生物学学术速递[1.10]
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q-bio生物学，共计7篇
【1】 A whitening approach for Transfer Entropy permits the application to narrow-band signals
标题：传递熵的白化方法允许将其应用于窄带信号
链接：https://arxiv.org/abs/2201.02461
摘要：Transfer Entropy, a generalisation of Granger Causality, promises to measure
"information transfer" from a source to a target signal by ignoring
self-predictability of a target signal when quantifying the source-target
relationship. A simple example for signals with such self-predictability are
narrowband signals. These are both thought to be intrinsically generated by the
brain as well as commonly dealt with in analyses of brain signals, where
band-pass filters are used to separate responses from noise. However, the use
of Transfer Entropy is usually discouraged in such cases. We simulate
simplistic examples where we confirm the failure of classic implementations of
Transfer Entropy when applied to narrow-band signals, as made evident by a
flawed recovery of effect sizes and interaction delays. We propose an
alternative approach based on a whitening of the input signals before computing
a bivariate measure of directional time-lagged dependency. This approach solves
the problems found in the simple simulated systems. Finally, we explore the
behaviour of our measure when applied to delta and theta response components in
Magnetoencephalography (MEG) responses to continuous speech. The small effects
that our measure attributes to a directed interaction from the stimulus to the
neuronal responses are stronger in the theta than in the delta band. This
suggests that the delta band reflects a more predictive coupling, while the
theta band is stronger involved in bottom-up, reactive processing. Taken
together, we hope to increase the interest in directed perspectives on
frequency-specific dependencies.
【2】 Control Theory Illustrates the Energy Efficiency in the Dynamic Reconfiguration of Functional Connectivity
标题：控制理论阐释功能连通性动态重构中的能效
链接：https://arxiv.org/abs/2201.02340
摘要：The brain's functional connectivity fluctuates over time instead of remaining
steady in a stationary mode even during the resting state. This fluctuation
establishes the dynamical functional connectivity that transitions in a
non-random order between multiple modes. Yet it remains unexplored how the
transition facilitates the entire brain network as a dynamical system and what
utility this mechanism for dynamic reconfiguration can bring over the widely
used graph theoretical measurements. To address these questions, we propose to
conduct an energetic analysis of functional brain networks using resting-state
fMRI and behavioral measurements from the Human Connectome Project. Through
comparing the state transition energy under distinct adjacent matrices, we
justify that dynamic functional connectivity leads to 60% less energy cost to
support the resting state dynamics than static connectivity when driving the
transition through default mode network. Moreover, we demonstrate that
combining graph theoretical measurements and our energy-based control
measurements as the feature vector can provide complementary prediction power
for the behavioral scores (Combination vs. Control: t = 9.41, p = 1.64e-13;
Combination vs. Graph: t = 4.92, p = 3.81e-6). Our approach integrates
statistical inference and dynamical system inspection towards understanding
brain networks.
【3】 PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences
标题：PWM2Vec：一种有效的从冠状病毒棘突序列中嵌入病毒宿主的方法
链接：https://arxiv.org/abs/2201.02273
摘要：COVID-19 pandemic, is still unknown and is an important open question. There
are speculations that bats are a possible origin. Likewise, there are many
closely related (corona-) viruses, such as SARS, which was found to be
transmitted through civets. The study of the different hosts which can be
potential carriers and transmitters of deadly viruses to humans is crucial to
understanding, mitigating and preventing current and future pandemics. In
coronaviruses, the surface (S) protein, or spike protein, is an important part
of determining host specificity since it is the point of contact between the
virus and the host cell membrane. In this paper, we classify the hosts of over
five thousand coronaviruses from their spike protein sequences, segregating
them into clusters of distinct hosts among avians, bats, camels, swines, humans
and weasels, to name a few. We propose a feature embedding based on the
well-known position-weight matrix (PWM), which we call PWM2Vec, and use to
generate feature vectors from the spike protein sequences of these
coronaviruses. While our embedding is inspired by the success of PWMs in
biological applications such as determining protein function, or identifying
transcription factor binding sites, we are the first (to the best of our
knowledge) to use PWMs in the context of host classification from viral
sequences to generate a fixed-length feature vector representation. The results
on the real world data show that in using PWM2Vec, we are able to perform
comparably well as compared to baseline models. We also measure the importance
of different amino acids using information gain to show the amino acids which
are important for predicting the host of a given coronavirus.
【4】 Invasion of cooperative parasites in moderately structured host populations
标题：合作寄生虫在中等结构宿主群体中的入侵
链接：https://arxiv.org/abs/2201.02249
摘要：Certain defense mechanisms of phages against the immune system of their
bacterial host rely on cooperation of phages. Motivated by this example we
analyse invasion probabilities of cooperative parasites in host populations
that are moderately structured. More precisely we assume that hosts are
arranged on the vertices of a configuration model and that offspring of
parasites move to nearest neighbours sites to infect new hosts. We consider
parasites that generate many offspring at reproduction, but do this (usually)
only when infecting a host simultaneously. In this regime we identify and
analyse the spatial scale of the population structure at which invasion of
parasites turns from being an unlikely to an highly probable event.
【5】 Fractional calculus modeling of cell viscoelasticity quantifies drug response and maturation more robustly than integer order models
标题：细胞粘弹性的分数微积分模型比整数阶模型更可靠地量化药物反应和成熟度
链接：https://arxiv.org/abs/2201.02589
备注：20 pages, 6 figures
摘要：It has recently been discovered that the viscoelastic properties of cells are
inherent markers reflecting the complex biological states, functions and
malfunctions of the cells. Although the extraction of model parameters from the
viscoelasticity data of many cell types has been done successfully using
integer order mechanical and power-law viscoelastic models, there are some cell
types and conditions where the goodness of fits falls behind. Thus, fractional
order viscoelastic models have been proposed as more general and better suited
for such modeling. In this work, we test such proposed generality using
published data already fitted by integer order models. We find that cell
viscoelasticity data can be fitted using fractional order viscoelastic models
in more situations than integer order. For macrophages, which are among the
white blood cells that function in the immune system, the fractional order
Kelvin-Voigt model best captures pharmacological interventions and maturation
of the cells. The steady state viscosity of macrophages decreases following
depolymerization of F-actin using the drug cytochalasin D, and also decreases
following myosin II breakdown using Blebbistatin. When macrophages are treated
with a bacterium-derived chemoattractant, the steady state viscosity decreases.
Interestingly, both the steady state viscosity and elastic modulus are
progressively altered as the cells become mature and approach senescence. Taken
together, these results show that fractional viscoelastic modeling, more
robustly than integer order modeling, enables the further quantification of
cell function and malfunction, with potential diagnostic and therapeutic
applications especially in cases of cancer and immune system dysfunctions.
【6】 AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models
标题：增强的PCA：一个Python包，包含监督和对抗的线性因素模型
链接：https://arxiv.org/abs/2201.02547
备注：NeurIPS 2021 (Learning Meaningful Representations of Life Workshop)
摘要：Deep autoencoders are often extended with a supervised or adversarial loss to
learn latent representations with desirable properties, such as greater
predictivity of labels and outcomes or fairness with respects to a sensitive
variable. Despite the ubiquity of supervised and adversarial deep latent factor
models, these methods should demonstrate improvement over simpler linear
approaches to be preferred in practice. This necessitates a reproducible linear
analog that still adheres to an augmenting supervised or adversarial objective.
We address this methodological gap by presenting methods that augment the
principal component analysis (PCA) objective with either a supervised or an
adversarial objective and provide analytic and reproducible solutions. We
implement these methods in an open-source Python package, AugmentedPCA, that
can produce excellent real-world baselines. We demonstrate the utility of these
factor models on an open-source, RNA-seq cancer gene expression dataset,
showing that augmenting with a supervised objective results in improved
downstream classification performance, produces principal components with
greater class fidelity, and facilitates identification of genes aligned with
the principal axes of data variance with implications to development of
specific types of cancer.
【7】 Fixation Maximization in the Positional Moran Process
标题：位置性Moran过程中的注视最大化
链接：https://arxiv.org/abs/2201.02248
备注：11 pages, 6 figures, to appear at AAAI 2022
摘要：The Moran process is a classic stochastic process that models invasion
dynamics on graphs. A single "mutant" (e.g., a new opinion, strain, social
trait etc.) invades a population of residents spread over the nodes of a graph.
The mutant fitness advantage $\delta\geq 0$ determines how aggressively mutants
propagate to their neighbors. The quantity of interest is the fixation
probability, i.e., the probability that the initial mutant eventually takes
over the whole population. However, in realistic settings, the invading mutant
has an advantage only in certain locations. E.g., a bacterial mutation allowing
for lactose metabolism only confers an advantage on places where dairy products
are present. In this paper we introduce the positional Moran process, a natural
generalization in which the mutant fitness advantage is only realized on
specific nodes called active nodes. The associated optimization problem is
fixation maximization: given a budget $k$, choose a set of $k$ active nodes
that maximize the fixation probability of the invading mutant. We show that the
problem is NP-hard, while the optimization function is not submodular, thus
indicating strong computational hardness. Then we focus on two natural limits.
In the limit of $\delta\to\infty$ (strong selection), although the problem
remains NP-hard, the optimization function becomes submodular and thus admits a
constant-factor approximation using a simple greedy algorithm. In the limit of
$\delta\to 0$ (weak selection), we show that in $O(m^\omega)$ time we can
obtain a tight approximation, where $m$ is the number of edges and $\omega$ is
the matrix-multiplication exponent. Finally, we present an experimental
evaluation of the new algorithms together with some proposed heuristics.
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