Quantitative Biology

In the last decades, the interest to understand the connection between brain and body has grown notably. For example, in psychoneuroimmunology many studies associate stress, arising from many different sources and situations, to changes in the immune system from the medical or immunological point of view as well as from the biochemical one. In this paper we identify important behaviours of this interplay between the immune system and stress from medical studies and seek to represent them qualitatively in a paradigmatic, yet simple, mathematical model. To that end we develop a differential equation model with two equations for infection level and immune system, which integrates the effects of stress as an additional parameter. We are able to reproduce a stable healthy state for little stress, an oscillatory state between healthy and infected states for high stress, and a "burn-out" or stable sick state for extremely high stress. The mechanism between the different dynamics is controlled by two saddle-node in cycle (SNIC) bifurcations. Furthermore, our model is able to capture an induced infection upon dropping from moderate to low stress, and it predicts increasing infection periods upon increasing before eventually reaching a burn-out state.

Heart failure (HF) affects at least 26 million people worldwide, so predicting adverse events in HF patients represents a major target of clinical data science. However, achieving large sample sizes sometimes represents a challenge due to difficulties in patient recruiting and long follow-up times, increasing the problem of missing data. To overcome the issue of a narrow dataset cardinality (in a clinical dataset, the cardinality is the number of patients in that dataset), population-enhancing algorithms are therefore crucial. The aim of this study was to design a random shuffle method to enhance the cardinality of an HF dataset while it is statistically legitimate, without the need of specific hypotheses and regression models. The cardinality enhancement was validated against an established random repeated-measures method with regard to the correctness in predicting clinical conditions and endpoints. In particular, machine learning and regression models were employed to highlight the benefits of the enhanced datasets. The proposed random shuffle method was able to enhance the HF dataset cardinality (711 patients before dataset preprocessing) circa 10 times and circa 21 times when followed by a random repeated-measures approach. We believe that the random shuffle method could be used in the cardiovascular field and in other data science problems when missing data and the narrow dataset cardinality represent an issue.

Time-dependent diffusion behavior is probed over sub-millisecond timescales in a single shot using an NMR static gradient, time-incremented echo train acquisition (SG-TIETA) framework. The method extends the Carr-Purcell-Meiboom-Gill (CPMG) cycle under a static field gradient by discretely incrementing the ? -pulse spacings to simultaneously avoid off-resonance effects and probe a range of timescales ( 50??00 microseconds). Pulse spacings are optimized based on a derived ruleset. The remaining effects of pulse inaccuracy are examined and found to be consistent across pure liquids of different diffusivities: water, decane, and octanol-1. A pulse accuracy correction is developed. Instantaneous diffusivity, D inst (t) , curves (i.e., half of the time derivative of the mean-squared displacement in the gradient direction), are recovered from pulse accuracy-corrected SG-TIETA decays using a model-free, log-linear least squares inversion method validated by Monte Carlo simulations. A signal-averaged, 1-minute experiment is described. A flat D inst (t) is measured on pure dodecamethylcyclohexasiloxane whereas decreasing D inst (t) are measured on yeast suspensions, consistent with the expected short-time D inst (t) behavior for confining microstructural barriers on the order of microns.

The rapid increase of transportation infrastructure during the recent decades has caused a number of effects on bird species, including collision mortality, habitat loss, fragmentation and noise. This paper investigates the effects of traffic noise and road-induced fragmentation on breeding bird richness in Cyprus. Cyprus, situated along one of the main migratory routes for birds, has a rich and diverse avifauna threatened by an ever-expanding road network and a road density among the highest in Europe. In this first island-wide study we used data from 102 breeding birds recorded in 10 km x 10 km grid cells. Within every cell we calculated road traffic noise and eight road-related properties. Most of the grid cells are subject to intense fragmentation and traffic noise with combined impact hotspots located even within protected areas (such as Cape Greco, and the Troodos Massif). Results from variance partitioning indicated that road-related properties (total road extent and road length) accounted for a combined 59% of variation in species richness, followed by fragmentation-related properties and noise properties. The study posits the need for further in-depth research on the effects of road networks on birds, and road construction, particularly in protected areas within Mediterranean islands.

Modern cancer -omics and pharmacological data hold great promise in precision cancer medicine for developing individualized patient treatments. However, high heterogeneity and noise in such data pose challenges for predicting the response of cancer cell lines to therapeutic drugs accurately. As a result, arbitrary human judgment calls are rampant throughout the predictive modeling pipeline. In this work, we develop a transparent stability-driven pipeline for drug response interpretable predictions, or staDRIP, which builds upon the PCS framework for veridical data science (Yu and Kumbier, 2020) and mitigates the impact of human judgment calls. Here we use the PCS framework for the first time in cancer research to extract proteins and genes that are important in predicting the drug responses and stable across appropriate data and model perturbations. Out of the 24 most stable proteins we identified using data from the Cancer Cell Line Encyclopedia (CCLE), 18 have been associated with the drug response or identified as a known or possible drug target in previous literature, demonstrating the utility of our stability-driven pipeline for knowledge discovery in cancer drug response prediction modeling.

Suppressing SARS-CoV-2 will likely require the rapid identification and isolation of infected individuals, on an ongoing basis. RT-PCR (reverse transcription polymerase chain reaction) tests are accurate but costly, making regular testing of every individual expensive. The costs are a challenge for all countries and particularly for developing countries. Cost reductions can be achieved by combining samples and testing them in groups. We propose an algorithm for grouping subsamples, prior to testing, based on the geometry of a hypercube. At low prevalence, this testing procedure uniquely identifies infected individuals in a small number of tests. We discuss the optimal group size and explain why, given the highly infectious nature of the disease, parallel searches are preferred. We report proof of concept experiments in which a positive sample was detected even when diluted a hundred-fold with negative samples. Using these methods, the costs of mass testing could be reduced by a factor of ten to a hundred or more. If infected individuals are quickly and effectively quarantined, the prevalence will fall and so will the costs of regularly testing everyone. Such a strategy provides a possible pathway to the longterm elimination of SARS-CoV-2. Field trials of our approach are now under way in Rwanda and initial data from these are reported here.

Patagonian fjords' area is one of the largest estuarine regions in the world. Every one of its water bodies displays a unique hydrodynamic behavior with enormous effects on the biogeochemical characteristics of the ecosystems. In this context, algal blooms are ecological phenomena of major relevance. Numerical simulation has proved to be a promising tool to understand their impacts. It has not been used for studying algal blooms in this zone. This article focuses on proposing a novel numerical model for simulating brief algal blooms occurring in water bodies of western Patagonia. The proposed model presents a trade-off between complexity and applicability since field-data sparsity in the zone discourages using more sophisticated approaches. The model is based on a two-layer description of the water column. The first layer represents the euphotic zone where an embedded biogeochemical model of NPZD-type is used to model a mass-conserving trophic web. High intensity wind drives the water column mixing, introducing an upward flux of nutrients that boosts high rates of primary production. A time-dependent Gaussian pulse is used to describe this process. Mass losses due to detritus sinking are also included. Then, the ecosystem's dynamics is represented by means of an externally forced, non-autonomous system of ordinary differential equations which is characterized by strictly positive trajectories but that it is not longer mass-conserving. A structure-preserving time integrator based on a splitting-composition technique is designed for solving the system's equations. It is cast as a three-steps algorithm and provides an exact estimations of biomass fluxes. Additionally, a genetic algorithm-based tool is used to calibrate the model's parameters in realistic scenarios. The proposed model is applied im a detailed study of a winter bloom in an austral fjord.

Pediatric obstructive sleep apnea affects an estimated 1-5% of elementary-school aged children and can lead to other detrimental health problems. Swift diagnosis and treatment are critical to a child's growth and development, but the variability of symptoms and the complexity of the available data make this a challenge. We take a first step in streamlining the process by focusing on inexpensive data from questionnaires and craniofacial measurements. We apply correlation networks, the Mapper algorithm from topological data analysis, and singular value decomposition in a process of exploratory data analysis. We then apply a variety of supervised and unsupervised learning techniques from statistics, machine learning, and topology, ranging from support vector machines to Bayesian classifiers and manifold learning. Finally, we analyze the results of each of these methods and discuss the implications for a multi-data-sourced algorithm moving forward.

The first part of the paper presents a review of the gold-standard testing protocol for Covid-19, real-time, reverse transcriptase PCR, and its properties and associated measurement data such as amplification curves that can guide the development of appropriate and accurate adaptive group testing protocols. The second part of the paper is concerned with examining various off-the-shelf group testing methods for Covid-19 and identifying their strengths and weaknesses for the application at hand. The third part of the paper contains a collection of new analytical results for adaptive semiquantitative group testing with probabilistic and combinatorial priors, including performance bounds, algorithmic solutions, and noisy testing protocols. The probabilistic setting is of special importance as it is designed to be simple to implement by nonexperts and handle heavy hitters. The worst-case paradigm extends and improves upon prior work on semiquantitative group testing with and without specialized PCR noise models.

Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification. AMICI is published under the permissive BSD-3-Clause license with source code publicly available on this https URL. Citeable releases are archived on Zenodo.