dataesr/scientific-paragraphs-categorization huggingface dataset:
A final analysis on agri-food systems impacts on total GHG emissions would not be complete without a focus on component gases in addition to quantities expressed in CO2eq. The FAOSTAT data confirm the trends form 1990 to 2019 seen for total CO2eq emissions, with important features (Tab. 5). First, the impact of agri-food systems on world total CO2 emissions was 21% in 2019 (down from 31% in 1990), a respectable share considering the importance of carbon dioxide in any effective long-term mitigation strategy. While most regions had contributions around this value, ranging 13%-23% for North America, Oceania, Europe and Asia, the CO2 contribution of agrifood systems was higher in Africa (52%) and South America (70%), largely in relation to the land use change emissions that are still significant therein.
, 2020; IIR, 2021; Tubiello et al. , 2021b). Another aspect of the dataset underlying this study is that it provides food and agriculture relevant information across IPCC and FAO definitions and classifications. In terms of national GHGH inventories, it is worth pointing out that while agri-food systems were found to be about one-third of total anthropogenic emissions, our data indicated that emissions from land use, land use change and forestry (LULUCF) in 2019 only represented 3-4%, while emissions from agriculture, forestry and other land use (AFOLU), were a mere 15% of the total anthropogenic emissions.
Conclusions This paper provided details of a new FAOSTAT domain characterizing GHG emissions along the entire agri-food systems chain, including crop and livestock production processes on the farm, land use change activities from the conversion of natural ecosystems to agricultural land, and processes along food supply chains, from input manufacturing to food processing, transport and retail, including household consumption and waste disposal. The data are provided in open access mode to users worldwide and are available by country over the time period 1990-2019. The major trends identified in this work help identify emissions hotspots across agri-food systems and by country, helping to identify areas for effective mitigation actions in food and agriculture. This work adds to knowledge well established in the literature but limited in terms of datasets to farm gate processes and land use change, by adding a wide range of additional details on emissions from pre-and post-production processes.
The new data highlight the increasingly important role that these play in the overall emissions footprint of agri-food systems, reflecting a pattern of development from traditional to modern agri-food systems and overall economic growth. The granularity of the dataset allows, for the first time, to highlight specific processes of importance in specific countries or group of countries with similar characteristics. The relevance of the information being provided cuts across several national and international priorities, specifically those aiming at achieving more productive and sustainable food systems, including in relation to climate change. To this end, the work presented herein completes a mapping of IPCC categories, used by countries for reporting to the climate convention, to food and agriculture categories that are more readily understandable by farmers and ministries of agriculture in countries.
Due to the scarcity of stations on the Tibetan Plateau, the Chinese Academy of Sciences (CAS) launched the Tibetan Plateau Network of Isotopes in Precipitation (TNIP) in 1991  (Yu et al. , 2016a). However, most Chinese stations in GNIP stopped monitoring in the early 2000s  (Zhang and Wang, 2016). In order to continue the systematic study, the CAS established the Chinese Network of Isotopes in Precipitation (CHNIP) based on the Chinese Ecosystem Research Network (CERN) in 2004. Due to the difficulty and high cost of measuring precipitation isotope ratios  (Allen et al. , 2018) , most of the observed data are short in length. The spatial distribution of observation stations is uneven, with few stations in inaccessible areas  (Wang et al. , 2015). The stable isotopes in precipitation can also be simulated by isotope-equipped general circulation models (iGCMs).
Furthermore, outputs of iGCMs are usually subjected to systematic bias when compared with gauged observations, and there is no single iGCM that consistently performs better than the others  (Zhang et al. , 2012; Wang et al. , 2015; Conroy et al. , 2013). The use of ensemble mean of multiple iGCMs is a desirable approach for better use of information available from all iGCMs. Overall, both observations and iGCM simulations have advantages and disadvantages. The effort of constructing a database by taking advantages and circumventing disadvantages of both becomes a challenge. Accordingly, in order to make full use of the advantages of high quality observations and continuous iGCM simulations, this study aims at constructing a high-resolution precipitation oxygen isoscape in mainland China by data fusion and bias correction of iGCM simulations.
In 70 order to determine the best scheme to build the dataset, two bias correction methods (BCMs) and three neural network data fusion methods (DFMs) are first compared in terms of bias correcting and fusing iGCM simulations. The new isoscape in monthly temporal and approximately 0. 5° spatial resolutions is produced by combining the Convolutional Neural Networks (CNN) fusion method and bias correction method for the 1870-2017 period. The spatial and temporal distribution characteristics of oxygen isotopes in precipitation are then analysed for China. 75.
Datasets There are 78 oxygen isotope observation stations in the study area (Fig. 1 ), including 29 GNIP stations (available at https://nucleus. iaea. org/wiser), 27 CHNIP stations  (Liu et al. , 2014) , 13 TNIP stations  (Yao et al. , 2013)  and 9 stations from other sources (mainly from references). Monthly oxygen isotope composition of precipitation (δ 18 O p ) is used for analysis. The time span of GNIP data mostly ranges from 1980 to 2000, and the length of the time period is basically 5-15 years. For CHNIP, most time periods are about 2-5 years ranging from 2005 to 2010. Most TNIP data were between 1995 and 2005, with varying lengths. Eight δ 18 O p spatio-temporal fields simulated by five iGCMs (CAM2, GISS E, HadAM3, LMDZ4 and MIROC32) are used, which are selected from the SWING2 archive (available at https://data.
Evaluation of bias correction and data fusion methods Prior to applying BCMs and DFMs to build the isoscape, the performance of iGCM simulations was evaluated by comparing gauged observations for the 1969-2007 period. Fig. 3  shows the cumulative distribution functions (CDFs) of δ 18 O p for observations and iGCMs simulations in each sub-region. Generally, the CDFs of observed δ 18 O p can be well represented by iGCM simulations for each sub-region, as the observed CDFs distribute in the centre of simulated ones. For specific regions, the envelope of CDFs is the narrowest for NE and SE, indicating that iGCMs perform consistently better for these two regions. For NC, SC and NW, the CDFs of LMDZ4 (free and nudged) simulated δ 18 O p are relatively close to the observations, while other iGCM simulations generally overestimate the δ 18 O p.
Data application The dataset includes the stable oxygen isotope of precipitation for mainland China over the 1870-2017 period. Since the 375 observation is limited and climate model simulations have different time durations, the entire duration was constructed by using the various methods and datasets for different periods as shown in Fig. 10. Based on this new isoscape, the physical mechanisms driving the spatio-temporal variation of δ 18 O p can be deeply explored. This dataset is useful for tracing atmospheric and hydrological processes. For example, it can be used to study the effect of meteorological variables and air mass trajectory on stable isotope distribution, and quantify the source and fate of moisture  (Tian et al. , 2007; Peng et al. , 2020b; Gao et al. , 2011). The data can also help track the water cycle, to assess the source of water resources using complementary isotopic data from lakes, rivers and groundwater  (Yao et al.
, 2013; Gao et al. , 2009). The generated dataset is also useful for paleoclimate studies, by studying the relationship between modern isotopes and meteorological variables  (Yu et al. , 2016b). When using this dataset, it should be noted that δ 18 O p for the 1979-2007 period generated using the CNN fusion method should be more reliable, because the performance comparison for BCMs and DFMs show that CNN fusion method performed better than BCMs, and more iGCM simulations were used for data fusion in this period that other periods.
Data availability The generated dataset is available in Zenodo at https://doi. org/10. 5281/zenodo. 5703811 (Chen et al. , 2021). The GNIP data can be obtained from the GNIP Database of IAEA/WMO (https://nucleus. iaea. org/wiser). The TNIP data was derived from the National Tibetan Plateau Data Centre (DOI: 10. 11888/Geogra. tpdc. 270940). The detailed information and sources of observations from other sources are shown in Table  3. The iGCM data can be downloaded from SWING2 (accessible at https://data. giss. nasa. gov/swing2/), where LMDZ4 zoomed data was provided by Dr. Camille Risi at Laboratoire de Météorologie Dynamique in France.
Acknowledgements We would like to thank the  International Atomic Energy Agency (IAEA)  for providing the GNIP data, the  Chinese Academy of Sciences (CAS)  for providing the CHNIP and TNIP data, and everyone who took samples and shared data. We would like to thank the  Stable Water Isotope Intercomparison Group (SWING)  and the second phase of SWING for providing the iGCM simulated data.
Financial support This work was partially supported by the  National Key Research and Development Program of China  (grant no. 2017YFA0603704 ), the  National Natural Science Foundation of China  (grant nos. 52109007  and  52079093 ), the  Overseas Expertise Introduction Project for Discipline Innovation  ( 111 Project ) funded by the  Ministry of Education and State Administration of Foreign Experts Affairs, P. R. China  (grant no. B18037 ).
(2020) (DOI: 10. 17632/vndfs3dpyn. 1) SE Xishuangbanna 21. 93 101. 27 2002-2004 Liu et al. (2007) https://doi. org/10. 5194/essd-2021-460 Discussions Preprint. Discussion started: 1 https://doi. org/10. 5281/zenodo. 5703811, 2021.
Data acquisition and preprocessing EEG signals were recorded using a HD-EEG system (EGI, Electrical Geodesic Inc. , 256 channels), with a sampling frequency of 1000 Hz. 199 electrodes were kept, removing most of the jaw and neck electrodes, as shown in the channel file available in the GitHub repository (see section 2. 8 ). EEG preprocessing was performed manually using the Brainstorm toolbox  ( Tadel et al. , 2011 ). Preprocessing was performed as follows. First, DC offset removal was applied. Second, a notch filter at 50 Hz and a band-pass filter of 1-100 Hz were applied. Third, signals were visually inspected, and bad channels were removed before being interpolated using Brainstorm's default parameters. Fourth, Independent Component Analysis (ICA) was used to remove eye blinks and muscle artifacts.
EEG source connectivity i. Forward Model Following the equivalent current dipole model, EEG signals measured from Q channels can be expressed as linear combinations of P time-varying current dipole sources as follows: 𝑋( 𝑡 ) = 𝐺. 𝑆( 𝑡 ) + 𝑁( 𝑡 ) (1) Where (  ×  ) represents the forward model, often called the lead field matrix, and (  ) denotes the additive noise. The lead field matrix is computed from a realistic head model along with the position of electrodes. Here, we used the Boundary Element Method (BEM) head model fitted to the ICBM MRI template  ( Kötter et al. , 2001 ) , downloaded from https://www. mcgill. ca/bic/software/toolsdata-analysis/anatomical-mri/atlases/icbm152lin using the OpenMEEG toolbox  ( Gramfort et al. , 2010 ) , and used the Electrical Geodesic Inc (EGI) configuration for the EEG electrodes.
Since the choice of the number of cycles and window length remains subjective, we tested how the networks estimated from one dataset were similar with varying number of cycles  (4, 6 or 8)  and overlap between  windows (70, 80 or 90% ). We extracted the networks following the steps Fig. 2. Dynamic FC pipeline used in our study. First, HD-EEG data was recorded during the Simon task (only correct incongruent trials were considered). After preprocessing, cortical-level sources were reconstructed using the weighted Minimum Norm Estimate (wMNE) and the Destrieux atlas (148 Regions of Interests, ROIs). Then, dynamic functional connectivity (dFC) was estimated for each subject and trial using a sliding window. Phase Locking Value (PLV) was used to quantify the statistical coupling between ROIs. Finally, the temporal Independent Component Analysis (tICA) was applied on the dFC tensor to extract dynamic brain network states, including spatial network maps and temporal activity.
, 2017 ; Yaesoubi et al. , 2015 ). This technique assumes maximal independence between the time courses of the extracted dBNS. Here, tICA was performed using the JADE algorithm (Joint Approximate Diagonalization of Eigen-matrices)  ( Cardoso and Souloumiac, 1993 ; Rutledge and Jouan-Rimbaud Bouveresse, 2013 ). Briefly, JADE applies the Jacobi technique to optimize contrast functions based on high statistical order (Fourth Order: FO) cumulants of the data. One advantage of JADE compared to other ICA methods (FastICA, infomax,. ), in our situation, is its high robustness for small sample sizes:  Tabbal et al. , 2021  showed that group-subject similarity was above 90% when there were only 5 subjects, and 100% with as few as 7 subjects. Here, the JADE function in Matlab was used (The Mathworks, USA, version 2019a).
i. Behavior RT and accuracy are the variables measured during the Simon task. Comparing congruent and incongruent RT of correct responses provides an estimate of the congruence effect that informs about the additional time needed to solve conflict. Incongruent trials are typically associated with increased RT and decreased accuracy. In addition to the analysis of the congruence effect, behavioral data were also analyzed in light of the activation-suppression model  ( Ridderinkhof, 2002 ) , which informs about the temporal characteristics of cognitive action control. These are based on distributional analyses that measure impulsive action selection (incongruent accuracy for the fastest trials) and suppression (slope value of the congruence effect for the slowest trials). A complete description of the model and analysis steps can be found in van den Wildenberg et al.
RT were log-transformed for increased compliance with the model's assumptions in that case. The formulas used for the models were the following: Fig. 3. A. Temporal ICA applied to the concatenation of the HC and PD groups. B. Back-Fitting approach assigns PLV network at each temporal window with one of all the ICA networks (microstate) having the highest spatial similarity value (strongest correlation). This was applied on all HC and PD subjects, followed by a calculation of the main network states metrics as shown in C. Fixed effects significance were computed through the Anova function of the {car} package  ( Fox and Weisberg, 2019  ) that calculates type II Wald chi-square tests. Marginal ( m R 2 ) and conditional ( c R 2 ) R 2 were calculated using the {MuMin} package  ( Barton, 2009 ).
Acknowledgements The authors would like to thank all the participants who took part in this study. We also thank  Dr. Adrien Bénard  (MD) and  Dr. Sina Potel  ( MD ) for their help in data acquisition. We also would like to thank  Bretagne Atlantique Ambition (BAA)  as well as the  Rennes Clinical Neuroscience Institute  (INCR: www. incr. fr), and the  Ille-et-Vilaine Parkinsonian Association (APIV)  who funded this work.
Methods: We extracted monthly data on cases and deaths for AIDS, gonorrhea, syphilis, hepatitis B, and hepatitis C between January 2015 and December 2021 from the notifiable disease reporting database on the official website of the National Health Commission of China. We used descriptive statistics to summarize the number of cases and deaths and calculated incidence and case-fatality ratios before and after the implementation of a nationwide lockdown (in January 2020). We used negative binominal segmented regression models to estimate the immediate and long-term impacts of lockdown on cases, deaths, and case-fatality ratios in January 2020 and December 2021, respectively. Results: A total of 14,800,330 cases of and 127,030 deaths from AIDS, gonorrhea, syphilis, hepatitis B, and hepatitis C were reported from January 2015 to December 2021, with an incidence of 149.
Acknowledgments This study was supported by the  Natural Science Foundation of China Excellent Young Scientists Fund  ( 82022064 ), the  Special Support Plan for High-Level Talents of Guangdong Province  ( 2019TQ05Y230 ), the  High Level Project of Medicine in Longhua, Shenzhen  ( HLPM201907020105 ), the  Sanming Project of Medicine in Shenzhen  ( SZSM201811071 ), the  Shenzhen Nanshan District San-Ming Project  ( SZSM202103008 ), and the " Pearl River Talent Plan" Innovation and Entrepreneurship Team   Project of Guangdong Province  ( 2021ZT09Y544 ). No funding parties had any role in the design of the study or in the explanation of the data. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit it for publication.
LC-MS/MS Data Processing and Analysis Label-free quantification was performed on Progenesis QI for Proteomics (Waters, Milford, MA, USA) in Hi-3 mode for protein abundance calculation. Proteins were filtered with a fold change ≥ 2 and a p-value ≤ 0. 05. MGF peak files from Progenesis were processed by Proteome Discoverer 1. 4 with the Mascot search engine (Version 2. 5. 1). The Swissprot database (release 2014_06, 545,657 entries) with Homo sapiens taxonomy (20214 entries) was used. A maximum of 2 missed cleavages was authorized. Precursor and fragment mass tolerances were set, respectively, to 7 ppm and 0. 5 Da for the Orbitrap Fusion, and 6 ppm and 0. 02 Da for the Orbitrap Qexactive Plus. The following post-translational modifications were included as variables: Acetyl (Protein N-term, K), Oxidation (M), Phosphorylation (STY), LRGG(K), EQIGG (K), and Deamidation (NQ).
Randomization, blinding, and unblinding Assignment to study treatment is randomized in a 1:1 ratio, stratified by site. An Interactive Response Technology System (IRTS) is used for randomization of patients (random permuted blocks of variable size) and IP supply. The treatment group randomization list and the IP kits list are generated prior to the initiation of the study by the IRTS vendor and approved by an unblinded statistician (not involved in the study data analyses). Eligible patients are enrolled by the investigator and randomized by the IRTS. Investigators and medical staff as well as study participants are blinded to the study treatment. Emergency unblinding will only be done via the IRTS by an investigator and/or dedicated authorized personnel (e. g. , Pharmacovigilance Department of the Sponsor).
Primary endpoint The mean difference in cystatin-C-based eGFR, calculated from the highest cystatin-C level measured during PODs 1-3, will be estimated with a two-sided 95% confidence interval by analyzing the change from the preoperative baseline eGFR value in an analysis of covariance model, which includes treatment and study site as factors and baseline eGFR as a covariate. Non-inferiority of Volulyte 6% compared to Ionolyte will be tested with a one-sided contrast (α = 0. 025) for a non-inferiority margin of δ = 8. 1 mL/min/1. 73 m 2 (see the "Sample size" section for justification). The primary endpoint will be analyzed in the full analysis set (FAS) and in the perprotocol analysis set (PPS). Since the use of the FAS may not be conservative in a non-inferiority trial (ICH-E9 guideline)  [23] , the non-inferiority test performed in both datasets is considered as co-primary.
By definition, the FAS will comprise all patients reaching the post-operative period and monitored at least once with respect to the changes in cystatin-C-based eGFR. The results of both analyses (i. e. , PPS and FAS) will be compared and possible differences assessed. Secondary outcomes will be compared by means of descriptive statistics and appropriate statistical tests (time to event analyses (Kaplan-Meier plots, Cox regression), analysis of covariance, logistic regression, Mann-Whitney U test, χ 2 test). Strong efforts will be made to collect all data points in the study (e. g. , by training of investigators and other authorized staff, regular monitoring of data entries). Missing pre-operative baseline values for serum creatininebased eGFR will be imputed by assuming a numerical value at the lower end of the normal range (i.
Trial ethics and governance This clinical study is being conducted in accordance with the Declaration of Helsinki and in compliance with the study protocol, Good Clinical Practice (GCP), designated SOPs, and local laws and regulations relevant to the country of conduct. The study protocol (version 4. 5 for Germany, dated 5 November 2019) was approved by the respective competent authorities and ethic committees involved and used as the basis for this manuscript (country-specific protocol modifications not considered). The study is registered at the European clinical trial database EudraCT database, No. : 2016-002162-30, and in the ClinicalTrials. gov Protocol Registration and Results System, ClinicalTrials. gov ID: NCT03278548. Written informed consent, in accordance with the origins of the Declaration of Helsinki and the applicable laws of the country, has to be obtained from all patients before entering the study.
Informed consent forms for individual countries are available from the corresponding author upon request. The investigator explains the nature, purpose, and risks of the study and provides the patient with a copy of the patient information. The patient will be given sufficient time to the study's implications before deciding whether participate. There is no compensation for trial participation. All study participants are insured in accordance with the respective national legislations. Information for the patients on the handling of their personal medical data and on the storage and handling of blood samples is part of the informed consent form. Blood samples that are collected specifically for this clinical study are kept until the end of the clinical study. Any remaining blood samples are destroyed after the completion of the clinical study.
All patient data obtained in the context of the clinical trial are subject to data protection. The patient's name in addition to other personal data (excluding age and sex) are not to be disclosed by the investigator. The storage of data for statistical assessment shall likewise be performed only under the patient's study identification. Only the investigator will have the means to identify a patient's name/ other personal details via the study identification. If personal data and study-related documentation are stored and processed, the requirements of pertinent data protection legislation are to be observed. Data generated in this study is recorded using a computerized system in accordance with applicable regulations. The system generates an individual eCRF for each patient participating in the study.