dataesr/scientific-paragraphs-categorization huggingface dataset:
Investigations X-ray absorption spectra (XAS) collected in fluorescence mode are better suited for determining the speciation of elements in low concentration  [19]. Unfortunately, the scanning of a large zone with a submicrometric resolution is too time-consuming for analyzing even a few samples. Also the technical advantages of the two techniques were combined. At first, XRF maps were collected in scanning mode on large areas. The PyMca software was used to batch-fit the XRF spectra, to generate the elemental maps (from Na to Co) and to normalize these maps taking into account the incident beam intensity  [10]. A typical result showing the main differences of elemental distribution in the various layers of a Ming Qinghua porcelain is presented in Figure  9. The colored zone is clearly identified by the high rate of calcium.
For Co-rich particles, the recorded spectra were similar to the one of cobalt aluminate crystals (CoAl 2 O 4 ), while in lower concentrated zones, the recorded spectra correspond to Co 2+ diluted in glass (  [18] , Figure  5 ). These spectra were acquired only for a few points. In order to have a full 2D picture of Co speciation and in particular to verify that all Co-rich particles are cobalt aluminate and not cobalt oxide phase, FF-XANES maps were acquired in a second step. Series of radiographies were recorded through the Co K-edge, and the data processing was carried out as previously described. Figure  10  shows one of the obtained results. The edge energy position map confirmed the presence of the only one state of valence and the phase map, the two types of cobalt speciation, that is, Co 2+ in the glassy matrix and Co 2+ in cobalt aluminate crystals.
Conclusion FF-XANES analysis is a very powerful technique to study complex structure and provide pertinent chemical information. It combines spatial and energy resolution with large FOVs and fast acquisition in a virtually limitless variety of samples. The spatial resolution can be adapted to the problematic from a few hundred to a few tens of nanometer following the geometry used (defocalized beam or transmission X-ray microscope). Software packages, such as TXM-Wizard or PyMca, provide user-friendly tools to successfully plan and collect the series of radiographies, to build from these images the 2D XANES map (advanced averaging, image alignment, filtering), and to process these big data in order to extract chemical information such as rate, valence state, and speciation. These tools are rather easy to use and allow obtaining pertinent information in the form of easily exploitable maps.
Acknowledgements The authors would like to thank  AEli Barjesteh  ( ASET Stiftung ) who kindly provided the photography of the Ming vessel. We would like also to thank  Philippe Goudeau  ( Pprime Institut, Poitiers ) and  Marine Cotte  ( ESRF, Grenoble ) for their suggestions and comments. Research was supported by the  CAI YUANPEI 2016-2018 program  (No. 36708RD ), the preresearch foundation of  Shaanxi University of Science and Technology  (No. 2018GBJ-08 ), and the  ARCHIMEDE Labex program :  Investissement d'Avenir   ANR-11-LABX-0032-01.
Systems-level analysis is essential for efficient resolution of the underlying molecular complexity and the extraction of actionable interpretation, in terms of systemic biological processes and pathways that are systematically perturbed. In this work we introduce PepSimili, an integrated workflow tool, which performs mapping of massive peptide repertoires on whole proteomes and delivers a streamlined, systems-level biological interpretation. The tool employs modules for modeling and filtering of background noise due to random mappings and amplifies the biologically meaningful signal through coupling with BioInfoMiner, a systems interpretation tool that employs graph-theoretic methods for prioritization of systemic processes and corresponding driver genes. The current implementation exploits the Galaxy environment and is available online.
The application is implemented on an instance of the Galaxy platform  (Afgan et al. , 2018). Through its user-friendly visual editor, the execution of the workflow is easily accessible to the basic user without prior experience in bioinformatics or in command-line oriented analyses. Additionally, the Galaxy platform already provides the tools necessary for the manipulation of the raw fastq files including quality filtering, trimming of the sequences to isolate the variable part of the recombinant phages and DNA to protein translation. PepSimili is the first application for the phage display technology implemented in Galaxy and which provides efficient mapping of short peptides on whole proteome databases. The tool is available online at http://pepsimili. e-nios. com:8080.
Mapping of Test and Control and Mock Repertoires on the Proteome The problem of mapping a set of peptides on a set of proteins, respecting a threshold of similarity h, was previously addressed in  Hume et al. (2013). Similarity between two peptides is evaluated using the PAM30 substitution matrix  (Jones et al. , 1992). The algorithms, producing all the ungapped matches, of the peptides of a repertoire, on a set of proteins, were implemented in C++ and the code of the four modules necessary to produce the mappings and provide the resulting profiles is available at https: //github. com/cbib/pepteam. In total three mappings are performed, respectively for Test (T), Control (C) and Mock (M) repertoire (built in step 2). In the resulting files are reported the matching peptides, the similarity score and the matching position on the corresponding protein.
Size of the Experimental Repertoires The quality of the experimentally obtained peptide repertoires determines the mappings and thus the m-scoring. PepSimili uses the unique peptide sequences present in a repertoire to produce the mappings and calculate the m-scores. It is necessary to dispose of repertoires large enough to optimize the density of the mappings. A minimum of 40000 unique peptide sequences of 7mers is necessary. Usually NGS provides millions of reads for each repertoire, corresponding at hundreds of thousands of unique peptide sequences for targets as simple as cell cultures, thus covering the requirements of PepSimili. For a visualization of the distribution of the m-score the script Sat_distri can be used; it produces a table with the distribution of the scores from any profile file.
Proteomes and Fasta File Header Format It is mandatory to use a particular header format for the proteins fasta file, so the script correctly extract the gene symbols for biological interpretation: >ENSGXXXXXXXX| ENSTXXXXXX| Gene Symbol| ENSPXXXXXXXX. Such files can be obtained from BioMart  (Smedley et al. , 2015) , using a simple query with, as filters, Gene stable ID, Transcript stable ID, Gene symbol and Protein stable ID. Two sets of human proteins are already included, one of general use (proteome 20k) and one minimal (proteome 10k), more restricted to interaction molecules, adapted for selections performed in vivo or on cells accessible via the blood vessels (e. g. , endothelial cells such as HUVEC). These proteins belong in either of the following three classes: plasma membrane, extracellular matrix, or secreted proteins, and were selected based on their annotation with GO terms Cellular localisation (GO:0051641) and Extracellular Region Part (GO:0044421), in addition to the proteins of the human plasma proteome, taken from the Human Plasma Peptide Atlas  (Schwenk et al.
, 2017). For each protein isoform, the most complete in terms of exons was included. However, the user can upload and use any proteome respecting the above-mentioned header format and perform manually the biological interpretation, using the BioInfoMiner module available on the PepSimili server. The functional analysis currently supports Homo sapiens, Mus Musculus, Rattus norvegicus, Gallus gallus, Sus scrofa, Danio rerio, Drosophila melanogaster, Caenorhabditis elegans, and Arabidopsis thaliana.
Galaxy-Based Tool for Integrated Analysis of Phage Display Data The Galaxy instance of PepSimili is available at http://pepsimili. enios. com:8080. The tool is easily accessible in the left-hand menu ("Tools"), under the section pepSimili. There are two additional tool sections, PepSimili tools and PepSimili subworkflows, which comprise the collection of satellite/intermediary scripts and partial worklows for mapping repertoires and scoring proteins, respectively. PepSimili main input and outputs are shown in Figure  2. The tool accepts a Test and a Control repertoire, the aminoacid frequencies of the phage display library, the homology and confidence cut-offs and the p-values for the enrichment analysis (see section "Materials and Methods") (Figure  2A ). All steps are executed automatically, including the biological interpretation.
Case Study We present a case study using published data of phage display repertoires  (Brinton et al. , 2016). We used two of the described repertoires. Both were selected in vitro, on HUVEC cells that were cultured either in normal medium (Control) or in tumor conditioned medium (tcm) by tumoral cells (pancreatic adenocarcinoma, PDAC), which is serving as Test. The two samples of cultured cells were separately used for the biopanning with a combinatorial library of cyclic 7mers on phage display. In these studies, the recombinant portion of the pIII of the phage was amplified by PCR and used as template for deep sequencing. The aim of the study was to identify binders specific of the tcm-treated endothelial cells, expecting that their targets would  be also expressed in vivo by cells in the microenvironment of cancerous tumors.
However, the development of analytical tools and strategies is far less advanced. Most software solutions have been in-house developed and not made widely available, by using standard, generic sequence comparison techniques, such as BLAST, or they had a limited scope such as PhastPep  (Brinton et al. , 2016)  taking into account only identical sequences to compare repertoires. Computational tools for Phage Display data analysis include RELIC  (Mandava et al. , 2004) , PEPTIDE  (Pizzi et al. , 1995) , SiteLight  (Halperin et al. , 2003) , and SLiMFinder  (Edwards et al. , 2007) , which enable sequence alignment and motif detection. However, these tools were designed for smallscale analyses, whereas deeper characterization emerged as a necessity with the advent of NGS techniques. For this purpose, newer methodologies have been developed for high throughput data processing and detection of consensus sequences  (Fowler et al.
, 2011; Alam et al. , 2015; Reich et al. , 2015) , although these techniques did not address the issue of selectivity and comparison among different physiological conditions. This particular problem was addressed by PHASTpep  (Brinton et al. , 2016) , a MATLAB-based tool, which enables differential analysis and selection of peptides that discriminate among different cellular states. PepSimili combines selectivity and networkbased functional analysis for prioritization of targets and derivation of biomarkers. In addition, little has been done to help identifying the spectrum of proteins that are potentially mimicked by the plethora of selected peptides, and to aid in the elucidation of the biological circuits, on which the selection is made. Such information is particularly interesting for biopanning performed in tissues or in vivo, where the complexity of the obtained repertoires reflects the complexity of the biological process under investigation.
However, the abundance of each peptide is reported for mere convenience, in the final results. Computational derivation of a set of proteins, with domains mimicked by the peptides, can also be helpful for the identification of the targets against which the peptides were selected, while studying their natural binders, as described in interactome databases. In complex systems, for which the peptides or protein domains were identified by our analysis, with the intention to be used as targeting tools for the homing of either therapeutic molecules or imaging agents, it is important to exclude those reacting with targets on healthy tissues. Usually, experimental strategies include the selection of peptides in a system as close as possible to the Test system, to produce a Control repertoire. In the example presented here, the Test system being endothelial cells cultured in tumor conditioned medium, is compared to the Control system being the same cells cultured in fresh medium.
However, further in silico screening of the oligopeptide repertoires using PepSimili may, on one hand, dramatically reduce the number of candidate oligopeptides, and on the other hand provide a rational basis for peptide selection based on their functional interpretation in terms of impacted biological mechanisms. Importantly, our methodology enables a systems-level interpretation, through streamlined mapping of the selected mimicked proteins to ontological and pathway networks, providing actionable insights. The BioInfoMiner module derives a small set of orthogonal, systemic processes, accompanied by the respective master regulatory genes linked with a significant part of them, altogether constituting a biomarker signature with actionable potential for clinical, therapeutic or diagnostic processes.
Finally, PepSimili is presented in a user-friendly environment, Galaxy, as an integrated tool that performs a complete analysis at a push of a button. A collection of satellite scripts and workflows is also provided, to propose tentative discovery paths that can be followed to complement the actual results, intending to encourage power users to develop, and share with the community new tools/scripts, adding to our work. This implementation facilitates the development of future extensions of the workflow and, importantly, the adaptation of the methodology to other high-throughput technologies, as mentioned above.
Morphology and Diet Our meta-analysis of available morphological data confirmed that Pachyptila species are distinguishable (fig. 2 ) not only by bill width (F ¼ 364. 2, n individuals ¼ 1,524, n populations ¼ 24, P < 0. 001, multiway ANOVA) and bill length (F ¼ 931. 7, n individuals ¼ 1,375, n populations ¼ 25, P < 0. 001), but also by wing length (F ¼ 81. 1, n individuals ¼ 1,673, n populations ¼ 25, P < 0. 001), tarsus length (F ¼ 26. 4, n individuals ¼ 1,010, n populations ¼ 21, P < 0. 001), and tail length (F ¼ 6. 2, n individuals ¼ 914, n populations ¼ 13, P < 0. 05) (see details in supplementary table  S2 , Supplementary Material online). All available data on the diet of the Pachyptila species studied here were considered in relation to bill morphology. We found that Salvin's prion, possessing a medium-width bill with pronounced palatal lamellae (fig.
Species, Samples, and Morphometric Data Between 2010 and 2012, samples (all from breeding adults) from 77 thin-billed prions (P. belcheri), 80 Antarctic prions (P. desolata), 118 broad-billed prions (P. vittata), 18 Salvin's prions (P. salvini), and 34 fairy prions (P. turtur) were obtained in breeding colonies located on temperate to subAntarctic island groups (fig. 1.
; supplementary table S1, Supplementary Material online). A summary of the morphometric data used in this study, as well as the source references are provided in the supplementary table  S2 , Supplementary Material online. To obtain missing morphometric data for P. belcheri corresponding to New Island, Falkland/Malvinas Islands (fig. 1 ; supplementary tables S1 and S2, Supplementary Material online), we measured wing length (6 1 mm; n ¼ 157) with a stopped wing rule, tale length (6 1 mm; n ¼ 157) with a feather rule, bill length (n ¼ 86) and width (n ¼ 98), and tarsus length (6 0. 1 mm; n ¼ 155) with callipers.
Parameterizing the Pachyptila Species Tree Coalescent To determine how Pachyptila species were related to each other, we reconstructed the phylogeny of the genus using all the available DNA sequence data (mtDNA and nuclear intron markers). The species tree was inferred using a Bayesian multispecies coalescent framework in BEAST 2  (Bouckaert et al. 2014). The best nucleotide substitution model was determined for each of the six alignments using JMODELTEST  (Posada 2008). Site models were thus unlinked for each partition. Each gene tree was unlinked, and a species tree using a birth-death model prior was used to account for gene tree incongruence. The population prior allowed for changes in effective population size, but assumed a constant ancestral population size. Five H. caerulea individuals, sequenced for the same marker set, were used as the outgroup of Pachyptila.
Times were set in generations with a generation time set to 18 years. Following recommendations of the software (DIYABC, see below), we employed a Stepwise Mutation Model (SMM) with a mean mutation rate given by a log-uniform prior (10 À6 -10 À2 ) and individual locus rates given by a gamma prior with shape parameter set at 2. 0 and constrained to the interval 10 À6 -10 À2. We employed an overall of 50 summary statistics: for each of the five species we used the mean number of alleles, mean genic diversity, mean size variance, and mean Garza-Williamson's M, whereas for each species pair we used Fst, shared allele distance and (dm) 2 distance. We visually assessed the fit of the best supported model to the empirical data by means of Principal Components Analysis (PCA) performed on the summary statistics (supplementary fig.
We estimated the best model with the Masello et al. doi:10. 1093/molbev/msz090 MBE function "predict. abcrf" trained on the reference table and using as a predicting point the set of summary statistics (introduced by hand). This function also estimates the posterior probability of the chosen model by means of a regression random forest  (Pudlo et al. 2016). Following recommendations  (Pudlo et al. 2016; Estoup et al. 2018) , the inference was performed with a large set of summary statistics, for which we included all those used in the classic ABC analysis plus classification index for each pair of populations (two indices per pair) and maximum likelihood of admixture, resulting in an overall 71 summary statistics. Simulations, model choice by the direct method and for logistic regression, as well as PCA analyses were carried out in the software DIYABC 2.
Acknowledgments This work was supported by the  Deutsche Forschungsgemeinschaft (Germany) ,  Heisenberg program  (grant number  DFG, Qu 148-5  to P. Q. ), a  CGS award  from the  Natural Environment Research Council (NERC) , the  Institut Polaire Francais Paul Emile Victor (Programme N   109 , to H. W. ), the  Terres Australes et Antarctiques Francaises (Kerguelen) ,  Mohamed bin Zayed Species Conservation Fund  and the  Brian Mason Scientific and Technical Trust (Chatham Islands). The study involved wild individuals and was carried out under permits from the  Falkland Islands Government (Environmental Planning :  R21. 2012 ),  Government of South Georgia  and the  South Sandwich Islands, Animal Ethic Committee of the Institut Polaire Francais Paul Emile Victor ,  Pr efet des Terres Australes et Antarctiques Francaises , the  Animal Ethics Committee of Charles Sturt University  ( 15/092 ), and the  New Zealand Department of Conservation.
I n recent years neuronal spike train data have been collected at an increasing pace, with the ultimate aim of unraveling how neural circuitry implements computations that underlie behavior. Often these data are acquired from extracellular electrophysiological recordings in vivo without knowledge of neuronal input and connections between neurons. To interpret such data, the recorded spike trains are frequently analyzed by fitting abstract parametric models that describe statistical dependencies in the data. A typical example consists in fitting generalized linear models (GLMs) to characterize the mapping between measured sensory input and neuronal spiking activity  [1] [2] [3] [4] [5]. These approaches are very useful for quantifying the structure in the data, and benefit from statistically principled parameter estimation and model selection methods.
We applied methods 1a and 2 to detect these artificial synaptic inputs based on the presynaptic and postsynaptic spike times only, and compared the detection performance with that of a CCG-based method. Specifically, input perturbations were described by delayed pulses for method 1a (which yields improved computational efficiency) and alpha functions for method 2 (cf. evaluation above). We quantified how much data is necessary to detect synaptic input of a given strength. For this purpose we computed the log-likelihood ratio between the I&F model with (J ≠ 0) and without (J = 0) coupling in a crossvalidated setting, similarly to the approach that has been applied to this dataset previously using a Poisson point process GLM  50. For the model-free CCG method we compared the CCG peak for positive lags to the peaks from surrogate data generated by distorting the presynaptic spike times with a temporal jitter in a large number of realizations.
Comparing the detection performance for fixed recording duration across all perturbation strengths and available cells shows that method 1a is more sensitive than the CCG method (success rate 0. 86 vs. 0. 78, Fig. 4d ). We conclude that both methods, 1a with delayed pulses and 2 with an alpha function kernel, are well suited to detect weak aEPSCs in this in vitro dataset, and their sensitivity is increased compared to a model-free CCG method. Inference of synaptic coupling. In the previous section we showed that we can successfully estimate the perturbations in the spiking of an individual neuron that may be elicited by inputs from another neuron. We now turn to estimating synaptic couplings in a network. We consider the situation in which we have observed spike trains of N neurons. We fit these data using a network model in which each neuron i receives independent fluctuating background input with neuron-specific mean μ i (t) and variance σ 2 i , and neurons are coupled through delayed current pulses which cause postsynaptic potentials of size J i,j with time delay d i,j , for i, j ∈ {1, …, N}.
J i,j = 0 J i,j = 0 J i,j (mV) 1 0 -1 J i,j (mV) 1 0 -1 = 0. 81 Although spontaneous activity was highly nonstationary, our inference of the connectivity appeared to be very accurate. Comparisons with ground truth estimates demonstrated accuracy of up to 0. 95 (for the intermediate timescale variant; Fig. 6c ). Moreover, reducing the number of spikes used for inference did not lead to an appreciable decrease of reproduction accuracy (Supplementary Fig. 7a ). Nevertheless, using instead a model-free CCG method on the spontaneous spike trains yielded comparable detection accuracy (Supplementary Fig. 7b , for an example CCG see Fig. 6b ). This fact and the observation that the timescale affects detection performance only weakly (cf. Fig. 6c ) may be explained by the large signal-tonoise ratio in this dataset (Supplementary Fig.
By applying our methods to suitable electrophysiological datasets, we could successfully infer the statistics of in vivo-like fluctuating inputs, detect input perturbations masked by background noise, reveal intrinsic adaptation mechanisms, and reconstruct in vivo synaptic connectivity. Previously several likelihood-based methods related to ours have been proposed, considering uncoupled I&F neurons with  61, 62, 72  or without adaptation  73, 74  for constant  73  or timevarying  61, 62, 72, 74  input statistics. All of these methods employ the Fokker-Planck equation and numerically calculate the spike train likelihood with high precision  73  or using approximations  61, 62, 72, 74. We benchmarked our methods against that from  61, 62  , which was applicable to the estimation of single neuron parameters and for which an efficient implementation is available (cf.