arXiv Quantitative Biology @arxiv_bio@qoto.org

This paper seeks to study the evolution of the COVID-19 pandemic based on daily published data from Worldometer website, using a time-dependent SIR model. Our findings indicate that this model fits well such data, for different chosen periods and different regions. This well-known model, consisting of three disjoint compartments, susceptible , infected , and removed , depends in our case on two time dependent parameters, the infection rate $β(t)$ and the removal rate $ρ(t)$. After deriving the model, we prove the local exponential behavior of the number of infected people, be it growth or decay. Furthermore, we extract a time dependent replacement factor $σ_s(t) ={β(t)}s(t)/{ρ(t) }$, where $s(t)$ is the ratio of susceptible people at time $t$. In addition, $i(t)$ and $r(t)$ are respectively the ratios of infected and removed people, based on a population of size $N$, usually assumed to be constant. Besides these theoretical results, the report provides simulations on the daily data obtained for Germany, Italy, and the entire World, as collected from Worldometer over the period stretching from April 2020 to June 2022. The computational model consists of the estimation of $β(t)$, $ρ(t)$ and $s(t)$ based on the time-dependent SIR model. The validation of our approach is demonstrated by comparing the profiles of the collected $i(t), r(t)$ data and those obtained from the SIR model with the approximated parameters. We also consider matching the data with a constant-coefficient SIR model, which seems to be working only for short periods. Thus, such model helps understanding and predicting the evolution of the pandemics for short periods of time where no radical change occurs.

Periodic travelling waves (PTW) are a common solution type of partial differential equations. Such models exhibit multistability of PTWs, typically visualised through the Busse balloon, and parameter changes typically lead to a cascade of wavelength changes through the Busse balloon. In the past, the stability boundaries of the Busse balloon have been used to predict such wavelength changes. Here, motivated by anecdotal evidence from previous work, we provide compelling evidence that the Busse balloon provides insufficient information to predict wavelength changes due to a delayed loss of stability phenomenon. Using two different reaction-advection-diffusion systems, we relate the delay that occurs between the crossing of a stability boundary in the Busse balloon and the occurrence of a wavelength change to features of the essential spectrum of the destabilised PTW. This leads to a predictive framework that can estimate the order of magnitude of such a time delay, which provides a novel ``early warning sign'' for pattern destabilization. We illustrate the implementation of the predictive framework to predict under what conditions a wavelength change of a PTW occurs.

Density-dependent population growth is a feature of large carnivores like wolves ($\textit{Canis lupus}$), with mechanisms typically attributed to resource (e.g. prey) limitation. Such mechanisms are local phenomena and rely on individuals having access to information, such as prey availability at their location. Using over four decades of wolf population and range expansion data from Wisconsin (USA) wolves, we found that the population not only exhibited density dependence locally but also at landscape scale. Superficially, one may consider space as yet another limiting resource to explain landscape-scale density dependence. However, this view poses an information puzzle: most individuals do not have access to global information such as range-wide habitat availability as they would for local prey availability. How would the population "know" when to slow their range expansion? To understand observed large-scale spatial density dependence, we propose a reaction-diffusion model, first introduced by Fisher and Kolmogorov, with a "travelling wave" solution, wherein the population expands from a core range that quickly achieves local carrying capacity. Early-stage acceleration and later-stage deceleration of population growth can be explained by early elongation of an expanding frontier and a later collision of the expanding frontier with a habitat boundary. Such a process does not require individuals to have global density information. We illustrate our proposal with simulations and spatial visualizations of wolf recolonization in the western Great Lakes region over time relative to habitat suitability. We further synthesize previous studies on wolf habitat selection in the western Great Lakes region and argue that the habitat boundary appeared to be driven by spatial variation in mortality, likely associated with human use of the landscape.

The thrombotic microangiopathies (TMAs) manifest in renal biopsy histology with a broad spectrum of acute and chronic findings. Precise diagnostic criteria for a renal biopsy diagnosis of TMA are missing. As a first step towards a machine learning- and computer vision-based analysis of wholes slide images from renal biopsies, we trained a segmentation model for the decisive diagnostic kidney tissue compartments artery, arteriole, glomerulus on a set of whole slide images from renal biopsies with TMAs and Mimickers (distinct diseases with a similar nephropathological appearance as TMA like severe benign nephrosclerosis, various vasculitides, Bevacizumab-plug glomerulopathy, arteriolar light chain deposition disease). Our segmentation model combines a U-Net-based tissue detection with a Shifted windows-transformer architecture to reach excellent segmentation results for even the most severely altered glomeruli, arterioles and arteries, even on unseen staining domains from a different nephropathology lab. With accurate automatic segmentation of the decisive renal biopsy compartments in human renal vasculopathies, we have laid the foundation for large-scale compartment-specific machine learning and computer vision analysis of renal biopsy repositories with TMAs.

The epidemiology of pandemics is classically viewed using geographical and political borders; however, these artificial divisions can result in a misunderstanding of the current epidemiological state within a given region. To improve upon current methods, we propose a clustering algorithm which is capable of recasting regions into well-mixed clusters such that they have a high level of interconnection while minimizing the external flow of the population towards other clusters. Moreover, we analyze and identify so called core clusters, clusters that retain their features over time (temporally stable) and independent of the presence or absence of policy measures. In order to demonstrate the capabilities of this algorithm, we use US county-level cellular mobility data to divide the country into such clusters. Herein, we show a more granular spread of SARS-CoV-2 throughout the first weeks of the pandemic. Moreover, we are able to identify areas (groups of counties) that were experiencing above average levels of transmission within a state, as well as pan-state areas (clusters overlapping more than one state) with very similar disease spread. Therefore, our method enables policymakers to make more informed decisions on the use of public health interventions within their jurisdiction, as well as guide collaboration with surrounding regions to benefit the general population in controlling the spread of communicable diseases.

Mutualistic interactions, where species interact to obtain mutual benefits, constitute an essential component of natural ecosystems. The use of ecological networks to represent the species and their ecological interactions allows the study of structural and dynamic patterns common to different ecosystems. However, by neglecting the temporal dimension of mutualistic communities, relevant insights into the organization and functioning of natural ecosystems can be lost. Therefore, it is crucial to incorporate empirical phenology -- the cycles of species' activity within a season -- to fully understand the effects of temporal variability on network architecture. In this paper, by using two empirical datasets together with a set of synthetic models, we propose a framework to characterize phenology on ecological networks and assess the effect of temporal variability. Analyses reveal that non-trivial information is missed when portraying the network of interactions as static, which leads to overestimating the value of fundamental structural features. We discuss the implications of our findings for mutualistic relationships and intra-guild competition for common resources. We show that recorded interactions and species' activity duration are pivotal factors in accurately replicating observed patterns within mutualistic communities. Furthermore, our exploration of synthetic models underscores the system-specific character of the mechanisms driving phenology, increasing our understanding of the complexities of natural ecosystems.

Identifying cerebral cortex layers is crucial for comparative studies of the cytoarchitecture aiming at providing insights into the relations between brain structure and function across species. The absence of extensive annotated datasets typically limits the adoption of machine learning approaches, leading to the manual delineation of cortical layers by neuroanatomists. We introduce a self-supervised approach to detect layers in 2D Nissl-stained histological slices of the cerebral cortex. It starts with the segmentation of individual cells and the creation of an attributed cell-graph. A self-supervised graph convolutional network generates cell embeddings that encode morphological and structural traits of the cellular environment and are exploited by a community detection algorithm for the final layering. Our method, the first self-supervised of its kind with no spatial transcriptomics data involved, holds the potential to accelerate cytoarchitecture analyses, sidestepping annotation needs and advancing cross-species investigation.

The origin of life must have been preceded by Darwin-like evolutionary dynamics that could propagate it. How did that adaptive dynamics arise? And from what prebiotic molecules? Using evolutionary invasion analysis, we develop a universal framework for describing any origin story for evolutionary dynamics. We find that cooperative autocatalysts, i.e. autocatalysts whose per-unit reproductive rate grows as their population increases, have the special property of being able to cross a barrier that separates their initial degradation-dominated state from a growth-dominated state with evolutionary dynamics. For some model parameters, this leap to persistent propagation is likely, not rare. We apply this analysis to the Foldcat Mechanism, wherein peptides fold and help catalyze the elongation of each other. Foldcats are found to have cooperative autocatalysis and be capable of emergent evolutionary dynamics.

The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity.

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.

Nuclear magnetic resonance (NMR) spectroscopy plays an essential role across various scientific disciplines, providing valuable insights into molecular dynamics and interactions. Despite the promise of AI-enhanced NMR prediction models, challenges persist in the interpretation of spectra for tasks such as molecular retrieval, isomer recognition, and peak assignment. In response, this paper introduces Multi-Level Multimodal Alignment with Knowledge-Guided Instance-Wise Discrimination (K-M3AID) to establish meaningful correspondences between two heterogeneous modalities: molecular graphs (structures) and NMR spectra. In particular, K-M3AID employs a dual-coordinated contrastive learning architecture, and incorporates a graph-level alignment module, a node-level alignment module, and a communication channel. Notably, the framework introduces knowledge-guided instance-wise discrimination into contrastive learning within the node-level alignment module, significantly enhancing accuracy in cross-modal alignment. Additionally, K-M3AID showcases its capability of meta-learning by demonstrating that skills acquired during node-level alignment positively impact graph-level alignment. Empirical validation underscores K-M3AID's effectiveness in addressing multiple zero-shot tasks, offering a promising solution to bridge the gap between structural information and spectral data in complex NMR scenarios.

What determines biodiversity in nature is a prominent issue in ecology, especially in biotic resource systems that are typically devoid of cross-feeding. Here, we show that by incorporating pairwise encounters among consumer individuals within the same species, a multitude of consumer species can self-organize to coexist in a well-mixed system with one or a few biotic resource species. The coexistence modes can manifest as either stable steady states or self-organized oscillations. Importantly, all coexistence states are robust to stochasticity, whether employing the stochastic simulation algorithm or individual-based modeling. Our model quantitatively illustrates species distribution patterns across a wide range of ecological communities and can be broadly used to explain biodiversity in many biotic resource systems.

In advancing the understanding of decision-making processes, mathematical models, particularly Inverse Reinforcement Learning (IRL), have proven instrumental in reconstructing animal's multiple intentions amidst complex behaviors. Given the recent development of a continuous-time multi-intention IRL framework, there has been persistent inquiry into inferring discrete time-varying reward functions with multiple intention IRL approaches. To tackle the challenge, we introduce the Latent (Markov) Variable Inverse Q-learning (L(M)V-IQL) algorithms, a novel IRL framework tailored for accommodating discrete intrinsic rewards. Leveraging an Expectation-Maximization approach, we cluster observed trajectories into distinct intentions and independently solve the IRL problem for each. Demonstrating the efficacy of L(M)V-IQL through simulated experiments and its application to different real mouse behavior datasets, our approach surpasses current benchmarks in animal behavior prediction, producing interpretable reward functions. This advancement holds promise for neuroscience and psychology, contributing to a deeper understanding of animal decision-making and uncovering underlying brain mechanisms.

Transitive Inference (TI) is a cognitive task that assesses an organism's ability to infer novel relations between items based on previously acquired knowledge. TI is known for exhibiting various behavioral and neural signatures, such as the Serial Position Effect (SPE), Symbolic Distance Effect (SDE), and the brain's capacity to maintain and merge separate ranking models. We propose a novel framework that casts TI as a probabilistic preference learning task, using one-parameter Mallows models. We present a series of simulations that highlight the effectiveness of our novel approach. We show that the Mallows ranking model natively reproduces SDE and SPE. Furthermore, extending the model using Bayesian selection showcases its capacity to generate and merge ranking hypotheses as pairs with connecting symbols are encountered. Finally, we employ neural networks to replicate Mallows models, demonstrating how this framework aligns with observed prefrontal neural activity during TI. Our innovative approach sheds new light on the nature of TI, emphasizing the potential of probabilistic preference learning for unraveling its underlying neural mechanisms.

The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography (EEG) experiments on children listening to amplitude modulated (AM) noise with slow-rythmic prosodic (0.5-1 Hz), syllabic (4-8 Hz) or the phoneme (12-40 Hz) rates, aimed at detecting differences in perception of oscillatory sampling that could be associated with dyslexia. The purpose of this work is to check whether these differences exist and how they are related to children's performance in different language and cognitive tasks commonly used to detect dyslexia. To this purpose, temporal and spectral inter-channel EEG connectivity was estimated, and a denoising autoencoder (DAE) was trained to learn a low-dimensional representation of the connectivity matrices. This representation was studied via correlation and classification analysis, which revealed ability in detecting dyslexic subjects with an accuracy higher than 0.8, and balanced accuracy around 0.7. Some features of the DAE representation were significantly correlated ($p<0.005$) with children's performance in language and cognitive tasks of the phonological hypothesis category such as phonological awareness and rapid symbolic naming, as well as reading efficiency and reading comprehension. Finally, a deeper analysis of the adjacency matrix revealed a reduced bilateral connection between electrodes of the temporal lobe (roughly the primary auditory cortex) in DD subjects, as well as an increased connectivity of the F7 electrode, placed roughly on Broca's area. These results pave the way for a complementary assessment of dyslexia using more objective methodologies such as EEG.

We previously demonstrated that the abrogation of the ICOS pathway prevents type 1 diabetes development in the Non Obese Diabetic (NOD) mouse, but results in a CD4+ T-cell dependent autoimmune neuromyopathy in aged mice. Pancreatic islet infiltrates in conventional NOD mice and neuromuscular infiltrates in Icosl-/- NOD mice have in common that they exhibit a strong enrichment in CD4+TIGIT+ T-cells, whilst TIGIT expression in the peripheral CD4+ T-cells is limited to the CD4+FoxP3+ T-cell population.When deleting Tigit on the NOD background, diabetes incidence was found increased. Peripheral CD4+CD226+ effector T-cells exhibited an increased frequency of IL-17 producing CD4+CD226+RORgt+ T-cells versus a decreased frequency of IFN$γ$-producing CD4+CD226+Tbet+ T-cells. ICOS is expressed in both CD4+FoxP3+ and CD4+CD226+ splenic T-cell subsets. Icosl deletion leads to a decrease of CD4+FoxP3+ cells, with decrease of PD1 but increase of ICOS and CCRX3. Also in the Icosl-/- model, CD4+CD226+ T-cells are decreased by Tigit deletion, and showed an increase of CD4+CD226+RORgt+ T-cells and a decrease of CD4+CD226+Tbet+ T-cells.However, deletion of Tigit in aged Icosl-/- NOD mice population did not increase the incidence of the autoimmune neuromyopathy observed in Icosl-/- NOD mice. Interestingly, the upregulation of CD4+CD226+RORgt+ T-cells was partly rescued.We conclude from our study that both Icosl and Tigit deletions on the NOD background lead to a shift between the ratio of IFN$γ$ and IL-17-producing CD4+CD226+ effector cells. The ICOS-dependent neuromyopathy development remains dominant and is not further altered in the absence of TIGIT.

Discriminating between physiological waves, linked to functional brain properties, and pathological waves, due to brain malfunction, is problematic, e. g., for high-frequency oscillations (HFO) as high $γ$ waves. Brain rhythms observed in EEG can also be observed in excitatory/inhibitory (E/I) NN models. Such models allow to study the relationship between waves and neuronal circuit functions, which could lead to fundamental insights to discriminating waves. To address this and other questions, we explore the emergence of high gamma rhythms in an E/I balanced neural population of integrated and fire neurons with short-term synaptic plasticity. This model generates a rich repertoire of EEG-like rhythms, including high-frequency excitatory and inhibitory oscillations. Using the integrated information decomposition framework (Phi-ID), we explore the information dynamics of the network and its relationship with excitatory and inhibitory activity and high-frequency rhythms. In regions where high gamma rhythms emerge only in the excitatory population, we see informational properties in the network more favorable for computation and processing information, corresponding then to functional regimes. However, regions where high gamma rhythms also emerge in the inhibitory population present lower mutual information in general, and the system becomes less predictable, a fact that can be linked to a less functional or ``pathological" regime. In the second case, we observe that both excitatory and inhibitory populations oscillate with the same dominant frequency, which is higher than in the first case. Higher frequency oscillations and synchronization are commonly associated with epileptic seizures so adopting an information dynamics approach could help differentiate between high-frequency oscillations related to cognitive functions from those related to neuronal disorders such as epilepsy.