dataesr/scientific-paragraphs-categorization huggingface dataset:
(a-d) Pairs of symmetric unilateral assemblies. (e-f) Single bilaterally symmetric assemblies. (g) All ROI assemblies found with the PCA-promax method, displayed over the maximal intensity projection of all imaged optical sections (for individual optical sections see Supplementary Video 2). In all panels, assemblies are displayed with the same color code, according to the similarity of their activity dynamics (the more temporally correlated, the more similar the color; except for the temporally correlated assembly pair shown in c, which was colored differently to facilitate visualization). (h) Same as g, but for assemblies found using k-means, color-coded according to the similarity of their activity dynamics. To allow for a comparison between g and h, the dimensionality of the dataset was reduced through PCA before clustering (otherwise, clustering did not converge).
ACKNOWLEDGEMENTS We thank  Simon Peron ,  Karl Svoboda  and collaborators, and also  Nicholas Priebe ,  Boris Zemelman  and collaborators for sharing with us the mouse somatosensory and visual cortex data, respectively, through the crcns. orgdata sharing portal (datasets ssc-1 and pvc-10). We thank the members of the  Sumbre group  for helpful discussions and continuing testing of the toolbox. We thank the  Marin Burgin lab at IBioBA  for their support. This work was supported by  EraSysBio+ Zebrain ,  ERC  stg  243106 ,  ANR-10-LABX-54   MEMO LIFE ,  ANR-11-IDEX-0001-02   PSL* Research University, Avenir  grant  INSERM ,  Argentine Agency  from the  Promotion of Science and Technology  ( PICT 2013-0182 ), and  Structural Convergence Fund for MERCOSUR (FOCEM).
It consists of zebrafish larva in vivo two-photon calcium imaging data (see Results). Software setupInstallation of the toolbox. Download the analysis toolbox from github. com/zebrain-lab/Toolbox-Romanoet-al. It contains all the Matlab source code and a Readme. pdffile that explains all the relevant variables used during the pipeline (this file is vital if the user wishes to adapt or further develop the toolbox). It also contains test data for a case study. Double-click on the downloaded toolbox zip file to extract its contents. Add the toolbox to Matlab path. For this, open Matlab and type the command:addpath(genpath('Extracted_Folder')).
During a time period of 36 h, the Ab levels peaked at 12 and 23 h with troughs at baseline, and 25 h showing significant fluctuations of more than 50% within 6 h. A sinusoidal pattern of Ab levels was described across participants, supposed to be due to time of day, activity or dynamic changes in the production or clearance rate of Ab. This study was the first to arise the issue of a possible diurnal variation of CSF biomarkers that could represent a significant obstacle when using CSF biomarkers as diagnostic tools. In the study reported by  Bjerke et al. (2010) , 14 psychiatrically and neurologically healthy subjects underwent knee surgery. CSF was serially collected by LP with an 18-Gauge Portex epidural needle at baseline, after 4-6 h (mean 5. 3 h) and after 24 h. If compared to Bateman's results, data showed more stable levels with a slight but significant decrease in CSF Ab42 after 4-6 h, which tended to return to baseline levels after 24 h.
A possible reason for these results is that, as opposed to Bateman's study, a smaller CSF volume was taken; this could have led to a minor impact on the CSF dynamics. More recently,  Slats et al. (2012)  studied the withinsubject variability of other AD biomarkers, i. e. Tau and pTau, and found no diurnal variation in CSF dynamics during a 36-h sampling (6 ml/h). While previous data were principally obtained in mostly young healthy participants, this study enrolled older subjects and patients with AD, in order to determine daily variability in these relevant age groups. Six patients suffering from mild AD (59-85 years, MMSE 16-26), and six healthy volunteers (64-77 years) underwent insertion of an intrathecal catheter, from which 6 ml of CSF were collected each hour for 36 h. Variability of CSF Ab40, Ab42, Tau and pTau concentrations was lower than expected, and the data observed by Bateman et al.
Diurnal variation in aSyn species As it will be reviewed in more detail later in this paper, many studies have investigated the diagnostic and/or prognostic performance of CSF aSyn species, i. e. total, oligomeric and phosphorylated aSyn, in PD and AD cohorts, reporting conflicting results  (Wang et al. 2015; Parnetti, Cicognola, et al. 2016). With respect to CSF aSyn species diurnal variations, data are still scanty  (Mollenhauer et al. 2016). Spies et al. (2011)  led a pilot study performing repeated CSF sampling in healthy elderly and AD patients. CSF samples of six healthy controls (59-85 years old) and six AD patients (64-77 years old) who underwent repeated CSF sampling from an indwelling intrathecal catheter with 1-h intervals were analysed. No linear trend in aSyn concentrations over 33 h was observed.
2015). Two trials with the anti-amyloid antibody gantenerumab in patients with prodromal (clinicaltrials. gov ID: NCT01224106) and mild AD (clinicaltrials. gov ID: NCT02051608) also required low levels of Ab1-42 in CSF for inclusion. Data released from the trial in prodromal AD also supported the value of CSF measures in selecting the patient population and it is expected that more trials will incorporate the same design.
The ADNI1 study required LPs in at least 50% of participants at BASELINE study entry and at 1 year. An add-on study resulted in the collection of annual CSF samples out to 3-4 years in 142 ADNI1 participants (18 AD, 74 MCI, 50 cognitively normal). Longitudinal CSF samples have been collected out to 4 years in the ADNIGO/2 phases and analyses of these are planned for early 2017. In this review we describe results for the ADNI1 longitudinal dataset. The changes in CSF Ab1-42, Tau and pTau181 in ADNI1 participants over time, from BASELINE visit out to 3-4 years have been described in several publications  (Beckett et al. 2010; Vemuri et al. 2010; Lo et al. 2011; Landau et al. 2013; Toledo, Xie, et al. 2013; Mattsson, Insel, et al. 2015). During 1 year following BASELINE little change was observed for these three CSF biomarkers except for a modest increase in Tau in cognitively normal controls  (Beckett et al.
2013). According to analyses of the chronology of biomarker changes in ADNI1 participants using linear regression models adjusted for age, gender and APOE e4 genotype, pathological values of BASELINE Ab1-42 predicted later increases in pTau181, but neither Tau or pTau181 BASELINE values predicted changes in Ab1-42 over time  (Toledo, Xie, et al. 2013). The observed time dynamics are consistent with observations in autosomal-dominant participants in the Dominantly Inherited Alzheimer Network (DIAN) study in which the proposed timeline for CSF Ab1-42, Tau and pTau181, based largely on cross-sectional data and using the expected year of symptom onset for each participant  (Bateman et al. 2012; Fagan et al. 2014). Planned further analyses in expanded numbers of the ADNI and other study cohorts over longer periods of time will be essential to confirm these results and provide greater insight and understanding of disease heterogeneity in late-onset AD, and inform clinical trial planners and ultimately routine clinical practice on expected CSF biomarker trajectories and their relationships to cognitive decline.
Acknowledgements This is an  EU   Joint Programme -Neurodegenerative Disease Research (JPND)  project, supported through the funding organisations under the aegis of JPND (www. jpnd. eu). The research leading to these results has received support from the  Innovative Medicines Initiative Joint Undertaking  under EMIF grant agreement n  115372 , resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007(FP7/ -2013 ) ) and EFPIA companies' in kind contribution. This study has been supported by a grant from the  German Federal Ministry of Education and Research (BMBF) :  Kompetenznetz Demenzen  ( 01GI0420 ). Piotr Lewczuk is supported by funds from the  Leading National Research Centre (KNOW) , and grants for neurodegenerative diseases,  Medical University of Białystok, Białystok, Poland , and received consultation and lectures honoraria from  Innogenetics/Fujirebio Europe ,  IBL International, AJ Roboscreen, Beckman Coulter, Virion/Serion GmbH , and  Roche.
The research leading to these results has received funding from the  programme 'Investissements d'avenir'   ANR-10-IAIHU-06  ( Agence Nationale de la Recherche -10- IA Agence Institut Hospitalo-Universitaire-6). He serves as Senior Associate Editor for the journal Alzheimer's & Dementia; he has been a scientific consultant and/or speaker and/or attended scientific advisory boards of Axovant, Anavex,  Eli Lilly  and company,  GE Healthcare ,  Cytox Ltd ,  Jung Diagnostics GmbH ,  Roche ,  Biogen Idec ,  Takeda-Zinfandel, Oryzon Genomics, Qynapse, Merck, Sharp & Dohme (MSD) ; and receives research support from the  Association for Alzheimer Research (Paris) ,  Pierre and Marie Curie University (Paris) ,  Pfizer  & Avid (paid to institution); and has patent applications, but receives no royalties.
jpnd. eu). For the Netherlands, the  Netherlands Organisation for Health Research and Development (ZonMw) ); The  Internationale Stichting Alzheimer Onderzoek  (projects  12506  and  14502 );  American Alzheimer Association  (project  IIRG-10-173389 ). Maria Bjerke  and Sebastiaan Engelborghs are partially funded by: the  University of Antwerp Research Fund ; unrestrictive research grants from  Janssen Pharmaceutica NV and ADx Neurosciences ; the  Flemish Government  initiated Methusalem excellence grant (EWI, www. ewi-vlaanderen. be); the  Flanders Impulse Program on Networks for Dementia Research (VIND) ; the  Agency for Innovation by Science and Technology (IWT , www. iwt. be); and the  Research Foundation Flanders (FWO , www. fwo. be). Catharina Klijn has received a clinical established investigator grant of the  Dutch Heart Foundation  (No  2012T077 ) and an  Aspasia grant  from the  Netherlands Organisation for Health Research and Development, ZonMw  ( 015008048 ).
80GHz CPUs. Each pair of benchmark and solver was run on one CPU with a timeout of 2500 seconds and 20 GB memory. The experimental data and the library are also available on Zenodo  5. Since our method focuses on showing satisfiability, only brief statistics on unsat benchmarks are provided. Among the 1546 unsat benchmarks, 200 benchmarks are found unsatisfiable already by the linear arithmetic theory reasoning in veriT. For each of the remaining ones, the method quickly returns unknown within 0. 002 to 0. 096 seconds, with a total cumulative time of 18. 45 seconds (0. 014 seconds on average). This clearly shows that the method can be applied with a very small overhead, upfront of another, complete or less incomplete procedure to check for unsatisfiability. Table  1  provides the experimental results on benchmarks with sat or unknown status, and the cumulative times.
Acknowledgments We are grateful to the anonymous reviewers for their comments. This research has been partially supported by the  ANR/DFG  project  SMArT  ( ANR-13-IS02-0001 & STU 483/2-1 ) and by the  European Union  project  SC 2  (grant agreement No. 712689 ). The work has also received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 713999 , Matryoshka). The last author would like to acknowledge the  JAIST Off-Campus Research Grant  for fully supporting him during his stay at  LORIA, Nancy. The work has also been partially supported by the  JSPS KAKENHI   Grant-in-Aid for Scientific Research(B)  ( 15H02684 ) and the  JSPS Core-to-Core Program (A. Advanced Research Networks ).
Shoots of each type were collected, and the leaves were scanned using a flatbed scanner (HP Scanjet G4010) with a resolution of 150 dpi and saved in portable network graphics (png) format. Leaf blade area, length and width were measured using ImageJ 1. 48v software, assuming that leaf shape is elliptical  (Freeman and Bolas, 1956). Total leaf area on each shoot was calculated as the sum of its individual leaves. The models predicting individual/total leaf area use allometric relations between easily recorded input variables such as the length of the ellipse of the biggest leaf on the shoot (L max ), the FIGURE 1 | Representation of the three shoot types on the first-order branch of apple. RO, rosette; BS, bourse shoot; and VS, vegetative shoot. Figure produced using the GroIMP platform  (Hemmerling et al.
To develop an allometric model for the prediction of final leaf area, we needed a sufficiently large data set. However, the data set available in this study  (2014 experiment, Bairam et al. , unpublished)  already included three defoliation treatments. In order to enlarge the database for the parameterization of our model beyond the control data, we checked whether the defoliation treatments had a significant impact on the following variables used in the model: total leaf area and number of leaves of bourse shoot and rosette, respectively. The statistical tests were carried out separately, on each genotype for each sampling date and each type of shoot. Results (ANOVA, Kruskal-Wallis) showed that there was no significant influence of the defoliation treatments  (Bairam et al. , unpublished).
For FU, ANOVA and Kruskal-Wallis analysis were carried out on 2015 and 2014 data to compare total leaf area and number of leaves (rosettes and bourse shoots) as a function of developmental stages (expressed in GDD) in order to establish the sampling stages at which the spur shoots were fully developed. Therefore, fully developed bourse shoots and rosette shoots, respectively, were pooled for each genotype. All shoots of the same type and the same genotype considered to be fully developed in terms of total leaf area and numbers of leaves were used in this study for building the allometric models. Total leaf areas and numbers of leaves of bourse shoots of AR, FU, and RB, were fully developed at 345, 201, and 275 GDD, respectively. Groups of bourse shoots of each genotype were selected from these physiological ages onwards, until harvest.
However, in this second equation two parameters of the model [s RO,G and N RO,G (R Amax )] had to be fixed. Both equations 8a and 8b were solved using the solver functionality of Microsoft Excel software (Microsoft, Redmond, WA, USA). Starting from Equation  7 , the model is expressed as: N(A R ) = 1 1 + (N j (R) -N j (R Amax )) 2 s 2 j,G ; jǫ{BS; RO} A R = A max 1 + (N j (R) -N j (R Amax )) 2 s 2 j,G ; jǫ{BS; RO} (9) A R = A max 1 + (R -R Amax ) 2 nl 2 s 2 j,G ; jǫ{BS; RO} A R = A max 1 + (R -R Amax ) 2 nl 2 •s 2 j,G ; jǫ{BS; RO} (10) By fixing the latter parameters, it was possible to calculate the normalized area of each leaf, using only the number of leaves on the shoot and the area of its biggest leaf. However, as the leaf area is not easily measurable non-destructively, further allometric relations are required for predicting leaf areas using variables that are more easily accessible.
s 2 RO,G (16b) The testing set was then used for calculating individual and total leaf areas. Using the testing data set, comparisons were made between measured and calculated data (i) for each established parameter, (ii) for individual leaf area and (iii) total leaf area on each shoot of each genotype using the linear model. P-values and coefficients of determination R 2 were calculated in order to analyze and interpret the significance and the goodness of fit of the models. For rosettes, as the ranks of leaves on the bourse were not recorded in the 2014 experiment, comparisons between measured and calculated values of individual area were done only on data from 2015.
Statistical Analyses Statistical analyses were done using R Studio software version 0. 98. 1062. 0 running R version 3. 2. 2. and the statistical computing environment of the R-package "agricolae, " version 1. 2-2. Normality of data used for each analysis was tested on residuals using the Shapiro-Wilk test for data sets with more than 50 samples per group or sample distributions considered to be normal (P > 0. 05); ANOVA tests were used to check if there was a significant difference among groups (P < 0. 05). Otherwise differences between groups were tested with the Kruskal-Wallis test (P < 0. 05), or both ANOVA and Kruskal-Wallis. Fit of data to selected models was checked using the lm function in R software for linear distribution models. The logistic model was parameterized using the SSlogis function in R software and coefficients of determination (R 2 ) and root mean squared errors (RMSE) were calculated to test the fit of the model.
Total leaf area on each shoot was calculated as the sum of individual leaves. Calculated and measured total leaf areas (CTLA BS,G and TLA BS,G ) of the bourse shoots were compared for each genotype using a linear regression where the slope α BS,G of each regression model of TLA BS,G as a function of CTLA BS,G was expected to be equal to 1 if the calculated and the measured area were identical. The slope parameter was equal to 1. 2, 0. 99 and 1. 87 for AR, FU and RB, respectively (Figures  9A-C ), which led us to assume that the parameter α BS,G was genotype dependent. Therefore, values of α BS,G fixed using the training set were retained for calibrating the model. The model defined in Equation  15 and built for bourse shoots including the three parameters (s BS,G , k BS,G and α BS,G ) and using the variables L max and nl for the prediction of individual leaf areas was used to calculate individual areas for each leaf of the testing set and the comparison with measured individual data was conducted using a linear model.
Modeling the Leaf Area of Rosettes (RO) Both ANOVA and Kruskal-Wallis test showed there was no significant difference between the three genotypes with respect to β RO,G. Thus β RO,G was fixed to 0. 69 using a linear regression model on the training set of rosette data of bourses sampled before harvest of the three genotypes (Figure  11A ). The model was significant (P < 2. 10 -16 ) and the coefficient of determination was high (R 2 = 0. 97). Afterwards, the values of parameters s RO,G and N RO,G (R Amax ) were fixed to 0. 38 and 0. 63, respectively. e RO,G was significantly different among genotypes (P = 3. 46. 10 -  < 0. 05), with means of e RO,G equal to 0. 65, 0. 66 and 0. 68 for AR, FU and RB, respectively. When comparing e BS of different ranks on rosettes of FU sampled in 2015, both ANOVA and Kruskal -Wallis test showed there were no significant differences among the shapes of leaves of different ranks on the bourse.
e RO,FU did in fact significantly vary between the 2 years (P = 2. 162. 10 -7 ). k RO,G was established for AR, FU, and RB using linear regression models on the training set of each genotype (Figures  12A-C ) and the leaf lengths of each genotype were significantly correlated with leaf width (P < 2. 10 -16 for all the genotypes' models) with R 2 = 0. 86, 0. 85 and 0. 85 for AR, FU and RB, respectively. k RO,G values were fixed to 0. 75, 0. 74 and 0. 71 for AR, FU and RB, respectively. Regression models of TLA RO,G as a function of CTLA RO,G conducted on the training data set showed a significant relationship (P < 2. 10 -16 ) for the three genotypes with R 2 of 0. 83, 0. 88 and 0. 91 for AR, FU and RB, respectively. However, the CTLA RO,G were bigger than the TLA RO,G as can be seen from the slopes.
Modeling the Leaf Area of Vegetative Shoots (VS) Neither ANOVA nor Kruskall-Wallis tests showed any significant difference in e VS,G between AR and FU (P = 0. 54 and 0. 49, for each test, respectively). Thus, k VS,G was fixed (k VS,G = 0. 56) using a linear regression model using data of both genotypes. The regression model showed a significant relationship between leaf length and width (P < 2. 10-16) and R 2 was equal to 0. 79. Besides, neither ANOVA nor Kruskal-Wallis test showed any significant difference in the variable p between genotypes AR and FU. The linear model used to fix p VS,G (p VS,G = 0. 95) was significant (P < 2. 10 -16 ) with R 2 = 0. 83.
The data set used for the establishment of the leaf length model for bourse shoots was based on leaves sampled from shoots from a wide range of dates. This was justified as we determined the time (in GDD) from which onwards shoot leaf areas did no longer differ significantly from final leaf areas in the three different genotypes. This time roughly coincides with the date found by Da Silva et al. (  2014 ), who indicated the 30th of June of each year as the end of annual primary shoot extension for most shoot types. We found that the shape of rosette leaves, independent of genotype, was always more circular than that of bourse shoots which is not surprising as rosette leaves are preformed  (Lauri, 2007). In this study, AR exhibited extreme values for the parameter that describes leaf shape (k), with bourse shoot leaves being much more elongated (k BS,AR = 0.
Admittedly, some aspects of this procedure are arguable: first of all, it could be claimed that the splitting (two thirds training to one third testing) was arbitrary. However, enough data was available to have sufficiently large training and testing sets and more data was needed for calibration than for testing. Secondly, we confounded the FU data sets of the two experimental years (2014 and 2015)-whereas for AR and RB only data of 2014 was available-and randomly distributed the data into the two sets, instead of using 1 year's data set for calibration and the other one for testing. However, the data sets revealed no significant inter-annual variation, except for the shape of rosettes in FU. As the 2015 data set was smaller than the one from 2014 and contained data only for FU, future testing with independent data sets has to be conducted to further prove the robustness of our models.
, 1995; Chen et al. , 1997; Da Silva et al. , 2008) , the next step in the reconstruction of branch architecture has to be to position and orient the different shoot types. In doing so, it has to be considered that the proportion of long and short shoots differs among genotypes  (Lauri et al. , 1995). The genotypes used in this study exhibited such a numerical variability at the scale of the first-order branch, with bourse shoots in AR being much more important than in RB (data not shown). The analysis of the distribution of shoots within the branch will be the subject of a follow-up study  (Bairam et al. , unpubl. ). In any case, it is thus necessary to count the number of shoots of the different types or better still, create a topological map of the branch and its different organs (see  Buck-Sorlin and Bell, 2000 , for an example) in order to finalize the reconstruction of branch architecture.
ACKNOWLEDGMENTS Sylvain Hanteville and Julienne Fanwoua helped with measurements in the field and the laboratory, and Gilles Hunault gave valuable statistical advice. Michael Henke developed the initial ImageJ script for the automatic measurement of leaf surfaces from scanned images. Antoine Roche helped with scanning leaves and saving images for leaf area analysis. Julienne Fanwoua helped to improve the readability of the manuscript. The staff of the experimental unit at Beaucouzé provided day-to-day maintenance of the orchard. All help is gratefully acknowledged.