dataesr/scientific-paragraphs-categorization huggingface dataset:
1A ). Most varieties flowered at similar times under NLD and LD, indicating that photoperiodic flowering was similarly promoted both under constant and varying day lengths. Flowering of Thaibonnet, Balilla and Nipponbare was delayed under LD compared to NLD, indicating that these varieties could discriminate between different long photoperiods and that continuous LD of 16h caused stronger floral repression. Based on heading dates under LD and SD, we calculated a photoperiod sensitivity index (PS). All varieties tested showed reduced PS compared to Nipponbare (Fig. 1B ), and some varieties, including Augusto and Sant'Andrea, were almost completely insensitive and flowered at the same time regardless of day length (Fig. 1A ). These data indicate that flowering of mini-panel varieties can be promoted by SD but not repressed under LD and NLD compared to Nipponbare.
Sequence data were compared to Nipponbare that bears functional alleles at all repressor loci with the exception of Hd6 that bears a premature stop codon, resulting in a truncated non-functional protein  (Takahashi et al. , 2001). Several allelic variants were identified (Table  1 , Supplementary Fig. S1 ). Ten out of 16 varieties carry at least one loss-of-function allele at Hd1, Ghd7 or Ghd8. Seven different alleles at the Hd1 locus were detected. The 2 bpdeletion observed in Fragrance, Eolo, Thaibonnet, Gladio, Panda and Augusto (Hd1-V, Hd1-VI), and the 43bp-deletion observed in Lido and Selenio (Hd1-II) were previously isolated from the cultivars Kasalath and HS66 respectively, and were associated to loss-of-function alleles  (Yano et al. , 2000). Balilla bears the same Hd1 allele of Ginbouzu which was suggested to encode a stronger LD repressor allele than Nipponbare  (Yano et al.
6A , Supplementary Table  S1 ). Selected varieties of the European collection were grown across a latitudinal cline at five locations in Europe, comprising Vercelli (Italy, 45. 3°N), Arles (France, 43. 6°N), Thessaloniki (Greece, 40. 6°N), Salvaterra de Magos (Portugal, 39°N) and Alcala del Rio (Spain, 37. 5°N). Flowering time data were recorded and made publically available at http://tropgenedb. cirad. fr and in Supplementary Table S1  (Feyt et al. , 2001). For each gene, all loss-of-function alleles were grouped into a single NF and genotypic data were used for targeted association analysis. Results of association statistics are presented in Fig. 6B , following a MLM2. The p-values of association calculated according to other models are reported in Supplementary Table  S3. The mean effect of allelic substitutions between wild type and mutant alleles on days to heading were calculated as the phenotypic difference between the two homozygotes, by setting to 0 the effect of the functional allele and computing the effect of the non-functional allele.
Sequences were then trimmed to keep only high quality reads (Q ! 35). Chimeras were removed using the chimera. uchime mothur command. Singletons were included in the analysis. Sequences were aligned and classified according to the SILVA bacterial SSU reference database v. 102  [14]. They were then assigned to genus-level phylotypes using the naive Bayes classifier implemented in Mothur and clustered into operational taxonomic units (OTU) using the average neighbor algorithm and a sequence identity cutoff of 97%. The Greengenes database  [15] , and the DictDb reference database v. 2. 3  [6]  were used for the taxonomic assignment of OTUs. The shared OTUs (97% sequence identity) between life stages (eggs, L2 larvae and workers) were determined using the Venn diagram package implemented in Mothur.
The Venny 2. 0. 2 software was used to plot resulting data. Relative abundance of each group of OTUs was added manually. Sequences are available in NCBI Sequence Read Archive under BioSample accession ID SAMN03114856 and SAMN03114878 to SAMN03114880 associated with BioProjects PRJNA270400-PRJNA270403. In each pyrotag library, the relative abundance of an OTU is the percentage of reads included in this OTU with respect to the total number of reads. The relative abundance of each taxon in a given sample is the sum of abundances of all the OTUs included.
The sequences were quality checked and processed in the same way as for pyrotag sequences. A phylogenetic analysis was performed using OTUs classified within the most representative phylum (Spirochaetes). A nucleotide Blast search was run and the five closest sequences imported for each OTU. Other Spirochaetes 16S rRNA gene sequences were randomly imported. The redundant sequences were removed and the sequences were aligned using SILVA SINA (www. arb-silva. de/aligner/) with the reference dataset SILVA SSU Ref NR  [17]. The alignment was improved manually by removing conserved gaps and ambiguously aligned regions. The phylogenetic tree was reconstructed using the Maximum Likelihood method implemented in MEGA v 6. 0  [18]  and the Tamura-Nei model with 1000 bootstraps.
The same number of sequences was, therefore, randomly subsampled in L2 larvae and workers. The resulting sequences from these three stages were assigned to 687 OTUs unequally distributed depending on the stage. The DictDB database  [6]  provided the best taxonomic assignment of OTUs and classified them into 18 bacterial phyla. Most of the OTUs fell within the following phyla: Proteobacteria (31. 46%), Spirochaetes (27. 94%), Bacteroidetes (15. 44%), Firmicutes (7. 27%), Actinobacteria (6. 02%), Fibrobacteres (4. 71%), Candidate phylum TG3 (4. 54%), Acidobacteria (0. 98%) and Synergistetes (0. 29%). The remaining OTUs fell within 9 minor phyla, each accounting for less than 1% of the reads with fewer than 5 OTUs per stage: Cyanobacteria, Caldiserica, Candidate division SR1, Chlorobi, Chloroflexi, Deferribacteres, Elusimicrobia, Planctomycetes, and Verrucomicrobia.
Our results consistently suggest that the succession of bacterial communities tends towards the promotion of symbionts capable of degrading wood components through the developmental stages of N. arburum. Many bacterial genes encoding glycoside hydrolases putatively involved in the degradation of cellulose and hemicellulose have been detected in the hindgut of Nasutitermes spp. [37, 38]. Diverse endoglucanases and nitrogenases were putatively assigned to both Treponema (Spirochaetes species) and Fibrobacteres while diverse endoxylanases and glycosidases were only assigned to Treponema  [37]. A phylogenetic comparison of Spirochaetes-related OTUs, the major lineage in adult workers of N. arborum, with previous published sequences available in databases indicated that the sequences retrieved in the present study mainly clustered with those from allopatric Nasutitermes species.
(b) Growth: use of yolk sac, size-at-age and body width -length ratio Photographs were taken of five to 10 fish from each rearing bin at 1 dph (before first feed), 2 dph (after first feed) and 16 dph, under a microscope with 10 mm graticule connected to a digital camera. One bin from each treatment could not be sampled at day 16 due to low survival. The maximum length and width measures of the yolk sac were digitized using four landmarks via TPSDIG software  [30]. Yolk sac centroid size (a metric of size calculated as the square-root of the sum of squared distances of individual landmarks from the centroid of the landmark configuration  [31] ) was determined using TPSRELW  [32]. Body length was digitized using six landmarks from the tip of the top lip to the base of the tail, and myotome length was digitized in TPSDIG and PAST  [32]  by two landmarks either side of the myotome at the position of the anus (electronic supplementary material, figure (c) Anti-predator response at 16 days post-hatching We developed an independent-measures anti-predator response experiment, whereby flight behaviour was assessed in response to attempts to catch the fish using a pipette (the same method used for transferring fish).
Acknowledgements. We thank  Ardtoe Marine Laboratories  for providing us with the facilities to carry out this study, and  Rogelio San Flores  for assisting with all aspects of fish husbandry and aquaculture. Marc Holderied ,  Thorin Jonsson ,  Sally Wood ,  Nathan Merchant  and particularly  Michael Ainslie  provided very helpful advice for acoustics analysis. Geospectrum Technologies  provided us with the accelerometer for recording particle acceleration.
Murine cle We used the "Cellvizio Dual Band" system integrated with Proflex Ultra Mini Z probes (Mauna Kea Technologies) and a flexible endoscope (MAS-GM, Olympus, Italia Srl, Milano, Italy) as previously described. 25 Briefly, the tumor was first localized with the use of the endoscope under endotracheal intubation. The hand-held confocal probe, inserted in the operative channel of the endoscope, was positioned to achieve full contact with the tissue. Images from the whole tumor were collected. The whole distal rat esophagus was screened by carefully moving the probe across the surface and adapting the imaging plane depth. Post-processing imaging analysis was performed by the IC-viewer software. CLE was carried out on anesthetized non-operated and operated rats. CLE images were acquired at different time points, according to animal group.
Important proteins with homology to the Golgi mammalian system have been described in Entamoeba, such as the N-ethylmaleimide-sensitive factor (NSF), which is responsible for the disassociation of SNARE complexes after fusion with the target membranes by ATP hydrolysis  [24]. The NSF protein shares a 40% homology with human NSF and has ATPase activity and intra-Golgi transport activity in vitro. In addition, the gene homologues of SNAP, t-SNARE, and v-SNARE were also identified in the E. histolytica genome databases. The data implies that the parasite contains the essential components for vesicle trafficking and fusion, which has yet to be fully described. Further analysis of the amoebic genome identified an unusual high number of genes encoding Ras-related proteins from the small GTPase family exceeding those of mammals or plants dedicated to vesicular sorting  [25].
Proteomic data analysis Data analysis was performed using Thermo Proteome Discoverer software suite (version 1. 4). For the search engine SEQUEST, the peptide precursor mass tolerance was set to 10 ppm, and fragment ion mass tolerance was set to 0. 6 Da. Carbamidomethylation on cysteine residues was used as fixed modification, and oxidation of methionine along with N-terminal acetylation was used as variable modifications. Spectra were queried against the E. histolytica UniProt database. In order to improve the rate of peptide identification percolator node in Proteome Discoverer was utilized with the false discovery rate (FDR) set to 1% for peptide and protein identification. In order to determine the absolute abundance of different proteins within a single sample we used iBAQ feature of MaxQuant version 1.
We utilized high resolution proteomics technology along with bioinformatic approaches to identify and annotate all the identified proteins into various functional classes. We undertook this proteomics analysis in order to distinguish ER-and Golgi-resident proteins as well as markers of diverse vesicle trafficking pathways. The procedure will give less insight on soluble proteins temporarily interacting with those subcellular compartments. The LC-MS/MS analyses of all three biological samples were performed using the LTQ-Orbitrap Velos mass spectrometer in high-resolution MS1 and MS2 modes (MS and MS/MS at 30000 and 7500 resolution with HCD fragmentation) resulting in excellent quality fragmentation spectra providing increased specificity during database searching. For peptide and protein identification, the raw data files were screened using both the Proteome Discoverer and MaxQuant software against the E.
histolytica Uniprot database. The entire dataset is in open access (ProteomeXchange library identifier PXD000770). Due to existing diverse gene annotations for E. histolytica, we kept the largest protein list given by Uniprot, which assembles all the proteins identified in E. histolytica independently of the way that it was used for such identification (i. e. , prediction from the genome, experimental data); consequently some of these entries should be redundant and may concern the same protein. Using a peptide and protein FDR of 1%, a total of 5683 peptides matching with 1531 proteins (representing 1198 groups) were identified among the three internal membrane samples which corresponds to roughly 20% of the total amoebic proteome. Details of this analysis can be found in Data in Brief from the Journal of Proteomics.
Although we did not detect Vps4, we identified proteins (EHI_167710 and EHI_165220) that share homologies with mammalian Alix (a direct interactor with Snf7), which has been found to be necessary for membrane abscission during cytokinesis and virus budding in association with ESCRT-III  [58]. Another E. histolytica protein identified and characterized with homology to Alix  [59]  (Fig. 7 ), is the adhesin protein ADH112 (EHI_181220) that has a role in phagocytosis  [60, 61]  and was found in our analysis. The Alix-homologue (EHI_167710) appeared in all three independent experiments and has differences at the amino acid sequence level (Fig. 7A-B ) indicating that in addition to the BRO1 domain (IPR004328), characteristic of the Alix family, it bears specific domains (Fig. 7C ). Besides these described MVB componentswe also found a high number of related ATPase (i.
We then, carefully reviewed the significance of identified proteins with respect to their mammalian or yeast cell counterparts. The data confirmed that the principal participants for the TGN, ER to Golgi and plasma membrane trafficking are present in E. histolytica, although the morphology of these subcellular compartments is still not completely delineated. TGN and ER to Golgi network transport components were, in the majority, well identified. Both COPI and COPII subunits were accounted for, as well as NSF. The presence of the SNARE complex and SNARE interacting proteins for the fusion and abscission of vesicles were recognized. Also, the retromer complex (EhVps26-29 and -35) previously identified during phagocytosis  [63] , was detected and localized in non-phagocyting trophozoites analyzed here at the perinuclear region, in the cytoplasm and enriched at the plasma membrane.
Acknowledgments The authors thank  Pascal Roux  ( Imagopole, Pasteur ) for assistance in image acquisition experiments. We acknowledge  Sherri Smith  for critical reading and correcting the manuscript. This work is supported by grants to NG from the  French National Agency for Research  ( ANR-10-INTB-1301-PARACTIN ) and from the  French parasitology network of excellence ParaFrap  (Grant  ANR-11-LABX0024 ). DP was supported by fellowships from the  French Ministère de la Recherche et la Technologie (MRT)  and from  Fondation pour la Recherche Médicale (FRM). GDJ is supported by the  Wellcome Trust-DBT India Alliance  (Grant  500080/Z/09/Z ).
We propose two strategies to improve the quality of tractography results computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both methods are based on the same PDE framework, defined in the coupled space of positions and orientations, associated with a stochastic process describing the enhancement of elongated structures while preserving crossing structures. In the first method we use the enhancement PDE for contextual regularization of a fiber orientation distribution (FOD) that is obtained on individual voxels from high angular resolution diffusion imaging (HARDI) data via constrained spherical deconvolution (CSD). Thereby we improve the FOD as input for subsequent tractography. Secondly, we introduce the fiber to bundle coherence (FBC), a measure for quantification of fiber alignment.
An overview of HARDI techniques can be found in  [5]. Here we use the method of constrained spherical deconvolution (CSD)  [6] , that from the initial diffusion data constructs a fiber orientation distribution (FOD), which models the distribution of fibers along different directions. Tractography methods are often used in the DW-MRI pipeline to provide insight in the structural connectivity of the white matter bundles. Independently of the model used for interpreting the DW-MRI data, noise originating from the scanner, acquisition artifacts and partial volume effects  [7]  are likely to result in spurious (aberrant) fibers in the tractography output. To improve the data on which the tractography is performed, different regularization methods can be used. Methods exist that apply filtering for the reduction of noise directly on the DW-MRI data  [8] [9] [10] , other methods aim to regularize the DTI tensor fields  [11] [12] [13] [14] [15].
Structure of the Article Section 2 covers theory of the individual parts of the pipeline as outlined in Fig. 1 , consisting of CSD, PDE enhancements, tractography and coherence quantification in Sections 2. 1-2. 4, respectively. In Section 3 we provide extensive validation of the combination of CSD and PDE enhancements and the FBC, using three experiments: 1. First we use the Tractometer evaluation system  [51, 52]  on the ISBI 2013 HARDI reconstruction challenge dataset  [53] , a digital phantom with known ground truth, to demonstrate how contour enhancement improves both the local FOD reconstruction and the global connectivity of fiber bundles compared to CSD, see Section 3. 1. 2. In Section 3. 2 we show on a human DW-MRI dataset, containing different crossing bundles, that CSD combined with enhancements yields an FOD that is more robust with respect to the b-value and the number of gradient directions used in the acquisition.
Methods In this paper it is assumed that we have HARDI data as input, from which we derive an FOD U that models the orientation of fibers in each voxel, i. e. U : R 3 × S 2 → R +. For this we use CSD  [6] , concisely described in Section 2. 1, as it gives sharp angular profiles and is able to distinguish multiple fiber directions within a voxel. Then we use the enhancement PDE for diffusion of the FOD U , coupling spatial and angular information. The combination of CSD and such enhancement is a powerful method to obtain an enhanced FOD in which the coherence inherent in the data is included, providing a more coherent input for the tractography. The enhancement technique is explained in Section 2. 2. We use the MRtrix algorithm  [6]  for both deterministic and probabilistic tractography to estimate the structural connectivity in the brain.
Experiments and Results In this section we extensively test the performance of our CSD enhancement method (A) and the FBC method (B), recall Fig. 1  and Sections 2. 2 and 2. 4, in three different experiments: • We use the HARDI Reconstruction Challenge dataset  [66] , which is artificial data with known ground truth, to quantitatively evaluate the CSD enhancement method (A) on deterministic tractography in Section 3. 1. • In Section 3. 2 we show on DW-MRI human brain data that the enhancement (A) have a positive effect on deterministic tractography, for different acquisition protocols of the data. Furthermore, on this DW-MRI dataset and on the phantom dataset we compare our method to previous work  [27] , where a DTI-based FOD is used in combination with nonlinear PDE flow. • In the third and last experiment, we reconstruct the optic radiation in human clinical data, see Section 3.
3. We include an extensive evaluation of our methods, the enhancement of the FOD (A) and the use of the FBC to classify and remove spurious fibers (B), and the combination of both methods. We show that the reproducibility of the probabilistic tractography has increased, resulting in a more stable localization of the tip of the Meyer's loop. For all datasets Mathematica  [67]  was used to perform the contour enhancement algorithm and the CSD, which in practice produces the same results as the MRtrix CSD implementation when the same deconvolution kernel is used. MRtrix software  [6]  was used to perform fiber tractography. The coherence quantification was implemented in C++. In Section 3. 1 we make use of the Tractometer  [51, 52]  (http://www. tractometer. org/) to evaluate tractography results.
HARDI Reconstruction Challenge The following experiment is performed on a digital phantom dataset that was designed for the ISBI 2013 Reconstruction Challenge  [66, 69]. It is used in combination with the Tractometer  [51, 52] , as a benchmark to compare different reconstruction and tracking methods. The phantom is inspired by the Numerical Fiber Generator  [70]  and the code to reproduce it is freely available as part of the Python package Phantomas (http://www. emmanuelcaruyer. com/phantomas. php). This synthetic dataset is of size 50 × 50 × 50 voxels with a resolution of 1 × 1 × 1 mm 3. It consists of 27 simulated white matter bundles, designed to resemble challenging branching, kissing and crossing structures at angles between 30 and 90 degrees, with various curvature and bundle diameters ranging from 2 mm to 6 mm.
A smaller ratio D 33 /D 44 is better suited in regions where the curvature of bundles is higher (visualized by thicker glyphs). The higher the diffusion time t, the more context is taken into account. When t is too large, fiber bundles with high curvature can be damaged or false positives could be created. Taking this into consideration, we choose our parameters as follows: we fix spatial diffusivity parameter D 33 = 1. 0, we take the angular diffusivity parameter D 44 ∈ {0. 005, 0. 01, 0. 02, 0. 04} and diffusion times t ∈ [0, 5]. Tractography results for the entire dataset are shown in Fig. 7. We can recognize the positive effect of the enhancements on deterministic tractography: we see less dropouts, better aligned fibers and better continuation of fibers The tractography on enhanced CSD results in better aligned fibers and a fuller reconstruction of the bundles.
They indicate the percentage of tracks that correctly connect, incorrectly connect or do not connect gray matter areas in the dataset, respectively. We also use the Average Bundle Coverage (ABC), the percentage of voxels in a bundle that is crossed by a valid streamline, averaged over all bundles. We combine the (VC), (IC) and (NC) in two metrics introduced in  [73] : • Connection to Seed Ratio (CSR), which represents the probability that a generated fiber actually connects two gray matter areas, computed as 100%-NC. • Valid Connection to Connection Ratio (VCCR), the probability that a connecting fiber is correct, computed as VC/(VC+IC). The results for the ABC, CSR and VCCR with the same enhancement parameters and SNRs as for the local metric are given in Fig. 8. Similar remarks hold for the global metrics as for the angular error.
For all three metrics and all SNRs the enhancements lead to an improvement compared to CSD, the only exception being the ABC for SN R = 10 and D 44 = 0. 02. Furthermore, as the SNR decreases, the larger diffusion times are beneficial and the more significant the improvement is. The best results are obtained for D 44 = 0. 005. We expect that truncation of the spherical harmonics already introduces some angular smoothing of the FODs on this artificial dataset, explaining the small effect of D 44 in the experiments. Furthermore, we see that the diffusion time t truly acts as a regularization parameter, resulting in a robustness for the metrics with respect to the SNRs: the higher the diffusion time, the smaller the differences in the metrics between the different SNRs. Seeding from the white matter voxels can lead to an over-representation of the number of fibers in longer fiber bundles with respect to the shorter bundles  [74].