dataesr/scientific-paragraphs-categorization huggingface dataset:
In the one-mode network, all nodes are treated as the same type, and directionality is lost (Figure  S1 ). For each country node, we calculated the degree of centrality within the network with the 'igraph' function degree. Degree centrality assigns an importance score based on the number of links (edges) held by each node. Thus, degree centrality represents a simple measure of node importance, whereby nodes with higher centrality are expected to quickly connect with the wider network. We used a generalized linear mixed model to explore the in uences of different country-level attributes to the degree centrality of each country. We extracted country-level attributes deemed potentially important in in uencing the system under study (full list of predictors is described in Supplementary text S1, including decisions taken after data exploration).
2  This first fact motivates an analysis which is based on the use of nonlinear technique to better understanding the relationship between the financial sector and the real economy. Fig. 2  shows the fitted distribution of real GDP growth in the euro area conditional on its location in the ''distress'' or in the ''non-distress" period. 3 I define distress periods as the highest one-third of realizations of the CISS index and I require that the 1 On the ECB -statistical Data Warehouse, the code series for the real GDP series and CISS index are MNA. Q. Y. I8. W2. S1. S1. B. B1GQ. _Z. _Z. _Z. EUR. LR. N and CISS. D. U2. Z0Z. 4F. EC. SS_CI. IDX, respectively. I perform a first log difference transformation to GDP series to compute the growth rates. 2 Using a Markov-switching business cycle model,  Lhuissier and Tripier (2021)  point to worsening credit-market conditions during distress periods.
14 The joint VAR-ES score is computed as 𝑆 𝛼,𝑡+ℎ =𝑄 𝛼,𝑡+ℎ ( 1 (𝑦 𝑡+ℎ ≤𝑄 𝛼,𝑡+ℎ ) -𝛼 ) -𝑦 𝑡+ℎ 1 (𝑦 𝑡+ℎ ≤𝑄 𝛼,𝑡+ℎ ) + 𝑒 ES 𝛼,𝑡+ℎ 1 + 𝑒 ES 𝛼,𝑡+ℎ [ ES 𝛼,𝑡+ℎ -𝑄 𝛼,𝑡+ℎ + 𝛼 -1 ( 𝑄 𝛼,𝑡+ℎ -𝑦 𝑡+ℎ 1 (𝑦 𝑡+ℎ ≤𝑄 𝛼,𝑡+ℎ ) )] + ln 2 1 + 𝑒 ES 𝛼,𝑡+ℎ , where ES ,+ℎ denotes the expected shortfall forecast at quantile. I have run a similar exercise with the Covid-19 crisis, as reported in the online appendix. Using the same real-time estimation approach, I compute here the predictive distribution of GDP for the first quarter of 2020. Clearly, financial variables do not provide useful timely information about the COVID-19 downturn. A caveat of the analysis is the use of quarterly data, while financial variables are often available at higher frequency. Considering a real-time nowcasting exercise with high-frequency data might be more accurate to predict the COVID-19 downturn, as shown in  Ferrara et al.
A. C. has received funding for this project from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 101022760. The effort of coauthors S. R. and A. Soto was supported through their institutional overhead, through a substantial donation of their personal time, and specifically not through any government funding. R. H. was supported by grant PID2019-109467GBI00 funded by MCIN/AEI/10. 13039/ 501100011033/ and by Grupos Gobierno Vasco IT1366-19.
For the first step, the database TRANSFAC  [34]  is employed together with the TF binding site identification algorithms Match and CMA  [35]  and genome enhancer software (https//gene xplain. com/genome-enhancer) [36]. Comparing the results for samples of light-induced engineered cells, it was concluded that while the effect of induction is noticeable at 2 h, the complete activation of the pathway seems to be achieved only at 4 h. This is consistent with model simulation results, which predicted some delay before the complete activation of the pathway. On the other hand, at 4 h the genome enhancer analysis explicitly detected interferon signaling pathways from interferon induced IFIT1, IFIT2 (Fig. 5A, B ). Furthermore, the mapping of the differentially expressed gene to the TRANSPATH gene ontology suggests that the system was also activating through IκBα the TLR 3 pathway (Fig.
The device loading port was sealed with silicon (732; Dow Corning). NIR activation; in vitro, cells were placed on custom designed 690 nm LED array in a 37 • C. 5% CO 2 incubator. Device activation; power antenna integrating implant devices were placed in a 6 well culture plate with 2 mL medium 2 cm above a 30 × 15 cm (RD5101 HF, Andrea Electronics) high frequency RFID wireless powering emitter antenna, tuned at 13. 56 MHz. Device manufacturing: the device architecture was drawn with Rhinoceros 3D design software. Device parts were printed using LCD SLA printer and polyJet printer (Stratasys). Silicon injection molding was performed with a medical grade NUSIL MED-4210 that presents mild curing temperature (around 100 • C). Flexible optoelectronic; Ink; the first-and third-layer ink are composed of the ELG UV curable conductive silver solids screen printable thick film paste (ELG410S UV; Norcote).
) Antifibrotic coating; antifibrotic coating of the devices is performed by integrating 25 μg GW2580 (HY-10917; MedChemExpress) crystals in the silicon assembly. GW2580 crystals preparation is described elsewhere  [38]. Bioinformatic transcriptomics NGS pathway analysis; transcriptomics profiling using NGS was performed by Eurecat Technology Centre of Catalonia and the analysis conducted with GeneXplain analysis platform (https://genexplain. com/genexplain-platform/). Aligned BAM files were fetched to FeatureCounts tool and generated tables with gene counts were filtered, normalized, and used as Limma input. The lists of significantly up and down regulated genes prepared with limma were used as inputs for the Genome Enhancer pipeline (https://gene xplain. com/genome-enhancer/), geneXplain TRANSFAC database is employed together with the TF binding site identification algorithms Match and composite module analyst CMA  [34].
The second analysis step involves the signal transduction database TRANSPATH and special graph search algorithms. Target genes were identified from the uploaded experimental data. EdgeR tool (R/Bioconductor package integrated into the pipeline)  [52]  was applied and compared gene expression with the following sets: "Optoferon NIR 4 h " with "Optoferon dark 4 h". EdgeR calculated the LogFC, the p-value, and the adjusted p-value (corrected for multiple testing) of the observed fold change. As a result, 2039 genes were found as significantly upregulated (p-value<0. 1), and 2373 genes were significantly downregulated (p-value<0. 1). Functional analysis of differentially expressed genes was done by mapping the significant up regulated to the ontology of signal transduction and metabolic pathways from the TRANSPATH® database.
The dehydrated sections mounted with Eukitt® (VWR) were acquired using a slide scanner (Axioscan, Zeiss) and quantified using the ZEN software (Zeiss). Quantification of demyelination with Luxol staining; spinal cord myelin sections were stained with Luxol (L0294, Sigma). Slices were acquired using digital slide scanner (Hamamatsu Photonics). Slides were analyzed using FIJI software (ImageJ). Measurement of the total area of myelin was integrated on the lumbar spinal cord using Fiji software and normalized by the total spinal cord area and done by a blind experimentator in view of the group. The myelin percentage was reported as 100% in WT and results are reported compared to WT untreated animals. Immunostaining; Microgliosis were analyzed by immunostaining with rabbit anti-Iba1 (1:500 dilution; G-3893; Sigma-Aldrich).
Participants The present sample is a subset of the German nationally representative panel study 'Socio-economic Panel' (SOEP;  Goebel et al. , 2019; Liebig et al. , 2019). The SOEP annually surveys over 30 000 participants in more than 20 000 households which come from a stratified random sample of the German household population. For the current study, a random subset of 12 000 households (one participant per household) were contacted via telephone interviews between 1 April 2020 (67 366 confirmed cases of COVID-19 and 732 confirmed deaths related to COVID-19 in Germany so far) and 4 July 2020 (196 096 confirmed cases and 9010 confirmed deaths) in the context of the SOEP-CoV study  (Entringer et al. , 2020; Kühne, Kroh, Liebig, & Zinn, 2020). Data collection was split into nine tranches. Overall, n = 6684 individuals participated in the survey in 2020.
Data preprocessing Data cleaning and analyses were performed in R v4. 0. 0 (R Core Team, 2020). The code used for preprocessing and analyses is available at https://osf. io/znwjt/. Missing values (4. 5%) were imputed by means of the MICE R package  (Buuren & Groothuis-Oudshoorn, 2011 ) using classification and regression trees with m = 5 imputations and 50 iterations. Statistical outliers were all within the range of the used scales, therefore considered meaningful and not removed. Predictor variables were z-standardized; outcome variables were not z-standardized. This enabled (a) comparison between different PD outcomes irrespective of their variance and (b) clinically interpretable evaluation of the relation between psychological factors and absolute change in PD. As shown in Table  1  and Fig.
Acknowledgements. The authors thank  Anna Daniels  and  Sarah A. Wellan  for helpful comments on the manuscript. Financial support. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement numbers 777084 (DynaMORE) and 101016127 (RESPOND). ' SOEP-CoV: The Spread of the Coronavirus in Germany: Socio-Economic Factors and Consequences'  was funded by the  German Federal Ministry of Education and Research (BMBF). AR was supported by  Studienstiftung des deutschen Volkes. HK was supported by the  Deutsche Forschungsgemeinschaft (DFG, German Science Foundation) Grants -415809395 ,  427279591 , and  40965412. The data can be accessed via the research data center of the SOEP.
Methodology In order to compare the smart and conventional buildings, simulations are performed with the dynamic building energy simulation and building LCA software Pléiades †. The methodological steps are summarised in Fig. 1. Firstly, the two design alternatives (90s and passive design) are modelled in the software. Secondly, the energy consumption is assessed for the two designs. In order to take into account the diversity of families that can live in the six dwellings as well as the various set of domestic equipment that a family owns, the generator of realistic occupancy scenarios of the module Amapola of the software Pléiades is run. 840 occupancy scenarios are obtained for each dwelling. Then, for each possible family, a saving scenario is randomly sampled in the savings distribution presented in 2.
1 for the "monitoring" and "management" alternatives. Thirdly, building LCA calculations are performed for all cases assuming a building lifetime of 80 years and a replacement of smart devices every 10 years. The inventory data for the † Software Pléiades: https://www. izuba. fr sensors come from EPDs of different manufacturers or from generic data from ecoinvent database ‡. The Brightway2 framework is used for the impact assessment of the smart devices. Four environmental indicators are assessed: the climate change (CC) from Intergovernmental Panel on Climate Change (GWP100), the cumulative energy demand (CED), and the shortterm damages to ecosystem quality and the human health from ImpactWorld+.
Comparison of smart and conventional buildings The comparisons of the three alternatives are given in Fig. 4  using three distributions: one distribution for the "without sensor" case, another for the "monitoring" case and a last one for the "management" case. For the "building monitoring" and "building management" alternatives, the uncertainties on the energy savings are included in the distributions. The alternative comparison is performed for the two designs (90s design in shade of orange, and passive design in shade of green) and for the four environmental indicators. For all environmental indicators and building designs, there is a large overlap between the distributions of the "without sensor" and the "monitoring" alternatives. As the ‡ ecoinvent database: https://ecoinvent. org/ "management" alternative leads to highest energy savings, this option seems to perform better than the two others even when considering the impacts of smart devices and all life cycle stages.
Data Availability Statement The initial catalog can be downloaded by querying the FDSN web service of ISTerre (http://ist-sc3-geobs. osug. fr) with the following request: http://ist-sc3-geobs. osug. fr:8080/fdsnws/event/1/query?starttime=2017-02-28&end-time=2019-10-10&minlatitude=45. 35&maxlatitude=45. 53&minlongitude=6. 22&maxlongitude=6. 43&for-mat=text. The continuous recordings can be downloaded using the FDSN web service of RESIF (http://ws. resif. fr). The high-resolution catalog is part of the supplementary material and is also available on Zenodo at https:// doi. org/10. 5281/zenodo. 6488975. Template matching was carried out with Seismatch version 0. 7. 6  (Herrmann et al. , 2019) , available on GitLab https://gitlab. seismo. ethz. ch/microEQ/TM and licensed under GNU GPLv3. To fit the displacement spectra, we used lmfit version 1.
0. 2 (Newville et al. , 2021) , published on GitHub https:// github. com/lmfit/lmfit-py and licensed under BSD. The differential evolution code to estimate the initial values for the fit was part of SciPy version 1. 7. 6  (Virtanen et al. , 2020) , available on GitHub https://github. com/scipy/ scipy and licensed under BSD. The b-values and magnitude of completeness were calculated using Magnitude-Distributions. jl version 0. 1. 0 (Minetto, 2021) , available on GitHub https://github. com/riccmin/MagnitudeDistributions. jl and licensed under MIT.
Despite the fact that changes in two-dimensional lake area have been qualified by the GSW (i. e. , Global Surface Water) database derived from Landsat images  [14] , the water volume of a lake is three-dimensional, tightly linked to water level, thus challenging to be estimated consistently across space and time  [10]. Several radar missions, such as Topex/Poseidon, ENVISAT, Jason, and CryoSat-2, provide lake water level measurements  [15] [16] [17]. Nevertheless, existing radar-based research still focused on a small proportion of global lakes with uneven temporal coverage, due to data quality issues or the difficulty of coordinating different sensors  [13]. ICESat (i. e. , Ice Cloud and land Elevation Satellite) and ICESat-2 have the potential to bring a significant step forward. The GLAS (i.
e. , Geoscience Laser Altimeter System) instrument onboard ICESat provides laser altimetry data from 2003 to 2009 with a fine spatial resolution (~70 m)  [18]. The seven-year data collected by GLAS has triggered many applications in hydrological studies  [19] [20] [21] [22]. The newly available ATLAS (i. e. , Advanced Topographic Laser Altimeter System) instrument onboard ICESat-2 extends the altimetry data to 2018-2020 (when this research was initiated), which have been successfully used to qualify the seasonal variability of lake water level at the global scale  [23]  and lake volume variations at the regional scale  [24, 25]. Thus, water level changes based on ICESat/ICESat-2 data, combined with area changes, offers an opportunity to estimate global lake volume changes spanning nearly two decades, with potentially high accuracy.
However, one challenge that needs to be engaged is the data gap of lake water level between 2009 and 2018 during which ICESat and ICESat-2 missions were disconnected. Fortunately, lake area is continuously available since the 1980s  [14] , and three approaches have been developed to reconstruct water level series from area series. The first approach is to assume a constant morphology for all lakes (e. g. , a conical shape), such that lake area change can be geometrically linked to lake water level change  [26]. However, this approach is challenging for a global application, because lake morphology differs across regions  [10]. The second approach estimates the topographic slope of a lake using high-resolution DEM and terrain indices, based on the assumption that the topography surrounding a lake is indicative of lake morphology  [27] [28] [29].
Methodological Framework We located lakes with an area of at least 10 km 2 (16689 in total) using the HydroLAKES database  [30] , and then calculated a monthly time series of lake area for each lake using the GSW database, after controlling for quality issues such as cloud contamination (Figure  S1 ). Note that months with no valid lake area observations were excluded from all subsequent analyses. We focused on lakes with an area larger than 10 km 2 because ICESat/ICESat-2 have relatively sparse observations over smaller lakes. To reconstruct a continuous series of lake water level, we calculated the regression slope (s) between lake water levels and lake area (i. e. , s = d level d area ), using a new sampling-based method (Figure  S2 ), and further generated area-derived water levels from 2003 to 2020.
The monthly time series of lake area and water level were then combined to generate monthly lake volumes. The results were validated against in situ (Tables  S1-S3 ) and satellite radar (i. e. , ESA CCI database) measurements. Lake volume changes were then compared to changes in terrestrial water storage (TWS) observed by the GRACE satellites (i. e. , Gravity Recovery and Climate Experiment)  [32, 33]. Climate data were used to explore the driving forces of the observed lake volume changes. Spatial data processing was performed using the GDAL/OGR package  [34]  in Python. Lake volumes were calculated and analyzed using pracma and ggm packages in R  [35] [36] [37]. The methodological framework is shown in Figure  1  and is explained in more detail below.
Locating Lakes The locations and boundaries of 16689 lakes with an area of at least 10 km 2 were obtained from the HydroLAKES database (https://www. hydrosheds. org/page/hydro lakes; accessed on 18 January 2022). This database provides the state-of-art distribution of global inland water bodies by combing and correcting several near-global and regional datasets such as the SRTM water body data  [30]. Reservoirs and natural lakes with regulation structures were included in this study and all are referred to as "lakes".
Lake Area from the GSW Database Pekel et al. generated a surface water database using Landsat images (https://globalsurface-water. appspot. com/download; accessed on 18 January 2022)  [14]. We used the occurrence map and monthly water history maps, two key components of the GSW database, to derive time series of lake area for lakes ≥10 km 2. Since the water history maps were affected by issues such as cloud contamination, data unavailability, and ETM+ sensor scan line failure  [25] , we rectified the water history maps using the method proposed by Zhao and Gao  [38] , which can be summarized as follows (Figure  S1 ): (1) For each lake, a mask is generated by buffering its lake boundary as defined in the HydroLAKES database (Figure  S1a ). Extraction of lake area is confined within the masked region.
A large buffer distance possibly includes surrounding water bodies, whereas a small one fails to capture the enlargement of the lake during the study period. The buffer distance (in meters) was chosen to be set as the 1 /4th power of lake area (in meters squared), after trial and error. This distance was validated to be neither too large (only 24 km for the Caspian Sea-the largest lake on Earth), nor too small (for 99% of the lakes, the maximum lake area did not exceed the buffer zone during 2003-2020). (2) The water history map of the GSW dataset describes whether each pixel was covered by water in a given month (Figure  S1b ), and the water occurrence map describes the frequency of each pixel marked as water bodies during the past decades (Figure  S1c ). We clipped the water occurrence map taking the cloud-contaminated water history map as a mask (Figure  S1d ), and then decided on an occurrence threshold (Figure  S1e )  [38] , which was in turn used to binarize the water occurrence map and derive the final corrected lake area (Figure  S1f ).
Locating Lakes The locations and boundaries of 16689 lakes with an area of at least 10 km 2 wer obtained from the HydroLAKES database (https://www. hydrosheds. org/page/hydr lakes; accessed on 18 January 2022). This database provides the state-of-art distributio  For each lake, we made certain that it had more than four valid area observations since 2003. To improve data quality, we removed outliers (if any) in each time series of lake area using a "hampel" filter  [37] , with the window length and the standard deviation of the filter set at 5 and 2, respectively, after trial and error. 2. 4. Lake Water Level from ICESat and ICESat-2 Data GLAH14, one of the level 2 ICESat/GLAS products, provides global surface elevation data from 2003 to 2009 (https://nsidc. org/; accessed on 18 January 2022).
Accuracy Assessment of Lake Water Levels and Lake Volumes The accuracy of ICESat/ICESat-2 data was assessed against the in situ water levels of 132 lakes (see Table  S1  and Figure  S5  for detailed information on these lakes). After eliminating systematic errors caused by the difference in reference ellipsoid by normalizing in situ and satellite-derived water levels, we obtained a Pearson's r of 0. 997 and a root mean square error (RMSE) of 0. 375 m (Figure  S6a ). ICESat-2 showed a higher accuracy than ICESat (Figure  S6a ). Among the 132 lakes, 85% had a Pearson correlation coefficient (r) greater than 0. 8, and 78% has a normalized RMSE (NRMSE, not influenced by the very different volumes of 132 lakes) lower than 30% (Table  S1 ). We also compared water levels retrieved from ICESat/ICESat-2 with ESA CCI water levels.
We found lower accuracy for ESA CCI product, as compared to the performance of ICESat/ICESat-2 (Figures  S6a, b ): the NRMSE of ICESat/ICESat-2 water levels was much lower than that of ESA CCI (0. 5% vs. 22%). We therefore conclude that water levels retrieved from ICESat/ICESat-2 data are highly accurate. Regarding the accuracy of area-derived water levels, although the RMSE was higher compared with the accuracy of water levels directly observed by ICESat/ICESat-2, the NRMSE remained low (Figure  S6c ): among the 132 lakes with in situ water levels, 94 showed NRMSEs lower than 30% (Table  S2 ). This indicates that our proposed sampling-based approach is effective. The uncertainties of estimated lake volumes were also assessed. Compared with lake water levels, lake volumes are much more difficult to be measured on the ground.
GSW and ICESat/ICESat-2 Data for Estimating Lake Volume Changes Accurate and long-term information on lake volume is essential for water budget assessment and water resource management  [1]. To monitor long-term changes in global lake volume, this study used the most recent advances in satellite remote sensing: the Landsat-based GSW database and the ICESat/ICESat-2 altimetry data. The GSW database was produced using a massive number of Landsat images and various data mining techniques (e. g. , expert system and visual analytics)  [14]. This database provides accurate information about the dynamics of the Earth's surface waters, which has been widely used in hydrological and ecological studies  [52] [53] [54]. Here, we further refined the GSW layers to alleviate the impacts of clouds and scan line failure, using the method of Zhao and Gao  [38]  (Figure  S1 ).