dataesr/scientific-paragraphs-categorization huggingface dataset:
Synteny Analysis, Duplication Pattern, and Gene/Protein Structure of Dehydrins in Fabaceae We retrieved the physical location of the putative DHN genes in the chromosomes of the eight Fabaceae species from the GFF-formatted file in their respective databases. The MCScanX software  (Wang et al. , 2012)  was used to determine the syntenic relationship and duplication patterns between these species and Circa 1  to plot graphical representation of DHN genes location and their syntenic relationships. The intron/exon organization of dehydrin genes was extracted from the GFF-formatted file of each genome and submitted to the GSDS software 2  for graphical representation. We predicted the consensus sequence of the DHN motifs of the proteins using the Multiple Expectation maximization for Motif Elicitation (MEME)  (Bailey et al.
AdDHN1 Cloning To identify the complete coding sequence of AdDHN1, the Aradu. IF4XP gene model 4  was aligned with the four best BLASTn hits of A. duranensis databases available on NCBI 5. The consensus sequence (675 bp) was synthesized and cloned (Epoch Life Science Inc. , TX, United States), under the control of the Arabidopsis actin 2 promoter (ACT-2) and the nopaline synthase (NOS) terminator, at the XhoI restriction site of pPZP-201BK-EGFP  (Chu et al. , 2014). This binary vector, hereafter called pPZP-AdDHN1, also contains two additional cassettes for the constitutive expression of the enhanced green fluorescent protein (eGFP) reporter gene and the hygromycin phosphotransferase (hpt) selection marker gene. The pPZP-AdDHN1 binary vector was then introduced into the disarmed Agrobacterium tumefaciens strain "GV3101" by standard electroporation protocol.
The Arabidopsis OE lines with enhanced freezing tolerance were then selected for subsequent stress assays. For that, OE and WT plants, grown in the controlled growth chamber, were submitted to a gradual water deficit (dry-down) treatment and to infection with M. incognita. We analyzed the data using single factor ANOVA (P < 0. 05). For the dry-down assay, irrigation of 3 weeks old plants grown on substrate was interrupted for a group of 10 individuals per OE line and the WT (stressed group) during 8 days whilst the control group of individuals was kept under irrigated conditions, i. e. , around 70% of field capacity (FC). During the assay, two SPAD chlorophyll meter readings (SCMR; SPAD-502, Konica Minolta Sensing, Japan) was recorded every 2 days from the same leaf of each individual. At the end of the water stress (8th day), we collected three leaf discs (0.
4 cm 2 ) per individual to assess Relative Water Content (RWC) according to  de Brito et al. (2011). The aerial and root plant fresh biomass were also determined at the end of the assay and the leaf area was estimated using the Rosette Tracker software  (De Vylder et al. , 2012). For the M. incognita bioassay, roots from 4 weeks old plants grown on a 2:1 sand:substrate mixture (v:v) were inoculated with approximately 500 J2 infective larvae of M. incognita, essentially as described by  Morgante et al. (2013). At 60 days after inoculation (DAI), roots were stained with acid fuchsin and the number of nematode females on roots (10 individuals per OE line and WT) assessed under a stereomicroscope.
Analysis of Transcription Levels in Arabidopsis by qRT-PCR We conducted qRT-PCR analysis in the T3 seedlings to study the expression levels of the AdDHN1 transgene and other stress-responsive genes in Arabidopsis OE lines and WT plants. Total RNA was extracted using an RNeasy Plant Mini Kit (Qiagen, Hilden, Germany), treated with DNAse and reverse transcribed as previously described  (Morgante et al. , 2013). qRT-PCR reactions were performed in three biological replicates on the StepOne Plus Real-Time PCR System (Applied Biosystems, Foster City, United States), as previously described  (Vinson et al. , 2018) , using specific primers (Supplementary Table  2 ). The online real-time PCR Miner tool  (Zhao and Fernald, 2005)  was used to estimate the average cycle threshold (Cq) values. The relative quantification (RQ) of mRNA levels was normalized with AtACT2 and AtEF-1α reference genes (Supplementary Table  2 ) and determined for the AdDHN1 transgene using the qGENE software 6  and for the stress-responsive Arabidopsis genes using the REST 2009 v.
Identification and Characterization of DHN Genes in Fabaceae We searched for DHN proteins in the proteomes predicted from the whole genome sequences of eight Fabaceae species belonging to the Papilionoideae sub-family based on the presence of the conserved dehydrin PFAM domain PF00257. Initially, we identified 21 putative DHN proteins in the eight species (Supplementary Table  1 ). However, two protein sequences from C. arietinum, and their corresponding genes, were 100% identical and hence eliminated, yielding a total of 20 non-redundant putative DHN proteins. Based on their motif numbers and organization, we could classify all 20 putative DHNs identified into only three out of the five known subclasses: Y n SK n , Y n K n , and SK n. Subsequently, we named each DHN according to the corresponding chromosomal position of the gene in each species (Supplementary Table  1 ).
These two DHN motifs have not yet been described, and hereafter are considered as -segments, previously noticed as poorly conserved segments located between the K-, S-, and Y-segments  (Graether and Boddington, 2014). We studied the predicted subcellular localization of the 20 legume DHNs using the WoLF PSORT Prediction Software 7. Interestingly, 19 out of 20 proteins studied were predicted to have a nuclear localization with only the Y n SK n -type CaDHN1 predicted to be cytoplasmic (Supplementary Table  1 ). This preferential nuclear localization of legume DHNs is not in accordance with previous studies showing that plant DHNs subcellular localization does not tend to be more nuclear than the cytoplasmic and are present in different parts of the cell  (Abedini et al. , 2017; Yu et al. , 2018).
A. duranensis DHN Genes Regulation Under Abiotic and Biotic Stresses We studied in more detail the regulation of two A. duranensis genes (AdDHN1 and AdDHN2) in response to abiotic and biotic stresses. This wild Arachis species has been used by our group in functional genomics studies due to its contrasting responses to abiotic and biotic stresses: tolerant to water deficit but somewhat susceptible to Meloidogyne arenaria infection  (Proite et al. , 2008; Leal-Bertioli et al. , 2012; Guimaraes et al. , 2017). The in silico expression profile of AdDHN1 and AdDHN2 was determined using our previous RNA-seq data including A. duranensis roots submitted to a dehydration treatment  (Vinson et al. , 2018)  and roots challenged with the RKN M. arenaria  (Mota et al. , 2018). We then validated the in silico expression analysis based on RNA-seq using qRT-PCR.
Gene Expression Microarray Data Analysis and Statistics. The raw CEL data files from microarray profiling were imported into the Partek Genomics Suite (v. 6. 6; Partek, St. Louis, MO) for analysis, and two-way analysis of variance (2-way ANOVA) was applied with a fold change of 1. 5 for the selection of differentially expressed genes at a significance level of p < 0 05. The differentially expressed gene lists were further correlated for their relevant biological function and reaction pathway by analysing the GSEA (Gene Set Enrichment Analysis) and KEGG (Kyoto Encyclopedia of Genes and Genomes) using the Partek Genomic Suite. A significance level ofp < 0 05in the GSEA analysis to identify the significant biological process involved was observed, whereas an enrichment score ofp < 0 05in the KEGG pathway to identify the significant pathway was observed.
2. 6. Quantitative Real-Time PCR (qPCR). The microarray data was validated by using qualitative qPCR. Genes for validation, i. e. , GDF15, EREG, RRM2B, SHC3, SHC1, SESN1, MSTN, MYOD1, and SMAD3, were chosen from pathway analysis. By using 2 μL total RNA as template and iScript Reverse Transcription Supermix (Bio-Rad, USA), the cDNA is generated from RNA. The reactions were carried out as follows: priming for 5 minutes at 25 °C, then reverse-transcription for 30 minutes at 42 °C and inactivation of the reverse-transcription for 5 min at 85 °C. Primer sequences for GDF15, EREG, RRM2B, SHC3, SHC1, SESN1, MSTN, MYOD1, and SMAD3 are shown in Table  1 (a). qPCR was carried out by using 1 μL cDNA as template, 1 μL of forward and reverse primers for genes of interest, and SSoAdvanced SYBR Green Supermix (Bio-Rad, USA).
7. Statistical Analysis. Microarray data was analysed by using Partek Genomic Suite (v. 6. 6; Partek, St. Louis, MO), and the differentially expressed gene lists were filtered based on a fold change of 1. 5 and a significance level of p < 0 05 by using two-way analysis of variance (2-way ANOVA). The relevant biological function and reaction pathway was identified based on GSEA analysis at a significance level of p < 0 05 and KEGG analysis at an enrichment score p < 0 05 by using the Partek Genomic Suite. The REV data in qPCR are presented as mean ± standard error of the mean (SEM). Statistical analysis was performed with the software IBM SPSS Statistics (version 20). Independent sample T test was used to determine the significant differences in between the SIPS control and TRF-treated groups.
Biological Processes and Pathways Affected by the TRF-Post-treatment on SIPS Myoblasts. GSEA analysis revealed the selected significant biological processes involved in response to TRF-posttreatment SIPS myoblasts compared to SIPS control myoblasts (p < 0 05) (Table  3 ). TRF treatment has significantly increased the regulation of JNK cascade, cell growth, and adult walking behavior, but decreased the cell cycle activity. KEGG pathway analysis was carried out by using Partek Genomic Suite, and the data can be categorised according to the cluster of differentially expressed genes (fold change<-1 5 or fold change > 1 5; p < 0 05) and the pathway ANOVA statistical method (p < 0 05). Our KEGG pathway analysis showed that TRF treatment significantly modulated the p53 signalling pathway, FoxO signalling pathway, Wnt signalling pathway, cell cycle, and ErbB signalling pathway (Table  4 ).
Information on the origin of pollution constitutes an essential step of air quality management as it helps identifying measures to control air pollution. In this work, we review the most widely used source-apportionment methods for air quality management. Using theoretical and real-case datasets we study the differences among these methods and explain why they result in very different conclusions to support air quality planning. These differences are a consequence of the intrinsic assumptions that underpin the different methodologies and determine/limit their range of applicability. We show that ignoring their underlying assumptions is a risk for efficient/successful air quality management as these methods are sometimes used beyond their scope and range of applicability. The simplest approach based on increments (incremental approach) is often not suitable to support air quality planning.
Based on simplified theoretical examples,  Clappier et al. (2017)  and  Grewe et al. (2010)  clearly made the point that some methods were conceptually designed to address different questions and in particular that some methods were not always suited to inform on the impact of emission abatement strategies on air quality. Source apportionment, however, continues to be used as a general support to air quality planning, regardless of the approach followed. The main motivations of this work are therefore to focus on the specific use of source apportionment in the context of air quality planning and to support the conclusions of  Clappier et al. (2017)  with real-world examples. In the context of FAIRMODE, the "Forum for Air Quality Modelling in Europe" (http://fairmode. jrc. ec. europa. eu), an inter-comparison exercise was organized to compare different source apportionment approaches on a common dataset  (Belis et al.
, 2019). A series of models using different methods were run over the region of Lens, France, and results inter-compared. The exercise highlighted important differences among the approaches and provided the elements for an initial investigation of the causes behind these differences. However, the fact that different methods were implemented in different Air Quality Models (AQM) did not allow to quantify precisely the difference caused by the source apportionment method alone versus the one related to the AQM uncertainty. To correct for this, the current study uses both theoretical examples and real-world datasets to highlight and explain the differences among source apportionment methods. We explore the potential inconsistencies among the approaches, quantify the differences and discuss the implications that these differences may have on air quality planning.
, 2008; Belis et al. , 2013; Watson et al. , 2008; Hopke, 2010). Mass-transfer models include also source-oriented models (MT-SM). These are based on AQM in which tagging/labelling techniques are implemented to keep track of the origin of air pollutants throughout a model simulation  (Kranenburg et al. , 2013). These approaches require all traditional AQM inputs to be available, in particular detailed emission inventories. Examples of MT-SM models are the particle source apportionment technology (PSAT) within the CAMx model  (Yarwood et al. , 2004; Wagstrom et al. , 2008; Kwok et al. , 2013; ENVIRON, 2014) , the tagged species source apportionment algorithm (TSSA, ISAM) within CMAQ  (Bhave et al. , 2007; Wang et al. , 2009)  and the labeling module built in LOTOS-EUROS  (Kranenburg et al. , 2013).
Impacts (ERI) vs. contributions (MT-SM) Differences between contributions and impacts have been discussed in many studies  (Burr and Zhang, 2011a; Kranenburg et al. , 2013; Clappier et al. , 2017; Grewe et al. , 2010; Mertens et al. , 2018). We highlight these differences with results obtained with a real-world modelling dataset over the Po-Valley (Italy) where both contributions and impacts are calculated consistently. It consists of a series of AQM simulations performed with the CAMx model (ENVIRON, 2014) applied in ERI mode for different emission reduction scenarios. Three sectors of activity: agriculture, transport and industry are analysed and reduced by 50 and 100% over the Po-valley area (Italy). The area was selected because of the high levels of PM considerable anthropogenic emissions that facilitate the analysis of the processes described in the theoretical examples.
We refer to  Pepe et al. (2016)  for additional details on the base case model set-up. The three sectors: agriculture (A), transport (T) and industry (I) are reduced independently from each other and/or in a combined way. All possible combinations of reductions are performed leading to seven different simulations (A, T, I, AT, AI, TI, ATI, where A, T and I represent the agriculture, transport and industry, respectively) for each level of reduction, in addition to the baseline simulation. Based on the same set of input data, an additional simulation has been performed with CAMx using the built-in tagging species module (PSAT) to deliver contributions for the same three sectors with a MT-SM based approach. All simulations are performed with a resolution of 5 km for an entire meteorological year (2010 -reference year for the Italian emission inventory).
e. the measures not covered by the ERI-LOW range. In other while the ERI-LOW impacts provide insight for single sector mitigation measures over a limited range of emission reductions (< α_t), tagging contributions provide information on the potential for combined/single measures beyond α_t. Tagging is therefore used as an additional information to support the interpretation of the ERI impacts. Because tagged contributions depend on arbitrary choices in terms of chemical pathways and in terms of splitting weights (molecular weight, mole…), this additional information remains however qualitative. This real-world modelling dataset can also be used to define the boundary between the ERI-HIGH and ERI-LOW regimes, as discussed below.
e. neither at 50% for daily, nor at 100% for both yearly and daily). In order to determine more precisely the value of the threshold, this type of comparison should be repeated for different levels of emission reductions. Regarding the first condition, an additional simulation with a lower reduction (e. g. 25%) would be needed. As these are not available in the current datasets, we rely on  Thunis et al. (2015)  who performed these tests over the Po-valley with another AQM. In theory, all tests should be performed each time a new version of the model (change in resolution, meteorology, emissions…) is available; in practice, this is quite demanding and tests are generally performed in a piecewise manner, i. e. some tests with a specific model version, others with another version, assuming that the basics of the physical and chemical behaviours of the atmosphere remain unchanged.
Conclusions Different approaches are available to policy makers to support them in the identification of sources responsible for air pollution levels and to inform on the efficiency of effective air quality plans. In this work, we reviewed three source apportionment methods: the incremental, the mass-transfer and emission reduction impacts (often referred to as brute force) approaches. The fact that these source apportionment methods generally serve the same purpose (support to air quality planning) but deliver different messages was analysed and discussed. Both the theoretical and real-case modelling dataset presented in this work highlight the important differences among the three methods, in terms of result and therefore also in terms of their possible use for air quality planning purposes.
Although our conclusions are drawn from simple theoretical examples and from a unique dataset in one particular region (Po Valley), we believe them to be generally valid because differences in the results are driven by assumptions in the source apportionment methodologies that lead to systematic biases. For air quality planning, our findings show the need to consider carefully the choice of source-apportionment method. The biases and limitations of the different source-apportionment methods can explain why methodological choices may result in inefficient air quality control options.
Most radiative transfer models assume that dust in spiral galaxies is distributed exponentially. In this paper our goal is to verify this assumption by analysing the two-dimensional large-scale distribution of dust in galaxies from the DustPedia sample. For this purpose, we make use of Herschel imaging in five bands, from 100 to 500 µm, in which the cold dust constituent is primarily traced and makes up the bulk of the dust mass in spiral galaxies. For a subsample of 320 disc galaxies, we successfully perform a simultaneous fitting with a single Sérsic model of the Herschel images in all five bands using the multiband modelling code galfitm. We report that the Sérsic index n, which characterises the shape of the Sérsic profile, lies systematically below 1 in all Herschel bands and is almost constant with wavelength.
Introduction Dust is one of the several constituents of galaxies which holds a major role in the interstellar medium (ISM). First, it acts as a catalyst in the transformation of the atomic hydrogen into molecular hydrogen from which stars form  (Wolfire et al. 1995). Thus, the dust is an important element in the chemical evolution of the ISM. Second, it allows the gas to cool and condense to form new stars  (Draine 1978; Dwek 1986; Hollenbach & Tielens 1997). Third, it absorbs some fraction of the emitted starlight and reemits it in the infrared (IR) domain, where the thermal emission by dust grains dominates in the spectral energy distribution ⋆ DustPedia is a project funded by the EU under the heading 'Exploitation of space science and exploration data'. It has the primary goal of exploiting existing data in the Herschel Space Observatory and Planck Telescope databases.
(2018, hereafter C18)  and can be freely retrieved from the DustPedia database 1. Below, we use the DustPedia sample and its imagery to select galaxies for our subsequent analysis. To study the cold dust distribution in galaxies, we make use of the Herschel observations which have the best spatial resolution in the FIR/submm to date. We consider five Herschel wavebands: PACS 100 µm, PACS 160 µm, SPIRE 250 µm, SPIRE 350 µm, and SPIRE 500 µm. We do not exploit PACS 70 data as they are available for only 244 galaxies of the DustPedia sample. On top of that, in this band emission from stochastically heated warm dust can be substantial and, therefore, could potentially bias our current study. In order to select galaxies for a Sérsic modelling of their Herschel images, we impose the following criteria.
For SPIRE 250, SPIRE 350, and SPIRE 500, we adopted the kernels which were used in  Smith et al. (2016)  (created by means of SPIRE calibration observations of Neptune). These extended PSFs are mandatory to have correct fits of the SB profiles. Complementary NIR data were also used to trace the old stellar population (see e. g. Elmegreen & Elmegreen 1984; Rix & Zaritsky 1995; Grosbol 1993). The WISE observatory scanned the whole sky in the W1 band (3. 4 µm), and so we make use of its observations for the entire sample of 875 DustPedia galaxies. We do this to obtain some important general characteristics of these galaxies (e. g. galaxy inclination, see the Appendix D), as well as some galaxy structural properties. These quantities are used in the current study and in the papers to follow. Since no error-maps are provided for the WISE data in the DustPedia database, we applied the same scripts used by C18 to retrieve the WISE W1 intensity maps and the related pixel-uncertainty maps from the NASA/IPAC Infrared Science Archive (IRSA 2  ).
2007; Gadotti 2008 Gadotti , 2009;; Mosenkov et al. 2010). In this paper we apply single Sérsic modelling to describe 2D stellar and dust emission of the selected galaxies. Apart from the parameters in Eq. 1, the free parameters of the 2D model include the coordinates of the galaxy centre, its apparent flattening, and position angle. The applicability of this fitting method to the Herschel observations and its robustness is discussed in the Appendix B. In this study we implement the galfit and galfitm software to fit a Sérsic profile to the galaxy light profile. galfit performs a Levenberg-Marquardt minimisation of the χ 2 residual between a galaxy image and its PSF-convolved model by modifying the free parameters. The final model is found when χ 2 reaches a minimum. The calculation of χ 2 is weighted by a sigma map, which is provided for each galaxy in our sample.
This code has been successfully applied to individual galaxies with a diverse morphology  (Gabor et al. 2009; Huang et al. 2013; Reshetnikov & Combes 2015)  and to large samples of galaxies consisting of thousands of objects  (Häussler et al. 2007; Hoyos et al. 2011; Salo et al. 2015). Recently, a new fitting code galfitm  (Bamford et al. 2011; Häußler et al. 2013; Vika et al. 2013 Vika et al. , 2014 Vika et al. , 2015) )  has been created (as an extension of galfit) to fit a wavelength-dependent model by using multiple images of the same galaxy in a simultaneous and consistent manner. Rather than fitting the parameter values at the wavelength of each band, galfitm fits the coefficients of a smooth function describing the wavelength dependence of each parameter. It has been shown that multiband mod-elling significantly improves the extraction of information, particularly from bands with low signal-to-noise ratio when combined with higher signal-to-noise images  (Vika et al.