dataesr/scientific-paragraphs-categorization huggingface dataset:
Database To the extent of our knowledge, there is not a public labeled subtle motion database available. Thus we decide to create our own database consisting of image sequences with subtle motions. We create 18 image sequences using real and artificially generated images which elements are for the most part static with the exception of one or two instances of subtle motion. We simulate the subtle motion by translating either an object in the image or the whole image by one pixel per frame during two or three frames. We also label the time when the subtle motion starts (onset) and ends (offset).
Evaluation Methodology The first step is, for each video, to add a specific level of noise. Then we evaluate the methods accu-racy by comparing all the detected peak frames in each method with the ground truth labels from each video in order to tell whether they are true or false positive subtle motions. Within a certain threshold level (p = 0. 75 see Eq. 24), if one spotted peak is located within the frame range of [ONSET, OFFSET] of a labeled subtle motion video, the detected sequence is be considered as one true positive subtle motion. Otherwise a penalization of a possible detected subtle motion (ψ frames) is counted as false positive. Since the noise is added randomly we repeat the test and measure the methods' accuracy 20 times. We define the true positive rate (TPR) as the percentage of frames of correctly detected subtle motion divided by the total number of ground truth subtle motion frames in the database.
Discussion The results in the previous section show that our method has, in general, a better spotting accuracy compared to the other methods tested. However, a closer examination of our database has shown that our method performs better in images with overall strong edges and its performance decreased in images with blurry edges. A possible explanation for this behavior is that our amplitude masking method (see section 3. 2) aims to bypass areas of higher amplitude in which the phase noise is lower and discard areas of low amplitude in which the phase noise is higher. However, in images with blurry edges, the computed amplitude will be low all over the image and our system will end up bypassing areas of higher noise, thus, compromising our method's accuracy. The performance of the OF method is comparable to our method.
CONCLUSIONS We presented a subtle motion analysis and spotting method based on the Riesz pyramid. Our method adapted the quaternionic representation of the Riesz monogenic signal by proposing a new filtering scheme. We were also able to mask regions of interest where subtle motion might take place in order to reduce the effect of noise using the image amplitude. Furthermore, we illustrated the power of our subtle motion analysis method by briefly presenting a couple of potential real-life applications. After testing our method using our own database under different levels of Gaussian additive noise and salt and peper noise, we can conclude that our method surpasses other state of the art methods. Due to unavailability of a public labeled subtle motion database we had to test our experiments in a rather limited dataset.
Statistical Analysis Differences between the varying actinobacterial strains and control cultures were analyzed using a One-Way Analysis of Variance (ANOVA) followed by a Dunnett Post-Hoc Test. Statistical analysis for OTA absorption on ACFs were analyzed using Two-Way ANOVA (p-value < 0. 05) followed by a Bonferroni Post-Hoc Test. Data analysis was carried out with GraphPad Prism 4 software (GraphPad Software, La Jolla, CA, USA). Differences were considered to be statistically significant when the p-value was lower than 0. 05.
Temperature was set to cool at a rate of 0. 2°C per minute, from +5°C to -30°C. Data was extracted from the data-logger using Testo Comfort Software 5. 0. SCP corresponds to the onset of the exotherm produced by the latent heat of freezing, just before the death of the insect  (Colinet et al. , 2007; Renault et al. , 2002).
Mass, volume and size measurements We determined if the SCP was influenced by the mass of the parasitoids and the size of the adults or the volume of the mummies. Mass was measured using a ± 0. 1 µg precision balance (Mettler-Toledo XP2U). Aphid mummies were weighted before and after each SCP experiment to test for potential effect of freezing on the mass. No difference was found (before SCP measurements: 0. 36 ±0. 04 mg, and after: 0. 35 ±0. 04 mg, Student paired t-test t=1. 42, p=0. 19, n=17), thus only the mass obtained after SCP measurements was kept for analyzes. Size of the adults and volume of the mummies were measured using a camera AxioCam ERc 5s (Zeiss, Germany) and the ImageJ software (v1. 51). For adults, the length of the left hind tibia was measured. For mummies, length (L) and width (W) were measured and the volume (V) was obtained using the relation: ܸ ൌ ‫6/²ܹܮߨ‬  (Colinet et al.
Statistical analyses A generalized linear model (GLM) was fitted to the data to test for differences in SCP among the five modalities (diapausing prepupae, non-diapausing prepupae, control prepupae, adult which went through immature diapause and adult which emerged without diapause) and using the sex and the ratio mass/size or mass/volume as covariables. Tukey post hoc tests for linear models (package 'multcomp') were then performed to test for differences among treatments. Data was then split between adults and prepupae and GLMs were fitted to the data in order to assess for differences in mass/size or mass/volume ratio between diapausing and non-diapausing parasitoids (sex was used as a covariable for adults). Differences between explanatory variables were assessed using a type-II Anova from the package 'car'.
Acknowledgments We thank  S. Llopis  for technical assistance and  N. Bidet  for drawing insects in Fig. 1. KT was supported by the  French Région Bretagne  and the  Canada Research Chair in Biological Control. LB and KT performed the experiments and analyzed the results. KT wrote the manuscript. All co-authors contributed to the protocols and made substantial revisions to the manuscript. This study was supported by the  LTER France Zone Atelier Armorique.
Spatial, historical and structural characteristics of the forest samples The map of recent forests came from the national topographic vector database (BDTopo) produced by France's National Institute of Geographic and Forest Information (IGN). BDTopo is typically defined to make maps on a 1:25,000 scale. The forest layer in the study area date from 2010. This layer was used to estimate the current area of each forest  [32]. The amount of forest habitat was considered by calculating the forest density in the area surrounding each sampled forest (i. e. the percentage of forest cover including the focal forest)  [33]. This variable was computed on two spatial levels (within a 0. 5 and a 1 km radius) and on two dates (1850 and 2010). Forest within a radius of 0. 5 km and 1 km from the centre of a sampled stand were assumed to provide appropriate habitats for FSLB  [34].
Species survey Beetles were sampled in the 27 forests in February 2014 using the litter sieving method  [14, 23, 36]. In each sampled forest, one homogeneous hectare in the center of the forest was selected to constitute the entomological sampling plot. In each plot, 10 trees representing the tree diversity were selected by the same person and adjacent litter sieved using a Winkler sieve with a 5 mm mesh. One litre of sieved litter was collected per tree, making ten litres per stand. The number of trees sampled and the litter volume are sufficient to collect 97. 2% of the FSLB present, even in rich sites  [14]. Arthropods were extracted from the litter using a Berlese extractor  [14]. Species were sent to a specialist for identification by morphological analysis. Only flightless (without wings or with knitted elytra) saproxylic litter beetles were kept and included in the dataset.
Statistical analysis Ecological differences between the three zones (east, west and south) were tested by means of the Kruskal-Wallis test for each variable: Forest_amount 2010_0. 5, Forest_amount 2010_1, Forest_amount 1850_0. 5, Forest_amount 1850_1, Area 2010, STW, CWD, ITS, Histo_continuity (Table  1 ). Generalised linear models (GLM) were used to link the presence-absence of species and the explanatory variables related to the forest characteristics. All the analyses were performed with R software 3. 1. 0 [37] , the "stats" package  [37]  and the lme4 package  [38]. Binomial distribution was used to fit the models based on the presence-absence surveys. The spatial effect of the zone was taken into account through the random effect in the model. Since multicollinearity of predictors may inflate the variance of regression parameters, checks were undertaken to verify that the variables were not highly correlated (│r│< 0.
GLM tested to explain the presence-absence of the four FSLB species recorded in a fragmented forest landscape. Explanatory variables DescriptionModel 0. 5 with current area, past and present amount of habitat within a 0. 5 km buffer radius, structural heterogeneity variables and historical continuity Model form: E(y) = β 0 + β 1 + β 2_0. 5 + β 3_0. 5 + β 4 + β 5 + β 6 + β 7 +(1|β 8 ) Area 2010 β 1 Present area of woodland (topographic database 2010) (ha) Present forest amount in a zone with a 0. 5 km radius (topographic database 2010) Past forest amount in a zone with a 0.
Acknowledgments The authors are grateful to  A. Brin ,  C. Bouget  and  M. Deconchat  for their helpful comments during the project, to  O. Courtin  for the species identification, to  N. Defarge  for his work in the field and it the laboratory and to  R. Carrieand M. Goulard  for their helpful comments during the analyses. We thank  D. Goodfellow  and  Claire Tarring  for corrections to the English. This research was funded by the  Midi-Pyrenees Region  and the  Ecole d'Ingenieur de Purpan.
Acknowledgments We thank  Andreas Kindel ,  André L. Luza ,  Danis Kiziridis ,  Kirsten Henderson ,  Paulo I. K. L. Prado ,  Rodrigo S. Bergamin  and  Sandra C. Muller  and two anonymous reviewers for revising and providing valuable suggestions that greatly improved this manuscript. VAGB received support from  CAPES  (grant # 1002302 ), the  TULIP Laboratory of Excellence  ( ANR-10-LABX-41  and  394 ANR-11-IDEX-002-02 ) and from a  Region Midi-Pyrenees  project ( CNRS 121090 ). VP received support from  CNPq, Brazil  (grant #  307689/2014-0 ). PRG was supported by  FAPESP  (grant # 2009/54422-8 ) and  CNPq.
Acknowledgments: The authors gratefully acknowledge financial support by the  German Ministry for Education and Research  under grant  OIUV1012. They are furthermore thankful for helpful comments by seminar participants at the  ESA Zurich , the  HSC New York , the  ZEW Mannheim , the  University of Chicago , the  University of Sterling , the  IMEBESS Oxford  and the SBRCC workshop in Kiel.
ACKNOWLEDGMENTS We thank  M. Eckstein  and  M. Schüler  for helpful discussion. This work was supported by the  Swiss National Science Foundation  through  NCCR MARVEL  and the  European Research Council  through  ERC Consolidator Grant   724103. The calculations have been performed on the Beo04 cluster at the  University of Fribourg , and the  CSCS  Dora cluster provided by MARVEL.
MinION sequencing and basecalling Read event data generated by MinKNOW control software (version 0. 50. 2. 15 to 0. 51. 3. 40) were basecalled using the Metrichor software (version 2. 34. 3 to 2. 39. 3). The data generated (pores metrics, sequencing, and base-calling data) by MinION software were stored and organized using a Hierarchical Data Format. Three types of reads were obtained: template, complement, and two-directions (2D). The template and complement reads correspond to sequencing of the two DNA strands. Metrichor combines template and complement reads to produce a consensus (2D) sequence  7. FASTA reads were extracted from MinION Hierarchical Data Format files using poretools  8. Pass and fail reads were both considered as 1D reads. This dataset is described in Table  1  and (Genomic datasets, Data Citation 5).
These SNPs were converted into 2,104 Bins. From all these markers, a saturated map comprising 20 linkage groups was constructed using MSTmap online. Long reads-based de novo assembly of L. biglobosa G12-14 and L. maculans Nz-T4 R7. 3 nanopore 2D reads (Table  3 ) were assembled into contigs using SmartDeNovo (https://github. com/ ruanjue/smartdenovo), with a k-mer size parameter set to 21. Eventually, the consensus of the assembly was polished by aligning the Illumina paired-end 2 × 250 bp reads (Table  2 ) with BWA mem  12  and using the resulting alignment file as input for Pilon  13. We iteratively polished twice the assembly. L. biglobosa G12-14 final assembly had a cumulative size of 34. 9 Mb and a N50 equal to 462 kb. L. maculans Nz-T4 final assembly had a cumulative size of 43. 4 Mb and a N50 equal to 383 kb.
Genome assembly update of L. maculans JN3 With 33 scaffolds, a N50 equal to 2. 40 Mb and a cumulative size of 44. 8 Mb, the JN3 rearranged reference genome was already contiguous enough for our analyses. However, it contained gaps that represented 1. 1 Mb in total. In addition, both the optical map 2 and the high-density genetic map indicated that nine mis-assembled super contigs had to be either splitted or fused. The reference genome was scaffolded and gap-filled using PBJelly  14  with the entire nanopore dataset as input (Table  3 ). As a result we obtained 33 scaffolds with a cumulative size of 45. 8 Mb, the N50 increased to 2. 43 Mb and we decreased by half the number of N's (570 kb). As the R7. 3 nanopore reads were prone to errors, particularly in the homopolymeric regions  15  , we polished the consensus of the gap closed assembly by aligning 150X of Illumina paired-end 2 × 250 bp reads with BWA mem  12  and giving the resulting alignment file to Pilon  13.
Genome annotation The gene prediction of the three Leptosphaeria genomes was done by combining gene models information computed from transcript and protein alignments (Fig. 2 ). First, repeated sequences in the reference genomes were masked using multiple tools. Tandem repeats were identified and masked using Tandem Repeats Finder  16. Low complexity regions were identified and masked using Dust  17. Interspersed repeats and other low complexity sequences were masked using RepeatMasker (http://www. repeatmasker. org). Furthermore 121 known transposable elements (identified in the L. maculans-L. biglobosa species complex in a previous study 2 ) were also masked using RepeatMasker. The Alternaria alternata, Cochliobolus heterostrophus, Pyrenochaeta sp. , Pyrenophora tritici-repentis and Zymoseptoria tritici proteomes included in UniProt (version available as of 07 September 2016) were used to detect conserved proteins in L.
2. 08, using a k-mer size of 63 bp. The results of this assembly for each sample are summarized in (Transcriptomic datasets, Data Citation 5). Reads were mapped back to the contigs with BWA-mem  12  and the consistent paired-end reads were selected. Chimeric contigs were identified and splitted (uncovered regions) based on coverage information from consistent paired-end reads. Moreover, open reading frames (ORF) and domains were searched using respectively TransDecoder 22 and CDDsearch  23. We only allowed breaks outside ORF and domains. At the end, the read strand information was used to correctly orient the RNA-Seq contigs. In a second step, RNAseq contigs were aligned to the relevant genome assembly (L. maculans JN3, L. maculans Nz-T4, L. biglobosa G12-14) using BLAT. The best matches (based on BLAT score) for each contig and other matches with a score > = 90% of the score of the best match were selected.
Then, Est2genome  24  was used to refine the alignments and we kept alignments with an identity percent > = 95% and at least 80% of the RNA-Seq contig that can be aligned (Transcriptomic datasets, Data Citation 5). Gene predictions from mRNA and proteomes were given as input to the GMOVE combiner  25  to build the gene models. This tool considers biological data such as RNA-seq from the organisms of interest and proteome from homologous species. Proteins give information about the frame of the coding sequence (CDS) and RNA-seq helps to find splicing sites with more accurate alignments. This tool is designed for all eukaryotes and de novo studies with no need of calibration pre-processing. After gene models were produced by GMOVE, a final filter was applied to remove artefactual untranslated regions (UTRs).
Illumina reads processing and quality filtering After the Illumina sequencing, an in-house quality control process was applied to the reads that passed the Illumina quality filters. The first step discards low-quality nucleotides (Q o 20) from both ends of the reads. Next, Illumina sequencing adapters and primer sequences were removed from the reads. Then, reads shorter than 30 nucleotides after trimming were discarded. These trimming and removal steps were achieved using in-house-designed software based on the FastX package. The last step identifies and discards read pairs that mapped to the phage phiX genome, using SOAP  26  and the phiX reference sequence (GenBank: NC_001422. 1). This processing resulted in high-quality data and improvement of the subsequent analyses. This dataset is described in Table  2  and (Genomic datasets, Data Citation 5).
A specific filter aiming to remove ribosomal reads was applied to data generated from RNA samples. The reads and their mates that mapped onto a ribosomal sequences database were filtered using SortMeRNA  27. It contains different rRNA databases and we used it to split the data into two files: rRNA reads in a file (ribo_clean) and other reads in another file (noribo_clean). This dataset is described in (Transcriptomic datasets, Data Citation 5).
Contamination detection In order to detect possible contamination in our assemblies, we first ran the metagene  28  gene predictor on NzT4 and G12-14 contigs and on JN3 scaffolds. Then, we aligned all predicted proteins against the NR database using blastp. Hits were then filtered by their e-value to only conserve the best hit with an e-value of at least 10 -10. Finally, a contig or scaffold was flagged as non-contaminant if at least 50% of its predicted proteins were assigned as fungi. Following this procedure, we couldn't detect any contaminant sequences in JN3 and G12-14 assemblies. We detected 13 small contigs that were not assigned as fungi in the NzT4 assembly, but further investigations (blat against the JN3 assembly and blastx using the NCBI blast server) revealed that they were not contaminants.
Acknowledgements The authors are grateful to  Oxford Nanopore Technologies Ltd  for providing early access to the  MinION device through the MinION Access Programme , and we thank the staff of  Oxford Nanopore Technology Ltd  for technical help. BI, CC, PW, and JMA are part of the  MinION Access Programme (MAP)  and JMA received travel and accommodation expenses to speak at  Oxford Nanopore Technologies conferences. This work was supported by the  Genoscope , the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA) and France Génomique  ( ANR-10-INBS-09-08 ). F. Dutreux was funded by a grant from  PROMOSOL (Association pour la promotion de la sélection des plantes oléagineuses ). The genetic map was obtained through a funding of the  French National research agency  ( ANR  project  ANR-12-ADAP-0009  ' Gandalf  ').
Alzheimer's and Parkinson's diseases are considered the most common neurodegenerative disorders, representing a major focus of neuroscience research to understanding the cellular alterations and pathophysiological mechanisms involved. Several natural products, including flavonoids, are considered able to cross the blood-brain barrier and are known for their central nervous system-related activity. Therefore, studies are being conducted with these chemical constituents to analyze their activities in slowing down the progression of neurodegenerative diseases. The present systematic review summarizes the pharmacological effects of flavonoids in animal models for Alzheimer's and Parkinson's diseases. A PRISMA model for systematic review was utilized for this search. The research was conducted in the following databases: PubMed, Web of Science, BIREME, and Science Direct.
Materials and Methods 2. 1. Search Strategy. The present systematic review was conducted according to the guidelines for Transparent Reporting of Systematic Reviews and Meta-Analyses (PRISMA statement)  [25]. In this review, the specialized databases Pubmed, Web of Science, "Biblioteca Regional de Medicina" (BIREME), and Science Direct were used for the literature search in September and October 2017, using the terms "flavonoids, bioflavonoids, flavonols, flavonoid, anthocyanin, flavone, flavones, 2-phenyl-benzopyrans, 2-phenyl-chromenes, isoflavonoids or flavonones," combined with "neurodegenerative diseases, Alzheimer disease or Parkinson disease. " We did not contact investigators, nor did we attempt to identify unpublished data.
Data Extraction. Data were extracted by one reviewer using standardized forms and were checked by a second reviewer. Extracted information included the substance, animal models, doses and concentrations, and evaluated parameters. 2. 4. Methodological Quality Assessment. The risk of bias and quality of preclinical evidences were assessed using a checklist adapted from Hooijmans et al. [26]  and Siqueira-Lima et al. [27]. This analysis allowed evaluating the methodological quality of the selected manuscripts regarding the outcome measurements, randomization of treatment allocation, blinded drug administration, blinded outcome assessment, appropriate description of the doses and routes of administration used, and appropriately reported statistical analysis of data. Studies that reported randomization of animals, blinding, outcome measurements, and statistical analysis were considered to be of higher methodological quality.
Results and Discussion 3. 1. Study Selection. The primary search identified 2456 articles, including 347 from PubMed, 1338 from BIREME, 595 from Web of Science, and 176 from Science Direct. Among these, 1157 documents were published more than five years ago and were therefore excluded from the review. In addition, 305 manuscripts were indexed in two or more databases and were considered only once, resulting in 994 eligible articles. After an initial screening of titles and abstracts, followed by a full text analysis, 31 articles were included in this systematic review, while the reminder (n = 728) was excluded since they did not meet the inclusion criteria. A flowchart illustrating the progressive study selection and numbers at each stage is shown in Figure  1.
For comparisons between mouse and human data, we first mapped the 513 identified mouse CGIs (mm9) to the corresponding CGIs in human (hg19), using the UCSC toolbox. 501 out of 513 CGIs were successfully matched between the two genomes. For 416 CGIs, human methylome data were available in TCGA dataset (Supplementary Fig. 3A ; Supplementary Data 2). We extracted the methylation β-value for the CpGs within these human CGIs and calculated the mean methylation difference for each CpG and for each of the 41 HCC patients. While the majority of these CpGs showed a methylation difference below 0. 2, a proportion of CGIs (24%) were differentially methylated (FDR < 0. 05) with a hypermethylation enrichment score similar to the Alb-R26 Met HCC (91%; Fig. 1e , Supplementary Fig. 3B ). As the analysed cohort includes patients with widely diverse aetiologies and characteristics, we next analysed the 416 CGIs in the individual patients.