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Spontaneous lipolysis results in the breakdown of milk fat by the lipoprotein lipase (EC: 3.1.1.34), an enzyme present in milk. Free fatty acids (FFAs) and by-products released in milk during lipolysis can alter both the organoleptic value of milk (off-flavors release) and technological properties of dairy products (decrease in creaming capabilities). Current climate change is having significant impacts on the feeding of grazing animals, with negative consequences on the availability and quality of grass. We and others have demonstrated that dietary restriction increases milk lipolysis in the cow species. However, no data about the impact of feed restriction on milk lipolysis is available in the ewe species. Thus, this paper aims to investigate the effect of feed restriction on milk characteristics with regard to lipolysis values in dairy ewes. Two groups of 24 multiparous Lacaune ewes in mid-lactation received a “non-restricted” control diet (100% of ad libitum DM intake) or a “restricted” (RESTR) diet (65% of ad libitum DM intake) according to a 2 × 2 crossover design. Milk gross composition together with lipolysis analyses were performed. Blood samples were also screened for metabolites or hormone concentrations. The RESTR treatment induced a decrease in milk production (− 21% compared with control treatment) and a modification of the metabolism of dairy ewes characterized by an increase in plasma non-esterified fatty acids (NEFAs), which represents the balance between adipose tissue mobilization and the use of NEFA by other tissues (+153%), cholesterol (+17%) and β-hydroxybutyrate (+4 %) levels. As a result, a decrease in BW of dairy ewes was observed (−7%). Feed restriction also resulted in a decrease in milk lipolysis estimated by the milk FFA measured by the copper-soap method (−63 and −62%, respectively, for morning and evening milking) or by the reference Bureau of Dairy Industry method (−51 and –57%, respectively, for morning and evening milking). The decrease in milk spontaneous lipolysis under feed restriction was not associated with a decrease in lipoprotein lipase activity in ewes. These results will be completed with proteomic and lipidomic studies in milk samples to better understand mechanisms initiated in the ewe species specifically with regard to lipolysis in milk.

ABSTRACT: Hormone-free (HF) reproduction in dairy sheep is a way to meet current societal demands, but it requires being prepared for collateral impacts on related system components. The efficiency of HF practices (e.g., using the male effect for estrus induction and synchronization) is uncertain compared with hormonal treatment (HT). For example, these practices can lead to higher variability in the flock physiological stage patterns throughout the year, which has direct consequences for feeding regimens. The objective of this work was to simulate the impacts of HF reproduction management, including artificial insemination (AI), on the temporal distribution of productive performance and nutritional requirements of a conventional dairy sheep flock. Using the REPROsheep2.0 model, 6 scenarios were compared over one typical production season for the same flock (n = 597 Lacaune ewes) intensively reared in the Roquefort region of France. These scenarios depicted reproduction with HT and AI in mid-May (Early); HT and AI in July (Summer Late); HT and AI in November (Autumn Late); and their HF versions (HF-Early; HF-Summer Late, and HF-Autumn Late, respectively). In all HF scenarios, a reduction in the number of ewes lambing and consequently in the annual milk production of the farm was observed (−1 to −7%). This affected annual performance with a subsequent decrease of total annual nutritional requirements (−2 to −6%). The HF scenarios resulted in a staggering of lambing events with a 7- to 14-d shift in the appearance of milk production peaks and related nutritional requirements compared with the HT scenarios. Transitioning from conventional to HF reproduction management, while preserving AI, would increase farm workload, lengthen milking period operations, and necessitate a readjustment of feeding management strategies with regard to available feed resources. Depending on the production season, the observed delay in the distribution of nutritional requirements could be either an attractive or an unfavorable outcome for farmers. The delay may be concordant, for example, with the recently observed impacts of climate change on seasonal forage availability in Mediterranean regions (less spring herbage production and warmer temperatures) that are affecting farmers' decision-making about the most efficient use of forage and feed resources.

The collective economic and environmental interest of the whole dairy sheep sector is to reduce feed costs and the negative impact of milk production on the environment. Thus, this study focused on the characterisation and genetic selection potential of feed efficiency in the Lacaune breed. Estimates for feed efficiency in dairy ewes are limited, mainly due to a lack of individual feed intake measurements in the sheepfold or in the pasture. We estimated the genetic parameters for two approximated (not entirely based on individual data) feed efficiency traits (lactation feed conversion ratio (LFCR) and residual energy intake (REI)) and daily milk yield (DMY) at different stages of lactation and throughout lactation. The accuracy of the efficiency traits was first evaluated on samples from Lacaune dairy ewes that were monitored individually, especially for their feed intake. Then, feed efficiency estimation methods were applied on eight commercial farms corresponding to 4 680 Lacaune dairy ewes over two milk lactations (30 854 records). Animals were collectively (for a large part of feed intake) or individually (for milk performance and dynamics of body fat reserves) monitored at different lactation stages. The heritabilities of LFCR and REI were estimated over lactations at 0.10 ± 0.01 and 0.11 ± 0.01, respectively. High genetic correlations were observed between the two efficiency traits and milk production traits, with a genetic correlation between LFCR and DMY of 0.74 ± 0.04 and between REI and DMY of −0.79 ± 0.04. A strong influence of environmental factors such as farm, year of milk production and lactation stage affected the genetic link between REI and milk production traits. Efficiency values observed in early lactation when animals were bred in the sheepfold were less genetically correlated with values obtained later in lactation when animals were grass-fed. However, individual characterisation of feed efficiency remains difficult due to the collective feeding context in dairy ewe farms.

Spontaneous lipolysis results in the breakdown of milk fat by the lipoprotein lipase (EC: 3.1.1.34), an enzyme present in milk. Free fatty acids (FFAs) and by-products released in milk during lipolysis can alter both the organoleptic value of milk (off-flavors release) and technological properties of dairy products (decrease in creaming capabilities). Current climate change is having significant impacts on the feeding of grazing animals, with negative consequences on the availability and quality of grass. We and others have demonstrated that dietary restriction increases milk lipolysis in the cow species. However, no data about the impact of feed restriction on milk lipolysis is available in the ewe species. Thus, this paper aims to investigate the effect of feed restriction on milk characteristics with regard to lipolysis values in dairy ewes. Two groups of 24 multiparous Lacaune ewes in mid-lactation received a "non-restricted" control diet (100% of ad libitum DM intake) or a "restricted" (RESTR) diet (65% of ad libitum DM intake) according to a 2 × 2 crossover design. Milk gross composition together with lipolysis analyses were performed. Blood samples were also screened for metabolites or hormone concentrations. The RESTR treatment induced a decrease in milk production (- 21% compared with control treatment) and a modification of the metabolism of dairy ewes characterized by an increase in plasma non-esterified fatty acids (NEFAs), which represents the balance between adipose tissue mobilization and the use of NEFA by other tissues (+153%), cholesterol (+17%) and β-hydroxybutyrate (+4 %) levels. As a result, a decrease in BW of dairy ewes was observed (-7%). Feed restriction also resulted in a decrease in milk lipolysis estimated by the milk FFA measured by the copper-soap method (-63 and -62%, respectively, for morning and evening milking) or by the reference Bureau of Dairy Industry method (-51 and -57%, respectively, for morning and evening milking). The decrease in milk spontaneous lipolysis under feed restriction was not associated with a decrease in lipoprotein lipase activity in ewes. These results will be completed with proteomic and lipidomic studies in milk samples to better understand mechanisms initiated in the ewe species specifically with regard to lipolysis in milk., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Simulating a consequence of a climate change event on feed availability, responses of Mediterranean meat ewes facing an acute undernutritional challenge ( CHA ; i.e., fed only low nutritional value cereal straw) were evaluated at a sensitive physiological stage (i.e., early suckling). Forty Romane ewes were chosen at early-mid pregnancy (around 2 mo) according to parity (20 primiparous, PRIM ; 20 multiparous, MULT ); feed efficiency genetic line of their sires (residual feed intake [ RFI ]; efficient, RFI - , n  = 10 per parity; inefficient, RFI + , n  = 10 per parity); litter size (i.e., bearing twins, diagnosed by ultrasonography); body weight ( BW , kg) and body condition score ( BCS ) (initial BW and BCS [mean ± SD]: 51.6 ± 7.41 kg; 2.5 ± 0.20, respectively; representing flock' averages per parity). Effects on dry matter intake ( DMI ), ewes' BW and BCS, subcutaneous dorsal fat thickness ( DFT ), energy metabolism (plasma non-esterified fatty acids [ NEFA ], β-hydroxybutyrate ( β-OHB ), glucose, urea, triiodothyronine [ T3 ]), and lambs' growth (BW and average daily gain [ ADG ]; g/d) were examined before, during and after CHA. Individuals' profiles of the response-recovery to CHA were described using a piecewise mixed-effects model. The fixed effect of parity and genetic line and the random effect of individual (ewe) were considered. A linear mixed-effects model was fitted to explore the effects on lambs' growth. The 2-d straw-only CHA had significant effects on most of the recorded parameters. Meaningful drops and recoveries were observed on ewes' DMI, BW, and DFT with effect on postchallenge levels. BW, BCS, DFT, or DMI were also affected by parity (MULT > PRIM) but not by genetic line. Plasma NEFA, β-OHB, glucose, urea, and T3 responded well to CHA with drops in T3, urea, and glucose levels, whereas NEFA and β-OHB significantly increased after CHA. MULT ewes presented sharper β-OHB recovery from CHA than PRIM ( P  ≤ 0.05). With this study, we provide tangible and necessary data for an emerging field of research. Our results give new insights into how such a short and abrupt CHA affects some key zootechnical and physiological parameters, and to what extent the impacts of CHA and the ewes' response-recovery are influenced. It also revealed potential between-individual differences in the adaptive capacities of ewes, which require further exploration., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science.)

The collective economic and environmental interest of the whole dairy sheep sector is to reduce feed costs and the negative impact of milk production on the environment. Thus, this study focused on the characterisation and genetic selection potential of feed efficiency in the Lacaune breed. Estimates for feed efficiency in dairy ewes are limited, mainly due to a lack of individual feed intake measurements in the sheepfold or in the pasture. We estimated the genetic parameters for two approximated (not entirely based on individual data) feed efficiency traits (lactation feed conversion ratio (LFCR) and residual energy intake (REI)) and daily milk yield (DMY) at different stages of lactation and throughout lactation. The accuracy of the efficiency traits was first evaluated on samples from Lacaune dairy ewes that were monitored individually, especially for their feed intake. Then, feed efficiency estimation methods were applied on eight commercial farms corresponding to 4 680 Lacaune dairy ewes over two milk lactations (30 854 records). Animals were collectively (for a large part of feed intake) or individually (for milk performance and dynamics of body fat reserves) monitored at different lactation stages. The heritabilities of LFCR and REI were estimated over lactations at 0.10 ± 0.01 and 0.11 ± 0.01, respectively. High genetic correlations were observed between the two efficiency traits and milk production traits, with a genetic correlation between LFCR and DMY of 0.74 ± 0.04 and between REI and DMY of -0.79 ± 0.04. A strong influence of environmental factors such as farm, year of milk production and lactation stage affected the genetic link between REI and milk production traits. Efficiency values observed in early lactation when animals were bred in the sheepfold were less genetically correlated with values obtained later in lactation when animals were grass-fed. However, individual characterisation of feed efficiency remains difficult due to the collective feeding context in dairy ewe farms., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Hormone-free (HF) reproduction in dairy sheep is a way to meet current societal demands, but it requires being prepared for collateral impacts on related system components. The efficiency of HF practices (e.g., using the male effect for estrus induction and synchronization) is uncertain compared with hormonal treatment (HT). For example, these practices can lead to higher variability in the flock physiological stage patterns throughout the year, which has direct consequences for feeding regimens. The objective of this work was to simulate the impacts of HF reproduction management, including artificial insemination (AI), on the temporal distribution of productive performance and nutritional requirements of a conventional dairy sheep flock. Using the REPROsheep2.0 model, 6 scenarios were compared over one typical production season for the same flock (n = 597 Lacaune ewes) intensively reared in the Roquefort region of France. These scenarios depicted reproduction with HT and AI in mid-May (Early); HT and AI in July (Summer Late); HT and AI in November (Autumn Late); and their HF versions (HF-Early; HF-Summer Late, and HF-Autumn Late, respectively). In all HF scenarios, a reduction in the number of ewes lambing and consequently in the annual milk production of the farm was observed (-1 to -7%). This affected annual performance with a subsequent decrease of total annual nutritional requirements (-2 to -6%). The HF scenarios resulted in a staggering of lambing events with a 7- to 14-d shift in the appearance of milk production peaks and related nutritional requirements compared with the HT scenarios. Transitioning from conventional to HF reproduction management, while preserving AI, would increase farm workload, lengthen milking period operations, and necessitate a readjustment of feeding management strategies with regard to available feed resources. Depending on the production season, the observed delay in the distribution of nutritional requirements could be either an attractive or an unfavorable outcome for farmers. The delay may be concordant, for example, with the recently observed impacts of climate change on seasonal forage availability in Mediterranean regions (less spring herbage production and warmer temperatures) that are affecting farmers' decision-making about the most efficient use of forage and feed resources., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection. The benchmark utilises SODA10M, a recently released large-scale dataset that concerns autonomous driving related problems. First, we review and discuss existing continual learning benchmarks, how they are related, and show that most are extreme cases of continual learning. To this end, we survey the benchmarks used in continual learning papers at three highly ranked computer vision conferences. Next, we introduce CLAD-C, an online classification benchmark realised through a chronological data stream that poses both class and domain incremental challenges; and CLAD-D, a domain incremental continual object detection benchmark. We examine the inherent difficulties and challenges posed by the benchmark, through a survey of the techniques and methods used by the top-3 participants in a CLAD-challenge workshop at ICCV 2021. We conclude with possible pathways to improve the current continual learning state of the art, and which directions we deem promising for future research., 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 Ltd. All rights reserved.)

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase. The resulting network resembles a static entity of knowledge, with endeavours to extend this knowledge without targeting the original task resulting in a catastrophic forgetting. Continual learning shifts this paradigm towards networks that can continually accumulate knowledge over different tasks without the need to retrain from scratch. We focus on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries. Our main contributions concern: (1) a taxonomy and extensive overview of the state-of-the-art; (2) a novel framework to continually determine the stability-plasticity trade-off of the continual learner; (3) a comprehensive experimental comparison of 11 state-of-the-art continual learning methods; and (4) baselines. We empirically scrutinize method strengths and weaknesses on three benchmarks, considering Tiny Imagenet and large-scale unbalanced iNaturalist and a sequence of recognition datasets. We study the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, and qualitatively compare methods in terms of required memory, computation time, and storage.

Background: Among the adaptive capacities of animals, the management of energetic body reserves (BR) through the BR mobilization and accretion processes (BR dynamics, BRD) has become an increasingly valuable attribute for livestock sustainability, allowing animals to cope with more variable environments. BRD has previously been reported to be heritable in ruminants. In the present work, we conducted genome-wide studies (GWAS) in sheep to determine genetic variants associated with BRD. BR (i.e. levels) and BRD (i.e. changes over time) were obtained through body condition score measurements at eight physiological stages throughout each productive cycle in Romane ewes (n = 1034) and were used as phenotypes for GWAS. After quality controls and imputation, 48,593 single nucleotide polymorphisms (SNP) were included in the GWAS., Results: Among the 23 QTL regions identified, a major QTL associated with BR during pregnancy and lactation was identified on chromosome 1. In this region, several significant SNPs mapped to the leptin receptor gene (LEPR), among which one SNP mapped to the coding sequence. The point mutation induces the p.P1019S substitution in the cytoplasmic domain, close to tyrosine phosphorylation sites. The frequency of the SNP associated with increased BR levels was 32%, and the LEPR genotype explained up to 5% of the variance of the trait. Higher fatness levels in ewes carrying the LEPR p.P1019S mutation were observed all along the productive cycle., Conclusions: These results provide strong evidences for involvement of LEPR in the regulation of BR in sheep and highlight it as a major candidate gene for improving adaptive capacities by genetic selection., (© 2022. The Author(s).)

Monitoring liveweight (LW) is an important part of sound management practices at the individual and flock level (e.g., controlling for nutritional status based on body condition, reproduction, and health-related issues), but it is time consuming and stressful. To our knowledge, no literature has reported on the evaluation of automated weighing systems in dairy sheep as an alternative to conventional static scales. The objective of this research was to evaluate the practical feasibility of using an automated walk-over-weighing (WoW) prototype to measure daily LW changes in dairy ewes without human intervention. We used adult Lacaune dairy ewes in 2 complementary trials conducted indoors. Trial 1 aimed at evaluating the repeatability, precision, and accuracy of LW measures recorded using WoW scales compared with a static scale (the gold standard). Forty-two adult ewes (LW ± standard deviation = 71.3 ± 10.4 kg) were randomly drafted from the main flock and used in a 1-day session. The trial included 3 passages. In each passage, ewes were weighed first on a static scale; once a static position was achieved and LW recorded, they continued the circuit and immediately traversed the WoW scale for an automated LW record. Trial 2 aimed to demonstrate the feasibility of using the WoW device under real-world conditions in a dairy sheep-farming system. The WoW scale was installed in the exit race of the milking parlor and evaluated over 7 wk with adult ewes in mid lactation (n = 93; LW 78.5 ± 8.1 kg). Once the ewes were acclimated to the WoW system, 1 group of ewes (n = 48) continued to receive the same feeding regimen (controls), and the other group (n = 45) underwent a nutritional challenge [challenged; 2 wk of undernutrition and then back to control regimen (refeeding) for 1 wk]. We evaluated the ability of the WoW to detect small changes in LW. We collected LW data (2 weighings per ewe per day) from the WoW after each of the 2 milking sessions (morning and evening). We also obtained LW values by weighing the ewes using a static scale once a week. The automated WoW system showed substantial agreement with the gold standard when assessed using Lin's concordance correlation coefficient and Bland and Altman's method, largely due to high repeatability. The WoW system was adequate for detecting small daily variations in LW during undernutrition and refeeding periods. Misbehaviors resulted in spurious WoW values in trial 2, requiring us to use filtration methods to exclude outlier weights and allow meaningful assessment of small LW changes. The WoW system evaluated here is an alternative to the static scales conventionally used on dairy sheep farms. If sound filtration of raw data is applied, WoW could contribute to the close (daily) monitoring of individual LW without operator intervention (i.e., voluntary weighing) and taking animal welfare into account (i.e., no stress related to the weighing session on static scales)., (Copyright © 2021 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Animal robustness may be defined as a complex trait characterizing the ability of an individual to be adapted, productive and healthy under contrasted and fluctuating environmental situations. Such a trait is now considered an essential criterion in order to meet the more ambitious goals of farming sustainability. In ruminants, one of the key mechanisms via which robustness is expressed is the capacity to mobilize or restore body reserves (BR) to cope with the challenges of negative energy balances. The objectives of this work were as follows: 1) to estimate the genetic parameters related to BR dynamics in ewes over successive production cycles and 2) to investigate BR management relationships between different physiological stages. For this, historical individual BW and BCS data from 2,920 phenotyped ewes were used for genetic analysis. The changes in BW (∆BW) and BCS (∆BCS) over time were analyzed. Eight physiological stages were considered to investigate these changes over time: mating, early pregnancy, mid-pregnancy, lambing, early suckling, mid-suckling, weaning, and postweaning. The estimated heritability were low for both ∆BW (h2 = 0.13 to 0.18) and ∆BCS (h2 = 0.04 to 0.16). Moderate to high positive genetic correlations (from 0.48 to 0.91) were obtained between BR mobilization phases and between BR accretion phases. Similarly, moderate to high negative genetic correlations (from -0.36 to -0.75) were estimated between the BR mobilization and accretion periods, suggesting that mechanisms driving BR mobilization and accretion processes were genetically correlated. This is the first study in ruminants that demonstrate that the extent and temporal changes in profiles of BR mobilization and accretion are heritable and genetically linked, indicating that such traits could be considered in genetic programs aimed at improving robustness. Nevertheless, further research is needed for a more comprehensive understanding of BR dynamics, notably by including other physiological parameters (i.e., metabolites and hormones) and additional information on the productive and reproductive life of the ewe.

Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes can represent individuals within a potentially large population (patients or healthy controls) accompanied by a set of features, while the graph edges incorporate associations between subjects in an intuitive manner. This representation allows to incorporate the wealth of imaging and non-imaging information as well as individual subject features simultaneously in disease classification tasks. Previous graph-based approaches for supervised or unsupervised learning in the context of disease prediction solely focus on pairwise similarities between subjects, disregarding individual characteristics and features, or rather rely on subject-specific imaging feature vectors and fail to model interactions between them. In this paper, we present a thorough evaluation of a generic framework that leverages both imaging and non-imaging information and can be used for brain analysis in large populations. This framework exploits Graph Convolutional Networks (GCNs) and involves representing populations as a sparse graph, where its nodes are associated with imaging-based feature vectors, while phenotypic information is integrated as edge weights. The extensive evaluation explores the effect of each individual component of this framework on disease prediction performance and further compares it to different baselines. The framework performance is tested on two large datasets with diverse underlying data, ABIDE and ADNI, for the prediction of Autism Spectrum Disorder and conversion to Alzheimer's disease, respectively. Our analysis shows that our novel framework can improve over state-of-the-art results on both databases, with 70.4% classification accuracy for ABIDE and 80.0% for ADNI., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non-trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non-matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Advances in neuroimaging have provided a tremendous amount of in-vivo information on the brain's organisation. Its anatomy and cortical organisation can be investigated from the point of view of several imaging modalities, many of which have been studied for mapping functionally specialised cortical areas. There is strong evidence that a single modality is not sufficient to fully identify the brain's cortical organisation. Combining multiple modalities in the same parcellation task has the potential to provide more accurate and robust subdivisions of the cortex. Nonetheless, existing brain parcellation methods are typically developed and tested on single modalities using a specific type of information. In this paper, we propose Graph-based Multi-modal Parcellation (GraMPa), an iterative framework designed to handle the large variety of available input modalities to tackle the multi-modal parcellation task. At each iteration, we compute a set of parcellations from different modalities and fuse them based on their local reliabilities. The fused parcellation is used to initialise the next iteration, forcing the parcellations to converge towards a set of mutually informed modality specific parcellations, where correspondences are established. We explore two different multi-modal configurations for group-wise parcellation using resting-state fMRI, diffusion MRI tractography, myelin maps and task fMRI. Quantitative and qualitative results on the Human Connectome Project database show that integrating multi-modal information yields a stronger agreement with well established atlases and more robust connectivity networks that provide a better representation of the population., (Copyright © 2017 Elsevier Inc. All rights reserved.)

OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments. OpenMOLE hides the complexity of designing complex experiments thanks to its DSL. Users can embed their own applications and scale their pipelines from a small prototype running on their desktop computer to a large-scale study harnessing distributed computing infrastructures, simply by changing a single line in the pipeline definition. The construction of the pipeline itself is decoupled from the execution context. The high-level DSL abstracts the underlying execution environment, contrary to classic shell-script based pipelines. These two aspects allow pipelines to be shared and studies to be replicated across different computing environments. Workflows can be run as traditional batch pipelines or coupled with OpenMOLE's advanced exploration methods in order to study the behavior of an application, or perform automatic parameter tuning. In this work, we briefly present the strong assets of OpenMOLE and detail recent improvements targeting re-executability of workflows across various Linux platforms. We have tightly coupled OpenMOLE with CARE, a standalone containerization solution that allows re-executing on a Linux host any application that has been packaged on another Linux host previously. The solution is evaluated against a Python-based pipeline involving packages such as scikit-learn as well as binary dependencies. All were packaged and re-executed successfully on various HPC environments, with identical numerical results (here prediction scores) obtained on each environment. Our results show that the pair formed by OpenMOLE and CARE is a reliable solution to generate reproducible results and re-executable pipelines. A demonstration of the flexibility of our solution showcases three neuroimaging pipelines harnessing distributed computing environments as heterogeneous as local clusters or the European Grid Infrastructure (EGI).

The delineation of functionally and structurally distinct regions as well as their connectivity can provide key knowledge towards understanding the brain's behaviour and function. Cytoarchitecture has long been the gold standard for such parcellation tasks, but has poor scalability and cannot be mapped in vivo. Functional and diffusion magnetic resonance imaging allow in vivo mapping of brain's connectivity and the parcellation of the brain based on local connectivity information. Several methods have been developed for single subject connectivity driven parcellation, but very few have tackled the task of group-wise parcellation, which is essential for uncovering group specific behaviours. In this paper, we propose a group-wise connectivity-driven parcellation method based on spectral clustering that captures local connectivity information at multiple scales and directly enforces correspondences between subjects. The method is applied to diffusion Magnetic Resonance Imaging driven parcellation on two independent groups of 50 subjects from the Human Connectome Project. Promising quantitative and qualitative results in terms of information loss, modality comparisons, group consistency and inter-group similarities demonstrate the potential of the method., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this paper, we present the construction of a probabilistic atlas mapping the preferential locations of diffuse WHO grade II gliomas in the brain. This is carried out through a sparse graph whose nodes correspond to clusters of tumors clustered together based on their spatial proximity. The interest of such an atlas is illustrated via two applications. The first one correlates tumor location with the patient's age via a statistical analysis, highlighting the interest of the atlas for studying the origins and behavior of the tumors. The second exploits the fact that the tumors have preferential locations for automatic segmentation. Through a coupled decomposed Markov Random Field model, the atlas guides the segmentation process, and characterizes which preferential location the tumor belongs to and consequently which behavior it could be associated to. Leave-one-out cross validation experiments on a large database highlight the robustness of the graph, and yield promising segmentation results.

The analysis of the connectome of the human brain provides key insight into the brain's organisation and function, and its evolution in disease or ageing. Parcellation of the cortical surface into distinct regions in terms of structural connectivity is an essential step that can enable such analysis. The estimation of a stable connectome across a population of healthy subjects requires the estimation of a groupwise parcellation that can capture the variability of the connectome across the population. This problem has solely been addressed in the literature via averaging of connectivity profiles or finding correspondences between individual parcellations a posteriori. In this paper, we propose a groupwise parcellation method of the cortex based on diffusion MR images (dMRI). We borrow ideas from the area of cosegmentation in computer vision and directly estimate a consistent parcellation across different subjects and scales through a spectral clustering approach. The parcellation is driven by the tractography connectivity profiles, and information between subjects and across scales. Promising qualitative and quantitative results on a sizeable data-set demonstrate the strong potential of the method.

Identification of functional connections within the human brain has gained a lot of attention due to its potential to reveal neural mechanisms. In a whole-brain connectivity analysis, a critical stage is the computation of a set of network nodes that can effectively represent cortical regions. To address this problem, we present a robust cerebral cortex parcellation method based on spectral graph theory and resting-state fMRI correlations that generates reliable parcellations at the single-subject level and across multiple subjects. Our method models the cortical surface in each hemisphere as a mesh graph represented in the spectral domain with its eigenvectors. We connect cortices of different subjects with each other based on the similarity of their connectivity profiles and construct a multi-layer graph, which effectively captures the fundamental properties of the whole group as well as preserves individual subject characteristics. Spectral decomposition of this joint graph is used to cluster each cortical vertex into a subregion in order to obtain whole-brain parcellations. Using rs-fMRI data collected from 40 healthy subjects, we show that our proposed algorithm computes highly reproducible parcellations across different groups of subjects and at varying levels of detail with an average Dice score of 0.78, achieving up to 9% better reproducibility compared to existing approaches. We also report that our group-wise parcellations are functionally more consistent, thus, can be reliably used to represent the population in network analyses.

In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model., (Copyright © 2014 Elsevier B.V. All rights reserved.)

In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.

Aim: To investigate intra-operator variability of semi-quantitative perfusion parameters using dynamic contrast-enhanced ultrasonography (DCE-US), following bolus injections of SonoVue(®)., Methods: The in vitro experiments were conducted using three in-house sets up based on pumping a fluid through a phantom placed in a water tank. In the in vivo experiments, B16F10 melanoma cells were xenografted to five nude mice. Both in vitro and in vivo, images were acquired following bolus injections of the ultrasound contrast agent SonoVue(®) (Bracco, Milan, Italy) and using a Toshiba Aplio(®) ultrasound scanner connected to a 2.9-5.8 MHz linear transducer (PZT, PLT 604AT probe) (Toshiba, Japan) allowing harmonic imaging ("Vascular Recognition Imaging") involving linear raw data. A mathematical model based on the dye-dilution theory was developed by the Gustave Roussy Institute, Villejuif, France and used to evaluate seven perfusion parameters from time-intensity curves. Intra-operator variability analyses were based on determining perfusion parameter coefficients of variation (CV)., Results: In vitro, different volumes of SonoVue(®) were tested with the three phantoms: intra-operator variability was found to range from 2.33% to 23.72%. In vivo, experiments were performed on tumor tissues and perfusion parameters exhibited values ranging from 1.48% to 29.97%. In addition, the area under the curve (AUC) and the area under the wash-out (AUWO) were two of the parameters of great interest since throughout in vitro and in vivo experiments their variability was lower than 15.79%., Conclusion: AUC and AUWO appear to be the most reliable parameters for assessing tumor perfusion using DCE-US as they exhibited the lowest CV values.

Low-grade gliomas (WHO grade II) are diffusively infiltrative brain tumors arising from glial cells. Spatial classification that is usually based on cerebral lobes lacks accuracy and is far from being able to provide some pattern or statistical interpretation of their appearance. In this paper, we propose a novel approach to understand and infer position of low-grade gliomas using a graphical model. The problem is formulated as a graph topology optimization problem. Graph nodes correspond to extracted tumors and graph connections to the spatial and content dependencies among them. The task of spatial position mapping is then expressed as an unsupervised clustering problem, where cluster centers correspond to centers with position appearance prior, and cluster samples to nodes with strong statistical dependencies on their position with respect to the cluster center. Promising results using leave-one-out cross-validation outperform conventional dimensionality reduction methods and seem to coincide with conclusions drawn in physiological studies regarding the expected tumor spatial distributions and interactions.

A case of right unilateral pulmonary interstitial emphysema in a compromised premature infant was managed by right selective bronchial occlusion with a favorable outcome. Occlusion with a Rashkind catheter occurred on day 8 and was well-tolerated. The balloon was deflated 10 hours later and no recurrence was observed. Right occlusion is easier than left selective intubation and seems to be the best method for treatment of unilateral interstitial emphysema in infants in extremely poor conditions.

We report an unusual case of congenital rubella. The infant was suffering from a serious encephalopathy, and both prenatal echography and neonatal CT scan showed passive ventriculomegaly with a calcified periventricular border. Usually, such lesions are strongly suggestive of cytomegalovirus (CMV) infection and have never previously been reported in congenital rubella. Classic cerebral lesions in rubella are related to a prominent obstructive vasculopathy. Conversely, encephaloclastic lesions in CMV infection are likely related to a necrosis of brain parenchyma following upon an initial ventriculitis, and perhaps also to a disturbance of neuronal proliferation. Recently, Carey described a neonate with proven congenital rubella and cranial ultrasound findings typical of ventriculitis. However, in spite of the close similarity between our patient's lesions and the typical CMV lesions, we think it's impossible to assert similar pathogenic mechanisms. Actually, it's quite conceivable that only a severe or extensive vasculopathy can lead to brain atrophy with periventricular calcification in congenital rubella.

Two sibs are reported with clinical and radiological features of the 3M syndrome. The differential diagnosis is discussed, particularly with regard to Russell-Silver syndrome.