Microsporidiosis in Patients with Autoimmune Diseases Undergoing Monoclonal Antibody Associated Therapy.

We present Enterocytozoon bieneusi infection in four patients with autoimmune diseases undergoing prolonged monoclonal antibody therapies. Two patients suffered from inflammatory bowel disease and received anti-TNF therapies, whereas two other patients suffered from systemic lupus erythematosus with renal involvement and received anti-CD20 or anti-BLyS protein therapies. Three out of four patients consulted for diarrhea with abdominal pain without intestinal inflammation or bleeding at the time of sampling.

Learning extreme expected shortfall and conditional tail moments with neural networks. Application to cryptocurrency data.

We propose a neural networks method to estimate extreme Expected Shortfall, and even more generally, extreme conditional tail moments as functions of confidence levels, in heavy-tailed settings. The convergence rate of the uniform error between the log-conditional tail moment and its neural network approximation is established leveraging extreme-value theory (in particular the high-order condition on the distribution tails) and using critically two activation functions (eLU and ReLU) for neural networks. The finite sample performance of the neural network estimator is compared to bias-reduced extreme-value competitors using synthetic heavy-tailed data.

DeepCTG® 2.0: Development and validation of a deep learning model to detect neonatal acidemia from cardiotocography during labor.

Cardiotocography (CTG) is the main tool available to detect neonatal acidemia during delivery. Presently, obstetricians and midwives primarily rely on visual interpretation, leading to a significant intra-observer variability. In this paper, we build and evaluate a convolutional neural network to detect neonatal acidemia from the CTG signals during delivery on a multicenter database with 27662 cases in five centers, including 3457 and 464 cases of moderate and severe neonatal acidemia respectively (defined by a fetal pH at birth between 7.

Individualized optimal strategy in team pursuit for track cycling.

In track cycling, performance in the team pursuit depends on the mechanical and physiological abilities of each member of the team, but also on the choice of racing strategy. Athletes must cover the 4000 m of the race, sharing the effort between them in successive relays. This raises the question of the optimum strategy.

Active Learning of Boltzmann Samplers and Potential Energies with Quantum Mechanical Accuracy.

Extracting consistent statistics between relevant free energy minima of a molecular system is essential for physics, chemistry, and biology. Molecular dynamics (MD) simulations can aid in this task but are computationally expensive, especially for systems that require quantum accuracy. To overcome this challenge, we developed an approach combining enhanced sampling with deep generative models and active learning of a machine learning potential (MLP).

A joint analysis proposal of nonlinear longitudinal and time-to-event right-, interval-censored data for modeling pregnancy miscarriage.

Pregnancy in-vitro fertilization (IVF) cases are associated with adverse first-trimester outcomes in comparison to spontaneously achieved pregnancies. Human chorionic gonadotrophin β subunit (β-HCG) is a well-known biomarker for the diagnosis and monitoring of pregnancy after IVF. Low levels of β-HCG during this period are related to miscarriage, ectopic pregnancy, and IVF procedure failures.

Coarse-Grained Molecular Dynamics with Normalizing Flows.

We propose a sampling algorithm relying on a collective variable (CV) of midsize dimension modeled by a normalizing flow and using nonequilibrium dynamics to propose full configurational moves from the proposition of a refreshed value of the CV made by the flow. The algorithm takes the form of a Markov chain with nonlocal updates, allowing jumps through energy barriers across metastable states. The flow is trained throughout the algorithm to reproduce the free energy landscape of the CV.

P1NP and β-CTX-1 Responses to a Prolonged, Continuous Running Bout in Young Healthy Adult Males: A Systematic Review with Individual Participant Data Meta-analysis.

Background: Circulating biomarkers of bone formation and resorption are widely used in exercise metabolism research, but their responses to exercise are not clear. This study aimed to quantify group responses and inter-individual variability of P1NP and β-CTX-1 after prolonged, continuous running (60-120 min at 65-75% V̇O) in young healthy adult males using individual participant data (IPD) meta-analysis.

Methods: The protocol was designed following PRISMA-IPD guidelines and was pre-registered on the Open Science Framework prior to implementation ( https://osf.

Drosophilids with darker cuticle have higher body temperature under light.

Cuticle pigmentation was shown to be associated with body temperature for several relatively large species of insects, but it was questioned for small insects. Here we used a thermal camera to assess the association between drosophilid cuticle pigmentation and body temperature increase when individuals are exposed to light. We compared mutants of large effects within species (Drosophila melanogaster ebony and yellow mutants).

Boundary conditions for the Boltzmann equation from gas-surface interaction kinetic models.

Boundary conditions for the Boltzmann equation are investigated on the basis of a kinetic model for gas-surface interactions. The model takes into account gas and physisorbed molecules interacting with a surface potential and colliding with phonons. The potential field is generated by fixed crystal molecules, and the interaction with phonons represents the fluctuating part of the surface.

Three-dimensional micro-structurally informed in silico myocardium-Towards virtual imaging trials in cardiac diffusion weighted MRI.

In silico tissue models (viz. numerical phantoms) provide a mechanism for evaluating quantitative models of magnetic resonance imaging. This includes the validation and sensitivity analysis of imaging biomarkers and tissue microstructure parameters.

SAMBA: A novel method for fast automatic model building in nonlinear mixed-effects models.

The success of correctly identifying all the components of a nonlinear mixed-effects model is far from straightforward: it is a question of finding the best structural model, determining the type of relationship between covariates and individual parameters, detecting possible correlations between random effects, or also modeling residual errors. We present the Stochastic Approximation for Model Building Algorithm (SAMBA) procedure and show how this algorithm can be used to speed up this process of model building by identifying at each step how best to improve some of the model components. The principle of this algorithm basically consists in "learning something" about the "best model," even when a "poor model" is used to fit the data.

Identification of glucocorticoid-related molecular signature by whole blood methylome analysis.

Objective: Cushing's syndrome represents a state of excessive glucocorticoids related to glucocorticoid treatments or to endogenous hypercortisolism. Cushing's syndrome is associated with high morbidity, with significant inter-individual variability. Likewise, adrenal insufficiency is a life-threatening condition of cortisol deprivation.

Multistage hematopoietic stem cell regulation in the mouse: A combined biological and mathematical approach.

We have reconciled steady-state and stress hematopoiesis in a single mathematical model based on murine experiments and with a focus on hematopoietic stem and progenitor cells. A phenylhydrazine stress was first applied to mice. A reduced cell number in each progenitor compartment was evidenced during the next 7 days through a drastic level of differentiation without proliferation, followed by a huge proliferative response in all compartments including long-term hematopoietic stem cells, before a return to normal levels.

Practical computation of the diffusion MRI signal based on Laplace eigenfunctions: permeable interfaces.

The complex transverse water proton magnetization subject to diffusion-encoding magnetic field gradient pulses in a heterogeneous medium such as brain tissue can be modeled by the Bloch-Torrey partial differential equation. The spatial integral of the solution of this equation in realistic geometry provides a gold-standard reference model for the diffusion MRI signal arising from different tissue micro-structures of interest. A closed form representation of this reference diffusion MRI signal, called matrix formalism, which makes explicit the link between the Laplace eigenvalues and eigenfunctions of the tissue geometry and its diffusion MRI signal, was derived 20 years ago.

Reciprocal association between voting and the epidemic spread of COVID-19: observational and dynamic modeling study.

Background: Whether voting is a risk factor for epidemic spread is unknown. Reciprocally, whether an epidemic can deter citizens from voting has not been often studied. We aimed to investigate such relationships for France during the coronavirus disease 19 (COVID-19) epidemic.

A random walk model that accounts for space occupation and movements of a large herbivore.

Animal movement has been identified as a key feature in understanding animal behavior, distribution and habitat use and foraging strategies among others. Large datasets of invididual locations often remain unused or used only in part due to the lack of practical models that can directly infer the desired features from raw GPS locations and the complexity of existing approaches. Some of them being disputed for their lack of biological justifications in their design.

Predicting the propagation of COVID-19 at an international scale: extension of an SIR model.

Objectives: Several epidemiological models have been published to forecast the spread of the COVID-19 pandemic, yet many of them have proven inaccurate for reasons that remain to be fully determined. We aimed to develop a novel model and implement it in a freely accessible web application.

Design: We built an SIR-type compartmental model with two additional compartments: (deceased patients); (individuals who will die but who will not infect anybody due to social or medical isolation) and integration of a time-dependent transmission rate and a periodical weekly component linked to the way in which cases and deaths are reported.

Understanding the Variability in Graph Data Sets through Statistical Modeling on the Stiefel Manifold.

Network analysis provides a rich framework to model complex phenomena, such as human brain connectivity. It has proven efficient to understand their natural properties and design predictive models. In this paper, we study the variability within groups of networks, i.

A mathematical approach to deal with nanoparticle polydispersity in surface enhanced Raman spectroscopy to quantify antineoplastic agents.

Antineoplastic agents are, for most of them, highly toxic drugs prepared at hospital following individualized prescription. To protect patients and healthcare workers, it is important to develop analytical tools able to identify and quantify such drugs on a wide concentration range. In this context, surface enhanced Raman spectroscopy (SERS) has been tested as a specific and sensitive technique.

Problem and non-problem gamblers: a cross-sectional clustering study by gambling characteristics.

Objectives: Gambling characteristics are factors that could influence problem gambling development. The aim of this study was to identify a typology of gamblers to frame risky behaviour based on gambling characteristics (age of initiation/of problem gambling, type of gambling: pure chance/chance with pseudoskills/chance with elements of skill, gambling online/offline, amount wagered monthly) and to investigate clinical factors associated with these different profiles in a large representative sample of gamblers.

Design And Setting: The study is a cross-sectional analysis to the baseline data of the french JEU cohort study (study protocol : Challet-Bouju , 2014).

Effect of fibrinogen concentrate administration on early mortality in traumatic hemorrhagic shock: A propensity score analysis.

Background: Fibrinogen concentrate is widely used in traumatic hemorrhagic shock despite weak evidence in the literature. The aim of the study was to evaluate the effect of fibrinogen concentrate administration within the first 6 hours on 24-hour all-cause mortality in traumatic hemorrhagic shock using a causal inference approach.

Methods: Observational study from a French multicenter prospective trauma registry was performed.

Parking 3-sphere swimmer: II. The long-arm asymptotic regime.

The paper carries on our previous investigations on the complementary version of Purcell's rotator (sPr): a low-Reynolds-number swimmer composed of three balls of equal radii. In the asymptotic regime of very long arms, the Stokes-induced governing dynamics is derived, and then experimented in the context of energy-minimizing self-propulsion characterized in the first part of the paper.