dataesr/scientific-paragraphs-categorization huggingface dataset:
Mapping Reads (or Contigs) to Reference Database Mapping tools are commonly used as a filtering step to remove host genome sequences or as a complement to similarity searches on short nucleotide sequences. Reads originating from the host genome can be partially removed by mapping the complete dataset to reference genomic sequences of corresponding host (if available) and then using only unmapped reads for further analyses. A reference genome sequence of the host must be chosen carefully, since it can affect the analysis. Choosing divergent variety/genotype of the host might reduce the efficiency of the host reads removal. Furthermore, reference host genomes might contain contaminating or genome-integrated viral sequences; thus, some viral reads can be lost in this step. Mapping tools can be also used to perform the alignment of reads or contigs against a reference viral database (e.
g. , NCBI Viral RefSeq database or a custom developed database containing one or more complete or partial viral genomes). In comparison to BLAST programs, most of the mapping tools such as Bowtie2  [98]  or BWA [84] build an index for the reference genome or the reads, increasing the speed of the analysis if used against a limited, virus-specific database. The mapping strategy is potentially more sensitive to detect viruses with low number of reads in analyzed datasets  [6] , in particular when using 21-24 nt sRNA sequences. Consequently, it is also sensitive to cross-sample contamination due to index-hopping, which may require the development of strategies to set a positivity threshold. On the other hand, mapping strategies are inefficient at detecting novel viruses or viroids that are absent from the database used.
Mapping stringency parameters (see Table  1 ) critically affect the outcome of the analyses and should be optimized keeping in mind the objective of the experiment. Too stringent parameters may result in the failure to detect divergent viral isolates. Too relaxed parameters may also give rise to erroneous results through the mapping of related host genes on a viral genome or through crossmapping the reads of a virus on the genome of a related virus. These problems can be minimized by first mapping all HTS reads against the reference viral database. Then, any reads that map to a virus are remapped against the host genome sequence. If the mapping score is higher for the host genome, the read is discarded. Tools such as Pathoscope  [99]  can help with cross-mapping between virus species by weighting reads that map to more than one viral sequence.
An efficient strategy, besides counting the number of mapped reads on a particular reference genome, considers the portion of this genome covered by the mapped reads and depth of coverage, the percent similarity between mapped reads, and the reference or other similar indicators to eliminate potential false positive results. Including suitable reference samples as controls during sample preparation and sequencing can help to eliminate such errors  [9]. Similar to reads, contigs generated by de novo assembly, can also be mapped to the reference databases. The longer the contig, the fewer erroneous mapping results are expected. However, the same recommendations for careful inspection of mapping results apply.
Protein Domain Searches Searching for known viral domains by matching translated amino acid sequences of reads/contigs with Hidden Markov Models (HMMs) of known protein domains using programs such as HMMER  [100]  or HMMScan is a popular alternative to BLASTx. With this method, sequences are first translated in all possible reading frames, and the translated protein sequences are compared to a database of conserved protein motifs such as Protein Families database-PFAM  [87] , viral profile HMMs-vFAM  [101] , and Conserved Domains Database-CDD  [88]. These approaches are faster than BLAST-based homology searches and more effective than mapping or BLAST searches for the detection of very distant homologs  [102]  and therefore possibly for the detection of novel, very divergent viruses. Similar to BLAST, a significance e-value is calculated, allowing the evaluation of the significance of a match.
g. , the usability of k-mer tools on small RNA datasets  [107]. Methods based on machine learning are being developed for the detection of viral sequences in metagenomics datasets. Several tools have already been published, e. g. , ViraMiner  [108] , DeepVirFinder  [109] , or Virnet  [110]  for human virus detection purpose. Given a metagenome with known composition, machine learning approaches attempt to find some meaningful patterns that allow differentiating the host from the virus. When the unknown metagenome dataset is provided, the software should be able to discriminate virus sequences from host sequences using the learnt pattern. Machine learning tools are new in this field; thus, we still lack their in-depth comparison with the more known approaches discussed above.
"Quick Start" Methods Depending on the computational background of the user, there are different ways to approach the analysis. Many software solutions are available for detecting the presence of (plant) viruses in HTS datasets, which have been summarized recently by several reviews  [118, 119]. For beginners or newcomers in the field, all these tools can be overwhelming. The quick-start guide (Figure  5 ) might be handy to select an appropriate tool or pipeline. Among these options, easy-to-use pipelines that do not require extensive computational expertise might be a good start. These pipelines present a user-friendly interface online or directly on the computer. A first group of pipelines can be considered as "all in one": they automatically start on the raw data to deliver the final results as a list of viruses detected.
If available, validated tests should be used and a new sample extract obtained for analysis. The selection of confirmatory tests depends on the performance characteristics required; the general characteristics of methods for plant virology have been reviewed  [120]. If no other tests are available to confirm the identity of the pest (i. e. , poorly characterized and uncharacterized organisms), primers should be designed and tested, based on the HTS sequence data and available sequence information in the sequence databases. Alternatively, generic primers that enable the amplification of viruses within a genus or family, including the targeted one(s), followed by Sanger sequencing of the amplicons could be used to confirm the identity.
In addition to sharing sequence data itself, sharing of analysis pipelines, protocols, and experiences between labs can greatly contribute to the harmonization of the field and provide useful resources for newcomers to the field. The recently established Plant Health Bioinformatics Network (PHBN) aims to foster this approach and provide protocols, pipelines (https://gitlab. com/ilvo/phbn-wp2-training, accessed on 13 April 2021), and reference datasets (https://gitlab. com/ilvo/VIROMOCKchallenge, accessed on 13 April 2021)  [130]  that can be widely employed. It also aims to organize community efforts to advance certain aspects of plant health bioinformatics (https://gitlab. com/ilvo/PHBN-WP4-RNAseq_ Community_Screening, accessed on 13 April 2021).
Recombination Analysis Recombination is common in some genera of plant viruses, and the presence of recombination events can have impacts on downstream analysis such as phylogenetics. Thus, identification of recombination is a useful first step, prior to further genome analysis. The most popular software solutions, which detect recombination patterns comparing full or partial viral genomes and run on Windows, are RDP4  [131] , SimPlot  [132] , and TreeOrder Scan  [133]. ViReMa (Viral Recombination Mapper) can be used for the detection of recombination junctions, as well as insertion/substitution events and multiple recombinations within single reads [134], and it has been successfully applied for the analysis of recombination events in plant virus genomes  [22, 135, 136].
Additional Bioinformatics Analyses Further analyses, beyond viral detection and taxonomic classification, can be performed on HTS data, depending on the goal of the study. For instance, the large amount of sequence data generated by HTS allows a good resolution of the within-host genetic diversity of the viral populations  [22]. Assessing the genetic diversity within and among viral populations can provide a better understanding of virus evolution and help to determine population genetic parameters or epidemiological patterns  [137, 138]. This can be done using single nucleotide polymorphism (SNP) calling algorithms, which need to allow the detection of low-frequency variants expected in virus populations. Phylogenetic relationships among the detected and previously known viruses can also be investigated using fast neighbor-joining algorithms  [139] , more precise maximum likelihood approaches  [140, 141] , or Bayesian analysis approaches  [142].
Freeware phylogenetic analysis suites, such as MEGAN  [143] , or phylogenetic analysis algorithms integrated within commercial software, such as CLC Genomics Workbench and Geneious Prime, can be used. Studying the time of emergence of viral species and strains including the distribution of the genetic diversity across geographical sites can be done using software such as BEAST [144], TempEst  [145]  and SPAGeDI  [146].
Conclusions and Outlook In this review, we aimed to provide an informative primer on the generation and analysis of HTS data for the detection of plant viruses. Even though the field of HTS is transforming rapidly and new platforms and analysis tools are being developed constantly, the basic concepts of data analysis reviewed here will remain relevant in the future. In the next few years, we expect a great increase in the use of the long-read HTS platforms. New algorithms and pipelines for analysis of data will continually be developed, building on some of the concepts described above. These developments are likely to focus on two main areas. Firstly, the adoption of deep learning approaches will likely be more and more integrated into the field of virus detection, on different levels, from similarity searches to the estimation of detection confidence levels, to enable the more robust detection of virus sequences that are more distantly related to those we currently recognize.
13, 14 However, in-home care service use was only assessed as a binary variable (yes/no). Thus it remains unclear whether frequency use of in-home care services makes a difference. This study aimed to investigate the association between the frequency of in-home care service use provided by care workers under the LTC insurance system and the probability of in-home death using national LTC claims data in Japan. Because demand for in-home care is endogenous (ie, individuals who express a strong preference for in-home deaths are more likely to be frequent users of in-home care services), we sought to implement an instrumental variables approach, leveraging exogenous determinants of in-home care supply. Several types of in-home services are provided under universal health and LTC insurance in Japan.
Single-cell data from one representative experiment. Green dots, nontreated Lpp-WT-infected single cells; orange dots, BTB-treated Lpp-WT-infected single cells. (F) Same infection conditions as in (E) but using nontreated cells. Bacterial replication was monitored in each single cell infected with Lpp-WT. Single-cell analyses (12 hpi) of ∆ψ m (TMRM SD/mean), area of intracellular GFP-expressing bacteria (μm 2 ), a proxy for intracellular replication, and cell death (Annexin-V intensity) in more than 3800 cells are shown. Single-cell data from one representative experiment; color scale (yellow) represents Annexin-V intensity per cell (AU). (G) Same as in (F) at 18 hpi. *p<0. 05; **p<0. 01; ***p<0. 001; ****p<0. 00001; ns, nonsigni cant (Mann-Whitney U test). The online version of this article includes the following gure supplement(s) for gure 4:   respiration is abruptly reduced.
Infection and transfection of HEK-293 cells HEK-293 cells stably expressing the FcγRII receptor (gift of Prof. Craig Roy)  (Arasaki and Roy, 2010)  were maintained in DMEM +10% FBS. Before use, the cells were tested negative for Mycoplasma contamination. The HEK-293 cells stably expressing the FcγRII receptor were not authenticated. However, the only entry of misidentified HEK cells at the ICLAC database is ICLAC-00063, when HEK cells were misidentified in 1981 with HeLa cells. Therefore, our HEK cells were identified by morphology and attachment as HEK cells are morphologically different from HeLa cells and they attach very weakly to tissue culture-treated plastic, contrary to HeLa cells. Most importantly, the remote possibility of misidentification with HeLa cells would not change any consideration or conclusion in our study.
Automatic high-content analyses (HCA) All analyses were performed with Harmony software v. 4. 9 (PerkinElmer) using in-house scripts developed in Harmony running Acapella Assay Language version 5. 0. 1. 124082 (available at https:// github. com/bbi-ip/Legionella_and_mitochondrial_ATPase. git; Rusniok, 2021; copy archived at swh:1:rev:657b662b912e3b3c630619f648489cc28652dcd9). For the HCA of the mitochondrial membrane potential (∆ψ m ), the Hoechst signal was used to segment nuclei in the 405/450 channel (excitation/emission), Hoechst background signal in the cytoplasm was used to segment the cytoplasm region in the 405/450 channel, L. pneumophila was identified by measuring the GFP signal in the 488/540 channel, and TMRM (10 nM) signal in the 561/600 channel was used to measure ∆ψ m by calculating SD/mean TMRM intensity values in each infected and noninfected cell.
Whole-genome sequencing for mutant validation Chromosomal DNA was extracted from BCYE-grown L. pneumophila using the DNeasy Blood and Tissue Kit (QIAGEN). The Illumina NGS libraries were prepared using the Nextera DNA Flex Library Prep following the manufacturer's instructions (Illumina Inc). High-throughput sequencing was performed with a MiSeq Illumina sequencer (2 × 300 bp, Illumina Inc) by the Biomics Pole (Institut Pasteur). For the analysis, we first removed adapters from Illumina sequencing reads using Cutadapt software version 1. 15  (Martin, 2011)  and then used Sickle (https://github. com/najoshi/sickle, Buffalo, 2021)  with a quality threshold of 20 (Phred score) to trim bad quality extremities. Reads were assembled using Spades  (Nurk et al. , 2013)  and different K-mer values.
Acknowledgements We acknowledge CB's,  P Glaser 's lab members, and  F Stavru  at  Institut Pasteur  for fruitful discussions. We specially thank  Daniel Schator  for help and discussions regarding HEK-293 infection and transfection. We thank  N Aulner ,  A Danckaert ,  Photonic BioImaging (PBI) UTechS and M Hassan, Center for Translational Science (CRT) at Institut Pasteur , for support. This research was funded by the  Institut Pasteur ,  DARRI-Institut Carnot-Microbe et santé  (grant number  INNOV-SP10-19 ) to PE; the  Agence National de Recherche  (grant number  ANR-10-LABX-62-IBEID  to CB and  ANR-21-CE15-0038-01  to PE), the  Fondation de la Recherché Médicale (FRM)  (grant number  EQU201903007847 ) to CB, and the  Région Ile-de-France  (program DIM1Health) to PBI (part of  FranceBioImaging ,  ANR-10-INSB-04-01 ).
Primary outcome We will extract pain-related information using noninvasive multimodal sensors. Specific features of the physiological/behavioural indicators of infant pain will require unique processing algorithms. We will record pain signals using facial electromyography (EMG), ECG, electrodermal activity (skin conductance/SC), oxygen saturation (SpO 2 ) and electroencephalography (EEG) in real time. Dedicated algorithms for each sensor will extract pain-related information such as facial grimacing or heart rate variability (HRV). The reliability and validity of these algorithms will be tested prospectively on data from preterm and term neonates, and infants aged 1-6 months experiencing acute pain during invasive procedures.
Sample size calculations We assume an α-error=0. 05, 1-β error=0. 8, and a mean:SD ratio of 2:1 for clinical and sensor-based pain scores (based on prior studies of clinical assessments of procedural pain in infants). 23 24 Based on these assumptions, to detect a slope that corresponds to a 0. 5-unit change in the outcome (eg, sensor pain score) per 1-unit change in the predictor (eg, clinical pain score), we will require a minimum of 40 infants in the training dataset. For a binary predictor with 25% or 50% prevalence (eg, mild vs moderate pain, or male vs female neonate), with this sample size, we will be able to detect a 0. 7-unit or 0. 8-unit change in the outcome, respectively. Given the number of sensors used for the first step of this study, some artefacts may occur requiring data corrections or greater variability, with a need for recruiting more patients.
Interventions/experimental design After applying inclusion/exclusion criteria and parental consent, infants' medical data will be recorded including: date of birth, perinatal/medical history, birth weight, gestational age, Apgar scores, congenital anomalies, metabolic defects, other diagnoses, previous surgeries, recent labs, prior imaging, major physical findings, number and types of painful procedures, and all medications used in the 24 hours immediately preceding the study. We will document the type of procedure, time of day, its location and duration, number of attempts and behavioural state of the infant before and after the procedure. Prior to a clinically indicated procedure such as a heel stick, subcutaneous or intramuscular injections (vaccine, drug shot), we will attach SC leads to measure galvanic skin responses either on a hand palm or foot sole, and EMG to record facial muscle activity from cheek and forehead, and directly obtain recordings from clinical monitors (ECG and SpO 2 ).
In addition, the infant will wear a cap with EEG leads. For study procedures, research staff will clinically assess pain using the Neonatal Pain and Sedation Scale (N-PASS) before and after the procedure, the Premature Infant Pain Profile-Revised (PIPP-R) and the Neonatal Facial Coding System (NFCS) during the procedure. 6] [27] [28]  Physiological and audio recordings will start 10-30 min before a planned procedure and continue for up to 20-30 min after the procedure. All sensors will be monitored and displayed on the same laptop. We will use the Brain Vision software to display and record EEG, EMG and SC responses and the Medi-Collector software for ECG and SpO 2. The recording time of the two softwares will be synchronised based on the laptop digital clock. The event marker will be triggered by the researcher using a dedicated function of the Brain Vision software to time lock and record times of noxious and non-noxious events for all sensors (Brain Vision and MediCollector).
At the completion of recording, all the above sensors will be disconnected from each patient and study procedures will be terminated. The entire study will last approximately 30-60 min; however the study may be stopped earlier if any infant shows signs of distress or if a bedside nurse or parent has any concerns. Data collection includes physiological recordings and medical information. Other than the facial EMG, SC leads and 32-channel BrainAmp EEG data acquisition system, all other sensors are used routinely as the standard of care. All sensors are non-invasive and safe. We will use standard electrodes which are routinely used for recording vital signs in the hospital (ECG, SpO 2 ). All recordings will use sticky pads attached to the skin. Other than a potential for mild skin irritation from the adhesives used, there are no significant risks associated with these devices, or other study-related procedures.
Signal-filtering and information extraction from sensors For each sensing modality, we will develop a software algorithm to process signals recorded by sensors and extract the clinically relevant information related to pain. Proof-ofconcept algorithms will be tested using the collected dataset. The feasibility of each sensing modality will be based on: (1) sensitivity and specificity of detecting clinically relevant painrelated changes, and (2) robustness of sensor readings in the presence of non-pain-related disturbances (eg, movement artefact). In order to address artefacts due to movement or suboptimal electrode-skin contact, we will initially use filtering techniques (eg, to remove power line interference). In addition, we will identify channels exhibiting artefacts by considering the range of signal values, where signals showing extreme deviation from average values or channels showing virtually zero activity will be excluded.
Our sensor-fusion framework will classify the neonatal/infant responses to infer pain intensity based on observed changes from baseline. A probabilistic relationship between pain intensity and sensor measurements can be established, where the unknown parameters of the statistical relationships are identified by a training dataset. The training dataset will also be used to estimate the importance of each feature, which can then be used to identify the optimal set of sensors. 61 estational age, postnatal age, and days of life and in hospital will be taken into account in the statistical analyses. We will initially focus on recruiting term neonates who are studied within 1 week after birth and have minimal exposures to prior painful events. This will increase the homogeneity of our sample and minimise the variability in physiological responses due to gestational age, postnatal age, days in the hospital and long-lasting effects of previous painful experiences.
The S-parameters were measured with a vector network analyzer (VNA HP8510C) in conjunction with a DC source (HP4142B) for biasing, a temperature control unit (Temptronic TP03200, Temptronic Corporation, Mansfield, MA, USA) for setting the ambient temperature, and a PC with a specialized software (IC-CAP) for controlling the full measurement procedure through the GPIB interface. The off-wafer calibration was performed using line-reflect-reflect-match (LRRM) standards on the alumina calibration substrate from Cascade Microtech and a commercial calibration software (WinCal). The comparative analysis is performed using S-parameters at the following two bias points in the saturation region: V ds = 3 V and V gs = -0. 1 V for the GaAs HEMT and V ds = 9 V and V gs = -4 V for the GaN HEMT. This choice has been made based on the analysis of the DC output characteristics of the two transistors at different T a (see Figures  2  and 3 ), in order to enable, as much as possible, a fair comparison between the two different technologies.
The microwave experiment consists of S-parameters measured from 45 MHz to 50 GHz at nine different ambient temperatures: -40 °C , -25 °C , 0 °C , 25 °C , 50 °C , 75 °C , 100 °C , 125 °C , and 150 °C. The S-parameters were measured with a vector network analyzer (VNA HP8510C) in conjunction with a DC source (HP4142B) for biasing, a temperature control unit (Temptronic TP03200, Temptronic Corporation, Mansfield, MA, USA) for setting the ambient temperature, and a PC with a specialized software (IC-CAP) for controlling the full measurement procedure through the GPIB interface. The off-wafer calibration was performed using line-reflect-reflect-match (LRRM) standards on the alumina calibration substrate from Cascade Microtech and a commercial calibration software (WinCal). The comparative analysis is performed using S-parameters at the following two bias points in the saturation region: Vds = 3 V and Vgs = -0.