pwc-archive/papers-with-abstracts huggingface dataset:
From trees to traits: A review of advances in PhyloG2P methods and future directions
Authors: Arlie R. Macdonald, Maddie E. James, Jonathan D. Mitchell, Barbara R. Holland
Date: 2025-01-13 00:00:00

Mapping genotypes to phenotypes (G2P) is a fundamental goal in biology. So called PhyloG2P methods are a relatively new set of tools that leverage replicated evolution in phylogenetically independent lineages to identify genomic regions associated with traits of interest. Here, we review recent developments in PhyloG2P methods, focusing on three key areas: methods based on replicated amino acid substitutions, methods detecting changes in evolutionary rates, and methods analysing gene duplication and loss. We discuss how the definition and measurement of traits impacts the utility of these methods, arguing that focusing on simple rather than compound traits will lead to more meaningful genotype-phenotype associations. We advocate for the use of methods that work with continuous traits directly rather than collapsing them to binary representations. We examine the strengths and limitations of different approaches to modeling genetic replication, highlighting the importance of explicit modeling of evolutionary processes. Finally, we outline promising future directions, including the integration of population-level variation, as well as epigenetic and environmental information. No one method is likely to identify all genomic regions of interest, so we encourage users to apply multiple methods that are capable of detecting a wide range of associations. The overall aim of this review is to provide practitioners a roadmap for understanding and applying PhyloG2P methods.
A model of predation and survival in a system of three interacting species
Authors: Anca Radulescu, Richard Halpern, Drew Kozlowski, Conor O'Riordan
Date: 2025-01-11 00:00:00

The study of interactions between multiple species in an ecosystem is an active and impactful direction of inquiry. This is true in particular for fragile systems in which even small perturbations of their functional parameters can produce dramatic effects like species endangerment or extinction, leading the system to enter an unsustainable regime and eventually collapse. In this context, it is important to understand which factors can lead to such effects and for which systems, so that one can act proactively and timely to prevent them. We built and studied a mathematical model that captures the natural interactions between three species, in which two species are predators of the third, but such that one of the predators also consumes the other (to which we refer as Owls, Snakes and Mice). The nonlinear components of the model were documented on existing literature and assembled as a system of Lotka-Volterra ordinary differential equations. Our analytical computations and numerical exploration explorations revealed sequences of transcritical and Hopf bifurcations that underlie counterintuitive transitions of the system into regions of vulnerability to external noise. We conclude that, in order to avoid extinction,one needs to rigorously prescribe a well-documented, prediction-based approach to population control.
Extinction and Extirpation Conditions in Coalescent and Ecotonal Metacommunities
Authors: Martin Heidelman, Dervis Can Vural
Date: 2025-01-10 00:00:00

Here we present extinction, extirpation and coexistence conditions where / when two communities combine. We consider one specific model where two communities coalesce, and another model where the communities coexist side by side, blending in a transitionary zone called the ecotone. Specifically, (1) we analytically calculate the shifts in abundances as a function of mixing strength. (2) Obtain a critical value for the mixing strength leading to extinction. (3) Derive an inequality condition for full coexistent mixing. (4) find how the individual communities penetrate into one other as a function of mixing strength. (5) derive the conditions for one species to cross the ecotone and invade an neighboring community and (6) conditions for a native species to get extirpated. Lastly, (7) we spatially investigate the species richness within the ecotone and derive a condition that determines whether the ecotone will have higher or lower richness compared to its surrounding habitats.
Solar radiation and atmospheric CO$_2$ predict young leaf production in a moist evergreen tropical forest: Insights from 23 years
Authors: Laura Lüthy, Colin A. Chapman, Patrick Lauer, Patrick Omeja, Urs Kalbitzer
Date: 2025-01-13 00:00:00

Climate change impacts ecosystems worldwide, affecting animal behaviour and survival both directly and indirectly through changes such as the availability of food. For animals reliant on leaves as a primary food source, understanding how climate change influences leaf production of trees is crucial, yet this is understudied, especially in moist evergreen tropical forests. We analyzed a 23-year dataset of young leaf phenology from a moist tropical forest in Kibale National Park, Uganda, to examine seasonal and long-term patterns of 12 key tree species consumed by folivorous primates. We described phenological patterns and explored relationships between young leaf production of different tree species and climate variables. We also assessed the suitability of the Enhanced Vegetation Index (EVI) as a proxy for young leaf production in moist evergreen tropical forests. Our results showed that tree species exhibited distinct phenological patterns, with most species producing young leaves during two seasonal peaks aligned with the rainy seasons. Rainfall, cloud cover, and maximum temperature were the most informative predictors of seasonal variation in young leaf production. However, solar radiation and atmospheric CO$_2$ were most informative regarding long-term trends. EVI was strongly correlated with young leaf production within years but less effective for capturing inter-annual trends. These findings highlight the complex relationship between climate and young leaf phenology in moist evergreen tropical forests, and helps us understand the changes in food availability for tropical folivores.
Temporal Dynamics of Microbial Communities in Anaerobic Digestion: Influence of Temperature and Feedstock Composition on Reactor Performance and Stability
Authors: Ellen Piercy, Xinyang Sun, Peter R Ellis, Mark Taylor, Miao Guo
Date: 2025-01-12 00:00:00

Anaerobic digestion (AD) offers a sustainable biotechnology to recover resources from carbon-rich wastewater, such as food-processing wastewater. Despite crude wastewater characterisation, the impact of detailed chemical fingerprinting on AD remains underexplored. This study investigated the influence of fermentation-wastewater composition and operational parameters on AD over time to identify critical factors influencing reactor biodiversity and performance. Eighteen reactors were operated under various operational conditions using mycoprotein fermentation wastewater. Detailed chemical analysis fingerprinted the molecules in the fermentation wastewater throughout AD including sugars, sugar alcohols and volatile fatty acids (VFAs). Sequencing revealed distinct microbiome profiles linked to temperature and reactor configuration, with mesophilic conditions supporting a more diverse and densely connected microbiome. Significant elevations in Methanomassiliicoccus were correlated to high butyric acid concentrations and decreased biogas production, further elucidating the role of this newly discovered methanogen. Dissimilarity analysis demonstrated the importance of individual molecules on microbiome diversity, highlighting the need for detailed chemical fingerprinting in AD studies of microbial trends. Machine learning (ML) models predicting reactor performance achieved high accuracy based on operational parameters and microbial taxonomy. Operational parameters had the most substantial influence on chemical oxygen demand removal, whilst Oscillibacter and two Clostridium sp. were highlighted as key factors in biogas production. By integrating detailed chemical and biological fingerprinting with ML models this research presents a novel approach to advance our understanding of AD microbial ecology, offering insights for industrial applications of sustainable waste-to-energy systems.
Heuristics based on Adjacency Graph Packing for DCJ Distance Considering Intergenic Regions
Authors: Gabriel Siqueira, Alexsandro Oliveira Alexandrino, Andre Rodrigues Oliveira, Zanoni Dias
Date: 2025-01-11 00:00:00

In this work, we explore heuristics for the Adjacency Graph Packing problem, which can be applied to the Double Cut and Join (DCJ) Distance Problem. The DCJ is a rearrangement operation and the distance problem considering it is a well established method for genome comparison. Our heuristics will use the structure called adjacency graph adapted to include information about intergenic regions, multiple copies of genes in the genomes, and multiple circular or linear chromosomes. The only required property from the genomes is that it must be possible to turn one into the other with DCJ operations. We propose one greedy heuristic and one heuristic based on Genetic Algorithms. Our experimental tests in artificial genomes show that the use of heuristics is capable of finding good results that are superior to a simpler random strategy.
Unsupervised detection and fitness estimation of emerging SARS-CoV-2 variants. Application to wastewater samples (ANRS0160)
Authors: Alexandra Lefebvre, Vincent Maréchal, Arnaud Gloaguen, Obépine Consortium, Amaury Lambert, Yvon Maday
Date: 2025-01-11 00:00:00
Tasks: Model Selection, parameter estimation, Time Series

Repeated waves of emerging variants during the SARS-CoV-2 pandemics have highlighted the urge of collecting longitudinal genomic data and developing statistical methods based on time series analyses for detecting new threatening lineages and estimating their fitness early in time. Most models study the evolution of the prevalence of particular lineages over time and require a prior classification of sequences into lineages. Such process is prone to induce delays and bias. More recently, few authors studied the evolution of the prevalence of mutations over time with alternative clustering approaches, avoiding specific lineage classification. Most of the aforementioned methods are however either non parametric or unsuited to pooled data characterizing, for instance, wastewater samples. In this context, we propose an alternative unsupervised method for clustering mutations according to their frequency trajectory over time and estimating group fitness from time series of pooled mutation prevalence data. Our model is a mixture of observed count data and latent group assignment and we use the expectation-maximization algorithm for model selection and parameter estimation. The application of our method to time series of SARS-CoV-2 sequencing data collected from wastewater treatment plants in France from October 2020 to April 2021 shows its ability to agnostically group mutations according to their probability of belonging to B.1.160, Alpha, Beta, B.1.177 variants with selection coefficient estimates per group in coherence with the viral dynamics in France reported by Nextstrain. Moreover, our method detected the Alpha variant as threatening as early as supervised methods (which track specific mutations over time) with the noticeable difference that, since unsupervised, it does not require any prior information on the set of mutations.
Automating Care by Self-maintainability for Full Laboratory Automation
Authors: Koji Ochiai, Yuya Tahara-Arai, Akari Kato, Kazunari Kaizu, Hirokazu Kariyazaki, Makoto Umeno, Koichi Takahashi, Genki N. Kanda, Haruka Ozaki
Date: 2025-01-10 00:00:00
Tasks: Scheduling

The automation of experiments in life sciences and chemistry has significantly advanced with the development of various instruments and AI technologies. However, achieving full laboratory automation, where experiments conceived by scientists are seamlessly executed in automated laboratories, remains a challenge. We identify the lack of automation in planning and operational tasks--critical human-managed processes collectively termed "care"--as a major barrier. Automating care is the key enabler for full laboratory automation. To address this, we propose the concept of self-maintainability (SeM): the ability of a laboratory system to autonomously adapt to internal and external disturbances, maintaining operational readiness akin to living cells. A SeM-enabled laboratory features autonomous recognition of its state, dynamic resource and information management, and adaptive responses to unexpected conditions. This shifts the planning and execution of experimental workflows, including scheduling and reagent allocation, from humans to the system. We present a conceptual framework for implementing SeM-enabled laboratories, comprising three modules--Requirement manager, Labware manager, and Device manager--and a Central manager. SeM not only enables scientists to execute envisioned experiments seamlessly but also provides developers with a design concept that drives the technological innovations needed for full automation.
Practical parameter identifiability of respiratory mechanics in the extremely preterm infant
Authors: Richard R. Foster, Laura Ellwein Fix
Date: 2025-01-15 00:00:00
Tasks: Sensitivity

The complexity of mathematical models describing respiratory mechanics has grown in recent years, however, parameter identifiability of such models has only been studied in the last decade in the context of observable data. This study investigates parameter identifiability of a nonlinear respiratory mechanics model tuned to the physiology of an extremely preterm infant, using global Morris screening, local deterministic sensitivity analysis, and singular value decomposition-based subset selection. The model predicts airflow and dynamic pulmonary volumes and pressures under varying levels of continuous positive airway pressure, and a range of parameters characterizing both surfactant-treated and surfactant-deficient lung. Sensitivity analyses indicated eleven parameters influence model outputs over the range of continuous positive airway pressure and lung health scenarios. The model was adapted to data from a spontaneously breathing 1 kg infant using gradient-based optimization to estimate the parameter subset characterizing the patient's state of health.
Pricing Quanto and Composite Contracts with Local-Correlation Models
Authors: Andrea Pallavicini
Date: 2025-01-13 00:00:00

Pricing composite and quanto contracts requires a joint model of both the underlying asset and the exchange rate. In this contribution, we explore the potential of local-correlation models to address the challenges of calibrating synthetic quanto forward contracts and composite options quoted in the market. Specifically, we design on-line calibration procedures for generic local and stochastic volatility models. The paper concludes with a numerical study assessing the calibration performance of these methodologies and comparing them to simpler approximations of the correlation structure.
Heath-Jarrow-Morton meet lifted Heston in energy markets for joint historical and implied calibration
Authors: Eduardo Abi Jaber, Soukaïna Bruneau, Nathan De Carvalho, Dimitri Sotnikov, Laurent Tur
Date: 2025-01-10 00:00:00

In energy markets, joint historical and implied calibration is of paramount importance for practitioners yet notoriously challenging due to the need to align historical correlations of futures contracts with implied volatility smiles from the option market. We address this crucial problem with a parsimonious multiplicative multi-factor Heath-Jarrow-Morton (HJM) model for forward curves, combined with a stochastic volatility factor coming from the Lifted Heston model. We develop a sequential fast calibration procedure leveraging the Kemna-Vorst approximation of futures contracts: (i) historical correlations and the Variance Swap (VS) volatility term structure are captured through Level, Slope, and Curvature factors, (ii) the VS volatility term structure can then be corrected for a perfect match via a fixed-point algorithm, (iii) implied volatility smiles are calibrated using Fourier-based techniques. Our model displays remarkable joint historical and implied calibration fits - to both German power and TTF gas markets - and enables realistic interpolation within the implied volatility hypercube.
Optimal Execution among $N$ Traders with Transient Price Impact
Authors: Steven Campbell, Marcel Nutz
Date: 2025-01-16 00:00:00

We study $N$-player optimal execution games in an Obizhaeva--Wang model of transient price impact. When the game is regularized by an instantaneous cost on the trading rate, a unique equilibrium exists and we derive its closed form. Whereas without regularization, there is no equilibrium. We prove that existence is restored if (and only if) a very particular, time-dependent cost on block trades is added to the model. In that case, the equilibrium is particularly tractable. We show that this equilibrium is the limit of the regularized equilibria as the instantaneous cost parameter $\varepsilon$ tends to zero. Moreover, we explain the seemingly ad-hoc block cost as the limit of the equilibrium instantaneous costs. Notably, in contrast to the single-player problem, the optimal instantaneous costs do not vanish in the limit $\varepsilon\to0$. We use this tractable equilibrium to study the cost of liquidating in the presence of predators and the cost of anarchy. Our results also give a new interpretation to the erratic behaviors previously observed in discrete-time trading games with transient price impact.
Follow the Leader: Enhancing Systematic Trend-Following Using Network Momentum
Authors: Linze Li, William Ferreira
Date: 2025-01-13 00:00:00
Tasks: Time Series

We present a systematic, trend-following strategy, applied to commodity futures markets, that combines univariate trend indicators with cross-sectional trend indicators that capture so-called {\em momentum spillover}, which can occur when there is a lead-lag relationship between the trending behaviour of different markets. Our strategy utilises two methods for detecting lead-lag relationships, with a method for computing {\em network momentum}, to produce a novel trend-following indicator. We use our new trend indicator to construct a portfolio whose performance we compare to a baseline model which uses only univariate indicators, and demonstrate statistically significant improvements in Sharpe ratio, skewness of returns, and downside performance, using synthetic bootstrapped data samples taken from time-series of actual prices.
Pricing American options under rough volatility using deep-signatures and signature-kernels
Authors: Christian Bayer, Luca Pelizzari, Jia-Jie Zhu
Date: 2025-01-12 00:00:00

We extend the signature-based primal and dual solutions to the optimal stopping problem recently introduced in [Bayer et al.: Primal and dual optimal stopping with signatures, to appear in Finance & Stochastics 2025], by integrating deep-signature and signature-kernel learning methodologies. These approaches are designed for non-Markovian frameworks, in particular enabling the pricing of American options under rough volatility. We demonstrate and compare the performance within the popular rough Heston and rough Bergomi models.
Optimal Insurance under Endogenous Default and Background Risk
Authors: Zongxia Liang, Zhaojie Ren, Bin Zou
Date: 2025-01-10 00:00:00

This paper studies an optimal insurance problem for a utility-maximizing buyer of insurance, subject to the seller's endogenous default and background risk. An endogenous default occurs when the buyer's contractual indemnity exceeds the seller's available reserve, which is random due to the background risk. We obtain an analytical solution to the optimal contract for two types of contracts, differentiated by whether their indemnity functions depend on the seller's background risk. The results shed light on the joint effect of the seller's default and background risk on the buyer's insurance demand.
VIX options in the SABR model
Authors: Dan Pirjol, Lingjiong Zhu
Date: 2025-01-11 00:00:00
Tasks: model

We study the pricing of VIX options in the SABR model $dS_t = \sigma_t S_t^\beta dB_t, d\sigma_t = \omega \sigma_t dZ_t$ where $B_t,Z_t$ are standard Brownian motions correlated with correlation $\rho<0$ and $0 \leq \beta < 1$. VIX is expressed as a risk-neutral conditional expectation of an integral over the volatility process $v_t = S_t^{\beta-1} \sigma_t$. We show that $v_t$ is the unique solution to a one-dimensional diffusion process. Using the Feller test, we show that $v_t$ explodes in finite time with non-zero probability. As a consequence, VIX futures and VIX call prices are infinite, and VIX put prices are zero for any maturity. As a remedy, we propose a capped volatility process by capping the drift and diffusion terms in the $v_{t}$ process such that it becomes non-explosive and well-behaved, and study the short-maturity asymptotics for the pricing of VIX options.
A note on bequest preferences in utility maximisation for modern tontines
Authors: Thomas Bernhardt
Date: 2025-01-15 00:00:00
Tasks: Common Sense Reasoning

In this short note, we address two issues in the literature about modern tontines with bequest and utility maximisation: how to verify optimal controls and the decreasing allocation of funds in the tontine. We want to raise awareness in the actuarial community about the dual approach to solve optimal control problems when working with power utilities. Additionally, we point out that bequest preferences should be time-dependent or otherwise yield unrealistic investment strategies. We base our attempt at modelling bequest preferences on common sense rules like 100% payback upon death at the start that vanishes over time. Our modelling shows that the resulting investment strategy almost linearly adjusts the allocation in the tontine from 0% to 100% over time.
Dynamic loan portfolio management in a three time step model
Authors: Deb Narayan Barik, Siddhartha P. Chakrabarty
Date: 2025-01-14 00:00:00
Tasks: Management

This paper studies the bank dynamic decision problem in the intermediate time step for a discrete-time setup. We have considered a three-time-step model. Initially, the banks raise money through debt and equity and invest in different types of loans. It liquidates its assets and raises new funds at the intermediate-time step to meet the short-term debt holders claim. Further, it has to meet specific capital requirements given by the regulators. In this work, we have theoretically studied the effect of raising new equity and debt. We show that in some cases, raising equity and debt may increase the return on equity, and in some cases, it may decrease the return on equity. We have discussed several cases and given a bound on the capital that can be raised. We have added an equity holders constraint, which ensures the return on equity and desists the bank from defaulting at the final time point.