dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis. Age was compared between groups with a classical univariate ANOVA. Equality of distribution of women and right-handed participants across groups was assessed with a chi-squared test. e neuropsychological characteristics of the subgroups at inclusion time and at follow-up were evaluated using a nonparametric repeated measures ANOVA with a parametric bootstrap  [106]  with factor group and the two neuropsychological assessments (baseline, 18-month followup) as within subject factor. We chose a semiparametric repeated measures MANOVA that only requires metric data, but allows for nonnormality and variance heterogeneity  [106]. is method is implemented in the R-package MANOVA. RM  [107]. We used it with the parametric bootstrap which showed the most favourable performance in unbalanced designs and was therefore generally recommended  [106].
When these values were combined with MRI or neuropsychology, we simply added the same MRI feature vector or neuropsychological feature vector to each of the EEG feature vectors. is was done because an accurate noise model that would allow for classical data augmentation could not be estimated reliably based on the small dataset. is is a rather conservative approach and at worst would lead to low classification accuracies. For the k-fold cross validation, we grouped the segments participantwise, so that all segments of one patient were included in one partition. is rather conservative approach was chosen because it can be assumed that the features are more similar within one participant than between participants. We performed a classification in the sense of supervised learning with a linear kernel function (dot product) and quadratic programming in order to find the separating hyperplane, resulting in a 2-norm soft-margin support vector machine, by using the Matlab functions svmtrain and svmclassify from the statistics and machine learning toolbox.
healthy controls of approximately 93% in Alzheimer's Disease Neuroimaging Initiative dataset and 97% in the Open Access Series of Imaging Studies dataset  [34]. However, classification into stages rather than into progression of decline is much easier to accomplish. In general, by comparing our results to previous research, it must be considered that the reported accuracies of 70-79% based on Alzheimer's Disease Neuroimaging Initiative  [25]  were obtained for the clinical question of conversion from mild cognitive impairment to Alzheimer's disease, while the presented analysis aimed to predict progression in specific cognitive domains. As an outlook, we may anticipate that not only other modalities but also other characteristics may be assessed by MRI or EEG. For instance, the assessment of structural connectivity by means of diffusion tensor imaging has proven to be especially useful to predict memory decline in patients at risk for Alzheimer's disease  [119, 120].
Nevertheless, there are still many open questions with respect to a potential dementing course in subtypes of epilepsy and there exist no studies using these biomarkers in order to predict decline on memory subscales, except for studies assessing the mainly short-term effect of seizures and interictal epileptiform events  [10, 13]. Machine learning is commonly used to predict progression of dementia  [37, 109]. Nevertheless, the use of machine learning techniques needs to be done carefully, since many of the early studies using machine learning in neuroscience have not fully addressed the problem of overfitting the model to the training data, either because of the use of lengthy feature vectors that exceeded the size of the sample or because no strict separation between training and testing datasets was done.
Yeast as a Model Organism Budding yeast (Saccharomyces cerevisiae) is a popular experimental model organism for the study of cellular processes. S. cerevisiae is unicellular and non-motile and because it is a eukaryote, An extensive set of genome-wide tools have been developed for use with S. cerevisiae. As well as genome-wide collections of gene knock-out and regulated knock-down mutant strains there are now fluorescently-tagged versions of most S. cerevisiae gene products and these have been used to create a database of the subcellular localization patterns and protein abundance under different environmental conditions of most S. cerevisiae gene products  [10, 11]. There are also databases of genome-wide gene expression data that include data showing the effect of changing various environmental conditions on relative gene expression levels  [12, 13].
Moreover, databases are available on genome-wide genetic interactions (e. g. , gene-gene phenotypic enhancement or suppression)  [14]  as well as genome-wide physical associations of gene products and formation of protein-protein, protein-DNA, and protein-small molecule complexes e. g. ,  [15] [16] [17] [18] [19] [20]. (1) to late-G1 phase (2). (2) Late G1 phase: Cells choose a new (nascent) bud site and then actin patches start to polarize to this nascent bud site and actin cables orient towards this nascent bud site (N. B. the spatial relationship of the nascent bud site to the previous bud site differs in haploids and diploids). (3) S phase: Cortical actin patches cluster at the tip of the bud and actin cables in the mother cell are oriented towards the newly formed bud. (4) G2 phase: Actin patches remain polarized to the growing bud but are no longer clustered and become isotropic within the bud while actin cables in the mother cell remain oriented to the growing bud.
These natural compounds are used by soil bacteria such as Streptomyces to inhibit the growth of fungi, with which they compete for nutrients in their natural environment. The pharmacological effects of these natural products and their derivatives on human cells result from the high degree of conservation of biological processes between single-cell and multiple-cell eukaryotes. As fungal (including yeast) proteins are the natural targets that these natural products have evolved to inhibit, S. cerevisiae is sensitive to a wide range of human therapeutics Cells 2020, 9, 672 4 of 42 and is therefore ideal for use in identifying target pathways of uncharacterized natural products using chemical-genetic interactions. An extensive database of chemical-genomic interactions is now available for S. cerevisiae  [22].
Data used in this work were drawn from 2011 to 2016, and comprised around 33,688 adult, non-traumatic, treated cases in Canada, and 55,358 in France, leading to estimated incidence rates of 75,3/100,000 inhabitants in France and 83/100,000 in Canada. In both countries, out-of-hospital cardiac arrest predominantly occurred in male patients, in their late sixties, at home, of presumed cardiac aetiology. Bystander cardiopulmonary resuscitation was provided in half of the cases. First assessed cardiac rhythm was shockable in 16% (France) vs. 22% (Canada). Professional resuscitation was attempted in 82% (France) and 60% (Canada). Prehospital organizations and response modes differed in the constitution of responding teams (France: physician-led advanced life support, Canada: trained paramedics), in response time intervals (call to first professional responders' arrival at scene 6.
Settings We are creating ReACanROC, an international research network between two nation-wide, population-based, Utstein-style prospectively implemented registries for EMS-assessed OHCA in France and Canada: RéAC (Registre électronique des Arrêts Cardiaques, data from 2011, France), and CanROC (Canadian Resuscitation Outcomes Consortium, previously ROC Epistry, data from 2005 in Ontario (the OPALS communities organized from Ottawa; the former Rescu (now R3) communities organized from Toronto) and in British Columbia urban communities)), covering approximately 80 million people (fig. 1 ). CanROC was created in 2016 after the ROC funding was discontinued. CanROC is currently expanding to include all provinces in Canada. Over 87% of the 350 French prehospital mobile medical teams (MMT) participate in the RéAC registry (n=305), covering a total population of 60 million inhabitants in 82 out of 96 regions on mainland.
Data We used preliminary, available, aggregate data drawn from the RéAC (nation-wide) and CanROC historical sites (ex-ROC Epistry: British Columbia and Ontario) registries. Both RéAC and CanROC are Utstein-style based. They record a broad range of variables, of which core elements and time events are synthesized in table 1. In Canada, data from 2011 to 2016 were drawn from the Canadian sites of the North-American ROC Epistry, comprising 8 sites in United States of America and 3 in Canada. CanROC now aggregates data from its former ROC sites and data drawn by its current sites from 2016 to date. Because ReACanROC will only use French and Canadian datasets, and in order to ensure comparability between the two countries, no data that were drawn from the previous ROC American sites were used in this article.
Main results Preliminary comparison of data drawn from RéAC and CanROC highlighted several differences in prehospital system organizations, care delivery, practices and outcomes. Detailed analyses, conducted on full and merged datasets from creation to date, are required in order to identify possible reasons to explain differences in outcomes, and to gain further insight into the optimal EMS configuration for OHCA management. One of the core questions that needs to be explored is why a system with physician-led triage and advanced life support, and systematic emergency department (ED) bypass towards catheterization laboratory and intensive care unit, presents with overall survival apparently over two times poorer than a system without prehospital physicians (5% vs. 11%), and in which ED bypass is inconsistent.
Implications for future work The ReACanROC network will be set during 2020. Once the datasets are merged and fully explorable, ReACanROC will be the first Europe-North America research network on OHCA. It will help to better understand the differences in outcomes between the countries and territories, and their associations with geographic, socioeconomic and EMS system-related factors. Because we suspect that several of the differences between France and Canada, and in extenso between systems of either type (e. g. physician-or paramedic-led), will rely on predictors that are not purely technical, we seek to perform observational studies (including propensity matched analyses), comparative effectiveness analyses (including simulation modeling), and subsequently international randomized clinical trials focusing on new practices and organizations.
Limitations This preliminary work has limitations. First, because the ReACanROC network is not created yet, and because the present work was aiming at discussing the need for its creation, we did not have full access to merged and homogenized datasets, but only to aggregate data; hence we were neither able to perform statistical tests of hypothesis, nor to describe, quantify, or address missing data between the two datasets. Nevertheless, every core Utstein elements and time events are mandatory for each registry, in order to reduce the amount of missing data, and to ensure sufficient data quality. Second, as depicted in previous works, the variability we are discussing may be related to the quality, exactitude or definition of the variables recorded in each of the registries  (28). Despite using the Utstein template, each of the registries sometimes uses some specific categories for core elements, and also different optional variables.
Acknowledgments We acknowledge with gratitude the funding agencies that made the collection of these data possible (Canada:  US National Institutes of Health, Canadian Institutes of Health Research , and the  Heart and Stroke Foundation of Canada ; France: the  French Society of Emergency Medicine ("Société Française de Médecine d'Urgence, SFMU) , the  French Federation of Cardiology ("Fédération Française de Cardiologie", FFC) , the  Mutual of National Education ("Mutuelle de l'Éducation Nationale", MGEN) ,  Lille University , the  French northern region ("Hauts-de-France") , and the  European community). We would like to thank the research staff and clinicians of all participating sites and investigators of both registries ( Gr-ReAC and CanROC ). We also wish to express gratitude to people involved in the data management in France and  Canada , including  Courtney Truong ,  Audra Stitt ,  Kosma Wysocki , and to the research assistants and administrative personnel involved in this project in each team ( Magali Bischoff ,  Sylvie Besnier ,  Manya Charrette ,  Lauren Tierney ,  Helen Connolly ).
Acknowledgements Open Access funding provided by Projekt DEAL. We would like to acknowledge the help and assistance from the whole MEDICIS collaboration; from  CERN-ISOLDE Technical and Physical groups. This research project has been supported by a  Marie Skłodowska-Curie Innovative Training Network Fellowship  of the  European Commission 's  Horizon 2020 Programme  under contract number  642889  MEDICIS-PROMED; by the  German Federal Ministry of Education and Research  under the consecutive projects  05P12UMCIA  and  05P15UMCIA ; by the  Research Foundation Flanders FWO (Belgium)  and by a  KU Leuven START  grant.
The clear enrichment of translational control pathways among translationally derepressed mRNAs at E16 implicated these mRNAs as being particularly dynamic between E13 and E16. We also discovered several mRNAs encoding RBPs in our dataset to be translationally dynamic between E13 and E16 (Fig. 1e ). Taken together, these findings reinforce the importance of precise translation control during neocortical development and especially as it proceeds from early to late neurogenesis  3, 5, 7, 10, 11, 13, 27. Elavl4 is derepressed as neocortical neurogenesis proceeds. Among the translationally derepressed RBP mRNAs was Elavl4. Elavl4 is widely used as an early marker of post-mitotic neuronal differentiation and Elavl4-deficient embryonic neurospheres and adult subventricular zone progenitors demonstrate reduced capacity to differentiate into neuronal lineages  28, 29.
Given the conservation of the expression patterns of ELAVL4 and CELF1 in ventral RG during neocortical development in humans, we sought to explore the disease implications of the translational regulatory mechanisms. Initially, evolutionary constraint scores were computed by the Exome Aggregation Consortium (ExAC) as a measure of intolerance to damaging mutations  46. We found that RBPs are highly constrained, including the members of the ELAVL family (Supplementary Fig. 10 ). Next, we performed enrichment analyses for published mRNA targets of the neuronal ELAVLs (ELAVL1, CELF1, and CELF4). Various degrees of enrichment were found in risk genes for neurodevelopmental disorders (NDDs) as defined by the DDG2P database, intellectual disability (ID; in OMIM), epileptic encephalopathy (EE; in OMIM) and ASD  47  (Fig.
9e , Supplementary Fig. 10a ). We also found several translationally regulated mRNAs to be targets of an RBP associated with ID and ASD, Fragile X mental retardation protein (FMRP)  48, 49  (Supplementary Data 3). The human orthologues of the translationally-regulated mRNAs during neocortical development (Fig. 1 , Supplementary Data 1) were also found to be constrained (Supplementary Fig. 10b ). Given that these genes are under strong selective pressure, we conjectured that they might be essential for physiological neurodevelopment and vulnerable targets in NDDs. To explore this hypothesis, we cross referenced the list of differentially translated mRNAs at E16 with risk genes for NDDs in the DDG2P database; 61 of the 850 unique human orthologs derepressed at E16 are NDD genes (57 OMIM disease genes); 62 of the 1,070 genes that are repressed at E16 were NDD genes (59 in OMIM) (Supplementary Data 2).
DAPI shown in blue. d Representative images of in situ hybridization (ISH) for Elavl4 on human VZ, ISVZ, PVFRZ, OSVZ at 11, 13, 15, 17, and 20 PCW in the frontal lobe. Scale bar: 100 μm. e The heatmap shows the degree of enrichment for mRNA targets of neuronal ELAVLs (nELAVLs), ELAVL1, CELF1 and CELF4 across neurodevelopmental disorders genes defined by the Developmental Disorders Genotype-Phenotype Database (NDD_ DDG2P), epileptic encephalopathy genes in OMIM (EE_OMIM), intellectual disability genes in OMIM (ID_OMIM) and ASD risk genes (PMID: 31981491). Color coding indicates -log10(p-value) after 10,000 permutations. Number of overlapping genes is indicated in each cell. f Examples of top translationally regulated genes implicated with ASD, ID, and EE. Note that some of these genes can manifest with co-morbidities.
Libraries were quantified by using the Library Quantification Kit Illumina/Universal (KAPA Biosystems) and then diluted and symmetrically pooled. We performed 2 × 75-bp paired-end sequencing using the Illumina Hiseq2500. Sequencing data have been deposited in the Gene Expression Omnibus (GEO) database, www. ncbi. nlm. nih. gov/geo (accession no. GSE77674). Results were aligned with the mm10 mouse genome found in the University of California Santa Cruz transcript map (Illumina iGenomes) using TopHat (v 2. 1. 1), and comparisons between groups were made in Cufflinks (v 2. 2. 1) and cummerbund (v 2. 22. 0) 66. Significant differences were judged using a 5% false discovery rate (FDR). The detection of up-or down-regulated mRNAs is more sensitive to lower expression levels in the polysome fraction than in total cellular RNA, so differences in polysome mRNAs were assessed whether these mRNAs were detected in total or not.
Relatively few translationally derepressed mRNAs were below the detection threshold in total RNA (97 out of 971 mRNAs). Lists of regulated genes were assessed for enrichment of functional groups or pathways using g:Profiler  26. Transcript isoforms that were significantly increased or decreased (from E13 to E16) in polysomes, but unchanged in total RNA were used to prepare unique Ensembl Gene ID lists (using the biomaRt package in R)  67  for upload to the g:Profiler site. Significantly enriched gene ontology groups, as represented by the best term per parent ontology chosen by p-value, are shown (Supplementary Fig. 1 ). Similarly, the best terms from a KEGG pathway search are included. Identifying common RNA-binding motifs. Transcript isoforms that were significantly up-or down-regulated in E13 vs.
Positive staining in individual channels was determined first. Then colocalizations were determined by the presence of two different markers on the same cell. Two-tailed unpaired t-tests were used to test comparisons for significant differences. Confocal imaging. All images except from human were taken with an Olympus BX61WI confocal microscope using 10 ×, 20×, or 60× objectives and processed using Fluoview FV-1000. Human IHC was analyzed using Leica TCS SP8 X FLIM confocal microscope. All representative images and images used in analysis were taken with the same confocal settings per experiment to allow for accurate comparisons of fluorescent intensity. Sections were imaged from the top of the cortical plate to the VZ and merged in the photo editing software Gimp2 and Fiji  71. For quantifications of binned images, a rectangle approximately a third of the width of the image was drawn from CC to the tops of the apical tufts at the pia.
Enrichment analyses. We extracted the 1216 unique PAR-CLIP targets of ELAVL1  (Lebedeva et al. , 2011 72  , Supplementary Data 2, genes described as "Conservative targets", PMID:21723171), 924 unique top HITS-CLIP targets of nELAVLs (Scheckel  et al. , 2016   73  , Supplementary File 1, PMID: 26894958), 431 human orthologs of the top 490 iCLIP targets of CELF4 (Wagnon et al. , 2012   74  , Supplementary Data 1, PMID:2320943), 1439 unique HITS-CLIP targets of CELF1 (Le Tonquèze et al. 75  , Supplementary Data 3, genes described as "inLETONQUEZE=1", PMID:27222809). We also used 915 unique genes in the Developmental Disorders Genotype-Phenotype Database (DDG2P, https://decipher. sanger. ac. uk/info/ddg2p) with "brain" included in organ specificity (referred to as NDD_DDG2P), 61 unique gene associated with EE in the Online Mendelian Inheritance in Man (OMIM) database (EE_OMIM), 142 unique genes associated with ID in OMIM (ID_OMIM), and 102 unique genes associated with ASD (ASD_Satterstrom) (PMID: 31981491).
Acknowledgements This work was supported by  National Institutes of Health (NIH)  grants ( NS064303  and  NS075367 ) and  Robert Wood Johnson Medical School  start-up funds (M. R. R). This work was also supported in part by the Assistant Secretary of Defense for Health Affairs endorsed by the  Department of Defense , through the  Epilepsy Research Program  under Award No. W81XQH-18-1-0338  to H. Z. and M. R. R. Opinions, interpretations, conclusions and recommendations are those of the author and are not necessarily endorsed by the  Department of Defense. The  U. S. Army Medical Research Acquisition Activity , 820  Chandler Street ,  Fort Detrick MD   21702-5014  is the awarding and administering acquisition office. In addition, this work was supported in part by the  "Research Cooperability" Program of the Croatian Science Foundation  funded by the  European Union  from the  European Social Fund  under the  Operational Programme Efficient Human Resources   2014-2020 PSZ-2019-02-4710  to Z.
K. and M. R. R. This publication was also supported by  Adris Foundation (ZK) ,  Croatian Science Foundation  projects  CSF-IP-09-2014-4517  (IK) and co-financed by the  Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience  (project " Experimental and clinical research of hypoxic-ischemic damage in perinatal and adult brain ";  GA KK01. 1. 1. 01. 0007  funded by the  European Union  through the  European Regional Development Fund) , and  NIH  grant  NS089578  to HZ. N. F. P. received support from the  Aresty Undergraduate Research Fellowship  and the  Thermo Fisher Scientific Antibody Scholarship Program. J. D. S. was funded by an  Undergraduate Research Award  from the  Rutgers Honors College-New Brunswick. M. L. K. was supported by a  NIH NRSA   F30 MD/PhD Fellowship  ( 1F30MH106220 ).
Data availability The unprocessed sequencing data have been deposited in the NIH GEO with accession number: GSE77647. Processed data used for the analyses are included in Supplementary Data files 1-3. The source data underlying Figs. 2b, 3b, 3e, 4b, 4d, 4g, 4h, 6d , 7b-e, 8e-h and Supplementary Figs. 1a, 2b, c, 4c-e, 5a, 5c-e, 6a-h, 7b, c, 8c, 9b, c are provided as a Source Data file.
Thus, while established guidelines for treating emotional eating in eating and weight disorders exist, hardly any innovative non-face-to-face interventions exist. Only recently have researchers proposed to use online interventions or smartphone-based interventions in daily life, lowering the threshold for treatment engagement. To illustrate, the so-called just-in-time adaptive interventions  (99)  use subjectively and objectively derived data from several state variables (e. g. current emotions, social context) to detect an optimal time point for sending brief therapeutic text messages, potentially adapted to the participant (e. g. with or without eating disorder). Hence, future research on the construct of emotional eating might pave the way towards personalised treatments for eating and weight disorders.