Classifying flow cytometry data using Bayesian analysis helps to distinguish ALS patients from healthy controls.

Introduction: Given its wide availability and cost-effectiveness, multidimensional flow cytometry (mFC) became a core method in the field of immunology allowing for the analysis of a broad range of individual cells providing insights into cell subset composition, cellular behavior, and cell-to-cell interactions. Formerly, the analysis of mFC data solely relied on manual gating strategies. With the advent of novel computational approaches, (semi-)automated gating strategies and analysis tools complemented manual approaches. Methods: Using Bayesian network analysis, we developed a mathematical model for the dependencies of different obtained mFC markers. The algorithm creates a Bayesian network that is a HC tree when including raw, ungated mFC data of a randomly selected healthy control cohort (HC). The HC tree is used to classify whether the observed marker distribution (either patients with amyotrophic lateral sclerosis (ALS) or HC) is predicted. The relative number of cells where the probability q is equal to zero is calculated reflecting the similarity in the marker distribution between a randomly chosen mFC file (ALS or HC) and the HC tree. Results: Including peripheral blood mFC data from 68 ALS and 35 HC, the algorithm could correctly identify 64/68 ALS cases. Tuning of parameters revealed that the combination of 7 markers, 200 bins, and 20 patients achieved the highest AUC on a significance level of p < 0.0001. The markers CD4 and CD38 showed the highest zero probability. We successfully validated our approach by including a second, independent ALS and HC cohort (55 ALS and 30 HC). In this case, all ALS were correctly identified and side scatter and CD20 yielded the highest zero probability. Finally, both datasets were analyzed by the commercially available algorithm 'Citrus', which indicated superior ability of Bayesian network analysis when including raw, ungated mFC data. Discussion: Bayesian network analysis might present a novel approach for classifying mFC data, which does not rely on reduction techniques, thus, allowing to retain information on the entire dataset. Future studies will have to assess the performance when discriminating clinically relevant differential diagnoses to evaluate the complementary diagnostic benefit of Bayesian network analysis to the clinical routine workup.

Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.

Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the development of tools and strategies to efficiently find these regions of high importance to advance brain research. Exploring the high dimensional parameter space using numerical simulations has been a frequently used technique in the last years in many areas of computational neuroscience. Today, high performance computing (HPC) can provide a powerful infrastructure to speed up explorations and increase our general understanding of the behavior of the model in reasonable times. Learning to learn (L2L) is a well-known concept in machine learning (ML) and a specific method for acquiring constraints to improve learning performance. This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model, or a whole brain simulation. In this work, we present L2L as an easy to use and flexible framework to perform parameter and hyper-parameter space exploration of neuroscience models on HPC infrastructure. Learning to learn is an implementation of the L2L concept written in Python. This open-source software allows several instances of an optimization target to be executed with different parameters in an embarrassingly parallel fashion on HPC. L2L provides a set of built-in optimizer algorithms, which make adaptive and efficient exploration of parameter spaces possible. Different from other optimization toolboxes, L2L provides maximum flexibility for the way the optimization target can be executed. In this paper, we show a variety of examples of neuroscience models being optimized within the L2L framework to execute different types of tasks. The tasks used to illustrate the concept go from reproducing empirical data to learning how to solve a problem in a dynamic environment. We particularly focus on simulations with models ranging from the single cell to the whole brain and using a variety of simulation engines like NEST, Arbor, TVB, OpenAIGym, and NetLogo.

Amyotrophic lateral sclerosis patients show increased peripheral and intrathecal T-cell activation.

Several studies suggest a role for the peripheral immune system in the pathophysiology of amyotrophic lateral sclerosis. However, comprehensive studies investigating the intrathecal immune system in amyotrophic lateral sclerosis are rare. To elucidate whether compartment-specific inflammation contributes to amyotrophic lateral sclerosis pathophysiology, we here investigated intrathecal and peripheral immune profiles in amyotrophic lateral sclerosis patients and compared them with controls free of neurological disorders (controls) and patients with dementia or primary progressive multiple sclerosis. Routine CSF parameters were examined in 308 patients, including 132 amyotrophic lateral sclerosis patients. In a subgroup of 41 amyotrophic lateral sclerosis patients, extensive flow-cytometric immune cell profiling in peripheral blood and CSF was performed and compared with data from 26 controls, 25 dementia and 21 multiple sclerosis patients. Amyotrophic lateral sclerosis patients presented with significantly altered proportions of monocyte subsets in peripheral blood and increased frequencies of CD4+ and CD8+ T cells expressing the activation marker HLA-DR in peripheral blood (CD8+) and CSF (CD4+ and CD8+) compared with controls. While dementia and multiple sclerosis patients exhibited a comparable increase in intrathecal CD8+ T-cell activation, CD8+ T-cell activation in the peripheral blood in amyotrophic lateral sclerosis was higher than in multiple sclerosis patients. Furthermore, intrathecal CD4+ T-cell activation in amyotrophic lateral sclerosis surpassed levels in dementia patients. Intrathecal T-cell activation resulted from in situ activation rather than transmigration of activated T cells from the blood. While T-cell activation did not correlate with amyotrophic lateral sclerosis progression, patients with rapid disease progression showed reduced intrathecal levels of immune-regulatory CD56bright natural killer cells. The integration of these parameters into a composite score facilitated the differentiation of amyotrophic lateral sclerosis patients from patients of all other cohorts. To conclude, alterations in peripheral monocyte subsets, as well as increased peripheral and intrathecal activation of CD4+ and CD8+ T cells concomitant with diminished immune regulation by CD56bright natural killer cells, suggest an involvement of these immune cells in amyotrophic lateral sclerosis pathophysiology.

Stability analysis of a hyperbolic stochastic Galerkin formulation for the Aw-Rascle-Zhang model with relaxation.

We investigate the propagation of uncertainties in the Aw-Rascle-Zhang model, which belongs to a class of second order traffic flow models described by a system of nonlinear hyperbolic equations. The stochastic quantities are expanded in terms of wavelet-based series expansions. Then, they are projected to obtain a deterministic system for the coefficients in the truncated series. Stochastic Galerkin formulations are presented in conservative form and for smooth solutions also in the corresponding non-conservative form. This allows to obtain stabilization results, when the system is relaxed to a first-order model. Computational tests illustrate the theoretical results.

Oscillation behavior for neutral delay differential equations of second-order.

In this paper, new criteria for oscillation of neutral delay differential equations of second-order are presented. One objective of this study is to complement and extend some well-known related results in the literature. To support our main results, we give illustrating examples.

Generation of a Model to Predict Differentiation and Migration of Lymphocyte Subsets under Homeostatic and CNS Autoinflammatory Conditions.

The central nervous system (CNS) is an immune-privileged compartment that is separated from the circulating blood and the peripheral organs by the blood-brain and the blood-cerebrospinal fluid (CSF) barriers. Transmigration of lymphocyte subsets across these barriers and their activation/differentiation within the periphery and intrathecal compartments in health and autoinflammatory CNS disease are complex. Mathematical models are warranted that qualitatively and quantitatively predict the distribution and differentiation stages of lymphocyte subsets in the blood and CSF. Here, we propose a probabilistic mathematical model that (i) correctly reproduces acquired data on location and differentiation states of distinct lymphocyte subsets under homeostatic and neuroinflammatory conditions, (ii) provides a quantitative assessment of differentiation and transmigration rates under these conditions, (iii) correctly predicts the qualitative behavior of immune-modulating therapies, (iv) and enables simulation-based prediction of distribution and differentiation stages of lymphocyte subsets in the case of limited access to biomaterial. Taken together, this model might reduce future measurements in the CSF compartment and allows for the assessment of the effectiveness of different immune-modulating therapies.

Human T cells in silico: Modelling dynamic intracellular calcium and its influence on cellular electrophysiology.

A network of ion currents influences basic cellular T cell functions. After T cell receptor activation, changes in highly regulated calcium levels play a central role in triggering effector functions and cell differentiation. A dysregulation of these processes might be involved in the pathogenesis of several diseases. We present a mathematical model based on the NEURON simulation environment that computes dynamic calcium levels in combination with the current output of diverse ion channels (KV1.3, KCa3.1, K2P channels (TASK1-3, TRESK), VRAC, TRPM7, CRAC). In line with experimental data, the simulation shows a strong increase in intracellular calcium after T cell receptor stimulation before reaching a new, elevated calcium plateau in the T cell's activated state. Deactivation of single ion channel modules, mimicking the application of channel blockers, reveals that two types of potassium channels are the main regulators of intracellular calcium level: calcium-dependent potassium (KCa3.1) and two-pore-domain potassium (K2P) channels.

Human T cells in silico: Modelling their electrophysiological behaviour in health and disease.

Although various types of ion channels are known to have an impact on human T cell effector functions, their exact mechanisms of influence are still poorly understood. The patch clamp technique is a well-established method for the investigation of ion channels in neurons and T cells. However, small cell sizes and limited selectivity of pharmacological blockers restrict the value of this experimental approach. Building a realistic T cell computer model therefore can help to overcome these kinds of limitations as well as reduce the overall experimental effort. The computer model introduced here was fed off ion channel parameters from literature and new experimental data. It is capable of simulating the electrophysiological behaviour of resting and activated human CD4(+) T cells under basal conditions and during extracellular acidification. The latter allows for the very first time to assess the electrophysiological consequences of tissue acidosis accompanying most forms of inflammation.