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Background: Cardiac disease (CD) is a primary long-term diagnosed pathology among childhood cancer survivors. Dosiomics (radiomics extracted from the dose distribution) have received attention in the past few years to assess better the induced risk of radiotherapy (RT) than standard dosimetric features such as dose-volume indicators. Hence, using the spatial information contained in the dosiomics features with machine learning methods may improve the prediction of CD., Methods: We considered the 7670 5-year survivors of the French Childhood Cancer Survivors Study (FCCSS). Dose-volume and dosiomics features are extracted from the radiation dose distribution of 3943 patients treated with RT. Survival analysis is performed considering several groups of features and several models [Cox Proportional Hazard with Lasso penalty, Cox with Bootstrap Lasso selection, Random Survival Forests (RSF)]. We establish the performance of dosiomics compared to baseline models by estimating C-index and Integrated Brier Score (IBS) metrics with 5-fold stratified cross-validation and compare their time-dependent error curves., Results: An RSF model adjusted on the first-order dosiomics predictors extracted from the whole heart performed best regarding the C-index (0.792 ± 0.049), and an RSF model adjusted on the first-order dosiomics predictors extracted from the heart's subparts performed best regarding the IBS (0.069 ± 0.05). However, the difference is not statistically significant with the standard models (C-index of Cox PH adjusted on dose-volume indicators: 0.791 ± 0.044; IBS of Cox PH adjusted on the mean dose to the heart: 0.074 ± 0.056)., Conclusion: In this study, dosiomics models have slightly better performance metrics but they do not outperform the standard models significantly. Quantiles of the dose distribution may contain enough information to estimate the risk of late radio-induced high-grade CD in childhood cancer survivors., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Bentriou, Letort, Chounta, Fresneau, Do, Haddy, Diallo, Journy, Zidane, Charrier, Aba, Ducos, Zossou, de Vathaire, Allodji and Lemler.)

Background: Identifying risk factors contributing the most to mortality of childhood cancer survivors is essential to guide harm reduction efforts in childhood cancer treatments, and long-term follow-up of childhood cancer survivors., Methods: We assessed Life Years Lost from childhood cancer treatments and their health-related late effects among the French Childhood Cancer Survivors Study, a cohort of 7670 5-year childhood cancer survivors. Using a landmark strategy, we also assessed time-varying effects of risk factors, and how the multi-morbidity affects life years lost., Results: We found subsequent malignant neoplasm (9.0 years [95 %CI: 4.3-13.7]), severe cardiac disease (8.0 years [95 %CI: 1.2-14.9]), and the use of radiotherapy (6.0 years [95 %CI: 4.7-7.3]) to be the highest contributors to Life Years Lost among childhood cancer survivors. We found no interaction impact on life years lost between health related late effects considered., Conclusions: Those findings suggest that radiotherapy is the root cause of early mortality among childhood cancer survivors. Moreover patients experiencing a subsequent malignant neoplasm or a cardiac disease should be monitored closely after the event, as comorbidity is common and causes premature deaths., Competing Interests: Declaration of Competing Interest None, (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Ascertaining the presence of weakly positive anti-HLA donor-specific antibodies (DSA) in organ transplantation with multiplex single antigen beads assays may be challenging despite their high sensitivity due to technical variability issues. Through extensive datasets of Next-Generation Sequencing HLA typings and single antigen analyses, we reassessed the mean fluorescence intensity (MFI) positivity threshold of the assay to enhance accuracy. By showing that some beads were more prone to false positivity than others, we propose a nuanced approach that accounts for nonspecific intrinsic reactivities at the HLA antigen level, that is, on a bead-by-bead basis, as it enhances assay precision and reliability. This is substantiated by a comprehensive statistical analysis of MFI values and the implementation of the determination of a "Quantile Adjusted Threshold 500" (QAT500) value for each bead. Applied to DSA detection during patients' follow-up, this approach discriminated better and earlier low-strength DSA that would later raise their MFI above the clinically relevant threshold of 3000. Moving from a subjective interpretation to a more objective and precise methodology allows for standardizing HLA antibody and DSA detection. The study emphasizes the need for further research with real clinical data to validate and refine this approach., (© 2024 The Author(s). European Journal of Immunology published by Wiley‐VCH GmbH.)

Amid the rise of machine learning models, a substantial portion of plant growth models remains mechanistic, seeking to capture an in-depth understanding of the underlying phenomena governing the system's dynamics. The development of these models typically involves parameter estimation from experimental data. Ensuring that the estimated parameters align closely with their respective "true" values is crucial since they hold biological interpretation, leading to the challenge of uniqueness in the solutions. Structural identifiability analysis addresses this issue under the assumption of perfect observations of system dynamics, whereas practical identifiability considers limited measurements and the accompanying noise. In the literature, definitions for structural identifiability vary only slightly among authors, whereas the concept and quantification of practical identifiability lack consensus, with several indices coexisting. In this work, we provide a unified framework for studying identifiability, accommodating different definitions that need to be instantiated depending on each application case. In a more applicative second step, we focus on three widely used methods for quantifying practical identifiability: collinearity indices, profile likelihood, and average relative error. We show the limitations of their local versions, and we propose a new risk index built on the profile likelihood-based confidence intervals. We illustrate the usefulness of these concepts for plant growth modeling using a discrete-time individual plant growth model, LNAS, and a continuous-time plant population epidemics model. Through this work, we aim to underline the significance of identifiability analysis as a complement to any parameter estimation study and offer guidance to the modeler., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Jean Velluet et al.)

Assessing donor/recipient HLA compatibility at the eplet level requires second field DNA typings but these are not always available. These can be estimated from lower-resolution data either manually or with computational tools currently relying, at best, on data containing typing ambiguities. We gathered NGS typing data from 61,393 individuals in 17 French laboratories, for loci A, B, and C (100% of typings), DRB1 and DQB1 (95.5%), DQA1 (39.6%), DRB3/4/5, DPB1, and DPA1 (10.5%). We developed HaploSFHI, a modified iterative maximum likelihood algorithm, to impute second field HLA typings from low- or intermediate-resolution ones. Compared with the reference tools HaploStats, HLA-EMMA, and HLA-Upgrade, HaploSFHI provided more accurate predictions across all loci on two French test sets and four European-independent test sets. Only HaploSFHI could impute DQA1, and solely HaploSFHI and HaploStats provided DRB3/4/5 imputations. The improved performance of HaploSFHI was due to our local and nonambiguous data. We provided explanations for the most common imputation errors and pinpointed the variability of a low number of low-resolution haplotypes. We thus provided guidance to select individuals for whom sequencing would optimize incompatibility assessment and cost-effectiveness of HLA typing, considering not only well-imputed second field typing(s) but also well-imputed eplets., (© 2023 The Authors. HLA: Immune Response Genetics published by John Wiley & Sons Ltd.)

In recent years, hydroponic greenhouse cultivation has gained increasing popularity: the combination of hydroponics' highly efficient use of resources with a controlled environment and an extended growing season provided by greenhouses allows for optimized, year-round plant growth. In this direction, precise and effective irrigation management is critical for achieving optimal crop yield while ensuring an economical use of water resources. This study explores techniques for explaining and predicting daily water consumption by utilizing only easily readily available meteorological data and the progressively growing records of the water consumption dataset. In situations where the dataset is limited in size, the conventional purely data-based approaches that rely on statistically benchmarking time series models tend to be too uncertain. Therefore, the objective of this study is to explore the potential contribution of crop models' main concepts in constructing more robust models, even when plant measurements are not available. Two strategies were developed for this purpose. The first strategy utilized the Greenlab model, employing reference parameter values from previously published papers and re-estimating, for identifiability reasons, only a limited number of parameters. The second strategy adopted key principles from crop growth models to propose a novel modeling approach, which involved deriving a Stochastic Segmentation of input Energy (SSiE) potentially absorbed by the elementary photosynthetically active parts of the plant. Several model versions were proposed and adjusted using the maximum likelihood method. We present a proof-of-concept of our methodology applied to the ekstasis Tomato, with one recorded time series of daily water uptake. This method provides an estimate of the plant's dynamic pattern of light interception, which can then be applied for the prediction of water consumption. The results indicate that the SSiE models could become valuable tools for extracting crop information efficiently from routine greenhouse measurements with further development and testing. This, in turn, could aid in achieving more precise irrigation management., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Florakis, Trevezas and Letort.)

Background: Childhood cancer survivors (CCS) are at an elevated risk of developing both a second malignant neoplasm (SMN) and cardiac disease., Objectives: This study sought to assess the excess of occurrence of cardiac disease after a SMN among CCS., Methods: Analyses included 7,670 CCS from the French Childhood Cancer Survivors Study cohort diagnosed between 1945 and 2000. To account for the time dependence of the occurrence of a SMN, we employed a landmark approach, considering an additive regression model for the cumulative incidence of cardiac disease. We estimated the effect of a SMN on the instantaneous risk of cardiac disease using a proportional cause-specific hazard model, considering a SMN as a time-dependent exposure. In both models, we adjusted for demographic and treatment information and considered death as a competing event., Results: In 7,670 CCS over a median follow-up of 30 years (IQR: 22-38 years), there were 378 cases of cardiac disease identified, of which 49 patients experienced a SMN. Patients who survived 25 years after their childhood cancer diagnosis and had a SMN in that time frame had a significantly increased cumulative incidence of cardiac disease, which was 3.8% (95% CI: 0.5% to 7.1%) higher compared with those without a SMN during this period. No SMN-induced excess of cardiac disease was observed at subsequent landmark times. SMNs were associated with a 2-fold increase (cause-specific HR: 2.0; 95% CI: 1.4-2.8) of cardiac disease., Conclusions: The occurrence of a SMN among CCS is associated with an increased risk of cardiac disease occurrence and risk at younger ages., Competing Interests: This work was supported and funded by the Gustave Roussy Foundation (Pediatric Program "Guérir le Cancer de l’Enfant"), the ITMO (Instituts thématiques multiorganismes) Cancer d’Aviesan Program (RadioPrediTool project no. 20CM112-00), the INCa (Institut national du cancer)/ARC (Foundation ARC for Cancer Research) foundation (CHART project), the Foundation ARC for Cancer Research (grant no. Pop-HaRC 201401208), the "START" PAIR Research Program (grant no. INCa-Fondation ARC-LNCC 11902), and the Ligue Nationale Contre le Cancer association. These funding agencies had no role in the design and conduct of the study, in the collection, management, analysis, and interpretation of the data, or in the preparation, review, and approval of the manuscript. The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2023 The Authors.)

Background and Purpose: Valvular Heart Disease (VHD) is a known complication of childhood cancer after radiotherapy treatment. However, the dose-volume-effect relationships have not been fully explored., Materials and Methods: We obtained individual heart Dose Volume Histograms (DVH) for survivors of the French Childhood Cancer Survivors Study (FCCSS) who had received radiotherapy. We calculated the Mean Dose to the Heart (MHD) in Gy, as well as the heart DVH parameters (V d Gy , which represents the percentage of heart volume receiving at least d Gy), fixing the thresholds to 0.1 Gy, 5 Gy, 20 Gy, and 40 Gy. We analyzed them furtherly in the subpopulation of the cohort that was treated with a dose lower than 5 Gy (V 0.1Gy | V5Gy=0% ), 20 Gy (V 5Gy | V20Gy=0% ), and 40 Gy (V 20Gy | V40Gy=0% ), respectively. We investigated their role in the occurrence of a VHD in this population-based observational cohort study using the Cox proportional hazard model, adjusting for age at cancer diagnosis and chemotherapy exposure., Results: Median follow-up was 30.6 years. Eighty-one patients out of the 7462 (1 %) with complete data experienced a severe VHD (grade ≥ 3). The risk of VHD increased along with the MHD, and it was associated with high doses to the heart (V 40Gy  < 50 %, hazard ratio (HR) = 7.96, 95 % CI: 4.26-14.88 and V 20Gy | V40Gy=0% >50 %, HR = 5.03, 95 % CI: [2.35-10.76]). Doses 5-20 Gy to more than 50 % (V 5Gy | V20Gy=0% >50 %) of the heart induced a marginally non-significant estimated risk. We also observed a remarkable risk increase with attained age., Conclusions: Our results provide new insight into the VHD risk that may impact current treatments and long-term follow-up of childhood cancer survivors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: With up to 200 published contributions, the GreenLab mathematical model of plant growth, developed since 2000 under Sino-French co-operation for agronomic applications, is descended from the structural models developed in the AMAP unit that characterize the development of plants and encompass them in a conceptual mathematical framework. The model also incorporates widely recognized crop model concepts (thermal time, light use efficiency and light interception), adapting them to the level of the individual plant., Scope: Such long-term research work calls for an overview at some point. That is the objective of this review paper, which retraces the main history of the model's development and its current status, highlighting three aspects. (1) What are the key features of the GreenLab model? (2) How can the model be a guide for defining relevant measurement strategies and experimental protocols? (3) What kind of applications can such a model address? This last question is answered using case studies as illustrations, and through the Discussion., Conclusions: The results obtained over several decades illustrate a key feature of the GreenLab model: owing to its concise mathematical formulation based on the factorization of plant structure, it comes along with dedicated methods and experimental protocols for its parameter estimation, in the deterministic or stochastic cases, at single-plant or population levels. Besides providing a reliable statistical framework, this intense and long-term research effort has provided new insights into the internal trophic regulations of many plant species and new guidelines for genetic improvement or optimization of crop systems., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background and Aims: Like other clades, the Coffea genus is highly diversified on the island of Madagascar. The 66 endemic species have colonized various environments and consequently exhibit a wide diversity of morphological, functional and phenological features and reproductive strategies. The trends of interspecific trait variation, which stems from interactions between genetically defined species and their environment, still needed to be addressed for Malagasy coffee trees., Methods: Data acquisition was done in the most comprehensive ex situ collection of Madagascan wild Coffea. The structure of endemic wild coffees maintained in an ex situ collection was explored in terms of morphological, phenological and functional traits. The environmental (natural habitat) effect was assessed on traits in species from distinct natural habitats. Phylogenetic signal (Pagel's λ, Blomberg's K) was used to quantify trait proximities among species according to their phylogenetic relatedness., Key Results: Despite the lack of environmental difference in the ex situ collection, widely diverging phenotypes were observed. Phylogenetic signal was found to vary greatly across and even within trait categories. The highest values were exhibited by the ratio of internode mass to leaf mass, the length of the maturation phase and leaf dry matter content (ratio of dry leaf mass to fresh leaf mass). By contrast, traits weakly linked to phylogeny were either constrained by the original natural environment (leaf size) or under selective pressures (phenological traits)., Conclusions: This study gives insight into complex patterns of trait variability found in an ex situ collection, and underlines the opportunities offered by living ex situ collections for research characterizing phenotypic variation., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: Health risk behaviors (HRB) of childhood cancer survivors (CCS) are generally studied separately, despite the evidence suggesting that HRB are not independent. To our knowledge, few studies have examined HRB profiles in the former pediatric cancer patients. In this study, we identified HRB profiles and examined predictors engaging in unhealthy behaviors in CCS., Methods: We used data from a French cohort of CCS that includes five-year survivors diagnosed between 1945 and 2000 and treated before reaching age 18, in five centers in France. A total of 2961 adult CCS answered a self-reported questionnaire pertaining to HRB. Latent class analysis was used to identify HRB profiles combining physical activity, smoking, cannabis use, and alcohol drinking. Multinomial logistic analyses examined predictors for engaging in unhealthy behaviors., Results: Three HRB patterns emerged: "Low-risk" (n = 1846, 62.3%) included CCS who exhibited the highest frequency for usual physical activity and the lowest probabilities for current smoking or cannabis use, but most drank at least moderately; "Moderate-risk behaviors" (n = 291, 9.8%), and "High-risk behaviors" (n = 824, 27.8%) for CCS who exhibited the highest frequencies for current smoking, cannabis use, and heavy drinking. The multivariable regression revealed that male CCS, less educated or not married were significantly more likely to be in the high-risk behaviors group than the low-risk group., Conclusions: As CCS remain a vulnerable population, screening for HRB should be routinized in long-term follow-up care and interventions targeting multiple HRB simultaneously among survivors should be developed., (© 2020 John Wiley & Sons Ltd.)

Background and Aims: Using internal trophic pressure as a regulating variable to model the complex interaction loops between organogenesis, production of assimilates and partitioning in functional-structural models of plant growth has attracted increasing interest in recent years. However, this approach is hampered by the fact that internal trophic pressure is a non-measurable quantity that can be assessed only through model parametric estimation, for which the methodology is not straightforward, especially when the model is stochastic., Methods: A stochastic GreenLab model of plant growth (called 'GL4') is developed with a feedback effect of internal trophic competition, represented by the ratio of biomass supply to demand (Q/D), on organogenesis. A methodology for its parameter estimation is presented and applied to a dataset of 15 two-year-old Coffea canephora trees. Based on the fitting results, variations in Q/D are reconstructed and analysed in relation to the estimated variations in organogenesis parameters., Key Results: Our stochastic retroactive model was able to simulate realistically the progressive set-up of young plant architecture and the branch pruning effect. Parameter estimation using real data for Coffea trees provided access to the internal trophic dynamics. These dynamics correlated with the organogenesis probabilities during the establishment phase., Conclusions: The model can satisfactorily reproduce the measured data, thus opening up promising avenues for further applying this original procedure to other experimental data. The framework developed can serve as a model-based toolkit to reconstruct the hidden internal trophic dynamics of plant growth., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Patients follow-up in oncology is generally performed through the acquisition of dynamic sequences of contrast-enhanced images. Estimating parameters of appropriate models of contrast intake diffusion through tissues should help characterizing the tumour physiology. However, several models have been developed and no consensus exists on their clinical use. In this paper, we propose a unified framework to analyse models of perfusion and estimate their parameters in order to obtain reliable and relevant parametric images. After defining the biological context and the general form of perfusion models, we propose a methodological framework for model assessment in the context of parameter estimation from dynamic imaging data: global sensitivity analysis, structural and practical identifiability analysis, parameter estimation and model comparison. Then, we apply our methodology to five of the most widely used compartment models (Tofts model, extended Tofts model, two-compartment model, tissue-homogeneity model and distributed-parameters model) and illustrate the results by analysing the behaviour of these models when applied to data acquired on five patients with abdominal tumours., (Copyright © 2016 Elsevier B.V. All rights reserved.)

High-intensity focused ultrasound (HIFU) enables the non-invasive thermal ablation of tumors. However, numerical simulations of the treatment remain complex and difficult to validate in clinically relevant situations. In this context, needle hydrophone measurements of the acoustic field downstream of seven rabbit tissue layers comprising skin, subcutaneous fat and muscle were performed in different geometrical configurations. Increasing curvature and thickness of the sample were found to decrease the focusing of the beam: typically, a curvature of 0.05 mm(-1) decreased the maximum pressure by 45% and doubled the focal area. A numerical model based on k-Wave Toolbox was found to be in very good agreement with the reported measurements. It was used to extrapolate the effect of the superficial tissues on peak positive and peak negative pressure at focus, which affects both cavitation and target heating. The shape of the interface was found to have a strong influence on the values, and it is therefore an important parameter to monitor or to control in the clinical practice. This also highlights the importance of modeling realistic configurations when designing treatment procedures., (Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Background: The lesions induced by high-intensity focused ultrasound (HIFU) thermal ablations are particularly difficult to simulate due to the complexity of the involved phenomena. In particular, boiling has a strong influence on the lesion shape. Thus, it must be accounted for if it happens during the pulses to be modeled. However, no acoustic model enables the simulation of the resulting wave scattering. Therefore, we propose an equivalent model for the heat deposition pattern in the presence of boiling., Methods: Firstly, the acoustic field is simulated with k-Wave and the heat source term is calculated. Then, a thermal model is designed, including the equivalent model for boiling. It is rigorously calibrated and validated through the use of diversified ex vivo and in vivo data, including usually unexploited data types related to the bubble clouds., Results: The proposed model enabled to efficiently simulate unitary pulses properties, including the sizes of the lesions, their morphology, the boiling onset time, and the influence of the boiling onset time on the lesions sizes., Conclusions: In this article, the whole procedure of model design, calibration, and validation is discussed. In addition to depicting the creative use of data, our modeling approach focuses on the understanding of the mechanisms influencing the shape of the lesion. Further work is required to study the influence of the remaining bubble clouds in the context of pulse groups.

Antibiotic-use policies may affect pneumococcal conjugate-vaccine effectiveness. The reported increase of pneumococcal meningitis from 2001 to 2009 in France, where a national campaign to reduce antibiotic use was implemented in parallel to the introduction of the 7-valent conjugate vaccine, provides unique data to assess these effects. We constructed a mechanistic pneumococcal transmission model and used likelihood to assess the ability of competing hypotheses to explain that increase. We find that a model integrating a fitness cost of penicillin resistance successfully explains the overall and age-stratified pattern of serotype replacement. By simulating counterfactual scenarios of public health interventions in France, we propose that this fitness cost caused a gradual and pernicious interaction between the two interventions by increasing the spread of nonvaccine, penicillin-susceptible strains. More generally, our results indicate that reductions of antibiotic use may counteract the benefits of conjugate vaccines introduced into countries with low vaccine-serotype coverages and high-resistance frequencies. Our findings highlight the key role of antibiotic use in vaccine-induced serotype replacement and suggest the need for more integrated approaches to control pneumococcal infections.