dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Supported by grants from the  Ligue Nationale contre le Cancer  (M. S. , E. H. ),  Fonds Carnot  (M. S. ),  Hadassah-France  (S. R. ),  Cancéropole IdF  (M. S. ),  Fondation ARC pour la recherche sur le cancer  ( PJA 20151203562  to F. B. ; PJA 20131200481 and PJA 20151203259 to E. H. ),  FP7 Marie Curie CIG  (to E. H. ). The research leading to these results has received funding from the program " investissements d'avenir "  ANR-10-IAIHU-06. We are indebted to  Gabriel Viennet ,  Anne-Marie Bergemer-Fouquet ,  Alain Nere  for providing samples;  Justine Guegan ,  Karim Labreche ,  Ludovic Prevost ,  Philippe Domineaux ,  Pierre de la Grange  for bioinformatics support and advises;  Philippe Ravassard  for providing the pDEST-RfAsens-IRES-Puromycin-dU3 construct.
7 kg). We chose a range of 15-50 lb (6. 8-22. 7 kg) to represent the size (weight) of seed batch (uniform distribution). The prevalence of Salmonella contamination in seeds was based on FDA data for a variety of seeds and beans (mung, alfalfa, clover, soybean, and other sprout varieties) reported in the FDA "FY 2014-2016 microbiological sampling assignment summary report: sprouts" (referred to henceforth as "FDA FY14-16 Sprouts Assignment"). (28) Overall, 170 samples were taken from various seed lots and four samples were positive for Salmonella. The analytical sample size was equivalent to 750 g. The four seed samples positive for Salmonella were obtained from four different lots from three sprouters. A literature search for peer-reviewed studies through searches in the National Center for Biotechnology Information PubMed database and the Google Scholar search engine was unfruitful to find any other studies on Salmonella prevalence in seeds for sprouting not involved in an outbreak, except a study from the United Kingdom.
Growth was eventually modeled using a stochastic process based on the Yule process,  (42, 44)  as: N t = N 0 + Negative Binomial (N 0 , exp(-g)), where N t is the number of bacteria after growth, N 0 is the initial number of bacteria, NegativeBinomial(s, p) is the negative binomial distribution with s the size parameter and p the probability parameter, and g is the overall expected growth, in log 10 (Table  I ). After the amount of growth was simulated and the total number of cells was determined per 25 g unit, the final maximum population density was set to be 4 log 10 CFU/g based on the data from the naturally contaminated seeds. We defined the yield of alfalfa sprouts as 6-7 times the mass of seeds;  (45)  this was used to determine the Salmonella level (CFU/g) of sprouts at the end of growth simulation.
Data and Assumptions for Testing of Spent Irrigation Water During irrigation, Salmonella cells present on the seeds/sprouts can be washed off and transferred to the SIW. A portion of SIW can be sampled for microbiological testing. (9) The model evaluated the effect of SIW testing when the water sample was taken after 48 hours of sprouting (and at other time intervals in alternative scenarios; see below). If SIW testing is positive for Salmonella, the associated production batch is not distributed for consumption, i. e. , only the fraction of production batches contaminated with Salmonella that were not detected by the SIW testing regime contribute to predicted cases in the model. In an ideal situation, the sampled SIW would have traveled across and covered all the sprouts in the production batch (complete coverage, i.
Density The proportion of the Salmonella cells that transfers to the SIW may vary substantially, by as much as three orders of magnitude, as reported in a comprehensive review of published data by Montville and Schaffner in 2005. (16) In this study, we included additional data on the subject published since 2005 and, adapting the Montville and Schaffner approach, we explicitly included the mass of in-process sprouts and the SIW volume in modeling this transfer. The mathematical derivation of the number of bacteria in SIW as a function of the number of bacteria in the sprouts and the difference in log 10 concentration of bacteria in sprouts and water is provided in the Appendix. Five studies  (31, 35, 36, 50, 51)  regarding the transfer of Salmonella and E. coli O157:H7 from sprouts to SIW reported the pathogen concentrations in sprouts and corresponding concentrations in SIW immediately after irrigation or rinsing of the sprouts.
Consumption Patterns, Dose-Response Relationship, and Risk Characterization Consumption of alfalfa sprouts in the total U. S. population per year was determined by using data from What We Eat in America (WWEIA), the food consumption data collected as part of the National Health and Nutrition Examination Survey, for survey cycles 2003-2012. (53) For alfalfa sprouts, the estimated total number of servings per year was 8. 52 × 10 7 servings using the WWEIA data and the U. S. total population (census data as of 06/01/2016 from http://www. census. gov/popclock/). The serving size (amount per eating occasion), also from this source, was represented by an empirical distribution with a median and maximum of 8. 7 g and 49. 5 g, respectively. The Salmonella dose-response model used in the present risk assessment was adapted from the one published in the FAO/WHO 2002 risk assessment of Salmonella in eggs and broiler chickens,  (54)  which was based in part on outbreak data from a variety of foods.
By separately characterizing variability and uncertainty, the risk assessment model provides a means to integrate data with different weight of evidence separately. Furthermore, risk estimates were generated for a number of alternative scenarios to evaluate the potential impact of alternative assumptions. Model convergence was tested in both variability and uncertainty dimensions. Risk estimates were generated by using 30,000 iterations in the variability dimension and 1,000 iterations in the uncertainty dimension. Risk and prevalence reduction compared to a baseline (without treatment and no SIW test) were evaluated within each of the uncertainty iterations. The model was developed using the R software. (57).
003% contamination rate for 50 g sprout samples, given no interventions). As part of the USDA/AMS Microbiological Data Program in which samples were collected from distribution centers or at retail from more than 10 states in the United States,  (70)  a large number of alfalfa or alfalfacontaining sprout samples were analyzed, and the results show a Salmonella positive rate of 0. 26% (six of 2,277 samples, ß50 g analytical sample size) in 2009, 0. 22% (five of 2,313 samples) in 2010, and 0. 34% (seven of 2,082 samples) in 2011. In an FDA survey study of RTE foods collected from retail (T. Hammack, personal communication, December 2016), a total of 2,688 sprout samples (622 alfalfa sprouts and alfalfa-sprout-containing samples with mixed types of sprout) were collected from retail stores in four states in the United States between 2012 and 2014, and none of the samples were positive for Salmonella (analytical unit size 50 g); thus, the positive rate was estimated to be <0.
ACKNOWLEDGMENTS We thank  Dr. Donald Schaffner  from  Rutgers University  for sharing the model "Monte Carlo simulation of pathogen behavior during the sprout production process," ( 16 ) a great help for us to understand the published model and build this risk assessment. We also thank our colleagues from  FDA ,  Dr. Insook Son , for providing subject matter expert knowledge on sprouts production and pathogen controls, and  Joy Johanson  for providing subject matter expertise and review of this article. This work was supported in part by an appointment to the  Research Participation Program at the Center for Food Safety and Applied Nutrition  administered by the  Oak Ridge Institute for Science and Education (ORISE)  through an interagency agreement between the  U. S. Department of Energy  and the  U.
The Extended RAS Panel is a United States Food and Drug Administrationapproved, qualitative in vitro diagnostic NGS test developed for the simultaneous detection of 56 known activating mutations within exons 2, 3, and 4 of the KRAS and NRAS genes. The assay system includes sequence-specific reagents and consumables for DNA qualification, library preparation, and sequencing, as well as integrated data analysis and reporting software. The assay is intended for use with the MiSeqDx ® (Illumina, Inc. , San Diego, CA, USA) sequencing instrument. The goal of this analysis was to clinically validate a companion diagnostic (CDx) test to aid in the identification of patients with mCRC eligible for treatment with panitumumab (Vectibix ® , Amgen Inc. , Thousand Oaks, CA, USA). The clinical accuracy of the Extended RAS Panel was evaluated by comparing the results provided by the NGS assay to those of the reference method, Sanger bidirectional sequencing.
The software will fail the entire sequencing run and all samples will appear as invalid. The negative control serves to detect issues like cross-contamination. If an unexpected outcome occurs in the negative control, the software will fail the entire sequencing run and all samples will appear as invalid. Only results for the 56 mutations listed in Table  S1  are reported. The panel mutations were selected based on four panitumumab clinical sequencing studies  (Douillard et al. 2010; Patterson et al. 2013; Peeters et al. 2015; Schwartzberg et al. 2014) , the Catalogue Of Somatic Mutations In Cancer (COSMIC) database, the NCCN guidelines (National Comprehensive Cancer Network 2017), and the  Vaughn et al. publication (2011). For calculation of clinical accuracy, results from the Extended RAS Panel testing were compared with the output from the antecedent Sanger sequencing.
In the primary analysis of the PRIME study as reported by  Douillard et al. (2010) , the therascreen investigational use only PCR kit (QIAGEN) was used to identify KRAS exon 2 mutations by analyzing seven somatic mutations in KRAS codons 12 and 13 (ie, alleles G12A, G12D, G12R, G12C, G12S, G12V, or G13D). Tumor samples from patients identified as having one or more of the seven KRAS exon 2 mutations and with sufficient additional tissue sections that met previously described quality criteria underwent additional testing for mutations in exons 2, 3, and 4 of KRAS and NRAS using Sanger bidirectional sequencing. Samples identified to have no mutations in KRAS exon 2 as determined by the therascreen assay were also tested for other mutations in exon 2 of KRAS. These extended Sanger sequencing data, which included KRAS exons 3 and 4 and NRAS exons 2, 3, and 4 status, were compiled with Sanger data from Douillard et al.
(  2013 ) to provide complete exon coverage for the qualified PRIME samples  (Douillard et al. 2013). Both the Extended RAS Panel and Sanger sequencing provided qualitative assessments of mutation status for both KRAS and NRAS. After passing predefined quality parameters to meet sequencing validity requirements, the final result of each method was a binary qualitative assignment into either RAS Mutation Detected (RAS Positive) or RAS Mutation Not Detected (RAS Negative) based on the identification of any of the 56 panel mutations. For NGS, a RAS Positive result was assigned if at least one RAS panel mutation was detected, a RAS Negative result was assigned if no RAS panel mutations were detected, and an invalid result was assigned if the data from a sample or a sequencing run were of insufficient quality.
These features have led to the development of the Extended RAS Panel, an NGS-based in vitro diagnostic test that analyzes 56 mutations in KRAS and NRAS in a single assay (Table  S1 ). High depth of coverage, achieved by deep sequencing, increases detection of mutations at low frequency (below 10%). Multiplexing enables pooling of different patients' DNA so they can be sequenced together in one single mixture, leading to improved laboratory throughput and further reducing time and costs. Multiplexing 56 targets also enables interrogation of all targets using a single DNA input and does not require individual reactions for each target. In addition, multiple mutations can be detected simultaneously. On completion of the sequencing run, the MiSeqDx sequencing instrument software analyzes the sequencing data, performs variant interpretation, and creates a final report, thus minimizing the demand for bioinformatics support and variations in the interpretation of results.
Densitometric analysis 40 μm coronal sections were stained overnight in primary antibody mGlu2/3 (Rabbit 1:250; Merck Millipore) diluted in the blocking TNB-Tris Buffer and 0. 05% Triton at 4˚C. After washing in PBS and Cy3-conjugated secondary antibody incubation for 1h, we mounted the PBS-washed section on slides in 50mM Tris pH 8. 0, let the sections dry, and coverslipped with Mowiol. Images were acquired using a cooled CCD Camera (Coolsnap HQ, Photometrics, Tucson, Arizona, USA) attached to an Axiophot microscope (Zeiss) and collected using image analysis software (MCID Elite Software 7. 0, Imaging Research, St Catharines, Canada). The immunoreactivity in a given region was obtained from densitometric measurement of the mean gray value using ImageJ (https://imagej. nih. gov/ij/). The background corrected optical densities were averaged per hippocampus region and animal.
The light was centered above the pool and provided evenly-distributed low-level illumination to accommodate tracking software (~8 lux on water surface). To reduce stress, mice were pre-handled for one week and experiments were carried out by the same individual. 10 WT (6 female, 4 male) and 13 Nogo-A -/-(6 female, 7 male) mice were used. Training and probe trials were conducted as in maze 1. Full-cue probes were performed on days 14-15, single-cue on days 16-17, no-cue on days 18-19 and the final full-cue condition on days 20-21. As in maze 1, probe trials were always followed by 6 training sessions to reduce extinction. Tracking was carried out at 4. 2Hz with a 576 x 768 pixel resolution camera using a Noldus EthoVision 3. 0 system (Noldus Information Technology, Wageningen, The Netherlands) and data analyzed offline (Wintrack, http://www.
Statistical analyses Statistical analyses were performed using IBM SPSS (IBM Corp. , Armonk, NY, USA). Values are represented as mean ± SEM. Normalcy of distribution was determined via the Shapiro Wilk test. Except where noted, normally distributed data was analyzed via t-tests. For data sets with non-normal distributions, non-parametric tests were used (Mann-Whitney U test for independent samples or Wilcoxon signed-rank test for paired samples). Significance was defined as Ã P 0. 05, ÃÃ P 0. 01, ÃÃÃ P 0. 001.
2 ± 2. 8, Nogo-A -/-= 7. 0 ± 2. 0, mGlu3 -/-= 12. 3 ± 2. 7 ; P (WT-Nogo-A -/-) = 0. 011, P (WT-mGlu3 -/-) = 0. 241; Fig  5B ). Activation of mGlu3 receptors by DCG-IV also induced an inward current in CA3 pyramidal cells from WT mice (at 1 μM: -17. 9 ± 5. 4 pA, at 3 μM: -29 ± 2. 1 pA ; Fig  5C ) as reported previously  [26]. Interestingly, application of DCG-IV failed to induce a significant current in CA3 pyramidal cells in Nogo-A -/-(at 1 μM : 0. 1 ± 2. 8 pA; P =. 001 at 3 μM: -1. 5 ± 2. 6 pA; P =. 0006) and mGlu3 -/-(at 3 μM: -2. 2 ± 1. 4 pA; P =. 0001) hippocampus (Fig 5C ). Taken together these data are consistent with a downregulation of mGlu3 receptors in CA3 pyramidal cells of Nogo-A -/-mice. We therefore investigated whether reduction may occur at the RNA or protein level. First, qPCR was performed on RNA from isolated CA3 tissue.
RFI Flagging Algorithms 2. 3. AOFlagger The current default RFI flagging software for radio imaging at multiple observatories, which is used routinely during transient surveys such as MWA  [28]  and LOFAR, is called the AOFlagger. In recent years, AOFlagger has become one of the standard RFI flagging algorithms, and is increasingly used Time%(UTC)% Frequency%(MHz)% Visibility%(Jy)% Time%(UTC)% Frequency%(MHz)% Visibility%(Jy)%    [24]  and Arecibo. 1 n brief, the AOFlagger is an iterative algorithm which operates on a frequency resolved timeseries of visibilities from a single baseline, and can operate on either a single polarization or a chosen Stokes parameter. It fits a surface to the visibility amplitudes in the time-frequency plane in order to eliminate systematics, e. g. , due to fringes from bright sources in sidelobes, and then clips out visibilities with amplitudes crossing a chosen threshold.
The flagged visibilities are ignored in the next iteration. The algorithm detects low level RFI using a set of decreasing thresholds, implemented via the SumThreshold algorithm. It has been empirically determined that two iterations of SumThreshold are adequate for flagging  [26]. The algorithm consists of the following steps  [26] : • SumThreshold: This is a combinatorial thresholding algorithm where the same dataset is subjected to a set of decreasing thresholds. A threshold χ M is determined by M , the number of samples in the data set surrounding a candidate data point (visibility amplitude for one time/frequency sample), where M is predefined, and a chosen false detection rate  [25]. If the sum of amplitudes of M contiguous visibilities exceeds the threshold χ M , then all M visibilities are flagged.
Observations As we were interested in examining the consequences of RFI flagging on real data, we wanted to examine a field observed with LOFAR. For this purpose, we chose data taken with the LOFAR telescope as an extra target observation during routine transient monitoring observations (Fender et al. , in prep) using one sub-array pointing of the telescope, where the beam is formed using the innermost antennas from each station. We then focused on a low-RFI 11 minute measurement set, taken on February 10, 2013, using one beam at 149 MHz with a bandwidth of 781 kHz, along with calibrator source Cygnus A. First, the data were pre-processed using the standard methods with LO-FAR: they were flagged for RFI using AOFlagger  [23] , and the data calibration and imaging were carried out using the method described by Broderick et al.
Observational Detection Bounds on Transient Fluence We quantified the effect of a threshold based flagging strategy on a bright transient by simulating the presence of such a transient on a dataset with a realistic noise and RFI instantiation. Assuming no sources in the field, the measured LOFAR System Equivalent Flux Density (SEFD) for a core station in HBA mode is 3 kJy at 150 MHz  [12]. To calculate the expected noise on a single visibility amplitude of a single polarization, ∆S, under the assumption of a high SNR, we use the equation ∆S = 1 η SEF D √ 2 √ δνt int ( 1 ) where δν is the channel width, and t int is the integration time. For our data, the system efficiency η is assumed to be 1, and the two stations making up the baseline are assumed to have the same SEFD. The subband of 781 kHz was spectrally resolved into 16 channels, with visibility integration times of 10 seconds.
This was done in order to study the effect of the flagger on this one injected transient signal in particular, as this data was very low in the amount of RFI it contained. For these simulations a transient source was injected in the center of the image by taking a model point source, transforming it to visibility space, and adding this to the recorded visibilities. This was done using BlackBoard Selfcal (BBS) software  [18]  where the temporal profile of the point source was a top hat signal of a given strength and duration in the image's sky model. After this injection, the data were processed as usual through the imaging pipeline described in Section 3. 1, and the resulting image was then analyzed. This transients amplitude was varied from 0. 5-10 Janskys in half Jansky intervals, and the duration was varied from one second to 2 minutes on a logarithmic scale.
This range, based on estimates laid out in Section 3. 2, was selected to probe the range of χ thresholds which depend on both the amplitude and connectivity of visibilities. The resulting images for these data were then run through the sourcefinding script PySE where the integrated flux at the location of the injected transient was measured via a forced fit of a point source. We should note that the data we injected the transient into had already been run through RFI processing during pre-processing when it was first obtained for analysis that is, the typical RFI processing for LOFAR imaging. The tests we describe here involve a secondary RFI flagging step after the transient was simulated and injected into the data but before imaging in order to study the effect of the flagger on this one injected transient signal in particular, especially as these data contained a very low amount of RFI.
[32]  MWA survey for brief transients, flagged and calibrated using 2 minute measurement sets but imaged in 30 second increments. This was done in order to understand how flagging for transients would affect shorter measurement sets, because the number of data samples is much smaller for such observations. For these measurement sets, we focused on transients that were up to 60 seconds duration in the data, or half the length of the observation. Further, we note that because AOFlagger has a recommendation of at least 1,000 time steps to be passed through the flagger, and this is impossible for MWA-length measurement sets, we edited the time window in which the measurement sets are divided for flagging into smaller increments than is recommended.
If automated flagging is used, very brief transients (∼ ∆t < 60 s) of a detectable brightness will be flagged out altogether by the automated flagging software because of the single sample threshold, χ 1 , designed to eliminate very bright and sporadic RFI. It should be emphasized that the transient signal measured at the end of this procedure would be weaker than the original signal injected, as described by Equation  2. Thus, in Figure  3a , for example, the transient signal flux measured is less than the amount injected. Longer, brighter transients (60 s < ∆t <120 s), however, are no longer flagged completely by AOFlagger, but do appear to have a lower flux when compared to the original strength of the injected transient. A check of what is being flagged in the visibilities reveals that the transient signals are becoming "partially flagged," where some, but not all, of the signal is being flagged as RFI.
An example of such partial flagging can be seen in Figure  4. Exactly how much of the signal is being flagged is dependent on the intensity and duration of the signal. We also note that sections of the non-transient data are also being flagged by the flagger in order to eliminate any potential fringes of RFI within the data due to dilation, which can further affect the measured fluxes in the data stream. For example, for the unflagged data, the 10 Jy transient with 2 minutes duration had a measured flux of 1. 24 Jy (Figure  3a ). For the flagged data, the same transient was measured as 1. 44 Jy in brightness (Figure3b. This was because while ∼ 30% of the transient signal was flagged, a large fraction of data surrounding the signal which did not contain a transient was also flagged by AOFlagger, making the effective ∆t of the measurement set shorter than for the unflagged data.
We see a similar plot to what was seen before with the longer measurement set in that the briefest transients ( ∆t < 25 s) are all entirely flagged. After this, transients of longer duration (25 s < ∆t <60 s) are visible, but not at their full strengths because they are partially flagged Time%(UTC)% Frequency%(MHz)% Visibility%(Jy)% Time%(UTC)% Frequency%(MHz)% Visibility%(Jy)% in visibility space. This can have consequences: for example, a 10 Jy, 20 second transient would be detectable in the unflagged data, but would not exceed the detection threshold in the flagged data. Thus, flagging both template datasets results in loss of the brightest transients, as well as dilution of the recovered flux of unflagged transients.