dataesr/scientific-paragraphs-categorization huggingface dataset:
SECCHI'S CRUISE ABOARD THE STEAM CORVETTE IMMACOLATA CONCEZIONE Cruise Description, Materials, Methods, and Data Alessandro Cialdi (1807-1882), commander of the Papal Navy, was an engineer by training and a scientist. Influenced by the French physicist and astronomer François Arago (1786-1853), Cialdi wanted to study the correlation among transparency, waves, and currents. In April 1865, Cialdi invited Secchi to join 1 An old English translation of this book seems to have been made decades ago by the US Department of the Navy, but it cannot be found online. 2 "[…] commander Cialdi ordered to make several disks of various diameters and colors, that had to be immersed in the sea until their sight were lost". […] il sig. commend. Cialdi fece preparare varii dischi di diametro e di colore differenti che doveano immergersi nel mare fino a perdersi di veduta.
Secchi designed fire protection systems and made suggestions for the reconstruction of buildings that had been affected by earthquakes. He also studied Rome's climate and its influence on the flow of the Tiber River, carefully examining data on rainfall, wind, and temperature in the areas of the Tiber basin. Secchi was a brilliant communicator. His documents are written in a rich and pedagogical style. He did not hesitate to give public lectures and disseminate science among the less educated social classes. Secchi greatly encouraged amateur astronomers and promoted astronomy education. He was held in high esteem internationally, witnessed by the numerous treatises published and translated into several languages, the cordial welcome he received during his travels abroad, and the many international prizes he was awarded.
"Disagreement, if at all, was of a fraction of a meter. " 8 Any differences were derived from causes external to the water quality. 9 ecchi's data from this cruise were not georeferenced, which explains their absence in historical databases (NOAA,  2013). However, this lack of location accuracy can be remedied by using verbal descriptions of the position (e. g. , "6 miles south of Cape Linaro") and bottom depth, which are available in Secchi's cruise report. The combined use of these verbal descriptions with a modern geomatic tool such as Google Earth permits retrieval of latitude and longitude. The station map in Figure  2  shows an offshore transect, away from any major river mouth, in deep, clear waters, despite the short dis-tance to land. Secchi's georeferenced data set is comprehensively compiled in a table in the online supplementary materials.
SECCHI'S FINDINGS The Secchi disk depth (z SD ) depends on (1) water quality (its inherent optical properties), and (2) an array of factors such as the disk's characteristics (diameter, materials), sea state, cloud cover, shading, and illumination. While most researchers interested in marine and lake ecology have only paid attention to water quality, Secchi focused all Oceanography | Vol. 33, No. 3 his attention on the array of factors that could affect depth readings. This section is organized similar to Secchi's (1865) cruise report. When needed, data from other authors are added for comparative analysis. The Disk's Influence on Perceived Color Secchi reported that a white disk in water acquires a color that is different from that of the surrounding water, and this color varies as the disk sinks.
Causes for this divergent fate may be the strong influence of Forel among limnologists, 39 Willi Ule's article in 1897 showed that, during those years, Secchi's work was known within the German-speaking community: "The visibility was estimated with the aid of Secchi's disk. " (Ule, 1897)  and the characteristics of their field, more prone to the use of simpler and lighter instrumentation than oceanography. The science and the methodology of the Secchi disk, on the other hand, did not take off, and the Secchi disk was left as a semi-quantitative method, without any physical characterization, acquisition protocol, and data correction. Physical studies were performed many decades later (Duntley, 1952, reissued  1960; Preisendorfer, 1986), but they were largely ignored by the community of Secchi disk practitioners.
Fortunately, recent contributions to the physics of the Secchi disk (Bowers et al. , 2020; Jiang  et al. , 2019; Lee et al. , 2015, 2018) are reaffirming the method, and publications are booming again, especially from the Chinese community (He et al. , 2017;  Zhang et al. , 2018; Zhou et al. , 2019), and profiting from the retrieval algorithms for satellite data (Feng et al. , 2019;  Liu et al. , 2020). The Secchi disk depth is the optical property that allows the construction of the longest time series that can bridge the current status of the ocean and lakes to that more than a century ago. Connecting Secchi disk science to modern optical oceanography must advance on various fronts. A visibility theory of the Secchi disk that is derived from first electromagnetic principles will warrant the broadest range of applicability and will be much preferred to the simpler, and seriously limited, regression models.
In addition, such theory will naturally account for dependence on the disk's properties, sea state, illumination, observation conditions, and optical properties of the water. With certain assumptions, a simplified formula will be derived from it in order to permit retrieval of optical properties from Secchi disk depth measurements. A starting point could be a recent horizontal atmospheric visibility theory (Lee  and Shang, 2016), whose adaptation to the Secchi disk seems feasible. Achieving this task would also warrant comparability of in situ data to satellite estimations, the latter seen by many as the future of the Secchi disk.
ACKNOWLEDGMENTS I thank  Oceanography Editor Ellen Kappel  and  Associate Editor Grace Chang  for their guidance and feedback. Reviews and support by  Zhongping Lee  and  David Antoine  were greatly appreciated. Comrade Falcini  is thanked for feedback on an early manuscript draft. Translation of the memorial plaque in Figure B-1 from Latin was made by  Leopoldo Gamberale  of the  Sapienza University at Rome. Oksana Viktorovna Knyazeva , of the  North Caucasus Federal University at Stavropol , assisted with text in the Russian language. The  Copernicus Climate Change Service  ( C3S_511 ) has been financially supportive.
This fragmentation mode allows to maximize the MS 2 sampling for optimal analytical coverage of the peptide mixture  [31] , but also permits to preserve the characteristic hexose neutral loss ions signing the peptide glycosylation  [12, 32, 33]. The datasets of fragmentation spectra obtained for each of the 17 bands were individually processed for the data mining of the annotated Mtb TubercuList reference database (release 27; 4031 entries). The resulting search outputs were merged for protein validation using the in-house developed MFPaQ software  [34]. Given as an example, the analyses of the 17 SDS-PAGE gel slices of the Mtb WT culture filtrate (CF) fractionation resulted in the recording of 244,488 fragmentation spectra that were assigned to 13,602 unique peptide sequences mapping 1232 proteins identified with a protein false discovery rate of 1% (FDR 1%; p > 0.
004) (Table  S1 , Figure  S1 ). This protein repertoire covers about 31% of the total annotated gene-coded proteome and ~40% of the expressed proteome  [35]  amenable to discovery using MS-based proteomics  [36]. Most of the proteins detected herein (n = 1007; >81%) were previously reported by Albrethsen et al. (1362 proteins,  [37] ) from label-free 2D-DIGE proteomic analyses of the Mtb extracellular proteome (Table  S1 ). The results obtained from these independent studies reveal a remarkable consistency of the Mtb culture filtrate proteome composed of a thousand proteins. It is noteworthy that only a limited number (95/1007; <10%;) of the proteins identified are actually predicted to be secreted and hence susceptible to correspond to secreted mannoproteins  [38]. Similar analyses of the secreted proteome of the Mtb PMTub mutant (∆Rv1002c) led to very comparable results with the identification of 1187 proteins (FDR < 1%) present in the culture filtrate (Table  S2 ) including 935 (78%) proteins shared with the dataset of Albrethsen et al  [37].
Improvement of MS 2 Data Processing for Mtb Glycoproteins Discovery However, about 98. 5% of the 7779 MS 2 spectra attributed to glycosylated peptides remained speculative requiring further endorsement (Figure  S1 ). This considerable number of spectra to process makes such a task hardly manageable manually. To overcome this hurdle, we set up an automated procedure to search for fragmentation spectra exhibiting the characteristic signature of glycosylation that could strengthen the reliability of the putative assignments for new glycopeptides discovery. Indeed, CID fragmentation spectra of glycoconjugates bearing terminal hexoses are characterized by the presence of intense Y-type fragment ions resulting from the loss of the sugar ring(s) as neutral fragments  [12, 32, 41]. Most of the CID-MS 2 -data search engine algorithms can be parametrized to take advantage of this property for the recognition of glycopeptide fragmentation spectra.
Assessment of the PMTub Associated Mannoprotein Repertoire To verify whether the newly identified glycopeptides correspond to the PMTub associated mannoprotein repertoire we analyzed in parallel a CF protein extract from the Mtb ∆rv1002c mutant invalidated for the protein O-mannosyl transferase gene using the same multi-parametric selection workflow. In these conditions, we found 138 spectra assigned to putative glycosylated peptides that exhibit the neutral loss signature (Table  S3 ). Targeted search on the full MS 2 dataset failed to detect any fragmentation spectra fitting with known glycopeptides. Moreover, none of these spectra corresponded to any of the expected glycopeptides of the newly Mtb mannosylated proteins identified above. Finally, after the exclusion of the MS 2 spectra corresponding to unique sequencing events (123/138), only 15 fragmentation spectra attributed to 4 putative glycopeptides remained.
Maisch; Proxeon Biosystems, Odense, Denmark). Peptides were eluted using a 5%-50% gradient of solvent B during 80 min at 300 nL/min flow rate. The LTQ-Orbitrap was operated in data dependent acquisition mode with the XCalibur software. Survey scan MS were acquired in the Orbitrap on the 300-2000 m/z range with the resolution set to a value of 60,000. The twenty most intense ions per survey scan were selected for collision induced dissociation CID fragmentation and the resulting fragments were analyzed in the linear ion trap (LTQ). The normalized collision energy was set to 35% and activation times to 10 ms and 150 ms. Dynamic exclusion was employed within 30 s to limit repetitive selection of the same peptide. MS data are available via ProteomeXchange with identifier PXD012018 and the open source "Neutral-Loss finder" software is downloadable from https://github.
com/david-bouyssie/neutral-loss-finder/. Post-analytical bioinformatics data processing Database search and data validation-The Mascot Daemon software (version 2. 6, Matrix Science, London) was used to perform database searches, using the Extract_msn. exe macro provided with Xcalibur (version 2. 0 SR2, Thermo Fisher Scientific) to generate peak lists. The following parameters were set for creation of the peak lists: parent ions in the mass range 400-4500, no grouping of MS/MS scans, threshold at 1000. A peak list was created for each analyzed fraction (i. e. , gel slice) and individual Mascot (version 2. 6) searches were performed for each fraction. Data were searched against the TubercuList reference database (release 27; 4031 entries). The following list of variable modifications was used: carbamidomethylation of cysteines, propionamidation of cysteines, oxidation of methionine, mono-glycosylation (Hexose) of serine/threonine and di-glycosylation (2 Hexoses) of serine/threonine.
Searches were performed using semi-tryptic digestion mode, and the specificity of trypsin was set for cleavage after K or R with two missed trypsin cleavage sites allowed. The mass tolerances in MS and MS/MS were set to 10 ppm and 0. 6 Da, respectively, and the instrument setting was specified as "ESI-TRAP". In order to calculate the False Discovery Rate (FDR), the search was performed using the "decoy" option in Mascot. Protein groups were validated based on this Mascot MudPIT score to obtain a FDR of 1% at the protein level: FDR = number of validated decoy hits / (number of validated target hits + number of validated decoy hits) x 100. The mass spectrometry proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE  [1]  partner repository with the dataset identifier PXD012018 Filtering of glycosylated MS/MS spectra-To isolate MS/MS spectra resulting from the fragmentation of glycosylated peptides, peak lists (i.
e. , MGF files) were filtered using a dedicated algorithm searching for consecutive neutral losses of hexoses. The script written in the Perl language is executed on each MGF file. The algorithm starts by filtering the top N peaks (N = 30 by default, and minimum relative intensity >10%) of a given MS/MS spectrum. Then, knowing the precursor ion m/z value and its charge (z), it computes the expected m/z value corresponding to the neutral loss of a given hexose: NLpeak = precursor_mz-(hexose_mass/z). A neutral loss is considered as being detected if the difference between the observed m/z value of the considered peak and the expected m/z value for this neutral loss is lower than a given tolerance (500 ppm in this study). If a peak is matched in the Top N list then the algorithm searches iteratively other consecutive neutral losses of the same hexose until no further loss is observed or until the maximum number of losses is reached (8 in this study).
Finally, information about the putative glycosylated peptides are reported in a tabulated text file containing the precursor m/z and charge state, the spectrum title and the list of detected neutral losses. The neutral-loss finding software can be downloaded from https://github. com/david-bouyssie/neutral-loss-finder/. Infections of macrophages-MH-S cell line macrophage infections were performed as previously described  [4]. Briefly, macrophages in RPMI 1640 were infected with either the H37Rv wild-type strain or the rv1002c complemented ∆rv1002c mutant at a multiplicity of infection (MOI) of 10. Infection was allowed to proceed at 37 • C for 1 h, and the extracellular bacteria were removed by 3 successive washes with fresh medium. At the end of the infection period, cells were harvested for RNA extraction at 0, 5, 8 and 24 h post-infection.
Funding: This research was funded by the  Agence Nationale de la Recherche  (Grant  11BSVB6016  to M. R. & O. B. -S. ), the  Fondation pour la Recherche Médicale (Equipes FRM   DEQ20180339208  to JN), the  FEDER (Fonds Européens de Développement Régional) , the  Région Occitanie ,  Toulouse Métropole  and the  French Ministry of Research  with the  Investissement d'Avenir Infrastructures Nationales en Biologie et Santé program  ( ProFI, Proteomics French Infrastructure  project,  ANR-10-INBS-08 ) (to OBS). L. T. was supported by the  Center National de la Recherche Scientifique  and the  Région Midi-Pyrénées , B. S. was supported by the  ANR  grant  11BSVB6016 ). Acknowledgments: We thank  Mylène Camus  for the label-free quantification and its graphical help.
Data quality requirements (DQR) 3. 1. 2. 1. Age of data. Some metadata of datasets, which should be defined in the  DQR (ISO14044, 2006; Joint Research Center, 2010) , informs on their age and minimum length of time for data collection. Potential temporal discrepancies between used datasets and the targeted temporal scope of a modelled system can thus be partially evaluated. Such information also provides some insights on the temporal scope of a system model when it represents human activities  (Bessou et al. , 2013; Yuan et al. , 2015). For example, the description of solar energy List of proposed terms defining key temporal considerations in the LCA framework. The list is in alphabetical order so all terms from this glossary are underlined to highlight the links. Words in brackets are synonyms from the literature.
Period of validity The period over which datasets, LCIs or LCIA methods are considered valid representations. This information should be calendar-based. [Time context (ILCD), time frame, range of time, period of time, time period, timespan, temporal boundary, time scale and time horizon] Prospective modelling A prospective LCA addresses future life cycle impacts using different modelling strategies (e. g. scenario-based, technology development curves and agent-or activity-based models). The evolution of systems is thus defined and/or simulated using a list of explicit assumptions regarding the future. Prospective modelling can be applied to both the technosphere and ecosphere and is a subset of the dynamic of systems, which only concerns future forecasts.
3. 1. 2. 2. Technology coverage. In some cases, the definition of technology coverage in the DQR of datasets can inform on the actual temporal scope of the study  (ISO14040, 2006; ISO14044, 2006; Joint Research Center, 2010)  with the ensuing qualitative assessment of temporal representativeness. For example, ecoinvent  (Wernet et al. , 2016)  uses five levels of technology (i. e. new, modern, current, old and outdated) to describe transforming activities. Using datasets with new or modern technology levels should therefore be relevant for LCA studies on future products. However, this information is relative to each sector, as the modern level could be representative for 10 years of technology evolution in an established sector, whereas fast-paced sectors like electronics may use modern technologies for only 1 year before switching to new options.
3. 1. 2. 3. Source of data. The choice of data sources and the qualitative assessment of their overall representativeness provide an indirect assessment of the temporal scope for modelled systems and LCA studies  (Rebitzer et al. , 2004). For example, when data are sourced from scientific journals, date of publication is the primary indication for its period of validity. More precise temporal information is also often provided in case studies for systems with longer lifetimes or in DLCA studies like  (Heeren et al. , 2013; Pahri et al. , 2015; Sohn et al. , 2017a; Vuarnoz et al. , 2018). The use of up-to-date LCA databases can bring a false sense of security on the temporal scope and representativeness of the data for recent products or systems. Indeed, database updates do not always follow the changes in market shares or evolution of technology because of the lack of new data.
Nevertheless, different temporal metadata is given for most datasets. For instance, ecoinvent guidelines  (Wernet et al. , 2016)  require the definition of the date of generation, the date of review and the period of validity with a start date and end date for any dataset. These temporal considerations fulfil most of the requirements from  ISO 14044 (2006)  except for the definition of the averaging period of dataset inputs. The ILCD handbook (2010) has set further requirements defining temporal properties: the expiring year of datasets and the duration of the life cycle, which respectively relates to the period of validity for LCI datasets and the temporal scope of elementary flows for a dataset. This metadata is available in most datasets of the ELCD  (Recchioni et al. , 2013). Many of these temporal metadata are more relevant to assess the temporal scopes of studies than the choice of a database and its version, but the place (e.
g. in dataset descriptions) and the different definition under which they can be found hinder their use in most LCA studies. 3. 1. 2. 4. Uncertainty description. The description of the uncertainty associated with flows (e. g. in ecoinvent  (Wernet et al. , 2016) ) is another indirect source of information to clarify the temporal scope and period of validity. Indeed, the temporal correlation indicator provides a quantitative assessment of the discrepancy between the time when the data was acquired and the intended temporal scope for the dataset  (Weidema et al. , 2012). For example, a product flow with a temporal correlation indicator of 3 means that its value has been gathered between 6 and 9 years before or after the targeted temporal scope of the dataset. With the current definition of the temporal correlation indicator, the precision of this temporal information is rather low (i.
A way to address this limitation is to use a reference "time 0" in the temporal distribution as a period of occurrence relating to a starting period of a process  (Beloin-Saint-Pierre et al. , 2014; Tiruta-Barna et al. , 2016). This "time mark" creates process-relative descriptions, which can be reused in any period of a life cycle or even for different life cycles. Tiruta-Barna et al. (2016)  and  Pigné et al. (2020)  provided process-relative temporal distribution archetypes for ecoinvent v3. 2, applicable to foreground and background datasets. As underlined by Beloin-Saint-Pierre et al. (  2014 ), the additional efforts needed to provide temporal information for all the flows of LCA databases are still significant and the prioritisation of data-gathering remains important.
Modelling evolutions with process differentiation The basic strategy to describe evolution is to differentiate processes when a system is considered to change substantially over time. The key challenge here is to identifying when changes are significant enough without expert opinion on the modelled product. A simple application can be performed, if calendar-based periods of validity are consistently provided for all datasets in LCA databases; they could then be changed automatically when they are no longer valid representations over the full life cycle of any system. Such metadata is, however, required only in the (discontinued) ELCD database (see subsection 0) and, currently cannot be easily integrated in LCA software. Collet et al. (2011)  proposed an approach to tackle this problem and identify where temporal differentiation of processes during system modelling is needed.
Their general idea is to recognise when the combined emission and impact dynamics justify the additional effort for temporal differentiation. Moreover, the selective introduction of the time dimension in background processes has been studied by  Pinsonnault et al. (2014)  and more recently by  Pigné et al. (2020). The authors have shown that the temporal variations of a selection of background processes and the entire ecoinvent database can significantly affect climate change impacts for processes in some sectors (e. g. transport and building).
Phase of inventory analysis: LCI computation The computation of LCI transforms the information of a technosphere model into a set of elementary flows whose quantities are in relation to the FU of the assessed systems. The computation traditionally aggregates all flows of the same type over the entire life cycle. 3. 3. 1. Computational framework 3. 3. 1. 1. Matrix-based computation with process differentiation. The conventional matrix-based computational approach can be used to calculate DLCIs, but with larger technosphere and ecosphere matrixes  (Heijungs and Suh, 2002). Collinge et al. (2012 Collinge et al. ( , 2013b) )  used this approach on foreground processes to calculate the DLCI for each year of a building's life cycle. They concluded, similarly to  Heijungs and Suh (2002) , that the implementation brings significant challenges in data management when background databases are used.