dataesr/scientific-paragraphs-categorization huggingface dataset:
In the first one, each selected protease acts independently, simulating different experiments on multiple proteins. In the second mode, all selected proteases are acting at the same time, simulating multiple proteases digestions of a protein. Finally, the main contribution of RPG is the possibility for the user to easily define new proteases and use them in the software. RPG is freely available through the well established package management system pip (pip3 install rpg) and follows the standards for software development with continuous integration on Gitlab (https://gitlab. pasteur. fr/nmaillet/ rpg) and automatic on-line documentation (https://rapidpeptide-generator. readthedocs. io).
MATERIALS AND METHODS RPG is a python tool that takes (multi-)fasta/fastq file of proteins as input and digests each of them. Digestion mode can be either 'concurrent', i. e. all proteases are present at the same time during digestion or 'sequential'. In sequential mode, each sequence is digested by each protease, one by one. In both modes, the output information is the same as in PeptideCutter, plus an important property: an estimation of isoelectric point (pI) of each generated peptide. Shortly, the isoelectric point is the pH at which a peptide carries no net electrical charge and a good approximation can be computed on small molecules  (7). The results are outputted in multi-fasta, CSV or TSV file. At the moment, 42 proteases and chemical compounds are included in RPG. The user can easily design new proteases, using a simple yet powerful grammar.
This grammar enables the user to design complex proteases like trypsin or thrombin, including many exceptions and different cleavage sites. User-defined proteases are then interpreted by RPG and included in the local installation of the software. In the rest of the text, nomenclature of Schechter and Berger (8) is used. Amino acids before the cleavage site are designated as P1, P2, P3, etc. in the N-terminal direction, and as P1', P2', P3', etc. in the C-terminal direction. For ex- The rule also contains a list of rule objects (sub-rules), hence its recursivity. This list represents exceptions to the main rule they are linked to. In the previous example, i. e. RULE B = (E, 0, TRUE, AFTER), one can add an exception that specifies not to cleave if the glutamic acid is preceded by another glutamic acid.
New protease creation One of the main features of RPG is the possibility for users to easily define new proteases. As explained in section Definition of cleaving site, proteases are defined in RPG as instructions of Python code. To simplify the description of new proteases without having to write code, a new formal grammar has been created. This grammar, as any formal grammar, is composed of an alphabet, a syntax and a set of production rules to form valid strings, according to the grammar definition. To stay as close as possible to the nomenclature of Schechter and Berger, the grammar of RPG introduces only minor modifications, allowing it to be formal and easier to use. For example, hydroxylamine, which cleaves between N and G (N↓G), and represented as N(P1) G(P1') using the nomenclature of Schechter and Berger, is defined as (N,)(G) in RPG's grammar.
Real case studies RPG was used on two protein families: Actins and Globins. The Representative Proteome (RP15) of Actins and Globins were retrieved from Pfam (15) (id: PF00022 and PF00042). Actin dataset is composed of 5704 sequences  for a total of 1 545 777 residues. Globin dataset is composed of 1678 sequences for a total of 161 696 residues. On these datasets, RPG was used with two different sets of proteases. The results were then analyzed to determine the proportion of generated peptides for each set having a suitable molecular weight for tandem mass spectrometry analysis (600-5000 Da). The first set (set A, in blue on Figures  1  and 2 ) is composed of the three proteases Bromelain, Chymotrypsin-low and Thermolysin, used sequentially. The second set (set B, in orange on Figures  1  and 2 ) is composed of the three proteases CNBr, Lys-N and Papain, used sequentially.
DISCUSSION The recent increase of interest in proteases, linked to advancements in mass spectrometry techniques, can be enhanced by software tools. In 'bottom-up' analysis, protein digestions are required. In silico digestions are a quick and inexpensive way of selecting proteases for a particular class of proteins. To be efficient, in silico digestion must be realistic, accurate and easily adapted to user's proteases. In the following, contributions of RPG are discussed, in terms of functionalities and results.
Comparison with existing programs Few software exist that predict cleavage sites of proteases. Among those, the most commonly used are PeptideCutter and MaxQuant. The latter does not allow to directly digest fasta files. Moreover, while the user can design new proteases, the definition in MaxQuant only concerns the cleavage zone. Therefore, many proteases cannot be properly define. PeptideCutter does not allow to define new proteases. The already defined proteases were thoroughly tested using RPG. All but three proteases of PeptideCutter are identically defined in RPG. The last three were defined in the local installation of RPG using the internal grammar. Despite a completely different way of expressing and defining proteases, all results of RPG are strictly identical to pep-tidCutter, with a better accuracy on the calculation of the molecular weight.
CONCLUSION RPG is a new software dedicated to predict proteaseinduced cleavage sites. The main novelty of RPG is that it provides the user with the ability to define new proteases, hence to not be limited by RPG's predefined proteases. To define proteases, RPG proposes an innovating yet simple grammar. This grammar uses AND, OR and NOT operators giving to the user the possibility to easily define any kind of complex proteases and yet does not require any particular programming skills. Apart from peptides, the result of a digestion includes approximations of molecular weight and isoelectric point, computed with a better accuracy than other existing software. RPG also includes two distinct digestion modes: these modes can simulate either independent digestion experiments or a single experiment of concurrent digestions by several proteases.
In this paper, we show recent experimental results obtained using the open-source hardware and software development platform Open Air Interface (OAI), and a rail moving with a Digital Servo Amplifier, SERVOSTAR 300, along with a Rosier servo motor controlling the movement. OAI is a wireless technology platform that offers an opensource software-based implementation of the Long Term Evolution (LTE) system spanning the full protocol stack of 3 rd Generation Partnership Project (3GPP) standard both in Evolved Universal Terrestrial Radio Access Network (E-UTRAN) and Evolved Packet Core (EPC). The experiments were carried out using orthogonal frequency division multiplex (OFDM) frames at the carrier frequency of 2. 68 GHz. Each OFDM symbol consists of 512 carriers, out of which 300 are filled with random QPSK symbols and the rest are set to zero.
Piecewise signals appear in many application fields. Here, we propose a framework for segmenting such signals based on the modeling of each piece using a parametric probability distribution. The proposed framework first models the segmentation as an optimization problem with sparsity regularization. Then, an algorithm based on dynamic programming is utilized for finding the optimal solution. However, dynamic programming often suffers from a heavy computational burden. Therefore, we further show that the proposed framework is parallelizable and propose using GPU-based parallel computing to accelerate the computation. This approach is highly desirable for the analysis of large volumes of data that are ubiquitous. The experiments on both the simulated and real genomic datasets from the next-generation sequencing demonstrate an improved performance in terms of both segmentation quality and computational speed.
IV. EXPERIMENTS AND RESULTS To test the performance of the proposed framework in terms of segmentation accuracy and computation speed, we consider both simulated and real-world datasets. In the in silico studies, two types of data are simulated: one follows the Poisson distribution and the other follows the Gaussian distribution. These datasets are fed to the proposed method and circular binary segmentation (CBS)  [5] , which is one of the most famous segmentation algorithms. In the analysis of the real-world dataset, the detection of CNV in NGS data from a breast cancer cell line was conducted as an example. CNV detection in NGS data is an important problem in genomics. This signal y is segmented under the proposed framework, which includes the piecewise distributed Poisson model, the Gaussian model with distinct variance (Gaussian1), and the Gaussian model with the same variance (Gaussian2).
In addition, CBS is also included for comparison. The penalty parameter λ (or λ 1 ) of the proposed frame work is set over a wide range, from 2 -10 to 2 10 with a common ratio of 2. CBS has a similar hyperparameter α CBS (alpha in the MATLAB function cghcbs) which tunes the significance level for the statistical tests of breakpoint detection. We also implement CBS with several α CBS values from 2 -18 to 2 -1 with a common ratio 2, and from 0. 6 to 0. 9 with a common difference of 0. 1. To evaluate the average performance, 100 Monte Carlo replicates are conducted, i. e. , 100 signals y are simulated. For each signal y and hyperparameter ( λ for the piecewise Poisson process, 'Gaussian1' and 'Gaussian2', or α CBS for CBS), we implement these segmentation algorithms and calculate the statistics.
Furthermore, the Poisson parameter is proportional to the copy number locally. Therefore the read depth signal can be modeled as a piecewise Poisson process. As a result, CNVs can be detected by segmenting the read depth signal  [3] ,  [4]. In this study, we used the proposed piecewise Poisson model to process a real read depth signal that was extracted from the whole-genome sequencing data of cell line HCC1143, which was generated from a 52 year old Caucasian woman with breast cancer  [31]. This cell line has been studied extensively as a breast cancer model and sequenced with the Illumina platform. A condensed data after mapping, i. e. , a BAM file  [32]  was downloaded. This file includes several types of alignment information, such as the chromosome index, short-read mapping coordinates in the reference genome (homo sapiens NCBI37/hg19), and orientation flag of each mate pair.
B. COMPUTATIONAL PERFORMANCE We tested the computational performance of the proposed framework in terms of execution time. The MATLAB stopwatch timer function tic and toc were used to record the elapsed time on a desktop with an Intel Core i7 processor and 32 GB memory. For the proposed method, the computation time is at most 22. 5 seconds, and with increasing penalty parameter λ, the computation time decreases steeply to zero. Overall, the proposed method spends half as much time as the CBS. We implemented the GPU-based parallel computing version code (see Sec. III-C) of the proposed framework with the MATLAB computing toolbox and tested the performance on a workstation with an nVIDIA Tesla C2050 GPU. Fig. 5 (b) shows the computation time with respect to the problem dimension N. In this panel, 'CPU' and 'GPU' represent the serial computing version code and a parallel alternative on a CPU and GPU, respectively, while 'CPU+parallel' represents the parallel computing version code on a CPU, i.
This work was supported in part by the  National Science Foundation of China  under Grant  61771381  and Grant  61401352 , in part by the  China Postdoctoral Science Foundation  under Grant  2014M560786 , in part by the  National Institutes of Health  under Grant  R01GM109068 , Grant  R01MH104680 , and Grant  R01MH107354 , and in part by the  National Science Foundation  under Grant  1539067.
Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e. g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature of within-host populations of pathogens. Thus, these techniques provide a subsample of the pathogen variants that were present in the host at the sampling time. Such data are expected to give more insight on epidemiological links than a single sequence per host. In general, a mechanistic viewpoint to transmission and micro-evolution has been followed to infer epidemiological links from these data. Here, we investigate an alternative approach grounded on statistical learning. The idea consists of learning the structure of epidemiological links with a pseudo-evolutionary model applied to training data obtained from contact tracing, for example, and using this initial stage to infer links for the whole dataset.
Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions, it is sufficiently parsimonious to allow the handling of big datasets in the future, and it is versatile enough to be applied to very different contexts from animal, human and plant epidemiology. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.
Nevertheless, the progress of sequencing techniques revealing the within-host genetic polymorphism of pathogens fostered the development of model-based approaches accounting for the generation of within-host diversity and/ or leveraging the information provided by sets of sequences sampled from hosts  [4 -7,9,14,20]. Approaches based on a mechanistic vision of transmission and micro-evolutionary processes are the most obvious direction to follow for inferring epidemiological links between host units. Indeed, mechanistic assumptions underlying these approaches act as relevant constraints, which are expected to guide the inference. However, statistical learning techniques  [22]  adapted to the inference of epidemiological links should also be developed, in particular (i) when mechanistic assumptions could be inadequate and, therefore, misleading, (ii) when sequence data do not accurately reflect the within-host pathogen population because of sequencing bias or errors and (iii) when a fast method is required to tackle big datasets in terms of number of hosts, sequencing depth and sequence length.
(a) Tracing experimental swine influenza outbreaks The first dataset was generated from an experimentally controlled transmission chain of SIV in pigs with different immunological histories (naive and vaccinated;  [2] ). For each chain, pairs of pigs were successively settled in an experimental enclosure, with a temporal overlap between the arrival of the new pair and the departure of the preceding pair to allow the virus to be transmitted. Thus, the infection pathways are partly known and will be used to assess the efficiency of SLA-FEEL. For each pig, the virus population was sampled on a daily basis, and multiple clones of the hemagglutinin gene were sequenced using a capillary approach (Sanger sequencing). The naive chain consisted of five pairs of pigs from which 21 samples of the viral populations were collected with multiple time points for eight pigs.
The vaccinated chain consisted of seven groups of pigs from which 29 samples of the viral populations were collected with multiple time points for seven pigs. Further details about the SIV dataset are provided in electronic supplementary material, table  S1. Transmission chains were inferred for the two experimental outbreaks with SLAFEEL. The penalization was calibrated for each outbreak with contact information from two training hosts, which were either the two pigs of the last group of the outbreak or a pig from the third group and a pig from the fourth group. The training hosts and the hosts with which they have been in contact, including the host in the same group, are detailed in electronic supplementary material, table  S2. For this application, we chose the H1normal penalization (see §4b) that led to higher consistency between contact information and inferred transmissions.
Electronic supplementary material, figure  S1  shows how this uncertainty is also reduced by adding a third training host to the last group. Using more contact information allows a finer calibration of the penalization (electronic supplementary material, figure  S2 ) and, consequently, a more accurate resolution of transmissions. Moreover, the advantage of introducing a penalization is clearly illustrated by electronic supplementary material, figure  S3 , which displays transmissions estimated without penalization: for the naive chain, host 113 is erroneously identified as the source of infection of numerous hosts. In this section, we analyse the dataset generated during the 2014 Ebola virus disease (EVD) outbreak in Sierra Leone  [23]. We were able to include in our analysis 58 confirmed EVD patients, from which within-host populations of the virus were collected and sequenced.
This number of patients represents nearly 50% of the EVD patients diagnosed in Sierra Leone from late May to mid-June. Viral populations were sequenced using the Nextera library construction method and Illumina sequencing and the haplotypes were estimated in a sliding window of 1000 bases every 500 bases using Predict-Haplo  [24]. More details about the Ebola dataset are provided in electronic supplementary material, table  S1. Here, we simply highlight the rather low pathogen diversity that was observed: on average, 16. 1 haplotypes per fragment of 1000 bases were identified for the 58 patients included in the analysis (s. d. ¼ 8. 0), and 1. 37 haplotypes per fragment of 1000 bases per patient (s. d. ¼ 0. 64). Epidemiological links between patients were inferred by calibrating the penalization with contact tracing published in  [25].
(c) Assessing epidemiological links at the metapopulation scale This dataset was generated from a wild plant species (Tragopogon pratensis, hereafter called wild salsify), which is a reservoir for a potyvirus closely related to the endive necrotic mosaic virus (ENMV;  [27] ). Within-host virus variants were sequenced from 189 infected host plants sampled in 2014 in a 40Â10 km region of south-eastern France. High-throughput sequencing was applied on viral PCR amplicons (final length: 438 bp of the capsid gene) using the Illumina technology  [28]. Sequence data were merged at the scale of the patch (i. e. meadows, agricultural fields or urbanized areas) with the aim of assessing epidemiological links between a subset of the metapopulation formed by the potyvirus (the 189 sampled plants were distributed in 27 patches).
Further details about this dataset are provided in electronic supplementary material, table  S1. Epidemiological links between sampled patches were inferred by calibrating the penalization with information on inter-patch distances, assuming that, on average, geographically close host patches are infected by similar viral variants (isolation-by-distance process). Here, the H1-x 2 penalization (see §4b) was chosen because it led to a lower average distance between connected patches (see criterion (4. 7), §4c). Figure  4  shows the inferred links between sampled patches. Here, all the optimal values for the penalization parameter (shown in electronic supplementary material, figure  S9 ) led to the same set of links and, therefore, no secondary arrows are displayed (electronic supplementary material, figure  S10  shows links inferred without penalization).
Finally, we simulated 1000 datasets with the SEEDY package (simulation of evolutionary and epidemiological dynamics;  [20] ) by using parameter values chosen by Worby and Read to generate their 4th figure (mean epidemic size: 26. 6 infected hosts (s. d. ¼ 2. 3); 10 virus genomes sampled per host). The SEEDY package allows not only the generation of datasets, but also a very fast inference of transmissions given infection times, the mutation rate, the equilibrium viral population size within host and the transmission bottleneck size, which are generally not known in practice. Thus, we used SEEDY-based inferences of transmissions as a benchmark, and assessed how SLAFEEL compares with SEEDY in identifying the true source for each recipient of each of the 1000 simulated outbreaks. For the application of SLAFEEL to each simulated outbreak, we randomly drew four training hosts whose sources were supposed to be known, and we chose the H1-normal penalization.
The key property underlying our procedure is the combination of a learning stage and a penalization that can be used to constrain what is a link. This is expected to help in  appropriately dealing with sequencing errors because such errors should be accounted for non-training hosts as they are for training hosts. Nevertheless, as discussed below, the impact of sequencing errors on inference accuracy should be formally assessed in simulation studies. The training stage can use classical information such as contact tracing data  [25] , but also contact proxies such as geographical distances between host units, connectivities via air masses for airborne pathogens  [31]  and social connections  [4, 32]. To get a contact proxy, one could also infer some transmissions with a (generally more timeconsuming) mechanistic approach from a subset of observed cases and use the estimated transmissions as training data in our approach applied to the whole dataset.
Another important perspective is the implementation of an efficient computer code. The R code that we developed (available at https://doi. org/10. 5281/zenodo. 1410438) allowed us to test different model specifications, to exploit genetic data from multiple sequence fragments and to perform cross-validation in a limited time-span (e. g. a SLAFEEL run for the swine influenza case study or for a sequence fragment in the Ebola case study took approximately 10 -20 minutes with a laptop computer, whereas royalsocietypublishing. org/journal/rstb Phil. Trans. R. Soc. B 374: 20180258 BadTrIP takes several days; see caption of electronic supplementary material, figure  S12  and  [5] ). However, implementing further improvements in the code should allow us (i) to include multiple infections in transmission scenarios where an explanatory set of sequences would consist of a weighted mixture of several samples collected from several putative sources, (ii) to select a penalization shape among a large library of functions, and (iii) to tackle big data (e.
Funding. This work was funded by an  ANR  grant ( SMITID  project;  ANR-16-CE35-0006 ). J. H. is funded by the  Medical Research Council  ( MC_UU_12014/12 ). Field and laboratory work for the plant virus was funded by the  Division for Plant Health and Environment (SPE)  of  INRA  through the  AAP-SPE-2014 framework.
The living systematic review (LSR) is an emerging approach Background: for improved evidence synthesis that uses continual updating to include relevant new evidence as soon as it is published. The objectives of this study are to: 1) assess the methods of conduct and reporting of living systematic reviews using a living study approach; and 2) describe the life cycle of living systematic reviews, i. e. , describe the changes over time to their methods and findings. For objective 1, we will begin by conducting a cross-sectional Methods: survey and then update its findings every 6 months by including newly published LSRs. For objective 2, we will conduct a prospective longitudinal follow-up of the cohort of included LSRs. To identify LSRs, we will continually search the following electronic databases: Medline, EMBASE and the Cochrane library.