pwc-archive/papers-with-abstracts huggingface dataset:
Integrating Agent-Based and Compartmental Models for Infectious Disease Modeling: A Novel Hybrid Approach
Authors: Inan Bostanci, Tim Conrad
Date: 2024-07-30 00:00:00
Tasks: Computational Efficiency

This study investigates the spatial integration of agent-based models (ABMs) and compartmental models for infectious disease modeling, presenting a novel hybrid approach and examining its implications. ABMs offer detailed insights by simulating interactions and decisions among individuals but are computationally expensive for large populations. Compartmental models capture population-level dynamics more efficiently but lack granular detail. We developed a hybrid model that aims to balance the granularity of ABMs with the computational efficiency of compartmental models, offering a more nuanced understanding of disease spread in diverse scenarios, including large populations. This model spatially couples discrete and continuous populations by integrating an ordinary differential equation model with a spatially explicit ABM. Our key objectives were to systematically assess the consistency of disease dynamics and the computational efficiency across various configurations. For this, we evaluated two experimental scenarios and varied the influence of each sub-model via spatial distribution. In the first, the ABM component modeled a homogeneous population; in the second, it simulated a heterogeneous population with landscape-driven movement. Results show that the hybrid model can significantly reduce computational costs but is sensitive to between-model differences, highlighting the importance of model equivalence in hybrid approaches. The code is available at: git.zib.de/ibostanc/hybrid_abm_ode
Impact of opinion dynamics on recurrent pandemic waves: balancing risk aversion and peer pressure
Authors: Sheryl L. Chang, Quang Dang Nguyen, Carl J. E. Suster, Christina M. Jamerlan, Rebecca J. Rockett, Vitali Sintchenko, Tania C. Sorrell, Alexandra Martiniuk, Mikhail Prokopenko
Date: 2024-07-26 00:00:00

Recurrent waves which are often observed during long pandemics typically form as a result of several interrelated dynamics including public health interventions, population mobility and behaviour, varying disease transmissibility due to pathogen mutations, and changes in host immunity due to recency of vaccination or previous infections. Complex nonlinear dependencies among these dynamics, including feedback between disease incidence and the opinion-driven adoption of social distancing behaviour, remain poorly understood, particularly in scenarios involving heterogeneous population, partial and waning immunity, and rapidly changing public opinions. This study addressed this challenge by proposing an opinion dynamics model that accounts for changes in social distancing behaviour (i.e., whether to adopt social distancing) by modelling both individual risk perception and peer pressure. The opinion dynamics model was integrated and validated within a large-scale agent-based COVID-19 pandemic simulation that modelled the spread of the Omicron variant of SARS-CoV-2 between December 2021 and June 2022 in Australia. Our study revealed that the fluctuating adoption of social distancing, shaped by individual risk aversion and social peer pressure from both household and workplace environments, may explain the observed pattern of recurrent waves of infections.
Beta regression mixed model applied to sensory analysis
Authors: João César Reis Alves, Gabriel Rodrigues Palma, Idemauro Antonio Rodrigues de Lara
Date: 2024-08-06 00:00:00
Tasks: Experimental Design, regression

Sensory analysis is an important area that the food industry can use to innovate and improve its products. This study involves a sample of individuals who can be trained or not to assess a product using a hedonic scale or notes, where the experimental design is a balanced incomplete block design. In this context, integrating sensory analysis with effective statistical methods, which consider the nature of the response variables, is essential to answer the aim of the experimental study. Some techniques are available to analyse sensory data, such as response surface models or categorical models. This article proposes using beta regression as an alternative to the proportional odds model, addressing some convergence problems, especially regarding the number of parameters. Moreover, the beta distribution is flexible for heteroscedasticity and asymmetry data. To this end, we conducted simulation studies that showed agreement rates in product selection using both models. Also, we presented a motivational study that was developed to select prebiotic drinks based on cashew nuts added to grape juice. In this application, the beta regression mixed model results corroborated with the selected formulations using the proportional mixed model.
A four-step Bayesian workflow for improving ecological science
Authors: EM Wolkovich, T Jonathan Davies, William D Pearse, Michael Betancourt
Date: 2024-08-05 00:00:00

Growing anthropogenic pressures have increased the need for robust predictive models. Meeting this demand requires approaches that can handle bigger data to yield forecasts that capture the variability and underlying uncertainty of ecological systems. Bayesian models are especially adept at this and are growing in use in ecology. Yet many ecologists today are not trained to take advantage of the bigger ecological data needed to generate more flexible robust models. Here we describe a broadly generalizable workflow for statistical analyses and show how it can enhance training in ecology. Building on the increasingly computational toolkit of many ecologists, this approach leverages simulation to integrate model building and testing for empirical data more fully with ecological theory. In turn this workflow can fit models that are more robust and well-suited to provide new ecological insights -- allowing us to refine where to put resources for better estimates, better models, and better forecasts.
Mitosis, Cytoskeleton Regulation, and Drug Resistance in Receptor Triple Negative Breast Cancer
Authors: Alexandre Matov
Date: 2024-07-26 00:00:00

During cell division, the receptor triple-negative MDA-MB-231 mitotic spindles are the largest in comparison to other BC cell lines. Many of the MDA-MB-231 spindles exhibit rapid lateral twisting during metaphase, which remains unaffected by knockdown of the oncogene Myc and treatment with inhibitors of the serine/threonine-protein kinase B-Raf and the epidermal growth factor receptor (EGFR), alone or in any combination. The MDA-MB-231 cells are the most aggressive and rapidly form metastatic tumors in xenograft transplant models, and exhibited very high proliferation rates when plated as three-dimensional cultures in Matrigel. Quantitative image analysis of microtubules (MTs) in six BC cell lines - MDA-MB-231 (receptor negative), HCC-1143 (receptor negative), HCC-3153 (receptor negative), ZR75B (estrogen receptor-positive), LY2 (progesterone receptor-positive), HCC-1428 (estrogen receptor-positive, progesterone receptor-positive) - demonstrated that the rotational spindle rocking of MDA-MB-231 cells during metaphase appears coupled with a significant increase in MT polymerization rates during interphase, which likely shortens interphase and accelerates cell cycle progression and mitotic entry. Unlike the uniform treadmilling rates of about 21 um/min in kinetochore MTs during metaphase we measured across cell lines, MDA-MB-231 cells in interphase exhibit the fastest MT polymerization dynamics of about 19 um/min and this is coupled with abnormal mitotic spindle oscillations of almost 30 um/min. This aberrant behavior in MDA-MB-231 spindles may represent a therapeutically targetable disrupted mechanism of spindle positioning in receptor triple-negative breast cancer (TNBC) cells leading to tumor aggressiveness. In this manuscript, we outline a strategy for the selection of the most optimal tubulin inhibitor based on the ability to affect MT dynamics.
Efficient Approximate Methods for Design of Experiments for Copolymer Engineering
Authors: Swagatam Mukhopadhyay
Date: 2024-08-04 00:00:00
Tasks: Experimental Design

We develop a set of algorithms to solve a broad class of Design of Experiment (DoE) problems efficiently. Specifically, we consider problems in which one must choose a subset of polymers to test in experiments such that the learning of the polymeric design rules is optimal. This subset must be selected from a larger set of polymers permissible under arbitrary experimental design constraints. We demonstrate the performance of our algorithms by solving several pragmatic nucleic acid therapeutics engineering scenarios, where limitations in synthesis of chemically diverse nucleic acids or feasibility of measurements in experimental setups appear as constraints. Our approach focuses on identifying optimal experimental designs from a given set of experiments, which is in contrast to traditional, generative DoE methods like BIBD. Finally, we discuss how these algorithms are broadly applicable to well-established optimal DoE criteria like D-optimality.
GraphAge: Unleashing the power of Graph Neural Network to Decode Epigenetic Aging
Authors: Saleh Sakib Ahmed, Nahian Shabab, Md. Abul Hassan Samee, M. Sohel Rahman
Date: 2024-08-02 00:00:00
Tasks: Graph Neural Network

DNA methylation is a crucial epigenetic marker used in various clocks to predict epigenetic age. However, many existing clocks fail to account for crucial information about CpG sites and their interrelationships, such as co-methylation patterns. We present a novel approach to represent methylation data as a graph, using methylation values and relevant information about CpG sites as nodes, and relationships like co-methylation, same gene, and same chromosome as edges. We then use a Graph Neural Network (GNN) to predict age. Thus our model, GraphAge, leverages both structural and positional information for prediction as well as better interpretation. Although we had to train in a constrained compute setting, GraphAge still showed competitive performance with a Mean Absolute Error (MAE) of 3.207 and a Mean Squared Error (MSE) of 25.277, slightly outperforming the current state of the art. Perhaps more importantly, we utilized GNN explainer for interpretation purposes and were able to unearth interesting insights (e.g., key CpG sites, pathways, and their relationships through Methylation Regulated Networks in the context of aging), which were not possible to 'decode' without leveraging the unique capability of GraphAge to 'encode' various structural relationships. GraphAge has the potential to consume and utilize all relevant information (if available) about an individual that relates to the complex process of aging. So, in that sense, it is one of its kind and can be seen as the first benchmark for a multimodal model that can incorporate all this information in order to close the gap in our understanding of the true nature of aging.
Cooperative SIR dynamics as a model for spontaneous blood clot initiation
Authors: Philip Greulich
Date: 2024-07-31 00:00:00
Tasks: Sensitivity

Blood clotting is an important physiological process to suppress bleeding upon injury, but when it occurs inadvertently, it can cause thrombosis, which can lead to life threatening conditions. Hence, understanding the microscopic mechanistic factors for inadvertent, spontaneous blood clotting, in absence of a vessel breach, can help in predicting and adverting such conditions. Here, we present a minimal model -- reminiscent of the SIR model -- for the initiating stage of spontaneous blood clotting, the collective activation of blood platelets. This model predicts that in the presence of very small initial activation signals, macroscopic activation of the platelet population requires a sufficient degree of heterogeneity of platelet sensitivity. To propagate the activation signal and achieve collective activation of the bulk platelet population, it requires the presence of, possibly only few, hyper-sensitive platelets, but also a sufficient proportion of platelets with intermediate, yet higher-than-average sensitivity. A comparison with experimental results demonstrates a qualitative agreement for high platelet signalling activity.
Accelerating Drug Safety Assessment using Bidirectional-LSTM for SMILES Data
Authors: K. Venkateswara Rao, Dr. Kunjam Nageswara Rao, Dr. G. Sita Ratnam
Date: 2024-07-08 00:00:00
Tasks: Drug Discovery

Computational methods are useful in accelerating the pace of drug discovery. Drug discovery carries several steps such as target identification and validation, lead discovery, and lead optimisation etc., In the phase of lead optimisation, the absorption, distribution, metabolism, excretion, and toxicity properties of lead compounds are assessed. To address the issue of predicting toxicity and solubility in the lead compounds, represented in Simplified Molecular Input Line Entry System (SMILES) notation. Among the different approaches that work on SMILES data, the proposed model was built using a sequence-based approach. The proposed Bi-Directional Long Short Term Memory (BiLSTM) is a variant of Recurrent Neural Network (RNN) that processes input molecular sequences for the comprehensive examination of the structural features of molecules from both forward and backward directions. The proposed work aims to understand the sequential patterns encoded in the SMILES strings, which are then utilised for predicting the toxicity of the molecules. The proposed model on the ClinTox dataset surpasses previous approaches such as Trimnet and Pre-training Graph neural networks(GNN) by achieving a ROC accuracy of 0.96. BiLSTM outperforms the previous model on FreeSolv dataset with a low RMSE value of 1.22 in solubility prediction.
Microfluidic 3D Cell Culture: Potential Application of Collagen Hydrogels with an Optimal Dose of Bioactive Glasses
Authors: Faezeh Ghobadi, Maryam Saadatmand, Sara Simorgh, Peiman Brouki Milan
Date: 2024-08-06 00:00:00

We engineered a microfluidic platform to study the effects of bioactive glass nanoparticles (BGNs) on cell viability under static culture. We incorporated different concentrations of BGNs (1%, 2%, and 3% w/v) in collagen hydrogel (with a concentration of 3.0 mg/mL). The microfluidic chip's dimensions were optimized through fluid flow and mass transfer simulations. Collagen type I extracted from rat tail tendons was used as the main material, and BGNs synthesized by the sol-gel method were used to enhance the mechanical properties of the hydrogel. The extracted collagen was characterized using FTIR and SDS-PAGE, and BGNs were analyzed using XRD, FTIR, DLS, and FE-SEM/EDX. The structure of the collagen-BGNs hydrogels was examined using SEM, and their mechanical properties were determined using rheological analysis. The cytotoxicity of BGNs was assessed using the MTT assay, and the viability of fibroblast (L929) cells encapsulated in the collagen-BGNs hydrogel inside the microfluidic device was assessed using a live/dead assay. Based on all these test results, the L929 cells showed high cell viability in vitro and promising microenvironment mimicry in a microfluidic device. Collagen3-BGNs3 (Collagen 3 mg/mL + BGNs 3% (w/v)) was chosen as the most suitable sample for further research on a microfluidic platform.
The IBEX Knowledge-Base: Achieving more together with open science
Authors: Andrea J. Radtke, Ifeanyichukwu Anidi, Leanne Arakkal, Armando Arroyo-Mejias, Rebecca T. Beuschel, Katy Borner, Colin J. Chu, Beatrice Clark, Menna R. Clatworthy, Jake Colautti, Joshua Croteau, Saven Denha, Rose Dever, Walderez O. Dutra, Sonja Fritzsche, Spencer Fullam, Michael Y. Gerner, Anita Gola, Kenneth J. Gollob, Jonathan M. Hernandez, Jyh Liang Hor, Hiroshi Ichise, Zhixin Jing, Danny Jonigk, Evelyn Kandov, Wolfgang Kastenmueller, Joshua F. E. Koenig, Aanandita Kothurkar, Alexandra Y. Kreins, Ian Lamborn, Yuri Lin, Katia Luciano Pereira Morais, Aleksandra Lunich, Jean C. S. Luz, Ryan B. MacDonald, Chen Makranz, Vivien I. Maltez, Ryan V. Moriaty, Juan M. Ocampo-Godinez, Vitoria M. Olyntho, Kartika Padhan, Kirsten Remmert, Nathan Richoz, Edward C. Schrom, Wanjing Shang, Lihong Shi, Rochelle M. Shih, Emily Speranza, Salome Stierli, Sarah A. Teichmann, Tibor Z. Veres, Megan Vierhout, Brianna T. Wachter, Adam K. Wade-Vallance, Margaret Williams, Nathan Zangger, Ronald N. Germain, Ziv Yaniv
Date: 2024-07-26 00:00:00

Iterative Bleaching Extends multipleXity (IBEX) is a versatile method for highly multiplexed imaging of diverse tissues. Based on open science principles, we created the IBEX Knowledge-Base, a resource for reagents, protocols and more, to empower innovation.
Correlation emergence in two coupled simulated limit order books
Authors: Dominic Bauer, Derick Diana, Tim Gebbie
Date: 2024-08-06 00:00:00

We use random walks to simulate the fluid limit of two coupled diffusive limit order books to model correlation emergence. The model implements the arrival, cancellation and diffusion of orders coupled by a pairs trader profiting from the mean-reversion between the two order books in the fluid limit for a Lit order book with vanishing boundary conditions and order volume conservation. We are able to demonstrate the recovery of an Epps effect from this. We discuss how various stylised facts depend on the model parameters and the numerical scheme and discuss the various strengths and weaknesses of the approach. We demonstrate how the Epps effect depends on different choices of time and price discretisation. This shows how an Epps effect can emerge without recourse to market microstructure noise relative to a latent model but can rather be viewed as an emergent property arising from trader interactions in a world of asynchronous events.
Existence, uniqueness and positivity of solutions to the Guyon-Lekeufack path-dependent volatility model with general kernels
Authors: Hervé Andrès, Benjamin Jourdain
Date: 2024-08-05 00:00:00

We show the existence and uniqueness of a continuous solution to a path-dependent volatility model introduced by Guyon and Lekeufack (2023) to model the price of an equity index and its spot volatility. The considered model for the trend and activity features can be written as a Stochastic Volterra Equation (SVE) with non-convolutional and non-bounded kernels as well as non-Lipschitz coefficients. We first prove the existence and uniqueness of a solution to the SVE under integrability and regularity assumptions on the two kernels and under a condition on the second kernel weighting the past squared returns which ensures that the activity feature is bounded from below by a positive constant. Then, assuming in addition that the kernel weighting the past returns is of exponential type and that an inequality relating the logarithmic derivatives of the two kernels with respect to their second variables is satisfied, we show the positivity of the volatility process which is obtained as a non-linear function of the SVE's solution. We show numerically that the choice of an exponential kernel for the kernel weighting the past returns has little impact on the quality of model calibration compared to other choices and the inequality involving the logarithmic derivatives is satisfied by the calibrated kernels. These results extend those of Nutz and Valdevenito (2023).
Efficient simulation of the SABR model
Authors: Jaehyuk Choi, Lilian Hu, Yue Kuen Kwok
Date: 2024-08-04 00:00:00
Tasks: model

We propose an efficient and reliable simulation scheme for the stochastic-alpha-beta-rho (SABR) model. The two challenges of the SABR simulation lie in sampling (i) the integrated variance conditional on terminal volatility and (ii) the terminal price conditional on terminal volatility and integrated variance. For the first sampling procedure, we analytically derive the first four moments of the conditional average variance, and sample it from the moment-matched shifted lognormal approximation. For the second sampling procedure, we approximate the conditional terminal price as a constant-elasticity-of-variance (CEV) distribution. Our CEV approximation preserves the martingale condition and precludes arbitrage, which is a key advantage over Islah's approximation used in most SABR simulation schemes in the literature. Then, we adopt the exact sampling method of the CEV distribution based on the shifted-Poisson-mixture Gamma random variable. Our enhanced procedures avoid the tedious Laplace inversion algorithm for sampling integrated variance and non-efficient inverse transform sampling of the forward price in some of the earlier simulation schemes. Numerical results demonstrate our simulation scheme to be highly efficient, accurate, and reliable.
Evaluating Microscopic and Macroscopic Models for Derivative Contracts on Commodity Indices
Authors: Alberto Manzano, Emanuele Nastasi, Andrea Pallavicini, Carlos Vázquez
Date: 2024-07-17 00:00:00

In this article, we analyze two modeling approaches for the pricing of derivative contracts on a commodity index. The first one is a microscopic approach, where the components of the index are modeled individually, and the index price is derived from their combination. The second one is a macroscopic approach, where the index is modeled directly. While the microscopic approach offers greater flexibility, its calibration results to be more challenging, thus leading practitioners to favor the macroscopic approach. However, in the macroscopic model, the lack of explicit futures curve dynamics raises questions about its ability to accurately capture the behavior of the index and its sensitivities. In order to investigate this, we calibrate both models using derivatives of the S\&P GSCI Crude Oil excess-return index and compare their pricing and sensitivities on path-dependent options, such as autocallable contracts. This research provides insights into the suitability of macroscopic models for pricing and hedging purposes in real scenarios.
The indifference value of the weak information
Authors: Fabrice Baudoin, Oleksii Mostovyi
Date: 2024-08-04 00:00:00

We propose indifference pricing to estimate the value of the weak information. Our framework allows for tractability, quantifying the amount of additional information, and permits the description of the smallness and the stability with respect to small perturbations of the weak information. We provide sharp conditions for the stability with counterexamples. The results rely on a theorem of independent interest on the stability of the optimal investment problem with respect to small changes in the physical probability measure. We also investigate contingent claims that are indifference price invariant with respect to changes in weak information. We show that, in incomplete models, the class of information-invariant claims includes the replicable claims, and it can be strictly bigger. In particular, in complete models, all contingent claims are information invariant. We augment the results with examples and counterexamples.