dataesr/scientific-paragraphs-categorization huggingface dataset:
1. All priors were chosen to be non-informative. The outlier component was not used for inference in the small dataset, but it was used for the large dataset. The MCMC sampler ran first for a number of burn-in iterations, then the posterior distribution was explored in a number of production iterations. During the production iterations, apart from sampling parameters of the model, a value of Y was also drawn, i. e. a sample from the posterior predictive. For the first synthetic data set 10,000 burn-in and 100,000 production iterations were used. For the second, larger, data set we used 5,000 burn-in iterations and 5,000 production iterations. For the second data set the MCMC sampler was run on Amazon Cloud, using 192 cores. Each iteration took on average one second, so that about 2. 7 h was needed in total.
Flow cytometry data We analyze two flow cytometry data sets with BayesFlow: the data set GvHD from the FlowCAP I challengewith four markers, 12 samples and approximately 13,000 cells per sample-and a data set obtained from the R package healthyFlowData  [32]  with technical replicates of PBMC samples from healthy donors-in total 20 samples with approximately 20,000 cells, also measured with four markers. In the GvHD dataset we can compare the gating obtained from BayesFlow with manual gating provided from FlowCAP as well as automated gating from a wide range of other methods. In healthyFlowData we can instead compare gating between technical replicates to see if samples are treated in a consistent manner. For the healtyFlowData dataset we used an exploratory approach with non-informative priors.
We ran multiple simulations and gradually increased the number of components until we passed the quality criteria described under Quality control; we finally arrived at using K = 25 components. For the GvHD data set we started with an exploratory approach and gradually increased the number of components, but in the quality checks we noted one population in one of the samples which was very hard to capture. Then we decided to use an informative approach for this population. Using a scatter plot, Fig. 6 , we set boundaries for this population in the dimensions given by the CD4 and the CD8b marker and computed its mean and empirical s covariance matrix. We used the mean to set an informative prior for θ k and the mean and the empirical covariance to initialize the component. Prior parameters in both the informative and non-informative case are described in the Additional file 1: Section E.
2. BayesFlow applies three data preprocessing steps: 1) Data points with extreme values in at least one dimension (larger than 0. 999 times the largest data point or smaller Fig. 6  Cell population which is hard to detect in the GvHD dataset than 1. 001 times the smallest data point) are removed. Such data points can lead to components with singular covariance matrices, and a well designed flow cytometry experiment should not have significant populations with such values. 2) The data is scaled using the 1 and 99 % percentiles q 0. 01 and q 0. 99 of the pooled data, with the same scaling for all samples, so that q 0. 01 = 0 and q 0. 99 = 1 for each marker for the pooled data. This is done in order to be able to set informative priors in an intuitive way. 3) Before testing which components should be merged, a very small amount of noise is added to the data (standard deviation 0.
003). This is since the discreteness of the original flow cytometry measurements can lead to a striped pattern in the flow cytometry data  [33]  and also when it is not visible to the human eye it disturbs the dip test. After preprocessing, parameters for the MCMC sampler were initialized by running the EM algorithm on the pooled data, followed by the initialization scheme used for the large synthetic dataset, detailed in the Additional file 1: Section D. 4. We ran 16,000 burn-in iterations and 4,000 production iterations of the MCMC sampler for both experiments. The burn-in period consisted of five phases: In the first phase, the priors on variation in location and shape were modified to force clusters together. Before the second phase, priors parameters were set to normal again. After the second phase, components which were considered to be outliers were turned off.
We ran BayesFlow and ASPIRE on a 3. 2 GHz quad core CPU. A BayesFlow run took 0. 5 h for the GvHD dataset and 1. 4 h for healthyFlowData. ASPIRE took in total 2. 4 h for the GvHD dataset and 6. 6 h for healthyFlowData per run. Four runs of ASPIRE was needed to determine the κ i parameters. HDPGMM was run on a dual core GPU. It needed 0. 72 h for the GvHD dataset and approximately 1 h for the healthyFlowData dataset.
Simulated data We begin by analyzing the smaller data set. In Fig. 2  we show univariate and bivariate histograms of all synthetic cell measurements pooled together, as well as the corresponding histograms of the data from a single flow cytometry sample where all four clusters are present. Note that the data when pooled together has a complicated density, as it is in fact a mixture of 232 multivariate normal densities. In Fig. 3  we show the same univariate and bivariate histograms, but this time with samples from the posterior predictive distribution of Y. From the synthetic cell measurements generated from the inferred models of the datasets it is clear that the inferred models are accurate and capture the variation across samples, which a model only of pooled data cannot do. Figure  7  displays dots at the posterior mean locations of the mixture component centers μ jk whose posterior probability of being active is greater than 1 %; the true locations of the active clusters are displayed as circles.
Flow cytometry data GvHD For the analysis of the GvHD dataset we did twelve runs of BayesFlow in the informed setup described above. Seven were excluded due to confusion between populations, i. e. at least one sample component was closest to the wrong latent component; of the remaining five, one more run was excluded since it has not converged, and another two because of multimodal clusters. This leaved two runs that passed the quality control. Additional file 1: Figs. S2  and S3  show trace plots and projections of clusters with high dip test values respectively. Table  1  reports the accordance with manual gating for the two BayesFlow runs as well as what is obtained from ASPIRE and HDPGMM, as well as the top two performing methods for this data set in FlowCAP: flowMeans and SamSPECTRAL.
Discussion From different runs of BayesFlow we can get different representations of data, as in the case of the GvHD dataset. This is because with highly overlapping populations there might be multiple models representing the data equally well. But since all samples are gated jointly in every run, the gated populations can still be compared across samples. The user might have a preference for one representation or the other though, and informative priors can be used to guide BayesFlow to a preferred representation. The boxes go between the quantiles q km,0. 25 and q km,0. 75 , the whiskers extend to q km,0. 01 and q km,0. 99. The α-quantile for (merged) component k in dimension m, q km,α , is here defined as q km,α = min i j {Y i j m : α < ij:Yijm<Y i j m w ijk }. c Population proportions in each of the twelve flow cytometry samples BayesFlow is not aimed at discovery of rare cell populations, but it can be used together with an algorithm specifically designed for detecting rare cell populations in a sample, such as SWIFT  [12] , and then use informative priors to find how this population occurs across an entire set of samples, in a similar way as was done in the GvHD dataset.
Non-linear transformations such as logicle can make Gaussian populations non-Gaussian, which makes inference harder. The flow cytometry data we used for the experiments had already been compensated, the healthyFlowData data set had also been transformed with an asinh transform; details are given in the Additional file 1: Section E. 1. BayesFlow finds a joint representation of an entire set of samples. In order for this representation to be reasonable there has to be sufficient correspondences between samples. Even if for a data set with very little correspondences a joint model could be obtained by using a very large number of components, it would hard to gain any insights from such a model. In such a case an entirely computational pipeline without the cell population identification step would be preferred.
Conclusions In this paper we have presented a new Bayesian hierarchical model designed for joint cell population identification in many flow cytometry samples. The model captures the variability in shapes and locations of the populations between the samples and we have demonstrated its use in an exploratory as well as in a partly informed setting with some prior information. We showed that for synthetic datasets generated from the model, the parameters were recovered with high accuracy through a MCMC sampling scheme. The model was then applied to a real flow cytometry data set where a manual gating was available, and it was shown to have very high accordance with manual gating as compared to other automated gating methods, while at the same time the gating was more consistent across samples than either the manual gating or other automated gating methods.
In contrast, traditional physicochemical methods, like HPLC or GC-MS, mainly provide analytical information like absolute concentrations of known chemicals  [17, 18]. Thus, when substances are new, unknown or not yet deposited in the databases, they cannot be identified by these approaches. Biosensors will not compete but rather complement official physicochemical methods, with specific benefits in environmental monitoring, food safety and quality control, drug testing and other uses where genotoxicity tests are needed to determine potential genotoxic and mutagenic hazards. Our previous study reported that yeast-based biosensors carrying a green fluorescent protein (GFP)-encoding gene under the control of DNA damage-inducible promoters, DIN7, PLM2, RNR2, or RAD54, could be used to identify genotoxic or carcinogenic compounds  [19] , but were not able to detect procarcinogens, e.
In brief, the assay was performed in 384-well microplate (24 x 16 well format; Greiner Bioone, Germany) with black walls and transparent flat bottoms. Each well was pipetted in triplicate for determining mean values, standard deviation (SD), and the assay was repeated at least 3 times for reproducibility. Except for the preparation of stock compound solutions and sealing the microplate with breathable membrane (Diversified Biotech, USA), all these steps were automatically executed by a combination of robotic system (Genesis RSP-150 Liquid Handling System, Tecan, Switzerland) and microplate reader (Tecan). A set of R-scripts developed in our previous study  [29]  controls both the robot and reader by translating the instructions into machine commands, executing liquid handling and measurements, collecting the data, and processing it without user interaction.
Leverage approach Outlier detection is very prominent in evolving the mathematical models. In fact, outlier detection is to recognize individual datum which may vary from the bulk of the data present in a dataset  [52]. The corresponding ways commonly comprise of numerical and graphical procedures  [52] [53] [54]. The Leverage approach  [52] [53] [54]  is deemed to be a confident procedure for outlier detection that copes with the values of the residuals and a matrix identified as Hat matrix comprised of the measured data and the estimated values accomplished from a model. The main usage criterion of the procedure is to utilize a LSSVM method, which is able of adequate calculation/estimation of the data of interest. The Leverage or Hat indices are calculated based on Hat matrix (H) with the following definition  [52] [53] [54] : H = X X T X -1 X T ( 11 ) where X is a (n × k) matrix, in which n indicates the data (rows) and k represents the parameters of the model (columns), and T shows the transpose matrix.
The database JET2 Viewer, openly accessible at http://www. jet2viewer. upmc. fr/, reports putative protein binding sites for all three-dimensional (3D) structures available in the Protein Data Bank (PDB). This knowledge base was generated by applying the computational method JET 2 at large-scale on more than 20 000 chains. JET 2 strategy yields very precise predictions of interacting surfaces and unravels their evolutionary process and complexity. JET2 Viewer provides an online intelligent display, including interactive 3D visualization of the binding sites mapped onto PDB structures and suitable files recording JET 2 analyses. Predictions were evaluated on more than 15 000 experimentally characterized protein interfaces. This is, to our knowledge, the largest evaluation of a protein binding site prediction method.
Some interaction sites might be shared by partners at different times, and some other sites might be large enough to be used by several proteins at once. Predicting these differences is of crucial importance to understand the PPI network and to design artificial interactions. JET2 Viewer is a web server that provides binding site predictions for the full set of structures collected in the Protein Data Bank (PDB). The predictions were produced by JET 2 , a new tool  (16)  that addresses the problem of identifying multiple interaction sites. The predictive model for protein interfaces implemented in JET 2 is inspired by a thorough analysis of known protein complexes which revealed a geometrical pattern observed for many binding sites  (17). Namely, experimental sites can be described as comprising three concentric layers: a layer of residues mostly buried and occupying the central zone of the interface (support), a layer of surface residues that become buried upon association with the partner (core) and a layer of residues remaining partially exposed to the solvent in the complex (rim).
JET2 VIEWER DATA AND FUNCTIONALITIES JET2 Viewer reports pre-computed interface predictions obtained by running the iterative version of JET 2 (iJET 2 , 10 iterations) on the non-redundant set (at 40% identity) of all chains for which a high-quality three-dimensional (3D) structure is available in the PDB. The user can give as input a valid PDB identifier or he/she can browse the list of all treated PDBs. If the PDB code provided by the user is not part of the set of explicitly treated PDB files, the user will be guided to access the JET 2 results computed for chains homologous to the chains of the query structure. JET 2 results can be interactively visualized in 3D through the JavaScript version of Jmol  (18) , JSmol (Figure  2 ). The user can choose to display all chains in the PDB to see if the predicted sites lie at the interface between chains.
About 5 000 additional sites could be identified by considering the whole PDB, leading to 18 541 sites in total. The proportion of correctly detected sites drops down to 81% on the whole set (Table 1), which may be explained by conformational changes occurring in the PDB entries on which JET 2 was not run. JET 2 performance was computed on the 15 071 predicted sites (Table  2 ). On average, JET 2 covered 53% of their interacting residues with a precision of 51%. The performance varies with the size of the protein. Small proteins (<100 residues) display high sensitivity (>75%) and rather low specificity (<50%), indicating that a very large portion of the surface of these proteins is involved in interactions and is correctly detected by JET 2. The sensitivity decreases with the increasing size of the protein.
Multiple partners for a single site The patch detected by SC1 in the phosphorelay intermediate protein YPD1 (PDB code: 2R25, chain A) matches the homodimeric interface of the protein at 90% sensitivity and 51% precision (Figure  4A , on the left). It also matches the interface with the osmosensing histidine protein kinase SLN1 with sensitivity of 93% and precision of 49% (Figure  4A , on the right).
DETAILS ON THE INPUT DATA The non-redundant set of all chains in the PDB was assembled with the PISCES server (http://dunbrack. fccc. edu/ PISCES. php) (  21 ) by imposing a filter of ≤40% sequence identity. Chains smaller than 40 and longer than 10 000 residues were not considered. Only PDB files of acceptable resolution (<3. 5 Å) and quality (R-value ≤ 0. 3) were retained. Nuclear magnetic resonance (NMR) entries were included, C␣-only entries were excluded. The final set was comprised of 22 015 PDB files annotated as XRAY or NMR by PISCES. JET 2 produced results for 21 840 chains (0. 7% error rate). Let us stress that JET 2 was run on entire PDB files. Consequently, each chain of the non-redundant set may be present more than once in the database. Furthermore, as new JET 2 predictions are computed in the lab, we add them to the database.
To date, results have been produced for about 70 000 non-unique chains. The user has access to all the information contained in the database. JET 2 implementation allows to handle frequent problematic issues associated to structural data deposited in the PDB. For X-ray structures, only the conformation displaying the highest occupancy is considered. For NMR entries, the calculation is performed on every conformer. Residues whose number contains an alphabetic insertion code are included. The most common non-standard amino acid residue types: selenomethionine (MSE), methyllysine (MLY), hydroxyproline (HYP), phosphoserine (SEP), phosphothreonine (TPO) and phosphotyrosine (PTR), are replaced by the corresponding standard amino acids. Amino acids of other non-standard types are ignored. Chains with no, lower-case or numeric identifiers are treated as chains with upper-case identifiers.
DISCUSSION JET2 Viewer is a web server providing to the community binding site predictions for all structures available in the D240 Nucleic Acids Research, 2017, Vol. 45, Database issue The numbers of chains for which at least one biological binding site was found in the query PDB file on which JET 2 was run or in the whole PDB are reported, along with the total numbers of sites, the numbers of sites predicted by JET 2 (with Sens ≥15%) and of those missed by JET 2 (Sens <15%). Statistical performance values averaged over 15 071 predicted interacting sites are given in percentages. iJET 2 predictions were obtained from a consensus of 2 runs out of 10. For all interacting sites, the three scoring schemes were systematically used and the best patch or combination of patches was retained. x represents the size (number of residues) of the protein considered.
FUNDING MAPPING  project [ ANR-11-BINF-0003 ,  Excellence Program 'Investissement d'Avenir' in Bioinformatics]; Institute for Scientific Computing and Simulation at UPMC [Equip@Meso  project --ANR-10-EQPX-29-01 ];  Institut Universitaire de France  (to A. C. ). Funding for open access charge:  MAPPING  project [ ANR-11-BINF-0003 ,  Excellence Program 'Investissement d'Avenir' in Bioinformatics ].
To date, non-reproducibility of neurophysiological research is a matter of intense discussion in the scientific community. A crucial component to enhance reproducibility is to comprehensively collect and store metadata, that is, all information about the experiment, the data, and the applied preprocessing steps on the data, such that they can be accessed and shared in a consistent and simple manner. However, the complexity of experiments, the highly specialized analysis workflows and a lack of knowledge on how to make use of supporting software tools often overburden researchers to perform such a detailed documentation. For this reason, the collected metadata are often incomplete, incomprehensible for outsiders or ambiguous. Based on our research experience in dealing with diverse datasets, we here provide conceptual and technical guidance to overcome the challenges associated with the collection, organization, and storage of metadata in a neurophysiology laboratory.
ORGANIZING METADATA IN NEUROPHYSIOLOGY Metadata are generally defined as data describing data  (Baca, 2008; Merriam-Webster, 2016). More specifically, metadata are information that describe the conditions under which a certain dataset has been recorded  (Grewe et al. , 2011). Ideally all metadata would be available machine-readable at a single location that is linked to the corresponding recorded dataset. The fact that such central, comprehensive metadata collections are not common practice already today is by no means a sign of negligence on the part of the scientists, but is explained by the fact that in the absence of conceptual guidelines and software support, such a situation is extremely difficult to achieve given the high complexity of the task. Already the fact that an electrophysiological setup is composed of several hardware and software components, often from different vendors, imposes the need to handle multiple files of different formats.
If labels appear multiple times, they were iteratively enriched with information obtained from the corresponding components of the setup. In such a complex experiment, the relevance of some metadata does not become immediately apparent and is sometimes underestimated. For example, the immediate relevance of each minute detail of the experimental setup are of little interest for the interpretation of a single recording, and only when one attempts to rebuild the experiment, these metadata become valuable. Apart from that, any piece of metadata can become highly relevant for screening and selecting datasets according to specific criteria at a later stage. Different experiments produce different sets of metadata and it is therefore not possible to provide a detailed list of metadata that needs to be collected from any given type of experiment.
She has programming experience in MATLAB and performs the preprocessing step of spike sorting. • Bob: He is a data analyst and a new member of Alice's laboratory. He has programming experience in MATLAB and Python. His task is to support Alice in implementing the preprocessing and first analysis of the data. • Carol: She is a theoretical neuroscientist and an analyst of an external group that collaborates with Alice's laboratory. She is an experienced Python programmer. Information exchange with Alice's group is limited to phone, email and video calls, and few in-person meetings. She is not an experimentalist and therefore not used to work with real neuronal data. She never participated in an experiment, and thus never experienced the workflow of a typical recording session. Each of the use cases below illustrates a different aspect of the analysis workflow: enrichment of metadata information, metadata accessibility, selection and screening of datasets, and formal queries to reference metadata.
One of these is to identify and extract time points of the behavioral events describing the object displacement in each trial (OT, HS, OR, see Table  1 ) from the continuous force and displacement signals which were saved in the ns2-datafile (label 6b in Figure  1  and Table  2 ). For this, the signals were loaded by a program Alice developed in MATLAB to perform this preprocessing step. The extracted behavioral events are saved for each recording session along with the processing parameters in a respective. mat file (labels 11 in Figure  1  and Table  2 ). It is important that these preprocessing results are readily accessible to all collaboration partners including Bob, because they are needed to correctly interpret the timeline and behavior in each trial. In scenario 1, Alice does not create a comprehensive metadata collection, and Bob has to deal with the fact that the behavioral events are not only not stored along with the digitally available trial events (e.