dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We sincerely thank the sequencing, assembly and annotation teams at the  Baylor College of Medicine Human Genome Sequencing Center  for their efforts. We would like to acknowledge the following funding sources: sequencing, assembly and automated annotation was supported by  NIH  grant  NHGRI U54 HG003273  to RAG; the  UVM Agricultural Experiment Station Hatch  grant to YHC ( VT-H02010 ); the  NIH  postdoctoral training grant to RFM ( K12 GM000708 ); MMT's work with Apollo was supported by  NIH  grants ( 5R01GM080203  from  NIGMS , and  5R01HG004483  from  NHGRI ) and by the Director,  Office of Science, Office of Basic Energy Sciences , of the  U. S. Department of Energy  (contract No. DE-AC02-05CH11231 ); the  National Science Centre  ( 2012/07/D/NZ2/04286 ) and  Ministry of Science and Higher Education  scholarship to AM.
Availability of data and material. All data generated or analyzed during this study have been made publicly available (see Methods for NCBI accession numbers), or included in this published article and its supplementary information. The genome assembly and official gene sets can be accessed at: https://data. nal. usda. gov/dataset/leptinotarsa-decemlineata-genome-assembly-10_5667, https://data. nal. usda. gov/ dataset/leptinotarsa-decemlineata-genome-annotations-v053_5668 and https://data. nal. usda. gov/dataset/ leptinotarsa-decemlineata-official-gene-set-v11.
MRI Anatomical Acquisition Mouse brain MRI data acquisition was performed with a 7T small animal scanner (BioSpec 70/30USR, Bruker) using 23 mm volumetric coil (Bruker). Animals were anesthetized by inhalation of isoflurane during the image acquisition. Five minutes before the start of the acquisition, mice were placed in an anesthesia chamber receiving 5% isoflurane. Then, the animals were placed into a specific animal bed system (Bruker) receiving 1. 5-2% isoflurane during the whole procedure. Respiration was monitored and maintained between 45 and 70 breaths per minute using a 1025-IBP-50 Small Animal Monitoring Gating System (SA instruments). Body temperature was maintained constant at 37 • C inside the magnetic bore by blowing warm air on the animal and eye lubricant was applied. MRI data were collected using 3D True-fast imaging with steady state precession (3D-true FISP) with the following parameters: matrix 128 × 128 × 64, image resolution 140 × 140 × 140 µm 3 , echo time (TE)/repetition time (TR) = 2.
bic. mni. mcgill. ca/ServicesSoftware/ServicesSoftwareMincToolKit, RMINC (https://wiki. mouseimaging. ca/display/MICePub/ RMINC, (rstudio (https://www. rstudio. com/, anaconda (https:// repo. continuum. io/archive/and pydpiper (https://github. com/ Mouse-Imaging-Center/pydpiper. For statistical analysis, we used the general linear model, with the log of the local deformation being modeled with 18 covariates and group. Group differences were measured via the t-statistic from the linear model and corrected for multiple comparisons using FDR at 20% (RMINC, https://wiki. mouseimaging. ca/display/ MICePub/RMINCRMINC was also used for single voxel boxcar plotting from Jacobians of individual subjects from each group.
In this previous Rs-FC study, we used data-driven spatial independent component analysis (100-ICASSO) of Rs-fMRI datasets, and identified 87 functional components (clusters of voxel showing correlated and/or anticorrelated activities), which we used as nodes to establish whole brain FC matrixes for each MOR KO and CTL group and compare the two groups. A MOR-dependent FC signature emerged and, to identify most prominent alterations in MOR KO mice, we ranked these nodes based on the number of statistically significant FC changes  (14). Here we considered the top-10 nodes from the Rs-fMRI study, and found that these nodes visually correspond with groups of voxels, which also show significant changes in local volumes (Figure  2B ). Specifically, PAG/Thalamus (Th), SC/PRT, AMY/Piriform (PIR), right and left SS, entorhinal area (ENT), AMY, HB components from the Mechling study match regions with enhanced volumes in this study, while HPF and MRN/SC/PAG nodes match with reduced volumes (Figure  2B ).
ACKNOWLEDGMENTS We thank the staff at the animal facility of the  Neurophenotyping Center Douglas Research Center (Montréal, Canada) ,  Aude Villemain ,  Annie Salesse ,  Eujin Kim ,  Aimee Lee Luco ,  DaWoon Park , and  Karine Lachapelle  for animal care and genotyping,  Axel Mathieu  for his assistance with MRI acquisition and  Daniel Gallino  for his help regarding image visualization. This work was supported by  National Institute of Health (National Institute of Drug Abuse  Grant No. 05010  to BK and  National Institute on Alcohol Abuse and Alcoholism , Grant No. 16658  to BK), the  Canada Fund for Innovation  and the  Canada Research Chairs  to  BK and Centre d'imagerie Cérébrale (CIC, Douglas Research Centre , Second Pilot Project Competition to ED).
Functional MRI data was acquired from healthy participants. The data was then analyzed using Recursive feature extraction (RFE) that was applied on the data using a mask obtained from an ALE meta-analysis  46. This way the most relevant voxels could be extracted. To demonstrate the neural recycling effect, a 2-fold cross-validation scheme was used: The training data consisted of the fMRI neural activity maps acquired during verb and noun trials from all subjects, while the testing data composed these trial-wise fMRI maps from the objects and objects-with-hand conditions. As such, we trained the classification algorithm on one subset of the experimental conditions and evaluated the fitted classification algorithm on unseen, statistically independent brain images from the remaining conditions.
To ensure attentiveness, participants were required to press a button with their right index finger whenever they saw a red object or read a pseudo word. These trials were excluded from the analysis. In the scanner, visual stimuli were presented via MRI compatible 3D goggles (VisuaStim XGA, Resonance Technology) with a horizontal viewing angle of 30° and a vertical viewing angle of 22. 5°. Data recording and stimulus presentation were accomplished using Presentation (Neurobehavioral Systems, Albany, CA, USA).
52 ms, FA = 9°, FOV = 256 × 256, ST = 1 mm, spatial resolution 0. 98 mm × 0. 98 mm × 1 mm). Preprocessing and Image preparation. Images were analyzed with SPM10 software (http://www. fil. ion. ucl. ac. uk/spm/). The first 5 scans of each participant were removed to allow for full T1 saturation, the remaining images were slice timing corrected, realigned to mean EPI image. Subsequently the data was co-registered to The corresponding textual stimuli were written in white letters on a black background and presented centrally ("Kugel", "Ring", "Zylinder" for nouns, and "fangen", "nehmen", "rollen" for verbs). Stimuli were presented in a randomized fashion. Each trial started with the presentation of a fixation-cross in the center of the screen for 1000 ms, followed by the stimulus for a further 1000 ms.
Software implementation. All statistical-learning analyses were performed in Python. Scikit-learn  59  provided efficient, unit-tested implementations of state-of-the-art statistical learning algorithms (http:// scikit-learn. org). This general-purpose machine-learning library was interfaced with the neuroimaging-specific nilearn library  60  for high-dimensional neuroimaging datasets (http://github. com/nilearn/nilearn). Theano was used for automatic, numerically stable differentiation of symbolic computation graphs  61, 62. All Python scripts that generated the results are accessible online for reproducibility and reuse (https://github. com/banilo/ horoufchin2017scirep).
Consequently, we examined the heterogeneity of the effect across laboratories and also included measures of religiousness to test the prediction that the effect of moral priming will be larger in laboratories whose participants hold stronger religious beliefs. These measures were administered after the primary tasks so that they would not influence the main outcome measure. The RRR project was announced by the Association for Psychological Science and on social media, and laboratories were invited to apply to contribute. By the deadline of November 30, 2016, the Editor had received and approved 29 applications. Twenty-five out of these 29 contributing laboratories completed the study and provided data for the meta-analysis (see the appendix following the Discussion section for a list of the authors participating at each lab).
Preregistration The approved protocol for the RRR was posted on the Open Science Framework (OSF) project page at https:// osf. io/3bwx5/. Each laboratory preregistered their Editor-approved implementation of the official protocol on their individual project page, and those preregistrations are available by visiting the labs' project pages (linked from the Contributing Labs section at https:// osf. io/hrju6/wiki/home/). Each laboratory team reported (on their project page) how they determined their sample size and documented all data exclusions. Any departures from the official protocol or the lab's preregistered implementation are documented in the Lab Implementation Appendix at https://osf. io/uskr8/ (also at http://journals. sagepub. com/doi/suppl/10. 117 7/2515245918781032). Drafts of the meta-analysis scripts were written in a data-blind manner, using simulated data.
Data, materials, and online resources All materials are available at https://osf. io/rbejp/. All data and analyses are available at https://osf. io/mcvt7/wiki/ home/. Supplementary online materials include the Lab Implementation Appendix, which documents the individual labs' contributions to the project (https://osf. io/uskr8/ and http://journals. sagepub. com/doi/suppl/10. 1177/2515 245918781032).
Inclusion criteria To be included in the analyses, participants had to be part of a sample that was 20% to 80% female and had to be 18-to 25-year-old students at the time of testing. They also had to follow task instructions and to complete all the tasks necessary for this replication study. These last two criteria were underspecified in the preregistered protocol, and the lead labs and Editor clarified these criteria prior to examining the data and results. Not following task instructions included not having torn out the matrix or blank page or reporting having solved more than 20 matrices. Participants who listed no books or Commandments and also reported spending no time recalling them were excluded for not having completed all the tasks. Note that participants were not excluded for not having completed other tasks in the packet.
Finally, data were excluded when the experimenter did not administer the tasks correctly or when a testing session included fewer than 50 participants (to ensure adequate anonymity, as in the original study). Data-blind exceptions to the protocol were allowed (e. g. , we allowed labs to recruit samples that had less than 20% males). All exceptions are listed in Table  2 , which also reports the language used at each lab, the number of participants tested and the number included in analyses for each lab, and descriptive statistics for the dependent variable. Note that all data, both excluded and included, are available on the OSF project page (https://osf. io/vxz7q/). Labs indicated in their data file whether or not participants' data were included and, if not, the reason for the exclusion.
Results The R scripts (R Version 3. 4. 3; R Core Team, 2013) for the data analysis were written during the data-collection phase and registered before viewing the data (see https://osf. io/vxz7q/). Prior to the primary data analysis, a data-integrity script checked for potential errors in data entry or coding, and individual labs were asked to clarify or resolve potential errors. As is standard for RRR projects, the primary data analysis consisted of a random-effects meta-analysis  (Simons, Holcombe, & Spellman, 2014). For each lab, the effect size was calculated as the difference in the mean number of solved matrices between the books-cheat condition and the Commandments-cheat condition. To examine whether differences in the religiousness of participants across labs moderated the size of the effect observed, we conducted a preregistered exploratory metaregression using the random-effects model  (Thompson & Higgins, 2002).
Primary analyses The primary analyses included data from 4,674 participants from 19 laboratories that met all inclusion criteria or that were granted a data-blind exception. The exceptions included 7 labs with samples that were more than 80% female and 1 lab that allowed people up to 27 years of age to participate (exception granted but not needed). Our primary analysis concerned the meta-analytic difference between the Commandments-cheat and the books-cheat conditions. That is, we asked whether people given a moral prime report solving fewer matrices than do those given a neutral prime when they are given the opportunity to cheat. In the original study, participants in the Commandments-cheat condition reported solving 1. 45 fewer matrices than did those in the books-cheat condition. In our replication project, participants reported solving 0.
In sum, we did not observe the predicted reduction in cheating following priming with the Ten Commandments. These results call into question the effectiveness of using the Ten Commandments as a moral prime to reduce cheating. Appendix: Author Affiliations (The Supplemental Material includes an additional appendix with a one-paragraph summary for each lab that specifies any departures from the protocol or from their own preregistered plan, as well as which analyses included the data from that lab. This Lab Implementation Appendix is also available at https:// osf. io/vxz7q/). ciation for Psychological Science and the Arnold Foundation provided funding to participating laboratories to defray the costs of running the study.
Open Practices All data, analysis scripts, and materials have been made publicly available via the Open Science Framework. The data and scripts can be accessed at https://osf. io/mcvt7/wiki/home/, and the materials can be accessed at https://osf. io/rbejp/wiki/ home/. The design and analysis plans were preregistered at the Open Science Framework and can be accessed at https:// osf. io/3bwx5 and https://osf. io/hrju6/wiki/home/. The complete Open Practices Disclosure for this article can be found at http://journals. sagepub. com/doi/suppl/10. 1177/ 2515245918781032. This article has received badges for Open Data, Open Materials, and Preregistration. More information about the Open Practices badges can be found at http://www. psychologicalscience. org/publications/badges. Notes 1. Four labs were approved but did not meet the requirements of the protocol.
Two labs (Huntjens, Sumampouw) changed aspects of the procedure that were important to the design, and two labs (Batra, Willis) recruited fewer than 200 participants. The data from the Huntjens lab are available on the OSF project page. The exclusion criteria specified in the protocol turned out to be vaguely worded, a problem we discovered as labs began to code their data. Consequently, we made a results-blind decision that the ancillary analyses of all labs contributing data would include labs that tested at least 200 participants before exclusions but fewer than 200 after exclusions. Data from these labs were excluded from the primary analysis and from the ancillary analyses of data from labs that strictly adhered to all protocol requirements. 2. This additional instruction went unnoticed by nine labs during the translation process (see Table  2 ).
Experimental methodology Embryos derived from cad mutant germ-line clones were generated and collected as previously described  [39, 40] , and females were then mated to wild-type males. The resulting embryos all lack maternal cad activity but carry one paternal copy of the cad gene. mRNA expression patterns of the gap genes gt or kni, and the pair-rule gene even-skipped (eve) were visualized using an established enzymatic (colorimetric) in situ hybridization protocol  [36]. Images were taken and processed using FlyGUI (https://subversion. assembla. com/svn/flygui) to extract the position of expression domain boundaries, as described in  [41]. The image data and extracted boundary positions are available from figshare at https://figshare. com/s/ 839791c208e42b7e61fe (DOI: 10. 6084/m9. figshare.
Acknowledgments We are very thankful to Manu for sharing the Newton-Raphson code with us and for the help he provided when we were starting this project. We would also like to thank  Vitaly Gursky  and  Lena Panok  for their invaluable initial guidance. We acknowledge  Nick Monk  for countless discussions and help with the analysis of nonautonomous gene circuits. We thank  Anna Kicheva  for critical reading and comments that significantly improved our initial drafts of this paper. James Sharpe ,  Jordi Garcı ´a-Ojalvo ,  Astrid Hoermann ,  Barbara Negre ,  Damjan Cicin-Sain , and  Alba Jimenez-Asins  provided guidance and intellectual stimulation during this project. We would like to thank  Gregory Boyle  for his help in making the supplementary movies. The authors thankfully acknowledge the computer resources, technical expertise, and assistance provided by the  Barcelona Supercomputing Center-Centro Nacional de Supercomputacion.
MINECO (grant number  BFU2009-10184/ BFU2012-33775/SEV-2012-0208 ). BFU2009-10184/BFU2012-33775  were 'Plan Nacional' grants to the group of JJ. SEV-2012-0208 was an ' Centro de Excelencia Severo Ochoa   2013-2017 ' award to the  CRG. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. European Commission http://www. biopredyn. eu(grant number  FP7/KBBE-2011/5/289434 ). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. MEC-EMBL Agreement  for the  EMBL/ CRG Research Unit in Systems Biology. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. La Caixa Savings Bank. PhD Fellowship received by BV at the  CRG/EMBL Research Unit in Systems Biology.
Using the perfusion system, 250 lM LLOMe was added to HeLa cells stably expressing CHMP4B-eGFP and mCherry-Galectin-3 and images were acquired every 20 s for a period of 20 min. As shown in the representative movie montage and the quantification graph, CHMP4B recruitment precedes Galectin-3, which is an established marker for detection of damaged endolysosomal membranes. The quantification graph is normalized to the area occupied by the cells. Data were obtained from >40 cells per condition from three independent experiments. Error bars correspond to 95% confidence intervals. Scale bars: 5 lm. 2009), even though the kinetics appear to be different  (Wenzel et al, 2018). Another important question concerns the cues that trigger ESCRT recruitment to damaged lysosomes. Ca 2+ leakage out of the injured lysosome, detected by ALIX and its Ca 2+ binding partner ALG2, has been put forward as a mechanism  (Skowyra et al, 2018) , but we note that TSG101 appears to be more important than ALIX for mediating ESCRT-III recruitment.
Confocal fluorescence microscopy Stained coverslips were examined with a Zeiss LSM 780 confocal microscope (Carl Zeiss) equipped with an Ar laser multiline (458/ 488/514 nm), a DPSS-561 10 (561 nm), a laser diode 405-30 CW (405 nm), and a HeNe laser (633 nm). The objective used was a Zeiss Plan-Apochromat 63×/1. 40 Oil DIC M27 (Carl Zeiss). Image processing was performed with ImageJ software (National Institutes of Health). Intensity settings for the relevant channels were kept constant during imaging. Images shown in figures are representative of at least three independent experiments.
Statistical analysis The number of individual experiments and the number of cells analyzed are indicated in the figure legends. We tested our datasets for normal distribution and chose an appropriate test accordingly using GraphPad Prism version 5. 01. The unpaired, two-tailed Student's t-test was used to calculate the significance of number of foci per cell in Figs 1 and 2, and EV1-EV3. The statistical tests performed for each experiment are specified in the figure legends. P-values are indicated for each experiment, and P-values below 0. 05 were regarded as significant. In instances of multiple means comparisons, we used one-way analysis of variance (ANOVA) followed by the Bonferroni post hoc test to determine statistical significance.
Image analysis and post-processing Images were analyzed in Fiji/ImageJ using custom python scripts. Briefly, images were filtered to remove noise and foci of ESCRTs and/or associated proteins were segmented by the "Find maxima" function and scored (Figs  4  and 5 ). Alternatively, foci were segmented by manual thresholding and objects were then identified and scored by the "Analyze Particles" function of ImageJ (Figs 1, 2, 3 and 6, and EV1-EV3). Manual thresholds were chosen to detect prominent LLOMe-induced structures; therefore, only spots above a certain intensity were scored. All post-processing was performed using Python and the "pandas" package, plots were generated using Seaborn. For live-cell movies, foci counts were normalized to the area occupied by cells within a given field of view.
Spatial dependencies between the stations were taken into account using a moving block technique. Cross correlation is performed on the time series of precipitation maxima at all stations; each bootstrap sample includes a station chosen at random and other stations that are positively correlated (r > 1/e) with that station. Serial autocorrelations could be treated similarly, but are negligible here. The fit routine is available via the KNMI Climate Explorer (http:// climexp. knmi. nl). The GMST-dependent GEV methodology was applied to station data in order to quantify the exceptionality of the 28 July 2014 event in the observational record and the change in its likelihood over the last century. First of all, a GEV was fitted to annual maxima in April-September daily precipitation at the 102 homogenised volunteer stations for 1910-2016, excluding the year of the extreme event in question, 2014.
The maximum observed precipitation value for 28 July 2014 among this data is 107. 7 mm. However, as these stations record only data between 8 and 8 UTC, a sizeable fraction of the precipitation that fell during the morning is recorded on 29 July 2014. The 28 July 2014 maximum observed value is therefore still considered to be the 131. 6 mm total recorded at the 0-24 UTC automatic weather station at Deelen. A positive trend is found in precipitation maxima in all observational data (significant at the 95% level) (Fig. 3 ). When fitted to the 102 homogenised stations and scaled to the climate of 1961, a 2014-type event has a return period of approximately 8000 years (Fig. 3a ). In the climate of 2014 the return period has reduced to approximately 3000 years. The change in probability of an event of the magnitude of 2014 due to climate change may be quantified by the probability (or risk) ratio (PR) defined as PR = P 1 /P 0 where P 1 is the probability of exceeding a specified threshold in the current climate and P 0 is the probability of exceeding the same threshold is some past climate (e.