pwc-archive/papers-with-abstracts huggingface dataset:
Assessing biological control method on the progression of anaplasmosis disease in dominant cattle species in the Matabeleland north province Zimbabwe
Authors: Meshach Ndlovu
Date: 2024-04-24 00:00:00

This paper presents a compartmental model for the transmission dynamics of Anaplasmosis in resource limited farmers cattle subjected to a biological control method. The study seeks to evaluate the stability and control of cattle herds dynamics relative to finite agitation. Anaplasmosis disease pose a major threat in eradicating cattle population growth in resources limited communities. In gaining the insight of the disease, the following model analysis strategies were used in order to compute simulations, analysis of the model upon varying initial predator population and testing the effects of different predation rate on the disease dynamics. It is essential that the progression of Anaplasmosis be stable after the introduction of tick predators into cattle-tick system because that provides the usability of predation as a control measure. After analysing the effect of different prediction rates on the spread of the disease in resource limited communities the study asserted that tick predators like birds and bacteria have been neglected as contributors to natural mechanism of Anaplasmosis. Furthermore, the study brought to light that predictors have been neglected as major contributors to natural control mechanism of Anaplasmosis in Zimbabwe. Additional numerical simulations showed that predation method can be used in the eradication of Anaplasmosis disease thus improving rural livelihood. Investigation of natural tick enemies and predation behaviour can lead to better control of the Anaplasmosis disease efficiently and effectively. Finally, we recommend the necessity for resource limited farmers to capitalise on the use of biological disease control measures.
A pure dual approach for hedging Bermudan options
Authors: Aurélien Alfonsi, Ahmed Kebaier, Jérôme Lelong
Date: 2024-04-29 00:00:00

This paper develops a new dual approach to compute the hedging portfolio of a Bermudan option and its initial value. It gives a "purely dual" algorithm following the spirit of Rogers (2010) in the sense that it only relies on the dual pricing formula. The key is to rewrite the dual formula as an excess reward representation and to combine it with a strict convexification technique. The hedging strategy is then obtained by using a Monte Carlo method, solving backward a sequence of least square problems. We show convergence results for our algorithm and test it on many different Bermudan options. Beyond giving directly the hedging portfolio, the strength of the algorithm is to assess both the relevance of including financial instruments in the hedging portfolio and the effect of the rebalancing frequency.
Resource-rational reinforcement learning and sensorimotor causal states, and resource-rational maximiners
Authors: Sarah Marzen
Date: 2024-04-29 00:00:00
Tasks: reinforcement-learning

We propose a new computational-level objective function for theoretical biology and theoretical neuroscience that combines: reinforcement learning, the study of learning with feedback via rewards; rate-distortion theory, a branch of information theory that deals with compressing signals to retain relevant information; and computational mechanics, the study of minimal sufficient statistics of prediction also known as causal states. We highlight why this proposal is likely only an approximation, but is likely to be an interesting one, and propose a new algorithm for evaluating it to obtain the newly-coined ``reward-rate manifold''. The performance of real and artificial agents in partially observable environments can be newly benchmarked using these reward-rate manifolds. Finally, we describe experiments that can probe whether or not biological organisms are resource-rational reinforcement learners, using as an example maximin strategies, as bacteria have been shown to be approximate maximiners -- doing their best in the worst-case environment, regardless of what is actually happening.
A pretest-posttest pilot study for augmented reality-based physical-cognitive training in community-dwelling older adults at risk of mild cognitive impairment
Authors: Sirinun Chaipunko, Watthanaree Ammawat, Keerathi Oanmun, Wanvipha Hongnaphadol, Supatida Sorasak, Pattrawadee Makmee
Date: 2024-04-29 00:00:00
Tasks: Experimental Design

As cognitive interventions for older adults evolve, modern technologies are increasingly integrated into their development. This study investigates the efficacy of augmented reality (AR)-based physical-cognitive training using an interactive game with Kinect motion sensor technology on older individuals at risk of mild cognitive impairment. Utilizing a pretest-posttest experimental design, twenty participants (mean age 66.8 SD. = 4.6 years, age range 60-78 years) underwent eighteen individual training sessions, lasting 45 to 60 minutes each, conducted three times a week over a span of 1.5 months. The training modules from five activities, encompassing episodic and working memory, attention and inhibition, cognitive flexibility, and speed processing, were integrated with physical movement and culturally relevant Thai-context activities. Results revealed significant improvements in inhibition, cognitive flexibility, accuracy, and reaction time, with working memory demonstrating enhancements in accuracy albeit not in reaction time. These findings underscore the potential of AR interventions to bolster basic executive enhancement among community-dwelling older adults at risk of cognitive decline.
Diversity in the radiation-induced transcriptomic temporal response of mouse brain tissue regions
Authors: Karolina Kulis, Sarah Baatout, Kevin Tabury, Joanna Polanska, Mohammed Abderrafi Benotmane
Date: 2024-04-29 00:00:00
Tasks: Diversity, Hippocampus

A number of studies have indicated a potential association between prenatal exposure to radiation and late mental disabilities. This is believed to be due to long-term developmental changes and functional impairment of the central nervous system following radiation exposure during gestation. This study conducted a bioinformatics analysis on transcriptomic profiles from mouse brain tissue prenatally exposed to increasing doses of X-radiation. Gene expression levels were assessed in different brain regions (cortex, hippocampus, cerebellum) and collected at different time points (at 1 and 6 months after birth) for C57BL mice exposed at embryonic day E11 to varying doses of radiation (0, 0.1 and 1 Gy). This study aimed to elucidate the differences in response to radiation between different brain regions at different intervals after birth (1 and 6 months). The data was visualised using a two-dimensional Uniform Manifold Approximation and Projection (UMAP) projection, and the influence of the factors was investigated using analysis of variance (ANOVA). It was observed that gene expression was influenced by each factor (tissue, time, and dose), although to varying degrees. The gene expression trend within doses was compared for each tissue, as well as the significant pathways between tissues at different time intervals. Furthermore, in addition to radiation-responsive pathways, Cytoscape's functional and network analyses revealed changes in various pathways related to cognition, which is consistent with previously published data [1] [2] [3], indicating late behavioural changes in animals prenatally exposed to radiation.
Multi-centre normative brain mapping of intracranial EEG lifespan patterns in the human brain
Authors: Heather Woodhouse, Gerard Hall, Callum Simpson, Csaba Kozma, Frances Turner, Gabrielle M. Schroeder, Beate Diehl, John S. Duncan, Jiajie Mo, Kai Zhang, Aswin Chari, Martin Tisdall, Friederike Moeller, Chris Petkov, Matthew A. Howard, George M. Ibrahim, Elizabeth Donner, Nebras M. Warsi, Raheel Ahmed, Peter N. Taylor, Yujiang Wang
Date: 2024-04-27 00:00:00
Tasks: EEG

Background: Understanding healthy human brain function is crucial to identify and map pathological tissue within it. Whilst previous studies have mapped intracranial EEG (icEEG) from non-epileptogenic brain regions, these maps do not consider the effects of age and sex. Further, most existing work on icEEG has often suffered from a small sample size due to the modality's invasive nature. Here, we substantially increase the subject sample size compared to existing literature, to create a multi-centre, normative map of brain activity which additionally considers the effects of age, sex and recording hospital. Methods: Using interictal icEEG recordings from n = 502 subjects originating from 15 centres, we constructed a normative map of non-pathological brain activity by regressing age and sex on relative band power in five frequency bands, whilst accounting for the hospital effect. Results: Recording hospital significantly impacted normative icEEG maps in all frequency bands, and age was a more influential predictor of band power than sex. The age effect varied by frequency band, but no spatial patterns were observed at the region-specific level. Certainty about regression coefficients was also frequency band specific and moderately impacted by sample size. Conclusion: The concept of a normative map is well-established in neuroscience research and particularly relevant to the icEEG modality, which does not allow healthy control baselines. Our key results regarding the hospital site and age effect guide future work utilising normative maps in icEEG.
The Convergence of AI and Synthetic Biology: The Looming Deluge
Authors: Cindy Vindman, Benjamin Trump, Christopher Cummings, Madison Smith, Alexander J. Titus, Ken Oye, Valentina Prado, Eyup Turmus, Igor Linkov
Date: 2024-04-29 00:00:00

The convergence of artificial intelligence (AI) and synthetic biology is rapidly accelerating the pace of biological discovery and engineering. AI techniques, such as large language models and biological design tools, are enabling the automated design, build, test, and learning cycles for engineered biological systems. This convergence promises to democratize synthetic biology and unlock novel applications across domains from medicine to environmental sustainability. However, it also poses significant risks around reliability, dual use, and governance. The opacity of AI models, the deskilling of workforces, and the outdated nature of current regulatory frameworks present challenges in ensuring responsible development. Urgent attention is needed to update governance structures, integrate human oversight into increasingly automated workflows, and foster a culture of responsibility among the growing community of bioengineers. Only by proactively addressing these issues can we realize the transformative potential of AI-driven synthetic biology while mitigating its risks.
Computational Approaches of Modelling Human Papillomavirus Transmission and Prevention Strategies: A Systematic Review
Authors: Weiyi Wang, Shailendra Sawleshwarkar, Mahendra Piraveenan
Date: 2024-04-30 00:00:00
Tasks: Epidemiology

Human papillomavirus (HPV) infection is the most common sexually transmitted infection in the world. Persistent oncogenic Human papillomavirus infection has been a leading threat to global health and can lead to serious complications such as cervical cancer. Prevention interventions including vaccination and screening have been proved effective in reducing the risk of HPV-related diseases. In recent decades, computational epidemiology has been serving as a very useful tool to study HPV transmission dynamics and evaluation of prevention strategies. In this paper, we conduct a comprehensive literature review on state-of-the-art computational epidemic models for HPV disease dynamics, transmission dynamics, as well as prevention efforts. We summarise current research trends, identify gaps in the present literature, and identify future research directions with potential in accelerating the containment and/or elimination of HPV infection.
Stochastic dynamics of two-compartment cell proliferation models with regulatory mechanisms for hematopoiesis
Authors: Ren-Yi Wang, Marek Kimmel, Guodong Pang
Date: 2024-04-29 00:00:00

We present an asymptotic analysis of a stochastic two-compartmental cell division system with regulatory mechanisms inspired by Getto et al. (2013). The hematopoietic system is modeled as a two-compartment system, where the first compartment consists of dividing cells in the bone marrow, referred to as type $0$ cells, and the second compartment consists of post-mitotic cells in the blood, referred to as type $1$ cells. Division and self-renewal of type $0$ cells are regulated by the population density of type $1$ cells. By scaling up the initial population, we demonstrate that the scaled dynamics converges in distribution to the solution of a system of ordinary differential equations (ODEs). This system of ODEs exhibits a unique non-trivial equilibrium that is globally stable. Furthermore, we establish that the scaled fluctuations of the density dynamics converge in law to a linear diffusion process with time-dependent coefficients. When the initial data is Gaussian, the limit process is a Gauss-Markov process. We analyze its asymptotic properties to elucidate the joint structure of both compartments over large times. This is achieved by proving exponential convergence in the 2-Wasserstein metric for the associated Gaussian measures on an $L^{2}$ Hilbert space. Finally, we apply our results to compare the effects of regulating division and self-renewal of type $0$ cells, providing insights into their respective roles in maintaining hematopoietic system stability.
Coupling in situ and remote sensing data to assess $α$- and $β$-diversity over biogeographic gradients
Authors: Maxime Lenormand, Jean-Baptiste Féret, Guillaume Papuga, Samuel Alleaume, Sandra Luque
Date: 2024-04-29 00:00:00
Tasks: Diversity

The mapping of plant biodiversity represents a fundamental stage in establishing conservation priorities, particularly in identifying groups of species that share ecological requirements or evolutionary histories. This is often achieved by assessing different spatial diversity patterns in plant population distributions. In this paper, we present two primary data sources crucial for biodiversity monitoring: in situ measurements from botanical observations and remote sensing (RS). In situ methods involve directly collecting data from specific sites, providing detailed insights into ecological patterns but often constrained by resource limitations. Integrating in situ and RS data highlights their complementary strengths, which depend on factors such as study scale, resolution, and logistical feasibility. While in situ approaches are characterized by precision, RS offers efficiency and extensive, repeated coverage. This research integrates in situ and RS data to analyze plant and spectral diversity across France at a spatial resolution of 5 km, encompassing over 23,000 grid cells. We employ four established diversity metrics leveraging the spatial distribution of 6,650 plant species and 250 spectral clusters (derived from MODIS data at a 500-meter resolution). Through bioregionalization network analysis combining these data sources, we identified five distinct bioregions that capture the biogeographical structure of plant biodiversity in France. Additionally, we explore the relationship between plant species diversity and spectral cluster diversity within and between these bioregions, offering novel insights into the spatial dynamics of plant biodiversity.
Nested Inheritance Dynamics
Authors: Bahman Moraffah
Date: 2024-04-23 00:00:00

The idea of the inheritance of biological processes, such as the developmental process or the life cycle of an organism, has been discussed in the biology literature, but formal mathematical descriptions and plausible data analysis frameworks are lacking. We introduce an extension of the nested Dirichlet Process (nDP) to a multiscale model to aid in understanding the mechanisms by which biological processes are inherited, remain stable, and are modified across generations. To address these issues, we introduce Nested Inheritance Dynamics Algorithm (NIDA). At its primary level, NIDA encompasses all processes unfolding within an individual organism's lifespan. The secondary level delineates the dynamics through which these processes evolve or remain stable over time. This framework allows for the specification of a physical system model at either scale, thus promoting seamless integration with established models of development and heredity.
Noise propagation and MP-PCA image denoising for high-resolution quantitative T2* and magnetic susceptibility mapping (QSM)
Authors: Liad Doniza, Mitchel Lee, Tamar Blumenfeld Katzir, Moran Artzi, Dafna Ben Bashat, Dvir Radunsky, Karin Shmueli, Noam Ben-Eliezer
Date: 2024-04-30 00:00:00
Tasks: Denoising, Diagnostic, Image Denoising

Quantitative Susceptibility Mapping (QSM) is a technique for measuring magnetic susceptibility of tissues, aiding in the detection of pathologies like traumatic brain injury and multiple sclerosis by analyzing variations in substances such as iron and calcium. Despite its clinical value, achieving high-resolution QSM (voxel sizes < 1 mm3) reduces signal-to-noise ratio (SNR), compromising diagnostic quality. To mitigate this, we applied the Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising technique on T2* weighted data, to enhance the quality of R2*, T2*, and QSM maps. Denoising was tested on a numerical phantom, healthy subjects, and patients with brain metastases and sickle cell disease, demonstrating effective and robust improvements across different scan settings. Further analysis examined noise propagation in R2* and T2* values, revealing lower noise-related variations in R2* values compared to T2* values which tended to be overestimated due to noise. Reduced variability was observed in QSM values post denoising, demonstrating MP-PCA's potential to improve the
Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank
Authors: Thomas Le Menestrel, Erin Craig, Robert Tibshirani, Trevor Hastie, Manuel Rivas
Date: 2024-04-26 00:00:00
Tasks: Disease Prediction, Prediction

Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals, underscoring a critical gap in genetic research. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data. We evaluate the performance of Group-LASSO INTERaction-NET (glinternet) and pretrained lasso in disease prediction focusing on diverse ancestries in the UK Biobank. Models were trained on data from White British and other ancestries and validated across a cohort of over 96,000 individuals for 8 diseases. Out of 96 models trained, we report 16 with statistically significant incremental predictive performance in terms of ROC-AUC scores (p-value < 0.05), found for diabetes, arthritis, gall stones, cystitis, asthma and osteoarthritis. For the interaction and pretrained models that outperformed the baseline, the PRS score was the primary driver behind prediction. Our findings indicate that both interaction terms and pre-training can enhance prediction accuracy but for a limited set of diseases and moderate improvements in accuracy
The Effect of Data Types' on the Performance of Machine Learning Algorithms for Financial Prediction
Authors: Hulusi Mehmet Tanrikulu, Hakan Pabuccu
Date: 2024-04-30 00:00:00
Tasks: Binarization, Prediction

Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A small improvement in forecasting performance can lead to increased profitability; therefore, obtaining a realistic forecast is very important for investors. Successful forecasting provides traders with effective buy-or-hold strategies, allowing them to make more profits. The most important thing in this process is to produce accurate forecasts suitable for real-life applications. Bitcoin, frequently mentioned recently due to its volatility and chaotic behavior, has begun to pay great attention and has become an investment tool, especially during and after the COVID-19 pandemic. This study provided a comprehensive methodology, including constructing continuous and trend data using one and seven years periods of data as inputs and applying machine learning (ML) algorithms to forecast Bitcoin price movement. A binarization procedure was applied using continuous data to construct the trend data representing each input feature trend. Following the related literature, the input features are determined as technical indicators, google trends, and the number of tweets. Random forest (RF), K-Nearest neighbor (KNN), Extreme Gradient Boosting (XGBoost-XGB), Support vector machine (SVM) Naive Bayes (NB), Artificial Neural Networks (ANN), and Long-Short-Term Memory (LSTM) networks were applied on the selected features for prediction purposes. This work investigates two main research questions: i. How does the sample size affect the prediction performance of ML algorithms? ii. How does the data type affect the prediction performance of ML algorithms? Accuracy and area under the ROC curve (AUC) values were used to compare the model performance. A t-test was performed to test the statistical significance of the prediction results.
Mean Field Game of High-Frequency Anticipatory Trading
Authors: Xue Cheng, Meng Wang, Ziyi Xu
Date: 2024-04-28 00:00:00

The interactions between a large population of high-frequency traders (HFTs) and a large trader (LT) who executes a certain amount of assets at discrete time points are studied. HFTs are faster in the sense that they trade continuously and predict the transactions of LT. A jump process is applied to model the transition of HFTs' attitudes towards inventories and the equilibrium is solved through the mean field game approach. When the crowd of HFTs is averse to running (ending) inventories, they first take then supply liquidity at each transaction of LT (throughout the whole execution period). Inventory-averse HFTs lower LT's costs if the market temporary impact is relatively large to the permanent one. What's more, the repeated liquidity consuming-supplying behavior of HFTs makes LT's optimal strategy close to uniform trading.
Dynamic Black-Litterman
Authors: Anas Abdelhakmi, Andrew Lim
Date: 2024-04-29 00:00:00
Tasks: model, Portfolio Optimization

The Black-Litterman model is a framework for incorporating forward-looking expert views in a portfolio optimization problem. Existing work focuses almost exclusively on single-period problems with the forecast horizon matching that of the investor. We consider a generalization where the investor trades dynamically and views can be over horizons that differ from the investor. By exploiting the underlying graphical structure relating the asset prices and views, we derive the conditional distribution of asset returns when the price process is geometric Brownian motion, and show that it can be written in terms of a multi-dimensional Brownian bridge. The components of the Brownian bridge are dependent one-dimensional Brownian bridges with hitting times that are determined by the statistics of the price process and views. The new price process is an affine factor model with the conditional log-price process playing the role of a vector of factors. We derive an explicit expression for the optimal dynamic investment policy and analyze the hedging demand for changes in the new covariate. More generally, the paper shows that Bayesian graphical models are a natural framework for incorporating complex information structures in the Black-Litterman model. The connection between Brownian motion conditional on noisy observations of its terminal value and multi-dimensional Brownian bridge is novel and of independent interest.