dataesr/scientific-paragraphs-categorization huggingface dataset:
In default of standards for encoding read origins, every evaluation tool has to be made explicitly compatible with the simulator used to generate reads. Results: In order to solve this obstacle, we have created a generic format RNF (Read Naming Format) for assigning read names with encoded information about original positions. Futhermore, we have developed an associated software package RNFTOOLS containing two principal components. MISHMASH applies one of popular read simulating tools (among DWGSIM, ART, MASON, CURESIM etc. ) and transforms the generated reads into RNF format. LAVENDER evaluates then a given read mapper using simulated reads in RNF format. A special attention is payed to mapping qualities that serve for parametrization of ROC curves, and to evaluation of the effect of read sample contamination.
, 2012) , PBSIM  (Ono et al. , 2013) , SINC  (Pattnaik et al. , 2014) , FASTQSIM  (Shcherbina, 2014) ) as well as many evaluation tools (WGSIM EVAL, DWGSIM EVAL, CURESIMEVAL, RABEMA  (Holtgrewe et al. , 2011), etc. ). However, each read simulator encodes information about the origin of reads in its own manner. This makes combining tools complicated and makes writing ad-hoc conversion scripts inevitable. Here we propose a standard for naming simulated NGS reads, called Read Naming Format (RNF), that makes evaluation tools for read mappers independent of the tool used for read simulation. Furthermore, we introduce RNFTOOLS, an easily configurable software, to obtain simulated reads in RNF format using a wide class of existing read simulators, and also to evaluate NGS mappers.
Existing read naming approaches Depending on the read simulator, information about reads origin is either encoded in its name, or stored in a separate file, possibly augmented with information about the expected read alignment. While WGSIM encodes the first nucleotide of each end of the read in the read name, DWGSIM and CURESIM encode the leftmost nucleotide of each end. ART produces SAM and ALN alignment files, MASON creates SAM files, and PIRS makes text files in own format.
MIShmash read simulator. MISHMASH is a pipeline for simulating reads using existing simulators and combining obtained sets of reads together (e. g. , in order to simulate contamination or metagenomic samples). Its output files respect RNF format, therefore, any RNF-compatible evaluation tool can be used for evaluation. 2. 2. 2 LAVEnder evaluation tool for read mappers. LAVENDER is a program for evaluating mappers. For a given set of BAM files, it creates an interactive HTML report with several graphs. In practice, mapping qualities assigned by different mappers to a given read are not equal (although mappers tend to unify this). Moreover, even for a single mapper, mapping qualities are very data-specific. Therefore, results of mappers after the same thresholding on mapping quality are not comparable.
CONCLUSION We designed RNF format and propose it as a general standard for naming simulated NGS reads. We developed RNFTOOLS consisting of MISHMASH, a pipeline for read simulation, and LAVENDER, an evaluation tool for mappers, both following the RNF convention (thus intercompatible). Currently, MISHMASH has a built-in interface with the following existing read simulators: WGSIM, DWGSIM, ART, and CURESIM. We expect that authors of existing read simulators will adopt RNF naming convention as it is technically simple and would allow them to extend the usability of their software. We also expect authors of evaluation tools to use RNF to make their tools independent of the used read simulator. The main benefit for users is the ease of switching between read simulators and read evaluation tools, without need of writing any special conversion scripts.
Břinda et al. Coor 12345678901234-5678901234567890123456789 Source 1 -reference genome chr 1 ATGTTAGATAA-GATAGCTGTGCTAGTAGGCAGTCAGCCC chr 2 ttcttctggaa-gaccttctcctcctgcaaataaa Source 2 -generator of random sequences READS: r001 ATG-TAGATA -> r002/1 TTAGATAACGA -> r002/2 <-TCAG-CGGG r003/1 tgcaaataa -> r003/2 gaa-gacc-t -> r004 ATAGCT. TCAG -> r005 GTAGG -> <-agacctt <-TCGACACG r006 ATATCACATCATTAGACACTA (a) Simulated reads r. tuple LRN SRN r001 sim 1 (1,1,F,01,10) [single end] #1 r002 sim 2 (1,1,F,04,14),(1,1,R,31,39) #2 [paired end] r003 sim 3 (1,2,F,09,17),(1,2,F,25,33) #3 [mate pair] r004 sim 4 (1,1,F,15,36) [spliced], #4 C:[6=12N4=] r005 sim 5 (1,1,R,15,22),(1,1,F,25,29), #5 (1,2,R,05,11) [chimeric] r006 rnd 6 (2,0,N,00,00) [random] #6 1 http://github. com/lh3/wgsim 2 http://github. com/nh13/dwgsim c Oxford University Press XXXX.
1 ). A Shack-Hartmann wavefront sensor (Flexible Optical BV) was used to determine the relative wavefront change between different deformable mirror configurations. The sensor, which consists of a 30 × 30 microlens array having a focal length of 3. 5 mm and 150 µm pitch, was directly placed in the path of the converging beam after the focusing parabolic mirror. Reconstruction of the wavefront was performed using the FrontSurfer analysis software (Flexible Optical BV), typically with ≈ 450 measured local wavefront slopes and RMS error on the order of 0. 05λ. Rotating the neutral density filters and the half-wave plate did not change the focal spot or the wavefront measurement significantly, insuring the wavefront distortion introduced by attenuation was negligible. Deformable Mirror and Genetic Algorithm The deformable mirror (AOA Xinetics) has a 47mm clear aperture of a continuous face sheet with 37 piezoelectric actuators arranged on a square grid spaced 7 mm apart.
Acknowledgements This work was supported by  DARPA  (Contract No. N66001-11-1-4208 ), the  NSF  (Grant No. 0935197 ),  NSF  CAREER (Grant No  1054164 ), the  AFOSR   Young Investigator Program  (Grant No. FA9550-12-1-0310 ) and MCubed at the  University of Michigan. The authors acknowledge the  OSIRIS  Consortium, consisting of  University of California, Los Angeles (UCLA)  and  Instituto Superior Técnico (IST)  (Lisbon, Portugal), for the use of the OSIRIS 2. 0 framework. This research was supported in part through computational resources and services provided by  Advanced Research Computing at the University of Michigan ,  Ann Arbor.
zero-3 poles model In this model, we consider an additional integral time constant τ p3 between the input X (t ) and the output R(t ), represented in the following equation through an additional state variable X 2 (t ): τ p1 dX 1 (t ) dt = -X 1 (t ) + K X (t ) -τ z dX (t ) dt τ p2 dX 2 (t ) dt = -X 2 (t ) + X 1 (t ) τ p3 dR(t ) dt = -R(t ) + X 2 (t ) In Laplace transform notation: R(s) X (s) = K 1 -τ z s (1 + τ p1 s)(1 + τ p2 s)(1 + τ p3 s) In order to identify the model from the samples, we used an approximate discrete model based on the bilinear transform  (Oppenheim and Schafer, 2010) : s ← 2 T s z -1 z + 1 where z -1 corresponds to a pure delay in the sample and T s is the sampling frequency. Parameters of the discrete model were fitted by linear regression using the R package with the dynlm package  (Zeileis, 2014) , which was also employed to perform the simulations.
, , 2013)). All three plasmids displayed a clear dose-dependent fluorescence/OD pattern, well above the levels of the negative control (Figures  5A-C ). For matC-matB, pACYCmatCmatB was readily available as a low-copy alternative that functioned in a quite well-reproducible fashion (Figure  2 ), when tested with the pBFR1k_RFP_8FapR sensor plasmid. These results prompted us to subclone our pathway collection into a pACYC backbone, thereby reducing the copynumber of all constructs to ~15. When expressed this way, all five constructs (matCmatB, mmsA, cagg1256, M. accABCD, and matCatoDA) allowed cell growth and displayed a scalable, IPTGdependent fluorescence (Figures  2  and 6 ). As it turned out, the concise nature of the dataset obtained with the pACYC-collection allowed us to fit various models describing the response of the sensor to malonyl-CoA production (see below).
ACKNOWLEDGMENTS We thank  Prof. John Cronan ,  Prof. Mattheos Koffas ,  Prof. Fuzhong Zhang , and  Dr. Jingwen Zhou  for providing their plasmids and  Dr. Brian Jester  for helpful discussions. PC is supported by  Genopole  through an  ATIGE Grant  by  PRES UniverSud Paris , by  Agence Nationale de la Recherche , and by  UPFellows program  with the support of the  Marie Curie COFUND program. VL is supported by a  DGA  ( French Ministry of Defense ) graduate fellowship. The authors acknowledge funding provided by  Toulouse White Biotech (TWB)  and the  GIP GENOPOLE.
Background: Medical apps are widely available, increasingly used by patients and clinicians, and are being actively promoted for use in routine care. However, there is little systematic evidence exploring possible risks associated with apps intended for patient use. Because self-medication errors are a recognized source of avoidable harm, apps that affect medication use, such as dose calculators, deserve particular scrutiny. We explored the accuracy and clinical suitability of apps for calculating medication doses, focusing on insulin calculators for patients with diabetes as a representative use for a prevalent long-term condition. Methods: We performed a systematic assessment of all English-language rapid/short-acting insulin dose calculators available for iOS and Android. Results: Searches identified 46 calculators that performed simple mathematical operations using planned carbohydrate intake and measured blood glucose.
Assessment procedures Apps were assessed using a standardized method to examine each component of the calculation process (Additional file 1: Methods AF2). Expected inputs and outputs, supported unit systems, terminology, and any supplementary app features were characterized by inspection. Simulated data were used to define behavior in response to missing and extreme input values. Where the formula used for calculation was not displayed in the app or in associated documentation, the developer was contacted. For those apps where a formula was ultimately identified, performance was assessed using a set of test cases generated by permutation of the range of possible values for each input parameter (further described in Additional file 1: Methods AF3). Where present, clinical disclaimer text was extracted and coded to identify statements advocating discussion with a healthcare professional prior to calculator use and the role of personal judgment in interpreting generated results.
Simple descriptive statistics were used to summarize the prevalence of issues within the schema. Before starting analysis, we hypothesized that the prevalence of identified issues might vary both by platform and the payment model (free or paid), because apps for iOS are subject to a quality control process prior to release whereas Android has no such requirement, and because developers of paid-for apps may invest more in quality assurance. Fisher's exact test was used to calculate the two-tailed probability of an association between issue prevalence and platform or distribution model. A significance level of 0. 05 was prespecified. Statistics were computed with R (Version 3. 0. 0) using the package exact2x2 (Version 1. 4. 0).
However, because these apps were clearly advertised as dose calculators they were retained for analysis. One iOS app was associated with an evidence-based diabetes self-management education program  [30, 31]  and another, available for both iOS and Android, had been developed by healthcare professionals  [32]. Download counts were available for Android, but not iOS. While not necessarily reflecting the number of active users, they allow a conservative aggregate estimate of at least 105,000 global downloads of the calculators included in the study by October 2014. While the majority of Android calculators had relatively low download counts (median 1000), three apps had been downloaded at least 20,000 times each. All apps performed a basic meal bolus calculation, designed to work out the amount of insulin required to offset counted carbohydrate intake that was extended in most cases (83%, n = 38/46) with an adjustment for current blood glucose measurements (Additional file 2: Table  AF7 ).
All apps accepted inputs entered manually by the user. Eight apps provided a food database for quantifying carbohydrate intake. One (Glucose Meter for Android  [33] ) supported automatic transfer of blood glucose measurements from a Bluetooth-enabled meter. Input issues affect the entry of data and increase the risk that calculation will proceed without correct parameters. Output issues affect interpretation of the calculated value. They create a risk of incorrect dose recommendation even when all input parameters have been supplied correctly. While input issues may be amenable to corrective action taken by a user, for example, by double-checking data entry, output issues may only be effectively addressed through changes to the software. Apps extended the basic calculation by incorporating additional adjustments for exercise levels (n = 10), measured ketones (n = 2), and lipids (n = 1).
Acknowledgements This work received no specific funding. However, KH's doctoral studies are funded by the  National Institute for Health Research (NIHR)  under the  Collaborations for Leadership in   Applied Health Research and Care (CLAHRC)  programme for North West London. They played no role in study design, in the collection, analysis, and interpretation of data, in the writing of the report, or in the decision to submit the article for publication. The views expressed in this publication are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the Department of Health.
The methodology is applied here for NC turning. It is also applicable for any machining operations, as long as setting-up is made of isostatic points, or can be modeled by isostatic points. However, the developments presented in this paper cannot be envisaged in an industrial context without a calculation tool. This is the reason why a new methodology, based on the Computer Aided Tolerancing (CAT) software ANATOLE of Airbus Group Innovations, is currently being developed. This will provide manufacturing engineers with the necessary calculation tools to determine the production specifications. The case of a local WCS defined by probing is included. The process plan presented in this paper was addressed using this method. The results proposed by the software are identical to those which are given by the method described here.
An overflow of regulatory RNAs (sRNAs) was identified in a wide range of bacteria. We designed and implemented a new resource for the hundreds of sRNAs identified in Staphylococci, with primary focus on the human pathogen Staphylococcus aureus. The "Staphylococcal Regulatory RNA Database" (SRD, http://srd. genouest. org/) compiled all published data in a single interface including genetic locations, sequences and other features. SRD proposes novel and simplified identifiers for Staphylococcal regulatory RNAs (srn) based on the sRNA's genetic location in S. aureus strain N315 which served as a reference. From a set of 894 sequences and after an in-depth cleaning, SRD provides a list of 575 srn exempt of redundant sequences. For each sRNA, their experimental support(s) is provided, allowing the user to individually assess their validity and significance.
RNAseq analysis performed on strains N315, NCTC8325, and Newman allowed us to provide further details, upgrade the initial annotation, and identified 159 RNA-seq independent transcribed sRNAs. The lists of 575 and 159 sRNAs sequences were used to predict the number and location of srns in 18 S. aureus strains and 10 other Staphylococci. A comparison of the srn contents within 32 Staphylococcal genomes revealed a poor conservation between species. In addition, sRNA structure predictions obtained with MFold are accessible. A BLAST server and the intaRNA program, which is dedicated to target prediction, were implemented. SRD is the first sRNA database centered on a genus; it is a user-friendly and scalable device with the possibility to submit new sequences that should spread in the literature.
2012)  with some containing small open reading frames that were shown to be expressed  (Sayed et al. 2012; Pinel-Marie et al. 2014 ). However, and for the most part, their functions and mechanisms are unexplored yet. For the few sRNAs with associated functions, some detect bacterial density, modify cell surface properties for host immune escape, adjust central metabolism for optimal growth, regulate the expression of virulence factors, influence antibiotic resistance  (Lalaouna et al. 2014) , trigger cell death, and encodes toxins  (Felden et al. 2011; Guillet et al. 2013). The systematic and recent use of high-throughput RNAsequencing technologies substantially raised the number of sRNAs sequences identified per bacterial species. To cope with that plethora of sRNAs, several databases have emerged from generalists to more specialize.
Generalist databases such as fRNAdb  (Kin et al. 2007)  or NONCODE  (Xie et al. 2014 ) focus exclusively on eukaryotes, while Rfam 11. 0  (Burge et al. 2013)  partitions RNAs data into families for both the eukaryotes and prokaryotes. RNAdb was originally designed for mammalian noncoding RNAs but was officially retired in June 2012  (Pang et al. 2007 ). On the other hand, there are databases specifically devoted to the bacterial kingdom. sRNAMap is a repository for the microbial genomes but Gram-positive bacteria, and therefore Staphylococci, are absent from this browser  (Huang et al. 2009). sRNATarbase  (Cao et al. 2010 ) was implemented to provide a list of sRNA targets, but is out of date for Staphylococci. Recently, two bacterial sRNA databases were developed: (i) sRNAdb, which focused exclusively on Gram-positive bacteria  (Pischimarov et al.
2012 ) but provides only 39 sRNA sequences for S. aureus; (ii) BSRD is a generalist bacterial sRNA database with >700 species included  (Li et al. 2013). Finally, RNAspace. org is a platform devoted to the prediction, annotation, and analysis of noncoding RNA but does not provide data set  (Cros et al. 2011 ). In S. aureus and more generally in the Staphylococcal genus, a unified sRNA nomenclature is lacking, while many redundancies, as single sequence described under several IDs, and potential misannotated sRNAs (e. g. , repeated sequences, mRNA leader or trailer sequences) would require an in-depth manual cleaning. Therefore, there is an urgent need for additional sRNA databases focusing on a bacterial genus to provide an accurate and simple list of sRNAs. Here, we report a Staphylococcus Regulatory RNA Database (SRD, http://srd.
Construction of a database encompassing the Staphylococcal regulatory RNAs Staphylococcal sRNAs were identified and studied principally in several strains of S. aureus  (Tomasini et al. 2014). The chronological discovery of the Staphylococcal sRNAs expressed in S. aureus is listed in Table  1. Those RNAs were identified by combining diverse experimental and bioinformatics approaches  (Novick et al. 1989 (Novick et al. , 1993;; Pichon and Felden 2005; Anderson et al. 2006; Roberts et al. 2006; Marchais et al. 2009; Nielsen et al. 2011; Morrison et al. 2012; Xue et al. 2014)  including the use of Next-Generation RNA-Sequencing technologies  (Geissmann et al. 2009; Abu-Qatouseh et al. 2010; Beaume et al. 2010; Bohn et al. 2010; Howden et al. 2013). A total of 894 sequences transcribed as sRNA were compiled from the literature (Fig.
1 ; Supplemental Data S1). We then focused on the following extensively studied and completed S. aureus genomes: N315, Newman, NCTC8325, and JKD6008 (Table  2 ). The BLAST program was used to locate the coordinates of each sRNA gene in any of the four genomes. Some sequences appeared, as previously suggested  (Beaume et al. 2010; Howden et al. 2013) , to be repeated onto the genomes, that led to an increase in the total number of sRNA sequences collected. Therefore, sequences identified as DNA repeated sequences by these authors were removed after confirming the initial statements using Blast (Supplemental DataS2). In addition, sequences located in CDSs, rRNAs, tRNAs, or spacers within the four genomes as well as the RNA sequences flanking the genes transcribed as ribosomes (reads overlapping with the ribosomes or within the intergenic regions of ribosomes) were discarded  (Liu et al.
srn numbers were assigned by increments of 10 to anticipate the identification of new sRNAs in the upcoming future which would be also numbered based on their genomic locations. When a srn gene is present in other strains it keeps the srn number assigned onto the N315 genome. When a srn identified in N315 is present in multiple copies in N315 or in another strain, the number of copies is provided (srn_1930. 1, srn_1930. 2, and srn 1930. 3), unless experimental evidence indicated that they should be considered as distinct sRNAs (Supplemental Data S3, S4). srn identified in other strains and absent in N315 strain will be assigned with numbers starting from srn_9000 (srn_9480 formerly known as sRNA334). There were only four sRNAs (4. 5 S, 6S, tmRNA, and RNase P RNA) for which a srn identifier was not generated, based on their extensive nomenclature, sequence, and functional conservation beyond Staphylococcal genomes (Supplemental Data S5).
Some of the sRNAs previously identified by RNA-seq were of short lengths (<50 nt), mostly detected as cis-encoded antisense RNAs, including a wide antisense transcription that is not understood yet  (Lasa et al. 2011). Also, some of the previously published sRNAs were already suspected as potential 5 ′ UTRs or 3 ′ UTRs  (Beaume et al. 2010; Howden et al. 2013). Therefore, we searched for the presence of srn transcripts in strains N315 and Newman (data submitted to GEO with the accession number GSE64026), cultivated until the exponential phase of growth in a rich medium (BHI), and for strain NCTC8325 cultivated under 16 different growth conditions (growth phase, temperature, O 2 limitations, etc. ; data kindly provided by Drs. P. Bouloc and T. Rochat). Those three strains are representative as their genomes were completed and annotated  (Kuroda et al.
Similar results were obtained for Newman and NCTC8325 strains, respectively (see the SRD website). srn annotated as "ND" were either not detected by HTSeq count or did not meet some of the criteria retained for describing a sequence as transcript or a cis-antisense. Current Illumina sequencing kits are not perfectly adapted to the search of small bacterial transcripts and therefore it is difficult to know whether there are limits in the technology or that transcripts are absent. Fortyfive of the srn that were not detected were predicted to have a length <50 nt. Ninenty-seven srn were predicted to be between 50 and 100 nt and the others were longer than 100 nt. Overall, only 159 srn were detected as transcripts or as RNA antisense under our experimental conditions (Supplemental Data S5 and http://srd.
genouest. org/browsevalidated). Also, there were 24 ambiguous srn (Supplemental Data S6). These ambiguous srn presented criterion that did not allow us to add them in the list of 159 sRNA (see comments in Supplemental Data). All these results suggest that some sRNAs are expressed or detected under specific conditions that await further experimental assessment, while other srn may not be considered as independent sRNA transcriptional units or may be false positive and arise from transcriptional noise. sRNA predictions in other Staphylococcal species and strains Four S. aureus genomes were used to generate SRD. It is however important to include in a database a large number of strains and other species. Therefore, we performed srn predictions on a set of 28 strains which included 18 S. aureus subspecies and 10 other Staphylococci (.