dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioluminescence Imaging Bioluminescence imaging was performed using the IVIS Spectrum (PerkinElmer, Villebon-sur-Yvette, France) imaging scanner coupled to the Living Image Software (Perkin Elmer) on IVIA multimodal imaging platform (Clermont-Ferrand, France) (https://doi. org/ 10. 18145/ivia accessed on 17 May 2022). Briefly, 100 mg/kg of in vivo luciferase substrate (beetle luciferin substrate, Promega) were injected intraperitoneally into each CARE-LUC mouse. Mice were then anesthetized with isoflurane and scanned 10 min after luciferin injection. The abdominal cavity was shaved to allow for the accurate collection of bioluminescence signals. Light emission was quantified with imaging software from constant regions of interest (ROI) drawn manually. Photon emission was measured as radiance in p•s -1 •cm -2 •sr -1.
Acknowledgments: We would like to thank  Leslie Mazuel  and  Arnaud Briat  from IVIA multimodal imaging platform, Clermont-Ferrand, France (https://doi. org/10. 18145/iviaaccessed on 17 May 2022). We are grateful to  Julien Hermet ,  Mehdi Djelloul-Mazouz  and  Yoann Delorme  for animal care and technical assistance. We also thank  Cécile Polge  and  Etienne Lefai ,  UNH ,  INRAE  for the critical reading of the manuscript.
Mouse health was monitored with weekly body weight measurements. Tumor burden was monitored weekly using the IVIS Spectrum In Vivo Imaging System (PerkinElmer, MA, USA). To do this, mice were injected subcutaneously with 75 mg/kg D-luciferin potassium salt (Promega E1605) in sterile PBS and anesthetized with 2% isoflurane in medical air. Serial bioluminescence images were acquired using the automated exposure set-up. The peak bioluminescence signal intensity within selected regions of interest (ROI) was quantified using the Living Image Software (PerkinElmer) and expressed as photon flux (p/sec/cm2/sr). Relative bioluminescence is calculated by a ratio of bioluminescence at day 28 and day 7, BLI day28/ BLI day7. Mice were sacrificed at day 28, or when the mice showed disease symptoms, and terminal blood was collected by cardiac puncture.
DNA and RNA extraction RNA was extracted from cell lines using the RNeasy Mini Kit (Qiagen). RNA from tumor samples were extracted from formalin-fixed paraffinembedded (FFPE) using the RNeasy FFPE Kit (Qiagen). For the CTC enriched samples, RNA was extracted with the RNeasy Micro Kit (Qiagen). RNA quality was evaluated by automated electrophoresis with High Sensitivity RNA ScreenTape (Agilent, CA, USA). RNA integrity number (RIN) and DV200 were calculated using the TapeStation Analysis Software (Agilent)  7, 8. For sorted single cells, cells were sorted using the SONY SH800S with at least 16 single cells sorted twice in independent experiments. The SingleShot Cell Lysis Kit (Bio-Rad) was used according to the recommendations using 4 µl per cell of lysis buffer containing proteinase K and DNase.
Digital droplet PCR and pre-amplification PCR primers and probes were designed with Primer3Plus (http://www. bioinformatics. nl/cgibin/primer3plus/primer3plus. cgi) to amplify disease specific fusion transcripts using Bio-Rad settings for the QX200 Droplet Digital PCR system (Bio-Rad). Primers and probes were designed to target exons that are recurrently involved in the majority of the targeted translocations, with 2 probes per fusion transcript, one designed for each gene (Fig. 1A-C  and supplemental Table  1 ). cDNA was made using SuperScript IV VILO Master Mix (Invitrogen, MA, USA) using the entire volume of RNA extracted for a final volume of 20 µL. For the single cell experiment only, 1 µl of the Master Mix was combined with the 4 µL of RNA extracted. For cell lines, cDNA was made from 500 ng of extracted RNA.
EWSR1-FLI1-specific ddPCR was performed using ddPCR™ EvaGreen Supermix (Bio-Rad) and the QX200 Droplet Digital PCR system (Bio-Rad). For mouse CTC experiment, 8 µL of cDNA made from all extracted RNA was used for the EWSR1-FLI1 specific ddPCR and 5 µL was used of the multiplex ddPCR, exhausting the material. Primers were used at a final concentration of 900 nM and the concentration for each probe was 500 nM. Cycling conditions were: 10 min at 95 °C, 36 cycles of 30 sec at 95 °C and 1 min 30 s at 60 °C, then 10 min at 98 °C and hold at 4 °C. Analysis was performed using the QuantaSoft program (Bio-Rad).
5C ). At relapse, we detected the expected fusions in 4 out of 19 blood samples (21%). We also detected the expected fusions in all the pleural effusions, 2 EWSR1-FLI1 and one BCOR-CCNB3. No false positives were found in any sample. The time to result after sample collection was one to two days. While these experiments were largely done retrospectively in a research laboratory environment, we also reviewed clinical and radiological data from these patients to determine whether prompt clinical implementation of this assay could have impacted patient care. In multiple cases, we found that tiered multiplexed ddPCR was able to detect pathognomonic fusions in samples from which existing diagnostic workflows resulted in either a protracted delay to diagnosis or misleading diagnostic information that likely impacted the initiation of therapy (Fig.
National climate change mitigation objectives must be translated to the city level and to specific sectors, with locally-relevant policy measures. However, assessing the decarbonization value of these measures is a challenge for local policy makers and few local consumption assessment studies integrate a systemic vision. This research presents a multi-criteria city-scale Life Cycle Assessment method focusing on the three largest carbon-emitting sectors in cities: food, residential buildings and daily mobility, with an original combination of databases and models. For food, databases were used to evaluate environmental impacts based on socio-demographic characteristics. For building and mobility, modeling tools were used and adapted at different scales: with local spatial refinement for mobility and the creation of archetypes representing the city's most common residential buildings.
Some territories like the C40 Cities group or the Grenoble urban area are voluntarily starting to set ambitious targets by proposing measures that are likely to reduce GHG emissions, but without knowing the real consequences or effectiveness of these measures. Although sector-based tools exist to evaluate many of these measures (i. e. building, transport and consumption models, material and energy flow analysis), combination of these models and analyses are complicated and rarely done, due to the multitude of data required and their lack of a systemic vision in regard to urban systems complexity  (Mirabella et al. , 2018). The environmental assessment of cities represents an important challenge for urban development, and for leaders aiming to improve their cities' sustainability. Such environmental assessments are challenging because cities represent complex systems, where multiple and diverse anthropogenic activities interact with significant concentration of people, companies, energies and resources  (Moine, 2006; Wegener, 1994).
The knowledge of each of these research fields was thus mobilized to evaluate the environmental impacts at city scale and integrated in coherence in a LCA. More precisely, (i) the data on the city's buildings and their construction systems was used to create archetypes of buildings representative of the city's constructions. The thermal simulation of these archetypes and their environmental assessment allowed to estimate the balance at the city scale and by neighborhood. (ii) the transportation models used by large cities to plan their future infrastructure were used as a basis for quantifying the movement of the city's population at the city neighborhood level. Thus, an inventory for assessment in LCA was derived. Finally, (iii) the characteristics of the city's population coupled with consumption inventory databases provided an estimate of the food consumption by city's residents.
Consistent with a carbon footprint approach, we evaluated only the impacts of the city's residents and not those of non-resident users (i. e. commuters). By cross-referencing these results by food type with an LCA impact database for food, we obtained the environmental assessment of this sector. Finally, the aggregation of these environmental results provided comparisons at city and neighborhood scales, but also for an average Montreuil resident. The rest of the article is composed as follows. The second section details the methodology for calculating LCA impacts as well as the types of data needed. The third section presents the case study of a municipality in the Paris region, Montreuil, with the specificity of the databases used in a French context. Finally, the last section demonstrates the environmental assessment based on our methodology on the city of Montreuil, as well as the implementation of measures at the city-scale to lower the GHG emissions.
(Loiseau et al. , 2018; Mirabella et al. , 2018)  T Three consumption sectors were included to represent major environmental impacts of the residents: food consumption, residential building and daily mobility. These three sectors were found to be highly impactful in a review of urban metabolism studies by  Goldstein et al. (2017). Depending on the study approach adopted (production-based, consumption-based or geographically-based), cumulative contributions of mobility, buildings and food sectors vary but stay significant, from 50% to 80%. All three sectors were handled separately in our study with distinctive models, datasets and calculations developed and executed in different manners. Nevertheless, the spatial and socio-economic information related to the population was processed jointly to properly characterize and disaggregate individuals into various spatial units (census sectors, city blocks, buildings).
Food sector To calculate the environmental impacts of food consumption, we first modeled the amount and type of food consumed in the city. We based this information on national consumption surveys, as has been done in numerous other studies  (Ramaswami et al. , 2017). However, food consumption patterns can vary largely within a country, and in the context of this work focused on environmental impacts and levers at the city scale, we sought to improve the local precision of this data. Diets vary largely by socio-economic factors, and three key factors defining categories of consumer are identified: age, sex, and level of education (or for children, level of education of parents) (ANSES, 2017). The typical annual diet for each socio-economic category was compiled, based on a national food consumption survey which contained socio-economic data for each respondent.
Building sector The method used for the environmental assessment of buildings was based on numerous studies applied at the neighborhood level  (Ballarini et al. , 2014; Reinhart & Davila, 2016). The use stage accounts for the majority of the life cycle environmental impacts of existing buildings, so it is necessary to calculate accurately the energy consumptions. The chosen approach consisted in modeling buildings and determining their energy performance. However, building energy simulation (BES) requires numerous detailed data and is time-consuming on a large scale. Archetypes were then created to represent the building stock, and modeled on the BES software Pleiades 1  , which aids in the eco-design of buildings and optimization. The dynamic building energy simulation model COMFIE  (Peuportier and Sommereux, 1990)  was used to evaluate heating loads, and environmental impacts were calculated over the building life cycle including stages of construction, use, renovation and end of life, with the LCA tool EQUER  (Peuportier, 2013; Polster, 1995; Popovici, 2005).
After representing the mobility and distance travelled in each spatial unit of the city, our transport methodology converted these distances into environmental impacts. The technology description of transport modes is essential when processing transport LCA. Then, we described the individual car fleet based on regional data, leading to a lower diesel rate than the national fleet. The car fleet LCA calculator, ModEm-ACV  (François, 2022) , was used to get unitary environmental impacts associated to the regional fleet. This calculator crosses LCA database and emission models to assess the exhaust, the fuel production, the vehicle life cycle (production, maintenance and end-of-life) and the road infrastructures impacts, while considering road and speed conditions  (François et al. , 2017). The environmental impacts were obtained per spatial unit by multiplying unitary impacts related to the car fleet, expressed per vehicle.
Demographic data sources The population description was central to estimating building occupancy, mobility patterns and food consumption related to every spatial unit that composed Montreuil. The French national census from 2015 was the main source of demographic data, with details on population, job location, and socioeconomic characteristics. The French statistical agency, INSEE, is in charge of the census and it localizes individuals and jobs at a spatial unit named IRIS. There are 41 IRIS units in Montreuil. Because building and mobility sectors require finer localization of individuals, we distributed the population into buildings and traffic blocks based on the BDTopo database, from IGN (The French national institute of geography). Montreuil has 112 traffic blocks and around 20,000 buildings, and the exhaustive road network is also available on this database (map in annex, Fig.
Transport data sources and models The DRIEA's MODUS model is a transportation model that simulates the daily trips of residents in the Paris region according to the transportation networks and the distribution of jobs and populations by geographic sector defined for the model (DRIEA Île-de-  France, 2021 France, , 2012)). For our study, the distribution was retained for the whole urban area except for Montreuil, for which the distribution was refined by block as explained above. In the model, only a selection of the main roads constituted the network. In the same way, we specified all roads at the scale of the city using the base of the road sections of the BDTOPO of the IGN. The MODUS model thus simulated for all users of each block the daily modal share (public transport, private car, bicycle, or walking).
Assessed distances travelled by private cars are also modeled. The environmental impacts of personal vehicles were calculated based on the regional car fleet composition described in the regional mobility surveys. The most recent mobility survey for Paris was in 2010, and described a car fleet with 64% of diesel and 35% of gasoline cars and an average age of vehicle around 7 years. Then, through ModEm-ACV calculator  (François, 2022) , emissions and consumptions of vehicle use were first estimated based on Copert model  (Ntziachristos et al. , 2020). Next, LCA impacts related to vehicle manufacturing and maintenance, fuel production and road construction were added from the EcoInvent database  (Wernet et al. , 2016). Detailed ModEm-LCA parameters, including car fleet description, are presented in the Supplementary Material.
Food data sources The INCA3 survey was used to determine the average amount and type of food consumed for each socio-economic group per year in metropolitan  France (ANSES, 2017). This survey was done by the national government agency ANSES, and surveyed over 5,800 individuals in 2014 and 2015, and was published in 2017. Data from the French census were used to determine the number of inhabitants in Montreuil corresponding to each socio-economic group. The breakdown of the population in each socio-economic category for the city is provided in the supplementary material (Annex_FOOD. xlsx). We combined the individual consumption data and the population data of the city to calculate the mass of food consumed from 44 different food categories. Life cycle inventory data for average food products consumed in France came from the AgriBalyse database, which draws from the LCA database EcoInvent  (Asselin-Balençon et al.
Data sources Following the disaggregation presented in the previous sections, the number of inhabitants of individual and collective housing properties was known. To evaluate the environmental impact of the city's buildings, we created archetypes representing the housing stock to avoid modeling each building in the city. Archetypes were based on key characteristics identified through the study of several complementary databases giving information on buildings, such as the type of residence, the construction period or the type of energy production. This data collection took place during the pre-modeling stage, and the studied databases are presented in Table  1.
Modeling building archetypes • Building archetypes were determined from the analysis of these databases, according to the three main criteria of the French database BD TOPO, detailed hereafter. This standard national map database for France is used in various sectors, such as improvement of the Global Positioning System (GPS)  (Bétaille et al. , 2015)  or urban heat island studies and climate modeling  (Emery et al. , 2018; Richard et al. , 2018). Its level of details enables the creation of archetypal building envelopes representative of the city. The buildings are also differentiated by their heat production system, mainly represented by electric and gas heating The archetypes were based on: type of residence (individual housing, small collective residence i. e. from 2 to 9 dwellings, and large collective residencemore than 10 dwellings) • Construction date, divided into four periods  (before 1945, 1946-1970, 1971-1990, after 1991)  • Wall and roof materials Building characteristics used to create the archetypes are the averages of data associated with the real representative of each archetype in the databases.
These data were used as Pleiades inputs in order to process to BES and LCA. Given the variability of building characteristics (materials, heating production and different envelopes), energy efficiency of the building stock was heterogeneous. Heat production for heating and domestic hot water was split between gas and electricity for each archetype, with ratios coming from the French database TABULA. Only multi-family dwellings built before 1945 were considered to be heated solely by gas. At the city level, 60% of the heat production comes from gas boilers, and 40% from electricity. Details regarding building archetypes (envelope and roof type, proportion in the city and heat production) are specified in the supplementary data: Annex_Build. xlsx) The most common archetypes were the four buildings presented in Table  2.
Validation of building energy consumption Building energy simulations of each archetype were performed to calculate energy consumption. Results were then extrapolated to the city scale to represent all energy consumption. To validate the method chosen to create the archetypes, these simulated consumptions were compared to real energy consumption. The reference for the real consumptions is the French database ROSE which collects numerous energy data as energy, energy indicator, energy bill etc. This indicates energy consumption of the residential housing of the town. According to our calculations, Montreuil's residences consumed 774 GWh/year, with 445 and 329 GWh/year for respectively gas and electricity, while the reference value was 731 GWh/year according to the ROSE data of 2017. The gap of 6 % between simulated and calculated final energy consumptions can be considered as acceptable and validated the choice of the methodology.
57% of total GHG emissions were related to direct exhaust emissions, 14% to fuel production, 26% to vehicle manufacturing and 3% to road infrastructure. For the other environmental impacts the contribution can be found in Supplementary data (Vehicle_impacts. docx). Within Montreuil, small spatial variability emerged from our calculations: the North East part was 14% more emitting than the rest of the city, with respectively 973 and 853 kg CO 2 eq/cap/a. This part of the city is less accessible by public transport with more individual housing.
e. electrical vehicles, new heaters …). In most studies, the data collection and the construction of inventories to handle building, mobility and food sectors is a long process and may include cut-offs to simplify models  (Dias et al. , 2018; Dong et al. , 2016). In our case, we used existing models and databases, such as Pleiades for building energy simulation  (Peuportier, 2013) , MODUS and ModEm-ACV, for mobility and car fleets  (François, 2022) , and AgriBalyse, for food production and transformation  (Asselin-Balençon et al. , 2020). Still, the quality of foreground and background LCA data is important, especially because nutrition, mobility and energy use for housing are the three main contributing consumption sectors  (Goldstein et al. , 2017). In the building sector, the archetype approach allowed for the assessment of city at the expense of simplified building modeling.
This could be important because buildings are sensitive to many design variables (material, exposure, …), and it is difficult to conserve a full description of buildings at city scale  (Reinhart & Davila, 2016). In transport, the local traffic condition is a relevant factor that impacts environmental performance, and the representativeness and quality of background LCA data remain an issue in transport and required additional work  (Frischknecht et al. , 2018). In the food sector the main difficulty was the vast range of food products that only few databases can represent consistently  (Asselin-Balençon et al. , 2020; van Paassen et al. , 2019). Relying on general databases prevented us from considering specificities of the local food system that may largely affect environmental performance, such as agricultural practices, production location and food waste treatment  (Gheewala et al.
Study Design Disease and injuries in GBD 2019 were organized into a comprehensive hierarchy of nested levels, with neoplasms as 1 of 22 level 2 groups. 20 Cancers were classified into 30 level 3 cancer groups (eg, leukemia), 4 of which were further subdivided into level 4 groups (eg, chronic myeloid leukemia). While the GBD study estimates benign and in situ neoplasms as an important component of total health burden from all neoplasms broadly, this level 3 cancer group was not included in the estimates reported in this article to focus on malignant cancers (eAppendix in the Supplement). Similarly, because nonmelanoma skin cancer (NMSC) has relatively high incidence and low mortality compared with other cancers, this article presents estimates with and without NMSC. There are 5 major ways that this iteration of the GBD study improved on the data and methods used to estimate cancer burden in GBD 2017  22  (eAppendix in the Supplement).
First, GBD 2019 incorporated an additional 104 076 new cancer-, location-, and year-specific sources of data compared with GBD 2017 (eTable 1 in the Supplement). Second, data processing methods were improved for several cancers, particularly liver cancer, as described later. Third, the youngest age group estimated was increased or decreased for several cancers to align with cancer registry age patterns. Fourth, modeling parameters were updated to perform additional smoothing of mortality-to-incidence ratio (MIR) estimates across age and time, reducing improbable variation from sparse data. Fifth, cancer survival estimation methods were updated to improve uncertainty estimations and estimate age-specific instead of all-ages survival curves. Results are presented by SDI, a composite indicator of income per capita, mean years of education, and fertility rate for those younger than 25 years.