dataesr/scientific-paragraphs-categorization huggingface dataset:
7 and Agilent Genomic Workbench v5. 0 softwares (Agilent Technologies). Regions with DNA copy number abnormalities were detected using ADM-2 (seta s 6) statistic provided by DNA Analytics, with a minimum number of 5 consecutive probes. Genomic build mm7 was used for the experiment. For comparison of the murine lymphoma genomic changes with those present in human disease, two previously published studies including the aCGH analysis of 136 samples from patients with SMZL were used  35, 36. Chemotaxis assays. Purified B cells (3 Â 10 5 ) resuspended in 100 ml RPMI 10% FCS were added to the upper insert of Boyden chambers (Costar). Then, the upper insert was placed in the lower chamber that contains 600 ml RPMI 10% FCS alone or in the presence of chemokine (recombinant human CXCL12 and recombinant murine CXCL12, CXCL13, or CCL21; Preprotech) at the specified concentration.
Calcium mobilization assay. Splenic cells (10 7 ) from WT and transgenic Em-NKX2-3 mice were stained with anti-CD19. Cells were twice washed with RPMI-1640 medium and stained with 2 mM of Fluo-4 AM (Invitrogen) for 30 min. Cells were collected and resuspended in 1 ml of RINGEN buffer (155 mM NaCl; 4. 5 mM KCl; 3 mM MgCl 2 ; 10 mM D-Glucose; 5 mM HEPES). When indicated, cells were stimulated with 10 mg ml À 1 anti-IgM, and later, the buffer was supplemented with 2 mM of CaCl 2. Intracellular Ca 2 þ mobilization was measured in real-time by FACScalibur (BD Biosciences) with a laser tuned at 488 nm. Raw data files were analysed using FlowJo 7. 6. 4 software. Adhesion assays and time-lapse microscopy on planar lipid bilayers. Artificial planar lipid bilayers containing GPI-linked mouse ICAM-1 or VCAM-1 (density 150 molecules per mm 2 ) were prepared and used in the study  42, 53.
Assays were performed in PBS/0. 5% FCS/0. 5 g l À 1 D-glucose/2 mM MgCl 2 /0. 5 mM CaCl 2. Images were acquired on an Axiovert LSM 510-META inverted microscope with a Â 40 oil immersion objective (Zeiss). Imaging analysis of cell adhesion and dynamic parameters were done using Imaris 7. 0 software (Bitplane) and ImageJ software (NIH). Adhesion frequency was calculated as (no. of cells in contact with the ICAM-1-or VCAM-1-containing membrane (IRM þ )/total no. cells in the field) Â 100; we calculated polarization and motility frequencies similarly. Assays were performed using NKX2-3 transgenic and WT B cells isolated from mice of same age, and comparing data between them. Similar data were obtained with WT B cells independently of the mouse age. IF studies. Frozen and acetone-fixed sections were blocked with 5% BSA in PBS for 20 min.
After mounting, the sections were viewed under an Olympus BX61 fluorescent microscope. The acquisition of digital pictures with a CCD camera was performed using the analySIS software. IF antibodies are listed in Supplementary Table  8. Chemical inhibitors. For therapeutic experiments, mouse transgenic B-cell lymphoma cells were incubated at 2. 5 Â 10 6 cells per ml for 24 h with the following compounds at increasing concentrations: PI3K inhibitor (Idelalisib; Chemie Tek); BTK inhibitor (Ibrutinib; Chemie Tek); MALT1 proteolytic inhibitor (MI2); NF-kB inhibitor (Bay11-7082; Enzo LifeSciences); Lyn inhibitor (Dasatinib; Selleckchem); Syk inhibitor (P505-15; Selleckchem). For chemotactic and cell dynamics assays, mouse B cells were incubated at 3 Â 10 6 cells per ml for 1 h at 37 °C with the following compounds: CXCR4 antagonist (AMD3100; 10 mm); blocking rat anti-mouse LFA-1 (CD11a) antibody (clon M17/4; 1 mg ml À 1 ; BioLegend); LFA-1-ICAM-1 interaction inhibitor (A286982; 10 mm); Lyn inhibitor (Dasatinib; 1 mm) and Syk inhibitor (P505-15; 1 mm).
Survival curves were estimated using the product limit method of Kaplan-Meier and were compared using the log-rank test. Two-tailed t-tests (normally distributed data) or Mann-Whitney U-test (non-normally distributed data) were used for evaluating the significance of differences at gene expression of WT versus transgenic samples, as appropriate. Statistical analyses were carried out using GraphPad Prism 5. 0 software (GraphPad). In the case of chemotaxis, cell adhesion and other cell dynamics parameters, statistical analysis and graphs were done using GraphPad Prism 4. 0 software. Two-tailed unpaired Student's t-test was applied to compare each NKX2-3 transgenic B cells population with its control (WT B cells); *Po0. 05, **Po0. 001, ***Po0. 0001.
Acknowledgements We thank  Jerry Adams  ( WEHI, Melbourne, Australia ) for providing the pEmSR vector,  Vincent Dubus  ( Genoway, Lyon, France ) for support during transgenic mouse generation,  Carlos Ortiz de Solorzano  ( CIMA, Pamplona, Spain ) for spleen ultrasound studies in mice,  Lorena Maestre  and  Giovanna Roncador  ( CNIO, Madrid, Spain ) for generation of NKX2-3 mAbs,  Bela Kajta  ´r ( Department of Pathology, University of Pecs, Hungary ) for human spleen IHC,  Maria Dolores Odero ,  Marı ´a Jose  ´Larrayoz (CIMA) and  Anne Gardiner  ( Royal Bournemouth Hospital, Bournemouth, UK ), and  Maria Antonia Fortun ˜o  ( Biobanco Universidad de Navarra ) for providing patient samples and clinical data,  Teresa Flores  ( IBMCC/HUS, Salamanca ) for pathology advice,  Elena Ciordia  and  Eneko Elizalde  ( CIMA ) for excellent animal care,  Eduardo Martı ´nez-Anso  (CIMA),  Charlotte Cresson  ( CHU Purpan, Toulouse, France ) and  Enrique de A ´lava  ( Hospital Virgen del Rocı ´o, Sevilla, Spain ) for initial studies, and the staff of the cytogenetic and molecular cytogenetic laboratories of the  Institute of Human Genetics (Kiel)  for technical assistance.
This work was supported by Grants from the  Instituto de Salud Carlos III (ISCIII) ,  Spanish Ministry of Economy and Competitiveness ,  FIS-PI12/00202  (to J. A. M. -C. ),  RTICC-RD12/0036/0063  (to J. A. M-C. ), RTICC-RD12/ 0036/0068  (to M. J. C and F. P. ),  RTICC-RD12/0036/0022  (to J. L. F-L. ), RTICC-RD12/ 0036/0070  (to J. C. ),  RTICC-RD12/0036/0010  (to B. B. ),  RTICC-RD12/0036/0044  (to M. J. B. ) and  RTICC-RD12/0036/0069  (to J. M. H. R. and M. G. ); by  Worldwide Cancer Research  project grant  15-1322  (to J. A. M. -C. , Y. R. C. and M. -Q. D. ); by  BFU2011-30097  (to Y. R. C); by  MINECO   SAF2013-45787-R and Marie Curie Programme FP7-PIIF-2012-328177  (to S. R. ); by the  French-Spanish   CITTIL  project (to F. P. , X. A. , J. A. M. -C. , C. B. and P. Brousset); by  SAF2012-32810 ,  SAF2014-57791-REDC ;  PIE14/00066 ,  BIO/SA32/ 14  and  CSI001U14  (to I.
There have been a lot of works in computational homology in the last decades. Many of them  [16, 9, 17]  can compute the homology groups of more general spaces in cubical time. Computing only the Betti numbers (number of holes), which are the ranks of these groups, should be faster, but this has not been algorithmically proved. Delfinado and Edelsbrunner  [5]  introduce an algorithm with almost linear time complexity that computes the Betti numbers of a simplicial complex which is a subcomplex of a triangulation of S 3. The software library RedHom  [12]  is optimized for computing the homology in the context of cubical complexes. Wagner  [19]  also proposes an adapted algorithm for computing persistent homology on a cubical complex. We propose an algorithm that is based on the computation of connected components and avoids any matrix manipulation.
Technically it is enough to implement a mapping from the set of indices to a linear space of memory [0, L-1] containing the value i at the ith position. Taking advantage of this fact, features of the C++ language, and Boost. MultiArray, we do not have to allocate memory for the index set. We get the index set and the slicing operation without any additional cost. Of course we can achieve it in many ways, however with our approach we can reuse well tested code.
Validation Table  1  shows results of numerical experiments with the algorithm implementation. We compare also with standard approach for Betti numbers computa-tions using elementary reductions, coreduction, and Morse decomposition from CAPD::RedHom  [11]. All the computations were performed using one data structure, only algorithms vary. Data sets N0001 and P0001 come from computer assisted proofs in dynamics. Data sets rand pP S were generated randomly, where S is the size of the grid and each 3-cube (together with its faces) is included with probability P. The data sets are in binary format, thus reading time can be omitted. Computations were performed on a 2,3 GHz Intel Core i7 (4 real cores, 8 virtual) with 16GB RAM. The results show that the parallel implementation is around 4 times faster than the sequential one.
The FPKM ratio of the Fus gene confirmed that Fus was expressed in Fus DNLS/DNLS mice, but not in Fus À/À mice (Fig EV3A   EV1 and Fig 4B). Of note, only nine genes and 56 genes were upregulated and downregulated more than 1. 5-fold, respectively (Dataset EV1). Identical analysis in Fus À/À mice identified 669 upregulated genes (29 genes more than 1. 5-fold) and 889 downregulated genes (72 genes more than 1. 5-fold)  (Dataset EV2 and Fig 4C). Comparison of both models identified 353 genes that were altered in the same direction, consistent with loss of FUS nuclear function underlying the altered levels of these transcripts  (Fig 4D and Dataset EV3). Downregulation of selected genes in both mouse models was confirmed by qRT-PCR  (Appendix Fig S1A)  yielding data similar to the RNA-seq results  (Fig 4E).
Thus, Fus DNLS/DNLS expression profiles largely recapitulated expression profiles of Fus À/À brains, yet a subset of genes was found specifically associated with the expression of cytoplasmic truncated FUS in Fus DNLS/DNLS brains. Gene ontology analysis showed that transcripts whose expression was upregulated in Fus DNLS/DNLS animals were enriched for genes involved in mRNA translation and extracellular matrix constituents (Dataset EV4). In contrast, transcripts upregulated in Fus À/À brains revealed enrichment for nuclear and nucleolar proteins involved in the regulation of transcription, DNA replication, or regulation of RNA metabolic processes (Dataset EV5). Gene ontology.
05 adjusted for multiple testing. D Venn diagram showing the number of overlapping genes misregulated in Fus DNLS/DNLS (blue circle) and Fus À/À (red circle) brains with 353 genes similarly downregulated or upregulated upon cytoplasmic mislocalization or complete loss of FUS. E Normalized expression (based on FPKM from RNA-seq) of genes identified by RNA-seq to be significantly downregulated (Ahi1, Kcnip1, Nefm, Nefl, Tuba4a, Dmpk, Rad9b, Stac3, Hist1h2bc, Hist1h1c) or upregulated (Fam193b, Pmm2, Bphl, Taf15) in both Fus DNLS/DNLS and Fus À/À compared to their control. Error bars represent SEM in 4-5 biological replicates. **P < 0. 01, two-tailed student's t-test. F Normalized expression (based on FPKM from RNA-seq) of genes identified by RNA-seq to be uniquely changed in Fus analysis revealed a similar enrichment for synaptic activity and function among transcripts downregulated in Fus DNLS/DNLS and Fus À/À animals (Datasets EV4 and EV5), supporting that cytoplasmic mislocalization of FUS alters the expression of genes involved in synaptogenesis, as a direct consequence of FUS loss of function.
Seventy-five splicing events were commonly regulated in Fus DNLS/DNLS and Fus À/À mice, 100% of which were differentially included or excluded in the same direction  (Fig EV4D and E and Dataset EV8). Semi-quantitative RT-PCR of selected RNAs confirmed these FUS-dependent splicing changes (Figs 5C and EV4F), including transcripts previously implicated in neurodegenerative diseases such as the N-myc downstream-regulated gene 2 (Ndrg2) that is misaccumulated in Alzheimer's disease  (Mitchelmore et al, 2004) , the microtubule-associated protein tau (Mapt) gene mutated in frontotemporal dementia  (Hutton et al, 1998) , the ataxin 2 (Atxn2) gene mutated in ALS  (Elden et al, 2010) , spinocerebellar ataxia type 2 (SCA2)  (Imbert et al, 1996) , and the pro-neurotrophin receptor sortilin 1 (Sort1)  (Hu et al, 2010).
1). Motor neurons were counted at L1-L5 on both cresyl violet-and ChAT-stained sections at 20× magnification (for each genotype: Exact numbers of animals per group are provided in figure legends). The counting was performed per ventral horn in every tenth section for ten sections in total per animal. In Nissl-stained sections, only neurons with an area ≥ 80 lm 2 and located in a position congruent with that of motor neuron groups were counted  (d'Errico et al, 2013). All ChAT + profiles located in the ventral horns of immunostained sections clearly displayed on the plane of the section were counted. Total estimated motor neuron numbers were obtained using a computer-assisted microscope (Nikon Eclipse E800) and the software (Nis Elements version 4. 0). Cells were counted on the computer screen using a digital camera (Nikon Digital Sight DS-U3) mounted on a microscope.
4-58. 7% mapping rate. Cufflinks (version v2. 1. 1) was used to generate transcript abundance for each annotated protein-coding gene as fragments per kilobase of transcript per million mapped reads (FPKM), and statistical analysis and comparison of FPKM values were calculated using Cuffdiff (version v2. 1. 1). Genomewide unsupervised clustering analysis and heat maps with significant changes between different groups were generated using R (Bioconductor). qRT-PCR techniques used to confirm RNA-seq changes are described in Appendix Supplementary Methods and oligonucleotides sequences are provided in Dataset EV9.
Analysis of splicing alterations by RASL-seq RNA-mediated oligonucleotide annealing, selection, and ligation with next-generation sequencing (RASL-seq) analysis of alternative splicing changes was carried out as already described elsewhere  (Li et al, 2012; Zhou et al, 2012). In brief, a pool of oligonucleotides was designed to detect 3,859 alternative splicing events in mice. One hundred fmol of RASL-seq oligos were annealed to 1 lg of total RNA isolated from brains of Fus DNLS/DNLS , Fus À/À , and their control littermates. After ligation, 5 ll eluted ligated oligos were used for 16-18 cycles of PCR amplification, and the barcoded PCR products were sequenced using an Illumina HiSeq 2000 sequencer with 24-30 samples per lanes. Sequencing data were decoded allowing no mismatch with each barcode, and target sequences were mapped with RASL-seq oligo pool sequences using the short read alignment software Bowtie allowing for 1 mismatch at both the left and right side of the ligated oligos.
An average of ~5 million reads from each sample was mapped, with events with < 4 counts in one of the isoforms removed. Ratios of the counts of shorter to longer isoforms were calculated. The significantly changed events were identified by t-test and average fold change. Unsupervised clustering analysis and heat maps with significant changes between different groups were generated using R (Bioconductor). Semi-quantitative PCR techniques used to confirm RASL-seq changes are described in Appendix Supplementary Methods. Oligonucleotides sequences are provided in Dataset EV9.
Statistics For the animal experiments, the values from each animal were averaged for each genotype group and analyzed by Student's unpaired two-tailed t-test. Comparison of three or four groups was performed using one-way ANOVA and Tukey's post hoc test. Data were analyzed by using the Graphics Prism Program (Graph Pad Software, San Diego, CA) and expressed as mean AE SEM (standard error of the mean) and differences were considered significant when P ≤ 0. 05.
Acknowledgements We thank  Dr. Pico Caroni  for providing the ChAT-CRE knockin mice and helpful discussions. We thank  Marlene Bartos  for her help in achieving this project. We are grateful to  Dr. Don W. Cleveland  and the members of his group for tremendous support and fruitful discussions. This work is supported by an  ALS   Association Investigator Initiated Award  (grants  2235  and  3209 ; to LD and CLT); the  Frick Foundation  (award 2013 to LD and  CLT , award 2010 to ES);  Association Française contre les Myopathies  (grant # 18280 ; to LD, CLT and ES);  Target ALS  (grant  13-0840 ; to CLT);  Virtual Helmholtz Institute "RNA dysmetabolism in ALS and FTD " ( WP2 , to LD, AW and ACL);  Association de recherche sur la SLA  (ArSLA; to LD);  State North Rhine Westphalia  (to ES);  Minna-James-Heineman-Stiftung  (to ES); and the  Max Planck Society  (to ES).
During his well-known debate with Fisher regarding the phenotypic dataset of Panaxia dominula, Wright suggested fluctuating selection as a potential explanation for the observed change in allele frequencies. This model has since been invoked in a number of analyses, with the focus of discussion centering mainly on random or oscillatory fluctuations of selection intensities. Here, we present a novel method to consider nonrandom changes in selection intensities using Wright-Fisher approximate Bayesian (ABC)-based approaches, in order to detect and evaluate a change in selection strength from time-sampled data. This novel method jointly estimates the position of a change point as well as the strength of both corresponding selection coefficients (and dominance for diploid cases) from the allele trajectory.
Approximate Bayesian Computation (ABC) has the advantage of being flexible in integrating complex models due to computational efficiency and the lack of likelihood computation  (Beaumont 2010). Recently, a hierarchical ABC-based method based on the Wright-Fisher model was developed in order to infer genome-wide effective population size and per-site selection coefficients from whole-genome multiple-time point datasets  (Foll et al. 2014 (Foll et al. , 2015)). While the initial approach performs well overall, the authors noted the possibility for observations inconsistent with a single-s Wright-Fisher model; this was indeed observed at certain sites in their analysis of the influenza virus genome, which were simply excluded from consideration. Thus, as a natural extension, we here investigate the presence of changing selection intensities in these outlier single nucleotide polymorphisms (SNPs); in doing so, we also develop an extended Wright-Fisher ABC-based method capable of detecting and quantifying changing selection intensities through time (termed CP-WFABC).
Wright-Fisher ABC-based method This approach first relies on a previously developed  Wright-Fisher ABC method (WFABC -Foll et al. 2014 , 2015)  in order to estimate a constant effective population size (N e ). WFABC infers an effective population size from time-serial datasets through the average fluctuation of neutral trajectories over time (following  Wright 1948). If whole-genome multitime point datasets are available, the estimated value is referred to as the genome-wide effective population size. The posterior of the N e estimated from WFABC is used as a prior for the following extended method. The trajectory X of a given allele with a known N e consists of time-serial allele frequencies f t (t = 1,. ,T) where T is the total number of generations (with T. 4 to allow for a change point to be realizable with the Wright-Fisher model), from which a sample n m is taken at sampling time points m = 1,.
Initially, the random sampling of an allele from generation 1 to generation CP-1 is simulated using s 1 , and onwards from the change point (CP) using s 2. In order to simulate realistic allele trajectories with changing selection coefficients, the allele needs to be segregating at the time of the change point. This condition is necessary since the change in selection coefficient cannot occur if the allele is either lost or fixed beforehand, assuming the infinite-site model with no back mutations. Thus, only alleles segregating at the change point are accepted as simulated datasets. The associated summary statistic for these time-serial data are Fs', an unbiased estimator of N e based on the allele frequency change between two sampling time points that remains unbiased even in the presence of highly skewed allele frequencies and small sample size  (Jorde and Ryman 2007) , an important property for inferring change points along the allele trajectory.
; M 2 1 (6) where b ¼ mean and S 0 ¼ 0. The CUSUM value S is accumulated only when the statistic b is different from its average value in the dataset. The change point S CP is the sampling time point with the maximal absolute value of S i , which is the furthest point from the initial value Here, we take Fs' at each pair of consecutive sampling time points as the statistic b, since it is a time-serial measure of the allele frequency change, which is indicative of the selection strength change. Thus, when Fs' is used as the statistic b in the CUSUM, the maximal CUSUM value S CP is the potential change point of the allele trajectory, as illustrated with an example in Figure  1. In the change point Wright-Fisher ABC (CP-WFABC), an additional summary statistic S CP is used to characterize observed and simulated allele frequency trajectories for detecting a change point.
The performance study was conducted with a haploid population of N e = (100, 1000, or 10,000) and a diploid population with N e = (50, 500, or 5000) using the simulated datasets of the two scenarios described in the previous section as M 0 and M 1 , respectively. We considered two cases for the pseudoobservables to test the sensitivity and specificity of the ABC model choice: the first case when the pseudoobserved trajectories have a single selection coefficient, and the second case when they have changing selection coefficients. One thousand pseudoobservable trajectories were generated for each case with the data ascertainment minimum frequency set to 2% for at least one of the sampling time points. Additionally, for the second case, pseudoobservable trajectories were accepted only when the allele was segregating at the time of the change pointa constraint for realistic combinations of selection coefficients, change points, and dominance (for diploids)in order to reproduce changing selection trajectories in real datasets.
All other input values were kept constant as in the simulated datasets described in the previous section. The results of the ABC model choice from a haploid population with N e = 100, and a diploid population with N e = 500, are represented as ROC curves  (Xavier et al. 2011)  in Figure  2. Specificity is given on the x-axis showing the true negative rate, while sensitivity is given on the y-axis showing the true positive rate of the Bayes factor B 1,0 calculated from 1000 pseudoobservables of changing selection (where B 1,0 should be large) and 1000 pseudoobservables of constant selection (where B 1,0 should be small). The overall ROC curves in black (all trajectories) show that when the specificity threshold is most conservative in detecting no false positives (i. e. , B 1,0 = infinite), the Bayes factor B 1,0 has a sensitivity of $30% for all populations.
DATA APPLICATION Historical dataset of P. dominula A long-running dataset based on the medionigra morph responsible for darker wing color in wild populations of P. dominula (Figure  S8 ) began in 1939 with collections by  Fisher and Ford (1947)   Using the ABC model choice introduced here as a test for a change in selection strength, and to estimate the parameters of interest for the chosen model, we assume the medionigra allele is a single codominant locus responsible for the homozygous and heterozygous expressions of the phenotypic forms bimacula and medionigra, respectively (Cook and Jones 1996). The model M 0 assumes a single selection coefficient, thus the only parameter to estimate is s. The M 1 model assumes a change in selection strength, thus the parameters of interest are s1, s2, and CP.