dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding was provided by  P01CA247886  (to EMJ, NZ, LTK, JZ, EJF),  K08CA248624  (to NZ), the  Lustgarten Foundation  ' A Translational Convergence Program of Personalized Immunotherapy for Pancreatic Cancer Patients at Johns Hopkins ' (to EMJ, NZ, LTK, JZ, EJF), a  Lustgarten Foundation-AACR   Career Development Award for Pancreatic Cancer Research , in honor of  Ruth Bader Ginsburg  (ALK),  GI SPORE P50CA062924  (EMJ, EJF),  U01CA253403  (EJF, AD, EMJ),  U54CA274371  (EJF, AK, DW),  U01CA212007  (EJF, LTK),  U54CA268083  (DW, AK, PH, EMJ, EJF),  R00NS122085  (GSO),  T32GM148383  (JM),  T32CA153952  (DB),  NSF   1720625  (RH, YW, PM),  NSF   2303695  (RH, HR, MG, PM), the  National Foundation for Cancer Research  (EJF, LC), the  Jayne Koskinas Ted Giovanis Foundation for Health and Policy  (RH, MG, IG, JM, DG, HR, PM),  U01CA232137  (RH, FK, PM),  NSF   1818187  (RH, AS, PM),  Leidos Biomedical Research  contract  75N91019D00024  (RH, HR, PM),  Maryland Cancer Moonshot Research Grant  to the  Johns Hopkins Medical Institutions (FY24)  (AD, EJF), a  Luddy Faculty Fellowship  (HR, PM),  R01CA169702  (LZ),  R01CA197296  (LZ), and  P30CA006973  (LZ).
Code availability PhysiCell Version 1. 12. 0 136 and later includes a full reference implementation of the grammar and grammar-based simulation modeling. The specific modeling examples presented in the results section presented below are available on GitHub at https://github. com/physicell-models/grammar_samples. To get a list of all the example models: make list-user-projects To load and compile an example named myproject, use: make load PROJ=myproject && make Similar to our prior work to create cloud-based training materials  98  and cloud-based model dissemination  95  , and inspired by other recent advances on "zero-install" models 137 , we have created a cloud-based version  94, 138  of PhysiCell based on the nanoHUB platform 139. This cloud implementation allows scientists to create, execute, visualize, and explore grammar-based models interactively in a web browser, without need for programming expertise or software setup.
(See documentation and training materials in the supplementary information). The cloud-hosted model is available at https://nanohub. org/tools/pcstudio. Alternatively, scientists can download the latest release of the PhysiCell Studio 94 desktop application at https://github. com/PhysiCell-Tools/PhysiCell-Studio/releases. Assuming an executable model has been compiled, the Studio allows interactive creation and editing of rules (Fig. 7 ), running a simulation, and visualizing results. Refer to the Studio user guide at https://github. com/PhysiCell-Tools/Studio-Guide/blob/main/README. md for more information.
Study sites Nine long-term grassland sites across Europe and Israel were selected for this study, spanning data from five to 31 years (Fig. 1 , Table  1 ). These sites exhibit a wide range of climatic conditions (Table  S1  in Supplementary material) and soil properties (Table  S2  in Supplementary material), making them ideal candidates for evaluating the models' behaviour. Four of them -Laqueuille (LAQ), Monte Bondone (MBO), Grillenburg (GRI), Oensingen (OEN) -are long-established semi-natural grasslands with vegetation types representative of the zone, equipped with flux-tower measuring systems where the volumetric soil water content (SWC), evapotranspiration (ET) and gross primary production (GPP) were recorded with half-hourly or hourly resolution amongst others. Five yield-specific experimental sites are focused on the harvested aboveground biomass production.
At some sites (LAQ, KEM, ROT), data from two management options were available. The French flux-tower site of Laqueuille was grazed with two intensities of management using two levels of nitrogen (N) fertilisation (LAQ1: intensive; LAQ2: extensive). At the German site of Kempten, grassland biomass was harvested at two cutting intensities (KEM1: two cuts per year; KEM2: four cuts per year). In Rothamsted (United Kingdom), the same amount of N was applied in two forms (ROT1: NO 3 -N; ROT2: NH 4 -N). Hereafter, Fig. 1. Geographical location of the grassland sites used in the study. the term "site" identifies either a physical location or a locationmanagement combination. The range of climatic conditions represented by each site is given by indicators of aridity and heat wave frequency (Supplementary material, Fig.
Grassland models Sensitivity simulations were performed at each location using seven models that had undergone a full assessment in a previous study  (Sándor et al. , 2017). At the four flux-tower sites, the models were assessed against detailed soil temperature and water flux datasets, while the evaluation was limited to considering harvested biomass data at the other sites. A brief description of the models follows, while details of the processes and parameter settings that distinguish each of them are provided in Supplementary material of  Sándor et al. (2017). The identities of models were kept anonymous by using model codes from 1 to 7 (as in  Sándor et al. , 2017). Two models -PaSim and SPACSYSwere originally developed to simulate grassland systems. PaSim  (Ma et al. , 2015)  simulates water, C and N cycles in grassland plots at sub-daily time step via climate, soil biology and physics, vegetation and management (including grazing animals) modules.
SPACSYS  (Wu et al. , 2007)  is a multi-dimensional, field-scale, daily time step model of C and N cycles between plants, soils and microbes, with a fine representation of the root system. The following three models -ARMOSA, EPIC and STICSare generic crop models with grassland options. ARMOSA  (Perego et al. , 2013)  estimates N dynamics in the soil-crop-atmosphere continuum and evaluates the impact of management on shallow and groundwater quality via modules for energy, water, C and N balances, and plant development and growth. EPIC, originally developed to estimate soil productivity affected by erosion  (Williams et al. , 2008) , is designed to allow the simulation of a large variety of crops and grasses with unique parameter values. STICS  (Brisson et al. , 2003)  is a generic, daily-step, patch-scale model that covers many crops and climate, soil and management conditions, and is configured to simulate either sown or established mown grasslands.
, 2017). For our analysis, HAB was set equal to the modelled aboveground biomass data at sampling days in each year, for both intensive and extensive sites. The constructed yield dataset represents the overall productivity (and economic value of the grasslands) and is the production indicator at each site (although it is not identical to net primary production). In order to compare the response of the different models for the four output variables at the 12 study sites, a linear approximation was first used  (Cure and Acock, 1986). A linear regression was performed (using R's inbuild lm() function;  R Core Team, 2023)  to approximate the slope of the effect size as a function of change in the given environmental variable (T, PREC or [CO 2 ] in physical units). In order to determine if the slope of the regression was significantly different from zero (the null hypothesis posited a slope of zero, indicating no significant effect of the environmental variable), a t-statistic test was conducted using the lm() function.
Data collection on experimental data To assess the realism of the individual simulations, as well as the realism of the multi-model ensemble mean, we conducted a literature review in which we collected published data from single-factor ecosystem manipulation experiments focusing on the four studied output variables (SWC, ET, GPP and HAB). Google Scholar was used primarily for literature searches. We also used some of the data collected in the study by  Piseddu et al. (2021). Due to the diversity of climatic conditions, experimental designs and technologies applied for climate manipulation, some decisions were made prior to data processing. For better comparability, sites with extremely cold (tundra) or hot (deserts) conditions were excluded from the analysis. Experiments performed under waterlogged conditions (e.
Experiments in closed chambers or greenhouses were not used because of possible uncertain effects on other environmental factors, plant size or growth issues caused by constraints imposed by the enclosures. Due to the known issues with OTCs, the FACE technology was considered in this study as the most comparable to the single-factor modelling exercise we performed. After the literature review, the collected data were checked and critically filtered. All experimental data with missing information on the magnitude/characteristics of the manipulation or climatic conditions were removed. Experience showed that a relatively large number of publications used data from the same experimental sites with a wide temporal coverage. Particular attention was paid to exclude duplicate sites and to use the longest available datasets.
Only annually or multiyearly interpretable data were retained (i. e. results from short-term experiments were ignored). In many cases, data collection consisted of scanning and digitising the graphs presented in journal articles, using customised software written in IDL 6. 3 (NV5 Geospatial Solutions, Inc. ; formerly distributed by Research Systems, Inc. ). Considering observed SWC in the field trials we extracted data that is comparable to the simulations that refer to the 0-10 cm depth (observed SWC was typically representative to the topsoil 0-5 cm, 0-10 cm, 0-15 cm, 5-10 cm, 0-20 cm depths, or it was measured at 5 or 10 cm depth). In all cases, the mean effect size was calculated over multiple years. Site properties (mean annual temperature and precipitation sum, latitude and longitude) were retrieved from the published studies and used in the analysis presented.
Acknowledgements The present article was published under the auspices of the  MACSUR (Modelling European Agriculture with Climate Change for Food Security) Science-Policy Knowledge Forum (MACSUR SciPol Pilot) , June 2021-December 2022, with the support of the  INRAE metaprogramme "Climate change in agriculture and forests: Adaptation and mitigation" (CLIMAE ). The results of this research were obtained within the  MAC-SUR pilot , which received funding in 2012 by a multi-partner call of the  Joint Programming Initiative 'FACCE JPI ' through national financing bodies. We also acknowledge the  Hungarian Scientific Research Fund  ( OTKA K104816 ,  OTKA PD115637 ), the  Széchenyi 2020 programme , the  European Regional Development Fund  and the  Hungarian Government  ( GINOP-2. 3. 2-15-2016-00028 ), the  BioVeL project (Biodiversity Virtual e-Laboratory Project ,  FP7 -INFRASTRUCTURES -2011-2 , project number  283359 ), the  National Multidisciplinary Laboratory for Climate Change  ( RRF-2.
Renáta Sándor  and  Gianni Bellocchi  received mobility funding from the  French-Hungarian bilateral  partnership through the  BALATON  ( N • 44703TF)/TéT  ( 2019-2. 1. 11-T ÉT-2019-00031 ) programme. Zoltán Barcza  was supported by grant " Advanced research supporting the forestry and wood-processing sector´s adaptation to global change and the 4th industrial revolution ", No. CZ. 02. 1. 01/0. 0/ 0. 0/16_019/0000803  financed by  OP RDE ". We thank the individual site PIs ( Katja Klumpp ,  Christof Ammann ,  Damiano Gianelle ,  Christian Bernhofer ) and the technical staff for sharing their eddy covariance data. We also thank  Katharina Braunmiller  ( Thünen Institute of Market Analysis, Braunschweig, Germany ) for facilitating contacts with the Partner Institutions which provided grassland data, and acknowledge technical support from the European Fluxes Database Cluster (http://www.
europe-fluxdata. eu). We thank  Haythem Ben Touhami  for helping in the calibration of PaSim during his PhD at INRA (2011INRA ( -2014)). We also thank  Francesca Piseddu ,  INRAE PhD  at the  Unité Mixte de Recherche sur l' Écosystème Prairial (UREP ) from 2019 to 2022, who provided access to the core dataset of her meta-analyses (Piseddu et al. , 2021), which helped to support the discussion of this study. We thank  Mattia Sanna  for supporting the project and the modelling work. Biome-BGC version 4. 1. 1 (the predecessor of Biome-BGCMuSo used here) was provided by the  Numerical Terradynamic Simulation Group (NTSG)  at the  University of Montana, Missoula MT (USA) , which assumes no responsibility for the proper use by others. We thank the three Anonymous Reviewers for the valuable comments that helped us to improve the quality of the manuscript.
Possible emissions constrained by planetary boundaries To derive the compatible space of emissions without ex-ante socioeconomic assumptions, we propose a backward (or inverse) approach using the Path nder carbon-climate model. Path nder is a reducedcomplexity model calibrated through Bayesian inference on state-of-the-art complex Earth system models and constrained using observational and IPCC AR6 data  13. Ex-ante atmospheric CO 2 concentration and global temperature time series are generated randomly with analytical expressions as input to Path nder until year 2500 (Methods). The model is then used to back-calculate compatible CO 2 emissions through mass balance, given its simulation of the land and ocean carbon sinks, including permafrost emissions. The non-CO 2 radiative forcing is further obtained by inversion of the energy balance equations of the climate module.
Virtual reality high fidelity simulator The ORSIM 1 virtual reality bronchoscopy simulator (Airway Simulation Limited, Auckland, New Zealand) incorporates a replica video bronchoscope, desktop sensor module, and dedicated laptop computer. The ORSIM 1 contains software recreating a high-fidelity FB intubation scenario for the user. It includes learning modules on airway anatomy and dexterity and uses both normal and difficult airway intubation scenarios. (https://www. orsim. co. nz/ design).
ORSIM 1 score Validity and reliability of the ORSIM 1 simulator as an assessment tool have been shown in a previous study by Baker et al. and relied on external scoring of a large number of video recording, which was not felt to be feasible for research  [30]. Their results provided them the basis for establishing computer-generated metrics  [30]  and the development of a proprietary ORSIM 1 score. This ORSIM 1 Score (which has not been validated against expert scores) appears on the ORSIM 1 Session Results screen after the participant finishes a given scenario and was recorded to grade their performance. of Airway Simulation Limited, which manufactures the ORSIM. In this study, he was excluded from data entry, scoring, or data analysis. His commercial involvement in the ORSIM was made clear to the other authors and the subjects at the commencement of the study.
Data collection Demographic data were collected including age, gender, dominant hand, years of residency training, previous FB experience, previous ORSIM 1 and video game experience. The ORSIM 1 simulator's data were collected including type of scenario, time to successful completion, collision avoidance percentage score and minimal oxygen saturation, ORSIM 1 scores for each scenario and the degree of difficulty felt for each of the scenarios as judged by the study subjects.
Study design The participants recruitment, randomization and allocation are displayed in Fig 1. After obtaining written informed consent, a study form was completed. Participants then watched a video explaining the functionalities of the ORSIM 1 simulator and the flexible bronchoscope. A pre-test dexterity test (Gopher module) was done before and then again after five minutes of hands-on familiarization with the bronchoscope simulator. Afterwards participants were randomized (by random number drawing) into two groups: Group N trained on normal airway scenarios up to a maximum of 40 minutes and group D trained instead on one normal and then three difficult airway scenarios (a retropharyngeal abscess, an epiglottic cyst, and a macroglossia). Each of the participants in group D had up to 10 minutes per scenario before being prompted to move to the next scenario (i.
Statistical analyses Redcap 1 software (Research Electronic Data Capture 1 ) was used to collect data  [31, 32]. R 1 software (R Foundation for Statistical Computing, Vienna, Austria) was used for the statistical analyses. Power of this study had been estimated by using the data from the previous ORSIM 1 study by Baker et al. [30] : 150 participants were needed to show a significant difference of 25% with a power of 90%. A Chi square test and Fisher's exact test were used to test differences between group N and D concerning categorial variables. A T-test (or Wilcoxon-Mann-Whitney for not normally distributed data) was used to test differences concerning continuous or ordinal variables. Statistical significance was taken as p<0. 05. Results are expressed in median and interquartile ranges or in numbers and percentages.
Recruitment and exclusions The study began in June 2019 and was stopped in March 2020 due to the Covid-19 pandemic. This analysis includes the 86 participants out of the 150 expected enrolments (43 in group N, 43 in group D) from Toulouse (n = 25) and Clermont-Ferrand (n = 25) in France, the University Hospitals Coventry and Warwickshire NHS Trust (n = 16) and Oxford University Hospitals NHS Foundation Trust (n = 20) in the UK. 105 participations had been recruited, but nine were excluded because of missing or invalid data and a further nine because of a protocol violation (they had already performed more than 10 fiberoptic intubations). Finally, one participant had been recruited twice: only the first of those attempts was kept for analysis.
Background: Deep learning methods have been shown to be useful for segmentation of lower limb muscle MRIs of healthy subjects but, have not been sufficiently evaluated on neuromuscular disease (NDM) patients. Purpose: Evaluate the influence of fat infiltration on convolutional neural network (CNN) segmentation of MRIs from NMD patients. Study Type: Retrospective study. Subjects: Data were collected from a hospital database of 67 patients with NMDs and 14 controls (age: 53 AE 17 years, sex: 48 M, 33 F). Ten individual muscles were segmented from the thigh and six from the calf (20 slices, 200 cm section). Field Strength/Sequence: A 1. 5 T. Sequences: 2D T 1 -weighted fast spin echo. Fat fraction (FF): three-point Dixon 3D GRE, magnetization transfer ratio (MTR): 3D MT-prepared GRE, T2: 2D multispin-echo sequence.
Assessment: U-Net 2D, U-Net 3D, TransUNet, and HRNet were trained to segment thigh and leg muscles (101/11 and 95/11 training/validation images, 10-fold cross-validation). Automatic and manual segmentations were compared based on geometric criteria (Dice coefficient [DSC], outlier rate, absence rate) and reliability of measured MRI quantities (FF, MTR, T2, volume). Statistical Tests: Bland-Altman plots were chosen to describe agreement between manual vs. automatic estimated FF, MTR, T2 and volume. Comparisons were made between muscle populations with an FF greater than 20% (G20+) and lower than 20% (G20À). Results: The CNNs achieved equivalent results, yet only HRNet recognized every muscle in the database, with a DSC of 0. 91 AE 0. 08, and measurement biases reaching À0. 32% AE 0. 92% for FF, 0.
19 AE 0. 77 for MTR, À0. 55 AE 1. 95 msec for T2, and À 0. 38 AE 3. 67 cm 3 for volume. The performances of HRNet, between G20À and G20+ decreased significantly. Data Conclusion: HRNet was the most appropriate network, as it did not omit any muscle. The accuracy obtained shows that CNNs could provide fully automated methods for studying NMDs. However, the accuracy of the methods may be degraded on the most infiltrated muscles (>20%). Evidence Level: 4. Technical Efficacy: Stage 1.
1] [32] [33]  The obvious choice was U-Net 2D, the reference CNN for medical image segmentation. 30 U-Net 3D, a variant of U-Net for processing volumes, was selected to compare the 2D and 3D approaches. 31 Two other networks, transUNet and HRNet, were selected, as they address the recognized shortcomings of U-Net. TransUNet allows evaluating the relation between the different elements of the image thanks to the presence of the vision transformer module. 32 RNet overcomes the loss of information due to the compression of the image in the U-Net encoder. 33 he objective of this study was to evaluate the performance of these four networks, as a function of fatty infiltration. To enrich the training and evaluation of CNNs, a database consisting of patients from three NMDs and controls was collected.
Subjects Data were collected from a hospital database collecting the work of three previous studies, on three different diseases, from the reference center for NMD and ALS at the university hospital of La Timone. 12, 34 The cohort consisted of 67 patients with NMD and 14 controls (age: 53 AE 17 years, sex: 48 M, 33 F). The patients included 29 familial amyloid polyneuropathies (FAP), 18 CMT diseases, and 20 chronic inflammatory demyelinating polyneuropathies (CIDP). There was no recruitment or inclusion processes, since the data were only collected from previous studies, which had their own recruitment/inclusion process. A few patients have been scanned several times and the whole set of images have been integrated in the training database, to ensure proper training of the CNNs. The corresponding MRI dataset was composed of 218 MRI volumes (112 thighs and 106 legs).
Ground Truth Segmentations A total of 10 (respectively 6) individual muscles were delineated in the thigh (respectively in the leg). The ROIs for the thighs consisted of the following individual muscles: adductor (Ad), biceps femoris (BF), gracilis (Gr), rectus femoris (RF), sartorius (Sa), semimembranosus (SM), semitendinosus (ST), vastus intermedius (VI), vastus lateralis (VL), and vastus medialis (VM). For the leg, the following muscle groups were delineated: anterior compartment (AC), deep posterior compartment (DPC), gastrocnemius lateralis (GL), gastrocnemius medialis (GM), lateral compartment (LC), and soleus (So). The segmentations were performed by neurologists who had participated in the various research protocols from which the data were gathered, and each had at least 5 years of segmentation experience (E.
Implementation CNN architecture appears in Fig. 1. U-Net 3D is not presented since its architecture is very similar to U-Net 2D, with 3D convolutions instead of 2Ds. The network is four-layered and the number of channels is 24/48/96/192 for the encoder, and the decoder is built symmetrically. The volumes were padded with replicated slices on the extremities to create a 320 Â 320 Â 24 volume, making it easier to divide the number of slices per two at each layer. The characteristics of the other neural networks are described in Fig. 1. A Python 3. 8. 3 environment was used to implement the CNN training with PyTorch 1. 11. 0 Experiments were run on a Linux Xeon Silver Workstation (4214cpu@2. 2 GHz-96 Gb) with a Nvidia GeForce RTX 3090 GPU. The T 1 w images were used for the CNNs training and consisted of 112/106 thigh/calf volumes for the 3D set and 2240/2120 thigh/calf images for the 2D set.
Fat Infiltration Distribution As illustrated in Fig. 2  Ground Truth vs. Predicted Segmentation DSC, ΔFF (%), ΔMTR, ΔT2 (msec), and ΔV (cm  3  ) values for each model and each muscle are presented in Fig. 3. For the whole dataset, the average values were 0. 92 AE 0. 05 (DSC), À0. 28 AE 1. 00% (ΔFF), 0. 17 AE 0. 83% (ΔMTR), À0. 49 AE 2. 11 msec (ΔT2), and À0. 35 AE 16. 83 cm 3 (ΔV). As can be seen in Fig. 3 , DSC values were high, with the largest value obtained for Ad (0. 95 AE 0. 01) and the lowest for GM (0. 86 AE 0. 09). The smallest FF error was observed for So (0. 32 AE 0. 33) and the largest for GM (1. 19 AE 1. 16). ΔMTR reached the minimal value for So (0. 28 AE 0. 27) and the maximal for Gr (1. 09 AE 0. 98). Regarding the ΔT2 (msec), So was the muscle with the smallest error (À0. 81 AE 0.