dataesr/scientific-paragraphs-categorization huggingface dataset:
A compatible web browser with JavaScript support is required. Although user requests are queued and processed one at a time, the application benefits from multi-core processor architectures because the analysis itself is performed by Java execution framework and the algorithm is separated into four independent steps (determine the score for each nuclide, precalculate sum, calculate scores and aggregation) that are computed in parallel. Our application is implemented as a Single-Page-Application with REST API backend. This configuration allows us to use the back-end as a standalone computational server employed by custom scripts for specific analyses. Front-end client application is made with Vue. js (https:// vuejs. org/) framework. Back-end is a Java application based on Spring framework (https://spring.
The improvement over these two methods is particularly significant for much conserved protein families. We also show that our method's predictive performance is comparable with computational frameworks well suited to treat viral sequences  (Flynn et al. 2017; Louie et al. 2018). Beyond prediction assessment, we provide a clear readout of the contribution of epistasis by looking at how sequences observed in nature are spread and evolutionary related. Our method is implemented as a fully automated tool, Global Epistatic Model for predicting Mutational Effects (GEMME), available as a downloadable package and as a webserver at www. lcqb. upmc. fr/GEMME/. We show that GEMME is faster than state-of-the-art methods by several orders of magnitude. Given an input alignment, the calculation of the full mutational landscape of a protein takes a few minutes.
2 ). The red dot indicates the correlation obtained with GEMME's default model, where the epistatic term is assigned a weight of 0. 6 (and 0. 4 for the independent term, see Materials and Methods). The black dash indicates the best performance achieved by the independent or epistatic contribution alone. The blue thick segments highlight the range of values obtained when varying the epistatic term's weight between 0. 5 and 0. 8 (see also supplementary figs. S3 and S4, Supplementary Material online). (c) Amino acid grouping preferences observed in GEMME's best performing models (parameters optimized for each scan). The color code goes from white (grouping never observed) to red (grouping observed for all scans). The amino acids are ordered so as to highlight the reduced alphabet used by default in GEMME.
Discussion We have presented GEMME, a computational method for performing mutational scans of protein sequences. It exclusively exploits protein sequence data available in public databases. It relies on a few biologically sound assumptions about the relationship between protein sequence and function. Its algorithm is straightforward and requires setting only two parameters. It uses the mathematical tree structure underlying the evolution of natural sequences and it explores it by using simple new concepts (smallest path between wild-type and mutated sequences). This is markedly different from what has been developed previously. State-of-the-art methods feature tens to hundreds of thousands of parameters, infer some of them using sophisticated machine learning techniques, others empirically, reweight the input sequences to correct for sampling bias, and do not explicitly model the evolutionary history relating these sequences.
We have implemented our method as a fully automated package and webserver, available at www. lcqb. upmc. fr/ GEMME/. It can deal with single mutations as well as combinations of mutations. It has been carefully evaluated against experimental measurements from 9 low-throughput experiments and 41 high-throughput mutational scans comprising up to 496,137 data points. Moreover, we have provided an understanding of the contribution of "epistasis" to the discrimination of mutations, based on the analysis of the variability of the input sequence data, and have demonstrated that our evolutionary-informed measure of conservation is a good indicator of the extent to which a position is sensitive to mutations. Finally, we have systematically assessed the influence of changing GEMME's two parameters and the input alignment's depth on the quality of the predictions.
Comparison of Computing Times To measure GEMME's computing time, we ran the tool starting from the input MSA and the result of the PSI-BLAST search. Hence, for each protein, we measured the elapsed (wall clock) time required to compute the conservation levels and the predictions from all models (default, independent and epistatic). The computation of the conservation levels was realized by running one iteration of JET 2. The resulting matrices of predicted mutational effects were very similar (99. 6 6 0. 4% Pearson correlation coefficient on average) to those obtained when running ten iterations of JET 2 and retaining the maximum conservation level over the ten runs. EVmutation's code was downloaded from https:// github. com/debbiemarkslab/EVmutation and https://github. com/debbiemarkslab/plmc.
For each protein, we measured the elapsed (wall clock) time required to compute the pairwise couplings and the predictions from both the independent and epistatic models. The same input MSAs, taken from  Riesselman et al. (2018) , were given to GEMME and EVmutation. In these alignments, some positions are flagged because they are highly gapped. GEMME considered all positions whereas EVmutation disregarded the flagged ones. EVmutation also disregarded highly gapped sequences. The numbers of positions and sequences considered by each tool are reported in supplementary table  S3 , Supplementary Material online. Calculations were realized on a single-core processor Intel Xeon E5-2630 v4 at 2. 20 GHz. DeepSequence's code was downloaded from https://github. com/debbiemarkslab/DeepSequence. We ran the tool to train one model on the input MSA for the b-lactamase and compute the predictions.
Acknowledgments This work was partially undertaken in the framework of the LabEx CALSIMLAB, supported by the public grant  ANR-11-LABX-0037-01  constituting a part of the  "Investissements d'Avenir" program  (reference :  ANR-11-IDEX-0004-02 ; YK); funds from the  Institut Universitaire de France  (AC). We thank  Tristan Bitard-Feildel  for providing computing time for DeepSequence,  Raymond Louie  for providing us with an MSA of HIV gp160 and some experimental fitness data, and  Anne Lopes  and  Hugues Richard  for careful reading of the manuscript.
Phylogeny. fr (1) is widely used and cited, and CIPRES (6), TRex  (7)  and Phylemon (8) also belong to this category. In the same spirit, SeaView  (9)  and MEGA  (10)  offer integrative solutions for phylogenetic analysis, while providing a standalone software to be installed locally. These integrative solutions usually consider preselected tools and/or analyses, and may have dif-Nucleic Acids Research, 2019, Vol. 47, Web Server issue W261 ficulties to evolve in terms of tool updating and chaining. Moreover, while such integrative solutions were particularly interesting ten years ago, the analyses that run nowadays have drastically changed in terms of number and size of sequences and CPU requirements. In parallel, scientific workflow systems (Galaxy  (11, 12) ) have reached a level of maturity that makes them convenient for scheduling the execution of complex and largescale analyses, while properly managing data by tracking consumed and produced data.
PHYLOGENETIC WORKFLOWS All NGphylogeny. fr workflows are based on the tools listed in Table  1. The choice of tools will mainly depend on the size of the dataset and the application. For multiple sequence alignment, very large datasets will preferably be run with Clustal  (16) ; medium to large datasets can be run with MAFFT (2); and small to medium datasets can be computed using Muscle  (17). Regarding alignment curation, Gblocks  (18)  and trimAl  (21)  are the methods of choice for very large datasets; BMGE  (19)  will mainly be used for medium datasets to large datasets, while Noisy  (20) , though very accurate  (27) , will be dedicated to small datasets. Lastly, for tree inference, with very large datasets (>5000 sequences) users can choose FastTree (fast combination of distance and likelihood); with large datasets (in the order of several thousand sequences) users will typically select FastME (distance) or TNT (parsimony), while with small to medium datasets they will prefer PhyML+SMS (likelihood based plus model selection).
MrBayes will be a method of choice for relatively small datasets, when users are interested in the posterior distribution of phylogenetic trees induced by their data. All the workflows take a FASTA file as input, preferably unaligned, and produce multiple sequence alignment files (FASTA or PHYLIP) and phylogenetic tree files (Newick format). For each type of results, NGPhylogeny. fr proposes a dedicated viewer: Multiple sequence alignments are visualized dynamically using the BioJS MSAViewer plugin  (28) ; Phylogenetic trees are visualized dynamically using PRESTO (http://www. atgc-montpellier. fr/presto) built on the phylotree. js plugin  (29)  or via upload to iTOL  (30) ; Other formats such as images, text, or html are displayed in the browser. Several flavors of workflows are available, depending on user's level of expertise.
BLAST-SEARCH Beyond the needs associated with execution and configuration of phylogenetic analyses, there is also a need to guide users in selecting sequences on which the analysis will be performed. The Blast-Search module, provided by NG-Phylogeny. fr, implements such a sequence search interface. Blast-Search is a successor of BlastExplorer  (31) , and uses BLAST  (32)  to retrieve and compare sequences that are similar enough to a user input sequence. To do so, Blast-Search runs 'blastn', 'blastp', 'tblastn' or 'blastx' either by query- ing databases installed on the Institut Pasteur Galaxy server  (33) , or by querying the public NCBI BLAST databases (the latter is only available on standalone mode). The use of Blast-Search can be summarized as follows: First, the user pastes an input sequence of interest (in FASTA format) and submits the form.
Once the BLAST job is finished, only sequences passing the e. value and query coverage thresholds (given by the user) are considered. A fast multiple-alignment is then built by using the query sequence as reference, ignoring insertions on matching sequences, merging potential multiple High Scoring Pairs (HSP), and filling the holes with gaps. This fast alignment is then used to compute a distance matrix and a distance based tree, which is visualized dynamically to enable the deletion of unwanted sequences or groups of sequences, hence building a clean dataset. The final dataset, constituted of the user input and its matching sequences, can be downloaded in FASTA format or used as input of any of the NGPhylogeny. fr workflows.
value lower than 10 -5 and covering the query on at least 80% of its length. Once the run is finished, in ∼20 min, we obtain 100 sequences having a length of ∼500 amino acids. Using the tree visualizer, we select sequences from the ape clade (hominoidae), that is, orangutans (pongo), chimpanzees (pan), gorillas, human and gibbons (hylobatidae), and delete all other sequences. We obtain a dataset made of 38 sequences that we give as input of the PhyML+SMS 'One click' workflow, as it is very accurate and fast with dataset of such size. Results are obtained in less than 5 min, and are shown in Figure  1 , displaying the workflow monitoring page, the curated alignment, as well as the final phylogenetic tree (displayed also with SH-like supports in Supplementary Figure  S1 ). The input sequence is well-placed among other known Human sequences in the tree and the taxonomy of apes is globally well-structured and well supported (SHlike), with human sequences closest to chimpanzees, then gorillas, orangutans and finally gibbon sequences.
Analysing large viral sequence dataset ('A la carte') In this case study, we analyze a very large viral sequence dataset of several thousands of sequences for which we want to build a phylogenetic tree with branch supports. Such an analysis is particularly CPU-intensive and cannot be executed in other phylogenetic analysis solutions, including the former Phylogeny. fr. Here, thanks to the integration of Fast-Tree and bootstrap support in NGPhylogeny. fr, results can be computed in ∼8 h on Institut Pasteur web server. To run this use case, we downloaded the HIV data sequence file located at https://ngphylogeny. fr/static/hiv pol. fa. zip, which contains 9,147 HIV pol gene DNA sequences of length ∼1,050 nucleotides  (5). Using the workflow maker, we then build a workflow including the following steps: (i) sequence alignment with MAFFT; (ii) tree inference with FastTree and (iii) tree rendering with Newick Display.
DISCUSSION This paper introduces NGPhylogeny. fr, the new version of Phylogeny. fr. NGPhylogeny. fr is based on modern Web technologies and relies on the Galaxy workflow system, to provide flexible, modular and scalable analyses, via a userfriendly graphical interface. Thanks to this new architecture and the new integrated tools, NGPhylogeny. fr (i) allows any user to easily perform complete analyses (as shown in our first use case) and (ii) pushes the limits of what is possible with Phylogeny. fr and other solutions in terms of number and size of sequences, such as large viral datasets (as shown in our second use case). Last but not least, NGPhylogeny. fr is easy to maintain and update, and straightforward to install and deploy. Currently NGPhylogeny. fr is limited to single gene analyses.
ARCHITECTURE AND IMPLEMENTATION The architecture of NGPhylogeny. fr consists of two main components working together: (i) a Galaxy workflow system on which tools and workflows are stored and executed and (ii) a user interface implemented in Python/Django allowing end-users to run their workflows without having to know how to use the Galaxy system. The Galaxy instance stores workflows and tools, and is responsible for running the jobs, and monitoring their execution until completion. We wrapped phylogenetic tools in Galaxy XML wrappers, and built the workflows upon them. To facilitate tool interoperability and workflow de-W264 Nucleic Acids Research, 2019, Vol. 47, Web Server issue velopment, we implemented the Galaxy wrappers such that all alignment and tree inference tools take FASTA and PHYLIP formats as input.
Moreover, to prevent sequence name errors, wrappers first clean sequence names conflicting with the Newick format or temporarily rename sequences with automatically generated names. The user interface takes care of presenting available workflows, assembling tools, formatting input forms, storing previous runs, checking job runs, and visualizing output files. The two components communicate via the Galaxy API using the bioblend python library  (34). For advanced users interested in deploying their own instance of NGphylogeny. fr, and be able to add other tools and modify the workflows, we additionally provide an easy way to install, update and deploy NGPhylogeny. fr via Docker (https://www. docker. com/). The web interface as well as the custom Galaxy instance containing all phylogenetic tools and workflows are packed into two Docker images that are automatically configured to run a local NG-Phylogeny.
Software-defined radios (SDRs) constitute a modern and highly adaptive realization of a radio-frequency transceiver system. This work demonstrates how a particular radio transceiver, namely, the LimeSDR board, can be configured for pulsed and continuous-wave magnetic resonance spectroscopy. As a first step, the board needed to be extended by a bit pattern generator, so as to facilitate synchronization of other spectrometer equipment. The upgraded board was incorporated into two different spectrometers, namely, into a pulsed spectrometer operating at either 30 MHz for nuclear spins or 150 MHz for electron spins and into a continuous-wave spectrometer at 150 MHz for electron spins. Pulse sequencing capabilities were approved by relaxometry and Rabi oscillations of electron spins at a time resolution of 33 ns.
Upon exhaustive averaging of acquired transients, unwanted oscillations that are characteristic for fast analog-to-digital converters emerged out of the noise floor. Methods for cancellations of these oscillations are presented, in particular a new acquisition scheme with cyclic incrementation of the acquisition position. The continuous-wave spectrometer provided derivative spectra of absorption and dispersion by phase-synchronous sideband demodulation. Furthermore, this spectrometer featured a software-defined automatic frequency control to account for probe drift, which has been incorporated into the open-source spectrometer control software.
Indirectly, of course, the innovation in electronic components manifests in improved performance of test and measurement instruments, such as digitizers, arbitrary waveform generators, synthesizers, or lock-in amplifiers. These instruments are certainly easier to assemble into a spectrometer system since these are delivered with a documented software interface. 30  paradigm shift is expected from open-source hardware platforms. These devices unify state-of-the-art hardware with a readyto-use open-source interface at low cost. Due to the open interface, application-oriented adaptations to the hardware platform become feasible without building an entire system from scratch. This study focuses on such an open-source hardware platform, namely, the LimeSDR board. This low-cost SDR board is equipped with an FPRF that is interfaced by an FPGA and a universal serial bus (USB) 3.
0 controller. Already in a previous study,  31  the open-source firmware of the LimeSDR's FPGA could be adapted for pulsed NMR spectroscopy. In this work, a different approach for the integration of the LimeSDR into a spectrometer is presented. Moreover, this approach is tested with pulsed NMR and EPR, as well as with continuous-wave (CW) EPR. This paper is organized as follows. First, a description of the relevant specifications of the LimeSDR and of the spectrometers for pulsed and CW experiments is given in Sec. II A. Since the software of the spectrometers is available for further use, a separate description of the software follows in Sec. II B. The experimental results for pulsed NMR and EPR are presented and discussed in Sec. III. In addition to spectroscopic results, a dedicated aspect of signal acquisition is treated separately in Sec.
B. Control software The LimeSDR was controlled from the host computer (Lenovo Thinkpad X1 Yoga, 2016) via USB 3. 0. The control software had two components [see Fig. 1(a) ]: (i) a low-level C++ routine that interacts with the LimeSDR through the provided API and (ii) a Python class to ease scripting of the spectrometer. In Secs. II B 1 and II B 2, the most relevant details on these programs are given. For further insight, the entire code is available for noncommercial use. 38 Note that the gateware was modified from version 2. 17 from June 2018, which was used with API version 18. 04. 19. At the time of preparing this manuscript, the gateware evolved to version 2. 21 with corresponding API updates, indicating active development and rectification of pending issues.
Python class for scripting A Python class called limr serves as the main interface for spectrometer control. In essence, the limr class contains all the parameters that are required for the low-level C++ interface. Furthermore, an additional arbitrary one-dimensional parameter sweep can be added. All the pulse sequences that are presented in Sec. III B were realized by adding such an additional parameter sweep specification. For CW experiments, an additional sweep was not used since the low-level routine already provided the capability for recording fieldswept CW spectra. Nevertheless, such an additional sweep could be of interest for CW, as for instance for power saturation studies. In order to give an example, Fig. 2  illustrates the script for an echo sequence with swept interpulse delay.
All the variables that are relevant for the experiment are identified with a three-letter label, as FIG. 2. Python script that defines a Hahn echo with an eight-step phase cycle and a variation of the interpulse delay to record the echo decay. See text for further details. for instance lof, which stands for LO frequency. Based on the code comments, the designations can be identified within Fig. 2. As the sequence has two pulses, any pulse parameter is provided as a list with two entries. The phase cycle is specified using two variables that determine the number of phases, pcn, and the stacking level of multiple cycles, pcl. In this example, the first pulse has two phases and is at the lowest stacking level. The second pulse has four phases and is stacked on top, resulting in an exorcycle that selects exclusively pathways due to echo refocusing.
The arbitrary parameter sweep is implemented at the very end of the script (l. parsweep), just before the experiment is executed (l. run). Using this sweep command, any variable can be swept. In the example here, the variable pof, i. e. , the pulse delay, is modified individually for each of the two pulses. The parameter sweep specification involves an initial and a final data point, followed by the total number of sweep points and by an optional pulse index. In principle, it would be sufficient to sweep only the delay between the first and the second pulses (pof with pulse index 1). However, this would lead to a shift of the echo position in the RX output buffer. For a constant echo position throughout the experiment, the position of the first pulse is also shifted (pof with pulse index 0).
B. Pulsed EPR at 150 MHz Pulsed EPR was performed in order to outline the attainable time resolution with the LimeSDR. In fact, at a sampling rate of 30. 72 MSa/s, the time resolution is 33 ns. In principle, even a faster sampling rate of 61. 44 MSa/s would be supported. with the host computer used in this work, the data stream was not stable at this faster rate. Even at the chosen rate of 30. 72 MSa/s, there were actually occasional interruptions of the stream. In order to avoid missing data points in pulse sequences, acquisition was resumed upon such interruptions. For the chosen field strength of 5 mT, a 500 μM solution of the so-called "Finland trityl" radical  47, 48  dissolved in a 20 mM HEPES buffer at pH of 7. 5 was used. This radical is known for its very narrow single-line spectrum as well as its sensitivity to local oxygen concentration.
In Fig. 5(c ), the decay of the Hahn echo as a function of the interpulse delay is shown in black. An exponential decay with a time constant T 2 = 1. 34 μs was obtained upon least-square curve fitting (gray). Note that the sequence script for this experiment is shown in Fig. 2. The decay of longitudinal magnetization was monitored with the inversion recovery sequence [inset in Fig. 5(d) ]. The resultant inversion decay (black) in Fig. 5(d ) was fitted in least-squares sense by an exponential decay (gray) with T 1 = 1. 54 μs. Overall, the experiments in this section confirm that the pulse sequencing capabilities demonstrated for NMR in Sec. III A as well as in an independent study 31 can be extrapolated to EPR as long as the time scale of 33 ns is sufficient for the sample under study. Nevertheless, a critical aspect when working in this regime is the presence of spurious frequencies from the ADC, which needed to be removed in a postprocessing step for a number of time traces presented in this section.