dataesr/scientific-paragraphs-categorization huggingface dataset:
Framework Definition To structure the information for the construction of the knowledge base, an analysis framework is defined based on three axes: design stages, actors involved, and organizational context. These axes of analysis are selected considering that the focus of the research corresponds to the design process of chemical products, which is represented through the design stages, and the organizational context, which is represented with the involved actors and the business strategy of the company  [13, 22]. In a possible future expansion of the database, it is possible to include more dimensions, i. e. , innovative capacity, long-term goals of the company, among others. Based on the analysis of the information from experts, it was found that the different design processes have similarities, but also different elements, i.
Extraction To extract the implicit knowledge of the experts in terms of chemical product design, a 3-step process was followed: 1. The transcription of the interviews: to create a written record of the conversation; 2. Manually coding the data: to identify and tag significant topics or patterns; and 3. Analyze the data: to interpret the data and draw conclusions  [44]. Following that process, recommendations or design statements expressed by the experts were extracted from the collected information. The focus was the information considered implicit (based on their experience and context) and at the same time applicable to different design processes as recommendations for decision-making, considering the organizational dimension. The specific criteria used for the extraction of statements were:.
The use of the knowledge base can be useful for problem-solving in organizations since it allows the managers to access formalized and categorized information; the statements in this case will act as recommendations or considerations to be considered to act and intervene in design processes. The knowledge base is susceptible to being fed with fresh information from new actors and experts until it becomes a robust system. Finally, this knowledge database could be valorized through the development of a design product management recommendation tool. To facilitate the selection of the best recommendations, it is possible to apply multi-criteria analysis methods to select the most relevant recommendations (for example with ELECTRE method  [46] ) and rank them according to the criteria of the design team (for example with analytic hierarchy process  [47] ).
Its thickness is mainly correlated to age and force distribution along the tooth and seems to act as a compensatory mechanism against mechanical loading  (Nudel et al. , 2021). Currently, this tissue is removed prior to incremental dentin analysis to avoid intrusive adulthood data in early-life dietary reconstruction  (Scharlotta et al. , 2018). Since M1 is the first permanent tooth to develop, it accounts for the longest bio-physiological information when considering both primary and secondary dentin. A life-long dietary analysis is developed on an exceptional archeological case study from a Longobard Italian skeletal collection (Castel Trosino, 6th-8th c. CE, in Ascoli Piceno, central Italy)  (Micarelli et al. , 2021). The individual (CT1953), an elderly woman represented only by her cranium, was subjected to an unprecedented case of trepanation, discussed in a companion paper  (Micarelli et al.
The highest δ 13 C value (À16. 3‰) (Data S1) recorded at approximately 5. 5 years might be indicative of C 4 plants consumption, possibly millet and/or sorghum, which are crops known for their tolerance to drought and poor soils. The spread of these plants has been associated with the Longobard arrival in northern Italy  (Iacumin et al. , 2014). This low-quality crop is not suitable for breadmaking, but in the Early Middle Ages, it was ideal for soup and porridge-like meals  (Guglielmetti, 2014; Iacumin et al. , 2014). The consumption of C 4based gruels for CT1953 may be linked to her early-life displacement in an area where the reliance on C 4 crops had already taken place. It is noteworthy that δ 15 N values do not vary in this age-range (Figure  2a ); therefore, the peak in δ 13 C should be related to dietary change rather than nutritional stress, which would have caused a parallel increase in δ 15 N values (e.
, 2021)  (Data S1). It is worth noting that the trepanation, or the situation which led to it, could have strongly affected the woman's metabolism/physiology (e. g. ,  Giuffra & Fornaciari, 2017; Goude et al. , 2020). Whether dietary or physiological stress is responsible for the isotopic values recorded for secondary dentin, these were able to highlight short-term variation, a change otherwise invisible in bone collagen. Indeed, we suggest considering secondary dentin analysis in long-life dietary studies.
| LIMITATIONS OF THE STUDY The limit of this study relies on the difficulty of an accurate age assignment for secondary dentin sections due to its nonhomogeneous deposition driven by mechanical loading  (Nanci, 2013; Nudel et al. , 2021 ). An alternative sampling scheme is proposed in this paper, which can be applied when enough material is available. The lack of similar multi-isotopes analyses at the intra-population level (i. e. , on the other crania from Castel Trosino) and inter-population level (i. e. , Longobard contexts in Italy) limited the data interpretation as well. Complementary investigations of both bone and dental tissues from Italian Early Middle Age contexts are needed to evaluate whether the practices documented in CT1953 are related to a particular social status or reflect specific habits of the Italian Longobard community.
ACKNOWLEDGMENTS We thank  Dr. Rossella Bedini  and  Ing. Raffaella Pecci  of the  Istituto Superiore di Sanità in Rome (IT)  for acquiring the tooth Micro-CT scans. We are grateful to the two anonymous reviewers for the useful suggestions that improved this manuscript. This study does not involve any modern human or animal subject. Open Access Funding provided by  Universita degli Studi di Roma La Sapienza  within the  CRUI-CARE Agreement.
In megacities, the concentrations are usually large and a network of monitoring stations would even be necessary to document their spatial variability  (Wang & Christopher, 2003). However, because of their cost such networks are either not deployed in the megacities of the developing countries or they do not have the required density. When they exist, the measurement datasets are also often too short to analyse the long-term evolution of the surface concentrations. Hence, the importance of alternate methods is to estimate surface air quality in places where ground measurements are insufficient or lacking. Among these methods, the remote sensing of the atmosphere's composition is a promising tool. This explains that many studies have attempted to derive an estimate of the surface PM concentration from aerosol optical depth (AOD) measurements performed by satellite-borne instruments or, more rarely, ground-based sunphotometers  (Barladeanu et al.
In this case, their presence is detected by remote sensing techniques but not by the surface monitoring stations, which hinders the possibility of deriving PM from the AOD. In summary, the relationship between these two quantities must be evaluated for each climatic zone. In this work, we propose to perform such an evaluation primarily for Cairo (Egypt) for which the available dataset is the longest and also for Delhi (India). The fact that the first site has a dry climate and the second one a tropical one will help test the influence of the climate on our results. With its population of 24 million inhabitants, Cairo is located at the southern tip of the Nile Delta, has two desert areas on its west and east side (the Pyramids and the El-Mokattam plateaus, respectively), and is connected to the Nile River valley in the south.
Surface PM10 Mass Concentration The hourly surface mass concentration data of Cairo and Gual Pahari were obtained from the WMO-GAW World Data Centre for Aerosols (WDCA) via the database infrastructure developed and operated by the Norwegian Institute for Air Research (https://ebas. nilu. no/). These concentrations correspond to particles with aerodynamic diameters less than 10 lm (PM10). For Cairo, the measurements are those of the station located at the headquarters of the Egyptian Meteorological Authority (EMA) in El-Abbasiya (lat: 30. 08 N; long: 31. 29 E). The El-Abbasiya area is classified as an urban and residential location. At the station, the aerosol concentration is measured at 45 m above mean sea level with a beta-gauge sampler (Thermo Scientific SHARP Monitor, Model 5030, which combines light scattering photometry and beta-radiation attenuation).
The current study analyses the data from June 2010 to May 2015, which are those posted in the EBAS archive. In Gual Pahari (lat: 28. 43 N; long: 77. 15 E, 320 m amsl), the PM10 hourly surface concentration was measured with a Thermo Scientific beta-hybrid mass monitor from the end of 2008 to the beginning of 2010. The station, located about 25 km south of Delhi (in Gurgaon), was set up in the frame of the EUCAARI GAW-WDCA project  (Hyva ¨rinen et al. , 2010). The measurements were also downloaded from the aforementioned NILU website. In this study, we use only the 2009 data because they are the least lacunar. Finally, the daily means of the PM10 concentrations at the two sites were calculated from the hourly values.
For instance, the CE-318 model of Cairo_EMA_2 performs sun measurements in eight spectral bands: 340, 380, 440, 500, 670, 870, 940, and 1020 nm. The 940 nm wavelength is dedicated to the evaluation of the columnar content of the atmosphere in precipitable water vapour (PW) and the others to that of the AOD. At Gual Pahari, Aeronet measurements were taken in parallel with the surface PM concentrations from the end of 2008 to the beginning of 2010. In this study, we use the data of 2009 that are the most complete. The spectral dependence of this AOD can be quantified by the means of the Angstro ¨m exponent (AE), which provides useful information on the type of aerosol present in the atmospheric column  (El-Metwally et al. , 2008; Masmoudi et al. , 2015). Indeed, low AEs are associated with coarse (supermicron) dust-like particles, whereas large AEs correspond to finer (submicron) aerosols of anthropic origin  (El-Metwally et al.
Acknowledgements The authors are grateful to  STDF Egypt  for their support to project No  43088 , and the PIs ( Africa Barreto  and  Ashraf S. Zakey  for  Cairo_EMA_2 ;  Anti Arola  for  Gual Pahari ) of the AERONET sites and to the staff who maintain it. The authors are also grateful to the  Egyptian Meteorological Authority and Finnish Meteorological Institute  for providing the PM10 data through the WMO/GAW World Data Center of Aerosols hosted by  Norwegian Institute  for Air  Research-NILU  (http://ebas-data. nilu. no/). The authors are thankful to the  ECMWF  for providing ERA-5 data: (Fifth generation of ECMWF atmospheric reanalyses of the global climate) through  Copernicus Climate Change Service Climate Data Store   (CDS).
For detection, the FID temperature was set at 350 • C, and the FID detector was supplied with 350 mL/min of zero air and 50 mL/min of hydrogen, both gases being provided by two generators (airmoPure and Hydroxychrom from Chromatotec for zero air and hydrogen, respectively). Once the chromatogram acquisition was complete, the peaks were automatically integrated, and the data were saved for further data treatment. The repeatability of the GC-FID analytical method was evaluated, and the corresponding RSD (relative standard deviation) was determined. For this, the same standard PAH mixture solution was manually injected and analyzed three times on the same day. Peak resolution (R) was again determined using the tangent method.
Determination of the Chromatographic Resolution The chromatographic resolution (R) between two consecutive peaks was calculated according to the tangent method with Equation (1): R = t R1 -t R2 1 2 (W 1 +W 2 (1) where t R1 and t R2 are the retention times (min) of two consecutive peaks; and W 1 and W 2 are the tangent widths of the two consecutive peaks at the baseline. The software of both instruments provided the values for retention time for each peak, whereas the tangent widths were either provided by UHPLC software or calculated using Excel for the GC-FID. For each compound, the resolution was defined as the lowest resolution value between the resolution with its preceding peak and the resolution with its following peak.
ACY is not fluorescent and was easily detected by UV at 229 nm (Figure  S3 ). R= t R1 -t R2 1 2 (W 1 +W 2 ) (1) where tR1 and tR2 are the retention times (min) of two consecutive peaks; and W1 and W2 are the tangent widths of the two consecutive peaks at the baseline. The software of both instruments provided the values for retention time for each peak, whereas the tangent widths were either provided by UHPLC software or calculated using Excel for the GC-FID. For each compound, the resolution was defined as the lowest resolution value between the resolution with its preceding peak and the resolution with its following peak.
Acknowledgments: The authors thank the technical department of ICPEES and more particularly  Christophe Sutter  (Electronic engineer) and  Michel Wolf  (Assistant Mechanical Engineer) as well as all the administrative staff of  ICPEES  for their support. The authors would also like to thank the entire staff of the  MICA Carnot Institute  for the quality of the exchanges and the animation of this network of laboratories as well as the interdisciplinary thematic institute "Hierarchical and functional mate-rials for health, environment and energy" ( ITI HiFunMat ).
Acknowledgements The authors are thankful to the  NCAR Atmospheric Chemistry Division (ACD)  for the distribution of the  NCAR/ACD TUV: Tropospheric Ultraviolet & Visible Radiation Model  (http://cprm. acom. ucar. edu/Models/TUV/Interactive_TUV/)and the 410 availability of their quicktool. Financial support https://doi. org/10. 5194/gmd-2021-388Preprint. Discussion started: 30 November 2021 c Author(s)  2021. CC BY 4. 0 License. This research has been supported by the  Agence Nationale de la Recherche  ( PALEOx  project, grant no. ANR-16-CE31-0010 ). This work was granted access to the HPC resources of TGCC under the allocation A0090102212 made by  GENCI  ( Grand 415 Equipement National de Calcul Intensif).
The lockdown and control groups were comparable based on sex, smoking status and distance from air pollution monitoring stations; however, they had different average ages (34 versus 41. 9 years, respectively). Air pollution data were collected from the nearest monitoring station a month before EBC samples were collected. The study reported lower exposure to air pollution in the lockdown group than in controls. Higher ultrafine particle concentrations in EBC and lower ultrafine particle concentrations in serum were found in the lockdown group compared to controls. Konstantinos Dodos presented preliminary results on the differences between elderly COPD patients admitted onto a COVID-19-free respiratory ward before and during the pandemic in Greece  [27]. The study included 514 and 464 subjects admitted before and after the COVID-19 pandemic, respectively.
New evidence from longitudinal COPD cohorts and other key studies In this oral presentation session, a broad range of studies were presented covering the future COPD prevalence in Europe, impact of specific air pollutants, pharmacological treatment and clinical characteristics, and mortality rates in COPD. As emphasised earlier, there is mounting evidence for a role of air pollution in the development and worsening of chronic lung diseases. However, less is known about the specific pollutants, the impact of road traffic noise and the risk of incident COPD. Shuo Liu presented data from a large cohort of Danish nurses (n=28 731) which were linked to national health, air pollution and traffic noise databases  [30]. They specifically investigated the impact of PM The initiation and withdrawal of inhaled corticosteroids (ICS) in patients with COPD have been examined in several previous studies  [35, 36].
Methods Morphometric data. Our data set consists of lower jaw surface mesh reconstructions of 145 individuals belonging to 90 species, representing nine orders. These meshes are available as described in Kamminga et al. 102  and Dearden et al. 103. For subsequent analyses, we defined two data subsets: one comprising only shark species (n = 89); and a second subset based on species with available stomach content data (n = 75). We defined a total of six landmarks, 51 curve sliding landmarks and 53 surface landmarks, to describe the three-dimensional shape of the jaws. All the landmarks and curve semilandmarks were taken by the same person (F. A. L. -R. ) using the software Landmark Editor (Version 3. 0)  104. To capture the surface landmarks across all individuals, we first used a reference specimen as a template (Carcharhinus acronotus) onto which all the fixed, curve and surface landmarks coordinates are captured.
We used the template to place the surface landmarks on the rest of the individuals in a semiautomated process with the Morpho R package  105  (Supplementary Information Methods and Supplementary Fig. S1 ). Phylogenetic tree. For the phylogenetic relationships, we selected a topology reflecting resolved relationships at the order level based on the distribution of 1000 trees of the selected species, obtained from http://vertlife. org/sharktree/  106. From these trees, we generated a maximum credibility tree in TreeAnnotator version 1. 8. 2  107  and a subset of 100 trees to account for phylogenetic uncertainty. We used the subset of trees for further analyses with the geometric morphometric data (Available at https://github. com/Faviel-LR/Jaws_Sharks_Evolution). Trophic and ecology data. To categorise the stomach content among all the selected species, we gathered information from the literature.
We used mainly the data from Cortes 1 and complemented the information for several species not included in his study, as well as updating data for some of the species (Supplementary Data 1). With the prey categories defined by Cortes 1 , we used the proportions of prey to estimate diet dissimilarity between species with the Bray-Curtis index. Afterwards, we clustered the categories with a UPGMA to define feeding guilds (Supplementary Data 2). Since some of the species present only one diet category, we calculated the clustering dendrogram a second time, excluding species with monospecific diets. We performed the dissimilarity and clustering analyses using the vegdist function in the R vegan package  108  and the hclust function in the R stats package  109. With these results, we used the guilds as categorical variables to group the species as fish consumers (FISH; n = 27), cephalopod consumers (CEPH; n = 18), crustacean consumers (CR; n = 12), generalists (GEN; n = 7), larger prey consumers (e.
g. , marine mammals, other chondrichthyans), here referred to as big predators (BP; n = 6), molluscs consumers (excluding cephalopods) (MOLL; n = 3), invertebrate consumers (excluding molluscs, crustaceans and zooplankton) (INV; n = 2), and zooplankton consumers (ZOO; n = 1). We performed additional comparisons considering the trophic level value  110  , with values lower than 3. 8 as low-level predators (LP; n = 20), from 3. 81 to 4. 2 as mesopredators (MP; n = 41), and values greater than 4. 2 as top predators (TP; n = 29). We considered the ecological lifestyle to investigate possible associations of morphological variation and habitat. Accordingly, we follow the habitat categories defined by Dulvy et al. 111  , distinguishing reef, shelf, pelagic, and deep-sea distributions. In total, the species in our data set are reef (n = 28), shelf (n = 24), pelagic (n = 17) or deep-sea (n = 21) associated (see Supplementary Information Methods, Supplementary Data 3).
To investigate for shape differences between the group categories, we performed a phylogenetic multivariate analysis of variance type II (pMANOVA), using the averaged by species Procrustes aligned landmark coordinates. A pMACOVA of the shape and centroid size interaction suggests that size is significant but contributes little to the shape variation (r 2 = 0. 04085; F = 3. 705; Z = 2. 3226; p = 0. 027). Therefore, we did not correct for size. Likewise, to test for the effect of the categories on the shape data while accounting for size covariation (using the logarithm transformed centroid size), we performed a phylogenetic multivariate analysis of covariance type II (pMANCOVA). We fitted a multivariate phylogenetic linear model with Pagel's lambda using penalised likelihood with the mvgls function in the mvMorph package  116.
We estimated the significance of each of the generated models with the manova. gls function  117  using Wilks Λ as a test statistic and 1000 permutations to account for differences in sample size. We estimated the rates of morphological evolution for each category (order, diet content, trophic level, and habitat). First, we fitted the evolutionary model (equal rates, symmetric, all rates different) for each category with the 100 trees subsample using the fit_mk function in the castor package  118. We selected the model based on the distribution of the Akaike information criterion (AIC) and the log-likelihood from all the fitted models (Supplementary Table  S1 ). With the selected model for each category, we made a stochastic character mapping on the 100 trees subsample using the make. simmap function in the phytools package  119.
We used silhouettes from Ebert et al. 120. to illustrate trait evolution. Afterwards, we used the charactermapped trees and the aligned landmark coordinates averaged by species with the function mvgls in the package mvMorph under a Brownian Motion model  116. We extracted the model parameter estimates to obtain the evolutionary rates as described in Fabre et al. 121. Next, we compared the morphological disparity for each category with the aligned landmarks using the dispRity. per. group function, we used the sum of variances as metric and 100 bootstraps, to overcome differences in sample size. Likewise, we estimated the disparity through time with the dtt. dispRity function. For this, we used the trimmed tree with only shark species and their respective averaged aligned landmarks; the sum of variances was used as a metric.
Both analyses were performed with the dispRity package  122. To estimate differences in morphological disparity for each group subset (order, habitat, trophic level, diet composition), we used the Wilcoxon test with Bonferroni correction with the test. dispRity function. We applied a multivariate variable rates model in BayesTraits v 3. 0. 2 (http://www. evolution. rdg. ac. uk) to estimate the rates of morphological evolution under a Bayesian framework. We used as variables the principal components from the phylogenetically aligned PCA deemed important (first 4 PCs) based on a log-likelihood ratio  123  , as implemented in the package Morpho with the function getMeaningfulPCs. Next, we implemented in BayesTraits the reversible-jump Markov Chain Monte Carlo (rjMCMC) algorithm to analyse the rate shifts for continuous trait evolution.
We set five independent chains running for 200,000,000 iterations, with the first 25,000,000 discarded as burn-in and sampling was done every 20,000 iterations. Afterwards, we evaluated the chains' convergence by analysing the trace plots and the effective sample size (ESS > 200). To evaluate the chains' convergence, we used Gelman and Rubin's diagnostic in the coda package  124  (Supplementary Fig. S5 , Supplementary Tables  S2  and S3 ). With these results, we summarised the average branch-specific rate with the rjpp function in the BTRTools package (https://github. com/hferg/btrtools/tree/master/R) and plotted the rates on the maximum credibility tree with the plotBranchbyTrait function in phytools  119. Additionally, we evaluated whether evolutionary rates display shifts in distinct regimes (i.