dataesr/scientific-paragraphs-categorization huggingface dataset:
MODIS for Atmosphere Products. The dust outbreak states that are characterized by the AOD and Angstrom exponent (AE) values were used in this study and were collected by the MODIS platform aboard NASA's Earth Observing System-(EOS-) Terra and Aqua polar orbiter satellites. Aqua was launched on May 4, 2002, into a sunsynchronous orbit with an ascending orbit at 1330 LT and a descending orbit at 0130 LT. The Naval Research Laboratory (NRL) receives the global digital data from Aqua in near-real time. The MODIS data are distributed by NASA's Goddard Earth Sciences Data and Information Services Centre (GES DISC) and are provided not only for the atmosphere but also for the land, cryosphere, and oceans  [71, 72]. The MODIS sensor has 36 narrow spectral bands and measures in the 0. 4-14. 4 m range.
Specific channels, including visible and infrared bands (e. g. , bands 1, 2, 3, 4, 26, 31, and 32), have been effectively used to detect mineral particles  [30, 32, 34]. In particular, deep blue products (deep blue uses information from blue channels such as bands 3 and 8) are generally used over bright land surfaces to detect mineral dust. Deep blue was developed because the standard MODIS aerosol LAND measurements could not be used to retrieve data over bright surfaces, such as desert areas. For more details on the deep blue algorithm, refer to Hsu et al. [33]. In this study, the MYD04 L2 Aqua deep blue AOD (hereafter, MODIS-AOD) and AE datasets (550 nm) were employed because these datasets have already been extensively validated and have been successfully utilized in several studies  [10, 25, 29, 30, 73, 74].
GLDAS for Land Surface Products. GLDAS is a result of datasets assimilated through cooperation of the National Centres for Environmental Prediction (NCEP), NASA, the National Oceanic and Atmosphere Administration (NOAA), and the Goddard Space Flight Centre (GSFC). GLDAS is a global-scale assimilation and modeling system that has been developed using several satellite-and ground-based datasets to provide the best estimation of the land surface conditions in near-real time  [75] [76] [77]. The analyses were based on the GLDAS/Noah products because these provide various land surface products with a high spatial resolution (0. 25 ∘ ) for every 3-hour period. In this study, WS (m/s, height of 10. 0 m), soil temperature (K, depth of 0-10. 0 cm), and precipitation (mm/3 h) products were also obtained from GLDAS.
Methodology The SMOS, AMSR2, and MODIS overpass datasets are available at local times, whereas the GLDAS/Noah products are based on the Universal Time Coordinate (UTC) system. It is of utmost importance that the UTC time of GLDAS products must match the satellite local overpass time before they are used for dust outbreak prediction. The SMOS overpass time (ascending, 0600 LT) precedes the MODIS and AMSR2 overpass time (ascending, 1330 LT). AOD from MODIS, which detects the dust outbreak state, is assumed as the reference dataset in this study, whereas SMOS and AMSR2 which retrieved SM are used alongside the GLDAS wind speed product to obtain the modeled AOD. The SMOS overpasses the study areas from 2100 to 0100 UTC (0600 LT, ascending), and MODIS overpasses the study areas from 0430 to 0830 UTC (1330 LT, ascending).
Developing Modeled AOD Products from Satellites-Based SM and GLDAS Dataset. In this study, due to the shorter period of data availability, we assumed that SMOS-based and AMSR2-based SM retrievals have a negative relationship with AOD, as previously proposed by Kim and Choi  [24]  (Figure  3 ). Since AMSR2 and SMOS datasets are only available every four years and seven years, respectively, deriving the same number of equations as in the study by Kim and Choi  [24]  was not possible. Their study utilized an 11year dataset (containing 27. 261 million data points), which provided sufficient information to make feasible assumptions regarding the derived equations  [24]. The WS magnitude is divided into 10 groups (3 to 12 m/s of WS) at 1 m/s intervals, as in Kim and Choi  [24]. Alternatively, WS lower than 4 m/s were classified as 3 m/s, and WS higher than 12 m/s were classified as 12 m/s.
Furthermore, the AOD value may be almost unaffected by the SM dynamics in a weaker WS or in damped conditions (about 16% of the SM). Moreover, under a stronger WS (higher than 6. 0 m/s) and drier land surface conditions (less than about 16% of the volumetric SM), SM plays an important role in dust outbreak processes. Considering the equation in Figure  3 , the AOD values could be estimated using the following equation with different constants for  1 and  2 that are contingent on the magnitude of WS: Modeled AOD 𝑖 = 𝐶 1 × exp (-𝐶 2 × SM 𝑖 ). (1) From (1), we can establish an MA by using SM (%) and WS (m/s) datasets. In this study, we considered four different MAs: the SMOS SM and GLDAS WS-based MA (hereafter, SMOS-MA), the AMSR2 SM and GLDAS WS-based MA (hereafter, AMSR2-MA), the MA based on GLDAS dataset at 0600 (hereafter, GLDAS-MA 6), and the MA based on GLDAS dataset at 1200 (hereafter, GLDAS MA 12).
Soil Moisture Composition and Moving Average of MA Products. We used five different composite SM products (composited day: one-to five-day) to investigate the impact of antecedent SM on dust outbreaks. We composited SMOS, AMSR2, and GLDAS SM using following equation: composited SM doy = ∑ doy 𝑖=doy-comp day SM 𝑖 comp day , ( 2 ) where doy is day of year. To calculate the time series of the MA dataset of the desert regions, we consolidated the daily available MA data Advances in Meteorology over the desert regions and averaged them by using following equation: average MA doy = ∑ 𝑁 𝑖=1 MA 𝑖,doy 𝑁 , (3) where  is the number of available MA data over the desert pixels at doy and doy is day of year. We set the composited day from one to five days. Moreover, the 11-day moving averaged MA was calculated as follows: moving averaged MA doy = ∑ doy+5 𝑖=doy-5 MA 𝑖 11.
Validation of MAs with AOD Observations. Validation of the MAs with an assumed reference dataset from the MODIS-AOD was performed to check the reliability of the models for future prediction. Figure  5  shows the temporal pattern of the MODIS-AOD and different MAs in the desert regions of East Asia for 2015. A temporal comparison between the SMOS-MA and MODIS-AOD shows a good correlation (-value: 0. 56), but mostly higher than MODIS-AOD (Figure  5(a) ). In the case of the AMSR-MA, a low correlation coefficient indicates that the temporal pattern of the MA dataset does not follow the reference AOD well, as compared to the SMOS-MA, and has an overall negative bias demonstrating that it usually underestimates the AOD with respect to the MODIS-AOD (Figure  5(b) ). When the MA is based on the GLDAS SM product matched with the SMOS (i.
Summary and Conclusions In this study, SMOS, AMSR2, and GLDAS/Noah SM products in combination with GLDAS WS product were used to predict the AOD in 2015 over East Asian desert areas. SMOS-MA better estimated the AOD compared to the AMSR2-MA and GLDAS-MA in desert regions with highly polluted atmospheric conditions. The aerosol products, observed from the MODIS sensor, were used as a reference dataset to validate the MA. The validation results showed that the SMOS-MA captured the temporal patterns well, with a -value 0. 56; however, it overestimated the reference AOD. A shortterm trend analysis was conducted by calculating the 11-day moving average of MAs, the result of which showed that the SMOS-MA dataset demonstrated superior performance in capturing the short-term trends when compared to other MAs ( = 0.
65). In addition, the results from investigating the impact of the antecedent SM on dust outbreaks showed that -values have not changed much with respect to different antecedent SM conditions. In this regard, we assume that the antecedent SM condition could not be the major factor that governs the timing of dust outbreaks. This is supported by the fact that bare desert regions have persistently very low SM conditions so that the WS, which fluctuates relatively more severely than SM (Figures  4(b ) and 4(d)) over desert regions, would play a more pivotal role in triggering dust outbreaks. Moreover, spatial maps in different seasons also demonstrated that the SMOS-MA showed better consistency with the reference dataset compared to other MAs. The findings of this study reveal the application of microwave-based SM retrievals for near-real time dust outbreak predictions and short-term dust outbreak trend analysis.
Limitations and Advantages of MA Products. Figures 7(a), 7(b), and 7(c) depict exemplary maps of the SMOS-MA, masked MODIS-AOD dataset (i. e. , eliminating AOD values when AE > 0. 35), and original AOD measurements from MODIS-AOD (without AE masking) for investigating a feature of MA products. Box (a)-1 predicts AOD to be between 0. 3 and 2. 5 in the box area; however, box (b)-1 and box (c)-1 do not show any aerosol observations. There are two feasible reasons that might support this mismatch.
by MODIS because the SMOS-MA in box (a)-1 was predicted 7. 5 h before MODIS overpassed those areas. This is supported by the fact that particles in box (c)-1 were mostly masked out, as shown in box (b)-1, because they had high AE values, which means that most of the aerosols in box (c)-1 would have been susceptible to being blown away. Boxes (a)-2, (b)-2, and (c)-2 indicate the limitation of MA datasets because Advances in Meteorology Observed AOD from MODIS versus AOD from SMOS-.
Acknowledgments This work was supported by the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIP)  ( NRF-2016R1A2B4008312 ). This research was supported by  Space Core Technology Development Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science, ICT, and Future Planning  ( NRF-2014M1A3A3A02034789 ). The SMOS data were provided by the  Centre Aval de Traitement des Données SMOS (CNES-IFREMER ) and through the  ESA-Supported SMOS Validation and Retrieval Team program. The aerosol optical depth data that were used in this study were obtained from https://ladsweb. modaps. eosdis. nasa. gov/data/. The Global Land Data Assimilation System (GLDAS) data used in this study were acquired as part of the mission of  NASA 's Earth Division; these data are archived and distributed by the  Goddard Earth Sciences (GES) Data and Information Services Centre (DISC)  (https://disc.
4. 3. 3 Limitations and Advantages of MA Products. Figures 7(a), 7(b), and 7(c) depict exemplary maps of the SMOS-MA, masked MODIS-AOD dataset (i. e. , eliminating AOD values when AE > 0. 35), and original AOD measurements from MODIS-AOD (without AE masking) for investigating a feature of MA products. Box (a)-1 predicts AOD to be between 0. 3 and 2. 5 in the box area; however, box (b)-1 and box (c)-1 do not show any aerosol observations. There are two feasible reasons that might support this mismatch.
Study population Fresh-frozen tumor-biopsy specimens were obtained from 159 FL patients included in the open-label, international, multicenter randomized PRIMA study, which enrolled 1135 patients with untreated high-tumor burden FL (NCT00140582). 23 Tumor biopsies were obtained at FL diagnosis and stored at the bio-bank Plateforme de Ressources Biologiques, Henri Mondor Hospital, Créteil (bio-bank ID number: BB-0033-00021). Moreover, formalin-fixed paraffin-embedded (FFPE) biopsy specimens were also available for tissue micro-array (TMA) construction for 116 patients of this cohort. All tissues used for this study came from pre-treatment diagnostic biopsies and were confirmed as FL grade 1-3a 1 by expert hematopathologists. This study was conducted in accordance with the Declaration of Helsinki and approved by the Comité consultatif de protection des personnes se prêtant à la recherche bio-médicale (Hospices Civils de Lyon).
Genome alignment and variant calling were performed using TopHat, 24 GATK Haplotype Caller  25  and Picard (http://broadinstitute. github. io/picard/) software programs. Expression data were assessed using the HTseq library. 26 Count data normalization was performed using the DESeq2 package  27  and transformed using the rlogTransformation function in R (version 3. 2. 2, https://www. r-project. org/). EZH2 mutation screening by genomic PCR and Sanger sequencing DNA was extracted using the High Pure PCR Template Preparation kit (Roche, Basel, Switzerland) according to the manufacturer's instructions. The mutation statuses of EZH2 exons 16 and 18 were screened in all samples with available DNA (n = 156). The EZH2 mutated residues were numbered according to the transcript NM_004456. 4 in the NCBI nucleotide database.
Single-nucleotide polymorphism arrays and copy-number variation analysis Copy-number abnormalities were assessed in the 156 DNA samples using a high-resolution Cytoscan HD single-nucleotide polymorphism array (Affymetrix, Santa Clara, CA, USA). The data were first pre-processed using the Affymetrix apt-chp-to-txt. exe utility to compute summarized probeset values. Signal intensity values (log R ratio and allelic difference) from single-nucleotide polymorphism (SNP) probesets were then extracted and given as input to GAP  28  to perform joint segmentation and calling of log R ratio and allelic difference. GAP output is an absolute copy-number call that takes into account the inferred ploidy of the tumor and the allelic imbalances. These absolute copy-number values were then transformed into a normalized copy-number deviation value that takes into account the patient-specific 'neutral' state of each tumor and sex specificity of the X chromosome using the following formula: CNdev = ((CN/medianCN) × 2)-NormalState, where medianCN is the ploidy of the tumor calculated as its median over all probesets CN call, and NormalState = 2, except for the chromosome X-specific region in males, where NormalState = 1.
Statistical analyses Correlation with clinical data and outcome. The correlations between EZH2 mutational or copy-number status and initial characteristics or treatment group were assessed using Fisher's exact test. Expression levels between groups were compared using the Mann-Whitney test. Correlation between variant allele frequencies (VAF) and H3K27me3/me2 score has been assessed using a Spearman test. Time-to-event parameters (progression-free survival (PFS) and overall survival) were estimated by the Kaplan-Meier product-limit method and compared using a log-rank test. All tests are two-sided. Analysis of over-representation of the expression gene set between groups of patients. Patients were separated into two groups based on alteration status and a transcriptome-wide differential expression was performed using XLSTAT software.
We controlled for false positives using a false discovery rate (FDR) threshold of 5%. 29 Then, the resulting lists of upregulated and downregulated genes in each patient subgroup (carrying a mutation or a gain) were separately assayed to test for enrichment in collections of gene sets from the Molecular Signatures Database v5. 2 (MSigDB). 30 The overlap with MSigDB gene sets was tested using hypergeometric distribution corrected for multiple hypotheses testing with an FDR threshold of 5% as previously described (http://software. broadinstitute. org/gsea/msigdb/annotate. jsp).
37 This should also be assessed in other patient cohorts to rule out a potential sampling bias. Three EZH2 inhibitors are currently being tested in phase 1 and 2 clinical trials in patients with and without EZH2 mutations (NCT01897571, NCT02395601 and NCT02082977); however, it is presently unknown which patients experienced a more substantial benefit from this treatment. To this end, we show that the IHC H3K27me3/me2 score fully distinguished mutated from wild-type samples, thereby confirming the applicability of this score for the prediction of EZH2 mutation in FL patients. The use of IHC as a surrogate for EZH2 mutation analysis has been previously validated in DLBCL,  19  but it had not to been demonstrated in FL until now. In addition to the mutational status, we suggest that consideration of the copy-number status of EZH2 could also help to identify patients who are more likely to benefit from this targeted therapy.
■ ACKNOWLEDGMENTS This work was partially founded by the joint  French-Romanian  project  ANR-UEFISCDI  (Contracts  9ROFR/ 01. 02. 2013  and  ANR-12-IS07-0003-01 ) and by the  European Commission  through the  SPINSWITCH  project ( H2020-MSCA-RISE-2016 , Grant Agreement No. 734322 ).
advopticalmat. de relieved. Strain has been shown to enhance the band gap,  [22, 32]  so this effect is in line with previous observations. Another interesting point is that thus far, there are no reported data of the optical band gap of T' BFO on LAO substrates by transmission spectroscopy (Figure  1a , top panel). The most obvious question here is, why is there a bimodal distribution of band gap, with the measured gap being higher for ellipsometry measurements? Is it related to sample quality, strain, inhomogeneous strain, and/or growth method? In order to obtain some hints on this subject, we have measured, by transmission spectroscopy, the optical properties of a large set of epitaxial films. We have examined films grown in various PLD chambers under different oxygen pressure, temperature, and cooling conditions.
This inhomogeneous strain gives rise to a strain gradient in the z direction, which we estimate from XRD data using the W-H method. The (001)-(004) reflections from coupled θ-2θ scans were used and the peaks were fitted with an asymmetric Gaussian function. Instrumental resolution as a function of Q z was estimated by taking the peak widths of substrate reflections (001-004), then fit with a quadratic function. Representative W-H plots for BFO//STO (001) by YAG and excimer lasers are shown in Figure  5b, c  respectively. Since the inhomogeneous strain is taken from the slope of these plots, it is clear that for these samples (with the same thickness), the YAG film has a larger strain gradient than the excimer film. The inhomogeneous strain for both sample types as a function of thickness is presented in Figure  5a.
Funding We thank the Agence Nationale de la Recherche for funding (grant ANR-14-ACHN-0003-01). Wild, G. & Traulsen, A. 2007:  The different limits of weak selection and the evolutionary dynamics of finite populations. Journal of Theoretical Biology 247(2):382 -390. Wolfram Research, Inc. 2015: Mathematica Edition: Version 10. 1. Wolfram Research, Inc. , Champaign, Illinois.
Data source and processing The breast cancer gene expression and survival data used here was downloaded from The Cancer Genome Atlas (TCGA), release No. 20 of the International Cancer Genome Consortium (ICGC) data portal (https://dcc. icgc. org/releases/release_20/Projects/BRCA-US). This dataset provides the RNA-seq counts of 18,708 genes for 879 tumor samples, in which we also have records of the vital status of corresponding donors, namely the overall survival outcome of the cancer patients being alive or deceased at the end of clinical treatment (Table  1 ). Since TCGA cancer data are from different origins and underwent different management processes, non-biological experimental variations, commonly known as batch effect, associated to Genome Characterization Center (GCC) and plate ID must be removed from the RNA-seq data.
Modeling framework for signaling pathways We applied the canonical circuit activity analysis method  (Hidalgo, et al. , 2017) , as implemented in the hipathia R package available at https://github. com/babelomics/hipathia, in pursuit of modeling signaling activity. Within the modeling context, a circuit is defined as all possible routes the signal can traverse to be transmitted from a particular input node to a particular output node (see Supplementary Figure  1A ). A total of 6,101 circuits are identified and modeled in this study. The transmission of the signal depends on the integrity of the chain of nodes that connect the receptor to the effector and briefly, it is estimated as follows. The presence of the mRNA (the normalized RNA-seq counts rescaled between 0 and 1) is taken as a proxy for the presence of the corresponding protein in each pathway node  (Bhardwaj and Lu, 2005; Efroni, et al.
Thus, we use a total of 9 types of profiles (detailed in Table  2 ) From the viewpoint of machine learning, this study is formulated as a typical binary classification problem where we determine a positive or negative pseudo label for each sample. Based on the data available in this study (Table  1 ) we perform a 5-fold cross-validation repeated 10 times on the dataset and report the mean performance over the 5 × 10 = 50 splits to assess the prognostic power for each type of profile. The performance is evaluated by the Area Under the ROC Curve (AUROC) criteria  (Sing, et al. , 2005). Note that usually a classifier returns a continuous prediction between 0 and 1 for each sample denoting the probability of that sample being in the positive class rather than in the negative class, and then assigns either label to the sample according to some cutoff value thresholding the prediction.
, 2015)  is described in the COSMIC database (entry COSM458603). Regarding CHADL, it has been related to chondrocyte differentiation  (Tillgren, et al. , 2015)  and extracellular matrix remodeling (  Barallobre-Barreiro, et al. , 2012). Therefore, both genes are potentially involved in cancer processes, which suggest that further investigation of the complete list of top-ranked other-genes could render new cancer drivers and potential therapeutic targets. An expanded list containing the top 50 most important features among the other-genes can be found in Supplementary Table  4 , in which many genes with cancer-related functions can be seen. Functions for the genes have been taken from their Uniprot (UniProt_Consortium, 2015) annotations and, when absent, from GeneCards annotations  (Stelzer, et al.
Funding This work was supported by the  European Union   7th Framework Program  through the  Marie Curie ITN MLPM  grant No  316861 , by the  European Research Council  grant  ERC-SMAC-280032 , by grants  BIO2014-57291-R  from the  Spanish Ministry of Economy and Competitiveness and "Plataforma de Recursos Biomoleculares y Bioinformáticos "  PT13/0001/0007  from the  ISCIII , both co-funded with  European Regional Development Funds (ERDF) ; and  EU   H2020-INFRADEV-1-2015-1 ELIXIR-EXCELERATE (ref. 676559 ).
This technique of extracting comparable effect sizes across a body of work is called meta-analysis, and it may be described as a tool to bring together diverse studies in a broader analysis conceptually encompassing them all. In previous work  [12] , we have compiled a database con-taining all public studies testing discrimination of vocalic sounds by infants. Here, we assess whether there is statistical evidence in this public database for the following factors affecting effect sizes across studies: (1) the order in which the two vowel stimuli are presented; and (2) the distance between the vowels, measured acoustically in terms of spectral and quantity differences. It is conceivable that the effects of these variables are modulated by native language experience. Therefore, we systematically include interactions with infant age and whether the contrast is native (i.
Methods We drew from the InPhonDB meta-analysis, available from MetaLab metalab. stanford. edu. Since the construction of the meta-analysis has been described in general terms elsewhere  [12] , we provide here only some general statistics about the database today, given that it has been updated after publication  [13]. At present, InPhonDB contains data from 2735 infants (mean age = 233 days, range 3-912 days) from 39 papers, collectively containing 191 effect sizes. Whether one expects a greater effect given NRV was coded on the basis of the vowels' position in F1/F2 space. If the first vowel presented was less peripheral than the second vowel, this was coded as "yes" (because it should be easier to discriminate when the order is less to more peripheral), or "no" if the opposite order was used.