Improving HLA typing imputation accuracy and eplet identification with local next-generation sequencing training data.

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.

Practical Identifiability of Plant Growth Models: A Unifying Framework and Its Specification for Three Local Indices.

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.

Predicting tomato water consumption in a hydroponic greenhouse: contribution of light interception models.

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.

The risk of valvular heart disease in the French Childhood Cancer Survivors' Study: Contribution of dose-volume histogram parameters.

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 (Vd 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 (V0.1Gy|V5Gy=0%), 20 Gy (V5Gy|V20Gy=0%), and 40 Gy (V20Gy|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 (V40Gy < 50 %, hazard ratio (HR) = 7.96, 95 % CI: 4.26-14.88 and V20Gy|V40Gy=0% >50 %, HR = 5.03, 95 % CI: [2.35-10.76]). Doses 5-20 Gy to more than 50 % (V5Gy|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.

Increased Cardiac Risk After a Second Malignant Neoplasm Among Childhood Cancer Survivors: A FCCSS Study.

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.

Two decades of research with the GreenLab model in agronomy.

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.

Internal trophic pressure, a regulator of plant development? Insights from a stochastic functional-structural plant growth model applied to Coffea trees.

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.

Identifying clusters of health risk behaviors and their predictors in adult survivors of childhood cancer: A report from the French Childhood Cancer Survivor Study.

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.

Phenotypic diversity assessment within a major ex situ collection of wild endemic coffees in Madagascar.

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 pipe model theory half a century on: a review.

Background: More than a half century ago, Shinozaki et al. (Shinozaki K, Yoda K, Hozumi K, Kira T. 1964a. A quantitative analysis of plant form - the pipe model theory. I. Basic analyses. Japanese Journal of Ecology B: 97-105) proposed an elegant conceptual framework, the pipe model theory (PMT), to interpret the observed linear relationship between the amount of stem tissue and corresponding supported leaves. The PMT brought a satisfactory answer to two vividly debated problems that were unresolved at the moment of its publication: (1) What determines tree form and which rules drive biomass allocation to the foliar versus stem compartments in plants? (2) How can foliar area or mass in an individual plant, in a stand or at even larger scales be estimated? Since its initial formulation, the PMT has been reinterpreted and used in applications, and has undoubtedly become an important milestone in the mathematical interpretation of plant form and functioning. Scope: This article aims to review the PMT by going back to its initial formulation, stating its explicit and implicit properties and discussing them in the light of current biological knowledge and experimental evidence in order to identify the validity and range of applicability of the theory. We also discuss the use of the theory in tree biomechanics and hydraulics as well as in functional-structural plant modelling. Conclusions: Scrutinizing the PMT in the light of modern biological knowledge revealed that most of its properties are not valid as a general rule. The hydraulic framework derived from the PMT has attracted much more attention than its mechanical counterpart and implies that only the conductive portion of a stem cross-section should be proportional to the supported foliage amount rather than the whole of it. The facts that this conductive portion is experimentally difficult to measure and varies with environmental conditions and tree ontogeny might cause the commonly reported non-linear relationships between foliage and stem metrics. Nevertheless, the PMT can still be considered as a portfolio of properties providing a unified framework to integrate and analyse functional-structural relationships.

Parameter estimation of perfusion models in dynamic contrast-enhanced imaging: a unified framework for model comparison.

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.

Influence of Skin and Subcutaneous Tissue on High-Intensity Focused Ultrasound Beam: Experimental Quantification and Numerical Modeling.

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.

Simulation of high-intensity focused ultrasound lesions in presence of boiling.

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.

Interaction of Vaccination and Reduction of Antibiotic Use Drives Unexpected Increase of Pneumococcal Meningitis.

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.

Modeling tree crown dynamics with 3D partial differential equations.

We characterize a tree's spatial foliage distribution by the local leaf area density. Considering this spatially continuous variable allows to describe the spatiotemporal evolution of the tree crown by means of 3D partial differential equations. These offer a framework to rigorously take locally and adaptively acting effects into account, notably the growth toward light. Biomass production through photosynthesis and the allocation to foliage and wood are readily included in this model framework. The system of equations stands out due to its inherent dynamic property of self-organization and spontaneous adaptation, generating complex behavior from even only a few parameters. The density-based approach yields spatially structured tree crowns without relying on detailed geometry. We present the methodological fundamentals of such a modeling approach and discuss further prospects and applications.

Insights into persistence mechanisms of a zoonotic virus in bat colonies using a multispecies metapopulation model.

Rabies is a worldwide zoonosis resulting from Lyssavirus infection. In Europe, Eptesicus serotinus is the most frequently reported bat species infected with Lyssavirus, and thus considered to be the reservoir of European bat Lyssavirus type 1 (EBLV-1). To date, the role of other bat species in EBLV-1 epidemiology and persistence remains unknown. Here, we built an EBLV-1-transmission model based on local observations of a three-cave and four-bat species (Myotis capaccinii, Myotis myotis, Miniopterus schreibersii, Rhinolophus ferrumequinum) system in the Balearic Islands, for which a 1995-2011 serological dataset indicated the continuous presence of EBLV-1. Eptesicus serotinus was never observed in the system during the 16-year follow-up and therefore was not included in the model. We used the model to explore virus persistence mechanisms and to assess the importance of each bat species in the transmission dynamics. We found that EBLV-1 could not be sustained if transmission between M. schreibersii and other bat species was eliminated, suggesting that this species serves as a regional reservoir. Global sensitivity analysis using Sobol's method revealed that following the rate of autumn-winter infectious contacts, M. schreibersii's incubation- and immune-period durations, but not the infectious period length, were the most relevant factors driving virus persistence.

Automated cell tracking and analysis in phase-contrast videos (iTrack4U): development of Java software based on combined mean-shift processes.

Cell migration is a key biological process with a role in both physiological and pathological conditions. Locomotion of cells during embryonic development is essential for their correct positioning in the organism; immune cells have to migrate and circulate in response to injury. Failure of cells to migrate or an inappropriate acquisition of migratory capacities can result in severe defects such as altered pigmentation, skull and limb abnormalities during development, and defective wound repair, immunosuppression or tumor dissemination. The ability to accurately analyze and quantify cell migration is important for our understanding of development, homeostasis and disease. In vitro cell tracking experiments, using primary or established cell cultures, are often used to study migration as cells can quickly and easily be genetically or chemically manipulated. Images of the cells are acquired at regular time intervals over several hours using microscopes equipped with CCD camera. The locations (x,y,t) of each cell on the recorded sequence of frames then need to be tracked. Manual computer-assisted tracking is the traditional method for analyzing the migratory behavior of cells. However, this processing is extremely tedious and time-consuming. Most existing tracking algorithms require experience in programming languages that are unfamiliar to most biologists. We therefore developed an automated cell tracking program, written in Java, which uses a mean-shift algorithm and ImageJ as a library. iTrack4U is a user-friendly software. Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U. Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences. Finally, iTrack4U is adapted for phase contrast and fluorescent cells.

A multi-task learning approach for compartmental model parameter estimation in DCE-CT sequences.

Today's follow-up of patients presenting abdominal tumors is generally performed through acquisition of dynamic sequences of contrast-enhanced CT. Estimating parameters of appropriate models of contrast intake diffusion through tissues should help characterizing the tumor physiology, but is impeded by the high level of noise inherent to the acquisition conditions. To improve the quality of estimation, we consider parameter estimation in voxels as a multi-task learning problem (one task per voxel) that takes advantage from the similarity between two tasks. We introduce a temporal similarity between tasks based on a robust distance between observed contrast-intake profiles of intensity. Using synthetic images, we compare multi-task learning using this temporal similarity, a spatial similarity and a single-task learning. The similarities based on temporal profiles are shown to bring significant improvements compared to the spatial one. Results on real CT sequences also confirm the relevance of the approach.