dataesr/scientific-paragraphs-categorization huggingface dataset:
This leads to an increase in bare soil evaporation and a reduction of vegetation transpiration and a better agreement with in situ data  [46]. For surface water, many studies already demonstrated the good accuracy of lake volume variations from the combination of satellite images with radar altimetry (see  [71] ). The time series of TWS from CSR, JL CRI and JPL RL06 mascon solutions, and of SM from GLEAM v3. 3a and 3. 3b over 2003-2016 for TAS, NWSAS and NSAS are presented in Figure  2. The annual amplitude of TWS is rather small (below 20-30 mm), but higher for CSR than for JPL CRI and JPL mascon solutions, and no clear seasonal cycle can be observed (Figure  2a, c, e ). Biases and RMSD are found to be below 3 mm and 10. 5 mm respectively for all three TBA (Table  1 ). These latter values correspond to RRMSD ranging from 30 to 45% when comparing TWS from CSR and JPL.
R greater than 0. 8 were estimated between TWS from CSR and the two JPL solutions for NWSAS and NSAS but only greater or equal 0. 5 for TAS (Table  1 ). The annual variations of SM from GLEAM v3. 3a and 3. 3b are also rather low (below 10 mm). They exhibit a well-marked seasonal cycle, with a peak in summer, over TAS and NWSAS (Figure  2b ,d respectively) but almost no variations over NSAS (Figure  2f ). Low biases (below 0. 6 mm), low RMSD (below 0. 5 mm) corresponding to RRMSD lower than 15% and very high correlations (R = 1) were found over TAS, NWSAS and NSAS for the two versions of GLEAM SSM. The spatial distribution of the bias, RMSD, RRMSD and R are presented for the comparison between TWS from CSR and JPL CRI, CSR and JPL, JPL CRI and JPL mascon solutions on Figures 3, A2 and A3 respectively, for the comparison between SM from GLEAM v3.
Very good agreement is also found between the two versions of SM from GLEAM. Larger bias and RMSD are found in the central part of TAS and northern parts of NWSAS and NSAS (Figure  4a, b ) and in the northwest and in the northeast (close to Nasser Lake) parts of NSAS (Figure  4c, d ). As JPL CRI and JL GRACE-based mascon solutions and GLEAM v3. 3a and 3. 3b products are very similar, the results will be presented mostly using CSR and JPL CRI mascon solutions and GLEAM v3. 3b products in the followings.
Climate Impacts on Groundwater Storage (GWS) Considering basin-scale temporal changes, mean annual values of GWS were compared to annual hydrological fluxes (P, ET, P -ET) using cross-correlation (Table  3 ). In the three TBA, higher correlations were obtained between GWS and P-ET than between GWS and P, and especially than between GWS and ET, the former quantity representing the net contribution of the atmosphere to the land water cycle. High temporal correlation was found between mean annual GWS and P -ET in the TAS using TWS from the CSR mascon products over 2003-2016. The low correlation obtained using the JPL-CRI mascon solutions is likely to be attributed to the lower spatial resolution of this dataset which is not sufficient to provide a reliable monitoring of the TWS, and, hence, of the GWS in a TBA with an area lower than 200,000 km 2 , and variations in GWS lower than 10 km 3 (i.
Conclusions This study provides quantification of the GWS changes over three major TBA located in arid and semi-arid North Africa: TAS, NWSAS and NSAS during the GRACE era  (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016). GWS variations were estimated removing SM (and SW storage from for NSAS) from GRACE-based TWS. Mascon solutions from CSR and JPL RL06 were chosen instead of global ones to minimize the effects of leakage. SM were obtained from GLEAM 3. 3a and 3. 3b outputs, and surface water storage from a combination of radar altimetry and satellite images made available by the Hydroweb database. The different products agreed well with each other in the different TBA, except over TAS for the GRACE solutions (R = 0. 5), and their combination also.
Funding: This research was funded by the  Hydroweb CNES TOSCA  grant. Acknowledgments: F. F. wants to express his gratitude to  Philippe Guiochon  and  Conrad Kilian  for inspiring this study. We thank two anonymous Reviewers who helped improve the manuscript. Funding: This research was funded by the  Hydroweb CNES TOSCA  grant. Acknowledgments: F. F. wants to express his gratitude to  Philippe Guiochon  and  Conrad Kilian  for inspiring this study. We thank two anonymous Reviewers who helped improve the manuscript.
In this study, we examine the effects of the economic uncertainty of partners on the transition to first birth in Greece before and after the onset of the recent economic crisis. After selecting a sample of childless couples, we applied a random effects model to EU-SILC data for the period 2005-2013. Few studies have focused on the association between economic uncertainty and fertility in Greece considering characteristics of both partners. Even fewer studies have examined panel data in the context of the recent crisis. Our findings show that Greek couples in which both partners are employed, have a high income, or are highly educated are in a more favourable position to have a first child. During the Greek Great Recession, corresponding in our study to the period 2010-2013, the odds of having a first child decreased to half the odds in the 2005-2009 period.
However, few studies have focused on how the relationship between couples' economic uncertainty and first birth has changed as a result of the recent economic recession  (Kotzamanis, 2018; Tragaki and Bagavos, 2014). To our knowledge, no studies on Greece have addressed this issue by conducting multivariate and longitudinal analyses or by considering multiple indicators of economic uncertainty for both partners. Our analysis is based on longitudinal data from the EU-SILC survey (EU Statistics on Income and Living Conditions) for Greece. We select a sub-sample of childless couples and consider survey waves from 2005 to 2013, a period overlapping with the current economic crisis. In the EU-SILC data, information about the level of income, education, occupational status, and type of contract (permanent or not) is available for both partners.
The Greek context In Greece, from the mid-1990s to 2003, the total fertility rate (TFR) was less than 1. 3 children per woman (Eurostat database accessed in August 2018). As in other Southern European countries, the postponement of marriage and fertility has been observed, while new forms of unions and births out of wedlock remain exceptions  (Gavalas et al. 2014). In 2013, Greece was the only European country in which the percentage of births out of wedlock was still less than 10% (Eurostat database accessed in July 2019). Beginning in 2004, the increase in fertility ushered in a new trend. Greece's TFR increased from 1. 3 children per woman in 2004 to 1. 5 in 2008 (Eurostat database accessed in March 2019). This trend was interrupted by the recent economic crisis: the number of births decreased by 15.
2%  (Vrachnis et al. 2014) , and the TFR decreased to 1. 3 in 2013 (Eurostat database accessed in July 2019). In a recent study,  Salvati (2018)  showed that during the recession, economic conditions worsened in all Greek districts and led to a decline in the growth rate of the population, except in certain coastal and tourist districts. In addition to low fertility, another feature that Greece has in common with other Southern European countries is youth and female labour precariousness. In 2008, youth unemployment (15-to 24-year-olds) was already double the national average, reaching 28. 3%  (Kretsos, 2014 ; Eurostat database accessed in August 2018). In the same year, female unemployment was decreasing but still high compared to that in other European countries (11. 5% in 2008, Eurostat database accessed in August 2018).
The economic crisis exacerbated this situation. The youth (15-to 24year-olds) unemployment rate reached 58. 3% in 2013 (Eurostat database accessed in July 2019). Regarding female employment, Greek experienced a greater decline than other Southern countries, with an employment rate reaching 40% in 2013. A dramatic decline was also observed for Greek men, with an employment rate of 49% in 2013 (Figs. 1  and 2 ). As an effect of the crisis, dual-earner households with children decreased by 9% between 2008q2 and 2012q2, from 55. 1 to 46%. The percentage of households with children in which the man is the provider and the woman is inactive in the labour market decreased by 10% in the same period, from 34 to 24%  (Karamessini, 2014). Meanwhile, the share of male breadwinner households with unemployed women increased by 6%, and the share of female breadwinner households increased by 5%  (Karamessini, 2014).
Moreover, a 6% pension reduction penalty for each year of early retirement was introduced into the pension calculation  (Papadopoulos Roumbakis, 2013). Given the high percentage of young adults living with their parents in Greece  (Albertini et al. 2007 ), such measures were not without influence and might have contributed, between 2009 and 2010, to the increase in poverty rates by 12. 6% among the unemployed  (Tzanatos and Monogios, 2013). Public spending on families in Greece, among the lowest in Europe before the crisis, was also affected. In 2009, family benefits in cash stopped increasing, and in 2010, public spending on services for families, amounting to approximately 0. 43% of the GDP, started decreasing (OCDE, Family database accessed in February 2019). Researchers enquiring into the consequences of the economic crisis in Greece have typically identified 2008 as the beginning of the crisis  (Tragaki and Bagavos, 2014; Vaughan-Whitehead, 2013; Papadopoulos and Roumbakis, 2013).
Data and methods For the purposes of this paper, we used longitudinal data from the EU-SILC survey (EU Statistics on Income and Living Conditions). The EU-SILC survey was launched in 2003 as the continuation of the European Community Household Panel survey  (ECHP, 1994 (ECHP, -2000)). The survey is the instrument used by the European Union to monitor progress on its Europe 2020 strategy, the main objective of which is to reduce poverty. To these ends, the EU-SILC survey collects comparable and multidimensional micro-level data on income, poverty, social exclusion, housing, work, education, and health. In Greece, the EU-SILC survey has been conducted since 2004, collecting longitudinal data on 3500 households and 7250 individuals per wave. In this study, we focused on the period of 2005-2013, including 67,493 person-years of the Greek EU-SILC.
We selected couples whose partners are childless and younger than 50 years old. We assumed that the possibility of having a first child in Greece exists almost exclusively for couples (see section "The Greek context"). For the sake of simplicity, we excluded individuals who live with children born from previous unions. As in  Santarelli (2011) , we excluded individuals living with adults other than their spouses because they might provide economic support to the household. Our final sample consists of 1974 couple-years and 1185 couples. For the period of analysis, we observed 136 first births. In the EU-SILC database, new births are identified under the variable "membership status" in the category "newly born into the household since the last wave". We created the variable "First birth," which takes the value of 1 when there is a newborn (variable …) in a household included in the previous wave by two members (variable "hx040") being in partnership (variable "pb200").
Age is recoded into five categories: (1) up to 24 years, (2) 25 to 29 years, (3) 30 to 34 years, (4) 35 to 39 years, and (5) 40 to 49 years. The variable "union" is coded as follows: (1) union with a legal basis and (2) union without a legal basis (de facto partners living in the same household). Regarding the place of residence of the respondents, we used two variables: "region" and "degree of urbanization". Concerning the region of residence, in the SILC database, a NUTS 1 classification of territorial units is available. NUTS 1 corresponds to four "development areas" that aggregate Greece's 13 regions (without necessarily constituting official administrative units): (1) Northern Greece (Anatoliki Makedonia, Thraki, Kentriki Makedonia, Dytiki Makedonia, and Thessalia); (2) Central Greece (Ipeiros, Sterea Ellada, Dytiki Ellada, and Pelopponisos); (3) Attiki; and (4) the Aegean Islands and Crete (Voreio Aigaio, Notio Aigaio, and Kriti).
Bioelectric Impedance Analysis (BIA) was used to measure percent body fat (PBF). Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) was used to quantify insulin resistance. A person with HOMA-IR ≥ 2. 5 was considered as insulin resistant. Statistical analyses were performed using the Statistical Package for Social Sciences version 23 for Windows. P < 0. 05 was considered statistically significant. Results: Out of 286 study participants, 38. 0% were insulin resistant. Average dairy product intake in the total sample was 2. 2 ± 1. 0 servings per day. Among males, the odds of having insulin resistance were 3. 9 times higher (95%CI 1. 4-11. 0; p = 0. 009) for those having a risky waist circumference compared to those having a healthy waist circumference. Among females, being married (OR: 0.
Data collection procedures Questionnaires During the 30-min face-to-face interview, trained nutritionists filled out four questionnaires: background questionnaire, International Physical Activity Questionnaire, food frequency questionnaire, and a 24-h Multiple-Pass Method recall. All questionnaires were pre-tested using a sample of thirty NDU employees in the three campuses. Revisions and corrections were performed before initializing the study. Details about the background questionnaire (available as a supplementary file [Additional file 1]) and International Physical Activity Questionnaire  [37]  were previously described  [36].
Among males, these confounders included education, income, BMI, PBF, risky waist circumference, physical activity (PA) and TG levels. However, education and income were removed from the model because of high correlation (ρ > 0. 80). Among females, confounders that were controlled for were marital status, BMI, PBF, risky waist circumference, TG, HDL and CRP levels. Statistical analyses were performed using the Statistical Package for Social Sciences (SPSS) version 23 for Windows. A pvalue of less than 0. 05 was considered statistically significant.
Acknowledgements The authors are grateful to the study participants for their cooperation and participation. We also thank the nurses,  Ms. Najwa El Gerges ,  Ms. Micheline Abou Saleh  and  Ms. Marie Kosseify  who assisted in blood withdrawal. And last but not least, we would like to show our gratitude for  Notre Dame University ,  Zouk Mosbeh  for giving us access to its laboratories and professional machines, making this research project achievable.
Pressure Measurements The transient pressure at the inlet and the outlet was measured using two piezoresistive pressure sensors (Keller 10L series) calibrated in a range of 0-3 bar with a full-scale uncertainty of 0. 25%. Signals from the pressure sensors were collected synchronously through a NI USB-6216 data acquisition card and exported to a computer by LabVIEW software. The sampling frequency was set to be 10 kHz, and the sample size was 10,000 (corresponding to a recording time of 1 s) for all flow conditions. Figure  3  shows an example of the pressure signals recorded by the inlet and outlet sensors. The averaged inlet pressure P in was used to calculate the cavitation number σ: σ = P in -P vap 0. 5ρu 2 in , ( 1 ) where P vap is the vapour pressure at the flow temperature, u in is the bulk velocity at the inlet computed according to the volumetric flow rate measured by the flow meter and ρ is the liquid density.
The instantaneous velocity fields were evaluated using the iterative cross-correlation algorithm in the software Davis 8. 1 from LaVision. The interrogation window for the first pass was 64 × 64 pixels, followed by 2 passes with interrogation windows of 16 × 16 pixels, and an overlap of 50% was used. The correlation peak was determined with a subpixel accuracy by using the three-point Gaussian fitting scheme in two directions. The particle density was taken into account in the final pass, where the vectors in the interrogation windows with less than 4 particles were rejected and replaced by a bilinear interpolation from their valid neighbouring vectors. This implied that the calculated velocities in the attached cavity had a higher uncertainty, as the particle concentration there was not sufficient.
It can be seen that all experimental points collapse well around one curve, suggesting that the normalised pressure loss is only a function of the cavitation number, independent The instantaneous velocity fields were evaluated using the iterative cross-correlation algorithm in the software Davis 8. 1 from LaVision. The interrogation window for the first pass was 64 × 64 pixels, followed by 2 passes with interrogation windows of 16 × 16 pixels, and an overlap of 50% was used. The correlation peak was determined with a subpixel accuracy by using the three-point Gaussian fitting scheme in two directions. The particle density was taken into account in the final pass, where the vectors in the interrogation windows with less than 4 particles were rejected and replaced by a bilinear interpolation from their valid neighbouring vectors.
There are many blind spots in the taxonomy of the pathogenic fungi of Colletotrichum due to differences in the applied evaluation criteria. The reported number of Colletotrichum species ranges from 29 to more than 700  [1, 9, 10] , and 889 records have been entered into the Index Fungorum database to date  [11]  The genus Colletotrichum has been regarded as a model organism for hemibiotrophic pathogens, with a brief biotrophic phase followed by a necrotrophic phase of infection  [1]. Colletotrichum species rely on various infection strategies, including biotrophic, necrotrophic, hemibiotrophic, and endophytic, to colonize the tissues of a host organism and derive nutrients  [7, 12]. Due to their considerable variations, these pathogens have also been widely investigated as model organisms in genetic studies.
Characteristic and Comparative Analysis of the Mitogenomes The complete mitochondrial genomes of Colletotrichum fioriniae, C. lupini, C. salicis, and C. tamarilloi from the C. acutatum species complex were characterized in terms of their size and composition. Furthermore, the gene contents and orders of the four referred mitogenomes were compared to those of previously published mitochondrial genomes of other representatives of the genus Colletotrichum. For that purpose, the mitogenomes of seven Colletotrichum species were downloaded from GenBank: C. acutatum, four representatives of the C. gloeosporioides species complex (C. aenigma, C. fructicola, C. gloeosporioides, and C. siamense), C. graminicola (C. graminicola species complex), and C. lindemuthianum (C. orbiculare species complex) (Table  1 ).
Identification of Repetitive Elements The REPuter program  [32]  was used to detect and assess genomic repeats, including forward, reverse, palindromic, and complementary sequences with a minimal length of 30 bp, a Hamming distance of 3, and 90% sequence identity. Mitochondrial simple sequence repeats (SSR) or microsatellites were identified in Phobos v. 3. 3. 12 [33]  with default settings for perfect SSRs with a motif size of one to six nucleotide units. Standard thresholds for the identification of mitochondrial SSRs were applied  [34] , i. e. , a minimum of 12 repeat units for mononucleotide SSRs, six repeat units for dinucleotide SSRs, four repeat units for trinucleotide SSRs, and three repeat units for tetra-, penta-, and hexanucleotide SSRs.
Phylogenetic Analysis Phylogenetic analyses were performed on sequences of 15 protein-coding genes shared by 46 fungi species belonging the Sordariomycetes class, including 11 Colletotrichum species and two species of Penicillium as an outgroup. The appropriate sequences were downloaded from the NCBI database (Table  S1 ). The chosen sequences were aligned in MAFFT v7. 310. Bayesian Inference (BI) and Maximum-Likelihood (ML) methods were used for genome-wide phylogenetic analyses in MrBayes v. 3. 2. 6  [35, 36]  and PhyML 3. 0  [37]. Before BI and ML analysis, the best fitting substitution model was searched for in Mega 7  [38] , and the GTR + G + I model was selected. A BI partitioning analysis was carried out to develop a majority rule consensus tree with 1 × 10 7 generations using the Markov Chain Monte Carlo (MCMC) method.
The tree sampling frequency was 1000 generations. The first 2500 trees were discarded as burn-in, with a random starting tree. The ML analysis was performed in PhyML 3. 0 with 1000 bootstrap replicates. In order to infer the phylogeny of the rps3 gene, an analogical approach was applied. However, in the case of the set of rps3 sequences derived from 44 species representing Sordariomycetes and two Penicillium species, Mega 7 software marked out the GTR + G substitution model as the optimal model, and therefore, it was applied in the BI and ML analyses. A detailed comparison of rps3 nucleotide sequences from 11 representatives of Colletotrichum was also performed. For this purpose, Geneious software was applied. As a consequence, polymorphic regions within the gene were identified and characterized.
Genome Size and Organization Four Colletotrichum species were sequenced to produce 1,557,270 (C. lupini), 1,769,902 (C. tamarilloi), 1,918,044 (C. fioriniae), and 4,983,140 (C. salicis) raw reads with lengths ranging from 150 to 300 bp. These were then mapped separately to the reference genome of C. acutatum (NC_027280). Obtained contigs were enlarged and assembled de novo to build the final complete circular mtDNA of each species. The six Colletotrichum mt genome sequences were then submitted to GenBank and acquired the following accession numbers: NC_030052 for C. fiorinae, NC_029213 for C. lupini, NC_035496 for C salicis, and NC_029706 for C. tamarilloi. The complete mitochondrial genomes of Colletotrichum fioriniae, C. lupini, C. salicis, and C. tamarilloi appeared as circular molecules with sequence lengths ranging from 30,020 bp (C.
Geomagnetic activity was low in the mid-latitudes at the time, but a weak sub-storm at high latitudes reached its peak at the start of the observation. An analysis of Global Navigation Satellite Systems (GNSS) and ionosonde data from the time reveals a larger-scale travelling ionospheric disturbance (TID), possibly the result of the high-latitude activity, travelling in the north-west to south-east direction, and, simultaneously, a smaller-scale TID travelling in a north-east to south-west direction, which could be associated with atmospheric gravity wave activity. The LOFAR observation shows scattering from both TIDs, at different altitudes and propagating in different directions. To the best of our knowledge this is the first time that such a phenomenon has been reported.
In this paper, we perform an in-depth analysis of ionospheric scintillation seen in an observation of the strong natural radio source Cassiopeia A (Cas A) overnight on 18-19 August 2013. This observation was amongst the first of its kind taken with LOFAR and exhibited quite strong scattering effects across the 10-80 MHz band. The purpose of this paper is both technical and scientific: We first describe the observation itself, and then demonstrate several techniques to analyse LOFAR data and show how these can bring out the details of ionospheric structures. Finally, we use supporting data from GNSS and ionosondes to get a broader picture of conditions in the ionosphere at the time and how these give rise to the scintillation seen by LOFAR.