dataesr/scientific-paragraphs-categorization huggingface dataset:
The R package Tximport then concatenates the count tables of each sample into a single table. 5. Compilation of quality control (QC) files. MultiQC is a tool that compiles the QC information from the softwares used in the pipeline. It creates a full quality assessment (QA)/QC report which is finally used for the creation of the Firalink:FIMICS report. An overall workflow of the Firalink pipeline is presented in Fig. 1A. Construction of lncRNA matrix. We have written a python script that combines lncRNA counts into a single matrix file, which can be exported as comma separated file (CSV), tab separated file (TSV) or Excel file. The script reads all count files in a directory and combines them into a single file. Two files are generated as the result of this 1) count matrix and 2) QC matrix, which can be used for further analysis.
Quality assessment of lncRNA counts. In order to assess the quality of the lncRNAs from count matrix, we have written another python script that generates reports providing the list of zero variance lncRNAs, and lncRNAs with low read counts. This step generates a plot showing on the x-axis filter threshold and on the y-axis the number of filtered lncRNAs, which satisfy the filtering criteria in defined number of samples (Fig. 1B ). Each coloured line in Fig. 1B  shows the number of lncRNAs that satisfy the threshold criteria in different number of samples. For example, the green line shows the number of lncRNAs that satisfy the filtering criteria in minimum 500 samples. The filtering criteria is defined in term of sum of all lncRNAs that are greater than threshold values. For example, x = 10 and green line means that ~1020 lncRNAs have greater than 10 values in more than 500 samples.
Firalink facilitates the use and strengthens the benefit of targeted sequencing approaches which could be helpful not only for discovery but also for clinical application of molecular diagnostic tests, a major interest of the EU-CardioRNA COST Action network  [10]. Firalink pipeline has been developed at first instance to work with the FIMICS panel of cardiac-enriched lncRNAs but it is applicable to any targeted sequencing data, as shown using a lncRNA panel for brain disease. The pipeline can be adapted to other targeted sequencing panels or other transcriptomics data (e. g. whole transcriptome) through a change of the reference genome/panel. The use of the Firalink pipeline can therefore be extended to any research project and any disease, thereby covering a wide range of application of targeted sequencing.
Acknowledgments The authors thank all members of  COVIRNA  project for their contribution. The Predi-COVID study was supported by the  Luxembourg National Research Fund (FNR)  ( Predi-COVID , grant number  14716273 ), the  André Losch Foundation  and by  European Regional Development Fund (FEDER , convention  2018-04-026-21 ). We are thankful to all the participants of the Predi-COVID study. We also acknowledge the involvement of the interdisciplinary and inter-institutional study team that contributed to Predi-COVID. The full list of the Predi-COVID team can be found here: https://sites. lih. lu/the-predi-covid-study/about-us/p roject-team/.
Gridded bottom-up inventories of CO2 emissions are needed in global CO2 inversion schemes as priors to initialize transport models, and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO2 emission inventories, hereafter referred to as CoCO2-MOSAIC 1. 0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1. 0 provides gridded (0. 1˚×0. 1˚) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015 to 2018 disaggregated in seven sectors.
7 ppm  (Meijer et al. , 2020 ). The CO2MVS system will combine satellite and in-situ measurements with prior information using an advanced data assimilation scheme  (Ciais et al. , 2015; Pinty et al. , 2017 Pinty et al. , , 2019)). The initial design of this system is being supported by the H2020-funded CoCO2 project (https://coco2-project. eu/), which will develop a pre-operational prototype of the CO2MVS continuing the efforts started by CHE (https://www. che-project. eu/) and VERIFY (https://verify. lsce. ipsl. fr/) projects. The main challenges faced by CO2MVS in particular, and by CO2 inversions in general, are that satellites measure column concentrations rather than emissions and that the signal of anthropogenic fossil emissions in atmospheric concentrations is small (and with much smaller variation) relative to the oscillating signal of natural fluxes between the land and ocean surfaces and the atmosphere.
Bottom-up gridded emission inventories are a key component to address these challenges  (Ciais et al. , 2015; Pinty et al. , 2017). They supply essential prior information to initialize transport models reducing the uncertainty of topdown inversions. They are also complementary to the top-down estimates and provide traceability to the primary activity data. Despite the advances done in source attribution using co-emitters, high-resolution images, or radiocarbon, bottom-up inventories can identify with a much higher level of detail the exact source of anthropogenic emissions. During the last decade, several efforts have been made to produce anthropogenic bottom-up inventories of CO2 emissions. The most prominent examples at the global scale are the Emissions Database for Global Atmospheric Research (EDGAR)  (Janssens-Maenhout et al.
, 2019; Crippa et al. , 2021 , https://edgar. jrc. ec. europa. eu/dataset_ghg60), the Copernicus Atmosphere Monitoring Service global anthropogenic emissions (CAMS-GLOB-ANT)  (Soulie et al. , 2023) , the Open-source Data Inventory for Anthropogenic CO2 (ODIAC)  (Oda et al. , 2018) , the Community Emissions Data System (CEDS)  (Hoesly et al. , 2018; McDuffie et al. , 2020) , the Global Carbon Grid (GID) (http://gidmodel. org) and the near-real time Global gRidded dAily CO2 Emission Dataset (GRACED)  (Dou et al. , 2022). Gridded inventories used for operational global scale inversions using 2015 data, analysing their total and per sector emissions in each region, their spatial and temporal weight factors, and the location and magnitude of super-emitting pixels, among other aspects.
Masks CoCO2-MOSAIC 1. 0 includes a country and an inventory mask. The country mask is based on the Geographic Information System of the COmmission (GISCO) 2020 dataset (1m) (EUROSTAT, 2020). GISCO 2020 labels countries with their ISO Alpha-3 codes and their English name. For rasterization, the ISO Numeric (3-digit) code was used. At coastal borders, all pixels touching the coastal line were considered as land and assigned to the corresponding country (Fig  S2 ). At country borders, pixels including more than one country were assigned to the country covering most of the border pixel (Fig  S3 ). Note that this could introduce a small error when using the country mask to aggregate the emissions per country in those countries with a significant share of their emissions close to their borders. The inventory mask maps each pixel to one input inventory (Fig  1 ).
LULUCF emissions EDGAR LULUCF  (Crippa et al. , 2022)  net fluxes are used to complete the overview of CO2 anthropogenic emissions. They are available at the regional level in four different categories: 'forest land' (living biomass), 'deforestation', 'organic soil', fires, and 'other' (including all other land uses). EDGAR LULUCF provides independent estimates for the living biomass pool in forest land (including fires), while emissions from the other categories are based on a compilation of official country reports to the UNFCCC  (Grassi et al. , 2022). The forest land CO2 fluxes are obtained combining the forest area from satellite-derived land use data and the IPCC Tier 1 approach, which uses IPCC default forest growth factors and country statistics on harvest. These fluxes are calculated over managed forests, derived from country information or approximated by means of a non-intact forest layer.
Also biomass fire emissions are estimated by means of a Tier 1 approach, using the Global Wildfire Information System (GWIS) burned area product  (Artés et al. , 2019). For this study, emissions from firewood harvest (preliminary estimate based on country statistics) were removed from forest land fluxes because these emissions are already accounted as CO2bf. A gridded EDGAR LULUCF dataset is not available yet, so LULUCF emissions are used for the analysis, but they are not integrated into CoCO2-MOSAIC 1. 0.
Analysis of super-emitters The number and magnitude of super-emitters depend on the power and heat plant emissions (IPCC sector 1A1a) and the total number of emitting pixels, so we inter-compared both quantities per inventory and region. CEDS was excluded as the energy sector includes other activities besides power plant emissions. We used the CoCO2 1. 0 global power plant database  (Guevara et al. , 2023)  to analyse the geo-location of super-emitting pixels. We checked if all super-emitting pixels contained a power plant, defining the total number of true positives (TP), super-emitters collocated with a power plant, false positives (FP), superemitters not collocated with a power plant, and a special case of false positives (FP*), super-emitters not collocated with a power plant but with a power plant in one of the 8 surrounding pixels.
Uncertainty analysis According to the Guide of the expression of Uncertainty in Measurements (GUM)  (JCGM, 2008) , the pixel-level uncertainties of CoCO2-MOSAIC 1. 0 should be calculated by propagating the pixel-level uncertainties of the input inventories through the different steps of the methodology. This would allow propagating CoCO2-MOSAIC uncertainties in inversion models and closing the uncertainty budget in comparisons against other inventories. Similarly, pixel-level uncertainties of input inventories should be obtained by propagating the uncertainties of their input datasets (country emissions, spatial and temporal proxies, etc. ) through their models. However, we could not apply this methodology because, unfortunately, only VULCAN 3. 0 provides pixel-level uncertainties. Instead, we used the methodology described by  Choulga et al.
Description of CoCO2-MOSAIC 1. 0 The total CO2 emissions in 2015 based on CoCO2-MOSAIC 1. 0 are 36. 7 Gt of CO2ff and 5. 9 Gt of CO2bf (Table  7 ). These emissions are partly offset by a LULUCF sink of -10. 9 Gt (Table  8 ). This sink is much bigger than the -3. 7 Gt net LULUCF sink reported by  Crippa et al. (2022)  because here the emissions from firewood harvest (preliminary estimated around +7. 2 Gt, based on country statistics) have been removed from forest land, as these emissions are already counted as CO2bf emissions. The discrepancy between the +5. 9 Gt of CO2bf and the +7. 2 Gt of firewood harvests is due to the different years of harvest and burning, the additional sources of CO2bf besides firewood, and the different methodologies used by each dataset. Note that to calculate the total CO2 emissions according to the IPCC reporting guidelines, the 36.
The best agreement is again observed in Europe, where the main discrepancy is the larger number of low-emitting pixels of CAMS-REG-GHG 5. 3 in the energy and transport sectors. This pattern is also observed in South-East Asia, USA and Africa. In South-East Asia, the larger number of emitting pixels of REAS 3. 2. 1 is explained by the downscaling procedure applied to the original dataset (0. 25°×0. 25°), but otherwise, the distributions have similar shapes. The largest difference in the number of emitting pixels is observed in Africa. Fig  S6  shows that all DACCIWA 2. 0 pixels inside each country have non-zero emissions for energy, manufacturing and sectors, due to disaggregating part of the emissions based on the population density. This procedure was not applied in the transport sector but the number of transport emitting pixels of DACCIWA 2.
The emissions of 1A1a sector have a small uncertainty so in principle the regional differences between inventories should be small. This is true for Europe, but differences up to 10% are observed between regional and global inventories in South-East Asia and Africa (Table  9 ). However, the largest discrepancies are due to the different number of energy-emitting pixels in each inventory. Figure  6  analyses the geolocation of super-emitting pixels by evaluating their agreement with the CoCO2 power plant database. Regional inventories have a perfect match with the power plant database, with all super-emitting pixels containing a power plant. By contrast, both EDGAR 6. 0 and CAMS-GLOB-ANT 5. 3 have 6 and 8 false positives in the pixels covered by regional inventories. Countries without regional inventories present the worst agreement likely due to the lower quality of both global inventories and global power plant databases in Russia and the Middle East.
Analysis of super-emitting locations The total power plant emissions of global and regional inventories in Europe are similar, but CAMS-REG-GHG 5. 1 has five times more emitting pixels than the global inventories likely due to the use of CORINE land cover dataset to distribute emissions not linked to a specific point source. Despite this, the number of super-emitters in global and regional inventories is the same, and the magnitude of the regional super-emitting pixels is even 36% greater in the two common super-emitters. All the super-emitters identified by CAMS-REG-GHG 5. 1 contain a power plant. The largest discrepancies between regional and global inventories are observed in South-East Asia. REAS 3. 2. 1 has 8. 5% less power plant emissions than EDGAR 6. 0 spread over a much larger number of pixels  (200577 vs 4521-4512).
This is partly due to the coarse native resolution of REAS 3. 2. 1. However, REAS 3. 2. 1 super-emitters are not influenced by the downscaling process because the most-emitting stations are available as point sources and were mapped directly to the 0. 1°x0. 1° grid. Nevertheless, both the smaller power plant emissions and the higher number of energy sources could be the reason behind the smaller number of super-emitters in REAS (5 vs 18-20). The use of the regional data improves the agreement with the power plant database. All REAS super-emitters contain a power plant, whereas EDGAR 6. 0 and CAMS-GLOB-ANT 5. 3 have 5 and 6 false positives, respectively. Most of them are in South Korea and have a power plant in the surroundings pixels, so the chances of geo-location errors are again high.
7 vs 21. 7 Mt), but the main difference is caused by the additional 13218 LTO emitting pixels included by VULCAN 3. 0 that add 49. 62 Mt of CO2ff not accounted by EDGAR. This could be explained by the more extensive list of airports, including also helipads, used by VULCAN, whereas EDGAR uses a global database from the International Civil Aviation Organization (ICAO) that includes only the main airports and main flights.
Their uncertainty could be smaller than the values reported in this study based on LDS default uncertainties, due to the uptake of local information. This group also includes shipping emissions and aviation emissions above 1km, which both have been considered as LDS. The high uncertainty of aviation emissions agrees with the large discrepancies observed in the previous section. More work is needed to reduce the uncertainty of global datasets of shipping and aviation emissions. The last group of countries with room for improvement includes Canada and Australia, the only countries with a well-developed statistical system without regional, gridded information. The development of gridded inventories for these countries could especially reduce the uncertainty of their spatially explicit emissions.
0 power plant database is better than for global inventories. All super-emitting pixels from regional inventories contained a power plant whereas around 25% of the superemitters from global inventories were likely incorrectly geolocated. Some of the largest discrepancies were found in the aviation sector, both in the magnitude of the emissions and the spatial allocation of LTO emissions, which agrees with the large uncertainty reported in this sector. Finally, we estimated the overall uncertainty of mosaic emissions to identify sectors and countries where improvements could be more easily made to reduce the uncertainty of CO2 emissions at a global scale.
Data availability CoCO2-MOSAIC 1. 0 is freely available at zenodo (https://doi. org/10. 5281/zenodo. 7092358)  (Urraca et al. , 2023)  and at the JRC Data Catalogue (https://data. jrc. ec. europa. eu/) in NetCDF format. The main files include the monthly emissions per sector for one species (CO2ff and CO2bf) over one year (2015 to 2018). Three auxiliary layers are available: mask_inventory (inventory mask), mask_country (country mask), and cell_area (area of the grid cell). The aviation emissions above 1km are provided as a separate file.
https://doi. org/10. 5194/essd-2023-210 Preprint. Discussion started: 27 June 2023 c Author(s) 2023. CC BY 4. 0 License. uncertainties based on the IPCC uncertainty framework(IPCC, 2006). For each IPCC sector, the methodology takes the IPCC default uncertainties for activity data and emission factors and propagates them first to EDGAR sectors and then to the CoCO2-MOSAIC sectors. CoCO2-MOSAIC 1. 0 does not have this level of sector disaggregation. Thus, we combined the relative uncertainties reported byChoulga et al. (2021)  for EDGAR sectors with EDGAR 6. 0 emissions to calculate the relative uncertainty per CoCO2-MOSAIC sector and country, and then we applied CoCO2-MOSAIC 1. 0 emissions to obtain the absolute uncertainties at country level. We split countries into well-developed (WDS) and less well-developed (LDS) statistical systems as made byChoulga et al.
Longer-term outcomes with the anti-CD38 antibody isatuximab in combination with carfilzomib-dexamethasone (Isa-Kd) were evaluated in the randomized Phase 3 trial IKEMA (NCT03275285), in a prespecified, follow-up analysis of progression-free survival (PFS, primary study endpoint), final complete response (CR) using Hydrashift Isa immunofixation assay, minimal residual disease (MRD) negativity, and safety. Enrolled patients had relapsed/refractory multiple myeloma (1-3 prior treatment lines). Isa 10 mg/kg was administered intravenously weekly in cycle 1 then biweekly. Efficacy analyses were performed in the intent-to-treat population (Isa-Kd: n = 179, Kd: n = 123) and safety evaluated in treated patients (Isa-Kd: n = 177, Kd: n = 122). Consistent with the primary interim analysis, the addition of Isa to Kd prolonged PFS (HR 0.
METHODS Study design and patients IKEMA was a prospective, randomized, open-label, active-controlled, Phase 3 study (NCT03275285) conducted worldwide, at 69 study centers in 16 countries. Study eligibility and exclusion criteria have been described in detail previously  [15]. Briefly, enrolled patients had relapsed and/or refractory MM with 1 to 3 prior treatment lines and measurable evidence of disease (serum M-protein ≥0. 5 g/dL and/or urine M-protein ≥200 mg/24 h)  [19]. Patients with primary refractory MM, serum freelight chain measurable disease only, or Eastern Cooperative Oncology Group performance status (ECOG PS) > 2 were not eligible. Patients were also excluded if they had received prior carfilzomib therapy, had a contraindication to treatment with dexamethasone, had an estimated glomerular filtration rate (eGFR) <15 mL/min/1.
The MRD negativity rate was the proportion of patients for whom MRD was negative at a sensitivity of 10 -5 at any time point after the first dose of study treatment. Adverse events (AEs) and laboratory abnormalities were monitored and graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) version 4. 03. Safety was regularly reviewed by an independent Data Monitoring Committee until the PFS update.
Statistical analyses Efficacy analyses were conducted in the ITT population and summarized by randomized treatment. Median PFS (mPFS) and 95% CIs were calculated using the Kaplan-Meier method. HRs were estimated using a Cox proportional-hazard model stratified by stratification factors as entered in the Interactive Response Technology system (ie, number of previous lines of therapy and R-ISS). The odds ratios were also stratified. Treatmentemergent AEs (TEAEs) were evaluated in the safety population (all treated patients). Categorical and ordinal data were summarized using the number and percentage of patients. Continuous data were summarized for each treatment group using the number of available observations, mean, median, standard deviation, minimum, and maximum. SAS 9. 4 software (SAS, Cary, NC) was used for all the analyses.
To facilitate treatment administration, a onceweekly schedule of carfilzomib is currently being evaluated in a multi-arm Phase 1b study (NCT02332850) in combination with Isa and dexamethasone in patients with RRMM and in a Phase 2 study (NCT04430894) in combination with Isa-Rd in transplant-eligible patients with newly diagnosed MM. Anti-CD38 therapy with Isa was administered in this trial by IV infusion rather than the subcutaneous (SC) route. However, in this particular regimen, the combination with carfilzomib, which also requires IV administration, would limit the convenience of an Isa SC delivery. Nonetheless, SC administration of Isa is currently being evaluated in combination with pomalidomide-dexamethasone in a Phase 1b study with promising, preliminary results  [30]. After 2 additional years of follow-up and notwithstanding the risks associated with the Covid-19 pandemic, the safety profile of treatment with Isa-Kd remains similar to the results previously reported in the interim analysis  [15].
DATA AVAILABILITY Qualified researchers may request access to patient-level data and related study documents including the clinical study report, study protocol with any amendments, blank case report form, statistical analysis plan, and dataset specifications. Patientlevel data will be anonymized and study documents will be redacted to protect the privacy of our trial participants. Further details on Sanofi's data sharing criteria, eligible studies, and process for requesting access can be found at: https:// www. vivli. org/.
The experimental results validate that leveraging this variation improved the performances of the MTL-based implicit hate speech detection models. The rest of the paper is structured as follows: Section II describes the background knowledge and related works. Datasets used for the analysis and evaluation are presented in Section III. In Section IV, the analysis of the emotional dimensions of hate speech is provided, while Section V presents the proposed architecture of our system with the experimental methodology and results along with error analysis on system performance. Lastly, Section VI concludes the work and presents future directions.
II. RELATED WORK Hate speech is harmful and often targets an individual or group of people directly attacking hate towards them. Hate speech expressions can be explicit or implicit. Compared to explicit, implicit poses challenges such as fewer lexicons to learn the model, bias towards the dataset and its labelling etc. With the rise of hate speech in online social media, several automatic hate content detection models have been proposed in the literature which are based on natural language processing techniques. Most of the proposed models used supervised ML methods. However, the generalisability of those models is not noticeable when they are applied to other unseen datasets  [11]. In addition, existing models are severely overestimated  [12]  and therefore, it still dares to apply these models in real-world scenarios.