dataesr/scientific-paragraphs-categorization huggingface dataset:
The total delivery time was approximately one day, with 2 h of hands-on time and 19 or 24 h of NGS sequencing according to the flowcell (micro or standard). Data were analyzed using TRKengine software, which automatically calculates chimerism values. Quantification based on ratio short:long reads of the Indel. Genotyping is homozygous if marker between 0-10%/90-100% short/ long and heterozygous if marker between 40 and 60% short/long. For monitoring, a marker is informative if the recipient and donor are homozygous for opposite genotypes or if the recipient is heterozygous and the donor is homozygous (+/+ versus -/-or -/-versus +/+ and +/versus +/+ or -/-), respectively. The software allows for storage of data by patient and allows for tracking graphs based on dates and sample types.
Until now, there has been no small automatic dPCR instrument. Now, the different steps of partitioning cdPCR, amplification and reading are automated in the same instrument  (32) , reducing run time and manual steps that are a source of error. Furthermore, they allow a homogeneous partitioning in volume that is reproducible in number (26,000 for dPCR Qiacuity and 20,480 for dPCR Absolute Q). This is a major advance for the integration of this technology in diagnostic laboratories. We have already shown that ddPCR-based chimerism works  (21, 22) , and in this work, we show that cdPCR systems can be used for the quantification of chimerism in a sensitive and reliable method. Thus, our study highlights that the sensitivity of cdPCR is similar to that of other dPCRs (i. e. , 0. 1%). Regarding software, all systems require powerful computer equipment.
Although cdCPR software is increasingly adapted to routine use in medical laboratories (especially for the dPCR Absolute Q software), it is not very adapted to the application of At the time of this publication, other innovations are in progress. First, ddPCR techniques have evolved. For example, Stilla proposed a 6-color dPCR Naica, and Biorad proposed the QXONE, a fully automated multicolor automaton. Second, the Continuum dPCR system (originally Dropworks and recently acquired by Bio-Rad Laboratories, Inc. ) is a technical innovation. This digital flow PCR (dfPCR) is a continuous flow system in which samples are loaded and processed sequentially. Continuum is also an integrated instrument, which means that droplet partitioning -up to 30,000 droplets per sample -as well as thermal cycling and analysis are all performed in Reproducibility of chimerism results from Absolute Q and Qiacuity dPCR.
This eliminates the need for laboratory management of all markers and allows genotyping and monitoring to be performed at the same time. Our comparison of NGS chimerism methods reveals that the three existing kits, Dvysr ® (Devyser Chimerism) in our previous study  (22)  and CareDX ® (AlloSeq HCT) and GendX ® (NGStrack) in this study are notably similar in terms of analytical performance and have a result delivery time not compatible with the emergency. However, small differences exist that are technical and linked to the software. First, the marker panels are variable from 24 to 202 markers, and it is necessary to perform genotyping for all contributors, except for AlloSeq HCT, which proposes an analysis in the absence of a contributor. On the one hand, the high number of markers avoids false-negative results caused by chromosomal deletions in the relapse of some malignancies, but on the other hand, it has been shown that too many markers are unnecessary  (33).
e. , 0. 3% for AlloSeq HCT and 0. 5% for NGStrack. However, we show that for AlloSeq HCT, the sensitivity could be 0. 1%. This sensitivity exceeds that of STR analysis  (17, 22, 26, 34, 35)  but is less than that of dPCR techniques. In addition to analytical performance, serial management and automation are key elements for NGS. Nevertheless, the result requires at least 48 h and is therefore not feasible in the context of a unit or an emergency. Regarding software, all methods require powerful computer equipment that allows all three methods to give a quick result. However, there are some differences. For example, Advyser software is the only one allowing us to select or deselect markers of interest. Moreover, Advyser and AlloSeq HCT software have quality controls with color codes and allow us to comment on the results by adding patient follow-up events (treatment…).
The AlloSeq HCT software will soon allow the integration of results directly into the laboratory's computer system. However, Advyser and AlloSeq HCT software have already evolved several times. The TRKengine software, which is newest, should evolve into more software that is complete. Finally, The two main interests of the chimerism study are the monitoring of engraftment and the prediction of disease relapse. For the first case, all the techniques can demonstrate that the hematopoiesis is of donor origin with the sensitivity near to 5%  (36). In the second case, the chimerism detected at day +30 can guide appropriate therapeutic management. They require very sensitive techniques with sensitivity of at least 1% is required  (37). All new methods achieve this target. If the two methods are compared in clinical practice, digital PCR is faster (result within the day vs.
Only NGStrack has the ability to determine HLA loss relapse. All NGS reagents and interpretation software are equal and the choice will depend more on the operating practices of the laboratories than on their difference Reproducibility of chimerism results from AlloSeq HCT and NGStrack. Each chimerism percentage (50%, 10%, 5%, 0. 5%) was tested on at least three different experiments to calculate the coefficient of variation of each method. in sensitivity. For laboratories with only dPCR, the instrument is compatible for all chimerism activities. Moreover, this study proposes some elements to laboratories interested in developing the analysis of chimerism toward a new technology that also allows the development of new activities, such as the study of dd-cfDNA in organ transplantation. Indeed, in a review of the literature, Knight et al.
1%) Sensitivity 0. 1% 0. 1% 0. 1% 0. 1% Measurement range 0. 1 to 100% 0. 1 to 100% 0. 1 to 100% 0. 1 to 100% Type of quantification Absolute (cp/μL) Absolute (cp/μL) Absolute (cp/μL) Absolute (cp/μL) Need for replicates No No No No Workflow Total time (min) 180 150 90 90 Hands on time (min) 30 15 <15 <15 Technical steps 6 4 3 3 Ease of use + ++ +++ +++ Software Security/Tracability (21 CFR Part 11) Yes not reported Yes Yes Patients database No No No No Automatic calculation Yes No, No No Easy to use + ++ +++ +++ Custom adjusted cutoff Yes Yes Yes Yes Data export Yes Yes Yes Yes Report edition Yes Yes Yes Yes *Including PCR thermocycler. **Ability to reuse unused areas of the plate. for.
Results: From March to April 2023, we evaluated 50 non-English medical reports from 8 physicians and 12 different groups of diseases, which included neurodevelopmental disorders, congenital disorders, fetal pathology and oncology. Reports contain in median 15 PHI and 7 HPO terms. Deep learning method achieved a sensitivity of 99% and a specificity of 87% in de-identification, and a sensitivity of 78% and a specificity of 92% in summarizing medical reports, reporting an average number of 6. 6 HPO terms per report, which is equivalent to the number of HPO terms provided usually by physicians in databases (6. 8 in PhenoDB). Conclusions: De-identification and summarization of non-English medical reports using deep learning methods reports non-inferior performance, providing insights on AI usage to facilitate precision medicine.
STUDY DATA From March to April 2023, we have gathered 46 consultations reports and 4 hospitalization reports written between 2019 and 2023 by eight differents physicians, concerning 50 patients, whose median age in report was 6 years old coming from 12 differents indications of which neurodevelopmental disorders (54%), congenital disorders, fetal pathology and oncology. In median, a report contains 478 words, 3180 characters, 3 abbreviations, 15 PHI and 7 HPO terms. The distribution between the PHI and HPO terms is shown in Figure  2. The cohort characteristics are detailed in the Table  1.
CODE AVAILABILITY This deep learning method is open-source and can be installed for offline usage on a local machine by following instructions at https://github. com/kyauy/ClinFly or used directly online at https://huggingface. co/spaces/kyauy/ClinFly. This method manages French, German and Spanish language. A translated and de-identified report can be downloaded as a text file output. The list of symptoms can be downloaded in CSV or PhenoTips JSON format, compatible with most EHR entries especially in genome sequencing platforms. Deep learning method reports non-inferior performance as physician to de-identify and summarize in HPO format non-English medical reports with reliable and scalable results. This study provides insights on how to exploit medical reports that allows physicians to share structured clinical data to facilitate precision medicine.
Report was processed according to the following offline pipeline : a) Text was first expanded with abbreviations gathered previously, b) Expanded text was translated into English using the Marian translator, an open-source neural machine translation tool developed by OpenNMT 11 (https://marian-nmt. github. io), c) Translated and corrected text was de-identified from PHI using Microsoft's Presidio (https://microsoft. github. io/presidio/), d) Translated and de-identified text is summarized in HPO format, including symptoms' quantitative data, using a upgraded version of ClinPhen 12  , and flagged "high-confidence".
ch/), as well as the collaborative Wikipedia page "Liste d'abréviations en médecine" of 2998 medical associated proper names, 4939 officinal drugs and 109804 genes symbols from various databases, including the OMIM phenotype title (March 2023 update, https://omim. org/), Human Phenotype Ontology terms (March 2023 release), ANSM lists of official medication in allopathy without homeopathy and herbal medicine (March 2023 update), and complete HGNC approved gene dataset (March 2023 update) to excluded them from de-identification. Given that method location detection was insufficient concerning French territories, we implemented a list composed of 34981 propers names of French cities, departments and regions coming from INSEE' official geographic code (https://www. insee. fr/fr/information/6051727, January 2022 release) to force de-(https://fr.
nm (sample 2). To disentangle the contributions of Fe 3 O 4 and Mn 3 O 4 on the magnetic properties of the samples, a detailed PNPD study at 5 K in different applied fields was carried out. As a first step, to determine the nuclear structure data (lattice parameters, site occupancies, and so on) two different approaches were carried out. For sample 1, the sum of the spin-up and spin-down polarization patterns (equivalent to unpolarized powder diffraction measurements) was used (Figure  2a ,b) (refined using CrysPy, see Experimental section). For sample 2 (for which the polarized sum profile cannot be refined reliably), the conventional neutron powder diffraction was analyzed by a usual Rietveld procedure (using FullProf; see Experimental Section). As expected, the Fe 3 O 4 cores present an inverse cubic spinel structure (space group Fd 3m; i.
Conclusions To summarize, we have systematically explored the use of polarized powder neutron diffraction to unravel the contribution of the two magnetic phases in bi-magnetic Fe 3 O 4 /Mn 3 O 4 core/shell nanoparticles by using the newly developed CrysPy software. Despite the very challenging experimental conditions, quantitative information on both Fe 3 O 4 and Mn 3 O 4 magnetic phases has been obtained. The evolution of the Fe 3 O 4 and Mn 3 O 4 signals with the applied field shows that while the moment of the Fe 3 O 4 core remains roughly constant, the moment averaged over the unit cell of the Mn 3 O 4 shell reorients form antiparallel to parallel to the Fe 3 O 4 as the field increases. This reorientation is evidenced also in the changes in the local susceptibility of Mn 3 O 4. In addition, the coherence length of the Fe 3 O 4 core has been found to decrease at large fields due to the interface disorder induced by the rotation of the averaged moment of the Mn 3 O 4 shell.
Acknowledgements I. V. G. acknowledges financial support from the  Russian Foundation for Basic Research  under Grant No  20-02-00109. A. G. R. and J. N. acknowledge financial support from the grants  PID2019-106229RB-I0  funded by  MCIN/AEI/10. 13039/50110001103and 2021-SGR-00651  from  Generalitat de Catalunya. I. K. and A. G. acknowledge the  European Union 's  H2020 reserach and innovation program , Grant agreement No  871072. A. G. R. acknowledges financial support from  RYC2019-027449-I  funded by  MCIN/AEI/10. 13039/501100011033. ICN2  is funded by the  CERCA programme/Generalitat de Catalunya. The ICN2 is supported by the  CEX2021-001214-S grant  funded by  MCIN/AEI/10. 13039/501100011033. M. E. acknowledges the grants  RYC2018-024396-I  and  PID2019-106165GB-C22  funded by  MCIN/AEI/ 10.
13039/501100011033  and by " ESF Investing in your future. "A. L. O. acknowledges financial support from the grants  PID2021-122613OB-I00  funded by  MCIN/AEI/ 10. 13039/501100011033  and  PJUPNA2020  from  Universidad Pública de Navarra. The authors thank ILL (D1B https://doi. org/10. 5291/ILL-DATA. 5-31-2650,D20 https: //doi. org/10. 5291/ILL-DATA. 5-32-932 ), LLB  and  ALBA  for the provision of beam-time.
Background Improving or enhancing the sustainability of rice yield continues to be a crucial challenge in the breeding of this crop, especially in the context of a growing world population and with regard to climate change. Inflorescence architecture in rice directly affects yield potential through the regulation of grain number. Grain number per panicle depends on orders and numbers of branches (i. e. , primary, secondary and potentially tertiary branches as well as both lateral and terminal spikelets) and on the length of each axis. Rice panicle architecture is based on the production of a series of lateral meristems with distinct identities  [1]. After the floral transition, the Shoot Apical Meristem (SAM) is converted into an indeterminate Rachis Meristem (RM) which produces several axillary meristems, the primary branch meristems (PBMs) that subsequently give rise to primary branches (PBs).
Detection of QTLs associated with panicle architecture traits A Dunnett's test revealed a total of 37 lines with significant differences compared to the Os_Caiapó recurrent parent for all traits combined (Additional file 1: Table  S4 ). In general, these lines bear at least two Og_MG12 introgression fragments. For this reason, assigning the underlying QTLs to a single chromosome segment was not straightforward. The CSSL Finder software was therefore used to detect genomic regions associated with panicle phenotype variation with an F-test at each marker  [40]. Graphical genotype representations were also produced in which the CSSLs were ordered by trait value for each evaluated trait (Additional file 2: Figure  S1 ). We considered the F-test as significant when its value was higher than 10.
sativa cv. Nipponbare (IRGSPv1. 0) functional gene annotation databases (i. e. , MSU7 and RAP_db) for candidate gene prioritization in relation to panicle architecture and flowering. For all QTLs identified in the present study, we found several known genes with relevant functions that related to flowering and/or panicle development (Additional file 1: Table  S8 ; Additional file 2: Figure  S6 ). Genes such as LAX PANICLE1 (LAX1), OsMADS1/LEAFY HULL STERILE1 (LHS1), OsMADS14, OsMADS34/PANICLE PHYTOMER2 (PAP2), OsINDETERMINATE SPIKELET 1 (OsIDS1), OsMADS18, DENSE AND ERECT PANI-CLE2 (DEP2) and FRIZZY PANICLE (FZP) are known to be involved in the control of panicle development and were suggested as candidates that might contribute to the panicle branching diversity observed between the two parents Os_Caiapó and Og_MG12  [3] [4] [5].
Plant materials The plant materials consisted in a set of 60 interspecific introgression lines, or Chromosome Segment Substitution Lines (CSSL). The CSSLs were derived at CIAT, Cali, Colombia from a cross between O. sativa subgroup tropical japonica (cv. Caiapó) as the recurrent parent and O. glaberrima (cv. MG12; acc. IRGC103544) as the donor parent  [40]. The CSSLs used were BC 3 DH, that is, obtained after three rounds of backcrossing to the recurrent parent followed by double haploidization of BC 3 F 1 male gametes (Additional file 1: Table  S1 ). They were genotyped at CIAT using 200 simple-sequence repeat (SSR) markers  [40]. The program CSSL Finder v1. 0. 0 (http:// mapdi sto. free. fr)  [40]  was then used to select the 60 lines from an initial panel of 312 BC 3 DH lines, so that the entire donor genome was represented by overlapping chromosome segments.
A total of 1,443 panicles from the 60 BC 3 DH lines and the parents were dissected and scored manually. In January-April 2014 (Year 2), a total of nine BC 3 DH lines were re-evaluated using P-TRAP software  [76] , which performs automatic scoring of panicle traits. Five BC 4 F 3 /F 5 lines with parents, plus L_10 and L_46 BC 3 DH, were grown in the greenhouse in Montpellier, France (March 2021) at 28 °C / 80% relative humidity under short days conditions (11h light-13h dark), and were scored for panicle traits using P-TRAP. Panicle phenotypes were re-evaluated in the same conditions for three BC 4 lines (L_B, L_D and L_E in June 2021). For each analysis (2013, 2014 and 2021), 6 morphological panicle traits were scored: rachis length (RL); number of primary branches (PBN); number of secondary branches (SBN); number of tertiary branches (TBN); spikelet number (SpN); and average primary branch length (PBL) per panicle (Additional file 1: Table  S2 ).
Thus, we decided to carry a preliminary QTL analysis -with the methodology described hereby -on each repetition separately. As a result, the F-test profiles were much similar between the two repetitions (Additional file 2: Figure  S1 ). Therefore, the subsequent analyses presented in this work were done taking the average phenotypic values of the two repetitions. Trait heritability was computed using the line effect based on the variance among phenotypic measurements between the two replicates of the population phenotyping assay. The corrplot (function cor), ade4 (function dudi. pca using center = TRUE and Scale = TRUE as arguments) and devtool R packages were used to analyze phenotypic correlation between traits. The CSSL Finder program was used to display graphical genotypes of the 200 SSRs in the 60 BC 3 DH CSSLs together with phenotypic values for each trait.
Acknowledgements The authors acknowledge  Ndomassi Tando  and the ISO 9001 certified  IRD iTrop HPC  (member of the  South Green Platform)  at  IRD Montpellier  for providing HPC resources that have contributed to the research results reported within this paper (URL: https:// bioin fo. ird. fr/-http:// www. south green. fr). We thank  Lady Johanna Arbelaez Rivera  ( Alliance Bioversity-CIAT ),  Sophie Chéron  ( IRD ) and  Harold Chrestin  ( IRD ) for plant care. We thank  Marie-Christine Combes  ( IRD ) and  Carole Gauron  ( IRD ) for their help with the panicle phenotyping work. We thank  Jean-Francois Rami  ( CIRAD ) for his help with R scripts. We also thank  Laurence Albar  ( IRD ) for her helpful feedback on the manuscript.
, 2016). However, in the marine environment, the process of spatial planning is still scarcely considered for the attainment of environmental goals mostly focusing on economic demands  (Katsanevakis et al. , 2020; Trouillet, 2020). Yet, considering where restoration activities are undertaken can result more important than how they are carried out  (Fraschetti et al. , 2021). Marxan software  (Ball et al. , 2009)  is the most widely used open-source decision-support tool  (Watts et al. , 2017)  in conservation. Initially conceived for the design of protected areas network meeting several ecological, social and economic criteria at once  (Ball et al. , 2009 , Christensen et al. , 2009) , Marxan integrates cutting-edge conservation science alongside human uses shaping dialogue between scientists and decision-makers.
e. , cells with HSM values less than 0. 61. This value corresponds to the cut-off which allowed to optimize the accuracy of the HSM predictions by reaching the best compromise between the sensitivity and the specificity of the model  (Fabbrizzi et al. , 2020). This exclusion ensured that the analysis only retained those sites in which restoration efforts are more likely to be effective, indicating the presence of suitable conditions. Areas with high frequency of Sea Surface Temperature Anomalies (SSTA) were locked out from the analysis as well. Considering the distribution of SSTA hotspots into the spatial planning was crucial to exclude areas where the high frequency of extreme climatic events can compromise the effectiveness of restoration actions. SSTA data were retrieved from the NOAA's Environmental Modeling Center database (https://www.
Restoration features Restoration features are intended to represent the entities (e. g. , species, habitats, ecosystems) to be restored. In this study, we determined restoration features in three steps. Firstly, we considered the following types of fucalean forests: i) Regressing forests (Rf), i. e. , areas where a pattern of regression from a previous healthy status of the canopy was documented by literature analyses and expert knowledge; ii) Extinct forests (Ef), i. e. , areas where fucalean forests were historically documented but are currently absent. To map these forests, we used an existing dataset that assembled data about the current and historical distribution of fucalean forests across the Mediterranean Sea  (Fabbrizzi et al. , 2020) , refined by conducting a literature review and data collection.
For the literature review, which involved both peer-reviewed and grey literature, different databases were used: ISI Web of Science (WOS), Scopus, AlgaeBase (https://www. algaebase. org/) and GBIF (https://www. gbif. org/). The search of pertinent articles was conducted for the whole Mediterranean Sea, setting 2020 as the only temporal cut-off. A total of 1236 studies including the keywords "Cystoseira" and "Mediterranean" were evaluated, retaining only those reporting geographical information about the distribution (presence/absence) and, when available, the status (coverage, trend, and present-past conditions) of any Cystoseira species. The screening of the literature was completed before the recent split of the genus Cystoseira in the three separate genera Cystoseira, Ericaria and Gongolaria (Molinari-Novoa and Guiry, 2020).
All studies without georeferenced data were no further examined and were excluded from the review. The information obtained from the literature was also combined with new data collected in the field within the framework of the AFRIMED project (http://afrimed-project. eu/) (see  Orfanidis et al. , 2021). Additional data were provided by AFRIMED partners as personal information. The assembled dataset comprises a total of 39,293 occurrence records (25,145 digitized as a vector shapefile of points and 14,148 as a vector shapefile of polylines) covering a large span of time from 1789 to 2020. Table  S1  shows the literature used for data collection, composed of 335 articles, including both peer-reviewed and grey literature. Each article is labelled with an ID number which corresponds to the ID of the related records in the georeferenced dataset.