dataesr/scientific-paragraphs-categorization huggingface dataset:
Such a paradoxical, but not uncommon example of one day is shown in Fig. 4  where the AQ is good according to the governmental system and bad according to the online AQ website waqi. com, despite both platforms used the same data from the same sensors. Needless to say, such disagreement between these websites noted by users may undermine the trust towards the system of AQ monitoring, whereas the exact reason of disagreement would remain unclear and can be even exaggerated by a user (malfunctioning instrumentation or underreporting of AQ by purpose).
Communicational driver of decentralization: data provision and transparency policies The time-series of the governmental monitoring data is not publicly available (only the last 24-h observations can be downloaded from the monitoring website). Access is only given only upon request by organizations affiliated with air pollution-related projects. Simultaneously, information about AQ in Krasnoyarsk from the scientific literature is rather fragmented and suffers from the lack of details about the instruments used, their technical features, validation and potential uncertainties. Despite the promising results, most studies that used either of the AQ governmental networks (the Krasecology system or the Air-Krasn system) to estimate AQ or the concentration of hazardous pollutants in the city lacked the description of instrumental imperfections or error bars in the expressed concentrations of pollutants  (Badmaeva and Tsimmerman, 2015; Shaparev et al.
Discussion We summarized the existing information about AQ in Krasnoyarsk and concluded that the conditions for deteriorated AQ in Krasnoyarsk were established at the early stage of the urban planning (favorable location for capping temperature inversions, concentration of unprecedently intensive production of commodities around which the residential areas evolved in the Soviet-alike urban planning) and exacerbated in modern times (rebounded production of commodities, increased heating demand in winter supplied by highly-polluting lignite fuel, increased frequency of neighboring forest fires in summer). Nowadays, although appropriate infrastructure for AQ measurements is available in Krasnoyarsk, the corresponding data were not being best utilized for the public good. This gap in the governmental efforts for monitoring AQ was allegedly complemented by the crowd-funded participatory AQ monitoring system.
The participatory monitoring data were already used for informing a broad audience about alarming AQ conditions in the city (mainly based on PM2. 5 peaks compared to the background conditions), and utilized by researchers  (Lin et al. , 2020; Mikhailuta et al. , 2020). Moreover, it is unclear whether inspired by participatory monitoring or not, but  Zavoruev et al. (2019)  reported that the Russian government already started implementing cheap sensors for monitoring AQ in Krasnoyarsk. Overall, the civic involvement and broad utilization of the resultant data by users and scientists indicate that the current system of AQ monitoring in Krasnoyarsk is decentralized. This confirms the prediction of  Snyder et al. (2013)  about the transformative role of public engagement in the paradigm of urban AQ monitoring.
Moreover, while (b) the governmental network provided only a fragmented access to official AQ data, thus making it somewhat unvalidatable by third parties, the activists provided their data in open access and actively encouraged broad public, researchers, industry and officials to use their data. Lastly, (c) while government relied on less flexible system for feedback about bad AQ, the activists used modern information technologies of communication, namely, social networks for exchanging the information about AQ among the citizens. This decentralization of AQ monitoring in Krasnoyarsk is an illustrative example how civic activists can more efficiently reinforce their preferences for environmental quality using modern technologies and tools (and oppositely, to promote these technologies and tools through their demands and suggestions to the government).
Such synergy can improve the existing models for predicting AQ in Russia, making the information more transparent and providing an opportunity to participate in shaping the environmental agenda of the city, thereby meeting the interests of government, population and activists, respectively. In this way, a potential eco-immigration from Krasnoyarsk that may be triggered by bad air  (Lam et al. , 2021)  can be prevented. In future, (a) the quality of the provided data by all air quality networks in Krasnoyarsk should be thoroughly scrutinized by local-oriented studies, while (b) the introduction of any new air quality index should be supported with scientific evidence. We identified association between degraded air quality and interest about air in the city, thus prompting that the (c) phenomenon of social media/civic activism in the countries suffering from bad air quality and their role in environmental policymaking should be elucidated.
Some contacts declined our request, unaware of the GNI project that their organisation had partaken in, while others retracted their participation at the last minute. Thus, we did not follow a purposive sampling strategy. Considering these limitations, we aimed for geographical representation. We secured interviews from at least one representative from North and Central Africa, and the Middle East. In addition, we sought to cultivate a representative sample that reflects the ideological diversity and variety of media types (e. g. , digital native and traditional print-first outlets). For our data analysis, we conducted an inductive thematic analysis after all authors transcribed the interviews. This is a widely used method to draw themes from qualitative data, particularly in datasets composed of interviews  (Braun & Clark, 2006).
Findings In Africa and the Middle East, GNI Innovation Challenge had granted funds for 43 projects until 2021, 22 (51,16%) in 2019 and 21 (48. 84%) in 2021. These projects were concentrated in 18 countries in this geographic region. As shown in Figure  1 , many countries were not contemplated with these grants, expanding the power asymmetry in this region. The datafication and platformisation of the digital infrastructure create power asymmetries between those who embrace technological knowledge and those who are surviving in the digital age  (Nielsen & Ganter, 2022; Poell et al. , 2022). Using the typology proposed by de-Lima-Santos and Mesquita (2023), we classified these projects according to their aims to identify these discrepancies. According to the project description, our categorisation focused on aspects of the data we were most interested in exploring.
This tech company was responsible for helping other news organisations to deploy their innovative solutions using their GNI grants, such as L'Orient-Le Jour, Nida al Watan, and Rasseff22. Whitebeard also received a GNI grant to develop a Customer Relationship Management solution for smaller news outlets, allowing these newsrooms to manage subscriptions better using a tool that combines a metered paywall and a locked system based on data signals from audiences. The lack of technological knowledge in the region might not have been anticipated by these organisations or Google, which did not work with them to develop these projects. For example, Citizen Bulletin also relied on other organisations to build the project. Once the existing funding was over, they folded. All respondents bemoaned that their GNI were developed independently without help from Google.
In this aspect, the inclusiveness of these projects reveals to be poor. In some respects, being led and developed by organisations in Western countries, some projects limit their possibilities to contribute to local development and mitigate the low level of technological development in the region. Other organisations brought an inclusive spirit to their projects by engaging new publics or providing an open space to create solutions for their problems, such as Africa Uncensored (Kenya), 263Chat (Zimbabwe), and Egab (Egypt). Similarly, EcoNai+ from Ripples Nigeria promises to track and mark changes to environmental phenomena using geo-journalism and crowdsourcing data. This platform allows users to collect, visualise, and report on data from communities impacted by climate change. According to R4, EcoNai+ is an ecosystem of: A couple of tools to help across the value chain of the environmental report, tracking, data capture or unreported to help journalists, researchers, scientists, policy formulators, as well as community members, convert their fears and their worries and anxieties about the environment into actual data that can help to drive and attract intervention for change.
Last, we wish to contribute to the ongoing dialogue about how platforms' programs could be shaped and executed to empower newsrooms. Even if it might not always be possible to prevent adverse effects from occurring, it is at least helpful to anticipate them, be responsive, and attempt to mitigate their impacts as much as possible. Due to limitations of language and vast scope, we could not reach out to every Google-funded organisation in the region. Although this limits our study, as it does not represent the entire variety of technological innovations adopted by these grantees, we combined methods to achieve a representative model that depicts the essential standard features to understand the influence of the GNI Innovation Challenge across the region. Similarly, some organisations were afraid of sharing data about these projects, as they had signed non-disclosure agreements with Google, restricting what they could share.
Acknowledgments The authors would like to thank  Dr Arwa Kooli  for assisting us during the data collection and all the interviewees who took the time to share their knowledge and experiences with us. Furthermore, this study was partially funded by the  University of Amsterdam's RPA Human(e) AI  and by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska Curie  grant agreement No  765140.
| X-ray computed tomography (μ CT) μCT was employed to visualise the 2D and 3D structure of proto-soil cores, clumps and plants non-destructively using a Nikon Metrology HMX ST 225 μCT scanner with a tungsten reflection target. Variable scanning conditions were used for each sample (summarised in Table  S2 ). Voxel (3D pixel) sizes ranged between 15 and 38 μm. A copper filter was sometimes used to pre-harden the beam and remove unwanted lower energy X-rays. Other parameters, including the number of projections collected (3142), the μA (180), the exposure time (708 ms), and the frames per projection (1), were consistent for all scans. Average scan time was ~35 min. Scans were reconstructed into 3D tomographic datasets as. tiff stacks using CT Pro Software (Nikon Metrology) and were rendered using Drishti v2.
5  (Limaye, 2012)  and ORS Dragonfly software to reveal 3D and 2D (X, Y, Z axes) views (see Table  S2 ). In some cases, liverwort thalli were digitally segmented, 3D thickness surface meshes were applied, and imaging analysis was undertaken using ORS Dragonfly software v 2020. 1. No staining agents (e. g. , iodine) were used, and the fixation of soil and plant material with 10% formalin, together with short scan times, aided in prevention of plant desiccation (and movement) during scanning.
(2021). Compression and heating of the sample are conducted simultaneously to monitor in situ the transformations in pyrolite at the conditions of the '660'. Pressures and occurrence of the corresponding phases are identified during the experiment using powder diffraction images collected in situ at high P/T and analyzed in the software Dioptas  (Prescher and Prakapenka, 2015). The collection of MGC images allows us to observe the evolution of microstructures within the sample upon transformation by providing information on phases present and their associated textures, but requires quenching the sample to ambient T with the layout we used in this work. MGC image collections are taken as close to the transformation conditions as possible. The first series of MGC images are collected when ringwoodite and garnet form in the DAC at pressures between 18 and 19 GPa and temperatures between 1600 and 1900 K.
Processing MGC data Analysis of the MGC data is performed using a series of software packages discussed below. Only a brief outline of the process will be provided and more information can be found in  Rosa et al. (2015)  as well as  Langrand et al. (2017). A MGC image collection relies on the measurement of diffraction images at multiple ω angles in order to obtain a comprehensive view of the sample grain orientations in three dimensions. The aim of the procedure is to link the diffraction spots observed in the diffraction images to individual grains within the sample and extract microstructural information such as the number of grains and their orientations. This step is performed using the open-source software package FA-BLE 3DXRD, which can be found at https://github. com /FABLE -MGC and additional tools specifically designed for this project, TIME-leSS tools, available at https://github.
(2014). Fig. 3a  shows the extracted diffraction peaks in run Pyr05, plotted along the azimuth against inverse d-spacing. shows a histogram of the number of diffraction spots against inverse d-spacing. Peaks from all phases can be clearly identified. Unit-cell parameters of the mineral phases are then extracted using the software MAUD  (Lutterotti et al. , 1997). Note the analysis of this spectrum is not a true Rietveld refinement because the data in the histogram is a number of single grain peaks rather than a true peak intensity. It is, however, very efficient at detecting peak positions of phases that would otherwise be drowned out by the KCl pressure medium. After the peaks are extracted and the phases identified, we use ImageD11 to compute a list of g-vectors using the positions of diffraction peaks, refined unit cell parameters, and experimental conditions such as wavelength, sample to detector distance, and detector tilt.
Elasticity and calculations of seismic anisotropy Phase proportions in pyrolite, both before and after transformation, were calculated using the software package HeFESTo  (Stixrude and Lithgow-Bertelloni, 2010)  available at https://github. com /stixrude /HeFESToRepository. Above the '660', at conditions of 22 GPa and 1700 K, pyrolite is composed of 67% ringwoodite, 25% garnet, and 8% other minor phases by volume. Below the '660', at 27 GPa and 1900 K, pyrolite contains 77% bridgmanite, 17% ferropericlase, and 6% davemaoite by volume. Elastic constants for each phase are required in order to complete calculations of seismic velocities (Table  S2 ). In this work, garnet and ringwoodite are treated as isotropic materials, which is consistent with measurements on single crystals in the pressure range of the mantle transition zone (MTZ)  (Sinogeikin and Bass, 2000; Murakami et al.
For a vertically compressive stress, we predict that S-waves will show no splitting for waves traveling parallel to compression and 1. 28% splitting in waves traveling perpendicular to compression (Fig. 5 ). Some observations suggest average anisotropy values of 3% just below the '660' assuming anisotropy is confined to a 100 km layer  (Wookey et al. , 2002) ; however, in another study by  Yuan and Beghein (2013) , the root mean square model amplitude at 800 km depth has approximately 1% anisotropy. This is comparable to observations of radial anisotropy within the deep upper mantle in  Beghein et al. (2006)  and  Panning and Romanowicz (2006). Additionally,  Montagner et al. (2021) , observe fast vertically polarized S-waves (V S V > V S H ) at 800 km depth below western America, central Africa, and central Asia using surface-wave overtone datasets to generate 3D anisotropy models.
Conclusions In this paper we presented the results of phase transformation experiments at the pressure and temperature conditions of the 660 km discontinuity on pyrolite. Compression was carried out in a laser-heated DAC at pressures ranging from 17. 5 to 33 GPa and temperatures between 1500 and 2000 K; implementing multigrain X-ray diffraction to study the sample. We successfully observe the phase transition from (ringwoodite + garnet) to (bridgmanite + davemaoite + ferropericlase) in all three runs along with the associated microstructures. Multigrain data processing techniques resulted in the assignment of single crystal diffraction peaks to individual grains within the sample. Our indexing capability ranges from 508 to 736 grains post transformation in a multiphase material. Prior to transformation, garnet is isotropic and ringwoodite is weakly textured with 001 planes oriented perpendicular to the compressive stress.
Acknowledgements The study was financed by the bilateral  ANR-DFG   TIME-leSS project  ( ANR-17-CE31-0025 ;  TH 1530/18-1 ;  SA 2585/3-1 ;  SP1216/8-1 ) and the bilateral  PROCOPE-PPP program  ( PHC 40555PC ;  DAAD 57390184 ). The  Chevreul Institute  is thanked for its help in the development of this work through the  ARCHI-CM  project supported by the  "Ministère de l'Enseignement Supérieur de la Recherche et de l'Innovation", the région "Hauts-de-France" , the  ERDF program  of the  European Union  and the " Métropole Européenne de Lille ". This research was carried out at the P02. 2 beamline of the  PETRA III synchrotron at DESY , a member of the  Helmholtz Association (HGF). The research leading to this result has been supported by the  project CALIPSOplus  under the Grant Agreement 730872 from the  EU   Framework Programme for Research and Innovation HORIZON 2020.
Indeed, the sequence-based RNA adenosine methylation site predictor (SRAMP) program (http://www. cuilab. cn/sramp) identi ed several high-con dence methylation modi cation sites on the MMP3 mRNA (Fig. 6J ). MeRIP enrichment experiments using anti-m6A antibody followed by RT-qPCR analysis indicated that MMP3 mRNA was subjected to m6A modi cation, which is dependent on METTL3 function (Fig. 6K, L ). Importantly, RIP-qPCR assay showed that knockdown of METTL3 using short hairpin RNAs (shRNAs) targeting its mRNA signi cantly reduced the binding of IGF2BP3 with MMP3 mRNA (Fig. 6M , Supplementary Table  S3 ), suggesting IGF2BP3 interacts with MMP3 mRNA in an m6A-dependent manner. Altogether, these results clearly show that IGF2BP3 regulates MMP3 protein expression by directly binding to and stabilizing its mRNA through m6A modi cation.
RNA sequencing (RNA-seq) The quality of extracted RNAs was determined using NanoDrop2000 (Thermo Scienti c, Waltham, USA). Samples were tested for library construction and sequenced using the Illumina Novaseq 6000 (Illumina, San Diego, USA). Sequenced reads were mapped onto the mouse reference genome (http://asia. ensembl. org/index. html). FPKM (fragments per kilobase per million mapped reads) was used to determine gene expression levels. Differentially expressed genes were analyzed using EdgeR software. Kegg and GO enrichments were used to determine their involvement in different biological processes.
Masson staining The hearts were xed in 4% paraformaldehyde for 48 h at room temperature. They were then dehydrated in ethanol and xylene. Sections of 50 µm thickness were cut from para n-embedded tissues every 200 µm from the ligature site to the apex of the heart. Four sections were collected from each heart and stained with Masson trichrome (Merck, New Sawamoto State, USA). The area of brosis was measured by Image J software.
TTC staining Heart tissues dissected from euthanized animals were washed in PBS and immediately frozen at -80°C for 15 min. Small tissue slices were prepared and incubated in 1% TTC (Merck, New Sawamoto State, USA) at 37°C for 30 min. Non-infarcted area were stained in red, whereas infarcted area appeared white. Stained slices were imaged and analyzed by Image J software.
Flow cytometry analysis of cell cycle Cells were washed with PBS and xed overnight in 75% ethanol. They were incubated in the dark with 10 mg/mL of RNase A and 5 mg/mL of propidium iodide (Beyotime, Beijing, China) for 30 min at room temperature. The samples were analyzed using BD FACSCanto II ow cytometer (Becton, Dickinson and Company, New Jersey, USA) and the FlowJo software. Reactive oxygen species (Ros) assay Cardiomyocytes were treated with H 2 O 2 (500 µM) to induce weak injury. Cells were then incubated with 10 mM DCFH-DA probe (Solarbio, Beijing, China) and digested with EDTA-free trypsin. After centrifugation, cells were resuspended in serum-free solution and analyzed using the BD FACSCanto II ow cytometer. The mean uorescence intensity was used to represent ROS level.
We present a database of observational constraints on past Antarctic Ice Sheet changes during the last glacial cycle intended to consolidate the observations that represent our understanding of past Antarctic changes and for state-space estimation and paleo-model calibrations. The database is a major expansion of the initial work of  Briggs and Tarasov (2013). It includes new data types and multi-tier data quality assessment. The updated constraint database, AntICE2 (https://theghub. org/resources/4884,  Lecavalier et al. , 2022) , consists of observations of past grounded-and floating-ice-sheet extent, past ice thickness, past relative sea level, borehole temperature profiles, and present-day bedrock displacement rates. In addition to paleo-observations, the present-day ice sheet geometry and surface ice velocities are incorporated to constrain the present-day ice sheet configuration.
Introduction Numerical ice sheet models have been applied to reconstruct past continental-scale ice sheet changes in Antarctica for decades  (Whitehouse et al. , 2012a; Golledge et al. , 2014; Briggs et al. , 2014; Huybrechts, 2002; Pollard and DeConto, 2009). However, given the host of uncertainties in such modelling, assessment of the correspondence between model results and past Antarctic Ice Sheet (AIS) evolution requires (among other things) a quality-controlled constraint database with carefully assessed observational uncertainties. To date, only one database is publicly available  (Briggs and Tarasov, 2013) , and it suffers from some key limitations. Specifically, many regions, such as in the ice sheet interior, lack any observational constraints, and the data quality was not explicitly evaluated and specified through standardized criteria.
Paleo-ice-sheet modelling has a host of uncertainties associated with initial and boundary conditions, physical processes, and their numerical representation. As such, inferences of ice sheet evolution must be meaningfully constrained against Published by Copernicus Publications. B. S. Lecavalier et al. :  Antarctic Ice Sheet paleo-constraint database paleo-and present-day (PD) data. This requires an accessible database with well-defined observational uncertainties and a clear understanding of model limitations. The AIS has consistently been identified as a dominant source of uncertainty in predicting past and future global sea level change  (Meredith et al. , 2019; Fox-Kemper et al. , 2023). Previous studies have generated a wide range of future AIS projections  (Little et al. , 2013; Levermann et al.
, 2014; Ritz et al. , 2015; Ruckert et al. , 2017; Golledge et al. , 2015; DeConto and Pollard, 2016)  and paleo-retrodictions  (Whitehouse et al. , 2012a; Golledge et al. , 2014; Briggs et al. , 2014; Argus et al. , 2014; DeConto and Pollard, 2016; Huybrechts, 2002; Simms et al. , 2019; Albrecht et al. , 2020) , often with poorly defined confidence intervals. Most often, these issues are dealt with via parametric tuning to generate reasonable predictions and upper-or lower-bound estimates (e. g. Golledge et al. , 2014; DeConto and Pollard, 2016). The integration of a constraint database would help quantify what is deemed a reasonable result. Additionally, most previous studies inadequately explored parametric uncertainties, did not account for structural uncertainties of the model, and only applied a small set of observational constraints.