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When does humoral memory enhance infection?. (arXiv:2302.04340v1 [q-bio.CB]) arxiv.org/abs/2302.04340

When does humoral memory enhance infection?

Antibodies and humoral memory are key components of the adaptive immune system. We consider and computationally model mechanisms by which humoral memory present at baseline might instead increase infection load; we refer to this effect as EI-HM (enhancement of infection by humoral memory). We first consider antibody dependent enhancement (ADE) in which antibody enhances the growth of the pathogen, typically a virus, and typically at intermediate "Goldilocks" levels of antibody. Our ADE model reproduces ADE in vitro and enhancement of infection in vivo from passive antibody transfer. But notably the simplest implementation of our ADE model never results in EI-HM. Adding complexity, by making the cross-reactive antibody much less neutralizing than the de novo generated antibody or by including a sufficiently strong non-antibody immune response, allows for ADE-mediated EI-HM. We next consider the possibility that cross-reactive memory causes EI-HM by crowding out a possibly superior de novo immune response. We show that, even without ADE, EI-HM can occur when the cross-reactive response is both less potent and "directly" (i.e. independently of infection load) suppressive with regard to the de novo response. In this case adding a non-antibody immune response to our computational model greatly reduces or completely eliminates EI-HM, which suggests that "crowding out" is unlikely to cause substantial EI-HM. Hence, our results provide examples in which simple models give qualitatively opposite results compared to models with plausible complexity. Our results may be helpful in interpreting and reconciling disparate experimental findings, especially from dengue, and for vaccination.

arxiv.org

ER network heterogeneity guides diffusive transport and kinetics. (arXiv:2302.04377v1 [physics.bio-ph]) arxiv.org/abs/2302.04377

ER network heterogeneity guides diffusive transport and kinetics

The endoplasmic reticulum (ER) is a dynamic network of interconnected sheets and tubules that orchestrates the distribution of lipids, ions, and proteins throughout the cell. The impact of its complex, dynamic morphology on its function as an intracellular transport hub remains poorly understood. To elucidate the functional consequences of ER network structure and dynamics, we quantify how the heterogeneity of the peripheral ER in COS7 cells affects diffusive protein transport. In vivo imaging of photoactivated ER membrane proteins demonstrates their non-uniform spreading to adjacent regions, in a manner consistent with simulations of diffusing particles on extracted network structures. Using a minimal network model to represent tubule rearrangements, we demonstrate that ER network dynamics are sufficiently slow to have little effect on diffusive protein transport. Furthermore, stochastic simulations reveal a novel consequence of ER network heterogeneity: the existence of 'hot spots' where sparse diffusive reactants are more likely to find one another. Intriguingly, ER exit sites are disproportionately found in these highly accessible regions. Combining in vivo experiments with analytic calculations, quantitative image analysis, and computational modeling, we demonstrate how structure guides diffusive protein transport and reactions in the ER.

arxiv.org

A Hopfield-like model with complementary encodings of memories. (arXiv:2302.04481v1 [q-bio.NC]) arxiv.org/abs/2302.04481

A Hopfield-like model with complementary encodings of memories

We present a Hopfield-like autoassociative network that stores each memory as two different activity patterns with complementary properties. The first encoding is dense and mutually correlated with a subset of other dense encodings, such that each memory represents an example of a concept. The second encoding is sparse and exhibits no correlation among examples. The network stores each memory as a linear combination of its encodings, which allows for sparse and dense encodings to be retrieved at high and low activity threshold, respectively. At low threshold, as the number of examples stored increases, the retrieved activity shifts from representing densely encoded examples to densely encoded concepts, which are built from accumulating common example features. Meanwhile, at high threshold, the network can still distinctly retrieve many sparsely encoded examples due to the high capacity of sparse, decorrelated patterns. Thus, we demonstrate that a simple autoassociative network with a Hebbian learning rule can retrieve memories at two scales. It can also perform heteroassociation between them, such that one encoding of a memory can be used as a cue to retrieve another. We obtain our results by deriving macroscopic mean-field equations for this network, which allows us to calculate capacity formulas for sparse examples, dense examples, and dense concepts. We also perform network simulations, which verify our theoretical results and explicitly demonstrate the capabilities of our model.

arxiv.org

COVID-19 Susceptibility, Mortality and Length of Hospitalization based on Age-Sex Composition: Insights for Intervention and Stratification. (arXiv:2302.04569v1 [q-bio.QM]) arxiv.org/abs/2302.04569

COVID-19 Susceptibility, Mortality and Length of Hospitalization based on Age-Sex Composition: Insights for Intervention and Stratification

The coronavirus disease (COVID-19) has spread worldwide with an unprecedented impact on society. In the Philippines, several interventions such as mobility restrictions for different age groups and vaccination prioritization programs have been implemented to reduce the risks of infections and mortality. This study aimed to identify age-sex composition with greater susceptibility, longer hospitalization and higher fatality. The COVID-19 cases from March 2020 to April 2021 provided by the Department of Health Davao Region in the Philippines were analyzed. A Chi-square test was used to determine the difference in proportions of COVID-19 cases among age-sex compositions. A correlation plot of \c{hi}2 test residual was employed to investigate the differences in susceptibility. Boxplots and Kruskal-Wallis tests were utilized to compare the length of hospitalizations. The study found a significant difference in the COVID-19 susceptibility among age-sex compositions (p < 0.01). Male children and female senior citizens were the most susceptible age-sex compositions. Furthermore, senior citizens had the longest hospital days wherein the median and IQR days were 19 (15-27) for men and 18 (16-29) for women. Male senior citizen was the subgroup with the highest case fatality (21.4%, p < 0.01). It is recommended that the number of cases among senior citizens be used as an input in the planning and allocation of medical resources at the provincial and regional levels. The local government unit executives in the region can also take advantage of the availability of age-sex composition data in stratifying localities, planning, allocating COVID-19-related resources and imposing mobility restrictions.

arxiv.org

A prey-predator model with fear induced group defence and prey refuge. (arXiv:2302.04594v1 [q-bio.PE]) arxiv.org/abs/2302.04594

A prey-predator model with fear induced group defence and prey refuge

In this study, we investigate the dynamics of a spatial and non spatial prey-predator interaction model that includes the following: (i) fear effect incorporated in prey birth rate; (ii) group defence of prey against predators; and (iii) prey refuge. We provide comprehensive mathematical analysis of extinction and persistence scenarios for both prey and predator species. To better explore the dynamics of the system, a thorough investigation of bifurcation analysis has been performed using fear level, prey birth rate, and prey death rate caused by intra-prey competition as bifurcation parameter. All potential occurrences of bi-stability dynamics have also been investigated for some relevant sets of parametric values. Our numerical evaluations show that high levels of fear can stabilize the prey-predator system by ruling out the possibility of periodic solutions. Also, our model Hopf bifurcation is subcritical in contrast to traditional prey-predator models, which ignore the cost of fear and have supercritical Hopf bifurcations in general. In contrast to the general trend, predator species go extinct at higher values of prey birth rates. We have also found that, contrary to the typical tendency for prey species to go extinct, both prey and predator populations may coexist in the system as intra-prey competition level grows noticeably. The stability and Turing instability of associated spatial model have also been investigated analytically. We also perform the numerical simulation to observe the effect of different parameters on the density distribution of species. Different types of spatiotemporal patterns like spot, mixture of spots and stripes have been observed via variation of time evolution, diffusion coefficient of predator population, level of fear factor and prey refuge. The fear level parameter (k) has a great impact on the spatial dynamics of model system.

arxiv.org

MMA-RNN: A Multi-level Multi-task Attention-based Recurrent Neural Network for Discrimination and Localization of Atrial Fibrillation. (arXiv:2302.03731v1 [cs.LG]) arxiv.org/abs/2302.03731

MMA-RNN: A Multi-level Multi-task Attention-based Recurrent Neural Network for Discrimination and Localization of Atrial Fibrillation

The automatic detection of atrial fibrillation based on electrocardiograph (ECG) signals has received wide attention both clinically and practically. It is challenging to process ECG signals with cyclical pattern, varying length and unstable quality due to noise and distortion. Besides, there has been insufficient research on separating persistent atrial fibrillation from paroxysmal atrial fibrillation, and little discussion on locating the onsets and end points of AF episodes. It is even more arduous to perform well on these two distinct but interrelated tasks, while avoiding the mistakes inherent from stage-by-stage approaches. This paper proposes the Multi-level Multi-task Attention-based Recurrent Neural Network for three-class discrimination on patients and localization of the exact timing of AF episodes. Our model captures three-level sequential features based on a hierarchical architecture utilizing Bidirectional Long and Short-Term Memory Network (Bi-LSTM) and attention layers, and accomplishes the two tasks simultaneously with a multi-head classifier. The model is designed as an end-to-end framework to enhance information interaction and reduce error accumulation. Finally, we conduct experiments on CPSC 2021 dataset and the result demonstrates the superior performance of our method, indicating the potential application of MMA-RNN to wearable mobile devices for routine AF monitoring and early diagnosis.

arxiv.org

PyRates -- A Code-Generation Tool for Dynamical Systems Modeling. (arXiv:2302.03763v1 [cond-mat.dis-nn]) arxiv.org/abs/2302.03763

PyRates -- A Code-Generation Tool for Dynamical Systems Modeling

Systems of differential equations are commonly used to model real-world dynamical systems. In most cases, numerical methods are needed to study these systems. There are many freely available software solutions that implement numerical methods for dynamical systems analysis. However, these different software solutions have different requirements for user-supplied models, which makes it difficult to set up dynamical system analysis workflows using multiple tools and complicates sharing of workflows within the scientific community. PyRates is a software tool for modeling and analyzing dynamical systems using a variety of programming languages. It provides a unified interface for defining complex, hierarchical models, either via simple YAML files or via a Python user interface. PyRates uses code generation to translate user-defined models into "backend" implementations in languages such as Python, Fortran, and Julia, providing access to a wide range of dynamical system analysis methods. We demonstrate the capabilities of PyRates in three use cases, showing how it can generate (i) NumPy code for numerical simulations via SciPy, (ii) Fortran code for bifurcation analysis and parameter continuations via PyCoBi, and (iii) PyTorch code for neural network optimization via RectiPy. Furthermore, we show that PyRates is well suited as a model definition interface for other dynamical systems tools. To this end, we introduce PyCoBi and RectiPy, two software packages that we developed as extensions of PyRates for specific dynamical systems modeling applications.

arxiv.org

The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons. (arXiv:2302.03819v1 [cs.CV]) arxiv.org/abs/2302.03819

The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons

The wiring and connectivity of neurons form a structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabled mapping of circuit diagrams (connectomics) within local regions of the mouse brain. However, applying volume EM over the whole brain is not currently feasible due to technological challenges. As a result, comprehensive maps of long-range connections between brain regions are lacking. Recently, we demonstrated that X-ray holographic nanotomography (XNH) can provide high-resolution images of brain tissue at a much larger scale than EM. In particular, XNH is wellsuited to resolve large, myelinated axon tracts (white matter) that make up the bulk of long-range connections (projections) and are critical for inter-region communication. Thus, XNH provides an imaging solution for brain-wide projectomics. However, because XNH data is typically collected at lower resolutions and larger fields-of-view than EM, accurate segmentation of XNH images remains an important challenge that we present here. In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories. Manual voxel-wise annotation of ground truth is a time-consuming bottleneck for training segmentation networks. On the other hand, skeleton-based ground truth is much faster to annotate, and sufficient to determine connectivity. Therefore, we encourage participants to develop methods to leverage skeleton-based training. To this end, we provide two types of ground-truth annotations: a small volume of voxel-wise annotations and a larger volume with skeleton-based annotations. Entries will be evaluated on how accurately the submitted segmentations agree with the ground-truth skeleton annotations.

arxiv.org

Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study. (arXiv:2302.03991v1 [q-bio.GN]) arxiv.org/abs/2302.03991

Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study

As the availability of omics data has increased in the last few years, more multi-omics data have been generated, that is, high-dimensional molecular data consisting of several types such as genomic, transcriptomic, or proteomic data, all obtained from the same patients. Such data lend themselves to being used as covariates in automatic outcome prediction because each omics type may contribute unique information, possibly improving predictions compared to using only one omics data type. Frequently, however, in the training data and the data to which automatic prediction rules should be applied, the test data, the different omics data types are not available for all patients. We refer to this type of data as block-wise missing multi-omics data. First, we provide a literature review on existing prediction methods applicable to such data. Subsequently, using a collection of 13 publicly available multi-omics data sets, we compare the predictive performances of several of these approaches for different block-wise missingness patterns. Finally, we discuss the results of this empirical comparison study and draw some tentative conclusions.

arxiv.org

Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps. (arXiv:2302.04065v1 [stat.ML]) arxiv.org/abs/2302.04065

Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps

Optimal transport (OT) theory focuses, among all maps $T:\mathbb{R}^d\rightarrow \mathbb{R}^d$ that can morph a probability measure onto another, on those that are the ``thriftiest'', i.e. such that the averaged cost $c(x, T(x))$ between $x$ and its image $T(x)$ be as small as possible. Many computational approaches have been proposed to estimate such Monge maps when $c$ is the $\ell_2^2$ distance, e.g., using entropic maps [Pooladian'22], or neural networks [Makkuva'20, Korotin'20]. We propose a new model for transport maps, built on a family of translation invariant costs $c(x, y):=h(x-y)$, where $h:=\tfrac{1}{2}\|\cdot\|_2^2+τ$ and $τ$ is a regularizer. We propose a generalization of the entropic map suitable for $h$, and highlight a surprising link tying it with the Bregman centroids of the divergence $D_h$ generated by $h$, and the proximal operator of $τ$. We show that choosing a sparsity-inducing norm for $τ$ results in maps that apply Occam's razor to transport, in the sense that the displacement vectors $Δ(x):= T(x)-x$ they induce are sparse, with a sparsity pattern that varies depending on $x$. We showcase the ability of our method to estimate meaningful OT maps for high-dimensional single-cell transcription data, in the $34000$-$d$ space of gene counts for cells, without using dimensionality reduction, thus retaining the ability to interpret all displacements at the gene level.

arxiv.org
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