dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to thank  Marc-Michel Rohé  from  Inria Sophia Antipolis-Méditeranée  for his help and assistance regarding the non-parametric shape analysis method. MOCHA Collaborative Group:  Andrew Taylor ,  Alessandro Giardini ,  Sachin Khambadkone ,  Silvia Schievano ,  Marc de Leval  and  T. -Y. Hsia  ( Institute of Cardiovascular Science, UCL, London, UK );  Edward Bove  and  Adam Dorfman  ( University of Michigan, Ann Arbor, MI, USA );  G. Hamilton Baker  and  Anthony Hlavacek  ( Medical University of South Carolina, Charleston, SC, USA );  Francesco Migliavacca ,  Giancarlo Pennati  and  Gabriele Dubini  ( Politecnico di Milano, Milan, Italy );  Alison Marsden  ( University of California, San Diego, CA, USA );  Jeffrey Feinstein  ( Stanford University, Stanford, CA, USA );  Irene Vignon-Clementel  ( INRIA,Paris,France );  Richard Figliola  and  John McGregor  ( Clemson University, Clemson, SC, USA ).
Computer Analyses Domain homology analyses were performed with pfam  (Finn et al. , 2016 ; http://pfam. xfam. org/), HHpred  (Söding et al. , 2005 ; http://toolkit. tuebingen. mpg. de/hhpred), and Phyre 2  (Kelley et al. , 2015 ; http://www. sbg. bio. ic. ac. uk/phyre2). Predictions of trans-membrane domains were performed with TMPred  (Hofmann and Stoffel, 1993 ; http://www. ch. embnet. org/software/TMPRED_form. html), TMHMM  (Sonnhammer et al. , 1998 ; http://www. cbs. dtu. dk/services/TMHMM/), and HHMTop  (Tusnády and Simon, 1998 ; http://sbcb. bioch. ox. ac. uk/TM_noj/TM_noj. html).
Sequence Analyses of PorX and PorY To gain insights onto the PorX and PorY proteins, we performed a sequence and structural analysis using the HHPred and Phyre 2 algorithms. The 518-aminoacid PorX protein [PGN_1019, gene accession (GI): 188594563] is a putative cytoplasmic protein that is constituted of two domains: a N-terminal receiver domain of the CheY family (pfam identifier: PF00072) and a C-terminal effector domain of the PglZ family (pfam: PF08665) (Figure  1A ). CheY is a phospho-protein involved in chemotaxis, coupling signal sensing via chemoreceptors to the control of the directionality of the flagellar rotation  (Sourjik and Wingreen, 2012). This regulation occurs via the phosphorylation of a conserved aspartate residue that is also present in PorX, at position 58 (Figure  1A ).
ACKNOWLEDGMENTS We thank  Laure Journet  for critical reading of the manuscript,  Laure Journet ,  Bérengère Ize  and  Laetitia Houot  and the members of the  Cascales, Lloubès ,  Cambillau/Roussel , Bouveret and Sturgis research groups for discussions,  Isabelle Bringer ,  Annick Brun , and  Olivier Uderso  for technical assistance and  Martine Scort-Seize  for encouragements. This work was supported by the  CNRS , the  Aix-Marseille Université  and a grant from the  Agence Nationale de la Recherche  to  EC  ( ANR-15-CE11-0019-01 ). MV is a recipient of a doctoral fellowship from the  French Ministry of Research. The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.
With the rapid growth of NM production, HTS methods are needed to allow toxicity testing of large numbers of materials in a timely manner and with savings in labor costs. HTS in vitro facilitates the hazard ranking of NMs, through the generation of a database with all reported effects on biological and environmental systems; thus novel NMs can be prioritized for in vivo testing. An effective HTS model for investigating the toxic effects of several metal-oxide NPs,  10  based on a hazard ranking system using HTS, gave results that were mostly comparable to in vivo results in zebra fish embryos with the same NPs. Further investigation needed to ensure there is no interference with NMs.
g. , using ANOVA with post Bonferroni analysis, or general linear models, (6) welldesigned graphical display of data (e. g. , bar charts) and-in the case of multiparametric datasetsvarious graphical plots to visualize associations between NP/NM exposure and different endpoints. For example, a multilevel heatmap matrix has been used to illustrate effects of dose, concentration, and time for multiple NMs  [11] [12] [13]  (Figure  1 ). Various methods have been applied to the study of toxicity of NMs, employing diverse physical, chemical, and biological principles and endpoints; they will now be described in detail, with emphasis on high throughput adaptations.
Cell number, proliferation, viability, morphology, and adhesion are quantified. Electronic microtiter plates: 16-well (left), 96-well (middle), and 384-well (not shown), can be used for high throughput nanotoxicity screening. The instrument is placed in a standard CO 2 incubator and is cable-connected with analysis and control units outside the incubator (right). The data and the performance of the instrument are displayed real-time (right). (c) The Ampha™ Z30 impedance flow cytometer (left) (Amphasys AG, Lucerne, Switzerland) uses microfluidic chips with microelectrodes (right) to measure changes in the electrical resistance of the fluid, in which cells are suspended, when cells pass through the applied AC electric field. Cell viability and mode of cell death (apoptosis vs. necrosis) can be detected and quantified.
g. , with Luminex ® technology. 171 Future highthroughput transcriptomics platforms, e. g. , in the LINCS and the Tox21 Phase III projects, enable rapid gene profiling experiments with both several doses and biological replicates using multiple model of 800-1500 genes. 172, 173 NM effects analyzed using traditional microarrays, such as Affymetrix GeneChips ® , form the basis for most existing gene profiling analyses of NMs,  174, 175  providing reference values for recent next-generation sequencing (e. g. , Ref 176 and future generation of HTS data from selected toxicity-reflective gene sets. Open source or commercial bioinformatics tools, such as-respectively-InCroMap or Ingenuity Pathway Analysis (Ingenuity ® Systems, www. ingenuity. com), rapidly sort omics-derived data into mechanistically meaningful results, enabling grouping of NMs into clusters by gene or pathway activation levels.
177 Connectivity mapping, i. e. , grouping for similarities in gene expression profiles, can be viewed as a form of biological read-across. 178 Modeling efforts have indicated a need for investigating more than 200 and even thousands of agents, whether NMs or chemicals, to effectively characterize toxicity mechanisms through omics analysis. 179 The well-known Connectivity Map project,  180  and its successor LINCS, have addressed this issue with over 1. 5 million gene expression profiles covering over 10 5 variables to date. 173 Overall, data integration across highthroughput, high-content, pathway-based cellular assays and omics profiling will enable a diversified view of the potential toxicological activity of a NM. Tiered HTS approaches including toxicity assessment and immunochemical assays followed by omics analysis, lead to gradually broader characterization of intoxicating concentrations of selected, potentially class-representative NMs.
The crucial toxicity endpoints include cytotoxicity, oxidative stress, genotoxicity and markers indicative of cell transformation and carcinogenicity. Automation should further streamline testing procedures, and-linked with appropriate standard operating procedures-this should contribute substantially to reducing variability and operator bias. HTS is bound to generate large data collections, and to encourage research groups to establish databases on relevant toxicological determinants of NMs. The availability of a bank of reliable information about NM toxicity will facilitate grouping approaches and the selection of class-representative materials that require animal testing. Finally, future efforts of HTS and HCA should also consider means of potentially automating the preparation and dispersion of NMs, as this task is so far mostly manually performed, leading to a less than desirable output of data from the increasingly growing array of HTS approaches reviewed here.
Growth Inhibition Assay Growth inhibition assays were performed as previously described  (Pasquier et al. , 2011). Briefly, cells were seeded at 1500 cells/well in 96-well plates. After 24 h, cells were treated with a range of concentrations of chemotherapeutic drugs alone or in combination with propranolol and after 72 h drug incubation, metabolic activity was detected by addition of Alamar blue and spectrophotometric analysis. Cell proliferation was determined and expressed as a percentage of untreated control cells. The determination of IC50 values was performed by point-to-point fit spline analysis using GraphPad Prism 4 software (GraphPad Software Inc. , La Jolla, CA). Combination index (CI) values were calculated for all tested drug concentrations according to the Chou and Talalay method  (Chou, 2010)  using the following equation: CI ¼ D ð Þ 1 D X ð Þ 1 þ D ð Þ 2 D X ð Þ 2: where (D) 1 and (D) 2 represent the dose of agent 1 and 2 used in combination to induce X% growth inhibition, and (D X ) 1 and (D X ) 2 represent the dose of agent 1 and 2 required to reach X% growth inhibition when used alone.
Tumor Spheroid Assay and PARP Cleavage For the tumor spheroid assay, 5000 BMST-Ras cells were seeded in ultra-low attachment 96-well plates (Corning Inc. , Corning, NY) and allowed to form even tumor spheroids of 600 μm in diameter for 48 h. Drug treatment was then initiated and photographs were taken every 24 h using the 5 × objective of an Axiovert 200 M fluorescent microscope coupled to an AxioCamMR3 camera driven by the AxioVision 4. 7 software (Carl Zeiss, North Ryde, Australia). The volume of at least 10 tumor spheroids was determined using the AxioVision 4. 7 software and the following formula: V = 4/3 × π × r 3. To assess apoptosis induction, tumor spheroids were lysed in RIPA buffer containing a cocktail of protease inhibitors (Sigma-Aldrich, Castle Hill, Australia) after 120 h of drug treatment.
Acknowledgements This project was funded part by the  Children's Cancer Institute , which is affiliated with the  University of New South Wales (UNSW Australia ) and  Sydney Children's Hospital Network  and by grants from the  Balnaves Foundation (EP) ,  Cancer Council New South Wales (MK) , and  NHMRC Senior Research Fellowships  (MK;  APP1058299 ). The authors would also like to thank  LNlavie  and  Les copains de Charles  for their support. EP is funded by a  Marie Curie-Sklodowska Fellowship.
No feedback was given to the participant. The experiment was controlled using Matlab (The Mathworks, Inc, USA) and Psychtoolbox  34. Participants were given time to practice the experiment and the mapping between buttons and identities before entering the scanner. Experimental design followed a slow event-related paradigm to minimize contamination of consecutive hemodynamic responses, and hence minimize any potential cross talk between BOLD responses to the V and F stimuli. Inter-stimulus interval was set randomly between 10 and 18 seconds with the onset of the stimuli locked to BOLD volume acquisition. Data were recorded from each participant over several sessions each containing 6-8 blocks (number of total blocks per participant (mean ± s. d. ): 29. 2 ± 5. 4). Each block lasted around 8 minutes with each condition presented twice.
Two-minute breaks were given between each two consecutive blocks, during which the participants were instructed not to move their head. Overall 700. 8 ± 129. 7 trials were presented corresponding to the 4 identities and three modalities for each participant. Participants were instructed to perform a 4-alternative forced choice identity classification task using 4 buttons of an MR compatible response pad (NordicNeuroLab, Norway). Reaction times (relative to sound onset for V condition and face onset for F and FV conditions) were collected within a response window limited to two seconds after stimulus onset. fMRI Data Analysis. Preprocessing Data were analysed using Statistical Parametric Mapping (SPM8, Wellcome Trust Centre for Neuroimaging) in Matlab. All functional and anatomical images from all the sessions were reoriented in the AC-PC orientation.
e. two trials are combined per regressor. The grey matter tissue probability maps generated by the segmentation processing stage were used as explicit masks to restrict the data analysis to grey matter. For each participant the following univariate contrasts were calculated: three T-tests for Face vs. baseline, Voice vs. baseline and Face-Voice vs. baseline; three F-tests comparing the four identities for each presentation condition. Multivariate analysis. The main GLM design resulted in a dataset with 350. 4 ± 64. 84 beta images per participant covering the 12 conditions. To decode these samples a whole-brain searchlight paradigm was used  23. In this approach cross-validated classification is carried out at the level of a small sphere scanning the whole brain. As a result each voxel in the output image contains the average cross-validation accuracy of the sphere centered on it.
In order to choose a consistent number of voxels per sphere among all participants different sphere radiuses were tested, between 3 and 8 voxels. For our dataset, a 4-voxel radius (8 mm) produced the most consistent voxel number per sphere (21. 74 ± 1. 43). Using a relatively smaller sphere ensures the classifier is less likely to over-fit on training samples due to a good balance between the number of samples (observations) and the number of voxels within the sphere (features). The number of samples is large enough compared to their dimensionality so the samples are not too sparse in the feature space  35. The searchlight analysis covered the whole brain, but a sphere was only considered in the analysis if more than 50% of its voxels fell within the grey matter mask of the subject being analysed, in order to restrict analysis to grey matter across the whole brain in each subject.
The number of spheres included in the analysis was (mean ± s. d. ) n = 46500 ± 6400. In each sphere the cross-validation analysis only included as features the voxels corresponding to grey matter, the rest being discarded. Each participant's data were grouped into training (80% of samples) and testing (the remaining 20%) sets. The training samples were chosen randomly and were then used to train a linear Support Vector Machine (SVM) classifier (as implemented by libSVM) to decode the four identities from the samples in the testing set. This process is then repeated 100 times and the average accuracy value is assigned to the voxel in the centre of the sphere. SVM is usually able to solve only two class problems. In order to extend it to 4-class problems, we used the one-versus-one approach as implemented in libSVM  36.
Empirical Chance Level. In theory the chance level is c = 1/n, where n is the number of classes (c = 0. 25 in this study). However, practically the chance level might differ due to the structure of the data or bias in the labelling that affects the training of the classifier. To address this issue an empirical chance level (c') must be estimated. For each sphere the labels of the training set were shuffled, i. e. samples were assigned wrong labels, before feeding them to the classifier. The classifier was then tested on the test dataset. This step is usually repeated a large number of times (> 1000) to estimate the distribution of the chance level per voxel. However, due to the large number of spheres, this was impractical. Here, we only ran the chance level estimation 100 times per sphere as we were only interested in the mean value of the empirical chance level.
Acknowledgements We are grateful to  Frances Crabbe  for her assistance with data acquisition and to  Bruno L. Giordano  for his help with data analysis. Supported by grants  BB/E003958/1  from  BBSRC (UK)  to PB and JG, large grant  RES-060-25-0010  by  ESRC/MRC (UK)  to P. B. and J. G. , grant  098433  by  Wellcome Trust (UK)  to J. G. , and grant  AJE201214  by the  Fondation pour la Recherche Médicale (France)  to P. B.
Pressure, airflow and muscular pressure signals were measured with the ASL 5000 software at 512 Hz and stored for subsequent analysis. The data were then processed with the help of a code written by us and based on the definition of asynchrony events as detailed below. The inter-patient variability clinically observed can therefore be easily simulated. There is not yet a consensus for designing the evolution of the muscular pressure for driving an ASL  [3]  and all parameter values required to reproduce it are very rarely fully reported, with the exception of Chatburn's studies  [20]. Most often a semi-sinusoidal muscle pressure -as predefined in the ASL 5000 -is used. Based on the (rare) physiological data available, we thus developed a more realistic evolution of the muscular pressure (Fig.
Detecting cycles and asynchrony events Simulations were performed for each ventilator included in the protocol and data were stored for an automatic analysis using our detection algorithm working with the time series of the muscular pressure P mus , the pressure P v measured in the piston chamber (nearly corresponding to the pressure in the ventilatory circuit) and the mechanical lung airflow Q p (assimilated to the patient airflow). These three time series were provided by the ASL 5000 and were filtered with a Butterworth filter acting as a low pass filter whose frequency cutoff was set at 10 Hz to reduce the measurement noise. The breathing cycles were determined by detecting time T 0 and T max from each oscillation of the muscular pressure. During each of them, the number of times the pressure P v becomes greater than a threshold value (equal to P h 2 ) allowed to determine whether this is a non-triggered, single-triggered or double-triggered cycle (sometimes we may also observe multi-triggered cycles).
, 2014). Importantly, the obtained scores were compared to alternative methods that were previously tested on the same database. With respect to spindle detection, our method achieved higher performance than most of the alternative methods. This was corroborated with statistic tests that took into account both sensitivity and precision (i. e. , Matthew's coefficient of correlation (MCC), F1, Cohen κ). Our proposed method has been made available to the community via an open-source tool named Spinky (for spindle and K-complex detection). Thanks to a GUI implementation and access to Matlab and Python resources, Spinky is expected to contribute to an open-science approach that will enhance replicability and reliable comparisons of classifier performances for the detection of sleep EEG microstructure in both healthy and patient populations.
Goal of This Study In a recent study, we proposed a framework for joint spindle and K-complex detection, based on the combination of a discrete wavelet transform, known as the Tunable Q-factor Wavelet Transform (TQWT;  Selesnick and Bayram, 2009; Selesnick, 2011a,b,c)  and morphological component analysis (MCA). Appropriate Q-factor tuning allows for the decomposition of the EEG signal into transient (K-complex) and oscillatory (spindle) components. Our results from the sleep EEG recordings of 14 participants demonstrated that this framework could be a promising tool to facilitate and improve the reliability of the detection of spindles and K-complexes. This study extends our previous work in three significant ways. First, we provide critical validation and benchmarking of the TQWT-MCA approach by applying it to an open-access database, namely the Montreal Archive of Sleep Studies (MASS;  O'Reilly et al.
, 2014). Second, we extend on the performance measures by including a wide range of metrics (sensitivity, positive predictive value (PPV), Matthew's coefficient of correlation (MCC), Cohen's κ and the F1 measure). This is important for the comparisons with other methods in the field. Finally, with the publication of this report we provide an open-source version of the software (which we coined Spinky for automatic spindle and K-complex detection), and we describe all the processing steps necessary for users to test on their data or replicate our findings.
Article Outline The article is organized as follows. We first describe the open-access database that we used (''Databases'' Section). Next, in Sections ''Optimal Threshold Estimation and Detection'', we provide a thorough investigation of the threshold estimation step (training phase of our algorithm), followed by a presentation of the statistical assessment of detection results (''Statistical Assessment of Detection'' Section). Section ''Performance Evaluation and Comparison with other Algorithms'' provides the links to the open-access Matlab-based toolbox and associated Python (Jupyter) interactive notebook. In ''Open Access'' Sections, a user-oriented overview of the Matlab GUI software is overviewed. The results section provides an assessment of the robustness of the threshold estimation step (''Evaluation of the Detection Threshold Variability'' Section), followed by the results of the automatic detection of spindles and K-complexes (''Automatic Spindle and K-Complex Scoring with Spinky'' Section).
Databases To demonstrate the performance of the proposed detector and facilitate comparisons with other methods, we chose to examine its detection results on an open-access database: MASS  (O'Reilly et al. , 2014). More specifically, we used the second subset of the first cohort (C1/SS2). This contained 19 full night recordings of healthy young participants, all scored for spindles and K-complexes by experts. Scoring was performed on N2 epochs using the C3 derivation and a linked-ear reference. As discussed in O'Reilly and Nielsen (2015), the two experts for sleep spindles show relatively low inter-rater agreement (a median Cohen κ of about 0. 4) owing to the fact that the first one scored spindles using traditional AASM rules, whereas the second rater used an approach similar to the one employed in  Ray et al.
Performance Evaluation and Comparison with Other Algorithms An important advantage of using an open-access database is the ability to benchmark the performance of a new algorithm and compare it to other methods. After running Spinky on the MASS data sets, we compared its performance to those of other spindle detection algorithms published in the literature, including a Teager detector  (Ahmed et al. , 2009) , Sigma index  (Huupponen et al. , 2000 (Huupponen et al. , , 2007)) , RSP  (Devuyst et al. , 2011) , RMS  (Mölle et al. , 2002;  all four assessed in O'Reilly and Nielsen, 2015), as well as a detector based on matching pursuit (MP;  Durka et al. , 2015). The output of the different methods was compared using the five metrics described in the previous section (Sensitivity, PPV, MCC, Cohen's κ and F1).
These comparisons were expected to be highly reliable as the same assessment method, subject sample and expert scoring were used for all cases. We further compared our results with detectors that were applied on the same database, but using slightly different methods (e. g. , TP, TN, FP and FN determined on time windows instead of time samples) or using a sub-sample of subjects (e. g. , excluding, for the computation of test statistics, subjects that were used for training the detector). This second set included the eight detectors A1-A8 that were tested in  Tsanas and Clifford (2015) , a detector based on complex demodulation (CD;  Ray et al. , 2015)  and two detectors using random forest (RF) and ANN that were assessed in  Patti et al. (2015).
Open Access To allow others to replicate our results or use our method on their own data, we provide open-access code, GUIs and interactive resources for the developed tools. The code used for assessing the performance of the detector is available as a Jupyter (IPython) notebook at https://github. com/christian-oreilly/spinkyDemo/ blob/master/notebook/finalDemo. ipynb. The database used for spindle detection is freely available at http://www. ceamscarsm. ca/en/MASS. The MATLAB (Mathworks Inc. , MA, USA) source code and GUIs for the detector can be downloaded at https://github. com/TarekLaj/SPINKY.