dataesr/scientific-paragraphs-categorization huggingface dataset:
(  14 ) and  (17). Calculating the UFL expression for the Jacobian J can be performed automatically using the derivative function. The last step before the solver is to use the Composite framework of CutFEM to define the problem on different parts of the mesh domain. Eventually, we can solve the non-linear and time-dependent problem with the following two nested loops, the outer one for the timestepping, and the inner one for the Newton-Raphson algorithm. The FEniCS Project infrastructure (UFL + FFC + DOLFIN) automatically generates and executes low-level code to assemble the sparse matrices A and b. This linear system is then solved using PETSc and MUMPS. In the previous piece of code, we used the increment as stopping criteria for the Newton-Raphson algorithm. We refer the reader to the free online repository with an example where the stopping criteria is the tolerance to the residual.
Asymptotic solution ODEs The aim of this section is to validate our CutFEM implementation highlight that the solution of the reaction-diffusion system tends to the solution of the ODE system associated to the chemical reactions Eqs. (  1 )-(  5 ) for time going to infinity. The solution of the ODE system is computed in two ways 1. we use the solve_ivp of the package scipy in python 2. we manually compute the asymptotic solutions, which can be found in Appendix A together with the ODE system in Eq. (24). For the PDEs, we solve the reaction diffusion system in a circular domain defined in CutFEM using the level set function φ(x, y) = (x -4) 2 + (y -3) 2 -25. The Gaussian parameters locating the chemical reactions, that are shown in Fig. 5a , have been set as following: G HXK (x 0 = 0. 5, y 0 = 2.
Example one: two-dimensional circular domain In this section, we assess the accuracy of the CutFEM solution. First, we consider the cell, , as a circle defined by the level set function φ(x, y) = (x -4) 2 + (y -3) 2 -25 and we show that the results obtained with CutFEM are of the same order as those obtained with FEM, where the circular domain is explicitly meshed using package mshr. To investigate the behaviour of CutFEM for more complex boundaries we consider an irregular cell geometry defined by a perturbed circle represented by the level set function φ(x, y) = (x -4) 2 + (y -3) 2cos (4x) cos (5x)sin (4y) cos (5y) -25. We highlight how it is easy for CutFEM to work with a more complex shape just by changing the level set function. In this experiment the glucose source term and the reaction locations and parameters are set as in section , with the exception of the Mito reaction that is located at G Mito (x 0 = 4.
Conclusion and discussion Studying brain energy metabolism can be helpful to further our understanding of several neurodegenerative diseases such as Alzheimer's and Parkinson's but a mechanistic perspective is still lacking. To investigate energy metabolism in biological relevant morphologies, we presented a simplified model of metabolism in a single cell using reactiondiffusion equations and demonstrated how to discretize the model using the FEM and CutFEM numerical methods. CutFEM has the advantage of enabling the use of a single level set function per cell, independent of the complexity of the cell geometry. Implementing using FEniCS and libCutFEM ensures the usability by non-experts. In particular, modifying one or several of the reaction diffusion equations can be made by altering only a few lines of code and any linearisation and parallelisation is done automatically by the FEniCS framework.
Availability of data and materials Two fully commented examples can be found at https://bitbucket. org/sofiafarina/cutfem-energy-metabolism/ and are licensed under the LGPLv3. The standard FEM example was written against FEniCS Project version 2019. 2. 0. To run the CutFEM example access to the CutFEM library is required. This access can be granted on written request to the second author.
Leaf Gas Exchange and Chlorophyll Fluorescence Before the beginning of the experiment, five mature leaves per plant were selected and labeled for all physiological measurements (15 independent biological replicates per variety) which were performed between 09:00 AM and 11:00 AM. Net photosynthesis (P net ), stomatal conductance (g s ), transpiration (E), and intercellular CO 2 concentration (Ci) were monitored with an infrared gas analyzer LCPRO-SD (ADC, BioScientific Ltd. , United Kingdom) equipped with the broadleaf chamber at saturating light (PAR at 1400 µmol. m -2. s -  ), at 380 µmol. mol -  for CO 2 concentration, airflow 200 µmol. s -  while leaf temperature was determined by the environment. Data were recorded when the measured parameters were stabilized  (3-6 min). The carboxylation efficiency was estimated as P net /Ci while the instantaneous and intrinsic water use efficiencies were calculated as WUE = P net /E and iWUE = P net /g s , respectively.
Statistical Analysis The data were expressed as means values (±standard error) and analyzed with R statistical software 1. The qualitative factors studied are sampling period (Control, water deficit, and 1 http://www. R-project. org recovery period) and ploidy level. The variance homogeneity was checked using Levene test and the influence of these factors was analyzed with a two-way ANOVAs followed by the LSD test (P < 0. 05) when a significant difference was detected. The normality was checked using Shapiro-Wilk test to determine the appropriate correlation coefficient to apply. Pearson's correlation analysis was performed on normalized data during water deficit conditions and was used to verify the relation between the physiological and biochemical variables under water deficit. Heat map of Pearson's correlation was performed with RStudio software.
ICOS is not expressed in naïve T cells but is rapidly upregulated after activation and may represent a key pathogenic pathway unaddressed by CD28 antagonism. ICOS appears particularly important for the function of several activated and/or effector T cell subsets, including differentiated types 1, 2, and 17, as well as follicular helper T cells (TFH)  (12). Indeed, activated T cells often downregulate CD28 and/or become less dependent on CD28 costimulation, and CD28-negative T cells accumulate in various in ammatory diseases, correlating with disease activity and lack of responsiveness to abatacept  (13) (14) (15) (16) (17) (18) (19). In contrast, ICOS upregulation correlates with disease activity in several in ammatory diseases  (20) (21) (22) (23) (24) , and in preliminary studies, the anti-ICOSL mAb prezalumab (AMG-557) demonstrated some bene cial activity on the arthritis of systemic lupus erythematosus (NCT04058028 ;  (25) ), as well as on overall disease activity in Sjögren's syndrome (NCT02334306).
ALPN-101 serum measurement The concentration of ALPN-101 was measured in serum samples collected 24 hours after the 8 th or 13 th dose in the HOCL model, or after the 10 th and 13 th dose in the Fra-2 Tg model, using an ELISA method developed at Alpine Immune Sciences. ALPN-101 was captured by Fc-speci c donkey anti-human IgG antibody (Jackson ImmunoResearch), immobilized onto a 96-well microtiter plate and detected with F(ab') 2 fragment, Fc-speci c donkey anti-huIgG:HRP (Jackson ImmunoResearch). A calibration curve was generated for each assay plate using SoftMax Pro data acquisition and analysis software (version 7. 1, Molecular Devices).
Slide staining analysis was performed with CaseViewer software (version 2. 4). Histopathologic assessment of dermal brosis in HOCL-treated mice and brosing alveolitis in the Fra-2 model Fixed 6-mm skin punch biopsies from HOCL-treated mice or left lung from Fra-2 mice were embedded in para n. A 4-µm thick tissue section was stained with hematoxylin, eosin, and saffron. Slides were scanned with the Lamina Multilabel Slide Scanner. For HOCL skin sections, dermal thickness was evaluated at 100-fold magni cation by measuring the distance between the epidermal-dermal junction and the dermal-subcutaneous fat junction at ve sites on skin sections by two independent blinded examiners with the CaseViewer software (version 2. 4). The mean of the 10 values obtained by the two examiners was calculated for each skin section.
Our study revealed an increase of ICOS concentration in a large set of SSc patients, extending data obtained in previous studies  (26, 27). Moreover, a higher number of circulating T follicular helper cells expressing ICOS was reported in SSc (43) compared to controls; such changes have also been reported in Sjögren's syndrome  (44) , systemic lupus erythematosus  (45) , and rheumatoid arthritis  (22). These human data were complemented by preclinical work reporting reduced disease progression and humoral responses in lupus nephritis and collagen-induced arthritis mouse models after ICOS-L blockade (  46 ). An anti-ICOSL antibody (AMG557) has been evaluated in patients affected by Sjögren's syndrome (NCT02334306) or by active SLE (NCT04058028) but has revealed no statistically signi cant e cacy in treated patients compared to placebo group, suggesting that inhibition of the ICOS pathway alone may be insu cient to impact disease.
Altogether these and other preclinical data have supported development of acazicolcept (ALPN-101) through a phase 2 program in systemic lupus erythematosus (NCT04835441). Therefore, in order to minimise the number of animals used per experiment, we decided not to include control groups de ned by single monoclonal antibodies but to answer to the question of the effects of this bi-speci c antibody in the speci c context of a brotic CTD to evaluate its effects at pre-clinical levels. Notably, a recent clinical study revealed for the rst time a signi cant decrease of the dermatological score mRSS in SSc patients treated with a bi-speci c antibody targeting IL-4 and IL-13 (54) ; these ndings further support the strategy of targeting multiple pathways in a complex disease like SSc.
Acknowledgements We thank platforms from  Cochin Institute : photonic imaging platform IMAG'IC (Thomas Guilbert and Pierre Bourdoncle ), histology platform HISTIM ( Maryline Favier ) and cytometry platform CYBIO (Muriel Andrieu). We also thank  Drs. Stanford Peng ,  Jan Hillson ,  Jing Yang ,  Katherine Lewis , and  Pamela Holland  from  Alpine Immune Sciences  for their input on study design and for their review of the manuscript.
Statistical Analysis We used descriptive statistics to summarize the demographic and clinical characteristics of the study population. Continuous variables were reported as means with SDs or medians with 25th and 75th percentiles, as appropriate. Categorical variables were reported as numbers with percentages. We performed registry-level analyses and a pooled analysis of data across the 3 registries to estimate independent associations between exposure categories and COVID-19 outcomes. Registry-level effect estimates were reported for exposure categories that included 10 or more patients. Associations were estimated using multilevel multivariable mixed-effects logistic regression analysis and reported as odds ratios (ORs) with 95% CIs. We chose mixed-effects regression analysis for its ability to handle missing data using maximum-likelihood estimation and to fit nested random effects to account for multilevel clustering.
Moreover, a recent study of data from the SECURE-IBD registry reported that thiopurine monotherapy and thiopurines in combination with TNF inhibitor therapy were associated with worse COVID-19 outcomes compared with TNF inhibitor monotherapy. 24 In contrast, researchers have postulated that methotrexate therapy may decrease the cytokine storm associated with COVID-19. 25, 26 otably, the direction of association was the same for methotrexate used in combination with TNF inhibitors, although the effect estimate crossed the line of no effect, which is possibly associated with the use of lower methotrexate doses in combination therapy compared with monotherapy. 27, 28 e timing of treatment initiation with Jak inhibitors may be an important factor associated with COVID-19 outcomes. The second iteration of the Adaptive COVID-19 Treatment Trial (ACTT-2) suggested a protective effect of treatment with baricitinib in combination with remdesivir therapy against unfavorable COVID-19 outcomes among some subgroups of patients with confirmed severe COVID-19.
Strengths and Limitations This study has strengths. These strengths include the robust worldwide collaboration between 3 international registries, which enabled evaluation of a large geographically diverse sample of adults with IMIDs. To our knowledge, this study is the first to pool data across registries to evaluate COVID-19 outcomes among patients with IMIDs. Pooling data increased the power of the study, allowed for more granular analyses of medications, and improved generalizability across IMIDs. Notably, our analyses controlled for active disease, which is only possible through the use of registry data because this variable is not typically available in administrative databases or electronic health records. Furthermore, clinicians or trained staff reported directly to each registry, which likely increased the accuracy of the information.
Overviews of the Simulation Tool In this paper, a simulation tool developed on MATLAB ® and Simulink ® is introduced to address HEV technologies. This tool offers increased flexibility to choose different control and energy management strategies, enabling the user to explore a wide range of hybrid topologies. The tool enables the user to modify any sub-system according to their requirements. In the simulation, vehicle speed data are needed, e. g. , new European driving cycle (NEDC), WLTC, real driving emissions (RDE), etc. , and based on the speed profile, the tool calculates the rotational speed of the wheels, the acceleration, and the total forces using the vehicle dynamics equations. Forces that are taken into consideration are aerodynamic resistance force, rolling friction losses, driving force, and uphill driving force.
𝑇 𝐽 * 𝜔 Theoretical values for simulation purposes with the objective of optimizing the gearbox: ∑ T = J *. ω (6) ω = T ICE + T Generator J ICE + J Generator dt (7) m f = T ICE * ω ICE η ICE * LHV (8) T 0 Power * Distance Cycle LHV * 1000 dt ( 9 ) where T is the torque [Nm],. Efficiency maps for various sizes of EMs are used from the software advisor in the MATLAB ® and Simulink ® library, and the closest range size of the EM is selected from the list. Upon picking this from the list, and with the coast down test results, EM models are created. The torque and speed of the vehicle along with the voltage and efficiency map are used to calculate the current required from the EM model. The generator is a device that converts mechanical energy into electrical energy for use in an external circuit.
In this HEV simulation tool, it is more specific that the input to the generator is the ICE, which delivers energy, due to the fact being both ICE and generator are coupled on the same shaft with the help of a reduction gear, and both are operating in their most optimal areas. The torque and speed of the ICE, along with the voltage and the efficiency map, are used to calculate the current produced by the generator. η EM = T Veh * ω Veh V * I ( 10 ) η Gen = V * I T ICE * ω ICE ( 11 ) where Regarding the battery model, a generic battery model is used in the HEV simulation tool from the MATLAB ® and Simulink ® library  [15, 16]. Battery models based on equivalent circuits are preferred for system-level development and control applications due to their relative simplicity. The battery block implements a generic dynamic model that represents most popular types of rechargeable batteries.
Summary This paper presented a detailed HEV simulation method based on a rule-based control strategy. The model was introduced in order to provide design engineers with the ability to investigate the effects of component selection and to develop control systems and automatic optimization processes for HEVs. The full drivetrain systems of parallel, serial and serial/parallel hybrids were developed, including ICEs/transmissions, EMs/generators and battery packages. All aspects of rotational inertial dynamics, friction and stiffness properties were considered. The hybrid simulation was developed in the MATLAB ® and Simulink ® environment, and the interaction between all of the working modes was implemented using Fuzzy Logic Toolbox™ and the GS algorithm. The aspects of modularity, flexibility, and user-friendly interface were emphasized during the model development.
Characterization of sintered samples To determine the instantaneous relative density during the entire isothermal dwell, the following equation was applied on SPS displacement measurements: ρ(t) = ρ f. h f h(t) (1) where ρ(t) is the instantaneous relative density, ρ f is the final relative density measured by Archimedes method in absolute ethanol (with temperature correction of liquid density), h f the thickness of sintered sample, h(t) the instantaneous thickness of the sample during sintering from lower piston displacement. The computation of the average grain size of sintered ceramic samples was performed on a minimum of 300 grains (obtained from at least three different FESEM micrographs) by grains contouring and treatment on ImageJ software (ImageJ, NIMH, Bethesda, Maryland, USA). The equivalent disc area diameter and an intensification factor of 1.
Acknowledgments The authors are grateful to  Pierre Carles  ( IRCER, Limoges, France ) for TEM and STEM observations. This study was carried out with financial support of the  Region Nouvelle-Aquitaine  ( MISTRAL program  n  • 2017-1R50311 ) and was also supported by institutional grant from the  National Research Agency  under the  Investments  for the future program with the reference  ANR-10-LABX-0074-01 Sigma-LIM.
Recruitment and Consent Participants were recruited from the Hugh Sinclair Unit of Human Nutrition's volunteer database, University of Reading, mailing lists, social media (Facebook and Twitter), a university press release, web-based advertisements, and word of mouth. Interested parties received information with links to the consent form and participant information sheet hosted on the study website, where these documents were available on the home page for reading and downloading. The web-based account creation, using email and password, and the consent agreement were completed directly on the study website. It was made clear that participation was voluntary and that they were free to withdraw at any time without giving reason and without detriment. Participants were informed that they would need to complete web-based questionnaires at baseline, week 6, and week 12.
In the software version deployed in this study (eNutri v1. 0), the inputs to the decision engine generating the PN feedback were a participant's dietary data and sex. The ideal weight range of the participants was based on their BMI calculation. A healthy BMI ranges from 18. 5 to 25. 0 kg/m 2 ; hence, an ideal weight for a participant was presented as the midpoint at 21. 75 kg/m 2. Textual messages and visual representations in the app were also tailored according to BMI (eg, colored bars on the scale to represent the ideal weight range)  [15, 16]. The PA feedback was based on the Baecke questionnaire  [32]. Participants were presented with their overall PA scores, followed by scores for the three categories (sports, leisure, and work), as defined by Baecke et al  [32]. Scores were on a scale of 0 to 100, with higher scores representing greater levels of PA.
Statistical Analysis Statistical analysis was conducted using Python StatsModel (version 0. 11)  [38]. Normality was tested using the D'Agostino-Pearson and Shapiro-Wilk tests  [39] , and, where necessary, m-AHEI component data were square root transformed. Treatment effects were determined based on the change from baseline between groups, where P≤. 05 was considered significant. To account for mean lower-energy intake reported at the end of the study compared with baseline (PN: -1316, SD 2315 kJ/day; control: -726, SD 2549 kJ/day) in the absence of weight change, baseline energy intakes were included as a covariate for the m-AHEI analysis only. Secondary outcomes (PA and BMI) were adjusted for baseline values and presented as adjusted means  [40]. Unless specified, data are presented as means (SDs).
Primary Outcomes Evaluation Considering the protocol for selecting the end point FFQ (ie, the one closest to week 12), the trial resulted in an average interval between FFQs of 10. 8 weeks. The analysis by group confirmed that the intervals were equivalent across the control (10. 7 weeks) and PN (10. 8 weeks) groups. The results for the overall changes in m-AHEI scores from baseline to end point (10. 8 weeks) are presented in Table  3. c Scores are reported on a scale between 0 and 100, where higher scores reflect greater diet quality. d Change from baseline at end point. Data are presented as adjusted means with the baseline energy intakes as a covariate  [40]. e Square root transformation. Compared with that for the control group, the treatment effect observed in the overall m-AHEI score for the PN group was 3.
Similar to eNutri, most apps (83%) in the review used self-assessment for feedback and monitoring. Social support in the form of personalized coaching calls  [47]  and a phone call and social networking feature  [48]  were used in 33% (2/6) of the studies. However, as noted by the review's authors, this requires significant financial input  [49]. Regarding deployment costs, this version of eNutri can be deployed in Google Firebase (database and hosting) using the free plan, allowing reproducibility and scalability. It should be noted that this study was powered to measure the overall m-AHEI treatment effect in all the participants but not the individual components. The fact that individual m-AHEI scores started from different baselines and were presented to the participants more or less frequently according to the participant's dietary intake makes it more difficult to reach statistical significance.
Acknowledgments The authors would like to thank  Luke White  for his assistance in conducting the study. RZF was sponsored by the  National Council of Technological and Scientific Development of the Brazilian government. Participant retention incentives were funded by the  British Nutrition Foundation  under the  Drummond Pump Priming Award 2017  (RF). The follow-up feedback questionnaire was funded by  European Institute of Innovation and Technology Food , the innovation community on Food of the  European Institute of Innovation and Technology , a body of the  European Union (EU) , under Horizon  2020 , the  EU   Framework Programme for Research and Innovation  (project  ID :  Quisper 2018 ID 18064 ).
Methods We modeled the presence-absence data in a spatially explicit, binomial and zero-in ation binomial (ZIB) logistic regression with and without autoregression (iCAR). The presence data were extracted from WHO and Promed archives for the three EID groups and we generated pseudoabsence points within the spatial distribution of the mammalian reservoirs. Various environmental and demographical raster were used to explain the distribution of EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models.