dataesr/scientific-paragraphs-categorization huggingface dataset:
MPI Parallelization aims at dividing a computation into mutiple subtasks that can be performed concurrently, thereby reducing the overall elapsed time. Many parallel algorithms exist for the boundary element method, see for example  [39, 40, 41, 42]. The goal here is to speed up the existing code by avoiding big changes. To this end, we effect a multiprocessing parallelization by using OpenMP. OpenMP is an application program interface (API) for parallel computing on shared memory architectures, which simplifies writing multi-threaded applications by using compiler directives and library routines. Before parallelization, the sequential code is re-organized to reduce the number of operations. Only the M2M, M2L and L2L operations involved in the computation of far interactions, which are time-consuming, have been parallelized.
These methods are more time and resource intensive since they are dependent on retrieving the individual patient data from researchers and on establishing an often long-lasting back-and-forth communication with researchers regarding the individual patient data and any missing or discrepant information found during the data checking. Indeed, it took around 1. 5 years and an intense communication with the respective first authors in order to retrieve the raw datasets of the studies included in our meta-analysis, partially due to the very sensitive and confidential nature of clinical data. Further, individual patient data meta-analyses require making complex decisions about data handling in order to ensure the accuracy of outcomes (e. g. , how to harmonize different outcome measures across studies or how to handle missing data) and advanced statistical expertise due to the complexity of the hierarchical modeling involved.
Study Identification and Selection Process The meta-analysis was performed in compliance with the Preferred Reporting Items for Systematic Review and Metaanalysis (PRISMA) individual patient data Statement  (Stewart et al. 2015). PsychINFO, Medline, Web of Science, Embase, and the Cochrane Library bibliographic databases were systematically searched from inception to May 18, 2016. Three sets of keywords were used covering the constructs of interest, namely, cognitive bias, addiction, and study type. For the cognitive bias set the main keywords were "cognitive bias", "attentional bias", "approach bias", "response inhibition". The second group of keywords related to addiction included "alcohol", "drinking" and "tobacco". The third group of keywords covering intervention types consisted of "longitudinal", "(re)training", "intervention" and "task".
All sets of keywords and subject headings for all databases were compiled with the support of the health librarian of the University of Amsterdam. They were generated both from a set of relevant keywords compiled by two CBM researchers and using a reference set of articles known to the authors as meeting the inclusion criteria. For all sets, additional keywords were used based on the bibliographic categorization of relevant papers reporting behavior-change CBM studies. The full list of search strings and results per bibliographic database is reported in the Supplementary Material. The references of the included studies were also searched systematically for missed studies. Two of the authors independently examined titles and abstracts of 2579 search results and further screened the full text of potential studies for full eligibility.
Data Collection and Data Items Authors of eligible articles were contacted for permission to use their raw data sets. They were asked to provide individual raw data on demographic (age and gender), clinical (severity of substance use problems), and intervention characteristics, including information regarding randomized group, baseline, post-intervention, and follow-up total scores of outcomes (cognitive bias(es), substance use, relapse), and training adherence information (total number of training sessions completed * amount of training trials per session). Integrity of the data included in the collected datasets was screened against data reported in the published reports and when discrepancies were found, the first authors were contacted for clarifications. We also coded study-level variables, which were available from the full reports, including, targeted addiction, type of CBM intervention, type of control condition (active or inactive), intervention setting (supervised, such as lab or clinic, or unsupervised, such as online), assessment time points, and types of outcomes measures for cognitive bias, substance use, and relapse rate.
Individual Patient Data Meta-Analysis Included studies used a mixture of measures to assess the study outcomes, that is, reaction-time tasks for the targeted cognitive bias(es) and self-report measures of substance use during a defined time frame (see Table  1 ). Cognitive biases are always assessed with reaction-time tasks involving the presentation of substance-related cues (typically pictures) across trials involving a stimulus-response manipulation, for example, alcohol-related and non-alcohol-related stimuli are presented in both approach and avoid trial formats in the Approach Avoidance Task in order to assess approach bias towards the alcohol-related relative to the non-alcohol-related cues. Another example includes the presentation of a probe appearing at the location of either the alcohol-related or the non-alcohol-related stimulus in the Visual Probe Task in order to assess attentional bias.
A summary score is typically obtained by computing the relative difference in mean (or median) response times (RT) to substance-related stimuli presented in the different trial conditions (e. g. , [alcohol/avoidalcohol/approach] -[non-alcohol/avoidnon-alcohol/approach] for approach bias, or probe/non-alcoholprobe/ alcohol for attentional bias). Such scores index the strength of cognitive bias towards substance-related stimuli relative to control stimuli (e. g. , the difference in RT between approaching rather than avoiding alcohol-related cues and approaching rather than avoiding non-alcohol-related cues). The scoring logic is the same across the different paradigms belonging to this family of neuropsychological tasks. Therefore, cognitive bias scores were standardized within each study by transforming them into z scores, which has the added benefit of removing any confounder related to using, for example, slightly different task data cleaning procedures, or using mean or median RT scores.
Given that the same assessment task is normally recast for training, some studies included the evaluation of training effects on the targeted bias, or on a different type of cognitive bias, by also assessing it with an additional, different task paradigm (i. e. ,  Begh et al. 2015; Eberl et al. 2013; Schoenmakers et al. 2010; Wiers et al. 2011). This approach has the advantage of detecting whether training effects generalize beyond the same task paradigm, that is, "far generalization", which is different from "close generalization", referring to an effect on untrained stimuli in the same task used for training  (Wiers et al. 2013). Hence, when different measures of cognitive biases were used, all were included in the individual patient data analysis as a separate comparison. The same z-score transformation was applied to measures of substance use, all of which consisted of similar retrospective, calendar-based measures of consumption during a defined time window (e.
However, to minimize the spread of difference in the followup duration, for all studies we included the first cognitive bias measurement available after the conclusion of the training. For substance use and relapse rate we included the measurements taken at the longest follow-up time point available. A duration-of-follow-up variable measured in weeks from the end of training was extracted from the study reports or the datasets including individual patient data, standardized within each study, and added in the main analyses as a covariate. Note that for one study the duration of the longest follow-up was slightly different across participants (range = 19-26 weeks;  Elfeddali et al. 2016) , therefore for this study we computed the mean duration of follow-up across participants (mean = 24). For all studies, we contrasted the CBM intervention with the control condition.
2015b ) and  Wittekind et al. (2015) , since the training varieties administered to the groups only differed slightly, with no substantial difference in training effects in intention-to-treat analyses. 1 Finally, due to the original study design comparing multiple CBM trainings against the same control condition, the Attentional Bias Modification and Approach Bias Modification training conditions in  Wiers et al. (2015a, b)  were each contrasted against the same control group in the analyses. Missing outcome data was not estimated with imputation methods when the authors did not use one, that is, imputed data were used only when available in the original raw datasets. Note that, when including non-completers in the analyses, different missing data imputation methods were used across the studies, including single (i.
The posterior distributions for the parameters of our models were estimated with R (R Core Team 2017) using the rstan package (Stan Development Team 2017). To summarize these posterior distributions, we report posterior means to indicate the strength of an effect, and 95% central credible intervals to indicate the uncertainty that is associated with the effect. If such an interval ranges between two values a and b, we can be 95% confident that the true value of the parameter lies between these values. To compare the predictive accuracy of different models we use Bayes factors (e. g. ,  Etz and Wagenmakers 2017). A Bayes factor for comparing M 1 against M 0 , say, is expressed as BF 10 ¼ p datajM 1 ð Þ p datajM 0 ð Þ ; where p(data | M 1 ) is the marginal likelihood of M 1. The Bayes factors were computed in R using the Savage-Dickey density ratio representation  (Dickey and Lientz 1970; Wagenmakers et al.
Data Availability Due to the confidentiality and sensitive nature of the collected data, the dataframes including the individual patient data created for the analyses, cannot be shared open access and are available only upon request. The dataframes are solely usable to reproduce the results of the current meta-analysis or to update the meta-analysis to include additional studies published after May 2016. The dataframes will be provided solely under the condition that they cannot be distributed to other parties nor shared open access. For more information about the single studies or to access the individual raw datasets please contact the study authors. All scripts for both the Bayesian and frequentist analyses are available open access on the Open Science Framework platform at https://osf.
Study Selection and Individual Patient Data Obtained The systematic search resulted in 14 eligible studies out of 2579 search results screened (reference list of included studies reported in Supplementary Material). During the screening process a few conference abstracts could not be matched to a published study. The authors of these conference abstracts were contacted and one additional study was identified through this process. The remaining conference abstracts were associated to previous or later peer-reviewed publications. We obtained individual patient data from all 14 eligible studies, yielding a total of 2435 participants. Figure  1  shows the study selection process. Most studies involved a parallel-group experimental design, testing the CBM intervention against a control condition, both in combination with TAU (n = 8).
Eight and nine studies used some form of imputation or coding criteria to handle missing data in the cognitive bias and behavioral outcomes, respectively, or differences in attrition between conditions were non-significant, indicating a low risk of attrition bias for both types of outcomes in only 57 and 64% of the studies. For each outcome, three studies included completers only or applied stringent exclusion criteria without running sensitivity analyses, resulting in a high risk of bias. Finally, nine studies were evaluated as having a low risk of reporting bias due to either the presence of some form of preregistration of the study outcomes (e. g. , protocol article or registration in official registry of randomized clinical trials) or through formal confirmation from the authors. Five studies did not include one or more outcomes in the final report and have been evaluated at high risk for reporting bias.
Instead of excluding the data from these comparisons across all models, we opted to include all available data across models M 1 to M 6 including all comparisons, and estimate model M 7 separately. The 18 comparisons included 3369 observations for the analysis of cognitive bias data, with 2112 observations without any missing values for the covariates that were used in the models reported in Table  S1. An additional 92 observations were omitted for the analysis of model M 7 , of which 69 came from the two excluded comparisons. We report the results for model M 7 on the reduced dataset in Table  S2  (Supplementary Materials). When comparing the estimated effects of M 7 with the estimated effects of M 6 reported in Table  S1 , we found substantial differences. For instance, the overall effect size and the effect of type of addiction roughly halved their values, and the 95% central credible intervals for each effect overlapped with zero in M 7.
Severity of substance use problems showed almost no effect, with a posterior mean equal to -0. 01 and a 95% credible interval ranging from -0. 06 to 0. 03. Further, the log Bayes factor strongly supported the baseline model M 0 (both marginal likelihoods were computed on the reduced dataset). To summarize, CBM was found to modestly reduce cognitive bias, although this effect was associated with much uncertainty, and was not affected by moderators, with the exception that reduction in cognitive bias after the training intervention is more likely to be observed for bias toward alcohol, but not toward tobacco. Figure  4  displays the results of the sensitivity analyses carried out with the two additional prior distributions. The Bayes Factor for models M 1 -M 6 (against model M 0 ) further corroborate the lack of evidence for the inclusion of any of the covariates and moderators in models M 1 to M 6 , relative to the simpler model M 0 , since they all range in the region of acceptance of H 0 (i.
The effect of severity of substance use was very small with a posterior mean equal to 0. 13 and a 95% central credible interval ranging from -0. 01 to 0. 26. Since the Bayes factors in Table  S5  and Table  S6  have been computed on different datasets the comparison of their values should be done with caution. The Bayes factor in Table  S6  suggests that there is no evidence in favor or against M 7 when compared to the baseline model M 0. Further, the amount of evidence is lower than the amount of support that M 6 received. This result suggests that there is no evidence for the inclusion of addiction severity. In sum, the Bayesian analysis showed a small albeit unreliable effect of CBM on relapse and a positive moderation effect of the amount of trials completed by participants. However, this moderation effect was stronger in the control condition, with a greater reduction in relapse rate for the control relative to the training condition.
We show here that SAGA and NuA4 require a dedicated chaperone machinery to be fully assembled. A recent study in Fig. 5  Mechanism of Tra1 specific interaction with SAGA. a Close-up view of the putative region of Tra1 that contacts the rest of SAGA, which constitutes the flexible hinge in the structure of P. pastoris SAGA (EMD: 3804). Cartoon cylinders represent α-helices in P. Pastoris Tra1 structure (PDB: 5OEJ). This domain is located near the start of the FAT domain and corresponds to residues 2700-2753 (brown-coloured box). A homologous region, defined as the Cup SAGA Interacting (CSI), was identified in S. pombe Tra1 (residues 2623-2676) from multiple alignments of Tra1 orthologs, shown at the bottom. Residues that appear unique to S. pombe Tra2 are underlined (green). b Schematic illustration of the hybrid mutant alleles of S.
For insertion of loxP sites, the same resistance cassettes were amplified from the pUG6 or pUG75 plasmids (Euroscarf #P30114, and #P30671, respectively)  52. Constructions of point mutations, internal deletions or domain swaps in spt20 and tra1 were performed using a ura4 cassette in a two step in vivo site-directed mutagenesis procedure  53. Alternatively, CRISPR-Cas9-mediated genome editing was used, as described  54  , for example for marker-less N-terminal epitope tagging of tra1. DNA fragments used for homologous recombination were generated by PCR, Gibson assembly cloning (kit E2611L, New England Biolabs), or gene synthesis. Cloning strategies and primers were designed using the online fission yeast database, PomBase  55. All relevant primer sequences are listed in Supplementary Table  5.
LFQ intensity ratios of Tra1 were normalised to the bait, Spt7. Ratios from two independent experiments are plotted individually with the mean (black bar). d Western blot analyses of V5-tagged Spt20 truncation mutants in SAGA complex eluates from panel b. e GST pull-down of S. pombe protein extracts from WT, tra1-Sptra2, and tra1-Sctra1 strains, using GST fused to S. pombe (Sp) Spt20 HIT region (residues 282-324) (top right panel) or GST fused to S. cerevisiae (Sc) Spt20 HIT region (residues 468-537) (bottom right panel). GST alone serves as a negative control for background binding to glutathione beads. Anti-FLAG western blotting is used to detect WT and hybrid Tra1 proteins bound to GST or GST-HIT columns (right panel) and in a fraction (0. 6%) of the input used for the pull-downs. Data that are representative of three independent experiments.
Coomassie blue staining of purified GST fusion proteins are shown on the right. Source data are provided as a Source Data file RT-qPCR. Reverse transcription and quantitative PCR analyses of cDNA were performed using RNA extracted from 50 mL of exponentially growing cells, as described  57  , and according to the MIQE guidelines  58. Briefly, the total RNA was purified using hot, acidic phenol, and contaminating DNA was removed by DNase I digestion, using the TURBO DNA-free™ kit (AM1907, Ambion). One microgram of RNA was then reverse-transcribed (RT) at 55 °C with random hexanucleotide primers, using the SuperScript III First-Strand System (18080051, Thermo Fisher Scientific). Fluorescence-based quantitative PCR was performed with SYBR Green and used to calculate relative cDNA quantities, from the slope produced by standard curves for each primer pair, generated in each experiment.
The 12 most intense ions with charge states ≥ 2 were sequentially isolated to a target value of 1 × 10 5 with a maximum injection time of 45 ms and fragmented by HCD (Higher-energy collisional dissociation) in the collision cell (normalised collision energy of 28%) and detected in the Orbitrap analyser at 17,500 resolution. Raw spectra were processed using the MaxQuant environment (v1. 5. 0. 0 or v1. 5. 5. 1)  60  and Andromeda for database search with label-free quantification (LFQ), match between runs and the iBAQ algorithm enabled  61. The MS/MS spectra were matched against the UniProt reference proteome (Proteome ID UP000002485) of S. pombe (strain 972 / ATCC 24843) (Fission yeast) (release 2017_10, https://www. uniprot. org/), 250 frequently observed contaminants, as well as reversed sequences of all entries.
Silver staining reveals Spt7 and Tra1, which migrate around 150 and 400 kDa, respectively. Anti-HA and anti-MYC western blotting of Spt7-TAP and Sgf11-MYC in a fraction of the input (Input) and in TAP eluates (TAP) is shown below. An anti-tubulin antibody and Ponceau red staining are used as loading controls. Data are representative of four independent experiments, quantified and averaged in (e). e Quantification of the ratio of Tra1 to Spt7 from silver stained gels (top) and of the ratio of Sgf11-MYC to Spt7-TAP from western blots (bottom). Signal intensities were quantified from four independent experiments (n = 4), except at 24 h (n = 2). Each data point was plotted individually. f Working model for the last steps of SAGA assembly. The core subunits (Spt7, Ada1 and TAFs), the HAT module (Gcn5, Ada2, Ada3 and Sgf29) and Spt20 form a pre-assembled complex.
Acknowledgements We acknowledge  Kerstin Wagner  and  Elsa Cesari  for invaluable technical assistance and all members of the  Helmlinger laboratory  for helpful suggestions and discussions. We thank  Patrick Schultz  and  Adam Ben Shem  for kindly sharing results before their publication. We thank  Fred Winston  for critical reading of the manuscript and sharing S. cerevisiae strains. We are grateful to  Robin Allshire ,  Charles Hoffman ,  Hisao Masukata , and  Paul Russell  for sharing S. pombe strains. A. E. V. is a recipient of a post-doctoral fellowship from the  Fondation pour la Recherche Médicale  ( SPF20130526854 ). D. T. is a recipient of a graduate fellowship from the  French Ministry for Research and Higher Education (MESR) , supported by the  Labex EpiGenMed , an  « Investissements d'avenir » program  ( ANR-10-LABX-12-01 ).
This work was supported by funds from the  CNRS (ATIP-Avenir) , the  FP7 Marie Curie Actions  ( FP7-PEOPLE-2012-CIG/COACTI-VATOR ), and the  Agence Nationale de la Recherche  ( ANR-15-CE12-0009-01  and  ANR-15-CE11-0022-03 ) to D. H.
Data availability The raw sequencing data reported in this publication have been deposited in NCBI Gene Expression Omnibus  68  and are accessible through GEO Series accession number GSE128448. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  69  partner repository with the data set identifier PXD013256. Supplementary Table  6  cross-references all MaxQuant results from each figure or table with the raw data files. All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request. The source data underlying all figures and supplementary figures, including uncropped versions of all gels and blots and the raw data of all reported graphs, are provided as a Source Data file.
MALDI-TOF-MS Analysis Released, derivatized, and purified glycans (5 µL sample) were spotted on an anchor chip MALDI target plate (Bruker Daltonics) and co-crystallized with 0. 5 µL of 5 mg/mL superDHB in 50% can, supplemented with 1 mM NaOH. Spectra were recorded in positive-ion reflector mode, after calibration with a Bruker peptide calibration kit, using a Bruker UltrafleXtremeTM mass spectrometer, controlled by FlexControl 3. 4 software Build 119 (Bruker Daltonics). Mass spectra were obtained over an m/z range of 1000 to 5000 for a total of 10 000 shots (1000 Hz laser frequency, 200 shots per raster spot during complete random walk). Tandem mass spectrometry (MALDI-TOF-MS/MS) was performed for structural elucidation via fragmentation in gas-off TOF/TOF mode.
Data Processing and Analysis of MALDI-TOF-MS Spectra Spectra were smoothed (Savitzky Golay algorithm, peak width: m/z 0. 06, 4 cycles), baseline corrected (Tophat algorithm), and exported to xy-files using FlexAnalysis 3. 4 (Stable Build 76). Mean average spectra were generated per technical replicate, which were summed to one spectrum using the open-source software mMass (http://www. mmass. org; [38] ). The spectrum was internally re-calibrated using glycan peaks of known composition (H5N2 at m/z 1257. 423, H6N2 at m/z 1419. 476, H7N2 at m/z 1581. 529, H8N2 at m/z 1743. 581, H5N4F1 at m/z 1809. 639, H5N4F2 at m/z 1955. 697, H5N4E1 at m/z 1982. 709, H10N2 at m/z 2067. 687, H6N5F1 at m/z 2174. 7715, H5N4L1E1 at m/z 2255. 793, H5N4E2 at m/z 2301. 835, H6N5E1 at m/z 2347. 8403, H7N6F1 at m/z 2539.