In this article, we quantitatively study, through stochastic models, the effects of several intracellular phenomena, such as cell volume growth, cell division, gene replication as well as ...fluctuations of available RNA polymerases and ribosomes. These phenomena are indeed rarely considered in classic models of protein production and no relative quantitative comparison among them has been performed. The parameters for a large and representative class of proteins are determined using experimental measures. The main important and surprising conclusion of our study is to show that despite the significant fluctuations of free RNA polymerases and free ribosomes, they bring little variability to protein production contrary to what has been previously proposed in the literature. After verifying the robustness of this quite counter-intuitive result, we discuss its possible origin from a theoretical view, and interpret it as the result of a mean-field effect.
Virtual Reality (VR) has emerged as a promising tool in many domains of therapy and rehabilitation, and has recently attracted the attention of researchers and clinicians working with elderly people ...with MCI, Alzheimer's disease and related disorders. Here we present a study testing the feasibility of using highly realistic image-based rendered VR with patients with MCI and dementia. We designed an attentional task to train selective and sustained attention, and we tested a VR and a paper version of this task in a single-session within-subjects design. Results showed that participants with MCI and dementia reported to be highly satisfied and interested in the task, and they reported high feelings of security, low discomfort, anxiety and fatigue. In addition, participants reported a preference for the VR condition compared to the paper condition, even if the task was more difficult. Interestingly, apathetic participants showed a preference for the VR condition stronger than that of non-apathetic participants. These findings suggest that VR-based training can be considered as an interesting tool to improve adherence to cognitive training in elderly people with cognitive impairment.
Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and ...(neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 104 motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible.
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•Prediction of antibody-antigen binding is a central question in immunology•A motif vocabulary of paratope-epitope interactions governs antibody specificity•Proof of principle that antibody-antigen binding is predictable•Implications for de novo antibody and (neo-)epitope design
Prediction of antibody-antigen binding is a central question in immunology and of high relevance for predictive antibody and vaccine design. Akbar et al. prove the predictability of antibody-antigen binding by discovering a universal, compact, and immunity-specific motif vocabulary of paratope-epitope interactions.
We study the mean-field limit and stationary distributions of a pulse-coupled network modeling the dynamics of a large neuronal assemblies. Our model takes into account explicitly the intrinsic ...randomness of firing times, contrasting with the classical integrate-and-fire model. The ergodicity properties of the Markov process associated to finite networks are investigated. We derive the large network size limit of the distribution of the state of a neuron, and characterize their invariant distributions as well as their stability properties. We show that the system undergoes transitions as a function of the averaged connectivity parameter, and can support trivial states (where the network activity dies out, which is also the unique stationary state of finite networks in some cases) and self-sustained activity when connectivity level is sufficiently large, both being possibly stable.
Class-switch recombination (CSR) is a DNA recombination process that replaces the immunoglobulin (Ig) constant region for the isotype that can best protect against the pathogen. Dysregulation of CSR ...can cause self-reactive BCRs and B cell lymphomas; understanding the timing and location of CSR is therefore important. Although CSR commences upon T cell priming, it is generally considered a hallmark of germinal centers (GCs). Here, we have used multiple approaches to show that CSR is triggered prior to differentiation into GC B cells or plasmablasts and is greatly diminished in GCs. Despite finding a small percentage of GC B cells expressing germline transcripts, phylogenetic trees of GC BCRs from secondary lymphoid organs revealed that the vast majority of CSR events occurred prior to the onset of somatic hypermutation. As such, we have demonstrated the existence of IgM-dominated GCs, which are unlikely to occur under the assumption of ongoing switching.
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•Germline transcripts peak prior to GC formation and rapidly decline in GCs•IgM-dominated clones are found in late GCs, arguing against ongoing Ig switching•CSR largely ceases upon the onset of somatic hypermutation•CSR decline due to low GLT and APE1 expression is possibly orchestrated by BCL6
Germinal centers (GCs) have long been considered sites in which Ig class-switch recombination (CSR) is favored. Roco et al. show that CSR occurs during the initial T cell:B cell interaction prior to GC formation and rapidly declines as B cells differentiate into GC cells and somatic hypermutation commences.
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this ...regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
In this paper we consider a stochastic system with two connected nodes, whose unidirectional connection is variable and depends on point processes associated to each node. The
input
node is ...represented by an homogeneous Poisson process, whereas the
output
node jumps with an intensity that depends on the jumps of the input nodes and the connection intensity. We study a scaling regime when the rate of both point processes is large compared to the dynamics of the connection. In neuroscience, this system corresponds to a neural network composed by two neurons, connected by a single synapse. The strength of this synapse depends on the past activity of both neurons, the notion of
synaptic plasticity
refers to the associated mechanism. A general class of such stochastic models has been introduced in Robert and Vignoud (Stochastic models of synaptic plasticity in neural networks, 2020, arxiv: 2010.08195) to describe most of the models of long-term synaptic plasticity investigated in the literature. The scaling regime corresponds to a classical assumption in computational neuroscience that cellular processes evolve much more rapidly than the synaptic strength. The central result of the paper is an averaging principle for the time evolution of the connection intensity. Mathematically, the key variable is the point process, associated to the output node, whose intensity depends on the past activity of the system. The proof of the result involves a detailed analysis of several of its unbounded additive functionals in the slow-fast limit, and technical results on interacting shot-noise processes.
Objective
Disorders of executive functions are among the most frequent cognitive deficits, but they remain poorly defined and are subject to heterogeneous assessment. To address this major issue, the ...Groupe de Réflexion sur l'Evaluation des Fonctions Exécutives (GREFEX) group has proposed criteria for behavioral and cognitive dysexecutive syndromes and has designed a battery including a specific heteroquestionnaire and 7 cognitive tests. We investigated the frequency of behavioral and cognitive dysexecutive disorders in patients suffering from various diseases and the association of these disorders with loss of autonomy.
Methods
A total of 461 patients aged between 16 and 90 years with severe traumatic brain injury, stroke, mild cognitive impairment, Alzheimer disease, multiple sclerosis, and Parkinson disease were recruited into this prospective cohort study by 21 centers between September 2003 and June 2006. Behavioral and cognitive dysexecutive disorders were examined using the GREFEX battery.
Results
A dysexecutive syndrome was observed in 60% of patients, concerning both behavioral and cognitive domains in 26% and dissociated in 34%. All behavioral and cognitive dysexecutive disorders discriminated (p = 0.001, all) patients from controls. The pattern of cognitive syndrome differed (p = 0.0001) according to the disease. Finally, behavioral (odds ratio OR, 4.6; 95% confidence interval CI, 2. 3–9.1; p = 0.0001) and cognitive (OR, 3.36; 95% CI, 1.7–6.6; p = 0.001) dysexecutive syndromes and Mini Mental State Examination score (OR, 0.79; 95% CI, 0.68–0.91; p = 0.002) were independent predictors of loss of autonomy.
Interpretation
This study provided criteria of dysexecutive syndrome and showed that both behavioral and cognitive syndromes contribute to loss of autonomy. Profiles vary across patients and diseases, and therefore systematic assessment of behavioral and cognitive disorders in reference to diagnostic criteria is needed. ANN NEUROL 2010
In vivo imaging of cytotoxic T lymphocyte (CTL) killing activity revealed that infected cells have a higher observed probability of dying after multiple contacts with CTLs. We developed a ...three-dimensional agent-based model to discriminate different hypotheses about how infected cells get killed based on quantitative 2-photon in vivo observations. We compared a constant CTL killing probability with mechanisms of signal integration in CTL or infected cells. The most likely scenario implied increased susceptibility of infected cells with increasing number of CTL contacts where the total number of contacts was a critical factor. However, when allowing in silico T cells to initiate new interactions with apoptotic target cells (zombie contacts), a contact history independent killing mechanism was also in agreement with experimental datasets. The comparison of observed datasets to simulation results, revealed limitations in interpreting 2-photon data, and provided readouts to distinguish CTL killing models.
We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from collections of high-dimensional brain images. The model is based on spatio-temporal matrix ...factorization, where inference on the sources is constrained by anatomically plausible statistical priors. To model realistic trajectories, the temporal sources are defined as monotonic and time-reparameterized Gaussian Processes. To account for the non-stationarity of brain images, we model the spatial sources as sparse codes convolved at multiple scales. The method was tested on synthetic data favourably comparing with standard blind source separation approaches. The application on large-scale imaging data from a clinical study allows to disentangle differential temporal progression patterns mapping brain regions key to neurodegeneration, while revealing a disease-specific time scale associated to the clinical diagnosis.
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