dataesr/scientific-paragraphs-categorization huggingface dataset:
The half maximal inhibitory concentration (IC50) values were calculated with Prism ® v6 software (GraphPad Software LLC, La Jolla, CA, USA). For apoptosis analysis, 1 × 10 6 cells derived from patients or cell lines were resuspended in 1 mL culture medium and treated for 48 h and 72 h with either 500 nM OTX015 (MK-8628), 500 nM JQ1, 1 µM ATRA + 1 µM ATO or 0. 1% DMSO. Apoptotic cells were detected using a CytoFLEX flow cytometer (Beckman Coulter, Villepinte, France). Cells were stained with 5 µg/mL PI and Annexin-V-FITC (Becton Dickinson, Le Pont de Claix, France) according to the manufacturer's instructions for 15 min at room temperature. Apoptotic cells were defined as Annexin V + with or without PI uptake. Apoptosis was determined by cytofluorometric analysis and analyzed with the FlowJo flow cytometry software (FlowJo LLC, Ashland, OR, USA).
Differentiation Assessment Morphologic differentiation of OCI-AML3, IMS-M2, K562 or patient cells was determined by CD11b expression and morphologic assessment of cells stained with May-Grünwald-Giemsa. Cells were treated for 48 h and 72 h with either 500 nM OTX015 (MK-8628), 500 nM JQ1, 1 µM ATRA + 1 µM ATO, or 0. 1% DMSO followed by flow cytometric detection of the differentiation marker CD11b Alexa Fluor ® 488 (#557701, Becton Dickinson, Le Pont de Claix, France) using a CytoFLEX flow cytometer (Beckman Coulter, Villepinte, France) and analyzed with the FlowJo flow cytometry software (FlowJo LLC, Ashland, OR, USA). For morphologic analysis, cytospin preparations (Cytospin 4, Fisher Scientific, Illkirch, France) of OCI-AML3, IMS-M2, K562 or patient cells treated for 48 h by OTX015 (MK-8628) 500 nM, JQ1 500 nM, ATRA 1 µM + ATO 1 µM, or 0.
Targeting Sequencing Using NGS Targeting sequencing of all exons of 66 genes recurrently involved in myeloid hematological malignancies was performed using a customized SureSelect™ (Agilent) capture panel (List of genes below). Libraries were prepared from 200 ng of genomic DNA from leukemia cells according to manufacturer's instructions and sequenced on NextSeq 550™ using Illumina reagents. The mean depth was 995× and the coverage at a minimum depth of 200× was 97%. Data analysis was performed using an in-house pipeline using GATK, Varscan and Pindel and VISCAP tools. Interpretation of variants was performed with the help of human genome databases, COSMIC database, prediction algorithms (including SIFT and PolyPhen) and literature review. List of genes: ASXL1, ASXL2, ATRX, BCOR, BCORL1, BRAF, BRCA2, BRCC3, CALR, CBL, CEBPA, CREBBP, CSF3R, CTCF, CUX1, DDX41, D NMT3A, EP300, ERCC6L2, ETNK1, ETV6, EZH2, FLT3, GATA2, HRAS, IDH1, IDH2, IRF1, JAK2, KDM5A, KDM6A, KIT, KMT2A/MLL, KMT2D/MLL2, KRAS, MECOM, MPL, MYC, NF1, NPM1, NRAS, PHF6, PPM1D, PRPF8, PTEN, PTPN11, RAD21, RIT1, RUNX1, SAMD9, SAMD9L, SBDS, SETBP1, SF1, SF3B1, SMC1A, SMC3, SRP72, SRSF2, STAG2, TET2, TP53, U2AF1, U2AF2, WT1, ZRSR2.
We took advantage of the phase I dose-escalating multicenter trial in hematologic diseases (NCT01713582) including 1 patient with relapsing NPM1c AML in our center. The patient received 120 mg of OTX015 (MK-8628) once daily and bone marrow examination was performed on day 1 and day 22 and bone marrow blasts were subjected immediately to IF staining without further exposure to OTX015 (MK-8628) ex vivo. On day 1 of treatment with OTX015 (MK-8628) the NPM1c was detected in the cytosol while on day 22 NPM1c disappeared from the cytosol with the patient having stable disease at this time point (Figure  6B , Patient #7, Table  2 ). The patient remained stable for two cycles of treatment until progression. Taken together we demonstrate that BETi induce apoptosis, differentiation, and disappearance of NPM1c in the cytosol in NPM1c primary cells.
As reported formerly for ATO + ATRA treatment, we could show the disappearance of NPM1c pattern in the cytosol in NPM1c cell lines and primary AML blasts upon treatment with BETi. Interestingly, NPM1c is not degraded in IMS-M2 by BETi cells but we could observe NPM1c disappearance in the cytosol in those cells. This indicates that BETi may interfere with NPM1c/NPM1wt oligomerization and that resistance mechanisms via deregulated gene pathways exert their activity up-or downstream in the tumor suppressive action of NPM1wt in IMS-M2 cells. We could also demonstrate in vivo activity of OTX015 (MK-8628) in a patient with relapsing NPM1c AML treated in the phase I dose escalating multicenter trial in hematologic diseases (NCT01713582). On day 1 of treatment with 120 mg once daily OTX015 (MK-8628) the NPM1 protein (wt + c) was localized in the cytosol in bone marrow blast cells while on day 22 the NPM1c disappeared from the cytosol.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/ 10. 3390/biomedicines9111704/s1. Institutional Review Board Statement: Approval for this study was obtained from the local institutional review board of the CHU Saint Louis, Paris (France) for patient samples. One patient with relapsed NPM1c AML was treated in a phase I dose escalating multicenter trial in hematologic diseases (NCT01713582,approved date: 24 October 2012). The study was approved by the local institutional review boards of CHU Lille (France), CHU Saint Louis, Paris (France), CHU Lyon Sud (France), Institut Paoli Calmettes, Marseille (France), CHU Toulouse (France), Princess Margaret Cancer Center, Toronto (Canada) and Royal Marsden Hospital, London (UK) for all patients included in the trial NCT01713582  [22].
Informed Consent Statement: Patients provided informed consent prior to BM aspiration at diagnosis, according to the Declaration of Helsinki. One patient with relapsed NPM1c AML was treated in a phase I dose escalating multicenter trial in hematologic diseases (NCT01713582) and patient BM cells were obtained at day 1 and 22 during first treatment cycle. The patient provided informed consent prior to BM aspiration at diagnosis, according to the Declaration of Helsinki.
Data Availability Statement: Our genomic data will be public in the GEO data base if the paper is accepted: The following secure token has been created to allow review of record GSE146362 while it remains in private status: kbyjkicojdqtfsj. Please note the following points: -This token allows anonymous, read-only access to GSE146362 and associated accessions while they are private; -Treat the token as you would a password and realize that the token provides access to GSE146362 to anyone who uses it; -we recommend you do not include the token anywhere except in secure email to journal editors; -If you want to revoke this access token, you can click the 'Reviewer access' button on your record which exposes the option to revoke this token. Send the following information to journal editors who will circulate to reviewers requiring access to your private data: To review GEO accession GSE146362: Go to https://www.
Principal Findings During the pandemic, significant social media discourse has focused on COVID-19 and people with dementia  [13, 17, 18]. In this study, we examined Twitter data to understand stigma against people with dementia during the COVID-19 pandemic. Drawing on Smith's  [15, 16]  model of stigma communication, we identified 4 main themes: ageism and devaluing the lives of people with dementia (eg, group labeling), misinformation and false beliefs about dementia and COVID-19 (eg, peril and responsibility), dementia used as an insult for political ridicule (eg, marking), and challenging stigma against people with dementia. Overall, our study sheds light on stigma against dementia during the COVID-19 pandemic and highlights opportunities for policy and research to address this moving forward.
As such, future research with qualitative interviews or focus groups with people with dementia or their care partners may provide more comprehensive and in-depth information regarding COVID-19 and experienced stigma against dementia. Second, no sociodemographic or geographic information (eg, ethnicity, age, sex, gender, income, country) of the tweeters was collected in our study and accordingly limits our ability to further evaluate stigma beyond describing the Twitter discourse. For example, because no data were collected on sex or gender, it is difficult to make specific inferences or draw conclusions regarding stigma against dementia in relation to sex or gender during the pandemic. Further research is needed to explore the stigma against dementia in relation to sociodemographic information, including sex and gender.
Acknowledgments This work was supported by  Team   15  of the  Canadian Consortium on Neurodegeneration in Aging (CCNA). The CCNA is supported by a grant from the  Canadian Institutes of Health Research , with funding from several partners, including the  Saskatchewan Health Research Foundation , the  Centre for Aging and Brain Health , and the  Alzheimer Society of Canada  (ASC). The  ASC COVID Task Force  was a particular catalyst for this work.
g. , century-long simulations), realtime initialization for S2S prediction is not very closely related to the research objective the model centers intend to pursue. To conduct LS4P-type research, the modeling groups have to develop an approach in nudging the reanalysis data for a real-time initialization. Nudging is one of the simplest data assimilation methods  (Hoke and Anthes, 1976)  and has been widely used in climate model evaluation and sensitivity studies (e. g. ,  Xie et al. , 2008; Sun et al. , 2019; Tang et al. , 2019)  to constrain the simulations towards a predefined reference (the reanalysis data in this case) and hence to facilitate time-specific comparisons between model and observations. For the LS4P simulations, E3SM and CESM2 used 1 month worth of nudging of the horizontal wind components (U and V ) with a 6 h relaxation timescale before the land mask for the initial LST perturbation was applied.
A. Uploading data into the National Tibetan Plateau Data Center using Filezilla To upload data into the National Tibetan Plateau Data Center, we recommend using "Filezilla". With Filezilla, the host, username and password are generated automatically for the Filezilla when the data are uploaded. The following procedure is based on "Filezilla". The procedure will utilize the following steps. 1. Log into the online National Tibetan Plateau Data Center (http://data. tpdc. ac. cn/en/, last access: 1 June 2021) using the aforementioned login details (see II). Login name: LS4P_group. 2. Go to "LS4P_group"/"personal center"; select "My Data" on the left bar, and then select "Submit Data". 3. You will see the webpage "CREATE META-DATA". Please fill in your data information, such as (i) overview (title, abstract, file naming, file size, time range), (ii) reference, and (iii) keyword(s).
Data availability. The LS4P data are stored by and will be distributed through the National Tibetan Plateau Data Center (Li et  al. , 2020, http://data. tpdc. ac. cn/en/) and the U. S. Lawrence Livermore National Laboratory (LLNL) Data Center Earth System Grid Federation (ESGF) node  (Cinquini et al. , 2014 , https://esgf-node. llnl. gov/projects/esgf-llnl). The evaluation/reference data sets from CAMS, CFSR, CMA, CRU, ERA-Interim, MERRA-2, and NARR as well as model data discussed in this paper are archived at https://doi. org/10. 5281/zenodo. 4383284 (Xue and Diallo, 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-14-4465-2021-supplement.
Task 4 No Two months (late April-June 2003) Task 4 is the same as Task 1, but here the 2003 SST is replaced by the climatology (1981-2010) SST. and June precipitation in eastern China is just a necessary condition for LS4P to pursue its approach, i. e. , to propose a hypothesis. It is not sufficient to guarantee the model can improve the June precipitation prediction by using improved May T -2m initial conditions. Only Task 3, as discussed below, will serve this purpose. 3. 2. 2 Task 2A number of LS4P modeling groups are from big climate modeling centers, and, as such, already have the required climatological runs in their respective databases. Those groups are required to send each year's global May T -2m and June precipitation from their climatological runs. Since different centers have different years in their climatology, LS4P only requires the climatological data set covering the time period from around 1981 to around 2010.
Since 2019, all three networks have merged in a single network (acronym NETSARC+). The shared online database (see rreps. org and netsarc. org) gathers all cases of sarcoma reviewed by a multidisciplinary tumor board. It collects data related to diagnostic, therapeutic management, relapse and survival. Since the January 1 st 2010, this database prospectively has included over 60000 patients with sarcoma and connective tissue tumor of intermediate malignancy (TIM), as defined according to the WHO 2013 classification  [1]. Since 2013, the overall accrual in the database reached a plateau, providing a near exhaustive collection of cases in this country. The global nationwide incidence of sarcoma has seldom been reported. Taking advantage of the organized reference center networks and expert pathology reviewing, we report here the incidence of the different histological subtypes of sarcomas and TIM from the NETSARC + database from 2013 to 2016.
The RRePS/NetSarc database These databases have been approved by the French Ethic Committee and Agency in charge of non-interventional trials: Comiteconsultatif sur le traitement de l'information en matière de recherche dans le domaine de la Sante(CCTIRS: number of approval 09. 594) and Commission Nationale Informatique et Liberte(CNIL: number of approval 909510). THe consent was obtained orally. All sarcoma/TIM or suspected sarcoma/TIM patient cases discussed during the multidisciplinary tumor board (MDTB) of all 26 centers were recorded in the electronic online database, by a dedicated team of Clinical research assistant (CRAs), supervised by the Coordinating centers (Unicancer Comprehensive Cancer Centers: Centre Leon Berard, Gustave Roussy, Institut Bergonie, as well as CHU Tours & CHU Nantes).
Patients files may be presented at different stages of care process, before any diagnostic procedure, before initial biopsy, before primary surgery, after primary surgery, at relapse, and/or in case of a possible inclusion in a clinical trial as previously described  [24, 25]. All patients had the option to opt out the initiave, or could orally consent to the pathology review of their biopsy, the registry in the national network database according to the national laws at that time, as well as benefit from the recommendations of the French National Cancer Institute. The databases were therefore not generated from clinical trial data, and we built to monitor and improve the management of patients with sarcomas in France. According to the national legislation, the activity of the three networks did not have to be reviewed by the national ethics committees, compared to clinical studies (the Comitede Protection des Personnes).
However, each local hospitals (i. e. the reference centers) used their internal procedure (internal institutional review board) to approve participation to this work. This approval was mandatory for the participation of the given center to any of the 3 networks. Oral consent is documented through a standard information sheet given to all patients in each institution in the welcome leaflet. Patients are able to opt out, and this information is taken into account during multidisciplinary tumor board discussions of the cases. Patients and treatment data were prospectively collected and regularly updated by the dedicated study coordinators. The cases were obtained directly from the pathologist laboratories, or referred to the expert pathology laboratory for diagnosis confirmation for a fraction of the patients who were first seen by the clinical MDT.
This double source of entry contributed to improve the exhaustiveness of the collection of cases. Of note, the database includes on purpose a limited set of data, describing for example patients and tumor characteristics, surgery, relapse and survival  [24, 25] , centers performing the first resection, as well as potential secondary surgery types and sites, the final quality of resection. The RRePS/NetSarc database may therefore give a nearly exhaustive representation of sarcoma cases to assess sarcoma incidence and prevalence in France. The database is not systematically updated for follow-up by the CRA, as for clinical trial processes, but all baseline and first therapeutic information are completed until the end of the first line treatment. This includes all pathology reviews, which are therefore as presented, the final diagnoses, with a median follow-up of the series of 17 months in the recent publications of the same dataset  (25).
Importantly, since 2019, the data (survival and all treatments) from the nationwide database of the social security system (SNDS, https://www. snds. gouv. fr/SNDS/ Accueil) is used to update the latest survival information of these patients as part of the Health DataHub Deepsarc project (https://www. health-data-hub. fr/outil-de-visualisation), now ensuring an exhaustive follow-up information. Of note, the diagnosis of sarcomas of TIM (e. g. lipoma, carcinoma, lymphoma. ) was not confirmed by the NetSarc MDTB for about 24% of the patients (not shown). All data presented here were extracted from the common online database available online for a period of 4 years between 2013 and 2016. These 4 years were selected since: 1) the yearly incidence of sarcoma and TIM started to plateau since 2013, and 2) data monitoring and implementation is still ongoing since 2017.
Presentation of the data The 2013 WHO classification of sarcomas and connective tissue tumors was used from January 1 st 2013 to describe the histological subtypes in the database, taking advantage of the contribution of the 2013 version (since April 2012) by one of the senior authors (JMC) of the current article,  [1]. Monthly physical meetings of the pathologist network to review complex cases have facilitated the homogeneity of data collection within this group. Both sarcomas and TIM were included in the database. Tumors of intermediate malignancy designates connective tissue tumors with the capacity to invade the surrounding tissues, with very rare metastases  (1). These include for instance, aggressive fibromatosis, dermatofibrosarcoma protuberans, atypical lipomatous tumors. The number of patients for each individual histological subtype of sarcoma or TIM per year, from 2013 to 2016, is therefore presented in the tables.
To facilitate the comparison with other databases using previous classifications, the incidence of tumor groups (e. g. "uterine sarcomas") are also presented in the tables. Each individual histotype, (e. g. WDLPS) and groups of histotypes are presented when clinically relevant (e. g. "WD and DDLPS", or "liposarcoma", "leiomyosarcomas"). Conversely, when a grouping did not exert relevance in clinical routine (e. g. "fibroblastic and myofibroblastic tumors" in Table  1 ), no new entity was described. To estimate the incidence of these tumors, we used the official number of French citizens from 2013 to 2016, which were respectively 65. 56, 66. 13, 66. 42 and 66. 60 million inhabitants.
Statistical analyses The number of patients per year with the different histotypes is presented in tables. To analyze the variation of incidence over the 4 years, a Poisson Regression was used. Six histotypes with a significant variation in the period of observation are graphically detailed by overlaying the observed incidence, a linear regression over time and a spline. The comparison of the frequency of published clinical trials per histological subtypes or groups of subtypes was performed using the chi square or Fisher's exact test with a threshold p value of p<0. 05. All statistical tests were two-sided. All statistical analyses were performed using SPSS (v 23. 0) (IBM, Paris, France).
Incidence of sarcoma and TIM in NetSarc Tables 1-3 present the incidence of the individual histological subtypes of soft tissue sarcomas/TIM (Table  1 ), visceral sarcomas/TIM (Tables  1  and 3  for uterine sarcoma), bone sarcomas/TIM (Tables  2  and 3 ) included in the database (gathering the RRePS, NetSarc, and ResOs) from 2013 to 2016, a period where the data are expected to be close to exhaustive nationwide. From 2013 to 2016, a total of 25172 patients were included in the database, with n = 5838, n = 6153, n = 6654, and n = 6527 of new patients each year. Of note, respectively N = 4435 N = 4977 and N = 5550 patients were included in each year from 2010 to 2012 (not shown). The NetSarc database contains over 150 individual histological subtypes (i. e. a single histological entity such as monophasic synovial sarcoma, low grade surface osteosarcoma, atypical lipomatous tumor) or groups of sarcomas (e.
Variable incidence of sarcoma histotypes over the 2013-2016 period We investigated then the variability of the yearly incidence of these different tumors in the database. The analysis of variance of the observed incidence indicated a significant interaction between time and histology. S2 Fig presents the six histological subtypes with the mst significant variation between 2013 and 2016. Adenosarcoma, desmoid tumors, malignant pecoma, UPS, endometrial stromal sarcoma-high-grade increased over the 4-year period, while myoepithelioma showed a decrease of incidence (S2 Fig). The significance of these variations remains unclear and needs further investigation using comparable registries with a centralized review.
Incidence of individual histotypes and published clinical trials Table  1  and S1 Fig gives a graphic presentation in decreasing order of the incidence of the different histotypes and groups of histotypes. These were matched with published clinical trial data collected from Pubmed on a given histological group (e. g. liposarcoma) or specific histotype (e. g. pleomorphic liposarcoma). Phase III studies, randomized phase II studies and nonrandomized phase II studies are indicated in green, dark blue and light blue respectively showing a variable access to clinical trial according to the incidence of the histotype. An histological subtype is considered "covered" by a trial only if the trial design contains a specific arm (phase II) or a specific strata (phase III) for a given histotype. As expected, phase III trials are made available mostly for histotypes or groups of histotypes with an incidence >1/10 6 per year (Fig  1 ).
14 of 35 (40%) histotypes and groups of histotypes with an incidence >1/10 6 had a dedicated phase III study vs 6 of 130 (4. 6%) histotypes for sarcomas with an incidence <1/10 6 (p<10 -6 ). 20100 (79,7%) patients of the database had a histotype for which no phase III trial had been reported. Twenty-one of 35 (60%) histotypes with an incidence >1/10 6 had a dedicated randomized phase II study vs 10 of 130 (7. 7%) histotypes for sarcomas with a incidence <1/10 6 (p<10 -10 ). 13154 (52. 1%) patients of the database had a specific histotype for which no randomized phase II trial had been reported. Twenty-eight of 35 (80%) histotypes with an incidence >1/10 6 had a dedicated phase II study vs 36 of 130 (27. 9%) histotypes for sarcomas with an incidence <1/10 6 (p<10 -8 ). 6516 (25. 8%) patients of the database had a specific histotype for which no phase II trial had been reported.