dataesr/scientific-paragraphs-categorization huggingface dataset:
STRONG pipeline STRONG processes co-assembly graph regions for multiple metagenomic datasets in order to simultaneously infer the composition of closely related strains for a particular MAG and their core gene sequences. Here, we provide an overview of STRONG. We start from a series of S related metagenomic samples, e. g. samples of the same (or highly similar) microbial community taken at different time points or from different locations. The Snakemake based pipeline begins with the recovery of metagenome-assembled genomes (MAGs)  [22]. We perform co-assembly of all available data with the metaSPAdes assembler  [28] , and then bin the contigs obtained by composition and coverage profiles across all available samples with CONCOCT  [3]. Each bin is then analyzed for completeness and contamination based on single-copy core genes, and poor quality bins are discarded.
The default criterion is that a MAG requires greater than or equal to 75% of the SCGs in a single copy. While we currently focus on this combination of software tools, in principle we could use any other software or pipeline for MAG recovery, e. g. we could use MEGAHIT as the primary assembler  [25]  or alternative binning tools or their combination. For each MAG we then extract the full or partial sequences of the core genes that we further refer to as single-copy core gene (SCG) sequences. The final coassembly graph produced by metaSPAdes cannot be used for strain resolution because, as with other modern assembly pipelines, variants between closely related strains will be removed during the graph simplification process. Instead, we output the initial graph for the final K-mer length used in the (potentially) iterative assembly following processing by a custom executable -spades-gsimplifier based on the SPAdes codebase -to remove likely erroneous edges using a 'mild' coverage threshold and a tip clipping procedure.
Relevant subgraph extraction Provided with the predicted (partial) gene sequence, T , and the upper bound on the length of the coding sequence, L, defined as 3α Ūn where Ūn is the average length in amino acids of that SCG in the COG database, and α = 1. 5 by default. The procedure for relevant HRAG subgraph extraction involves the following steps. First, the sequence T is split into two halves, T and T , keeping the correct frame (both T and T are forced to divide by 3). T and T are 19/33 then processed independently. Without loss of generality we describe the processing of T : 1. Identify the path P corresponding to T in the HRAG. We denote its length as L P. 2. Launch a graph search of the stop codons to the right (left) of the rightmost (leftmost) position of T ( T ). The stop codon search is frame aware and is performed by a depth-first search (DFS) on the graph in which each vertex corresponds to a pair of the HRAG position and the partial sequence of the last traversed codon 1.
Subgraph simplification While processing SCG subgraphs from a particular MAG we use the available information on the coverage of the MAG in the dataset. In particular, we set up the simplification module to remove tips (a node with no successors) below a certain length and edges with coverage below a fraction of the total coverage across all samples. If a tip is not removed it is labelled as a 'dead-end' to distinguish it from potential connections to the rest of the graph. While simplifying a COG subgraph, edges connecting it to the rest of the assembly graph should be handled with care (in particular, they should be excluded from the set of potential tips). This is because in the BayesPaths algorithm they form potential sources and sinks of the possible haplotype paths. Moreover, during the simplification the graph changes, and such edges might become part of longer edges.
Availability of data and materials The anaerobic digester time series have been uploaded to the ENA as part of the study PRJEB39861. The STRONG pipeline and synthetic community data are available from https://github. com/chrisquince/STRONG and the BayesPaths algorithm https://github. com/chrisquince/BayesPaths. The code for synthetic community generation from https://github. com/chrisquince/STRONG_Sim and the Nanopore EM algorithm https://github. com/chrisquince/NanoHap.
Based on the simulated channel gains, the optimal characteristics of the sources, in terms of half-power angles were determined. Then, using an on-off keying (OOK) modulation with a fixed transmission power, the maximum achievable data rate was evaluated according to the desired quality of service (QoS). However, this former study has some shortcomings in its approach to the propagation channel modeling: • The results presented in  [14]  were obtained for a 3D full detailed environment model, corresponding to a huge amount of data, often private and so not accessible, which leads to heavy computational effort; • All the materials were considered as perfectly diffuse surfaces with a single reflectivity value of 0. 5; • A single dynamic scenario (pilot's head movement) was studied, with no true mobility inside the cockpit; • The level of ambient optical noise was fixed, according to the literature, to the measured value in a conventional indoor environment at ground level, which differs from the proposed application; • The analyses were based on analytical cumulative density functions (CDF) of channel gain, fitted to simulated data.
B. Channel modelling To determine the channel impulse response, we adopt a modeling approach based on a stochastic Monte-Carlo method, associated with the ray-tracing algorithm. Our research laboratory has developed the RaPSor software (Ray Propagation Simulator), which is an extensible tool based on the Netbeans platform and coded in Java, for modelling wave propagation in realistic environments in different frequency domains, from the radio range to the optical one, in IR and in visible wavelengths  [15] -  [19]. This software allows determination of the impulse response h(t) for a defined link. The following inputs are required for the simulation: geometric model and reflection characteristics of the simulation environment, including pilot and co-pilot, definitions of key features as the maximum number of successive reflections, and the characteristics/locations of the optical transceivers.
In addition to the cockpit itself, important parameters affecting optical propagation are the pilots' bodies and the changes in transceiver orientation resulting from the pilots' movements. The body model representing the pilot and copilot is a 3D realistic human mesh of 1. 8 m height and composed of 1004 polygons (see Fig. 3 ). It was fully articulated and animated using the Blender software  [21]  in order to take into account realistic dynamic scenarios in our channel simulations. The choice of human body height doesn't really matter. As mentioned in Section II-A, the pilot and co-pilot seats are in extreme configurations. This applies particularly to the co-pilot's, which corresponds to a worst case scenario because his head is so close to the ceiling (15 cm) that the optical contributions arrive with a grazing incidence on the headset receiver.
Telemedicine, particularly telemonitoring, has been shown to be effective in the management of chronic pathologies such as heart failure. For example, the MyPredi remote monitoring platform, formerly known as E-care, has been tested for monitoring chronic diseases, both in hospitals and at home. The system makes it possible to combine the data collected daily with the operator's clinical knowledge of the patient using artificial intelligence tools for personalized and predictive monitoring. These systems are particularly suited for monitoring patients in nursing homes and their geriatric risks. As a result, the GER-e-TEC program was created and will be detailed in the present article.
Developed in England, TeleHealth  [14]  is a remote monitoring platform that monitors a patient's vital signs and activity. It is designed for elderly people who live alone and suffer from chronic diseases (congestive heart failure and chronic obstructive pulmonary disease). A primary study was conducted with 36 participants for a duration of up to one year, depending on the patient. Each day, the patients measured their vital signs (blood pressure and weight for CHF, oxygen saturation level for COPD) with sensors, as well as their activity (passive infrared motion sensor and/or a chair or bed sensor). An algorithm is used to process the data and sends an alert in case of an abnormality. Measurements and alerts are sent to the platform wirelessly and can be consulted online by both the patient and the physician.
) and to model the concepts related to the monitoring of heart failure  [32]. The effectiveness of the system for diagnostic purposes implies that operational semantics are included, semantics which specify how the knowledge modeled in the system will be processed and automatically produce further knowledge. The reasoning part is based on an inference engine where the rules are either introduced by medical experts or from generated data, then, validated by medical experts. As shown in Figure  1 , the MyPredi platform includes a patient module and a server module. The first is installed in the patient's residence, making it possible to collect and integrate data from different types of non-invasive medical sensors  [32]  or exchanges in the form of questions and answers. All of the functionalities are embedded in a mobile device, such as a tablet, in order to allow greater patient autonomy.
The Patient Module This module, installed in the patient's house, includes several sensors (devices, also called Agents) and a receiver (Aggregation Manager) for collecting data from physiological sensors (thermometer, oximeter, blood pressure monitor, scale, etc. ) as well as exchanges in the form of questions and answers. The receiver (computer, touch pad, or smartphone) sends the data to the server via the internet. In the MyPredi platform, touch pads are used with an ergonomic application that allows the patient or their relatives to submit their measurements for advice and to communicate directly with their doctor.
The Server Module The server receives data from all patient modules and manages authentication and communications security, as well as data consistency. These are stored to preserve the privacy of patients. The server uses systems and an intelligent component to detect risk situations and warn caregivers. A service portal is used to allow users, depending on their role (patient, caregiver, relatives, etc. ), to access and follow the evolution of vital signs as well as the general condition of the patient or even to enter medical information and consult transmitted alerts. The MyPredi platform was initially tested in a 20-bed Internal Medicine unit receiving patients from emergency facilities as part of a care system for patients with heart failure (HF) at the University Hospitals of Strasbourg  [33].
Development of the MyPredi Platform The MyPredi platform integrates the monitoring of other chronic diseases including diabetes, hypertension, and COPD  [34]. The MyPredi platform is an evolutive platform with original architecture and proven functionalities to manage patients with chronic pathologies who require long-term follow-up. It consists of a patient module, with a tablet and sensors, and a server module, which receives and processes the data collected from the various patient modules. The server module combines semantic web and artificial intelligence technologies (AI). An inference engine is used to monitor the patient's health and to detect any abnormal situation  [12]. We propose a medical telemonitoring platform with an open and flexible architecture, offering the ability to build predictive models of risk situations.
Main Developments and Functions The platform used helps caregivers by automating the processing of sensor information, questions, and questionnaires in order to detect and report medical risk situations early. MyPredi will provide personalized care for the main geriatric risks to avoid the occurrence of acute factors leading to decompensation in elderly patients. The information collected will be supplemented by codified therapeutic care, according to international recommendations in nursing homes. This platform provides all paramedical and medical health professionals with information on the resident's geriatric data. This information will be updated regularly, including anthropometric, nutritional, cognitive, and iatrogenic data. Together, the data will provide a real-time complete picture integrated into an electronic platform, creating a standardized gerontological assessment based on simple and rapid measures.
Discussion With the GER-e-TEC project, we now offer medical teams and paramedics a remote monitoring device that optimizes the management of patients with geriatric syndromes and their accompanying conditions. This remote monitoring system will allow monitored patients to record their daily physiological data (weight, oxygen saturation, blood pressure, heart rate, blood sugar) and complete questionnaires on the state of their health. The data will be automatically sent to our MyPredi platform, which is designed to predict situations that are at risk of deteriorating. A coordination unit will then be able to monitor the patient remotely, providing comprehensive and personalized treatment of the areas of concern detected by the platform and helping the patient with their therapy. Patients monitored remotely: -Physiological data analyzed daily; -Help with therapy-related questions; -Remote consultations.
Medical team: -Medical advice if needed. The GER-e-TEC project was tested on a patient (Figure  7 ) who, after providing written consent, was monitored daily by a team of healthcare professionals. The patient was given a pedometer to monitor his sleep and activity. The goal was to test the ergonomics and functionality of the remote monitoring platform on a daily basis before using it in the study. Medicines 2020, 7, x FOR PEER REVIEW 16 of 24 their daily physiological data (weight, oxygen saturation, blood pressure, heart rate, blood sugar) and complete questionnaires on the state of their health. The data will be automatically sent to our MyPredi platform, which is designed to predict situations that are at risk of deteriorating. A coordination unit will then be able to monitor the patient remotely, providing comprehensive and personalized treatment of the areas of concern detected by the platform and helping the patient with their therapy.
Being able to efficiently call variants from the increasing amount of sequencing data daily produced from multiple viral strains is of the utmost importance, as demonstrated during the COVID-19 pandemic, in order to track the spread of the viral strains across the globe. We present MALVIRUS, an easy-to-install and easy-to-use web application that assists users in two tasks: (1) computing a variant catalog consisting in a set of population SNP loci from the population sequences and (2) efficiently calling variants of the catalog from a read sample. Tests on Illumina and Nanopore samples prove the efficiency and the effectiveness of MALVIRUS in genotyping SARS-CoV-2 strain samples with respect to GISAID data.
Introduction The SARS-CoV-2 pandemic has put the global health care services to the test and many researchers are racing to face its swift and rapid spread. Since the outbreak of the virus in China and in other European countries, several studies are using sequencing technologies to track the geographical origin of SARS-Cov-2 and to analyze the evolution of sequence variants  (1, 2). In this context, the availability of efficient approaches to analyze variations from the growing amount of sequencing data daily produced is of the utmost importance. The typical pipelines for the analysis of variations within viral samples consists in aligning reads against a reference genome (3), then analyzing the alignments to discover the variants  (4, 5). However, the increasing number of viral assemblies available in public databases such as GISAID  (6) , GenBank  (7) , and the COVID-19 Data Portal allows to build a complete catalog of variants of a viral population.
Methods To efficiently genotype a viral sample from an individual with respect to the current knowledge, we propose MALVIRUS a web application based on five state-of-the-art tools. The application is divided into two logically distinct modules: the creation of the catalog containing the SNP loci of the viral species under investigation and the variant calling from the read sample. Fig. 1  shows the MALVIRUS pipeline. The first module requires as input the reference genome of the species under investigation, the assemblies of a set of strains of that species, and, if available, the annotation of the genes. The output of this module is the set of population SNP loci in VCF format. MALVIRUS first builds the full-length sequence alignment of the input sequences to the input reference genome using MAFFT  (8) , then extracts the set of population SNP loci from the multiple alignment using snp-sites  (9).
Since snp-sites is not able to output variants in positions with gaps, MALVIRUS fills the gaps in the alignment with the corresponding portions of the reference. Although this step might induce some artificial variants, it allows to preserve real ones that might be lost due to incomplete assemblies. If the population under investigation is well characterized and/or the user wants a finer control over the variant catalog, it is possible to upload a custom catalog of SNP loci in VCF format instead of relying on the automatic computation from a set of assemblies. The second module requires as input a sample of reads in FASTA/Q format and a catalog of population SNP loci along with the corresponding reference genome chosen among the ones computed or uploaded in the first module. The output of the second module is a VCF containing the genotype information of the sample and their qualities.
To call the genotype of each variant, this module counts the k-mers in the sample using KMC3  (10) , then it genotypes the variants using MALVA  (11) : an efficient and accurate mapping-free approach for genotyping a set of known SNPs and indels initially developed for human individuals. We extended MALVA to support haploid organisms and high-coverage samples. Additionally, if gene annotation is available, the module also annotates the functional effects of each variant using SnpEff  (12). Finally, the results of each analysis can be visualized as a table (see Fig. 2  for an example) or downloaded in VCF format or as a spreadsheet for further analysis. MALVIRUS is available as a self-hosted web application distributed as a Docker container image that can be installed and run on multiple platforms, from personal laptops to large cloud infrastructures.
For user convenience, the application is distributed with a set of precomputed catalogs of variants for SARS-CoV-2 based on the assemblies available on GenBank  (7) , therefore the user can immediately run MALVIRUS on a locally available (e. g. , private) viral sample. The precomputed catalogs can be easily updated from the application itself with a single click. Extensive documentation and a detailed tutorial are available at https://algolab. github. io/MALVIRUS.
Results To test the effectiveness of MALVIRUS, we considered 10 strains from the GISAID database for which a sample of raw reads is available on the Sequence Read Archive (SRA). These were the only samples that we were able to cross-reference between GISAID and SRA at the moment of writing, furthermore for 5 of such strains we analyzed reads sequenced using both Illumina and Oxford Nanopore technologies, showing that MALVIRUS achieves similar results on both data types. For simulating a real case scenario, where the goal is to genotype a newly-sequenced strain, before analyzing a sample, we removed it from the set of complete SARS-CoV-2 strains available on GISAID (accessed on July 17, 2020) and we ran MALVIRUS on the remaining 42709 strains for building the variant catalog. From the 42709 strains, the first module of MALVIRUS produced a VCF containing 13709/13710 variants (depending on which strains were removed).
Then, we genotyped such a catalog using the second module of MALVIRUS starting from the corresponding read samples. To evaluate the overall accuracy of MALVIRUS we computed its precision and recall in genotyping the set of known variants produced by its first module. To compute precision and recall, we used the first module of MALVIRUS to build the variant catalog with respect to the considered strain (i. e. , the strain we removed) and we used it as truth set. We then classified each variant as a reference variant if its real genotype is 0, i. e. the reference allele, and as an alternate variant if its genotype is not 0. Finally, we compute the precision and recall of MALVIRUS and reported the results of this analysis in Table  1. MALVIRUS scored a perfect precision (100%) on both reference and alternate alleles, while recall on the reference is almost perfect (99.
9-100%) with some loss of recall on the alternate alleles. This loss of recall on the alternate alleles is caused by the fact that, especially on ONT data, some SNPs exhibit an unexpected and extremely low coverage that together with the high error rate makes them harder to correctly genotype. A careful inspection of these cases showed that a different choice of parameters (especially the k-mer size) improves its accuracy, allowing it to correctly genotype most of these low-covered SNPs at the cost of slightly lower precision. However, we believe that the default parameters of MALVIRUS allow to achieve the best trade-off between precision and recall. Finally, a single SNP (5508:T>C) is unique to the specific strain considered (GISAID ID EPI_ISL_416410) and cannot be present in the variant catalog built by the first module of MALVIRUS.
Therefore, that variant could not be genotyped by the second module of MALVIRUS. However, since the rapidly increasing number of available complete sequences will broaden the variant catalog, we can expect that this situation will be uncommon in the next few months. On the other hand, such an increasing amount of data does not significantly challenge MALVIRUS since each step of the pipeline is efficient. We ran MALVIRUS using 8 threads and the analysis of each sample completed in 50/60 minutes requiring less than 7GB of RAM. Such amount of resources is nowadays available on any computer, allowing MALVIRUS to run even on laptops and desktop machines. The first module of our application (catalog creation) required less than 15 minutes and less than 12GB of RAM. Anyway, we point out that the catalog creation needs to be run only when new strains are available, that each catalog can be reused multiple times, and that the software is distributed with a precomputed variant catalog built using the sequences available on NCBI.
1. Results on real data. For each considered strain (GISAID ID, for ease of presentation we removed the EPI_ISL_ prefix) and the corresponding SRA sample, we report the Precision and Recall obtained by MALVIRUS on calling reference variants (i. e. those variants whose real genotype is the reference allele, REF) and alternate variants (i. e. those variants whose real genotype is the alternate allele, ALT). For each sample, we also report the technology used (ONT for Oxford Nanopore and ILLU for Illumina) and its coverage (in terms of number of bases).