dataesr/scientific-paragraphs-categorization huggingface dataset:
scRNA-seq and analysis of sequencing data Fresh single-cell suspensions (2 x 10 4 cells) were loaded onto the 10x Chromium to produce sequencing libraries, which were processed according to methods provided by 10x Genomics (v3 Chemistry). Cell cDNA was sequenced using the Truseq Illumina Stranded protocol and the Illumina NextSeq 550 sequencing machine (> 3x10 8 reads/sample). The reads were aligned with Cell Ranger v3. 1. 0 on the human genome using the GRCh38 assembly and the GTF file downloaded from Ensembl release 101. The 6 samples (3 times points, 2 donors) were pre-processed and normalized together using Seurat v4. 3. 0. Cells expressing less than 1000 genes were removed. Dead or lysed cells were excluded by removing cells with a percentage of mitochondrial genes above a threshold calculated using the Scater package (median percentage of mitochondrial genes across all individual cells + 3 median absolute deviations).
Definition of cell identities in the scRNAseq data sets The original cell x gene matrix of each donor were preprocessed and normalized separately as described above and cells were assigned the cluster number of the "integrated UMAP". Each donor dataset was then analyzed with Azimuth, an automated reference-based algorithm for single-cell annotation (https:// azimuth. hubmapconsortium. org/, version 2. 0. 0), using the Human Lung Cell Atlas as a core consensus reference model which encompasses 584,884 human cells of the lung and nose. The finest level of annotation was used. Cells presenting Azimuth annotation scores below 0. 6 were discarded. The remaining cells were hence associated to both a cluster number and a cell identity. In order to generate robust downstream analyses, the cell identities representing less than 10% of the associated cluster were eliminated for the subsequent analyses (Additional file 6).
The top markers of the 19 analyzed cell identities found in the "integrated UMAP-filtered" were extracted separately for each donor, using the normalized expression values of each dataset before integration [1000 top marker genes per cell identity versus the other identities (minimal log2FC>=0. 8, Bonferoni adjusted p-value<0. 05)]. The two lists were then merged into a single list to keep only common markers to both donors, ranked in a decreasing order using the lowest gene expression fold change between the two donors (Additional file 7).
Ingenuity pathway analysis The DEG lists were submitted to IPA software on the IPA knowledge database restricted to immune cells, epithelial, endothelial and fibroblasts, with stringent filters, according to IPA Ingenuity Web Site, www. ingenuity. com. We selected the meaningful pathways and functions with z-scores > 1. 9 or < -1. 9) in the two donors at least at one time point for each cell type family. The z-score values are systematically reported on Figures. IPA considers that z-scores > 2 or < -2 correspond to function/ pathways that are significantly predicted to be up-or downmodulated respectively. In all cases, apoptosis, cell viability, inflammation of lung and inflammatory response functions were additionally selected. For the most pertinent modulated functions or pathways, we illustrated the fold changes of the contributing genes (Additional files 11, 13-15).
GSEA results and BubbleGUMbased illustrations To better characterize the gene expression changes that occurred upon EVLP using an alternative method to IPA, we applied high-throughput GSEA with the BubbleMap module of the BubbleGUM software  (38)  on the scRNA-seq data normalized separately for each donor, comparing 0 h vs 4 h and 0 h vs 10 h. We used the Hallmark (H) geneset collection from the Molecular Signatures Database [MSigDB v2023, https://www. gseamsigdb. org/gsea/index. jsp (39) ]. The retrieved annotations were selected based on two criteria: i) a false discovery rate (FDR) < 0. 1 in GSEA and ii) a consistent and significant enrichment common to the two donors at least at one time point across all cell type identities. The leading edge genes that contribute the most to the enrichments are provided in Additional file 12.
Acknowledgments This work has benefited from the facilities and expertise of the high throughput sequencing core facility of  I2BC (Centre de Recherche de Gifhttp ://www. i2bc. paris-saclay. fr/)». We warmly thank  Nathalie Peynot  ( UniversitéParis-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas ) for her precious help in the cDNA bank generation. We thanks the  CIML CB2M group  for their technical and methodological support in bioinformatics analyses and  Valentin Loux  ( UniversitéParis-Saclay, INRAE, Maiage, Jouyen-Josas ) and  Patrice Dehais  ( INRAE, Sigenae, Toulouse ) for their help regarding easy access to the viewer.
Materials and Methods All scientific articles were identified and selected from PubMed, Scopus, and the ISI Web of Science (v5. 13. 1), which includes numerous relevant databases. To ensure thoroughness, this was completed through Google Scholar (https://scholar. google. com, accessed on 23 November 2023). This selection spanned the period from 1978 to 2020, using the search terms "Arbovirus exposure factors in the world", and with the related keywords obtained from the Web Medical Subject Headings site (a site that allows defining related vocabulary terms, which can aid in conducting more precise and exhaustive research, https://meshb. nlm. nih. gov/search, accessed on 23 November 2023). The keywords are: "flavivirus OR dengue OR chikungunya, zika OR japanese encephalitis OR yellow fever OR west nile AND exposure factors OR risk factors OR socio-economic factors OR anthropological factors".
This study was conducted before AD biomarkers like spinal fluid amyloid and tau levels, or amyloid and tau PET scans, were more commonly used, so the PPV of the clinical diagnosis is now likely higher than 83%; however, it remains imperfect. The PPV for a clinical diagnosis of probable LB dementia against the pathologic diagnosis is also around 80%  [21, 50, 45]. In general, it has been difficult to clinically distinguish between AD without Lewy bodies, AD with Lewy bodies, and LB dementia  [25]. Additionally, individuals who may have advanced pathology, but mild symptoms are frequently misdiagnosed clinically or missing from clinical datasets altogether because they do not seek medical attention. Motor function and neuropsychiatric and cognitive symptoms have been suggested as diagnostic clues of AD pathology, LB pathology, or co-pathology  [14, 48].
Moreover, developing AD pathology biomarkers such as assays of amyloid-β, tau, and phosphorylated tau levels in the cerebrospinal fluid or blood plasma has been valuable in closing the gap between diagnosis during life and pathologic AD diagnosis  [6, 32]. LB pathology biomarkers, including promising assays of α-synuclein aggregates in the cerebrospinal fluid, are similarly improving the diagnosis of LB dementia  [47, 43, 36]. Still, because LB biomarkers have been developed more recently, most existing genetic datasets consist of only clinically diagnosed subjects. Ultimately, as a histological and molecular endophenotype, pathologic diagnosis offers the most reliable insights into the genetic drivers of disease. Previous research has produced contrasting and somewhat ambiguous findings on the genetic risk loci for AD and LB pathology.
These observations emphasize the need for novel GWAS of AD/LB pathology to better characterize the genetic architecture of these complex dementias. To this end, we assembled a preliminary cohort of 5,254 individuals with genetic data and autopsyconfirmed AD and LB pathology status, the largest such cohort to date. We adapted criteria from the literature to categorize these individuals as AD + LB + , AD + LB -, AD -LB + , or AD -LB -, yielding 1,072 AD + LB + , 2,492 AD + LB -, 158 AD -LB + , and 1,263 AD -LB -individuals in our study cohort (total N = 4,985). We compared each disease category to controls by performing separate GWAS meta-analyses. We also compared AD + LB + pathology to AD + LB -pathology in another analysis.
Study cohort We analyzed data from individuals from the National Alzheimer's Coordinating Center (NACC) and Rush University Medical Center databases who were evaluated postmortem for both AD and LB pathology. We excluded NACC individuals who were classified as having Lewy bodies in the olfactory bulb or in an "unspecified" region (individuals for whom the NACCLEWY parameter was equal to 4). We also excluded individuals missing sex or age-at-death information. In total, our preliminary cohort comprised 5,254 individuals before classification according to AD and LB pathology status. This cohort was distinct from that analyzed in  [13] , the largest genetic study of LB dementia (which included subjects without pathology verification), and  [28] , the largest previous genetic study of subjects categorized by both AD and LB pathology status.
Genome-wide analysis We performed a meta-analysis of separate GWAS in the NACC and Rush subsets of our cohort for each of AD + LB + , AD + LB -, and AD -LB + pathology compared to AD -LB -pathology, as well as for AD + LB + versus AD + LB -pathology. We used PLINK 2. 0 for logistic regression and included sex, age-at-death, and the top ten principal components accounting for genetic ancestry as covariates  [11]. We removed duplicates and first-degree relatives within and between genomic datasets using KING  [37]. In each pair of relatives, the relative with younger age at death was preferentially kept or the oldest control in the absence of pathology cases. We filtered out genetic variants that had a minor allele frequency below 0. 01, departed from Hardy-Weinberg equilibrium with a significance below P < 10 -5 , or had a missingness rate above 20%.
We imputed data on the TOPMed reference panel as described in  [33]  and considered variants with R 2 > 0. 8. We meta-analyzed the separate NACC and Rush GWAS using the inverse variance weighted method in METAL  [55]. Manhattan plots from summary statistics were produced using the R package CMplot  [56]. The significance threshold was set at P < 5 × 10 -8 , the standard threshold for genome-wide significance. We estimated the association of APOE-ε4 with risk of AD + LB + , AD + LB -, and AD -LB + pathology relative to AD -LB -pathology, and the association with risk of AD + LB + pathology relative to AD + LB -pathology, in terms of odds ratio (OR). We also estimated the association of APOE-ε2. We compared our estimates to those in the literature  [52, 28, 13, 2, 10, 14, 18, 46, 48]  and when relevant we computed measures of linkage disequilibrium between variants in European ancestry populations using LDlink  [35].
| L I M I T A T I O N S This study has limitations. First, HLMs were not genotyped and thus an effect of CYP450 polymorphism cannot be excluded but it is unlikely because they consisted of a mixture of pooled HLMs with no donor of Asian origin. Then, the results of an in vitro study cannot be overestimated, especially in terms of clinical outcomes and need to be validated in patients. To further unravel the mechanisms underlying HTPR in the elderly, in addition to the present in vitro study, we are currently conducting a prospective multicenter study designed to assess the relationship between the clopidogrel-AM (pharmacokinetics) and the platelet reactivity (pharmacodynamics) according to age in patients treated with clopidogrel, with a clinical follow-up (NCT04990596).
Patients with myelodysplastic syndromes (MDS) frequently experience a significant symptom burden, which reduces health-related quality of life (HRQoL). We aimed to identify determinants of low HRQoL in patients recently diagnosed with MDS, for guiding early intervention strategies. We evaluated longitudinal data in 2205 patients with MDS during their first year after diagnosis. Median values of EQ-5D 3-level (EQ-5D-3L) index (0. 78) and visual analog scale (VAS) score (0. 70) were used as thresholds for low HRQoL. In addition, the 5 dimensions of EQ-5D-3L were analyzed for impairments (any level vs "no problem" category). After multiple imputation of missing values, we used generalized estimating equations (GEE) to estimate odds ratios (OR) for univariable determinant screening (P <. 15), and to subsequently derive multivariable models for low HRQoL with 95% confidence intervals (CI).
Analytic design and ethics Our analyses aimed to identify determinants of low HRQoL at any specific time point early after diagnosis. We used data from the EUMDS Registry, which has been described earlier. 27 Briefly, EUMDS Registry is a multicenter prospective registry with newly diagnosed patients with MDS who had their diagnosis within 100 days of enrolling in the study. 27 The EUMDS Registry was created by a large group of hematologists from the European LeukemiaNet organization and since early 2008 has been collecting data throughout 17 countries (Austria, Croatia, the Czech Republic, Denmark, France, Germany, Greece, Israel, Italy, The Netherlands, Poland, Portugal, Romania, Serbia, Spain, Sweden, and the United Kingdom) with more than 140 active sites. Currently, the EUMDS Registry has more than 3000 patients with MDS, the majority of whom are lower-risk patients, and includes comprehensive clinical-and patient-related information from baseline (within 100 days after diagnosis) and consequent followup visits at 6 month intervals.
Treatments are assigned based on local guidelines. Patients are followed until death, loss to follow-up, or study withdrawal. The importance of the EUMDS Registry and the numerous data analyses are described in a recent publication by de Witte et al. 28  Because we aimed to comprehensively analyze recently diagnosed patients with MDS, and the proportion of missing values for HRQoL continuously increased between the 1st and the 18th visit, we restricted the analyses to data from the first 3 time points (ie, baseline, and the 2 follow-up visits at 6 and 12 months). Country-specific ethical approvals were provided from the enrolled institutions in compliance with the Declaration of Helsinki. Ethical approval for the study was also obtained from the Research Committee for Scientific and Ethical Questions at UMIT -University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria.
In conclusion, we identified increased age, female sex, high SF level, low Hb count, reduced KPS, high MDS-CI, high BMI, and transfusion dependence as significant independent determinants of low HRQoL in MDS, assessed by EQ-5D or VAS. Our analyses minimized the potential missing-data bias and made use of the repeated measurements of an extensive array of variables in the EUMDS Registry. Knowing and considering the determinants of low HRQoL is expected to provide improved patient-oriented quality of care, increase patients' participation in the MDS disease management, and guide patient-shared decision making. Patients with a higher chance for early deterioration of HRQoL might be the primary candidates for early clinical evaluations and interventions that aspire to deliver systematic, multidisciplinary, and timely health care to fragile patients.
Acknowledgments The authors thank all patients and health professionals (list provided in supplemental Table 5) who have contributed to the EUMDS Registry, for sharing their time and helping us directly or indirectly in any segment of the research, as well as the collaborators from the  MDS-RIGHT  project. The authors also thank  Ian White  for his advice on the multiple imputation process. This study was financially supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement number  634789 ,  MDS-RIGHT , within the  Personalising Health and Care Programme   PHC-2014-634789 , and by  Translational Implementation of Genetic Evidence  in the management of  MDS (TRIAGE-MDS) (TRIAGE-MDS, Austrian Science Found I 1576)  within the  TRANSCAN -Primary  and secondary prevention of cancer call ( ERA Net ).
Acknowledgements This work has been supported by  Ministero dell'Istruzione, dell'Università e della Ricerca  ( Dipartimenti di Eccellenza Program  ( 2018-2022 ) (F11I18000680001)). The device has been designed using the open source Nazca design TM framework. Useful support in the analysis of experimental data was provided by  P. G. Kwiat  through free access to online tutorials and software (http://research. physics. illinois. edu/QI/Photonics/Tomography/).
The old (A2) chamber (previously for CaII K, Figure  38 ) was kept alive only for sporadic research purposes. The (A1) chamber was reserved for systematic observations. Photographic glass plates were abandoned, and 13 x 18 cm² films were used at the 3. 0 m focus of the chamber. The bandwidth of images remained 0. 15 Å and 0. 25 Å, respectively for CaII K and Hα. The films were systematically digitized with a 12 bits scanner after 1995. At this date, the spectroheliograms became freely and immediately available to the solar community and to the public owing to the development (by the author of this paper) of the BASS2000 solar data base (https://bass2000. obspm. fr). The 1989 version was ready for the implementation of a digital sensor, a Reticon linear array of 2048 elements, but this device was never installed, as CCD rectangular arrays were more promising.
The slices provide the backward compatibility with the photographic spectroheliograph. Data-cubes and monochromatic slices are freely available on-line at the BASS2000 database (https://bass2000. obspm. fr), in FITS format for scientific use and JPEG format for quick look, with the solar North up. the interplanetary medium. The chromosphere and the corona require spectroscopic means to reveal their structures, respectively via absorption lines of the visible spectrum or emission lines of the ultraviolet spectrum. The solar atmosphere follows a 11-year activity cycle and a 22-year magnetic cycle. Flares and coronal mass ejections occur in active regions a few years around the solar maximum; the maximum of the current cycle (number 25) is forecasted for 2025. The symbol K above (Kelvin) is the unit of the absolute temperature, related to the Celcius temperature by T(K) = T(°C) + 273.
ACKNOWLEDGEMENTS This paper is dedicated to the memory of  M. -J. Martres  and  G. Olivieri , who worked respectively during five and fifteen years with M. and  L. d'Azambuja , and transmitted us the knowledge of this pioneers. The author thanks  C. Calderari-Froidefond  for images of Paris observatory's library,  I. Bualé  and  F. Cornu  for spectroheliograms and archives of the Meudon solar collection.
Dataset Our dataset consists of 105 short promotional written pitches. These pitches were composed by start-ups in the G-17: a portfolio of companies that were trained in 2017 for 6 months. They received their training at Start-up Chile, an accelerator program financed by the Chilean government to promote entrepreneurship and innovation. These pitches are in English, which is the language promoted by the organization as an internationalization strategy. (Indeed, the Start-up Chile website is fully translated into English). According to the official website, "since 2016, the new objectives of Start-Up Chile are to maintain Chile as one of the most important centres of technological entrepreneurship and innovation in the world and that our start-ups have a positive impact on the local economy.
" As of 2021, it has supported 1,960 start-ups and is valued at US$2. 1 billion. Two authors emailed Start-up Chile executives, who agreed to provide data to conduct this research following ethical procedures. The project in which this specific research is embedded was reviewed and declared exempt by the Institutional Review Board of Universidad de La Serena, Chile (second author´s affiliation). The pitches in our dataset were submitted by the start-ups when they applied for the program. At the end of the program, Start-up Chile executives ask the start-ups to update their original written pitches for promotional purposes; they do not instruct the start-ups how to complete this task. These iterated pitches reflect what each start-up has learned after six months in the program. The pitches are the first discursive devices through which potential venture capitalists may evaluate the start-ups' offerings.
After the 6-month training, the pitches are published on the website of Start-up Chile permanently, as part of the portfolio of firms supported by the accelerator. Pitch texts usually range between 15 and 70 words; the start-up program does not impose a stable structure. Table  2  shows the pitches that we analyzed, organized by industry and customer segment. Some industries are covered by start-ups with specific business models: the educational field, for example, tends to attract B2C start-ups, while ICT tends to attract B2B. Education, financial services and ICT correspond to the industries including most of the start-up innovations of our dataset. The start-ups comprising our dataset had already been identified and socialized by Start-up Chile as belonging to a particular industry. This categorization process has been broadly researched from sociological and cognitive approaches (see  Granqvist & Siltaoja, 2020, for  ACCEPTED MANUSCRIPT / CLEAN COPY a literature review).
Deductive analysis To detect how logics of justification (orders of worth) were realized in pitch language, we first conducted a deductive analysis (e. g. ,  Bitektine, 2011; Bitektine & Haack, 2015) , based on the original inventory provided by  Boltanski and Thévenot (2006). In this analysis, we identified and labeled lexis (vocabulary) related to logics as well as populating our own database. For this identification task, we built a master description (Table  1 ) that guided our research team in recognizing lexical items that realize the logics of justification. To ensure credibility and trustworthiness of data and results, we conducted three rounds of analysis: 1. Round 1: Refining the master description. Two authors analyzed all pitches using the analysis matrix (Table  3 ), which included the ID of the pitch, the written pitch, and the logics identified.
For instance, when lexico-grammatical realizations referred to problems or challenges targeted by the start-up, they correspond to a pattern we labelled "Problem-Assistance. " When lexical realizations referred to time efficiency in industrial activities (such as "raise information in real time," "saving time" and "know beforehand"), they correspond to the pattern we labelled "Time. " Such semantic patterns constitute the "core values" of a business pitch; through them, specific types of start-ups construct meaning in pitch structures according to their industry sector and business model. Although our research considers a dataset corresponding to English pitches, such semantic patterns could be detected in other languages as well.
Language patterns Along the 105 pitches, we found 381 instances of logics (Table  3 ). The most frequent logic exploited along pitches corresponds to Industry (59%), followed by Inspiration (17%), Market (13%), Civic (9%) and Fame (2%). • Industrial logic presented six well-defined semantic patterns in our dataset: Tools (34%), Simplicity (17%), Quality (14%), Problem-Assistance (13%), Industrial Process (13%), and Time (9%). • Inspiration logic presented five semantic patterns: Emotional appealing (40%), Uniqueness (29%), Personalization (14%), Encouragement (9%), and Security (8%). • Market logic presented four semantic patterns: Market strategies (39%), Convenience (29%), Opportunity (20%), and Profitability (12%). • Civic logic presented four semantic patterns: Sociality (33%), Development (24%), Environment (24%), and Health (18%).