Exactly Solvable Random Graph Ensemble with Extensively Many Short Cycles

We introduce and analyse ensembles of 2-regular random graphs with a tuneable distribution of short cycles. The phenomenology of these graphs depends critically on the scaling of the ensembles' control parameters relative to the number of nodes. A phase diagram is presented, showing a second order phase transition from a connected to a disconnected phase. We study both the canonical formulation, where the size is large but fixed, and the grand canonical formulation, where the size is sampled from a discrete distribution, and show their equivalence in the thermodynamical limit. We also compute analytically the spectral density, which consists of a discrete set of isolated eigenvalues, representing short cycles, and a continuous part, representing cycles of diverging size

Int J Mol Sci

The placenta is a key organ for fetal and brain development. Its epigenome can be regarded as a biochemical record of the prenatal environment and a potential mechanism of its association with the future health of the fetus. We investigated associations between placental DNA methylation levels and child behavioral and emotional difficulties, assessed at 3 years of age using the Strengths and Difficulties Questionnaire (SDQ) in 441 mother-child dyads from the EDEN cohort. Hypothesis-driven and exploratory analyses (on differentially methylated probes (EWAS) and regions (DMR)) were adjusted for confounders, technical factors, and cell composition estimates, corrected for multiple comparisons, and stratified by child sex. Hypothesis-driven analyses showed an association of cg26703534 () with emotional symptoms, and exploratory analyses identified two probes, cg09126090 (intergenic region) and cg10305789 (), as negatively associated with peer relationship problems, as well as 33 DMRs, mostly positively associated with at least one of the SDQ subscales. Among girls, most associations were seen with emotional difficulties, whereas in boys, DMRs were as much associated with emotional than behavioral difficulties. This study provides the first evidence of associations between placental DNA methylation and child behavioral and emotional difficulties. Our results suggest sex-specific associations and might provide new insights into the mechanisms of neurodevelopment.Exposition prénatale au tabac et à la pollution atmosphérique et effets sur la santé respiratoire et le neurodévelopment de l'enfant: rôle de la méthylation placentaireHorizon 2020 research and innovation programm

Hematopoietic stem cell transplantation for DLBCL: a report from the European Society for Blood and Marrow Transplantation on more than 40,000 patients over 32 years

Autologous(auto-) and allogeneic(allo-) hematopoietic stem cell transplantation (HSCT) are key treatments for relapsed/refractory diffuse large B-cell lymphoma (DLBCL), although their roles are challenged by CAR-T-cells and other immunotherapies. We examined the transplantation trends and outcomes for DLBCL patients undergoing auto-/allo-HSCT between 1990 and 2021 reported to EBMT. Over this period, 41,148 patients underwent auto-HSCT, peaking at 1911 cases in 2016, while allo-HSCT saw a maximum of 294 cases in 2018. The recent decline in transplants corresponds to increased CAR-T treatments (1117 cases in 2021). Median age for auto-HSCT rose from 42 (1990-1994) to 58 years (2015-2021), with peripheral blood becoming the primary stem cell source post-1994. Allo-HSCT median age increased from 36 (1990-1994) to 54 (2015-2021) years, with mobilized blood as the primary source post-1998 and reduced intensity conditioning post-2000. Unrelated and mismatched allo-HSCT accounted for 50% and 19% of allo-HSCT in 2015-2021. Three-year overall survival (OS) after auto-HSCT improved from 56% (1990-1994) to 70% (2015-2021), p 40,000 transplants, providing insights for evaluating emerging DLBCL therapies

Contrôle de processus de diffusion sur graphe avec allocation séquentielle de ressources

The dynamic containment of an undesired network diffusion process, such as an epidemic, requires a decision maker (DM) to be able to respond to its evo- lution by taking the right control actions at the right moments. This task can be seen as managing the alloca- tion of a limited amount of resources to the graph nodes, with the objective to reduce the effects of the process.In this thesis we extend the Dynamic Resource Alloca- tion (DRA) problem and propose a multi-round dynamic control framework, which we realize through two derived models: the Restricted and the Sequential DRA (RDRA, SDRA). Contrary to the standard full-information and full-access DRA considerations, these new models take into account possible access restrictions regarding the the available information about the network and/or the ability to act on its nodes. At each intervention round, the DM has limited access to information related to a fraction of the nodes, and is also gaining access to act on them in a sequential fashion. The latter sequential as- pect in the decision process offers a completely new per- spective to the dynamic diffusion process control, making this work the first to cast the dynamic control problem as a series of specially designed sequential selection pro- cesses.In the Sequential Selection Problem (SSP), immediate and irrevocable decisions need to be made by the DM as candidate items arrive randomly and get examined for one of the limited selection slots available. For the needs of network diffusion control, what we propose translatesinto selecting the right nodes to allocate the control re- sources in a multi-round sequential process. However, standard SSP variants, such as the very well-known sec- retary problem, begin with an empty selection set (cold- start) and perform the selection process once over a single candidate set (single-round). These two limita- tions are addressed in this thesis. First, we introduce the novel Warm-starting SSP setting that considers hav- ing at hand a reference set, which is a set of previously selected items of a given quality, and tries to update optimally that set while examining the sequence of ar- riving candidates, constrained by being able to update the assignment to each selection slot (resource) at most once. The Multi-round Sequential Selection Process, the new online-within-online problem, is then introduced as a natural extension of the warm-starting selection.Both rank-based and score-based ob jective functions over the final selection are considered. A cutoff-based approach is proposed for the former, while the optimal strategy based on dynamic thresholding is derived for the latter assuming that the score distribution is known. These strategies are then put in comparison for their efficiency in the traditional selection setting as well as in solving network control problems that motivated this thesis. The generality of the introduced models allow their application to a wide variety of fields and problems; for instance, reoccurring recruiting processes, manage- ment of resources (e.g. beds, staff) in healthcare units, as well as tackling difficult combinatorial problems under constrains, such as the b-diversification problem found in data-stream processing applications (e.g. in robotics).L’endiguement dynamique d’un processus de diffusion indésirable sur réseau, comme une épidémie, exige d’un décideur (DM) qu’il soit capable de répondre à son évolution en prenant les bonnes mesures de con- trôle au bon moment. Cette tâche peut être considérée comme la gestion de l’allocation d’une quantité limitée de ressources aux nœuds du réseau, avec pour objectif de réduire les effets du processus.Dans cette thèse, nous étendons le problème de l’allocation dynamique de ressources (DRA) et pro- posons un cadre de contrôle dynamique à itéra- tions/tours multiples, que nous réalisons grâce à deux modèles dérivés: le DRA restreint et le DRA séquen- tiel (RDRA, SDRA). Contrairement aux considérations standards dans lesquelles l’information et l’accès sont complets, ces nouveaux modèles prennent en compte les éventuelles restrictions d’accès concernant les informa- tions disponibles sur le réseau et/ou la capacité à agir sur ses nœuds. À chaque cycle d’intervention, le DM a un accès limité aux informations relatives à une fraction des nœuds, et obtient également l’accès pour agir sur eux de manière séquentielle.Ce dernier aspect séquentiel dans le processus de décision offre une perspective com- plètement nouvelle au contrôle du processus de diffusion dynamique, ce qui fait de ce travail le premier à présen- ter le problème du contrôle dynamique comme une série de processus de sélection séquentielleDans le cadre du problème de sélection séquentielle (SSP), des décisions immédiates et irrévocables doivent être prises par le décideur, tandis que les candidats ar- rivent dans un ordre aléatoire et sont examinés pour l’un des créneaux de sélection disponible. Pour les besoins du contrôle de la diffusion en réseau, ce que nous pro- posons se traduit par sélectionner les bons nœuds afin deleur allouer les ressources de contrôle dans un processus séquentiel à plusieurs itérations. Cependant, les vari- antes standard du SSP, comme le très connu problème de la secrétaire, commencent par un ensemble de sélec- tion vide (démarrage à froid) et effectuent le processus de sélection une fois sur un seul ensemble de candidats (unique itération). Ces deux limites sont abordées dans la présente thèse. Tout d’abord, nous introduisons un nouveau paramètre de démarrage à chaud qui considère avoir à portée de main un ensemble de référence, c’est-à- dire un ensemble d’éléments préalablement sélectionnés d’une qualité donnée. Le DM tente ensuite de mettre à jour de manière optimale cet ensemble tout en exam- inant la séquence de candidats qui arrivent, contraint par la possibilité de mettre à jour l’affectation à chaque créneau de sélection (ressource) au plus une fois. Le pro- cessus de sélection séquentielle aux multiples itérations, est alors introduit comme une extension naturelle de la sélection de démarrage à chaud.Des fonctions objectif basées sur le rang et le score de la sélection finale sont prises en compte. Une approche basée sur la séparation de la séquence en deux phases est proposée pour la première, tandis que la stratégie optimale basée sur le calcul d’un seuil d’acceptation dy- namique est dérivée pour la seconde en supposant que la distribution des scores est connue. Ces stratégies sont ensuite mises en comparaison pour leur efficacité dans le cadre de la sélection traditionnelle ainsi que pour la résolution des problèmes de contrôle sur réseaux qui ont motivé cette thèse. La généralité des modèles introduits permet leur application à une grande variété de domaines et de problèmes; par exemple, les processus de recrute- ment récurrents, la gestion de ressources (par exemple, lits, personnel) dans les unités de soins de santé, ainsi que la résolution de problèmes combinatoires difficiles sous contraintes, comme le problème de b-diversification que l’on trouve dans les applications de traitement de flux de données (entre autres, en robotique)

Epidemic Models for Personalised COVID-19 Isolation and Exit Policies Using Clinical Risk Predictions

In mid April 2020, with more than 2.5 billion people in the world following social distancing measures due to COVID-19, governments are considering relaxing lock-down. We combined individual clinical risk predictions with epidemic modelling to examine simulations of isolation and exit policies.Methods: We developed a method to include personalised risk predictions in epidemic models based on data science principles. We extended a standard susceptible-exposed-infected-removed (SEIR) model to account for predictions of severity, defined by the risk of an individual needing intensive care in case of infection. We studied example isolation policies using simulations with the risk-extended epidemic model, using COVID-19 data and estimates in France as of mid April 2020 (4 000 patients in ICU, around 7 250 total ICU beds occupied at the peak of the outbreak, 0.5 percent of patients requiring ICU upon infection). We considered scenarios varying in the discrimination performance of a risk prediction model, in the degree of social distancing, and in the severity rate upon infection. Confidence intervals were obtained using an Approximate Bayesian Computation approach. The framework may be used with other epidemic models, with other risk predictions, and for other epidemic outbreaks