dataesr/scientific-paragraphs-categorization huggingface dataset:
Network robustness in nodes removal In this analysis, the proportion of removed nodes required to achieve a connectivity loss of 0. 80 was recorded for the OeMG, OeSG, OmMG, and OmSG networks after directed, cascading, degree or random node removal. Directed removal of nodes consists in removing first the nodes with higher betweenness centrality. The cascading effect is that the nodes with high betweenness centrality are removed first, but recalculated each time a node is removed. The robustness of the network was also measured after removing nodes with very high degree. The last type is a random removal of nodes. The robustness of the networks is calculated using the Network Strengths and Weaknesses Analysis (NetSwan) script  (Lhomme, 2015)  in RStudio  (RStudio Team, 2020).
Prediction and comparison of functional traits in the tick microbiome The 16S rRNA amplicon sequences from each dataset were used to predict the metabolic profile of each sample. PICRUSt2  (Douglas et al. , 2020)  implemented in QIIME2, was used to predict the metagenomes from the 16S rRNA amplicon sequences. The ASVs were inserted into a reference tree (NSTI cut-of value of 3) containing more than 20,000 complete 16S rRNA sequences from prokaryotic genomes and then used to predict the copy numbers of each gene family for each ASV. The predictions are based on Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KO;  Kanehisa, 2000). Pathways were generated based on the Meta-Cyc database  (Caspi et al. , 2018). The differentially frequent paths were identified based on log 2 fold change (LFC), using the Wald test implemented in the DESeq2 (v1.
38. 2) R package  (Love et al. , 2014). The DESeq2 pipeline was developed for differential gene analysis on smaller RNA-seq datasets with high overdispersion. The method assumes that the count data has a negative binomial distribution and models it using the maximum likelihood estimation (MLE) method. It includes a dispersion shrinkage estimation and fold-change estimation for each feature, resulting in accurate false discovery rates and FDR-corrected value of ps  (Love et al. , 2014).
Hierarchical clustering of samples based on Jaccard distance showed that the microbiome of OmMG and OmSG clustered separately, while OeSG and OeMG clustered closely together (Figure  2B ). Analysis of differential relative abundance (expressed as clr) showed that 30 taxa changed significantly between the four datasets (Figure  2C ). Overall, these results suggest that soft tick microbiome is species-specific, and that differences in the bacterial composition and diversity between tissues is higher in O. moubata than in O. erraticus.
Functional profiles of microbiome in salivary glands and midguts of Ornithodoros erraticus and Ornithodoros moubata Changes in composition and structure of the microbial community may affect the inferred functional profile of the tick microbiome. We analyzed and compared the composition, diversity and differential abundance of predicted metabolic pathways in SG and MG of O. erraticus and O. moubata. Significant differences were   S5A ). The Bray-Curtis index and a PERMANOVA test showed that the beta diversity of metabolic pathways was significantly different between all datasets (PERMANOVA, p < 0. 05), except in the SG of the two tick species (Figure  6B ; Supplementary Table  S5B ). The four datasets shared 421 (93. 1%, 452 total) metabolic pathways. Six unique metabolic pathways (1. 32%, 452 total) were found in each of the OmSG and OmMG microbiomes (Figure  6C ; Supplementary Table  S6 ), while no unique pathway was found for OeSG and OmMG.
, 2017) , Dermacentor silvarum  (Duan et al. , 2020)  and Ixodes ricinus    Frontiers in Microbiology 11 frontiersin. org (Wiesinger et al. , 2023). Differences in bacterial diversity and composition between tissues of the same tick species could be associated to differential microbe-microbe interactions (e. g. , competition caused by differences in nutrient availability) and/or host-microbe interactions (e. g. , tissuespecific immunity or immune evasion by microbes;  Wu-Chuang et al. , 2021). In our study, several genera of Proteobacteria (i. e. , Rickettsiella, Cellvibrio, Methylophaga, Xanthomonadaceae, Cupriavidus, and Ralstonia) presented significant changes in relative abundance between the four datasets. Kumar et al. (2022)  showed a significant difference in the relative abundance of Proteobacteria in SG and MG of unfed and partially engorged Ixodes scapularis ticks.
, , 2021;; Pérez-Sánchez et al. , 2022)  between MG  (Oleaga et al. , 2017a (Oleaga et al. , ,b, 2018) )  and SG  (Oleaga et al. , 2021; Pérez-Sánchez et al. , 2022)  of O. erraticus  (Oleaga et al. , 2018; Pérez-Sánchez et al. , 2022)  and O. moubata  (Oleaga et al. , 2017a (Oleaga et al. , ,b, 2021) )  demonstrates functional differentiation and specialization in these tissues, which may be influenced by the composition of the microbial community and its functional contribution as a result of its metabolic activity. Despite evidence of metabolic specialization in the SG and MG of O. erraticus and O. moubata, 93. 1% of the predicted metabolic pathways were shared by all the datasets. Likewise, a previous study found similar biochemical pathway composition across tick ontogeny  (Zolnik et al.
Funding UMR BIPAR was supported by the  French Government 's  Investissement d' Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant no. ANR-10-LABX-62-IBEID ). AW-C was supported by  Programa Nacional de Becas de Postgrado en el Exterior "Don Carlos Antonio López " (grant no. 205/2018 ). AM was supported by the  'Collectivité de Corse ' , grant: ' Formations superieures ' (grant code. SGCE-RAPPORT No. 0300 ). AC-A was supported by project " CLU-2019-05-IRNASA/CSIC Unit of Excellence , " granted by the  Junta de Castilla y León  and co-financed by the  European Union  ( ERDF "Europe drives our growth ").
These two approaches captured first order pandemic related reductions but were not tested for subsequent variations when lockdowns were finished, and did not continue to provide near-real-time, daily and country-level estimates emissions. Further, the private company Kayrros released a daily estimate of emissions from regulated sectors (European Trading Scheme) in November 2021 using site-level satellite monitoring of industrial activity, called Carbon Watch  20. The Carbon Monitor international research initiative developed a new near-real-time daily dataset of CO 2 emissions with global coverage and country-level estimates for 12 countries which lags reality by only one month  1, 3, [21] [22] [23] [24] [25] [26]. Building on this foundation, they have also developed the Carbon Monitor China  27  , which provides provincial-level near-real-time daily emissions data for China, and the Carbon Monitor United States  28  , which provides state-level daily emissions data for the US.
Additionally, they have launched the Carbon Monitor Cities  29, 30  , covering near-real-time daily emissions data for over 1500 cities worldwide, the Carbon Monitor Power  31  , which provides near-real-time hourly-to-daily data on power generation, and the Carbon Monitor GRACED  32, 33  , a dataset of global near-real-time grid-level CO 2 emissions. Here, we present a near-real-time, sector-specific, country-level estimates of daily fossil fuel and cement CO 2 emissions for 27 European Union countries and the United Kingdom based on an extension of Carbon Monitor called Carbon Monitor Europe (CM-EU). We present the methodology and the results for changes in emissions between January 1, 2019 and December 31, 2021. Details of our data sources and approach are in the Methods section. An evaluation of CM-EU against national quarterly emissions estimates and Carbon Watch is provided in the technical validation section.
Methods Carbon Monitor Europe (CM-EU) is a new regional improved version of the global Carbon Monitor system, providing near-real-time daily estimates of CO 2 emissions for six sectors over the globe with separate estimates for 12 countries or groups of countries  1, 21. CM-EU presents country-level estimates of daily CO 2 emissions from January 2019 through December 2021 for 27 countries of European Union (EU27) and the United Kingdom (UK). Following the method presented in the global Carbon Monitor publications  1, 21  , we used annual CO 2 emissions of EU27 & UK from the Emissions Database for Global Atmospheric Research (EDGAR)  8  as the baseline data for emissions in the year 2019, then disaggregated and extrapolated in time this estimate into daily scale for 2020 and 2021 based on time variations of activity data.
Below we describe the calculation process in detail. annual country-level and sectoral CO 2 emissions in the baseline year 2019. Annual fossil fuel combustion and cement production CO 2 emissions by sector in 2019 for all European Union countries and United Kingdom are directly obtained from the Fossil CO 2 emissions of all world countries -2020 Report  8  released by the Emissions Database for Global Atmospheric Research (EDGAR). Emissions of EDGAR are derived using the IPCC Tier 1 approach according to the 2006 IPCC Guidelines  35  , with 35 sectors based on IPCC categories. We aggregated 11 energy-related and cement production sectors of EDGAR into six main sectors, including power, industry, ground transportation, residential (public, commercial buildings and households), domestic and international aviation.
∑∑∑ = × where i, j, k denote regions, sectors, and fuel types respectively. We assume that emission factors and structure of each sector remain unchanged for each country in 2020 and 2021 compared with 2019. Thus, the rate of change of the emission is calculated based solely on the change of the activity data in 2020 and 2021 compared to the same period of 2019. The emissions were calculated following this equation separately for the power sector, the industrial sector, the ground transportation sector, the aviation sector (including the domestic and international aviation sector) and the residential sector. Power sector. For the power sector, daily emissions were calculated from hourly electricity generation data by production types at resolution of   2 ). We removed outliers and filled the N/A values by using the linear interpolation function in Python Pandas packages.
The electricity generation data used in this study from ENTSO-E and BMRS are include several fuel types with coal, gas, oil and peat. The average yearly emission factor of the whole power sector is assumed to remain constant at its 2019 value, so that daily emissions are estimated by: Emis Emis AD AD power daily power yearly power daily power yearly For Lithuania, Luxembourg, Malta, Romania and Sweden (other EU) not covered by ENSTO-E, we assume a linear relationship between their daily emissions and the total daily emissions of the 22 EU countries and United Kingdom (EU22 + UK) and their daily emissions are estimated as:   but does not mean there are no vehicles and zero emissions. The lower threshold of emissions when the congestion level is zero was estimated using real-time data from an average of 70 main roads in the city of Paris.
Emissions of the aviation sector separated into domestic and international aviation, are estimated by individual commercial flights data (Table  2 ) from the Flightradar24 database (https://www. flightradar24. com). We compute CO 2 emissions by assuming a constant emissions factor of aviation in CO 2 emissions per km flown, called EF aviation across the whole fleet of aircraft (regional, narrowbody passenger, widebody passenger and freight operations) as the share of flight types has not significantly changed since 2019. The aviation sector separates domestic flights and international flights departing from countries considered in this study. The flights within EU but different countries are considered as international. The daily emissions of the aviation sector are estimated as:.
Data Records The CM-EU dataset is an CSV file containing country-level emission of 27 European Union countries and the United Kingdom from 01/01/2019 to 31/12/2021 for six sectors: power, industry, ground transportation, domestic and international aviation. The dataset comprises five columns: country, date, sector, value, and timestamp. The value column denotes the daily CO 2 emissions in Mt CO 2. The timestamp is included to help servers recognize time. At the time of writing this article, this dataset has been updated to December 31, 2021, and the full dataset can be downloaded at Figshare  34. Latest updates and related information are available for view and download on our website https://eu. carbonmonitor. org/. The power sector emissions for Lithuania, Luxembourg, Malta, Romania, and Sweden, in addition to ground transport emissions for Croatia, Cyprus, and Malta, have been estimated by assuming a linear relationship with the daily emissions totals from the remaining countries in their respective sectors.
This estimation method was employed due to the lack of coverage or incorrect data provided by the activity data. Figure  1  shows daily CO 2 emissions for the years 2019, 2020, and 2021 in the EU27 & UK, as well as for specific countries including Germany, the United Kingdom, Italy, Poland, France, Spain, the Netherlands, and other European countries. Figure  2  represents the daily CO 2 emissions for six sectors within the EU27 & UK from 2019 to 2021. Figure  3  shows the changes in emissions within the EU27 & UK, from 2019 to 2020 and 2021, across regions and sectors. It highlights the contributions made by major countries and sectors during the study period. Figure  4  shows changes in annual CO 2 emissions at country level, making comparisons between 2020 and 2019, as well as between 2021 and 2020, and 2021 and 2019 with other published datasets.
The third evaluation data include four data sources: annual CO 2 emissions reported to the UNFCCC 6 , near-real-time daily and monthly CO 2 estimates of EU from CICERO (updated from ref. 41  ) and Centre of Research on Energy and Clean Air (CREA)  42  , and daily country-level CO 2 emissions of EU countries and UK from 2019 to 2021 from the Carbon Watch data of Kayrros  20  focusing on regulated sectors (ETS). Figure  5  shows the comparison of annual CO 2 emissions in 2019 and 2020 and changes between the two years for EU27 & UK, Germany, UK, Italy, Poland, France, Spain, Netherlands and the rest of EU between CM-EU, EDGAR, BP, IEA, GCP and Eurostat. In terms of annual total amount, the results of the six databases are relatively similar, and the main difference comes from the scope difference of each dataset.
As for changes between 2019 and 2020, our estimates are lying in the middle range, close to most other datasets. Figure  6  shows the comparison of annual or quarterly CO 2 emissions in 2019 and changes between 2020 and 2019 or 2021 and 2020 for Netherlands, UK, Sweden and Germany between this study and estimates from Citepa (France), Centraal Bureau van Statistiek (Netherlands), gov. uk (UK), Statistics Sweden (Sweden) and Umwelt Bundesamt (Germany). It illustrates that our estimates are close to the official estimates in terms of annual or quarterly totals for 2019 and for changes between 2019 and 2020 (at the beginning of COVID-19 pandemic), with relative differences from 0. 07% to 3. 17% at annual scale and 0. 2% to 5. 63% at quarterly scale. Our estimates differ more (in terms of relative differences) from these official estimates for quarterly and annual changes between 2020 and 2021 than between 2019 and 2020, with relative differences from 0.
81% to 3. 34% at annual scale and 0. 19% to 9. 94% at quarterly scale. In Fig. 7 , we compare CO 2 emissions across 27 EU countries from 2019 to 2021, utilizing yearly, monthly, and daily time-steps with other estimates with CREA and CICERO. We omitted the CM-EU emissions from the international aviation sector and UK emissions due to the absence of extra-territorial and UK emissions data in CREA and CICERO. Although CM-EU and CREA offer daily estimates, we averaged them into monthly steps for an easier visual comparison. For monthly data, the emission estimates from all three sources are relatively similar, with CREA reporting a slightly higher emission level. We conducted a moving window correlation analysis of these two sets of data with CM-EU and averaged the results. As can be seen from the shaded portion of the line graph, there is substantial agreement between CM-EU and the other two data sets for the majority of the time, with the exception of July 2021, where a discrepancy was observed.
A possible reason for this is the scope of Kayrros covering the regulated industrial installations is lower than the total industry sector of CM-EU, and there are the balances of attributions between power and industry for power production on industrial sites in Kayrros. While all the manufacturing industries, construction and cement production are considered in CM-EU and Citepa. Citepa also considers other industrial productions. For power, the CM-EU emissions in EU27 & UK are almost the same as Kayrros but small differences in winter peak and summer trough (Fig. 8 ). A possible reason for this is both datasets use the electricity production by fuel type as activity data from the same data sources. Kayrros can catch the differences from fuel burning efficiency in facility level while CM-EU can't.
Here we compared daily emissions from CM-EU with estimates derived from natural gas use from ENSTO-G  47   The ENTSO-G consumption data was completed with Trading Hub Europe (THE)  48  for the German and e-control  49  for Austria. Note that the consumption sectors are not provided by e-control dataset. We further split the consumption into more detailed sectors, including household and public buildings heating, industry, and others based on energy balance datasets from Eurostat 50 following the method from Zhou et al. 51  The activity data used here are only from gas fuel of household and public heating, and we assume that there is a linear relationship between gas fuel use and total fossil fuel use for residential sector. The results in Figs. 10, 11 show that the variation of residential emissions in our study are similar to those of ENTSO-G in most countries (R 2 ranges from 0.
Uncertainty analysis. There are two main sources of uncertainties in the CM-EU data. 1. The uncertainty inherited from the EDGAR annual national emissions used for the reference year 2019. 2. Uncertainty from daily activity data and models used to downscale them into daily emissions. The uncertainty analysis was conducted based on the 2006 IPCC Guidelines for National Greenhouse Gas Inventories  35  and the methodology outlined in the global Carbon Monitor  1, 21. First, uncertainties were calculated for each sector of CM-EU. For the power sector, CM-EU uses daily statistics of actual thermal production as activity data. When no uncertainty information is available, the 2-sigma uncertainty of the power activity data is assumed to be ±5% according to the IPCC recommended default uncertainty range of energy statistics  35.
IPCC code Sectors of EDGAR Sectors of CM-EU Industry sector. Daily emissions from industry are estimated using the monthly industrial production index (IPI) from several datasets and daily power generation data (Table2). As daily production data are not available for industrial and cement production, the monthly CO 2 emissions are estimated by using monthly statistics of industrial production and daily data of electricity generation to disaggregate the monthly CO 2 emissions into a 1A1a Public electricity and heat production Power 1A1bc Other energy industries 1A2 Manufacturing industries and construction Industry (incl. cement production) 2A1 Cement production 1A3a Domestic aviation Domestic aviation 1A3b Road transportation no resuspension 1A3c 1A3d Rail transportation Inland navigation Ground transportation 1A3e Other transportation 1A4 Residential and other sectors Residential 1C1 Memo: International aviation (bunker fuels) International aviation power daily , EU ,2019, 5 = Emis power yearly , ,2019, o ther EU × Emis Emis power yearly power daily , ,2019, 22 EU E U , , 2019, 22 + + UK UK Emis power daily , or ,2020 2021, other EU = Emis p ower daily , E U ,2019, 5 × + UK ,2020 2021, 22 + or EU power daily power daily , Emis Emis EU , ,2019, 22 UK.
The second approach was proposed by Lustig  17  and consists in estimation of a number of ensemble averages U , U 2 , ∂U ∂V , ( ∂U ∂V ) 2 , ∂ 2 U ∂V 2 , which in dfferent combinations result in macroscopic values for isobaric heat capacity C p , adiabatic speed of sound c s , and Joule-Thomson coefficient µ JT  17. Here U is the potential energy of the system and V -its volume. This methodology was implemented in the software package ms2  18  for molecular simulations with effective interatomic potentials. So far it is unclear whether this methodology could be applied with ab initio simulations, when the subsystem of valence electrons is treated explicitly via density functional theory and total energy of the system is a complex functional of electron density  19. The third approach is easily applicable with classical (with interatomic effective interactions) MD simulations and needs calculations of k-dependent correlators involving energy (or heat) density  10, 20  , that allows estimation of k-dependent ratio of specific heats γ (k).
Both calculated dependences in Fig. 1  show monotonic increase with density. Combining the density dependence in Fig. 1  according to (8) one obtains the adiabatic speed of sound c s as a function of density at T = 280 K as shown in Fig. 2. For comparison by open boxes we show the results of estimation of c s via the long-wavelength limit of the ratio of k-dependent quantities γ (k) and S(k) according to the right hand side of Eq. 2. The latter was calculated in a sophisticated way via sampling of k-dependent energy-(or heat-)density fluctuations in MD simulations and calculations of corresponding static correlators. One can see that there is practically good agreement of c s obtained by the proposed methodology via Eq. (  8 ) and via the previous sophisticated methodology via Eq. (  2 ). Both sets of results are in good agreement with the NIST database for supercritical Ar at 280 K.
It uses abstract interpretation techniques to perform value analyses, relational analyses. Verasco is written in Coq and the soundness of the analysis is guaranteed by a theorem: a program where the analysis does not raise an alarm is free of errors. Reasoning about program behaviours is possible as Verasco reuses the formalisation of the C semantics in Coq that was written for CompCert  [11]. CompCert is a proved semantic preserving C compiler written in Coq. Another example is the K  [18]  framework for writing semantics using rewriting rules. Rewriting rules make the formal definition of a semantics both flexible and relatively simple to write, and allows to mechanically derive objects from the semantics like an interpreter. However, this mechanization can be complex: K-Java  [2]  is a formalization of Java in K, with close to four hundred rewriting rules.
It is unclear if it is possible to derive an analysis from a mechanization in K. The key idea that we will pursue in this paper is that an abstract interpreter for a semantic metalanguage combined with language-specific abstractions for a particular property yield a correct-byconstruction abstract interpreter for the specific language and property. We describe how to obtain a correct program analyser for a programming language from its skeletal semantics. Skeletal Semantics  [1]  is a proposal for machine-representable semantics of programming languages. The skeletal semantics of a language L is a partial description of the semantics of L. Typically, a skeletal semantics will contain definitions of the constructs of the language and functions of evaluation of these constructs. A skeletal semantics is written in the meta-language Skel  [17] , a minimalist functional language.