pwc-archive/papers-with-abstracts huggingface dataset:
AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy
Authors: Yan Ding, Hao Cheng, Ziliang Ye, Ruyi Feng, Wei Tian, Peng Xie, Juan Zhang, Zhongze Gu
Date: 2023-12-28 00:00:00
Tasks: Attribute, Molecular Property Prediction, molecular representation, Property Prediction

We propose Adjustable Molecular Representation (AdaMR), a new large-scale uniform pre-training strategy for small-molecule drugs, as a novel unified pre-training strategy. AdaMR utilizes a granularity-adjustable molecular encoding strategy, which is accomplished through a pre-training job termed molecular canonicalization, setting it apart from recent large-scale molecular models. This adaptability in granularity enriches the model's learning capability at multiple levels and improves its performance in multi-task scenarios. Specifically, the substructure-level molecular representation preserves information about specific atom groups or arrangements, influencing chemical properties and functionalities. This proves advantageous for tasks such as property prediction. Simultaneously, the atomic-level representation, combined with generative molecular canonicalization pre-training tasks, enhances validity, novelty, and uniqueness in generative tasks. All of these features work together to give AdaMR outstanding performance on a range of downstream tasks. We fine-tuned our proposed pre-trained model on six molecular property prediction tasks (MoleculeNet datasets) and two generative tasks (ZINC250K datasets), achieving state-of-the-art (SOTA) results on five out of eight tasks.
Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion
Authors: Alex Morehead, Jeffrey Ruffolo, Aadyot Bhatnagar, Ali Madani
Date: 2023-12-21 00:00:00

Generative models of macromolecules carry abundant and impactful implications for industrial and biomedical efforts in protein engineering. However, existing methods are currently limited to modeling protein structures or sequences, independently or jointly, without regard to the interactions that commonly occur between proteins and other macromolecules. In this work, we introduce MMDiff, a generative model that jointly designs sequences and structures of nucleic acid and protein complexes, independently or in complex, using joint SE(3)-discrete diffusion noise. Such a model has important implications for emerging areas of macromolecular design including structure-based transcription factor design and design of noncoding RNA sequences. We demonstrate the utility of MMDiff through a rigorous new design benchmark for macromolecular complex generation that we introduce in this work. Our results demonstrate that MMDiff is able to successfully generate micro-RNA and single-stranded DNA molecules while being modestly capable of joint modeling DNA and RNA molecules in interaction with multi-chain protein complexes. Source code: https://github.com/Profluent-Internships/MMDiff.
Comprehensive Joint Modeling of First-Line Therapeutics in Non-Small Cell Lung Cancer
Authors: Benjamin Schneider, Sébastien Benzekry, Jonathan Mochel
Date: 2024-01-15 00:00:00

First-line antiproliferatives for non-small cell lung cancer (NSCLC) have a relatively high failure rate due to high intrinsic resistance rates and acquired resistance rates to therapy. 57% patients are diagnosed in late-stage disease due to the tendency of early-stage NSCLC to be asymptomatic. For patients first diagnosed with metastatic disease the 5-year survival rate is approximately 5%. To help accelerate the development of novel therapeutics and computer-based tools for optimizing individual therapy, we have collated data from 11 different clinical trials in NSCLC and developed a semi-mechanistic, clinical model of NSCLC growth and pharmacodynamics relative to the various therapeutics represented in the study. In this study, we have produced extremely precise estimates of clinical parameters fundamental to cancer modeling such as the rate of acquired resistance to various pharmaceuticals, the relationship between drug concentration and rate of cancer cell death, as well as the fine temporal dynamics of anti-VEGF therapy. In the simulation sets documented in this study, we have used the model to make meaningful descriptions of efficacy gain in making bevacizumab-antiproliferative combination therapy sequential, over a series of days, rather than concurrent.
Phenotypic switching mechanisms determine the structure of cell migration into extracellular matrix under the `go-or-grow' hypothesis
Authors: Rebecca M. Crossley, Kevin J. Painter, Tommaso Lorenzi, Philip K. Maini, Ruth E. Baker
Date: 2024-01-14 00:00:00

A fundamental feature of collective cell migration is phenotypic heterogeneity which, for example, influences tumour progression and relapse. While current mathematical models often consider discrete phenotypic structuring of the cell population, in-line with the `go-or-grow' hypothesis \cite{hatzikirou2012go, stepien2018traveling}, they regularly overlook the role that the environment may play in determining the cells' phenotype during migration. Comparing a previously studied volume-filling model for a homogeneous population of generalist cells that can proliferate, move and degrade extracellular matrix (ECM) \cite{crossley2023travelling} to a novel model for a heterogeneous population comprising two distinct sub-populations of specialist cells that can either move and degrade ECM or proliferate, this study explores how different hypothetical phenotypic switching mechanisms affect the speed and structure of the invading cell populations. Through a continuum model derived from its individual-based counterpart, insights into the influence of the ECM and the impact of phenotypic switching on migrating cell populations emerge. Notably, specialist cell populations that cannot switch phenotype show reduced invasiveness compared to generalist cell populations, while implementing different forms of switching significantly alters the structure of migrating cell fronts. This key result suggests that the structure of an invading cell population could be used to infer the underlying mechanisms governing phenotypic switching.
Understanding YTHDF2-mediated mRNA Degradation By m6A-BERT-Deg
Authors: Ting-He Zhang, Sumin Jo, Michelle Zhang, Kai Wang, Shou-Jiang Gao, Yufei Huang
Date: 2024-01-15 00:00:00

N6-methyladenosine (m6A) is the most abundant mRNA modification within mammalian cells, holding pivotal significance in the regulation of mRNA stability, translation, and splicing. Furthermore, it plays a critical role in the regulation of RNA degradation by primarily recruiting the YTHDF2 reader protein. However, the selective regulation of mRNA decay of the m6A-methylated mRNA through YTHDF2 binding is poorly understood. To improve our understanding, we developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated degradation of m6A-methylated mRNAs. We meticulously assembled a high-quality training dataset by integrating multiple data sources for the HeLa cell line. To overcome the limitation of small training samples, we employed a pre-training-fine-tuning strategy by first performing a self-supervised pre-training of the model on 427,760 unlabeled m6A site sequences. The test results demonstrated the importance of this pre-training strategy in enabling m6A-BERT-Deg to outperform other benchmark models. We further conducted a comprehensive model interpretation and revealed a surprising finding that the presence of co-factors in proximity to m6A sites may disrupt YTHDF2-mediated mRNA degradation, subsequently enhancing mRNA stability. We also extended our analyses to the HEK293 cell line, shedding light on the context-dependent YTHDF2-mediated mRNA degradation.
Balancing reaction-diffusion network for cell polarization pattern with stability and asymmetry
Authors: Yixuan Chen, Guoye Guan, Lei-Han Tang, Chao Tang
Date: 2024-01-14 00:00:00

Cell polarization is a critical process that separates molecular species into two distinct regions in prokaryotic and eukaryotic cells, guiding biological processes such as cell division and cell differentiation. Although several underlying antagonistic reaction-diffusion networks capable of setting up cell polarization have been identified experimentally and theoretically, our understanding of how to manipulate pattern stability and asymmetry remains incomplete, especially when only a subset of network components are known. Here we present numerical results to show that the polarized pattern of an antagonistic 2-node network collapses into a homogeneous state when subjected to single-sided self-regulation, single-sided additional regulation, or unequal system parameters. However, polarity restoration can be achieved by combining two modifications with opposing effects. Additionally, spatially inhomogeneous parameters favoring respective domains stabilize their interface at designated locations. To connect our findings to cell polarity studies of the nematode Caenorhabditis elegans zygote, we reconstituted a 5-node network where a 4-node circuit with full mutual inhibitions between anterior and posterior is modified by a mutual activation in the anterior and an additional mutual inhibition between the anterior and the posterior. Once again, a generic set of kinetic parameters moves the interface towards either the anterior or posterior end, yet a polarized pattern can be stabilized through spatial tuning of one or more parameters coupled to intracellular or extracellular cues. A user-friendly software, PolarSim, is introduced to facilitate the exploration of networks with alternative node numbers, parameter values, and regulatory pathways.
Quantifying energy landscape of high-dimensional oscillatory systems by diffusion decomposition
Authors: Shirui Bian, Ruisong Zhou, Wei Lin, Chunhe Li
Date: 2024-01-13 00:00:00

High-dimensional networks producing oscillatory dynamics are ubiquitous in biological systems. Unravelling the mechanism of oscillatory dynamics in biological networks with stochastic perturbations becomes paramountly significant. Although the classical energy landscape theory provides a tool to study this problem in multistable systems and explain cellular functions, it remains challenging to quantify the landscape for high-dimensional oscillatory systems accurately. Here we propose an approach called the diffusion decomposition of Gaussian approximation (DDGA). We demonstrate the efficacy of the DDGA in quantifying the energy landscape of oscillatory systems and corresponding stochastic dynamics, in comparison with existing approaches. By further applying the DDGA to high-dimensional biological networks, we are able to uncover more intricate biological mechanisms efficiently, which deepens our understanding of cellular functions.
"Bayesian anchoring" and the fourfold pattern of risk attitudes
Authors: Francesco Fumarola, Lukasz Kusmierz, Ronald B. Dekker
Date: 2024-01-13 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

Experiments on decision making under uncertainty are known to display a classical pattern of risk aversion and risk seeking referred to as "fourfold pattern" (or "reflection effect") , but recent experiments varying the speed and order of mental processing have brought to light a more nuanced phenomenology. We model experiments though a Bayesian formalization of the anchor-and-adjust heuristic observed in empirical studies on cognitive bias. Using only elementary assumptions on constrained information processing, we are able to infer three separate effects found in recent observations: (1) the reported enhancement of the fourfold pattern for quicker decision processes; (2) the observed decrease of fluctuations for slower decision-making trials; (3) the reported dependence of the outcome on the order in which options are processed. The application of Bayesian modeling offers a solution to recent empirical riddles by bridging two heretofore separate domains of experimental inquiry on bounded rationality.
Iron role paradox in nerve degeneration and regeneration
Authors: Samira Bolandghamat, Morteza Behnam-Rassouli
Date: 2024-01-13 00:00:00

Iron accumulates in the neural tissue during peripheral nerve degeneration. Some studies have already been suggested that iron facilitates Wallerian degeneration (WD) events such as Schwann cell de-differentiation. On the other hand, intracellular iron levels remain elevated during nerve regeneration and gradually decrease. Iron enhances Schwann cell differentiation and axonal outgrowth. Therefore, there seems to be a paradox in the role of iron during nerve degeneration and regeneration. We explain this contradiction by suggesting that the increase in intracellular iron concentration during peripheral nerve degeneration is likely to prepare neural cells for the initiation of regeneration. Changes in iron levels are the result of changes in the expression of iron homeostasis proteins. In this review, we will first discuss the changes in the iron/iron homeostasis protein levels during peripheral nerve degeneration and regeneration and then explain how iron is related to nerve regeneration. This data may help better understand the mechanisms of peripheral nerve repair and find a solution to prevent or slow the progression of peripheral neuropathies.
Spectral Topological Data Analysis of Brain Signals
Authors: Anass B. El-Yaagoubi, Shuhao Jiao, Moo K. Chung, Hernando Ombao
Date: 2023-12-01 00:00:00
Tasks: EEG, Functional Connectivity, Topological Data Analysis

Topological data analysis (TDA) has become a powerful approach over the last twenty years, mainly due to its ability to capture the shape and the geometry inherent in the data. Persistence homology, which is a particular tool in TDA, has been demonstrated to be successful in analyzing functional brain connectivity. One limitation of standard approaches is that they use arbitrarily chosen threshold values for analyzing connectivity matrices. To overcome this weakness, TDA provides a filtration of the weighted brain network across a range of threshold values. However, current analyses of the topological structure of functional brain connectivity primarily rely on overly simplistic connectivity measures, such as the Pearson orrelation. These measures do not provide information about the specific oscillators that drive dependence within the brain network. Here, we develop a frequency-specific approach that utilizes coherence, a measure of dependence in the spectral domain, to evaluate the functional connectivity of the brain. Our approach, the spectral TDA (STDA), has the ability to capture more nuanced and detailed information about the underlying brain networks. The proposed STDA method leads to a novel topological summary, the spectral landscape, which is a 2D-generalization of the persistence landscape. Using the novel spectral landscape, we analyze the EEG brain connectivity of patients with attention deficit hyperactivity disorder (ADHD) and shed light on the frequency-specific differences in the topology of brain connectivity between the controls and ADHD patients.
Vitamin K content of Australian-grown horticultural commodities
Authors: Eleanor Dunlop, Judy Cunningham, Paul Adorno, Georgios Dabos, Stuart K Johnson, Lucinda J Black
Date: 2024-01-15 00:00:00

Vitamin K is emerging as a multi-function vitamin that plays a role in bone, brain and vascular health. Vitamin K composition data remain limited globally and Australia has lacked nationally representative data for vitamin K1 (phylloquinone, PK) in horticultural commodities. Primary samples (n = 927) of 90 different Australian-grown fruit, vegetable and nut commodities were purchased in three Australian cities. We measured PK in duplicate in 95 composite samples using liquid chromatography with electrospray ionisation-tandem mass spectrometry. The greatest mean concentrations of PK were found in kale (565 ug/100 g), baby spinach (255 ug/100 g) and Brussels sprouts (195 ug/100 g). The data contribute to the global collection of vitamin K food composition data. They add to the evidence that PK concentrations vary markedly between geographic regions, supporting development of region-specific datasets for national food composition databases that do not yet contain data for vitamin K.
Sources of HIV infections among MSM with a migration background: a viral phylogenetic case study in Amsterdam, the Netherlands
Authors: Alexandra Blenkinsop, Nikos Pantazis, Evangelia Georgia Kostaki, Lysandros Sofocleous, Ard van Sighem, Daniela Bezemer, Thijs van de Laar, Marc van der Valk, Peter Reiss, Godelieve de Bree, Oliver Ratmann
Date: 2024-01-16 00:00:00

Background: Men and women with a migration background comprise an increasing proportion of incident HIV cases across Western Europe. Several studies indicate a substantial proportion acquire HIV post-migration. Methods: We used partial HIV consensus sequences with linked demographic and clinical data from the opt-out ATHENA cohort of people with HIV in the Netherlands to quantify population-level sources of transmission to Dutch-born and foreign-born Amsterdam men who have sex with men (MSM) between 2010-2021. We identified phylogenetically and epidemiologically possible transmission pairs in local transmission chains and interpreted these in the context of estimated infection dates, quantifying transmission dynamics between sub-populations by world region of birth. Results: We estimate the majority of Amsterdam MSM who acquired their infection locally had a Dutch-born Amsterdam MSM source (56% [53-58%]). Dutch-born MSM were the predominant source population of infections among almost all foreign-born Amsterdam MSM sub-populations. Stratifying by two-year intervals indicated shifts in transmission dynamics, with a majority of infections originating from foreign-born MSM since 2018, although uncertainty ranges remained wide. Conclusions: In the context of declining HIV incidence among Amsterdam MSM, our data suggest whilst native-born MSM have predominantly driven transmissions in 2010-2021, the contribution from foreign-born MSM living in Amsterdam is increasing.
Measuring multisensory integration in reaction time: the relative entropy approach
Authors: Hans Colonius, Adele Diederich
Date: 2024-01-15 00:00:00

A classic definition of multisensory integration (MI) has been proposed as ``the presence of a (statistically) significant change in the response to a cross-modal stimulus complex compared to unimodal stimuli''. However, this general definition did not result in a broad consensus on how to quantify the amount of MI in the context of reaction time (RT). In this brief note, we argue that numeric measures of reaction times that only involve mean or median RTs do not uncover the information required to fully assess the effect of multisensory integration. We suggest instead novel measures that include the entire RT distributions functions. The central role is played by relative entropy (aka Kullback-Leibler divergence), a statistical concept in information theory, statistics, and machine learning to measure the (non-symmetric) distance between probability distributions. We provide a number of theoretical examples, but empirical applications and statistical testing are postponed to later study.
Artificial Intelligence for Digital and Computational Pathology
Authors: Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson, Ming Y. Lu, Anurag Vaidya, Tiffany R. Miller, Faisal Mahmood
Date: 2023-12-13 00:00:00
Tasks: Prognosis, whole slide images

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis; however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data.
Optimal control of ribosome population for gene expression under periodic nutrient intake
Authors: Clément Soubrier, Eric Foxall, Luca Ciandrini, Khanh Dao Duc
Date: 2024-01-11 00:00:00

Translation of proteins is a fundamental part of gene expression that is mediated by ribosomes. As ribosomes significantly contribute to both cellular mass and energy consumption, achieving efficient management of the ribosome population is also crucial to metabolism and growth. Inspired by biological evidence for nutrient-dependent mechanisms that control both ribosome active degradation and genesis, we introduce a dynamical model of protein production, that includes the dynamics of resources and control over the ribosome population. Under the hypothesis that active degradation and biogenesis are optimal for maximizing and maintaining protein production, we aim to qualitatively reproduce empirical observations of the ribosome population dynamics. Upon formulating the associated optimization problem, we first analytically study the stability and global behaviour of solutions under constant resource input, and characterize the extent of oscillations and convergence rate to a global equilibrium. We further use these results to simplify and solve the problem under a quasi-static approximation. Using biophysical parameter values, we find that optimal control solutions lead to both control mechanisms and the ribosome population switching between periods of feeding and fasting, suggesting that the intense regulation of ribosome population observed in experiments allows to maximize and maintain protein production. Finally, we find some range for the control values over which such a regime can be observed, depending on the intensity of fasting.
A Two-Step Longstaff Schwartz Monte Carlo Approach to Game Option Pricing
Authors: Ce Wang
Date: 2024-01-16 00:00:00
Tasks: regression

We proposed a two-step Longstaff Schwartz Monte Carlo (LSMC) method with two regression models fitted at each time step to price game options. Although the original LSMC can be used to price game options with an enlarged range of path in regression and a modified cashflow updating rule, we identified a drawback of such approach, which motivated us to propose our approach. We implemented numerical examples with benchmarks using binomial tree and numerical PDE, and it showed that our method produces more reliable results comparing to the original LSMC.