arXiv Quantitative Biology @arxiv_bio@qoto.org

Functional magnetic resonance imaging (fMRI) has been widely utilized to study the motor deficits and rehabilitation following stroke. In particular, functional connectivity(FC) analyses with fMRI at rest can be employed to reveal the neural connectivity rationale behind this post-stroke motor function impairment and recovery. However, the methods and findings have not been summarized in a review focusing on post-stroke functional connectivity analysis. In this context, we broadly review the static functional connectivity network analysis (SFC) and dynamic functional connectivity network analysis (DFC) for post-stroke motor dysfunction patients, aiming to provide method guides and the latest findings regarding post-stroke motor function recovery. Specifically, a brief overview of the SFC and DFC methods for fMRI analysis is provided, along with the preprocessing and denoising procedures that go into these methods. Following that, the current status of research in functional connectivity networks for post-stoke patients under these two views was synthesized individually. Results show that SFC is the most frequent post-stroke functional connectivity analysis method. The SFC findings demonstrate that the stroke lesion reduces FC between motor areas, and the FC increase positively correlates with functional recovery. Meanwhile, the current DFC analysis in post-stroke has just been uncovered as the tip of the iceberg of its prospect, and its exceptionally rapidly progressing development can be expected.

In therapeutic design, balancing various physiochemical properties is crucial for molecule development, similar to how Multiparameter Optimization (MPO) evaluates multiple variables to meet a primary goal. While many molecular features can now be predicted using \textit{in silico} methods, aiding early drug development, the vast data generated from high throughput virtual screening challenges the practicality of traditional MPO approaches. Addressing this, we introduce a scaffold focused graph-based Markov chain Monte Carlo framework (ScaMARS) built to generate molecules with optimal properties. This innovative framework is capable of self-training and handling a wider array of properties, sampling different chemical spaces according to the starting scaffold. The benchmark analysis on several properties shows that ScaMARS has a diversity score of 84.6\% and has a much higher success rate of 99.5\% compared to conditional models. The integration of new features into MPO significantly enhances its adaptability and effectiveness in therapeutic design, facilitating the discovery of candidates that efficiently optimize multiple properties.

The analysis of biological networks encompasses a wide variety of fields from genomic research of protein-protein interaction networks, to the physiological study of biologically optimized tree-like vascular networks. It is certain that different biological networks have different optimization criteria and we are interested in those networks optimized for fluid transport within the circulatory system. Many theories currently exist. For instance, distributive vascular geometry data is typically consistent with a theoretical model that requires simultaneous minimization of both the power loss of laminar flow and a cost function proportional to the total volume of material needed to maintain the system (Murray's law). However, how this optimized system breaks down (or is altered) due to disease has yet to be characterized in detail in terms of branching geometry and geometric interrelationships. This is important for understanding how vasculature remodels under changes of functional demands. For instance, in polycystic kidney disease (PKD), drastic cyst development may lead to a significant alteration of the vascular geometry (or vascular changes may be a preceding event). Understanding these changes could lead to a better understanding of early disease as well as development and characterization of treatment interventions. We have developed an optimal transport network model which simulates distributive vascular systems in health as well as disease in order to better understand changes that may occur due to PKD. We found that reduced perfusion territories, dilated distributive vasculature, and vessel rarefaction are all consequences of cyst development derived from this theoretical model and are a direct result of the increased heterogeneity of local renal tissue perfusion demands.

Although all members of the ophthalmic community agree that distortion is an aberration affecting the geometry of an image produced by the periphery of an ophthalmic lens, there are several approaches for analyzing and quantifying this aberration. Various concepts have been introduced: ordinary distortion, stationary distortion and central static distortion are associated with a fixed eye behind the ophthalmic lens, whereas rotatory distortion, peripheral distortion, lateral static distortion, and dynamic distortion require a secondary position of gaze behind the lens. Furthermore, concept definitions vary from one author to another. The goal of this paper is to review the various concepts, analyze their effects on lens design and determine their ability to predict the deformation of an image as perceived by the lens wearer. These entities can be classified within 3 categories: the concepts associated with an ocular rotation, the concepts resulting from an optical approach, and the concepts using a perceptual approach. Among the various concepts reviewed, it appears that the Le Grand-Fry approach for analyzing and displaying distortion is preferable to others and allows modeling of the different possible types of distortions affecting the periphery of an ophthalmic lens.

In growing populations, the fate of mutations depends on their competitive ability against the ancestor and their ability to colonize new territory. Here we present a theory that integrates both aspects of mutant fitness by coupling the classic description of one-dimensional competition (Fisher equation) to the minimal model of front shape (KPZ equation). We solved these equations and found three regimes, which are controlled solely by the expansion rates, solely by the competitive abilities, or by both. Collectively, our results provide a simple framework to study spatial competition.

Focused ultrasound (FUS) can be used to open the blood-brain barrier (BBB), and MRI with contrast agents can detect that opening. However, repeated use of gadolinium-based contrast agents (GBCAs) presents safety concerns to patients. This study is the first to propose the idea of modeling a volume transfer constant (Ktrans) through deep learning to reduce the dosage of contrast agents. The goal of the study is not only to reconstruct artificial intelligence (AI) derived Ktrans images but to also enhance the intensity with low dosage contrast agent T1 weighted MRI scans. We successfully validated this idea through a previous state-of-the-art temporal network algorithm, which focused on extracting time domain features at the voxel level. Then we used a Spatiotemporal Network (ST-Net), composed of a spatiotemporal convolutional neural network (CNN)-based deep learning architecture with the addition of a three-dimensional CNN encoder, to improve the model performance. We tested the ST-Net model on ten datasets of FUS-induced BBB-openings aquired from different sides of the mouse brain. ST-Net successfully detected and enhanced BBB-opening signals without sacrificing spatial domain information. ST-Net was shown to be a promising method of reducing the need of contrast agents for modeling BBB-opening K-trans maps from time-series Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) scans.

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order and emerging technologies, such as optical genome mapping and long-read DNA or RNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to a consortium such as GREGoR, which is focused on elucidating the underlying cause of rare unsolved genetic disorders.

Many functional magnetic resonance imaging (fMRI) studies rely on estimates of hierarchically organised brain networks whose segregation and integration reflect the dynamic transitions of latent cognitive states. However, most existing methods for estimating the community structure of networks from both individual and group-level analysis neglect the variability between subjects and lack validation. In this paper, we develop a new multilayer community detection method based on Bayesian latent block modelling. The method can robustly detect the group-level community structure of weighted functional networks that give rise to hidden brain states with an unknown number of communities and retain the variability of individual networks. For validation, we propose a new community structure-based multivariate Gaussian generative model convolved with haemodynamic response function to simulate synthetic fMRI signal. Our result shows that the inferred community memberships using hierarchical Bayesian analysis are consistent with the predefined node labels in the generative model. The method is also tested using real working memory task-fMRI data of 100 unrelated healthy subjects from the Human Connectome Project. The results show distinctive community structures and subtle connectivity patterns between 2-back, 0-back, and fixation conditions, which may reflect cognitive and behavioural states under working memory task conditions.

Understanding the interplay between recombination and resampling is a significant challenge in mathematical population genetics and of great practical relevance. Asymptotic results about the distribution of samples when recombination is strong compared to resampling are often based on the approximate solution of certain recursions, which is technically hard and offers little conceptual insight. This work generalises an elegant probabilistic argument, based on the coupling of ancestral processes but so far only available in the case of two sites, to the multilocus setting. This offers an alternative route to, and slightly generalises, a classical result of Bhaskar and Song.

Why do we perceive illusory motion in some static images? Several accounts have been proposed based on eye movements, response latencies to different image elements, or interactions between image patterns and motion energy detectors. Recently, PredNet, a recurrent deep neural network (DNN) based on predictive coding principles was reported to reproduce the "Rotating Snakes" illusion, suggesting a role for predictive coding in illusory motion. We replicate this finding, and then use a series of "in silico psychophysics" experiments to examine whether PredNet behaves consistently with human observers for simplified variants of the illusory stimuli. We also measure response latencies to individual elements of the Rotating Snakes pattern by probing internal units in the network. A pretrained PredNet model predicted illusory motion for all subcomponents of the Rotating Snakes stimulus, consistent with human observers. However, we found no simple response delays in internal units, as found in physiological data. The PredNet model's detection of motion in gradients was based on contrast, not luminance as it is in human perception. Finally, We tested the robustness of the illusion on 10 identical PredNets trained on the same video data; we found large variation in the ability of the network to reproduce the illusion and predict motion for simplified variants of the illusion. Also, unlike human observers, none of the networks predicted illusory motion for greyscale variants of the pattern. Even when a DNN successfully reproduces some idiosyncrasy of human vision, a more detailed investigation can reveal inconsistencies between humans and the network, and between different instances of the same network. The inconsistency of the Rotating Snakes illusion in PredNets trained from different initializations suggests that predictive coding does not reliably lead to human-like illusory motion.

Porcine reproductive and respiratory syndrome virus (PRRSV) remains widely distributed across the U.S. swine industry. Between-farm movement of animals and transportation vehicles, along with local transmission are the primary routes by which PRRSV is spread. Given the farm-to-farm proximity in high pig production areas, local transmission is an important pathway in the spread of PRRSV; however, there is limited understanding of the role local transmission plays in the dissemination of PRRSV, specifically, the distance at which there is increased risk for transmission from infected to susceptible farms. We used a spatial and spatiotemporal kernel density approach to estimate PRRSV relative risk and utilized a Bayesian spatiotemporal hierarchical model to assess the effects of environmental variables, between-farm movement data, and on-farm biosecurity features on PRRSV outbreaks. The maximum spatial distance calculated through the kernel density approach was 15.3 km in 2018, 17.6 km in 2019, and 18 km in 2020. Spatiotemporal analysis revealed greater variability throughout the study period, with significant differences between the different farm types. Sow farms were consistently categorized as high risk farm types, while downstream farms (i.e., finisher and nursery farms) had more susceptible farms within areas of significant-high relative risk. Factors associated with PRRSV outbreaks were farms with higher number of access points to barns, higher numbers of outgoing movements of pigs, and higher number of days where temperatures were between 4°C and 10°C. Results obtained from this study may be used to guide the reinforcement of biosecurity and surveillance strategies at farms and areas within the distance threshold of PRRSV positive farms.

The goal of this study is to assess the feasibility of airway geometry as a biomarker for ASD. Chest CT images of children with a documented diagnosis of ASD as well as healthy controls were identified retrospectively. 54 scans were obtained for analysis, including 31 ASD cases and 23 age and sex-matched controls. A feature selection and classification procedure using principal component analysis (PCA) and support vector machine (SVM) achieved a peak cross validation accuracy of nearly 89% using a feature set of 8 airway branching angles. Sensitivity was 94%, but specificity was only 78%. The results suggest a measurable difference in airway branchpoint angles between children with ASD and the control population.

The Spot Prawn trap fishery off the west coast of British Columbia (BC) is managed using a fixed escapement strategy that aims to prevent recruitment overfishing while maximizing expected long-term yield by closing the fishery when the catch rate of spawners, projected to the following spring, drops below 1.7 spawners per trap (the de jure rule). We develop a management strategy evaluation framework for BC's Spot Prawn fishery that examines the expected performance of the management procedure implemented in practice (the de facto rule), which was significantly more conservative than the de jure rule, usually closing the fishery when spawner catch rates were at least twice as high as specified by the de jure rule. Simulations indicate that the de facto spawner index rule using average empirical March 31st targets from 2000 to 2019 maintains most stocks near or above 0.8 BMSY with or without accounting for environmental effects and/or increasing future SST on recruitment. Abundance indices were found to be strongly hyperstable, with fishing efficiency 1.5 to 3.0 times higher under low biomass than high biomass.

Investigating the mutational history of tumor cells is important for understanding the underlying mechanisms of cancer and its evolution. Now that the evolution of tumor cells is modeled using labeled trees, researchers are motivated to propose different measures for the comparison of mutation trees and other labeled trees. While the Robinson-Foulds distance is widely used for the comparison of phylogenetic trees, it has weaknesses when it is applied to labeled trees. Here, k-Robinson-Foulds dissimilarity measures are introduced for labeled tree comparison.

Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and drug manufacturing access to poorly available and brand-new molecules. Conventional rule-based or expert-based computer-aided synthesis has obvious limitations, such as high labor costs and limited search space. In recent years, dramatic breakthroughs driven by artificial intelligence have revolutionized retrosynthesis. Here we aim to present a comprehensive review of recent advances in AI-based retrosynthesis. For single-step and multi-step retrosynthesis both, we first list their goal and provide a thorough taxonomy of existing methods. Afterwards, we analyze these methods in terms of their mechanism and performance, and introduce popular evaluation metrics for them, in which we also provide a detailed comparison among representative methods on several public datasets. In the next part we introduce popular databases and established platforms for retrosynthesis. Finally, this review concludes with a discussion about promising research directions in this field.

Label-free and nondestructive mid-infrared vibrational hyperspectral imaging is emerging as an important ex-vivo tissue analysis tool, providing spatially resolved biochemical information critical to understanding physiological and pathological processes. However, the chemically complex and spatially heterogeneous composition of tissue specimens and the inherently weak interaction of infrared light with biomolecules limit the analytical performance of infrared absorption spectroscopy. Here, we introduce an advanced mid-infrared spectrochemical tissue imaging modality using metasurfaces that support strong surface-localized electromagnetic fields to capture quantitative molecular maps of large-area murine brain-tissue sections. Our approach leverages polarization-multiplexed multi-resonance plasmonic metasurfaces to simultaneously detect many different functional biomolecules. The resulting surface-enhanced mid-infrared spectral imaging (SE-MIRSI) method eliminates the non-specific effects of bulk tissue morphology on the quantitative analysis of fingerprint spectra and improves the chemical selectivity. We show that the metasurface enhancement increases the retrieval of amide I and II absorption bands associated with secondary structures of proteins. Moreover, we demonstrate that plasmonic metasurfaces enhance the chemical contrast in infrared images and enable the detection of ultrathin tissue regions that are not otherwise visible to conventional mid-infrared spectral imaging. While we tested our approach on murine brain tissue sections, this chemical imaging method is well-suited for any tissue type, which significantly broadens the potential impacts of our method for both translational research and clinical histopathology.

The syllable is a perceptually salient unit in speech. Since both the syllable and its acoustic correlate, i.e., the speech envelope, have a preferred range of rhythmicity between 4 and 8 Hz, it is hypothesized that theta-band neural oscillations play a major role in extracting syllables based on the envelope. A literature survey, however, reveals inconsistent evidence about the relationship between speech envelope and syllables, and the current study revisits this question by analyzing large speech corpora. It is shown that the center frequency of speech envelope, characterized by the modulation spectrum, reliably correlates with the rate of syllables only when the analysis is pooled over minutes of speech recordings. In contrast, in the time domain, a component of the speech envelope is reliably phase-locked to syllable onsets. Based on a speaker-independent model, the timing of syllable onsets explains about 24% variance of the speech envelope. These results indicate that local features in the speech envelope, instead of the modulation spectrum, are a more reliable acoustic correlate of syllables.

Olfactory navigation is observed across species and plays a crucial role in locating resources for survival. In the laboratory, understanding the behavioral strategies and neural circuits underlying odor-taxis requires a detailed understanding of the animal's sensory environment. For small model organisms like C. elegans and larval D. melanogaster, controlling and measuring the odor environment experienced by the animal can be challenging, especially for airborne odors, which are subject to subtle effects from airflow, temperature variation, and from the odor's adhesion, adsorption or reemission. Here we present a method to flexibly control and precisely measure airborne odor concentration in an arena with agar while imaging animal behavior. Crucially and unlike previous methods, our method allows continuous monitoring of the odor profile during behavior. We construct stationary chemical landscapes in an odor flow chamber through spatially patterned odorized air. The odor concentration is measured with a spatially distributed array of digital gas sensors. Careful placement of the sensors allows the odor concentration across the arena to be accurately inferred and continuously monitored at all points in time. We use this approach to measure the precise odor concentration that each animal experiences as it undergoes chemotaxis behavior and report chemotaxis strategies for C. elegans and D. melanogaster larvae populations under different spatial odor landscapes.

Tropical rainforests exhibit a rich repertoire of spatial patterns emerging from the intricate relationship between the microscopic interaction between species. In particular, the distribution of vegetation clusters can shed much light on the underlying process regulating the ecosystem. Analyzing the distribution of vegetation clusters at different resolution scales, we show the first robust evidence of scale-invariant clusters of vegetation, suggesting the coexistence of multiple intertwined scales in the collective dynamics of tropical rainforests. We use field data and computational simulations to confirm our hypothesis proposing a predictor that could be of particular interest to monitor the ecological resilience of the world's 'green lungs.'

Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource intensive due to the vast combinatorial search space. Recently, computational approaches, specifically deep learning models have emerged as an efficient way to discover synergistic combinations. While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines. In this study, we collected data from various datasets covering various drug-related aspects. Then, we take advantage of large-scale pre-training models to generate informative representations and features for drugs, proteins, and diseases. Based on that, a message-passing graph is built on top to propagate information together with graph structure learning flexibility. This is first introduced in the biological networks and enables us to generate pseudo-relations in the graph. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods on synergistic prediction benchmark datasets. We are also capable of inferencing new drug combination data in a test on an independent set released by AstraZeneca, where 10% of improvement over previous methods is observed. In addition, we're robust against unseen drugs and surpass almost 15% AU ROC compared to the second-best model. We believe our framework contributes to both the future wet-lab discovery of novel drugs and the building of promising guidance for precise combination medicine.