  (Gottesman et al. , 2013) (Transparent Methods). This dataset has been used previously for hypothyroidism variant discovery  (Denny et al. , 2011). Criteria for defining HT cases included the assessment of thyroid-stimulating hormone (TSH) and free T4 (FT4) levels (Transparent Methods). Although levels of biochemical markers are not necessarily correlated with morphological grading of HT, immune cell infiltration is pathognomonic of the disease  (Anila et al. , 2016).
We used logistic regression to test for associations between hypothyroidism status and HDAC9 and TXNDC5 genotype while correcting for patient age (decade of birth), sex, collection site, and ancestry (Transparent Methods). For both genes, the location of the most significant GTEx iQTL peaks, as determined by their IHW-adjusted p values, coincided closely with the highest eMERGE peaks  (Figures 3C and 3D vertical lines). Although a colocalization analysis did not identify a single causative SNP common to both datasets, we noted a high degree of similarity between the association profiles. To quantitatively assess the concordance of the association signatures between the GTEx and eMERGE data, we first selected the SNPs common to both datasets and grouped neighboring SNPs into bins of size 20 SNPs based on their genomic coordinates.
The lowest p value in each bin was then selected for both datasets. A correlation test revealed that the association signatures between eMERGE and GTEx data were significantly concordant across both HDAC9 (Figure  S7A1 ; Spearman r = 0. 59, P < 2. 9 3 10 À6 ) and TXNDC5 (Figure  S7A2 ; Spearman r = 0. 73, P = 9 3 10 À4 ). Next, we checked if linkage disequilibrium (LD) could be artificially driving the observed concordance. SNPs were pruned for LD using PLINK (Transparent Methods), and the binning and correlation analysis was repeated. To account for the reduction in the number of SNPs (HDAC9, 1,071 to 330; TXNDC5, 347 to 88), smaller bin sizes of six and five SNPs were used for HDAC9 and TXNDC5, respectively. A correlation test revealed a more modest but still significant correlation for HDAC9 (Spearman r = 0.
30, P = 0. 026) and a significant correlation for TXNDC5 (Spearman r = 0. 72, P = 0. 001). Thus, HDAC9 concordance was partially biased by LD, whereas TXNDC5 concordance was unaffected. To verify that it was hypothyroidism status driving the concordance, we performed 1,000 permutations of the hypothyroidism case/control labels in the eMERGE dataset and repeated the logistic regression, binning, and correlation analyses between the LD-pruned GTEx and eMERGE data. The resulting permutation p value did not reach significance for HDAC9 (P = 0. 127), likely due to the lower nominal p values remaining after the LD pruning. However, the TXNDC5 permutation value was significant (P = 0. 006) and demonstrated that it was unlikely to obtain the observed TXNDC5 concordance between eMERGE and GTEx profiles by chance (Figures  S7B1  and S7B2 ).
Outlook Our intuition, along with over a decade of progress in GWAS, tells us that genotype and phenotype are associated in ways that can be quantitatively assessed. The growth of integrated data resources such as GTEx, and the advancement of digital slide imaging technologies, provides the opportunity to explore that association at the cellular level. This study demonstrates the potential of histopathological imagebased QTL profiling for de novo discovery of disease variants, especially when unbiased, continuous, automatically extracted image features are used as quantitative traits. Our results indicate that iQTLs can identify different variants to cases vs controls GWAS, offering a complementary approach that leverages cell-level phenotypes for target discovery. Although we applied iQTLs to a thyroid dataset, the method is completely generalizable to other tissues and disease contexts.
ACKNOWLEDGMENTS This work was supported by grants from the  US National Institutes of Health , including  5U01CA190234  and  5U24CA194354  (J. D. B. , H. J. W. L. A. , J. Q. ),  5R01AI099204  (J. P. , J. Q. ),  5R01HL111759  (J. Q. , J. N. P. ), and  5P50CA127003  (J. Q. ). This work was conducted under dbGaP-approved protocols # 9112  and # 13896. The dsDNA and human schematics in the Graphical Abstract were obtained from the  Library of Science and Medical Illustrations  under the Creative Common License  CC BY-NC-SA 4. 0.
Soil and slag samples The agricultural soil (AS) used in this study was collected from a site in Italy where agricultural activities have been carried out. The soil was homogenized, air dried and sieved to 2 mm. Granulated slags (GS) used in this study are by-products derived from metallurgical exploitation, in particular copper production process. The slag is composed of amorphous glass characterized by the frequent occurrence of intermetallic droplets, which contain more than 90% of Cu and minor elements like Pb and Zn. Full structural characterization of GS was performed by  Potysz et al. (2016b)  and a summary can be found in supplementary data (Fig. S1 , Tables  S1  and S2 ). Physico-chemical characterization of AS and the mix AS þ GS 50% wt. was performed by SADEF (France), which included the following determinations: pH (electrometric procedure in water, according to NF ISO 10390), cation exchange capacity (measurement of cations displaced by ammonium acetate at pH 7, according to NF ISO 10390), organic carbon (sulfochromic oxidation, according to NF ISO 14235), organic matter content calculation, total N (dry combustion, according to NF ISO 13878), P 2 O 5 (Joret-Hebert method for the determination of soluble phosphorus in ammonium oxalate, according to NF X 31-16), K 2 O, MgO and CaO (agitation method for the determination of ammonium acetate extractable cations, according to NF X 31-108), Fe, Mn and B (DTPA extraction for exchangeable metal determination, according to NF X 31-12), particle size determination (sedimentation-pipette method, according to NF X 31-107), texture (soil texture calculator from the United States Department of Agriculture), metals (total Cd, Cr, Cu, Ni, Pb and Zn solubilized by HF attack, according to NF X 31-147 and quantification by ICP-MS; Hg by internal method MA7-82 v5).
Pot experiment Sunflower seeds (12) were sown in plastic pots (15 cm diameter) filled with 800 g of substrate. The experimental design comprised two conditions: (a) AS as control and (b) mixture of 50% wt. GS and 50% wt. AS (AS þ GS 50% wt. ). To limit the level of heavy metals present in GS and test plant tolerance, in a previous experiment GS were mixed in different proportions (from 5 to 50% wt. ) with AS. Sunflower was able to grow and performed well in the presence of GS up to a level of 50% wt. (data not shown). As a result, this proportion was chosen for the present study. Pots were placed in a growth chamber (Sanyo Versatile Plants were harvested after 90 d of growth. Plants were removed from pots and biometric parameters such as plant height, root length, number of leaves and number of flowers per plant were recorded.
Community level physiological profiling (CLPP) Microbial metabolic diversity of bulk and rhizosphere soil samples was assessed by analysing the utilization pattern of 31 carbon sources in Biolog EcoPlates™ (Biolog, Inc. , Hayward, CA, USA)  (Campbell et al. , 1997; Garland and Mills, 1991). Briefly, soil samples (1 g) were shaken at 80 rpm in 100 mL of sterile 0. 85% NaCl solution for 2 h at 28 C, 140 ml of soil suspension were inoculated in Biolog EcoPlates™ and incubated at 28 C for one week. Absorbance of formazan was determined at 590 nm and recorded daily over one week (Biotek Power Wave XS). Microbial activity in each microplate was expressed as the average well color development (AWCD) as described by  Garland (1996)  and  Hitzl et al. (1997). Further methodological details can be obtained from supplementary materials and the evolution of AWCD over the incubation period can be found in supplementary data (Fig.
An ultraviolet (UV) transilluminator (Universal Hood III, Bio-Rad, USA) was used for gel visualization and image acquisition. DGGE was performed in DcodeTM universal mutation detection system (Bio-Rad, USA). PCR products were loaded in a 8% (w/v %) acrylamide:bis-acrylamide (37. 5:1) gel containing a vertical linear denaturing gradient (40e70%) of denaturant solution (100% denaturant corresponds to 7 M urea and 40% (v/v) formamide. Electrophoresis (60 V, 32 mA) was carried out for 16 h at 60 C using 1Â TAE as running buffer. The gel was then stained for 20 min in ethidium bromide solution (0. 75 mg L À1 in 0. 5 Â TAE buffer) and gel images were acquired on a UV transilluminator. For gel images analysis and construction of single linkage phylogenetic tree, PyElph 1. 4 software was used  (Pavel and Vasile, 2012).
Acknowledgements The  French Île-de-France region  and the  European TECO Project  (grant agreement N  0001860 ) financially supported this work. The authors thank  Master student Fatchima Moudi  from the  Universit e Paris-Est Marne-la-Vall ee  for assistance with the management of pot experiment. A. C. Agnello is particularly grateful for the technical support of  Division of Plant-Microbial Interactions CSIR-National Botanical Research Institute, Lucknow, U. P. , India. A. Potysz thanks  National Science Centre in Poland  (grant agreement  UMO-2016/20/S/ST10/00545 ). The authors also thank  Carla Di Benedetto  for the graphical abstract polishing.
In vivo studies in NSCLC mice showed that afatinib penetrated the BBB and cerebrospinal fluid (CSF) levels correlated with plasma levels  (35). In a compassionate-use program including 31 patients, afatinib demonstrated a 35% CNS response in molecularly non-selected patients who had previously failed TKI therapy, with a median time to treatment failure of 3. 6 months  (36). In a combined dataset post hoc analysis in 81 EGFR-mutant NSCLC patients with BM (30% had received brain radiotherapy) in the first-line LUX-Lung 3 and LUX-Lung 6 phase III clinical trials, afatinib significantly improved PFS (8. 2 vs. 5. 4 months, hazard ratio (HR) 0. 50, p = 0. 0297) and RR (21 vs. 5%, p = 0. 0027), although without a significant difference in OS  (22. 4  vs. 25. 0 months; HR 1. 14, p = 0. 64) compared with platinum-based chemotherapy  (37).
Combined eGFR TKis and Antiangiogenics Activity and an acceptable safety profile of bevacizumab, an anti-VEGF monoclonal antibody, have been reported in NSCLC patients with asymptomatic and untreated BM  (59). Moreover, in a preclinical model of lung adenocarcinoma, bevacizumab prevented BM formation  (60). In a phase II trial in 154 Asian EGFRmutant NSCLC patients, the addition of bevacizumab to erlotinib as first-line treatment significantly improved PFS compared to erlotinib alone (16. 0 vs. 9. 7 months, HR 0. 54; 95% CI: 0. 36-0. 79, p = 0. 0015)  (61) , leading to EMA approval of the combination in this population in April 2016. Two ongoing phase III trials evaluating erlotinib combined with ramucirumab (NCT02411448) or bevacizumab (BEVERLY study, NCT02633189) compared with erlotinib, will hopefully further validate this strategy.