dataesr/scientific-paragraphs-categorization huggingface dataset:
The UNet and Mask-RCNN can then use this additional data for training. Finally, to further improve their results, they combine results obtained from their trained UNet and their trained Mask-RCNN to obtain a final instance segmentation map. More specifically, the semantic segmentations from UNet are post-processed to obtain instance segmentation masks, and merged (following a specific protocol) with those predicted by the trained Mask-RCNN. A few comments: Clarity regarding the purposes of the different training sets used could be improved. At first it was not clear to me how the CC/MIE datasets related to the final training dataset of interest (the 1868 x 1400 image). It could be made a bit more explicit that (1) the CC / MIE data is used to pre-train a neural network, (2) this neural network is then applied on the image of interest to provide the annotations, and (3) final training data is obtained by correcting these predictions.
Report 14 December 2021 https://doi. org/10. 5256/f1000research. 55252. r101244 © 2021 Laine R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Romain F Laine MRC Laboratory for Molecular Cell Biology, University College London, London, WC1E 6BT, UK Pecot et al present a nice set of ideas about how to improve the pipeline of nuclei segmentation. The premise of this work is that it is time consuming to generate good quality annotation for DL training. The authors are absolutely right here, it takes time and can be discouraging.
August 2021 https://doi. org/10. 5256/f1000research. 55252. r89422 © 2021 Lucas A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Alice Lucas Broad Institute, Cambridge, MA, USA The authors propose multiple strategies to improve segmentation results given a new dataset. Instead of manually annotating a training image from scratch, the authors recommend to leverage knowledge learned by networks pre-trained on other larger datasets. Therefore, they propose to first train a model on large existing datasets (that differ from the final dataset of interest). The trained model is then used to annotate the desired training image, and these predictions are then manually corrected using Annotater.
Data availability The five annotated images are available at https://github. com/ tpecot/DeepLearningBasedSegmentationForBiologists/tree/main/ Data/AnnotatedNuclei. This project contains the following data: • Polyp12_ [10837,39273]_component_data. tiff: image used for training U-Net and Mask R-CNN in all figures and for training pix2pix in Figure  2  • Polyp40_ [13694,34105]_component_data. tiff and Polyp42_ [12011,37598]_component_data. tiff: two images used for training U-Net and Mask R-CNN in Figure  3  • Polyp12_ [12699,39273] Thierry Pecot, Medical University of South Carolina, Charleston, USA We thank Romain Laine for his enlightening observations. To answer Dr Laine remarks, we changed the manuscript accordingly: We added a sentence about the cost/benefit analysis of manual annotation vs automated in the introduction.
scientific standard. Version 1 Reviewer 1 Report 14 December 2021 https://doi. org/10. 5256/f1000research. 55252. r101244 © 2021 Laine R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Romain F Laine MRC Laboratory for Molecular Cell Biology, University College London, London, WC1E 6BT, UK Pecot et al present a nice set of ideas about how to improve the pipeline of nuclei segmentation. The premise of this work is that it is time consuming to generate good quality annotation for DL training. The authors are absolutely right here, it takes time and can be discouraging.
Reviewer Report 03 03 August 2021 https://doi. org/10. 5256/f1000research. 55252. r89422 © 2021 Lucas A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Alice Lucas Broad Institute, Cambridge, MA, USA The authors propose multiple strategies to improve segmentation results given a new dataset. Instead of manually annotating a training image from scratch, the authors recommend to leverage knowledge learned by networks pre-trained on other larger datasets. Therefore, they propose to first train a model on large existing datasets (that differ from the final dataset of interest). The trained model is then used to annotate the desired training image, and these predictions are then manually corrected using Annotater.
Consequently, the wide spectrum of unresolved scales in the presence of so many correlated physical mechanisms leads to the introduction of several modeling parameters. Specific modeling developments are provided in this paper for turbulent combustion in the presence of residual burnt gases and the database takes into account the self-similar nature of the thermochemical properties. A detailed timefunction for the inlet and outlet mass flow rates and a new blending function for the wall heat fluxes are also introduced. The sensitivity of the numerical results to the values of key modeling parameters such as injected air mass m air , ejected mass m out , segregation rate S ∈ [0. 7 ; 0. 8] , wall heat losses parameter κ T ∈ [0. 41 ; 2. 36] is studied. A quantitative comparison of the results and computational data is performed based on the pressure and heat flux signals in the case of propane-air combustion.
This will also help in specifying boundary conditions from the partial experimental information. Eventually, this low computational cost tool will be used both to supplement experimental data and to provide the initial and boundary conditions for a high fidelity LES of a single specific cycle. The software, OpenFoam, with which these developments are implemented is free, open source and has a large community of users from both the engineering and research fields. The tools developed for this article are freely available to the community. The most efficient strategy to reduce computational costs is to decrease the mesh refinement. RANS models, mainly developed in the past century, provide the keystone of such a low resolution LES  [23, 24]. The relevance of these models when applied to LES has already been proven  [1]  and is the main reason for the very rapid standardization of LES tools in the combustion community over the last decades.
Thermochemistry database The construction of a thermochemical database that would include the effects of multicomponent molecular mixing, heat losses, and pressure variations still represents an ongoing and challenging work. Each additional mechanism to be included in the database would require additional dimensions of the table. For example, accounting for flame-wall interactions would require at least one more dimension for total energy to describe the effects of the heat losses on the flamelet characteristics. The effects that wall heat losses have on the flow are taken into account via boundary conditions although their effects on the chemistry are neglected. We have not considered the effects of heat losses in the database. The feedback on the flow of the chemistry near the walls is negligible but may play a significant role in the formation of pollutants.
Thus, the prediction of pollutant emissions is not addressed in this article as it would require both heavier data tables and additional transport equations. In addition, self-ignition mechanisms induced by the large increase in pressure and temperature have been observed in CVC chambers such as the one considered herein, see Michalski et al. [19]  , and it should also be included in an ultimate modeling proposal. However, in this first step of the CVC chamber study, the effects of pressure are also not considered in the database. Therefore, the laminar flame computations are performed for compositions corresponding to different values of the mixture fraction ξ and the RBG fraction ψ, see Eq. 10 , where the flamelet pressure is set to the pressure measured in the combustion chamber just before ignition.
Then, the fresh gas composition of flamelets is obtained from the air-inlet and fuel-inlet compositions, Y i | a and Y i | f respectively and from the RBG composition Y i | r which is considered homogeneous at the beginning of each cycle. The fresh gas temperature of flamelets is obtained in two steps: (i) the mixing of these three flows air/fuel/RBG gives the composition and the specific enthalpy (sensible + chemical) of the mixture before compression:  (22)  and (ii) the same compression (considered isentropic) as the one observed experimentally is applied to the mixture. h | u = ξ h | f + ψ h | r + (1 -ξ -ψ ) h | a Consequently, for each value of ξ and ψ, a laminar flame is computed using the Cantera library  [53]  associated with the gri3. 0 chemical mechanism. The minimum data set to be stored contains h (ξ , ψ ) , α(ξ , ψ ) and Y min (ξ , ψ ).
The heat release h between the fresh and burnt gas sides of the laminar flame is: h = n k =1 h 0 f,k b - n k =1 h 0 f,k u (23) Y min corresponds to the mass fraction of fuel at the burnt gas side and α is obtained by, see Bray et al. [35]  : α = x b x u ω c ρ dx x b x u c 2 dx - x b x u cdx  (24)  where x is the distance across the laminar flame and c is the normalized progress variable used to describe the internal structure of the flamelet. The temperature is considered here as: c = (T -T u ) / (T b -T u ). Following the flame brush layer theory of the flame described in Kha et al. [36]  , the Karlovitz number gives the integration thickness (x bx u ) in order to have a value of the progress variable at the leading edge consistent with that provided by Eq. (20). In order to reduce the size of the database, each variable K describing a laminar flame property is now analyzed with the normalized variable ξ | K instead of the mixture fraction ξ , see Serra et al.
(25 -26. Excellent agreement is observed for the normalization quantities given by the functions P 3 (ψ ) to P 6 (ψ ) , see the top four graphs in Fig. 6. Good agreement is also found for the main quantities feeding the turbulent combustion model, namely h and α, see the lower Fig. 6. This result validates the relevance of the reduced form of the database for the CV 2 simulation despite the slight differences observed. Eventually, as already discussed, the effects of sub-grid passive scalar fluctuations on the chemical source term are neglected so that the database is implemented as two functions of the filtered mixture fractions: h = f 1 ( ξ , ψ ) and α = f 2 ( ξ , ψ ).
Conclusions This numerical research work was devoted to the implementation of a comprehensive computational framework for the joint numerical experimental analysis of the flow in the CV 2 device. The LES tool developed includes a new combustion model, a database taking into account the self-similar nature of the thermochemical properties, a detailed time-function for the inlet and outlet mass flow rates and a new model for the wall heat fluxes. Different meshes including a significantly coarse one, have been used to validate the modeling strategy whose objective is to minimize the difference between the experimental and numerical results by optimizing a few numerical parameters. This LES strategy, whose computational cost is reasonable, has allowed parametric simulations as well as multiple cycles simulations of the real complex flow of CV 2.
Acknowledgments This work has been supported by the  CPER FEDER Project of Région Nouvelle Aquitaine. Part of this work has been performed using high-performance computing (HPC) resources from  GENCI  ( Grand Equipement National de Calcul Intensif )-[CINES/IDRIS] under Grant  A0092B07456  and from the mesocenter facility of Poitiers. This work pertains to the  French Government program  " Investissements d'Avenir" (EUR INTREE , reference  ANR-18-EURE-0010 ). The CV 2 facility was developed during the CAPA research program on Alternative Combustion Mode for air-breathing Propul-sion supported by  SAFRAN Tech ,  MBDA France  and  ANR (National Research Agency). We thank  Bastien Boust  and  Marc Bellenoue  of the  Pprime Institute  for providing us with the experimental data.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-022-04647-8. 9. The research protocol was approved by Innovations for Poverty Action Institutional Review Board and preregistered in the AEA RCT Registry (study 0002544). The pre-analysis plan is registered at https://www. socialscienceregistry. org/versions/52534/docs/version/document. All survey participants completed informed consent. They were not compensated for their time as they were all part of the national cash transfer programme.
Randomized controlled trial design and data Experimental design. In total, approximately 100,000 households have participated in the Niger cash transfer programme since 2012. This study focused on the 3rd wave of the programme, which reached 22,507 beneficiary households in 329 villages in 17 communes of the 5 most populous of Niger's eight regions (Dosso, Maradi, Tahoua, Tillaberi and Zinder; see Supplementary Fig. 1  for a map of study communes). All of the villages that received cash transfers in the 17 communes were included in our sample. After grouping small neighbouring villages that have less than 8 beneficiaries for ease of programme operations, 322 villages entered the randomization. The study is a cluster-randomized controlled trial in which villages with existing cash transfer beneficiaries were randomly allocated to one of the four arms (Table  1 ): one control group (81 villages), and three treatment arms with variants of the intervention components (80 villages in Capital, 78 villages in Psychosocial and 83 villages in Full).
Likewise, we test the importance of including psychosocial components on top of a design that includes the lump-sum capital transfer (by comparing the Full arm to the Capital arm). Note that earlier work on the Niger national cash transfer programme has shown that cash transfers (either alone or combined with group savings facilitation as in the core component) increased savings and livestock accumulation, but had little average effects on earnings from income-generating activities or economic diversification  55, 56. Sampling, timeline and data. Out of the 22,507 cash transfer beneficiaries that were assigned to the 4 treatment variants, 4,712 households were drawn into a sample for data collection (1,206 households in control, 1,191 households in capital, 1,112 households in psychosocial and 1,203 households in full).
Before the study, we conducted power calculations assuming an intracluster correlation of 0. 10 (based on data from Ghana 6 and a Niger national household survey) and equal sized arms. To maximize power, we sampled all villages in this phase. Sampling 15 households per village allowed for minimum detectable sizes of 0. 057s. d. between arms, before adjusting for baseline outcomes or strata. Extended Data Figure  1  summarizes the study timeline. Baseline data collection took place between April and June 2017. The public lotteries took place after data collection in June 2017. The intervention was delivered between September 2017 and January 2019. Two follow-up surveys were collected. The midpoint occurred in February and March 2019, a median of 6 months (3 to 9 months) post-intervention (that is, after the delivery of the lump-sum grant in treatment arms with the capital component).
Acknowledgements This study is a collaboration between the  Government of Niger , the  World Bank Sahel Adaptive Social Protection programme, Africa Gender Innovation Lab, DIME and Innovations for Poverty Action. The study was co-funded by the  Sahel Adaptive Social Protection Program  ( TF0A2522 ,  TF0B2819 ,  TF019115 ) at the  World Bank , the  Wellspring Philanthropic Fund  ( TF0A5780 ) and the  Niger Adaptive Safety Nets Project  ( P166602 ), managed by  Cellule Filets Sociaux (CFS)  in the Prime Minister's office of the  Niger Government. The study was pre-registered in the AEA RCT registry: https://www. socialscienceregistry. org/trials/2544. It received approval from the  Innovations for Poverty Action Institutional Review Board  (# 00006083 ). The study underwent blind review at the  World Bank Research Department , and results were presented to funders and implementers before publication, without leading to substantive changes in the results.
Outcome parameters were mortality and the modified Rankin Scale (mRS) score at 3 months assessed by either outpatient visits or telephone calls with patients and/or relatives. Poor functional outcome was defined as a mRS score of 3-6. As safety outcome, we defined the occurrence of sICH using the ECASS-II-criteria  [16]. Intracranial hemorrhage was monitored by follow-up CT or MRI as described in prior research  [17]. Included data were collected between June 1995 and December 2018. We excluded patients with missing data on (i) WBC on admission or (ii) 3-month outcome.
Statistical analyses Statistical analyses were performed with SPSS Statistics version 25 (IBM) and with R (R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https:// www. R-proje ct. org/) and RStudio  (RStudio Team (2022). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA URL http:// www. rstud io. com/). We investigated the associations between WBC and outcomes using WBC as a (i) continuous variable and as a (ii) categorical variable distinguishing leukocytosis (WBC > 10 × 10 9 /l) and leukopenia (WBC < 4 × 10 9 /l) according to current hematological definitions  [18, 19]. Normal WBC (4-10 × 10 9 /l) served as reference group. Continuous data were summarized as median and interquartile range (IQR).
One study, including 985 IVT-treated stroke patients, found an association between baseline leukocytosis and poor outcome  [10] , whereas in another study, including 846 IVT-treated stroke patients, baseline neutrophil count but not leukocytosis independently predicted poor outcome and mortality  [11]. A third study found baseline WBC < 8. 1 × 10 9 /l to be associated with favorable outcome in IVT-treated stroke patients (n = 657) and suggested further investigation of WBC in larger prospective datasets  [22]. Limitation of these studies is the relatively small number of patients with leukocytosis. In the present study, we found an independent association between leukocytosis and poor functional outcome and mortality, which remained stable over the long period of data collection. Interestingly, association with poor functional outcome and mortality was even stronger in patients with leukocytosis and elevated CRP.
Patients Patients included in the present study were those identified as infected with the SARS-CoV-2 Marseille-4 variant  1, 2. The present study has been approved by the ethics committee of University Hospital Institute (IHU) Méditerranée Infection (N°2020-016-3). Epidemiological and clinical data were retrieved for patients registered in the Assistance Publique-Hôpitaux de Marseille (APHM) information system. Access to the patients' biological and registry data issued from this system was approved by the data protection committee of APHM and was recorded in the European General Data Protection Regulation registry under number RGPD/APHM 2019-73. Statistical processes were performed using R software version 4. 0. 2 (https://cran. r-project. org/). A p< 0. 05 was considered statistically significant.
Assembly and analyses of genome sequences Genome sequences were assembled by mapping on the SARS-CoV-2 genome GenBank accession no. NC_045512. 2 (Wuhan-Hu-1 isolate) using CLC Genomics workbench v. 7 (with the following thresholds: 0. 8 for coverage and 0. 9 for similarity) (https://digitalinsights. qiagen. com/) as previously described 2 or Minimap2 (https://github. com/lh3/minimap2)  4. Samtools (https://www. htslib. org/) was used for soft clipping of Artic primers (https://artic. network/) and to remove sequence duplicates  5. Consensus genomes were generated using CLC Genomics workbench v. 7 and Sam2consensus.
3 (https://github. com/vbsreenu/Sam2Consensus) through a first in-house script written in Python language (https://www. python. org/). Mutation detection was performed using the Nextclade tool (https://clades. nextstrain. org/) and freebayes (https://github. com/freebayes/freebayes)  6  using a mapping quality score of 20 and results filtered by the Python script based on major nucleotide frequencies ≥ 70% and nucleotide depths ≥ 10 (when sequence reads were generated on the NovaSeq Illumina instrument (Illumina Inc. )) or ≥ 5 (when sequence reads were generated on the MiSeq Illumina instrument). SARS-CoV-2 genotyping was performed using a second in-house script written in Python language (https://www. python. org/) by comparing mutation patterns with those of our database of SARS-CoV-2 variants.
Nextstrain clades and Pangolin lineages provided in the present study were determined using the Nextclade web application (https://clades. nextstrain. org/)  7, 8  and Pangolin web application (https://covlineages. org/pangolin. html)  9  , respectively. The sequences described in the present study have been deposited in the GISAID sequence database (https://www. gisaid. org/)  10  and can be retrieved online using the GISAID online search tool with "Marseille" as a keyword, and then selecting sequence names containing "IHU" or "MEPHI". The sequences have also been deposited in the IHU Marseille Infection website: https://www. mediterraneeinfection. com/tout-sur-le-coronavirus/sequencage-genomique-sars-cov-2/.
Phylogenetic reconstruction and definition and naming of Marseille-4 subvariants and lineages Phylogenetic reconstruction based on SARS-CoV-2 genomes were performed for genome sequences >24,000 nucleotides using the 3extstrain/ncov tool (https://github. com/nextstrain/ncov) and then were visualized using Auspice (https://docs. nextstrain. org/projects/auspice/en/stable/).
Acknowledgments This work was supported by the  French Government  under the " Investments for the Future" program  managed by the  National Agency for Research (ANR) ,  Méditerranée-Infection   10-IAHU-03  and the  Emergen  Consortium (https://www. santepubliquefrance. fr/dossiers/coronavirus-covid-19/consortium-emergen). We also acknowledge support from the  Région Provence Alpes Côte d'Azur  and  European funding FEDER PRIMMI (Fonds Européen de Développement Régional-Plateformes de Recherche et d'Innovation Mutualisées Méditerranée Infection) ,  FEDER   PA 0000320 PRIMMI. We are thankful to  Mamadou Beye ,  Emilie Burel ,  Elsa Prudent ,  Céline Gazin ,  Sofiane Bakour ,  Laurence Thomas ,  Séverine Guillon ,  Véronique Filosa ,  Ludivine Bréchard , and  Claudia Andrieu  for their technical help.