dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Additional details are provided in the appendix. All analyses were performed using Stata software (version 14, Stata-Corp, College Station, TX) with a two-sided type I error of α = 5%. Comparisons of patient characteristics between survivors and non-survivors were performed using the chisquared or Fisher's exact tests for categorical variables, and Student's t test or Mann-Whitney test was used when the assumption of the t test was not met (normality and homoscedasticity studied using the Fisher-Snedecor test) for quantitative variables. Because the available severity scores (SOFA, APACHE II, APACHE III) differed among the studies included in this meta-analysis and because incorporating each of them as a covariate would have led to a reduced number of cases available for multivariate analyses, a risk score was calculated using an average z score as a composite of available scores based on the mean of the standardized variables (by subtracting the mean and then dividing by their standard error)  [25].
Data synthesis and patient characteristics Our database search retrieved 23 articles that were scrutinized with a full text review. In total, eight prospective studies fulfilled our eligibility criteria and were finally included  [7, 11, 12, 14, [29] [30] [31] [32]  (Fig. 1 ). This analysis included 746 participants (among whom 700 were complete cases available for multivariate analysis) with available individual records, plasma levels of sRAGE, and follow-up data: 6 were observational studies  [11, 12, 14, [29] [30] [31]  and 2 were analyses of saved samples and on-study variables from ARDS patients enrolled in multicenter randomized controlled trials (RCTs) comparing lower and higher V T ventilation  [7, 32]. The intraclass correlation coefficient associated with the study effect was equal to 0.
Funding This work was supported by grants from the  Auvergne Regional Council  (" Programme Nouveau Chercheur de la Région Auvergne " 2013) (J. M. Constantin), the  French Agence Nationale de la Recherche and Direction Générale de l'Offre de Soins  (" Programme de Recherche Translationnelle en Santé "  ANR-13-PRTS-0010 ) (M. Jabaudon), and by  NHLBI  Grants  HL133390  and  HL140026  (C. S. Calfee) and  HL51856  ( M. A. Matthay ). The funders had no influence on the study design, conduct, and analysis or the preparation of this article.
These were further subdivided into $800 delineated subregions over both hemispheres, representing >300 unique subregions aligned with those in the Allen Reference Atlas (ARA)  (Dong, 2008). We also developed an unsupervised mapping strategy devoid of a priori constraints on region boundaries in which the density of synapse parameters, type, or subtype (described below) were quantified in voxels (19 3 19 3 0. 5 mm). The sets of synaptome maps are available on our website (http://synaptome. genes2cognition. org). We have also developed an interactive software application called Synaptome Explorer that allows visualization of individual synaptic puncta and their intensities, sizes, types, and subtypes across the five coronal sections (STAR Methods; Video S1).
Thus, network analysis further reinforces the conclusion that synaptome architecture and the distribution of synapse diversity is a fundamental property of brain circuits and networks. To examine the relationship between synaptome architecture and dynamic brain activity in the global network, we turned to the observation that whole-brain network activity measured by resting-state fMRI (rs-fMRI) shows small-world topology. Using mouse brain rs-fMRI data  (Stafford et al. , 2014) , we compared the network node degree (i. e. , the number of connections linking a region to other regions, the most fundamental parameter of network topology)  (Bullmore and Sporns, 2009)  between the synaptome and rs-fMRI networks. We found a significant correlation between rs-fMRI and synaptome node degree (R = 0.
02 Hz. Peak amplitude of the negative part of fEPSPs was used as a measure of the synaptic response. Following at least 10-15 min of equilibration period inside an MEA well, I/O relationships were obtained and baseline stimulation strength was set to evoke a response that corresponded to $40% of the maximal attainable fEPSP at the principal recording electrode. Paired pulse facilitation (PPF) was observed after stimulating Schaffer collateral/commissural fibers with a pair of pulses at baseline stimulation strength and an interpulse interval of 50 ms. PPF value was calculated as fEPSP2/ fEPSP1*100%. Average data from five paired-pulse stimulations were used for each slice. LTP was induced after 60 min period of stable baseline responses by a theta-burst stimulation (TBS) train consisting of 10 bursts given at 5 Hz with 4 pulses given at 100 Hz per burst.
4), a CSU-X1 spinning-disk (Yokogawa) and an Andor iXon Ultra monochrome back-illuminated EMCCD camera. Images acquired with that system have a pixel dimension of 84 3 84 nm and a depth of 16 bits. A single mosaic grid was used to cover each entire brain section with an adaptive Z focus set-up by the user to follow the unevenness of the tissue using the Andor iQ2 software. In both systems, eGFP was excited using a 488 nm laser and mKO2 with a 561 nm laser. The CV1000 system is equipped with the following filters: BP 525/50 nm for eGFP and BP 617/73 nm for mKO2 whereas the Andor Revolution XDi is equipped with a Quad filter (BP 440/40, BP 521/21, BP 607/34 and BP 700/45). For both systems, mosaic imaging was set-up with no overlap between adjacent tiles.
We compared synaptome map responses to spike patterns recorded during trials ending in a correct response (the correct port visited) to those ending in an incorrect response (one of the wrong ports visited). Data from 10 neurons recorded during trials ending in a correct response (705 spikes in total) and 10 neurons from incorrect trials (917 spikes in total) were used. Data were taken from the waiting time period, between trial onset and stimulus cue presentation, a time period defined as the sustained attention period. Model synaptic map responses for each synapse were taken as the sum of amplitudes as described above. Responses from the two conditions were further analyzed. For the correct responses, the ratio of largest over smallest synaptic map response was 1. 7 and for incorrect responses it was 2.
QUANTIFICATION AND STATISTICAL ANALYSIS Detection of Synaptic Puncta Punctum detection was carried out using image detection algorithms, Ensemble Detection developed in house. Detection of the synaptic puncta is a key and fundamental step in the SYNMAP pipeline. Our synaptic punctum images were usually acquired in a low signal-to-noise-ratio (SNR) environment and puncta were diverse in the intensity distribution, as shown in the PSD95 and SAP102 intensity PDF (Figure  3A ). Existing punctum/particle detection algorithms  (Chenouard et al. , 2014)  in fluorescence microscopy can only process images with similar punctum intensities and background noise, or require significant fine tuning of algorithm parameters. Therefore, they can only be applied to fluorescence microscopic images collected within a small area of tissue and not perform robustly detect synaptic puncta in our data at the whole brain scale.
The remaining candidate puncta were finally classified as either true puncta or background noise using the corresponding feature vectors and the classifier. The classifier was pre-trained with the training image set and machine learning algorithms  (Zhou, 2012). The training set was randomly selected from the whole-brain synapse images of over $800 delineated subregions. The puncta in these images were then manually annotated independently by two different experts. Ensemble learning  (Zhou, 2012)  was selected as the classifier learning algorithm as it has proven performance in predicting generalization error  (Breiman, 2001)  in machine learning and hence, is suitable to classify puncta with diverse intensities in our dataset.
Classification of Synaptic Puncta Unsupervised classification was applied to $1 billion individual synapses to group synaptic puncta of similar parameters into 37 subtypes. A new classification method, weighted ensemble clustering (WEC) algorithm, was developed in order to automatically generate a robust and validated classification results by combining clustering results from different clustering methods, each of which often performs well only for a specific type of statistical PDF  (Wiwie et al. , 2015). Eight state-of-the-art clustering methods  (Wiwie et al. , 2015)  were independently applied to $1 billion of puncta and each method was used by varying number of clusters from 1 to 300 for initial clustering. This gave rise to a comprehensive pool of clustering that contained 2400 different clustering results on the same dataset.
PSD95/SAP102 Juxtaposition with VGlut1/VGluT2 For imaging of PSD95/SAP102/VGluT1/VGluT2 quadruple labeling in the VP, Andor Revolution XDi SDM was used and VGluT1/ VGluT2 were labeled with Cy5/Pacific Blue and excited using 640/405 nm wavelengths, respectively. Quantification of the juxtapositions of PSD95 and SAP102 with those of VGluT1 and VGluT2 was done using Imaris software (version 8. 1. 2). Spot detection function was used to segment synapses expressing VGluT1, PSD95 and SAP102 puncta in 3D datasets (Z stack depth of 2 mm with 11 slices separated by 0. 2 mm) from three mice (males, 4. 7 months). Three-way colocalization was then measured by Imaris Triple Colocalization plugin, developed by Michael Adams, using a distance threshold of 600 nm. Colocalization with VGluT2 was measured in two steps: first, PSD95 and SAP102 spots with an intensity above 3,000 in VGluT2 channels were filtered.
Striatal Compartment Synaptome Parameters For characterization of striatal compartments, PSD95/SAP102/MOR were labeled and imaged in the CP using Andor Revolution XDi SDM. Pairs of closely located images from each compartment were then analyzed using the Imaris software (version 8. 1. 2) in brain slices of three mice (males, 2-3 months). PSD95 and SAP102 were detected using spot detection in order to extract the number of puncta, mean punctum intensity and mean punctum size in each image. Colocalization was then measured as for synaptome mapping, using a distance threshold of 300 nm. Moreover, to consider the level of colocalization that might occur by chance, colocalization was also calculated between PSD95 and SAP102 images coming from different mice, in order to create a randomized control.
Bayesian Analysis Bayesian estimation was used for the analysis of mutant effects on synaptome maps (Figure  7 ). Posterior probabilities of model parameters were estimated from the data and these were then used to determine whether genotype dependent differences exist. Each of the synaptome mapping parameters (e. g. , intensity, density, and size) was modeled as having a t-distribution. For both studies, major brain regions (i. e. , hippocampus, cortex, medulla, and striatum) were modeled separately, while subregions (i. e. , CA1so, CA1slm and DGgrInf were contained within a single model). The model was written in JAGS based on the script 'Jags-Ymet-Xnom2fac-MrobustHet. R' associated with Chapter 20. 4 of Kruschke's textbook  (Kruschke, 2014). The key modification made to the base model is that the scale parameter of the t-distribution was adapted to depend only on one of the explanatory variables, x 2 (brain region) and not x 1 (genotype).
ACKNOWLEDGMENTS The authors thank  K. E. Elsegood ,  D. Fricker ,  E. Tuck ,  D. Kerrigan ,  C. L. McLaughlin , and  N. Afinowi  for technical assistance;  A. Tullo ,  J. Menendez Montes ,  J. Piatkowski , and  R. Gokhale  for computational support;  D. Maizels  for artwork;  C. S. Davey  for editing; Image Solutions (UK) and Yokogawa for loaning the CV1000; and  M. Carle  ´n and  H. Kim  for providing prefrontal cortex recording data. Funding for this study was provided by the  Wellcome Trust  ( Technology Development Grant   202932 ), the  European Union Seventh Framework Programme  (grant agreement numbers  HEALTH-F2-2009-241498  [Eurospin] and  720270 ), and  CIBERNED  ( CB06/05/0066 , Spain).
Data Analysis and Modeling For each time point, a preference index (PI) was calculated using the following formula: PI = (N odorant -N solvent )/(N total ) N odorant being the number of larvae in the odorant zone, N solvent being the number of larvae in the solvent zone and N total being the total number of larvae in the assay. As this FIGURE 1 | Schematic of the behavior assay design. Ten 3rd and 4th-instar caterpillars were put in the center of a Petri dish after being starved for 16-22 h. On one side of the dish, a filter paper with 10 µl of an odorant solution was placed. Another filter paper with 10 µl of solvent was put at the opposite side of the dish. The numbers of caterpillars in the different zones were recorded at 2. 5, 5, 10 and 15 min. The preference index (PI), ranging for 1 (attraction) to -1 (repulsion), was calculated for each observation time.
FUNDING This work has been funded by  Inra ,  Sorbonne Université  and the  French National Research Agency  ( ANR-16-CE21-0002-01  and  ANR-16-CE02-0003-01 ). AF and GC-V received doctoral fellowships from Inra and the  National Council of Science and Technology of Paraguay , respectively. XS received a grant from the  China Scholarship Council (CSC).
The received view of this entrainment pathway is that the SCN emits these clock signals to the rest of the body in a hierarchical, one-way communication stream. But our work comes as a surprise because it reveals that the communication is, in fact, reciprocal: the CP can also transmit clock signals to the SCN. This was tested in an in vitro system of cocultured CP and SCN explants; we found that in the presence of the CP, the SCN clock accelerated. Next, it was tested in vivo by targeted silencing of Bmal1 expression in the FoxJ1expressing CP and the ependyma; the transcription factor FOXJ1 regulates development of motile cilia, and its restricted expression in the CP can be visualized from the Allen Mouse Brain Atlas database  7  (Figure  1 ). Although the SCN controls behavioral circadian rhythms expressed through sleep-wake cycles and physiological rhythms of homeostasis, the "ticking" of the SCN clock results from feedback that emanates from the CP clock.
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by  Taipei Medical University-Shuang Ho Hospital Collaboration  Grant  107TMU-SHH-03  to JM and  DW and Taiwan Ministry of Science and Technology Grants   107-2311-B-038-001-MY2  to JM and  104-2420-H-038-001-MY3  to TJL. deClARATIon oF ConFlICTIng InTeReSTS: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
" The survey was conducted between January 29, 2018 and February 14, 2018. We asked 23 questions to the participants. We collected demographic data on the following: age, region, gender, year of highest degree, current position, number of publications, number of publications related to consciousness, number of publications on consciousness in public media, primary domain of expertise, conferences on consciousness attended (ASSC or Tucson), total amount of grants received for studying consciousness, overall amount of grants received. Other questions concerned the possibility of a complete biological explanation of consciousness, progress in the understanding of consciousness thanks to neuroscience and psychology, improvement in the field of consciousness science, theories of consciousness perceived as most promising, experimental rigor of the work on consciousness compared to other subfields in neuroscience, difficulty of successfully competing for funding while studying consciousness compared to other neuroscience subfields, difficulty of the job market in consciousness science compared to other neuroscience subfields, preference for public or private funding of the work on consciousness, accuracy of popular media articles on consciousness, and perceived benefits of popular media article on consciousness.
Background: Immune system dysfunction is a hypothesis in the psychopathology of schizophrenia, but the impact of antipsychotic treatment within this system is not clear. The aim of this meta-analysis was to investigate the impact of antipsychotic treatment on cytokine levels in in vivo studies on schizophrenia. Methods: After a systematic database search, original data were extracted with the help of certain authors. Means and SDs were extracted to calculate standardized mean differences. Cytokine levels were compared in vivo in schizophrenia patients, before and after antipsychotic treatment. Meta-regressions were performed to explore the influence of demographic and clinical variables on cytokine level standardized mean differences. Stratifications by treatment and diagnosis were also performed.
Data Sources and Study Selection Process We carried out a search on the MEDLINE and PsycINFO databases in January 2017, without any limits on year of publication, using the key words cytokines or immune inflammation and schizophrenia or first psychotic episode or psychosis. Studies were included if (1) they were published in English in a peerreviewed journal, (2) they included patients with a diagnosis of schizophrenia based on DSM, III, IV, or V criteria, and (3) they measured in vivo cytokines levels in plasma or serum, before and after antipsychotic treatment. Studies that did not fulfill these 3 criteria were systematically excluded from the analyses. To obtain additional data, an email alert was created after January 2017 on MEDLINE with the same keywords to detect any publications of potential interest.
For each study, we thus obtained the means and SD for all cytokine levels before and after treatment. Articles written by a given research group were carefully scrutinized to ensure the absence of redundancy among the populations included in the trials. A set of clinical variables was defined for the meta-regression analysis. We extracted the duration study (in weeks), the means and SDs for age (age variable), the percentages of females (sex variable), the means and SDs for years of illness (duration of illness variable), the mean age at onset of the disorder (onset disorder variable), and the means and SDs for schizophrenia severity scores before and after treatment (total, positive, negative, and general symptom scores). Data extraction was performed by one author (B. R. ) and verified by another (C.
Article Identification Process Figure  1  shows the article-selection process and supplemental Table  1  shows details of included studies. The database search found 1562 articles. Forty-six studies identified by database research and 1 other identified by email alert were included. Of these 47 studies, 41 studies specify in their article that they exclude patients (1) with somatic comorbidity that may explain variations in cytokine levels, (2) with an abnormality in standard physical and biological examinations, and (3) taking a treatment that impacts the immune system.
Compared with other meta-analysis  (Tourjman et al. , 2013; Goldsmith et al. , 2016) , our results are quite similar, but as we included a larger number of studies, we obtained a larger statistical database. Therefore, we found some difference: we did not find that IL-12 levels were impacted by treatment as contrary to 3 other meta-analyses  (Miller et al. , 2011; Tourjman et al. , 2013; Goldsmith et al. , 2016)  that found an IL-12 level increase. The significant result found in these 2 meta-analyses could merely be due to the small number of studies included (respectively 2 and 3 studies). We found a significant decrease in IFN-γ level like  Tourjman et al. (2013)  counter to  Goldsmith et al. (2016)  and  Miller et al. (2011). Finally, other meta-analyses  (Miller et al. , 2011; Tourjman et al.
Acknowledgments We thank all the following authors for their kind collaboration:  Dr Liina Haring  ( Psychiatry Clinic of Tartu University Hospital, Tartu, Estonia ) for providing seminal data for the study "Antipsychotic treatment reduces psychotic symptoms and markers of low-grade inflammation in first episode psychosis patients, but increases their body mass index";  Dr Milica Borovcanin  ( Department of Psychiatry, Faculty of Medical Sciences, University of Kragujevac Svetozara Markovica, Psychiatric Clinic, Clinical Center Kragujevac Zmaj Jovina, Kragujevac, Serbia ) for providing data for the studies "Antipsychotics can modulate the cytokine profile in schizophrenia: Attenuation of the type-2 inflammatory response" and "Increase systemic levels of IL-23 as a possible constitutive marker in schizophrenia.
org/10. 1016/j. jocit. 2018. 09. 009 Peer review under responsibility of Shanghai Hengrun Biomedical Technology Research Institute. of fracture delimited by the localization in the proximal third, middle third or distal third (Fig. 1 ). The specific stress, specific strength and Young's Modulus calculus were made with the SciDavis software (GNU license). Density was calculated using the pycnometer method for solids  [4]  (Eq. (1). ). The specific stress corresponds to maximum stress divided by density. The specific modulus corresponds to Young' modulus divided by density. Equations Density = (M1*d)/(M 1 +M 2 -M 3 ) (1) Where: M 1 = Nerve weight M 2 = Pycnometer + solution weight M 3 = Nerve + pycnometer + solution weight d = Solution density Beside the fact this study intend to characterize human digital collateral biomechanics; we hypothesized that density will affect the mechanical behavior of nerves.
Apparatus The experiment was run on a PC and programmed using Matlab. It was conducted in a small, darkened, quiet room, and participants were seated on a chair such that their eyes were roughly 40 cm from the center of the computer monitor (1600 x 900 pixels). Points served as the appetitive US, and participants were instructed that they had to earn 80 points to complete the task. Participants left the experiment when they completed the task, even if they failed to earn 80 points. The first-order CSs consisted of five 4 cm diameter colored circles (blue, green, red, yellow, and purple), and the second-order CSs were white, capital letters (A, B, C, D, and E; the font was Arial) with a font size value of 80 based on Matlab programming language (i. e. , approximately 2. 8 cm). The screen consisted of a black background with a points tally in the top right corner, and two 3.
Procedure Unlike the  Prével et al. (2016)  experiment, in this study we required an anticipatory response during the concurrent FR phases (Phases 1 and 5), as well as during the conditioning phases (Phases 2 and 4) and test for first-order conditioning (Phase 3; see Figure  1  for the complete design): On-screen targets "", "", and "SPACE" were used as discriminative stimuli (S D ) and were presented at the center of the monitor. Arrows were 3. 5 cm long, and the word SPACE was written with a font size of 60 based on the Matlab programming language. In Phases 1 to 5, during presentations of these targets, participants were required to press a key on the keyboard matching the target presented. Targets lasted 1200 ms, and a correct press (i. e. , pressing the key matching the S D ) was immediately followed by a 'positive' tone (i.
When the required ingredients to calculate the NP score were unavailable, we gathered them either from the producer's website (if available) or from the USDA Food Composition Databases 6  , for a highly similar product. Food products healthiness is inversely proportional to the NP score (the lower the NP score, the healthier the product). Moreover, a food (resp. drink) is deemed unhealthy with a NP score > 4 (resp. > 1). NP scores of our food products are presented in Figure  5.