dataesr/scientific-paragraphs-categorization huggingface dataset:
Boyce index values close to zero mean that the model is not different from a random model. Models with a mean Boyce index higher than 0. 7 were selected (Fig. S3 ). Consequently, five models were kept for modelling: GLM, GBM, GAM, FDA and MARS. These analyses were performed using the R package ecospat v2. 1. 1  72. Forecasts of current and future favourable areas were obtained by the ensemble forecasting method. Current and future ensemble forecasts represent consensual projections of the five modelling techniques, obtained through averaged distributions of favourability scores  89, 95, 96. To discriminate current favourable and non-favourable areas, and visualize shifts by 2050, continuous probability of favourability were transformed into binary projections. These binary projections were obtained using a probability threshold that maximized the Sørensen value  91.
Acknowledgements We sincerely thank  J. Thevenot  ( UMS Patrinat ),  O. Lorvelec  ( INRA, ESE, Rennes, France ),  B. Guillemot  ( Office National de la Chasse et de la Faune Sauvage ),  H. Magnin  and  G. Van Laere  ( Parc National de Guadeloupe ),  J. Chalifour  ( Réserve Nationale Naturelle de Saint-Martin ),  B. Angin  ( Ardops Environnement ),  A. Lenoble  ( PACEA, Université de Bordeaux ),  M. Rutherford  ( The University of West Indies, Trinidad ), and  F. Catzeflis  ( Institut des Sciences de l'Evolution, Université Montpellier 2, Montpellier, France ) for sharing occurrence data. We thank the curators of the museums for providing access to specimens and data (see Table  S1 ).
The green and red channels were acquired sequentially. For drift correction, 200 nm TetraSpeck beads (Life Technologies) were added to samples. Images were processed using the NanoJ-SRRF plugin  (Gustafsson et al. , 2016)  with the following parameters: Ring Radius 0. 5, Radiality Magnification 5, Axes in ring 6, Temporal Analysis TRPPM. SRRF time-lapses were produced by running SRRF analysis on groups of 50 frames. If aberrant PSF of Tetraspeck beads were observed, datasets were discarded.
Quantification and statistical analysis All confocal images were pre-processed using the ImageJ software (http://rsbweb. nih. gov/ij/) for the delimitation of the region of interest. Then the CZI image files were processed using a computational pipeline relying on the numpy, scipy, pandas, czi_file Python libraries, as well as other custom libraries. Extensive details about the computational methods and algorithms are given in Appendix 3, 4 and 5. Given the non-linear positive DR5 response, the raw values were logarithmically transformed in order to obtain a symmetric distribution of the noise. Nadaraya-Watson estimates and confidence intervals were then calculated with a confidence level of 95% in the R environment  (RStudio Team, 2015). The boxplots displayed in the article were obtained by computing the median (central line), first and third quartiles (lower and upper bound of the box) and first and ninth deciles (lower and upper whiskers) using the R environment or numpy percentile function and rendered using the matplotlib Python library.
Data and software availability All experimental data and quantified data that support the findings of this study are available from the corresponding authors upon request. Generic quantitative image and geometry analysis algorithms are provided in Python libraries timagetk, cellcomplex, tissue_nukem_3d and tissue_paredes (https://gitlab. inria. fr/mosaic/) made publicly available under the CECILL-C license. Specific SAM sequence alignment and visualization algorithms are provided in a separate project providing Python scripts to perform the complete analysis pipelines  (Cerutti, 2020 ; copy archived at https://github. com/elifesciences-publications/sam_ spaghetti).
3. This consolidates our previous conclusions, even in the case of an underestimation of the radial speed. confusion between P p and P pþ3. (E) The primordia points from the observed experimental data form perfectly separable clusters (F-G) Increasing the plastochron variability creates clusters that are more difficult to separate and generates confusion between P p and P pþ5. (H-J) Measured anglular and radial deviations of both observed and simulated primordia allow to determine plausible values for the angular varibility s a ¼ 3:6 (I) and plastochron variability s T ¼ 0:22 (J). The prmordia sdistriibution generated with this parameters (H) is the one that metches best the observed deviation of primordia clusters. (K) Separability evaluated by varying speed coefficient and plastochron variability.
Each point in P corresponds to a detected nucleus, identified by an integer index n and associated with a single spatial position P n in physical coordinates, so that we can write P ¼ P n j n 2 N ¼ ½½1; jPj½½ f g: (40) This detection method has been evaluated on a set of 4 manually expertized SAM images acquired at different voxel sizes with a 16 bit encoding, representing more than 5000 cells. Since the acquisition parameters for those images matched the one use in the rest of our analysis, we adjusted the method parameters to perform best on this expertized dataset. The parameter testing led to the determination of the optimal values K s ¼ 3, s min ¼ 0:8m, s max ¼ 1:4m, I min ¼ 3000, corresponding to an evaluated performance of 95. 6% recall (percentage of the manually labelled cells that were indeed detected) and 98.
Microscopy image preparation The confocal images saved as CZI files through the ZEN software (ZEISS International, 2012) of the LSM-710 microscope are opened using a Python script  (Gohlke, 2012)  and split into independent channels that are saved separately as INR image files. This operation preserves all the information contained in the raw image format. In the specific case of acquisitions for which both pPIN1:PIN1-GFP (PIN1) and pRPS5a:DII-VENUS (DII) are imaged, the close emission wavelengths causes the PIN1-stained cell membranes to appear in the DII nuclei images. In that only case, the PIN1 signal intensity is subtracted from the DII image channel before saving the file. We use the polygonal selection tool of the ImageJ software to manually define a region of interest in all the slices of every image, and doing so, digitally dissect the outermost organs to get meristem images with at most six visible organs.
This binary mask is then applied on all the channels, and masked channels are saved in separate INR files. Nuclei detection and quantification The nuclei detection algorithm has been implemented in the tissue_nukem_3d Python library using the Gaussian filtering functions provided by the SciPy library  (Virtanen et al. , 2020)  for the construction of the scale space. The surface mesh used for the extraction of L1 nuclei was computed using the implementations provided in the VTK (Visualization ToolKit) library  (Schroeder et al. , 2006)  for the Marching Cubes algorithm, the mesh smoothing and the quadric decimation  (vtkImageMarchingCubes, vtkWindowedSincPolyDataFilter, vtkQuadricDecimation). A version of the isotropic remeshing algorithm has been implemented in the cellcomplex library. Image registration The image registration operations are performed using the blockmatching computing library that comes embedded in the timagetk (Tissue Image ToolKit) image processing library dedicated to 3D microscopy images of multicellular tissues.
The confocal image stack was then processed through the automatic pipeline described above. This provides notably unique identifiers to cells and allows to identify the visible cell interfaces by pairs of cell labels that can be matched to the outcome of the pipeline. Individual cell interfaces were manually annotated by two independent experts in the ImageJ software using the same protocol. We designed a procedure to determine carrier polarities on 2D slices co-imaging membrane-located fluorescent carrier proteins and cell-wall staining inspired from  Shi et al. (2017). The following steps are repeated for each cell interface, identified by a pair of cell labels coming from the automatically segmented PI image:. Draw a minimal amount of 3 lines using the 'Straight line' tool: Going always from the same cell (left) towards the other (right) Keeping them orthogonal to the apparent interface plane (Appendix 4-figure  3B ).
Acknowledgements We thank  Fabrice Besnard  and the members of the SIGNAL team for insightful discussions;  Antoine Larrieu  for helping with RNA-Seq analysis; Helene  Robert-Boisivon  for the YUC transcriptional and mutant lines;  Sophie Ribes  for her contribution to the nuclei image processing pipeline;  Gwyneth Ingram ,  Olivier Hamant ,  Arezki Boudaoud  and  Jan Traas  for feedback on the manuscript. We acknowledge the contribution of  SFR Biosciences  ( UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon ,  UCBL ) facilities PLATIM, for assistance with microscopy; of the  PSMN (Pole Scientifique de Modelisation Numerique) of the ENS de Lyon  for providing us access to their computing resources. This work was supported by  Human Frontier Science Program organization (HFSP)  Grant  RPG0054-2013  to TV and  CG ,  ANR-12-BSV6-0005  grant ( AuxiFlo ) and  ANR-18-CE12-0014-02  (ChromAuxi) to TV and  DFG FOR2581  to JUL.
Data availability All data generated or analyzed during this study are included in the manuscript and supporting files. Generic quantitative image and geometry analysis algorithms are provided in Python libraries timagetk, cellcomplex, tissue_nukem_3d and tissue_paredes (https://gitlab. inria. fr/mosaic/) made publicly available under the CECILL-C license. Specific SAM sequence alignment and visualization algorithms are provided in a separate project providing Python scripts to perform the complete analysis pipelines (https://gitlab. inria. fr/mosaic/publications/sam_spaghetti. git, copy archived at https:// github. com/elifesciences-publications/sam_spaghetti).
Here, we report the results of Part A (single-dose PK phase) and Part B (continued dosing 12-week safety phase in patients with SRI) of a three-part phase I trial (NCT02923947) designed to characterize the impact of SRI on the PK of osimertinib and its metabolites (AZ5104 and AZ7550) in patients with advanced solid tumors. Table  S1 ]). 15 NRF was defined as CrCL >90 mL/min.
| Trial design The study was performed in accordance with the ethical principles that have their origin in the Declaration of Helsinki and that are consistent with International Council for Harmonisation (ICH)/Good Clinical Practice (GCP), applicable regulatory requirements and the AstraZeneca policy on Bioethics. Protocols were reviewed and approved by an independent ethics committee or institutional review boards in each participating country before implementation. Data underlying the findings described in this article may be obtained in accordance with AstraZeneca's data sharing policy described at https:// astra zenec agrou ptria ls. pharm acm. com/ST/Submi ssion /Discl osure.
| Population pharmacokinetic analysis An additional assessment of the impact of renal impairment on the PK of osimertinib was performed using a population PK analysis and the surrogate marker of CrCL as determined by the C-G formula. Previously published population PK analysis methods were updated with additional clinical data from this study. 17  this study, and five as mentioned previously). For each clinical trial dataset, analysis of variance (ANOVA) statistical analysis was performed and mean differences between groups and respective confidence intervals for each dataset were calculated.
The mean exposure to osimertinib and its metabolites (AZ5104 and AZ7550) in patients with NRF in this study was comparable to that observed in patients in other studies with advanced NSCLC (mean AUC of 11 070 nmol/L•h in this study and 10 590-13 520 nmol/L•h in other studies; Supplementary Section Table  S2 ) and in the population PK analysis (mean AUC ss 11 258 nmol/L•h). 17 Note: Relationship between renal function (CrCL calculated on baseline) and natural log-transformed osimertinib PK parameters AUC ss is presented here. Results are based on an ANOVA model with a fixed effect for renal function group. Abbreviations: ANOVA, analysis of variance. a Three subjects without PK exposure are not included in this analysis. b Ratio: Median ratio of 100 matched datasets. In patients with SRI, the higher osimertinib exposure cannot be explained by changes in protein binding.
The population PK data set included patient data from phase I, II, and III osimertinib trials AURA (NCT01802632), 11, 18  AURA2 (NCT02094261), 19 AURA3 (NCT02151981), 2  and FLAURA (NCT02296125). 3 Details of the studies included in the population PK dataset are provided in Supplementary Section and TableS2. A linear regression model was conducted, to assess the relationship between empirical Bayes estimate for total apparent plasma clearance (CL/F) and baseline CrCL including patients with SRI's data from this study (n = 7 SRI patients) to the population PK dataset (n = 1364, of which there were five patients with SRI). Statistical comparisons of osimertinib exposure parameters (area under the plasma concentration-time curve at steady state [AUC ss ]) from the population PK analysis set (n = 1364) combined with this study (n = 16) were also performed and the results were summarized.
While exposure to osimertinib was higher in the cohort with SRI, a correlation between osimertinib and/or metabolite(s) exposure with CrCL could not be established. Several individual patients with high osimertinib AUC values were also observed in the NRF population in this study, similar to those observed in prior studies (maximum AUC values: 74 500 nmol/L⋅h [NCT02161770], 25 500 nmol/ L⋅h [NCT02163733], and 40 400 nmol/L⋅h [NCT02157883]). Furthermore, the upper range of osimertinib C max observed in this study was similar in the two populations and below that observed under single-dose conditions in several other trials (1260 nmol/L [NCT02161770], 704 nmol/L [NCT02157883], and 803 nmol/L [NCT02908750]). The high individual exposure to osimertinib in this study observed in two patients with AUC above 35 000 nmol/ L⋅h could not be explained by their demographic and/or disease characteristics and suggests variability of exposure of osimertinib.
Background: Alpha and gamma adaptin binding protein p34 (AAGAB) was previously reported as a novel on-treatment biomarker can improve prediction of response to neoadjuvant chemotherapy in breast cancer. However, the expression and prognostic value of AAGAB in breast cancer is unknown, the function of AAGAB in breast cancer remains to be elucidated. Methods: Herein we investigated the role of AAGAB in human breast cancer from the TCGA database, immunohistochemistry, Gene set enrichment analysis (GSEA) and immune in ltration analysis. Results: Increased AABAB expression in breast cancer was signi cantly associated with age, gender, race, ER status, PR status, N-stage, PAM50 classi cation and histological type (all p-values<0. 05). Kaplan-Meier survival analysis showed that breast cancer patients with AAGAB-high had a worse prognosis than that with AAGAB-low (p=0.
Gene set enrichment analysis GSEA is a computational method to determine whether an a priori de ned gene sets is signi cant between two groups  [15, 16]. We used MSigDB database C2 collection to perform GSEA. For the purpose of analysis, we divided breast cancer patients into AAGAB high group and AAGAB low group based on the expression level of AAGAB. Gene set permutations were 1000 times for each evaluation. The nominal p value and normalized enrichment score (NES) were used to sort the enriched pathways of gene sets.
Results The expression of AAGAB in breast cancer was signi cantly up-regulated We screened the difference in AAGAB gene expression in the relevant cancer datasets from the TCGA databases, supplemented by the GTEx databases. The results demonstrated that the expression of AAGAB in BRCA, CESC and OV tissue was signi cantly higher than that in normal tissues (P <0. 05) (Figure  1 ). 1104 breast cancer samples were included in this analysis and 403 normal breast tissues used for comparison. The expression of AAGAB in breast cancer was signi cantly up-regulated (p <0. 001, log 2 FC = 1. 163) relative to normal tissue (Figure  2 ). For the purpose of analyzing the association between clinicopathologic variables and AAGAB expression, we divided breast cancer patients into AAGAB high group and AAGAB low group based on the expression of AAGAB, in which performed chi-square analysis (Table  1 ).
AAGAB maybe involving in endocytic recycling of growth factor receptors such as EGFR, which can result in increasing cell division. The level of AAGAB was found to be prognostic of response in renal cancers and in thyroid cancers from the TCGA database. AAGAB was identi ed as a novel on-treatment biomarker for accurate prediction of pathological complete response (pCR) and reaction in patients treated with neoadjuvant chemotherapy. However, the exact role of AAGAB in breast cancer is currently unclear and warrants further investigation. Herein we investigated the role of AAGAB in human breast cancer from the TCGA database, immunohistochemistry, GSEA and Immune in ltration analysis. Increased AABAB expression in breast cancer was signi cantly associated with age, gender, race, ER status, PR status, N-stage, PAM50 classi cation and histological type (all p-values<0.
Authors' contributions Dan Yin designed the study and performed data analysis; Cheng Zhang and Chen Gong revised the images and article; Yang Liu collected samples; Changcai Wang, Cheng Zeng and Kai Yang performed IHC experiment. All authors reviewed and approved the manuscript. We would like to thank Yuanyuan Zhao from Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology for her kindly help. Figure  2  The expression of AAGAB in breast cancer tissues and normal tissues from TCGA database. The expression of AAGAB in breast cancer tissues was signi cantly up-regulated as compared to normal breast tissues (p<0. 001, log2FC=1. 163).
The aim of this study was to elucidate the ecological structure of the human gut temperate bacteriophage community and its role in inflammatory bowel disease (IBD). Temperate bacteriophages make up a large proportion of the human gut microbiota and are likely to play a role in IBD pathogenesis. However, many of these bacteriophages await characterization in reference databases. Therefore, we conducted a large-scale reconstruction of temperate bacteriophage and bacterial genomes from the whole-metagenome sequence data generated by the IBD Multi'omics Database project. By associating phages with their hosts via genome comparisons, we found that temperate bacteriophages infect a phylogenetically wide range of bacteria. The majority of variance in bacteriophage community composition was explained by variation among individuals, but differences in the abundance of temperate bacteriophages were identified between IBD and non-IBD patients.
IBDMDB Data Depository This study was conducted using publicly available whole-metagenome shotgun sequencing (WMGS) data generated by the IBD Multi'omics Database (IBDMDB) research team as part of the Human Microbiome Project 2  [1, 10]. Specifically, 1338 sets of quality-filtered WMGS data were used. Sequences (i. e. , reads) were generated via DNA sequencing using the Illumina HiSeq (2 × 101 bp) system (Illumina, San Diego, CA, USA) from longitudinal stool samples from 65 CD patients, 38 UC patients, and 27 non-IBD patients. Non-IBD patients had gastrointestinal symptoms but were not diagnosed with IBD  [1]. Quality control of WMGS data was performed by the IBDMDB research team  [1]. Briefly, the AnADAMA2 pipeline (http://huttenhower. sph. harvard. edu/anadama2) was used to remove low quality regions from reads, and any reads matching the human genome were discarded.
Generation of Viral Operational Taxonomic Units (OTUs) for Temperate Bacteriophages Viral regions within contigs were predicted using VirSorter (version 1. 0. 5) (-db 2, -diamond)  [14]. To reduce false identification, predicted viral regions with at least one hallmark viral gene and/or enrichment of either viral-like genes or non-Caudovirales viral genes (i. e. , VirSorter categories 1, 2, 4, and 5) were selected for further analyses. The selected viral regions were grouped into viral OTUs at 95% sequence similarity using CD-HIT-EST (version 4. 6. 8) (-c 0. 95, -aS 0. 85, -n 10, -g 1)  [15]. To identify viral OTUs representing temperate bacteriophages, MetaGeneMark (version 4. 30) was first used to derive translated peptide sequences from the viral regions  [16]. Subsequently, the peptide sequences were searched against the Pfam-A database (version 33.
0) using hmmscan (version 3. 3) (-cut_ga)  [17, 18]. Viral OTUs encoding one or more of the following viral integrase protein domains were defined as temperate bacteriophage and were selected for the subsequent analyses: PF00589, PF02899, PF09003, PF14659, PF13356, PF13495, PF12834, PF12835, PF13009, PF14657, PF02914, PF02316, PF02920, PF09299. Taxonomic classification of the viral OTUs was performed by classifying representative sequences (the longest) from each OTU using vConTACT v2. 0 (version 0. 9. 12) (-rel-mode Diamond, -db ProkaryoticViralRefSeq94-Merged)  [19].
Assignment of Hosts to Viral OTUs Two strategies based on previous studies were used for host assignment of viral OTUs: binning-and basic local alignment search tool (BLAST)-based methods  [33] [34] [35]. In the binning-based method, a MAG cluster was assigned as the host of a viral OTU if the contigs from the MAG included a region corresponding to the viral OTU. In the BLAST-based method, bacterial sequences adjacent to prophage sequences (i. e. , VirSorter categories 4 and 5) were aligned against MAGs using BLASTn in the BLAST+ package (version 2. 8. 1) (-perc_identity 95, -evalue 1e-50, -word_size 11)  [36]. If both the upstream and downstream regions of a prophage sequence in a viral OTU aligned with contigs from a MAG cluster with sequence similarity >95% and query coverage >80%, the MAG cluster was considered to be the host.
Mapping Reads to Viral OTUs and MAGs Quality-filtered reads from 1338 gut metagenomes were mapped to the representative sequences of each viral OTU and MAG cluster using bowtie2 (version 2. 3. 4. 3) (-maxins 1000)  [37]. Resulting bam files were processed using CoverM (version 0. 3. 0, https://github. com/wwood/CoverM/) (-proper-pairs-only) so that only reads that aligned across ≥80% of their length with a minimum of 95% sequence identity were retained. The bam files were then further processed using samtools (version 1. 9) (-f 3, -F 2304)  [38] , and only the primary alignments from the properly paired reads were retained. Counts of mapped read pairs were retrieved using idxstat in samtools (version 1. 9) and placed into count tables for both viral OTUs and MAG clusters [38].
Analysis of Ecological Diversity For all subsequent analyses, a set of 1290 samples with more than 1,000,000 reads mapped to MAG contigs and viral OTUs was used. Alpha diversity within each stool sample was assessed using Shannon's diversity index analysis of genome-length-normalized count tables after rarefying at the minimum sample depth using phyloseq R package (version 1. 30. 0) [39]. This process was repeated 10 times and the average value for each sample was reported. The following linear mixed-effects model was used to assess the statistical significance of the effect of diagnosis factor on the alpha diversity of gut microbial communities using the nlme R package (version 3. 1-147)  [40] : Shannon s diversity index ∼ disease status + sex + consent age + antibiotics + chemotherapy+ immunosuppressants + site name + (1 participant ID).