dataesr/scientific-paragraphs-categorization huggingface dataset:
There is a strong relationship between regional integration and economic performance. This paper investigates the impact of regional integration on macroeconomic indicators in the Eurasian Economic Union (EAEU) -a trade block created by the former Soviet republics in 2014. This study compares two types of regional collaboration strategies: first, unilateral trade liberalization with the one-sided opening of market access that does not imply any mutual concessions, and second, reciprocal regional liberalization evaluated as the degree of regional economic integration between the countries. We apply a random effect panel data analysis with the dataset over 25 years since 1995 covering five countries (Armenia, Belarus, Kazakhstan, Kyrgyzstan, and Russia). We find that, under the current stage, the EAEU positively affects trade flows, negatively affects the level of employment, and has no impact on other economic performance indicators.
When pursuing these benefits, the countries have chosen between different strategies of trade liberalization, either following a "going alone liberalization"  (Bhagwati 2002)  and equally recusing customs tariffs to all trading partners or opening markets to selected groups of countries based on the principle of reciprocity and adopting a strategy of regional integration. According to  Agbetsiafa (2010) , the latter can ensure better control over trade liberalization and exposure to foreign competition. Since the 1980s, there has been a significant increase in regional integration activity. Around half of the 581 regional agreements notified to the WTO by 2022 were reported in the last ten years. According to the WTO Regional trade agreements database, today, almost all economies are parties to at least one regional initiative.
The panel data used in the analysis include the observations for 25 years since 1995 covering five countries (Armenia, Belarus, Kazakhstan, Kyrgyzstan, and Russia) that trade with 271 economies globally. The country-level data is retrieved from the World Bank (WB) website, which contains official statistics collected through national accounts, countries' balance of payment, and Bank's country reports, as well as from the World Integrated Trade Solution (WITS), online software which allows users to source trade-related information from the databases of the WB, United Nations Conference on Trade and Development, International Trade Center, United Nations Statistical Division and the World Trade Organization. The sample is then refined by cleaning out the missing values as well as the outliers.
Groundwater recharge feeds aquifers supplying fresh-water to a population over 80 million in Iran-a global hotspot for groundwater depletion. Using an extended database comprising abstractions from over one million groundwater wells, springs, and qanats, from 2002 to 2017, here we show a significant decline of around -3. 8 mm/yr in the nationwide groundwater recharge. This decline is primarily attributed to unsustainable water and environmental resources management, exacerbated by decadal changes in climatic conditions. However, it is important to note that the former's contribution outweighs the latter. Our results show the average annual amount of nationwide groundwater recharge (i. e. , ~40 mm/yr) is more than the reported average annual runoff in Iran (i. e. , ~32 mm/yr), suggesting the surface water is the main contributor to groundwater recharge.
role in this process  10, 11. In fact, spatial and temporal variability of groundwater recharge, as an index of groundwater availability  12  , is not accessible even locally in most parts of the world, including Iran. This scarcity of data indicates how rarely this variable is monitored. A possible way to obtain estimates of groundwater recharge over a large spatial scale is using global/regional hydrologic models. However, the recharge estimated by these models is not certain, especially in semiarid regions such as Iran, where uncertainties in evapotranspiration and precipitation may lead to large errors in the results  13, 14  , hindering the detection of regional imbalances between groundwater recharge and human withdrawals  8. Following the study conducted by Noori et al. 8  on Iran's groundwater depletion, which raised concerns about the lack of the country's groundwater recharge data, we estimate its recharge using an extensive, nationwide database in all 30 hydrological basins of Iran from 2002 to 2017.
Study area and data Iran's groundwater monitoring network has been gradually established since the early 1970s (Fig. 4  and Note S2). The network covers 492 out  of 609 plains (Fig. S3 ), including 666 aquifers (Fig. S4 ), and contains 12,293 active piezometers (Fig. S4 ) that measure the groundwater level at monthly intervals. The total area of aquifers is around 272,000 km 2 and the monitoring network covered more than 96% of this area (i. e. , 261,000 km 2 ) by the end of 2017. The remaining area (i. e. , ~11,000 km 2 ), includes 133 relatively small and less important aquifers distributed across the country  5. Although the groundwater levels were measured in active piezometers from the early 1960s, the comprehensive database of Iran's groundwater resources was established from the water year of 2001-2002, when ~78% of the aquifer areas became covered by the groundwater monitoring network  53.
Details about the aggregated data are given in Note S3 and also supplemented in https://zenodo. org/record/ 8382150. Egressed water from the aquifers (i. e. , Q off ) includes all anthropogenic groundwater withdrawals and natural groundwater losses. Anthropogenic groundwater withdrawals are fully measured based on sampling campaigns and field inventories in over a million wells and qanats on approximately a five-year cycle during wet and dry periods in Iran. The same measurements as the 5-year field campaigns are done twice a year during wet and dry periods at a small but representative set of selected extraction points in different aquifers. Natural groundwater losses can take place through different routes such as natural springs, evapotranspiration, and outgoing lateral fluxes from the aquifers.
Fortunately, the national groundwater database includes the annual groundwater discharge through natural springs, which is measured as frequently as the anthropogenic groundwater withdrawals and thus also based on sampling campaigns and field inventories. Evaporation through the saturated zone is usually negligible when the depth to the groundwater table is large (>3 m depth). We supposed that the groundwater loss through evaporation, including transpiration, is negligible because the groundwater depth was more than 3 m in almost all the aquifers 8. Groundwater losses through aquifer discharge to surface waters are also negligible due to the decline in the countrywide groundwater table during the last five decades (>20 m)  55. This hypothesis gains further support when data show both a widespread decline in Iran's river flows  38  and drying out the national wetlands and inland lakes  8, 56.
Data analysis Groundwater data processing, including aggregation, division, multiplication, normalization, and statistical analysis, for 666 national aquifers during the study period (2002-2017) For a description of the change in groundwater recharge and precipitation, the magnitude of temporal trends was calculated by Sen's slope estimator method  57. A Mann-Kendall test  58, 59  was also applied to differentiate between significant and insignificant trends in both groundwater recharge and precipitation. These statistical analyses were done by MAKESENS 1. 0 software, introduced by the Finnish Meteorological Institute  60.
Data uncertainty The results obtained in this study heavily rely on the data quality of the national groundwater database. Factors including poor information on aquifer type (e. g. , confined or unconfined aquifer types), specific yield, and hypotheses made can potentially introduce errors/uncertainties in the calculated groundwater recharge for Iran and in each of the 30 basins. Detailed information on the data and the corresponding sources of errors/uncertainties is given by Noori et al. 8. An uncertainty analysis was conducted by determining the lower and upper limits of the 95% confidence interval for magnitude of the trends in groundwater recharge calculated using Sen's slope estimator  53  (see the Methods section for details). Table  1  shows the results of the uncertainty analysis for the calculated groundwater recharge across 30 basins and the countrywide values.
LC-MS/MS experiments The fractionated peptides were reconstituted in 2% acetonitrile/0. 1% formic acid (FA). The peptides were analyzed by the data-dependent acquisition method on the EASY-nLC 1000 system coupled to the Q-Exactive Plus mass spectrometer (Thermo Scientific). The peptides were separated on an EASY-Spray ES803A analytical column (Thermo Scientific) applying a flow rate of 300 nl/min and a linear gradient from 3 to 40% acetonitrile in 0. 1% formic acid. The gradient length was 2 hours for pTyr phosphoproteome fractions, 4 hours for proteome and IMAC phosphoproteome fractions. See Table  S3  for mass spectrometry data acquisition parameters. Peptide identification and quantification were performed using FragPipe version 17. 1 (doi: 10. 1038/nmeth. 4256). The mass spectra were searched against the human SwissProt sequences (20409 entries, downloaded on December 2, 2021), supplemented with common contaminants.
For proteome data processing, the TMT10 workflow was loaded and the plex was changed to 11plex. Trypsin was specified as the proteolytic enzyme with up to two missed cleavage sites allowed. For IMAC-and superbinder (pTyr)-enriched datasets, the TMT10-phospho workflow was used, that includes phospho(STY) as an additional variable modification. For phosphoproteomic data, the minimal peptide length for the search was set as 6, whereas the value was set as 7 for proteome data. The virtual reference was used for the TMT multi-batch normalization. The median centering normalization was used for normalization between the sample channels. Other Fragpipe parameters are left as default values of the workflows.
Statistical analysis Statistical analyses were carried out using the GraphPad Prism9 software. Unpaired One-way ANOVA was conducted to test the significance of the difference of unpaired samples from different patient groups, and repeated measure ANOVA was done for paired patients' samples as described in the figure legends 2-negative ICU patients (the ICU group; median years of age = 58. 0; IQR = 52. 5-63. 0), and 5 healthy individuals (the HC group; median years of age = 57. 5; IQR = 52. 8-62. 8) (Tables  S1-3 ).
CONCLUSIONS The pathophysiology of SARS-CoV-2-induced ARDS has similarities to that of severe community-acquired pneumonia and sepsis caused by other viruses or bacteria  (41) , as is the case between the COV and ICU cohorts employed in the current study. Indeed, our deep proteome and phosphoproteome analysis of the PBMC of sepsis patients who are positive or negative for SARS-CoV-2 identified numerous common features, including compromised adaptive and innate immune responses to the pathogens. Besides the arsenal of potential therapeutic targets discovered in our study, the rich proteomics and phosphoproteomics datasets obtained herein may inform future investigations into the mechanisms underpinning pneumonia and sepsis associated with viral or bacterial infection and aid in the development of targeted immunomodulatory therapies for the treatment of these conditions  (40).
Phenotypic measurements For our analysis of trait change, we focused on descendants of individuals born towards the end of the experiment in "core" patches (here, the release patches or their immediate neighbours, ‫ݔ‬ = 0 or 1) or in the corresponding "edge" patches (i. e. the farthest populated patch in a landscape at the time of sampling, or the farthest two if there were not enough individuals in the farthest one). We compared them to wasps from the "stock" populations initially used to start the experimental expanding landscapes. Note that mentions of "X th generation" wasps below indicate the number of generations of experimental landscape expansion before sampling/ transfer to common garden conditions. For some traits (short-term movement, and fecundity and dispersal during the density-independent tests), data was also collected on one or two intermediate generations.
For consistency and simplicity, we only analysed (and described below) the latest tested generation for each trait, but made available all data, including intermediate samples (Data availability). To determine whether landscape connectivity and expansion had an effect on body size, we selected female wasps from the stock populations, and compared them to 12 th generation females from the experimental landscapes. Due to logistical constraints, the latter were selected in 8 edge-core pairs of populations (see Fig. 2  for how they were distributed among landscape treatments and strains). Adding the three stock populations, and accounting for the fact one edge-core pair was only sampled in the core due to limited numbers in the edge population, we measured 316 (91 to 116 per strain) wasps in 18 populations (mean ± SD: 17.
To reduce behavioural changes at the edge of the arenas, and their effect on our metrics, we only tracked individuals within a central 7×11 cm area, and the outer parts of the arena were kept in the dark to discourage individuals from approaching the edges. We studied 27 populations (core and edge from 12 of the 24 experimental landscapes + the three stock populations, see Fig. 2 ), with 8 replicate trials per stock population, and 16 replicate trials for each of the remaining populations (except one where this was 15), for a total of 119 replicate groups. Video files were analysed using Ctrax  (Branson et al. , 2009)  for tracking and the trajr R package  (McLean & Volponi, 2018)  for computation of movement statistics from trajectories. Most individuals were not tracked continuously for the entire five minutes due to either leaving the filmed area or the loss of individual identity information.
As a result, output data were in the form of a series of "tracklets" (i. e. any continuous sub-track longer than 2 seconds), that could not be assigned to a specific individual, only to a specific replicate trial. We therefore first computed metrics at the tracklet level, and then averaged them, weighted by tracklet duration, to generate replicatelevel metrics. We used the proportion of total tracked time individuals were active, the average speed and the average sinuosity  (Benhamou, 2004). All three movement metrics responded similarly to the experimental protocol; for simplicity, we only present and discuss results from the "proportion of time active" metric here, and models for the other metrics are included in the associated analysis code (see Data availability). Effective dispersal F1 offspring of 12 th generation wasps (reared in a low-density common-garden setting as described above) were used to evaluate dispersal differences between treatments.
Statistical analyses Analyses were done using R, versions 4. 0. 4 and 4. 1. 0 (R Core Team, 2021). We analysed data in a Bayesian framework using the brms R package  (Bürkner, 2017)  as a frontend for the Stan language  (Carpenter et al. , 2017). We mostly relied on the tidybayes (Kay, 2019), bayesplot  (Gabry et al. , 2019) , patchwork  (Pedersen, 2019)  packages, and on the tidyverse suite of packages  (Wickham et al. , 2019) , for data preparation, model diagnostics and plotting. We ran four Markov chains per model; the number of iterations per chain was model-dependent (but always ≥ 2000 after excluding warmup iterations), and set to be large enough to ensure convergence (ܴ 1. 01) and satisfactory effective sample sizes (both bulk-and tail-effective sample sizes sensu  Vehtari et al. , 2020 Vehtari et al.
Article DNase activation. 20, 24 Indeed, DNA damage following mitotic arrest can be decreased by inhibiting caspases, but also increased by targeting antiapoptotic factors such as Bcl-2 and Bcl-x. 7, 20 roteins preventing cytochrome c loss from mitochondria have been identified, among them Tp53 regulated inhibitor of apoptosis 1 (Triap1). 25 The expression of this protein is under the control of p53, and, surprisingly, it dampens canonical p53 outputs: Triap1 inhibits both p21-dependent cell-cycle arrest and apoptosis. 26, 27 Triap1 expression correlates with poor prognosis in melanoma and renal cancer patients, as found in Human Protein Atlas database (TGCA). Coherently, Triap1 overexpression has been implicated in progression of nasopharyngeal cancer  28  and leads to doxorubicin resistance in MCF7 breast cancer cell lines.
The fraction of polyploid cells after treatment was minimal (Figure  3A ). As expected, cells that survived treatment were arrested and largely senescent, $90% of them being positive to SA b-galactosidase (senescence-associated b-galactosidase) (Figure  1C ). Surprisingly, after 7 days cells resumed proliferation giving rise to an exponentially dividing population, Figure  1D. We collected these cells (first-cycle cells, or FC cells), with the aim of analyzing them as shown in the next sections. In summary, our data show that cells can escape from the G1 arrest induced by prolonged mitotic arrest and slippage and resume unhindered cell treated with nocodazole are de-sensitized to a second round of treatment We reasoned that cells that had managed to escape from the arrest may be less sensitive to a second round of treatment with nocodazole.
We sequenced FC cells with low coverage, and looked for changes in chromosomes number. Yet, we did not detect any specific aneuploidy or major structural changes except for the loss of chrXII arm duplication (Figure  S4C ), where we did not identify genes that could explain Triap1 upregulation. To understand whether Triap1 upregulation was due to some pre-existing mutation in the population, we repeated the treatment shown in Figures  1A  and 1B  on single clones. The results were in line with what we observed in bulk: similar growth rates (Figure  S1D , left), anticipated recovery after one treatment (Figure  S1D , right), and increased Triap1 protein levels after treatment (Figure  S4D ). In summary, these data do not support the hypothesis of pre-existing mutations. They leave open the possibility that epigenetic changes (i.
Confluence was determined by Incucyte S3. Data were fitted with a logistic model, and growth rate was measured as the slope at the middle point. Results of two experiments were used for the analysis. Details of the analysis and statistical significance analysis are reported in STAR Methods. N = 2. (D) Upper panels: we propose that the mitotic arrest induced by nocodazole results in mild caspase-3/7 activity and DNA damage (left). In cells that express higher levels of Triap1 (right), cytochrome c release from mitochondria is reduced, and cells experience less DNA damage and p21 activation. Lower panel: when cells undergo slippage, a few of them overexpress Triap1 (through an unknown process marked by ''?''). These cells are selected and will resist more efficiently to the second treatment.
Materials availability Materials generated in this study (e. g. cell lines) are available upon MTA completion on request from the lead contact. Data and code availability d Mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier: PXD037143. d Raw data are available from the lead contact upon request. All original codes generated in this study are available in supplemental information. d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
Incucyte growth curves After treatment (Recovery Day0) cells were released and 4x10 3 cells were plated in 24 well in duplicate. Cells were imaged every 6h by using Incucyte S3 and 36 images were acquired for each well. Media was replaced twice a week. Confluence was determined by Incucyte Software: it corresponds to the total imaged area divided by the area occupied by cells. The mean confluence value and the SEM of the pooled replicates and images were plotted. To estimate the slope as proxy of growth rate, a logistic model was fitted using R studio software.
DNA extraction Genomic DNA was extracted from exponentially growing RPE1, First Cycle cells using Qiagen genomic extraction Kit according to guidelines. NGS sequencing was performed by Biodiversa srl (Treviso, Italy). Time lapse imaging 2,5/3,5 x 10 4 cells were plated onto IBIDIâ slides and synchronized in thymidine. After 24h cells were released in DMEM-F12 medium (without phenol) containing nocodazole. The medium surface was coated with sterile mineral oil to avoid evaporation and cells dehydration. Images were acquired using DeltaVision Elite (GE-Healthcare) CoolSNAP HQ CCD camera using a 603 immersion objective (Olimpus). 4 z stacks (4 mm) were acquired for 40h using 32% of POL intensity exposed for 0. 1 s. Temperature of 37 C and 5%CO 2 were maintained with Okolab incubator. Images were then analyzed using ImageJ software.
Samples were washed with 1%BSA/PBS and re-suspended in 100mL of anti-mouse FITC (life-technologies) diluted 1:50 in 1%BSA/PBS for 1 h at room temperature in the dark. After washing once with 1%BSA/PBS pellets were re-suspended in 1 mL of Propidium Iodate (PI) (2. 5 mg/mL) and RNase (250 mg/mL) (Ribonuclea-seA from bovine pancreas, Sigma) and incubated overnight at 4 C. Acquisition was made with FACScalibur and data analysis was performed with FlowJo software.
MS database search Raw MS files were processed with MaxQuant software (1. 5. 2. 8). 36 MaxQuant enables high peptide identification rates, identifies ppb-range mass accuracies and proteome-wide protein quantification making use of the Andromeda search engine. 37 MS/MS peak lists were searched against the UniProtKB Human complete proteome database (uniprot_cp_human_2015_03) in which trypsin specificity was used with up to two missed cleavages allowed. Searches were performed selecting alkylation of cysteine by carbamidomethylation as fixed modification, oxidation of methionine, N-terminal acetylation and N-Deamination as variable modifications. Mass tolerance was set to 5 ppm and 10 ppm for parent and fragment ions, respectively. A reverse decoy database was generated within Andromeda and the False Discovery Rate (FDR) was set to <0.
ACKNOWLEDGMENTS We thank all members of the A. C. Lab and  Stefano Santaguida  for discussions and suggestions. Ylli Doksani ,  Jelena Vermezovic , and  Silke Hauf  read and commented on the manuscript. Research of the A. C. lab is financed by the  Italian Association for Cancer Research (AIRC)  ( IG-21556 ), by donations of the  Suma-Nesi  family and by the  Hungarian National Research, Development and Innovation Office  through grant  TKP2021-EGA-42. The L. L. F. lab is supported by the  Giovanni Armenise-Harvard Foundation  ( CDA 2017 ) awarded to L. L. F. ,  AIRC  ( MFAG 2019 -ID 23560  project). M. P. has been financed by an  AIRC  and a  Climbers Against Cancer  fellowship. We thank  Claudio Vernieri  for sharing doxorubicin,  Emanuele Martini  for g-H2AX quantification,  Ubaldo Gioia  for suggestions on the comet assay, and the  IFOM Cell Biology and Imaging Unit  for support.