dataesr/scientific-paragraphs-categorization huggingface dataset:
This is critically important, because it confirms that the solar spectrum residual is not air mass factor or solar zenith angle dependent, but is instead a product of inaccuracies in the measured solar spectrum. The stable concentrations in this all-afternoon test allowed us to investigate the ideal SO2 fit window. We did this by fitting the entire dataset with a range of fit windows, producing a mean and standard deviation for the entire set for each window. The results from this study are shown in Figure  9. We chose an optimal fit window of 308-318 nm, as this produces reasonably accurate results, within 10% of the gas cell amounts, and allows a stronger SO2 line to be included to increase the sensitivity of the retrieval to small amounts of SO2. We see that while the accuracy of the fit does change with the fit window, the empirical precision is impressively small, and We performed a BrO retrieval using a solar spectrum residual corrected fit, with a window of 337-352 nm, and simultaneously fitting BrO, SO2, O3, NO2 and Ring spectra, as well as an order 4 polynomial, shift, stretch and FWHM of the instrument lineshape.
We have shown that the residual solar spectrum is not air mass factor dependent, indicating that it is the result of inaccuracies in the solar spectrum. Errors at the 1-2% level are not surprising for atmospheric corrected solar spectra, but the key capability of the iFit code to accurately fit the main gas absorptions until the 1-2% solar spectrum error limit is reached means that the residual spectrum is an essentially pure solar spectrum correction function. This can then, in theory, be re-applied independent of solar zenith angle, air mass factor, weather conditions, and so on. To date all of our tests have supported this capability. The flat spectrum is pixel dependent, and will not shift with temperature induced wavelength shifts. By accurately characterising the flat spectrum we may elegantly remove its effects on the measured spectrum, making the resulting spectra robust to wavelength shifts, which may be accurately fitted in the iFit procedure.
Using the flat spectrum removal procedure to produce a residual which is in turn removed allows iFit to produce noise-limited fits of SO2 and BrO for traverse data. Scanning data requires a slightly more complex approach. Here we may produce a stored residual solar spectrum for each spectrometer, which is then applied to all fits, taking account of any shifts between the measured residual and the current fit spectrum. Alternatively, and perhaps more robustly, we may use the capability of the flat-equipped fit to produce a 1-2% solar spectrum residual by fitting the vertical viewing spectrum in a single scan. The elegance of this approach is that we account for nearly all SO2 (down to at most 10-20 ppm. m) which may be present, as opposed to the classic DOAS clear-sky spectrum method, which introduces unknown magnitude biases into the scan retrieval.
Atmos. Meas. Tech. Discuss. , doi:10. 5194/amt-2015-380, 2016   Manuscript under review for journal Atmos. Meas. Tech. Published: January 2016 c Author(s) 2016. CC-BY 3. 0 License. consistent with retrieved SO2 errors. This opens the possibility to correct the systematic differences between calibrated SO2 amounts and retrieved SO2 amounts. We show the time series of SO2 retrieved values using both solar spectrum residual and flat fits for the dataset up until light decreases to low levels in Figure10. There is a fairly flat systematic underestimate of ~10% for the 405 ppm. m cell and 10% overestimate in the 126 ppm. m cell. These can be readily corrected. A modest 2-3% decline in SO2 values is observed during the course of the day. The two retrievals produce near identical results, consistent with small residual of the flat fit.
5. 4 Test 3: Etna traverse: SO2Our third test case uses data from an example dataset of spectra collected during a carbased traverse of Mt. Etna, Sicily, Italy on 7 th January 2007. This dataset was chosen as the solar radiance in January is relatively low, and therefore represents a close to worst-case scenario. No flat spectrum was measured at the time of the traverse, but we produced one in July 2012 which improved the fit residual when it was removed from the measurement, demonstrating the stability of this feature. The abundant number of clear sky spectra between the plume measurements permitted an accurate determination of the residual solar spectrum with SO2 fixed to zero. An example fit of an SO2-rich spectrum is shown in Figure11, and the retrieved SO2 time series is shown in Figure12.
The ribosome profiling technique (Ribo-seq) allows the selective sequencing of translated RNA regions. Recently, the analysis of genomic sequences associated to Ribo-seq reads has been widely employed to assess their coding potential. These analyses led to the identification of differentially translated transcripts under different experimental conditions, and/or ribosome pausing on codon motifs. In the context of the ever-growing need for tools analyzing Ribo-seq reads, we have developed 'RiboProfiling', a new Bioconductor open-source package. 'RiboProfiling' provides a full pipeline to cover all key steps for the analysis of ribosome footprints. This pipeline has been implemented in a single R workflow. The package takes an alignment (BAM) file as input and performs ribosome footprint quantification at a transcript level.
It also identifies footprint accumulation on particular amino acids or multi amino-acids motifs. Report summary graphs and data quantification are generated automatically. The package facilitates quality assessment and quantification of Ribo-seq experiments. Its implementation in Bioconductor enables the modeling and statistical analysis of its output through the vast choice of packages available in R. This article illustrates how to identify codon-motifs accumulating ribosome footprints, based on data from. Escherichia coli.
e. transcript, codons, multi-codons motifs). Several tools for processing ribosome profiling data have previously been proposed. RiboTools'  (Legendre et al. , 2015)  and 'RUST'  (O'Connor et al. , 2015) ) were developed in python, 'riboSeqR'  (Hardcastle, 2014)  corresponds to an R package. Each of the above software integrates some, but not all, of the functions necessary for a standard Ribo-seq workflow from reads to quality assessment, recalibration and quantification. A previous effort to group the different approaches for Ribo-seq analyses has been developed with the Galaxy instance RiboGalaxy  (Michel et al. , 2016). These tools have been developed to answer specific questions related to ribosome occupancy: normalization of Ribo-seq reads ('RUST'), detection and characterization of reading frame usage ('riboSeqR'), or irregular translational behavior such as translational ambiguities (Ribo-seq footprints in different phases) and stop-codon read through ('RiboTools').
However, prior to any advanced Ribo-seq data processing, it is necessary to have standard pipelines for quality assessment of the experiments and specific ribosome footprint assignment to sequences. We implemented the features for a standard Ribo-seq workflow in a pipeline entitled 'RiboProfiling'. The pipeline takes an alignment file (BAM) as input, performs identification of the read offset, generates transcript and (multi-) codon coverage quantification data, and performs statistical analyses as well as graphical representations. Our pipeline is, to our knowledge, the most complete integration of a ribosome profiling standard analysis pipeline in an unique R framework. This includes the crucial step prior to quantifying ribosome footprints that consists in identifying the offset between Ribo-seq reads and the P-site of the ribosome.
We have given special attention to this step as it is essential to correctly associate ribosome footprints with codon resolution. Depending on the RNA digestion protocol, the assignment of the ribosome must be made specifically either to the 5', 3' or the center of the read. To our knowledge, there is only one implementation for the determination of ribosome offset that was published so far. It was proposed in 'riboSeqR' as a metagene plot and the determination of the offset was only possible from the 5' read end and for a particular read length. Our package offers several options, to compute the offset and recalibrate reads based either on the 5' or the 3' read ends. RiboProfiling also enables the graphical representation of ribosome density around the Translation Start Site (TSS) for multiple read lengths.
This option allows to perform analysis for a single read length or on the merge of several read lengths. It enables to group all lengths sharing a same and unique offset value. The R/Bioconductor implementation provides an easy-to-use comprehensive set of functions that requires a minimal knowledge in R programming. The package contains a function entitled 'riboSeqFromBAM' that treats multiple Ribo-seq BAM files in parallel. The automated workflow generates report summary graphs and data quantification. We illustrate the main features of the package using a Ribo-seq control sample from murine ES cells (GSM1655059), taken from a recently published ribosome profiling study using translation inhibitors  (Popa et al. , 2016). We then detail the analysis of ribosome accumulation on certain codons and tri-peptide motifs on a public dataset in Escherichia coli (GSE64488)  (Woolstenhulme et al.
Requirements The 'RiboProfiling' package, v. 1. 2. 0 can be used with R 3. 3. 0 and Bioconductor version 3. 3. The script needs a minimum memory limit in R of 3 Gb when analyzing tripeptide motifs. The package starts from alignment BAM files, from either Ribo-seq or RNA-seq experiments. We have validated BAM files from bowtie/ tophat, Hisat2, STAR, and Lifescope (Solid), both single-or paired-end (for RNA-seq reads). Reads from rRNA, tRNA, and PCR duplicates (if unique molecular identifiers are available) should be removed from the BAM files before starting the analysis (see package vignette for details).
RiboProfiling package All analyses can be performed either through a call to a function called 'riboSeqFromBAM', or through a step by step approach. Figure  1  describes the workflow of the package starting from BAM files with reads mapped to the genome of interest. The first step in processing Ribo-seq reads is to select only those with match lengths compatible with standard ribosome footprints. The function 'histMatchLength' allows the visual inspection of read match sizes distribution across the BAM file (Figure  2. A), which should be enriched in reads of sizes between 20 to 40 nucleotides  (Ingolia et al. , 2009; Popa et al. , 2016). A second particularity in the handling of Ribo-seq data comes from the shift existing between the extremities of the read (i. e. 5' or 3') and the P-site position of the ribosome.
Analysis of ribosome stalling on sequence motifs 'RiboProfiling' can also be useful for analyzing ribosome occupancy on multi-codons motifs. Codons accumulating ribosome footprints are indicative of slowed ribosome progression (stalling) during the translation elongation process. The 'RiboProfiling' package offers several features for quantifying footprint accumulation on sequence motifs (ranging from one to three consecutive codons), performs principal component analyses, and allows graphical representation of those data. To illustrate how 'RiboProfiling' can be used to explore the influence of sequence motifs (in this case tri-amino-acid sequences) on ribosome pausing, we analyzed an Escherichia coli Ribo-seq dataset  (Woolstenhulme et al. , 2015). We downloaded, filtered and mapped the reads of an efp-knockout sample (ΔEfp2, GSE64488), the elongation factor EFP being essential for the translation of polyproline motifs.
�ummary Our 'RiboProfiling' Bioconductor package offers a collection of tools for Ribo-seq data analysis. It provides an unique, straightforward R implementation of a ribosome profiling pipeline from BAM, to P-site calibration, quantification of reads on sequence features, and codon coverage. The packages' graphical features offer quality assessment and result representation across the analyses. Following the overview of Ribo-seq experiments with 'RiboProfiling, the output tables can then be easily integrated into more specialized dowstream analyses, either using more specialisez riboseq tools such as (XXX, YYY) or directly within R. We highlighted here the features of our package in characterizing ribosome stalling at sequence motifs along ORFs based on an example dataset from Woolstenhulme et al.
(Woolstenhulme et al. , 2015). The workflow we propose for the analysis of ribosome occupancy on codon motifs using the 'RiboProfiling' package will most surely prove an useful asset in the context of recent ribosome profiling applications such as the detection of tumor sensitivity to differential amino acid depletion  (Loayza-Puch et al. , 2016). I am not proficient with existing pipelines, and did not verify if Riboprofiling is more complete than them. However, I found that an R implementation is nice since most users will be familiar with its installation, organisation and mode of use and won't have to find other tools elsewhere. I have a few remarks on the installation and on the vignette example: I had to install the Bioconductor Rsamtools which was not installed alongside Riboprofiling, while other dependencies were installed.
8. (Figure  1 ). While determining the read lengths in a ribo-seq dataset is a useful assessment, it is only one determinant of the quality of ribo-seq data. The RUST tool  (O'Connor 2015 )  as mentioned by the et al. authors carries out normalisation of the data, but also allows for the quality of the ribo-seq and mRNA-seq data to be determined in terms of sequencing biases. Nevertheless, RiboProfiling adds to the repertoire of publicly available ribo-seq tools which is useful to the community.
Questions: In the manuscript the abbreviation TSS is used for Translation Start Site. However, on page 6 of the RiboProfiling manual "aroundPromoter: returns the genomic positions flanking the transcript (TSS) for the x% (3% default value) best expressed CDSs". Should this be Translation start site start site? It is not clear what the authors mean by tools such as (XXX, YYY) ' in the following sentence in the Summary section 'Following the overview of Ribo-seq experiments with 'RiboProfiling, the output tables can then be easily integrated into more specialized dowstream analyses, either using more specialisez riboseq tools such as (XXX, YYY) or directly within R. ' (There are also some typo errors in this sentence (dowstream; specialisez). While following the instructions in the RiboProfiling manual, there were several operations for which I was missing the required library (e.
g. library(Rsamtools), library(GenomicAlignments), library(TxDb. Hsapiens. UCSC. hg19. knownGene), etc). While it might be obvious which additional libraries are required after running the command, it may help users if they were specified in the manual along with library(RiboProfiling). I ran the 'quick start' riboSeqFromBAM function on one of our own datasets and four plots were generated fine. However, the 'list of per ORF per codon coverage' was not output. Should this have been generated as stated in the manual? What read lengths are considered in the RiboProfiling 'quick command' approach for the offset determination? On our own dataset the sumUp offset plot was generated showing an offset of -13 (which corresponds to our own estimation). However, this plot did not show the individual read lengths.
Are read lengths sharing the same offset value used? All of the step-by-step commands in the user manual ran fine on our own dataset and the corresponding plots were generated. However, there was an error when I tried to run the accompanying scriptWoolstenhulme_Defp2. R to generate the barplot in Figure  4B  of the average ribosome occupancy on PP(X) motifs in the E. coli dataset (see further). So unfortunately I was unable to verify this part of the manuscript. According to the manual, "consecutive motifs of 9 nucleotides (3 consecutive codons) overlap on 6 nucleotides. The Ribo-seq coverage is reported as the coverage on the 2nd codon in the motif considered as being in the P-site. " Is the average pause score in Figure  4B  for ribosome occupancy of the second codon (P) or the last codon (X) or across the three codons? The scriptWoolstenhulme_Defp2.
R is hard-coded for the PPX motif and E. coli. I did not get to check if it can be easily adapted for other codon motifs in other datasets. Below I provide a sub-set output of the scriptWoolstenhulme_Defp2. R with the error. If the sessionInfo() 2 F1000Research data and this package generates a great summary of these. However this package is not as complete as suggested by the authors. Indeed an important point is the periodicity of footprints that is not addressed here. Moreover it could be interesting to add the ability to detect which reading phase is indeed read. This should be feasible since the P-site is well defined. I include below a few comments for the authors. The algorithm of the readStartCov function is not sufficiently described. The only one sentence found, does not give enough information to understand how it works.
For instance, "the read frequency distribution centered on the translation start site": "Centered" needs more explanation. This is important, because using our own datasets we were not able to obtain any convincing results about the position of the P-site. The lack of information prevented us from understanding why. In the Package Manual, when the aroundPromoter function is defined, the TSS is described as the "Transcript Start Site", which does not match with the "Translational Start Site" found in the article. The pipeline requires an annotation file with a specific format (TxDb object). Users have to be aware of that if they want to use their own genome. We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
a partner of the National Grant information:  Infrastructure France Génomique  ( ANR-10-INBS-09-03  and  ANR-10-INBS-09-02 ) and PB's group, thanks to supports by the  Cancéropôle PACA and Commissariat aux Grands Investissements. RW was supported by  Fondation ARC pour la recherche sur le cancer  ( SFI20121205973 ), and  PB by ANR  ( ANR-12-BSVI-0023-02 ),  Fondation pour la Recherche Médicale  ( DEQ20130326464 ) and labex  Signalife  ( ANR-11-LABX-0028-01 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
�oftware availability The package is built in R (>=3. 3. 0) and freely available from Bioconductor website 1. https://www. bioconductor. org/packages/release/bioc/ html/RiboProfiling. html 2. We provide an associated-script to the analyses in this paper at http://genomique. info/data/public/Ribo Profiling/scriptWoolstenhulme_Defp2. R Zenodo: scriptWoolstenhulme_Defp2. R, doi: 10. 5281/ zenodo. 54567, (Popa A, 2016)  3. GPL-3 licence.
Specifically, we will discuss the direct experience we had within the Space4Agri project whose aim was the improvement of the agronomic sector in Northern Italy  [9]. Space4Agri exploits multisource information, among which remote sensing data products and VGI on crops observations created through the use of a smart app by volunteers (farmers, agronomists and citizens). The application supports the volunteer in selecting the correct crop type and its phenological stage that is related to its crop development, by a specific agronomic ontology (named Biologische Bundesanstalt, Bundessortenamt and CHemical industry scale, for short BBCH scale)  [10, 11]. Nevertheless, even with the help of this ontology, volunteers were faced with many uncertainties in identifying both the correct crops and their actual stages.
• Missing information on the created VGI on the consumer side, such as the lack of appropriate metadata describing the VGI creation conditions, lack of information on the imprecision of the geo-localization of VGI, etc. From the above, it is clear that the problem of VGI uncertainty and imprecision is strictly related to the representation of both VGI semantics and context of its creation. To enable semantic interoperability of VGI applications, Bakillah et al. [16]  proposed a VGI semantic data model that defines the characteristics of VGI from distinct applications, in terms of the possible types of data that might be requested to volunteers. The VGI data model is intended for ex-post semantic integration of VGI, when an application already exists, by providing the conceptual framework for the generation of common descriptions of the heterogeneous VGI data sets.
Creating and Managing Contextualized VGI within Space4Agri Project The Space4Agri (S4A) project defined and developed a Spatial Data Infrastructure (SDI) integrating multisource heterogeneous data from space, i. e. , satellite images and Unmanned Aerial Vehicle (UAV) photographs, authoritative databases from Lombardy Agricultural Authority and cadastral data, and in situ data on crop development and agro-practices created by volunteer agronomists, farmers and citizens to support sustainable and precision agriculture in Lombardy region, Northern Italy  [9]. A smart application, the S4A smart app, has been designed and implemented to create contextualized VGI by having as cornerstones the following design concepts: • the possibility to support data normalization and semantic interoperability by providing a domain ontology to the author in order to ease both the creation of observations and the interpretation of contents by potential consumers; • the reduction and, when possible, resolution of the geometric imprecision of the VGI geofootprint; and • the sharing of VGI by standard Web services recommended by the Open Geospatial Consortium (OGC) so as to enable its discovery and access with filtering options.
It can be seen that each code uniquely identifies a picture and an associated textual description. For inexperienced users, it may be questionable to tell apart the differences between some close codes by observing the pictures or by reading their textual descriptions which contain vague terms such as "30: beginning of stem elongation. " and "31: first node at least one cm. ". The S4A smart app stores a compact version of the same ontology on the mobile device. The volunteer then browses the two level menu and selects the BBCH codes whose description best matches the current observation (see Figure  3 ). The created VGI items are automatically geo-referenced by associating them with the geographic coordinates of the current position determined by the GPS sensor of the mobile device. In order to The created VGI items are automatically geo-referenced by associating them with the geographic coordinates of the current position determined by the GPS sensor of the mobile device.
VGI Management The S4A smart app communicates via wireless or mobile network with a back-end application installed and running on a Web server where a geodatabase stores the VGI items. The back-end application can send/receive JavaScript Object Notation (JSON) messages to/from the S4A smart app, and can read/write into the geodatabase. In this phase, the reduction and resolution of the imprecision of the VGI geofootprints is performed by applying a knowledge-based conflation. When receiving a new VGI item, the back-end application analyzes its geofootprint and possibly conflates it with those of other VGI items already stored in the geodatabase, by exploiting the "conflation data layer" (when provided). This represents the known geographic entities of interest for the current project which have to be considered as the targets of volunteers observations.