dataesr/scientific-paragraphs-categorization huggingface dataset:
Root mapping We collected data on root distribution during the year 2010 for each plot in which soil water was monitored using a modified trench profile method  (Tardieu 1988). First of all, a trench 300 cm deep was dug into the plot. The observed vertical profile (180 cm wide, 300 cm deep) which was perpendicular to the crop row was then prepared. After the working surface had been smoothened, roots were made visible by removing approximately 1 cm of soil with a knife. Next, roots were mapped on three adjacent 60 * 300 cm grids with cells of 1. 9 * 1. 9 cm. Since root counting was a very time-intensive operation, the number of root impacts in each cell was measured only on 20% of the cells for each 60 cm wide grid (the seven cells at the right of the grid), and the presence or absence of root impact was noted for the other cells.
Using data of precipitation (P), irrigation (I) and potential evapotranspiration (PET), the water balance (P+I-PET) was calculated each year and for each crop from March 1 to the date of maximal SWD. The water balance was -238 mm on an average and was very similar for most crops (-256 ± 5 mm), except for triticale (-148 mm). It was negatively correlated with maximal SWD (r = -0. 63; p < 0. 001; Fig. 3 ). The influence of aboveground biomass production on maximal SWD was also investigated. No significant correlation was found between biomass production and maximal SWD (Fig. 4 ). This was true not only when all crops were grouped together but also for each crop independently, despite large differences in biomass production between crops and years for some crops. Miscanthus, switchgrass and triticale had a rather stable biomass production over the period 2007-2013 but fescue, alfalfa and sorghum displayed a higher variability with low biomass production (< 10 t ha -1 ) some years.
PWC calculated at the date of maximal SWD were compared to PWC calculated at the end of the growing season, i. e. November 1 (data not shown). PWC changed very slightly for the three deeper layers (only 1% on average) but was reduced in the upper layers. This means that there was no or very little additional water retrieval in the deeper layers after the date of maximal SWD, even for crops for which maximal SWD was observed on average more than one month before the end of their growing period (switchgrass, fescue, alfalfa). Fig. 6  shows the root distribution of each crop observed in 2010 on the trench profiles. Miscanthus, switchgrass and alfalfa had a particularly deep root system: the maximum rooting depth was 300, 288 and 276 cm respectively. Sorghum had a more superficial root system (maximum rooting depth = 128 cm) and fescue and triticale were intermediate with a maximum rooting depth of 200 cm.
Acknowledgements We are grateful to  F. Mahu  for the maintenance of the soil moisture and temperature probes,  J. Duval  for the database development,  T. Laemmel  for his help in data processing and  L. Le Guen,  E. Mignot ,  C. Demay  and  F. Millon  for their technical assistance. This work was supported by the  French National Research Agency (ANR)  under the project " Regix " and by  BPI-France  under the project " Futurol ".
Remarkably, most of these discoveries have been made using three L. monocytogenes strains. These widely used strains are the 10403S, EGD, and EGD-e L. monocytogenes strains. The genome of the EGD-e strain was sequenced in 2001 (RefSeq accession number NC_003210  [7] ). The sequence and annotation of the 10403S genome have recently been released (NC_017544), as have those of several other strains  (8) (9) (10) (11) (12). Currently, NCBI's RefSeq database contains 39 L. monocytogenes genomes, and this number will probably continue to grow exponentially in the coming years. In this context, the unknown sequence of the extensively used strain EGD remained a gap to fill. The EGD strain is from the Trudeau Institute (NCTC7973) and derived from the original strain isolated from guinea pigs by E. G.
RESULTS Resequencing of the EGD-e genome sequence. Prior to sequencing the L. monocytogenes EGD genome, we resequenced the genome of strain EGD-e using the Illumina technique. Only five differences compared to the published sequence were found (Fig. 1A ), confirming the high quality of the first published sequence  (7, 28). As shown in Data Set S1 in the supplemental material, four of the five differences are in intergenic regions, where no small RNAs (sRNAs) have been identified so far, and only one difference induces an amino acid change, i. e. , a glycine to a valine, in Lmo0247, a hypothetical protein. EGD's genome sequence and its comparison to those of EGD-e and 10403S. The EGD genome was sequenced by the Illumina technique, assembled, annotated, and deposited in the European Nucleotide Archive (ENA) (accession number HG421741).
Strikingly, PrfA is the only protein in the whole virulence locus with an amino acid sequence in EGD that is different from that of 10403S. All the other proteins are similar in the two strains but show some differences in strain EGD-e (Fig. 3B  and Data Set S1). This result predicted that EGD might express the PrfA regulon in a way very different from that of EGD-e and 10403S (see below). It is noteworthy that our analysis of NrdD, a class III anaerobic ribonucleotide reductase (RNR), in EGD showed that a KITPFE motif present in strain EGD (Fig. S1C ), as well as in 10403S, is absent in EGD-e (53), revealing a higher capacity for the first two strains to live under anaerobic conditions, including the gastrointestinal tract. The PrfA core regulon in EGD is overexpressed. To assess the impact of the PrfA* mutation in EGD, we constructed a PrfA* mutant in EGD-e by generating a Gly145Ser mutation and compared the phenotypes of the two strains (EGD and EGD-e PrfA*) to the EGD-e strain in exponential phase, after growth in brain heart infusion (BHI) at 37°C.
We first performed a whole-genome transcriptomic analysis of the resulting EGD-e PrfA* strain using our Affymetrix tiling array  (35). As EGD-e and EGD share more than 95% of their ORFs and sRNAs, our tiling arrays could also be used for EGD transcriptomic analysis. We found that in both EGD and EGD-e PrfA*, the core PrfA regulon  (54) , which contains the whole virulence locus, the inlA-inlB operon, inlC (lmo1786), and hpt (lmo0838), is overexpressed compared to its expression in the reference strain EGD-e (Fig. 3C  and Data Set S3), confirming the effect of the Gly145Ser PrfA* mutation on the core PrfA regulon. All these genes have a canonical PrfA box upstream from their start codon, which allows the direct binding of PrfA. Notably, we found that only 15 genes in the EGD-e PrfA* strain (Fig.
S4B  and S5 and Data Set S3). Phenotypic effect of the PrfA* mutation. The two PrfA* strains EGD-e PrfA* and EGD grow more slowly in broth than strains EGD-e and 10403S. This was confirmed by a colony size analysis showing larger colonies for EGD-e than for EGD-e PrfA* and for 10403S than for EGD on BHI agar plates after 24 h of growth (Fig. 4A ). This is in accordance with the defect already observed in 10403S PrfA* strains  (57). We performed classical gentamicin invasion assays (58) using strains EGD, 10403S, EGD-e PrfA*, and EGD-e in three different cell lines: HeLa (in which entry is InlB dependent), JEG3 (in which entry is InlA and InlB dependent), and Raw264 (macrophages). In HeLa and JEG3 cells, strains EGD and EGD-e PrfA* were more invasive than EGD-e and 10403S (Fig. 4B ). There were also higher bacterial counts in mouse Raw264 macrophages for EGD-e PrfA* than for EGD-e and for EGD than for 10403S.
According to the NCBI genome database, the strain phylogenetically closest to EGD is strain SLCC5850. It is a serotype 4b strain isolated in 1983 from a man with meningitis, according to the Seeliger collection database  (63). Its main feature is the loss of important motifs in its PrfA protein (Fig. 3A ). The loss of these motifs was also found in the nonhemolytic SLCC53 strain when PrfA was sequenced in 1991  (49). SLCC53 is the type strain of Listeria (64) and thus originates from the rabbit strain isolated by E. G. D. Murray in 1924 (see reference 13). The strains phylogenetically closest to EGD-e are SLCC2372, a serotype 1/2c strain isolated from human in 1935, and SLCC2479, a serotype 3c strain which is of unknown origin, isolated in 1966  (12). By comparing the close phylogenetic neighbors of each strain, it seems more likely that EGD is closer to the original type strain and that EGD-e has been mislabeled and exchanged for another strain.
MATERIALS AND METHODS For more information on materials and methods, see Text S4 in the supplemental material. Listeria monocytogenes EGD and EGD-e sequencing and annotation. Briefly, genomic DNA was prepared as described in reference 75. Library preparation was achieved using NEBNext DNA sample prep mas-ter mix set 1 with the multiplexing sample preparation oligonucleotide according to the manufacturer's recommendations. Libraries were then sequenced on a HiSeq 2000 sequencer in 100-base single-end reads. Sequence files were generated using Illumina Analysis Pipeline version 1. 7 (CASAVA). After quality filtering, 25,827,948 reads were aligned with the Listeria monocytogenes 10403S genome sequence (GenBank accession number CP002002) using CLC Genomics Workbench (version 3. 20), and more than 98.
4% of reads mapped successfully. The remaining 407,195 reads were then used to sequentially fill gaps in the final sequence. The overall final coverage was 875ϫ, with only 47,125 unmapped reads. EGD-e was sequenced using the same protocol, with a total of 13,414,584 reads. The consensus sequence of EGD has been exported and annotated using RAST annotation software  (76). Automatic annotation provided by RAST was curated using homology to proteins in Listeria monocytogenes EGD-e  (7)  and Listeria monocytogenes 10403S, and the sequence was submitted to the ENA database (accession number HG421741). Interactive visualization of the syntenic organization of Listeria genomes is available with the Flash-based SynTView (77) software available at http://genopole. pasteur. fr/SynTView/flash/Listeria_monocytogenes/SynWebEGD_final.
html. Transcriptomic analysis. Bacterial overnight cultures were diluted in BHI, and bacteria were grown to an OD of 1. RNA was extracted and samples for each chip were prepared as previously described  (35). The tiling chip works with two types of arrays: the gene expression array (link E-MTAB-1676; https://www. ebi. ac. uk/arrayexpress/experiments/E -MTAB-1676/) and tiling array (link E-MTAB-1677; https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-1677/). Genes having an average false-discovery rate (Benjamini and Yekutieli method  [84] ) (FDRBY) under 0. 05 and an absolute log fold change (|logFC|) value of Ͼ1. 5 were selected as potential differentially expressed genes. For small RNAs, we applied the cutoff t test P value of Ͻ0. 05 and an |logFC| value of Ͼ1. 5; after manual curation, we obtained a potential list of differentially expressed sRNAs.
We thus selected the following MOIs: 0. 1 for EGD-e, 0. 001 for EGD-e PrfA*, 0. 5 for 10403S, and 0. 001 for EGD. Directly on a picture of the plaques, we measured plaque size (in square pixels) using the interior value of the region of interest (ROI) manually defined by Icy software  (80). Almost 30 plaque sizes were measured for each bacterium. An unpaired t test was used to assess plaque size differences between strains. Nucleotide sequence accession number. The sequence of EGD was submitted to the ENA database under accession number HG421741.
ACKNOWLEDGMENTS This work received financial support from the  European Research Council  (advanced grant  233348 ), the  French Agence Nationale de la Recherche  (grants  BACNET 10-BINF-02-01 ,  IBEID ANR-10-LABX-62-01 , and  ERA-NET ANR-2010-PATH ), the  Institut Pasteur , the  Institut National de la Santé et de la Recherche Médicale , and the  Institut National de la Recherche Agronomique. A. K. is a recipient of a scholarship from the  Pasteur-Paris University International Doctoral Program/Institut Carnot Maladies Infectieuses. We thank  Rich Calendar  and  Patrick Berche  for important discussions,  Nina Sesto  for the help in the construction of the PrfA* mutant,  Marie-Anne Nahori  for the help in performing in vivo studies, and  Guillaume Soubigou  for the help in performing the tiling array experiments.
miRNA BIOGENESIS To date, miRbase, a specialized miRNA database, comprises 1872 entries for the human genome  (Griffiths-Jones et al. , 2006; Kozomara and Griffiths-Jones, 2011). miRNA genes are present on all chromosomes, with the exception of the human Y chromosome. Nearly 50% of miRNA-coding genes are situated within the intergenic space and possess their own regulatory elements  (Lagos-Quintana et al. , 2001; Corcoran et al. , 2009). In contrast, 40% of miRNAs genes are positioned within introns  (Rodriguez et al. , 2004; Smalheiser et al. , 2008) , and 10% are located within exon terminals. As a consequence, the expression of half of the miRNA genes depends on the regulation of their host gene, so they may be involved in the control of genetic networks related to the expected function of the host gene product  (O'Carroll and Schaefer, 2013 ).
, 2003; Grimson et al. , 2007; Nielsen et al. , 2007; Saetrom et al. , 2007). Given the large and growing number of miRNA species, the enormous number of putative target mRNAs and the length of their 3 UTR sequences, the manual prediction of miRNA:mRNA interactions would be impossible. Therefore, computational analysis using algorithms and prediction databases is the usual method to identify miRNA targets. Hence, target prediction programs are based on seed region recognition tools that basically list all the sites with miRNA-binding properties and rank them accordingly to the kind of seed, conservation across evolution and some of the above-mentioned factors known to modulate site efficiency (for review, see  Reyes-Herrera and Ficarra, 2012). However, despite refinements of prediction tools, the percentage of predicted targets that pass the experimental validation stage is still sub-optimal.
In the past few years, many studies have screened for miRNA expression in various animal models and in cohorts of patients with diverse neurodegenerative diseases. Although highlighting biomarker miRNAs with a possible value for therapeutic approaches, this kind of approach does not improve our understanding of miRNA targeting and function in pathological mechanisms. Indeed, it would be risky to evaluate targets of candidate miRNAs on algorithm-based prediction databases only, since the number of putative targets is very large and confirmation of targeting sometimes difficult to achieve. Therefore, it will be important to improve miRNA target prediction tools maybe by considering recent experimental data suggesting that "seed" region may not be the only signature of miRNA targeting  (Helwak et al.
Since the deformations are expected to be spatially structured, we perform classification combining logistic loss and spatial regularization techniques, which have not been explored so far in this context, as far as we know. The main contribution of this paper is the comparison of regularization techniques enforcing the coefficient maps to be spatially smooth (Sobolev), piecewise constant (total variation) or sparse (fused LASSO) with standard regularization techniques which do not take into account the spatial structure (LASSO, ridge and ElasticNet). On a dataset of 103 patients out of ADNI, the techniques using spatial regularizations lead to the best classification rates. They also find coherent areas related to the disease progression.
) to be found across the population (i. e. on the common template) and we want this coherence to be driven by the disease progression. In this paper, we investigate the use of total variation, Sobolev and fused LASSO regularizations in 3D volumes. Compared to total variation, Sobolev enforces smoothness of the coefficient map, whereas fused LASSO adds a sparsity constraint. The deformation model used to assess longitudinal evolutions in the population is presented in Section 2. Machine learning strategies are discussed and the model of classification with logistic loss and spatial regularization is described in Section 3. The dataset used and numerical results are presented in Section 4. We illustrate that initial momenta capture information related to AD progression, and that spatial regularizations significantly increase classification performance.
Data Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http:// adni. loni. usc. edu). The ADNI was launched in 2003 by the National Institute on Aging, the National Instituteof Biomedical Imaging and Bioengineering, the Food and Drug Administration, private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year public private partnership. The primary goal of ADNI has been to test whether serial MRI, positron emission tomography, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials.
For up-to-date information, see http://www. adni-info. org. A dataset of 206 hippocampus binary segmentations from 103 patients enrolled in ADNI  (Mueller et al. , 2005)  has been used. The segmentations were computed and provided by ADNI, detailed information can be found on their website. For each patient, 'screening' and 'month 12' were the two time points selected. All patients were MCI at the screening point, 19 converted to AD by month 12, and the remaining 84 stayed MCI.
87 ± 0. 02, which validated the suitability of the template obtained for our study. The last paragraph of Section 4. 3 also provides another reason why such template can be used in our study. Second, we evaluated the empirical convergence of our optimization procedure. Fig. 4a  shows the relative distance to final estimate, i. e. ∥T k -T ∞ ∥ 2 L 2 ∥T ∞ ∥ 2 L 2 ; ð23Þ where T k is the Karcher estimate at iteration k, and T ∞ is approximated by the last computed estimate. Fig. 4b  shows the relative distance between two consecutive estimates, i. e. ∥T kþ1 -T k ∥ 2 L 2 ∥T k ∥ 2 L 2 ; ð24Þ with the same notations. On this dataset, we notice that (1) the convergence speeds are coherent with the ones presented in  Vialard et al. (2011 Vialard et al. ( , 2012b)) , i. e. only a few Karcher iterations are required for convergence, and (2) the alternate minimization over T and {R i } 1 ≤ i ≤ n provides a faster convergence than the one over T with the {R i } fixed.
Optimization of the logistic regression model In the training procedure, we have n = 103 observations (one for each patient). As initial momenta are scalar fields in space, each initial momenta has the same dimension as the number of voxels, so p = 12,531. Since stable and progressive classes in the dataset are unbalanced, the weighted version of the loss function defined in Section 3. 2. 3 was used. Solution of the optimization problems was computed via HANSO 4  with a maximum of 20 iterations.
Performance evaluation First, the effect of spatial regularizations was The spatial regularizations introduced in Section 3. 2 aim at enforcing local correlations between the coefficients in w. Using the whole dataset, the effects of the various regularizations were compared. Second, the model was evaluated in terms of classification of AD progression. All patients were classified using a leave-10%-out scheme. From the numbers of true/false positives/negatives (TP, FP, TN, FN), four indicators were used to measure classification accuracy: specificity Spec ¼ def: TN TN þ FP , sensitivity Sens ¼ def: TP TP þ FN , negative predictive value NPV ¼ def: TN TN þ FN , and positive predictive value PPV ¼ def: TP TP þ FP. Statistical tests were also performed to evaluate the significance of the differences.
Computation time The various algorithms were implemented in a mix of C++, MATLAB ® , mex and python. Table  3  reports approximate running time on a standard laptop (Intel ® Core™ i7-2720QM CPU at 2. 20 GHz, 8 GB of RAM). The geodesic shooting step is linear with the number of patients. The computation of the template is linear with the number of patients and the number of Karcher iterations. One should note that Karcher iterations can have decreasing number of gradient descent iterations, which decreases the total computation time. Then the transport is also linear with the number of patients. So far, it is interesting to notice that all the steps can be easily be divided into different jobs to take advantage of multi-core or distributed architectures. Finally come the learning and classification.
Regularization Standard Spatial LASSO Ridge Elastic Net Sobolev Total Variation Fused LASSO None <10 -5 <10 -5 <10 -5 <10 -5 <10 -5 <10 -5 Standard LASSO -1. 1*10 -04 <10 -5 <10 -5 <10 -5 <10 -5 Ridge -4. 2*10 -02 <10 -5 <10 -5 <10 -5 Elastic Net -6. 3*10 -05 <10 -5 <10 -5 Spatial Sobolev -<10 -5 <10 -5 Total Variation -0. 86 Fused LASSO -patient in a smooth representation, we saw that it is important to regularize spatially across the population (i. e. between patients) in order to be able to build meaningful statistical models for classification and biomarker discovery. On that point, the logistic regression model has proven to be efficient as it can be combined with complex regularizations. Our spatial regularizations gave the best results on our dataset, and another research direction is the study of other regularizations such as group sparsity.
Acknowledgements Data collection and sharing for this project were funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defense  award number  W81XWH-12-2-0012 ). ADNI is funded by the  National Institute on Aging , the  National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following:  Alzheimer's Association; Alzheimer's Drug Discovery Foundation ;  BioClinica, Inc. ;  Biogen Idec, Inc. ;  Bristol-Myers Squibb Company ;  Eisai Inc. ;  Elan Pharmaceuticals, Inc. ;  Eli Lilly and Company; F. Hoffmann-La Roche Ltd. and its affiliated company  Genentech, Inc. ;  GE Healthcare; Innogenetics, N. V. ; IXICO Ltd. ;  Janssen Alzheimer Immunotherapy Research & Development, LLC.
This paper proposes to exploit gravitational lensing effects to improve the sensitivity of neutrino telescopes to the intrinsic neutrino emission of distant blazar populations. This strategy is illustrated with a search for cosmic neutrinos in the direction of four distant and gravitationally lensed Flat-Spectrum Radio Quasars. The magnification factor is estimated for each system assuming a singular isothermal profile for the lens. Based on data collected from 2007 to 2012 by the ANTARES neutrino telescope, the strongest constraint is obtained from the lensed quasar B0218+357, providing a limit on the total neutrino luminosity of this source of 1. 08 × 10 46 erg s -1. This limit is about one order of magnitude lower than those previously obtained in the ANTARES standard point source searches with non-lensed Flat-Spectrum Radio Quasars.