pwc-archive/papers-with-abstracts huggingface dataset:
Deep multivariate autoencoder for capturing complexity in Brain Structure and Behaviour Relationships
Authors: Gabriela Gómez Jiménez, Demian Wassermann
Date: 2024-09-03 00:00:00
Tasks: Decoder, Diffusion  MRI

<div><p>Diffusion MRI is a powerful tool that serves as a bridge between brain microstructure and cognition. Recent advancements in cognitive neuroscience have highlighted the persistent challenge of understanding how individual differences in brain structure influence behavior, especially in healthy people. While traditional linear models like Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS) have been fundamental in this analysis, they face limitations, particularly with high-dimensional data analysis outside the training sample. To address these issues, we introduce a novel approach using deep learninga multivariate autoencoder model-to explore the complex non-linear relationships between brain microstructure and cognitive functions. The model's architecture involves separate encoder modules for brain structure and cognitive data, with a shared decoder, facilitating the analysis of multivariate patterns across these domains. Both encoders were trained simultaneously, before the decoder, to ensure a good latent representation that captures the phenomenon. Using data from the Human Connectome Project, our study centres on the insula's role in cognitive processes. Through rigorous validation, including 5 sample analyses for out-of-sample analysis, our results demonstrate that the multivariate autoencoder model outperforms traditional methods in capturing and generalizing correlations between brain and behavior beyond the training sample. These findings underscore the potential of deep learning models to enhance our understanding of brain-behavior relationships in cognitive neuroscience, offering more accurate and comprehensive insights despite the complexities inherent in neuroimaging studies.</p></div>
Cognitive Networks and Performance Drive fMRI-Based State Classification Using DNN Models
Authors: Murat Kucukosmanoglu, Javier O. Garcia, Justin Brooks, Kanika Bansal
Date: 2024-08-14 00:00:00
Tasks: Feature Importance

Deep neural network (DNN) models have demonstrated impressive performance in various domains, yet their application in cognitive neuroscience is limited due to their lack of interpretability. In this study we employ two structurally different and complementary DNN-based models, a one-dimensional convolutional neural network (1D-CNN) and a bidirectional long short-term memory network (BiLSTM), to classify individual cognitive states from fMRI BOLD data, with a focus on understanding the cognitive underpinnings of the classification decisions. We show that despite the architectural differences, both models consistently produce a robust relationship between prediction accuracy and individual cognitive performance, such that low performance leads to poor prediction accuracy. To achieve model explainability, we used permutation techniques to calculate feature importance, allowing us to identify the most critical brain regions influencing model predictions. Across models, we found the dominance of visual networks, suggesting that task-driven state differences are primarily encoded in visual processing. Attention and control networks also showed relatively high importance, however, default mode and temporal-parietal networks demonstrated negligible contribution in differentiating cognitive states. Additionally, we observed individual trait-based effects and subtle model-specific differences, such that 1D-CNN showed slightly better overall performance, while BiLSTM showed better sensitivity for individual behavior; these initial findings require further research and robustness testing to be fully established. Our work underscores the importance of explainable DNN models in uncovering the neural mechanisms underlying cognitive state transitions, providing a foundation for future work in this domain.
Analysis of Potential Biases and Validity of Studies Using Multiverse Approaches to Assess the Impacts of Government Responses to Epidemics
Authors: Jeremy D. Goldhaber-Fiebert
Date: 2024-09-11 00:00:00
Tasks: Time Series Regression, valid

We analyze the methodological approach and validity of interpretation of using national-level time-series regression analyses relating epidemic outcomes to policies that estimate many models involving permutations of analytic choices (i.e., a "multiverse" approach). Specifically, we evaluate the possible biases and pitfalls of interpretation of a multiverse approach to the context of government responses to epidemics using the example of COVID-19 and a recently published peer-reviewed paper by Bendavid and Patel (2024) along with the subsequent commentary that the two authors published discussing and interpreting the implications of their work. While we identify multiple potential errors and sources of biases in how the specific analyses were undertaken that are also relevant for other studies employing similar approaches, our most important finding involves constructing a counterexample showing that causal model specification-agnostic multiverse analyses can be incorrectly used to suggest that no consistent effect can be discovered in data especially in cases where most specifications estimated with the data are far from causally valid. Finally, we suggest an alternative approach involving hypothesis-drive specifications that explicitly account for infectiousness across jurisdictions in the analysis as well as the interconnected ways that policies and behavioral responses may evolve within and across these jurisdictions.
Advances in Nanoparticle-Based Targeted Drug Delivery Systems for Colorectal Cancer Therapy: A Review
Authors: Mahadi Hasan, Camryn Grace Evett, Jack Burton
Date: 2024-09-08 00:00:00
Tasks: Specificity

Colorectal cancer (CRC) continues to be a significant global health burden, prompting the need for more effective and targeted therapeutic strategies. Nanoparticle-based drug delivery systems have emerged as a promising approach to address the limitations of conventional chemotherapy, offering enhanced specificity, reduced systemic toxicity, and improved therapeutic outcomes. This paper provides an in-depth review of the current advancements in the application of nanoparticles as vehicles for targeted drug delivery in CRC therapy. It covers a variety of nanoparticle types, including liposomes, polymeric nanoparticles, dendrimers, and mesoporous silica nanoparticles (MSNs), with a focus on their design, functionalization, and mechanisms of action. This review also examines the challenges associated with the clinical translation of these technologies and explores future directions, emphasizing the potential of nanoparticle-based systems to revolutionize CRC treatment.
On the design and stability of cancer adaptive therapy cycles: deterministic and stochastic models
Authors: Yuri G. Vilela, Artur C. Fassoni, Armando G. M. Neves
Date: 2024-09-10 00:00:00

Adaptive therapy is a promising paradigm for treating cancers, that exploits competitive interactions between drug-sensitive and drug-resistant cells, thereby avoiding or delaying treatment failure due to evolution of drug resistance within the tumor. Previous studies have shown the mathematical possibility of building cyclic schemes of drug administration which restore tumor composition to its exact initial value in deterministic models. However, algorithms for cycle design, the conditions on which such algorithms are certain to work, as well as conditions for cycle stability remain elusive. Here, we state biologically motivated hypotheses that guarantee existence of such cycles in two deterministic classes of mathematical models already considered in the literature: Lotka-Volterra and adjusted replicator dynamics. We stress that not only existence of cyclic schemes, but also stability of such cycles is a relevant feature for applications in real clinical scenarios. We also analyze stochastic versions of the above deterministic models, a necessary step if we want to take into account that real tumors are composed by a finite population of cells subject to randomness, a relevant feature in the context of low tumor burden. We argue that the stability of the deterministic cycles is also relevant for the stochastic version of the models. In fact, Dua, Ma and Newton [Cancers (2021)] and Park and Newton [Phys. Rev. E (2023)] observed breakdown of deterministic cycles in a stochastic model (Moran process) for a tumor. Our findings indicate that the breakdown phenomenon is not due to stochasticity itself, but to the deterministic instability inherent in the cycles of the referenced papers. We then illustrate how stable deterministic cycles avoid for very large times the breakdown of cyclic treatments in stochastic tumor models.
When can few-species models describe dynamics within a complex community?
Authors: Stav Marcus, Guy Bunin
Date: 2024-09-10 00:00:00

Dynamics of species' abundances in ecological communities are often described using models that only account for a few species. It is not clear when and why this would be possible, as most species form part of diverse ecological communities, with many species that are not included in these few-variable descriptions. We study theoretically the circumstances under which the use of such models is justified, by considering the dynamics of a small set of focal species embedded within a diverse, sparsely-interacting community. We find that in some cases the focal species' dynamics are high-dimensional, making a few-variable description impossible. In other cases we show that such a description exists, even though the effect of the surrounding community on the focal species' dynamics is not small or simple. We give two different methods for approximating the dynamics, by using effective parameters that depend on the surrounding community, which are relevant under different assumptions on the relation between the explicitly modeled focal species and the rest of the species. Both methods work surprisingly well in many of the cases that we check, with effective dynamics that are often very similar and sometimes indistinguishable from the true dynamics, even when the effect of the community on the focal species is significant.
Mechanistic-statistical model for the expansion of ash dieback
Authors: Coralie Fritsch, Marie Grosdidier, Anne Gégout-Petit, Benoit Marçais
Date: 2024-09-10 00:00:00

Hymenoscyphus fraxineus is an invasive forest fungal pathogen that induces severe dieback in European ash populations. The spread of the disease has been closely monitored in France by the forest health survey system. We have developed a mechanisticstatistical model that describes the spread of the disease. It takes into account climate (summer temperature and spring rainfall), pathogen population dynamics (foliar infection, Allee effect induced by limited sexual partner encounters) and host density. We fitted this model using available disease reports. We estimated the parameters of our model, first identifying the appropriate ranges for the parameters, which led to a model reduction, and then using an adaptive multiple importance sampling algorithm for fitting. The model reproduces well the propagation observed in France over the last 20 years. In particular, it predicts the absence of disease impact in the south-east of the country and its weak development in the Garonne valley in south-west France. Summer temperature is the factor with the highest overall effect on disease spread, and explains the limited impact in southern France. Among the different temperature indices tested, the number of summer days with temperatures above 28{\textdegree}C gave the best qualitative behavior and the best fit. In contrast, the Allee effect and the heterogeneity of spring precipitation did not strongly affect the overall expansion of H. fraxineus in France and could be neglected in the modeling process. The model can be used to infer the average annual dispersal of H. fraxineus in France.
Mapping Evolution of Molecules Across Biochemistry with Assembly Theory
Authors: Sebastian Pagel, Abhishek Sharma, Leroy Cronin
Date: 2024-09-09 00:00:00
Tasks: Drug Discovery

Evolution is often understood through genetic mutations driving changes in an organism's fitness, but there is potential to extend this understanding beyond the genetic code. We propose that natural products - complex molecules central to Earth's biochemistry can be used to uncover evolutionary mechanisms beyond genes. By applying Assembly Theory (AT), which views selection as a process not limited to biological systems, we can map and measure evolutionary forces in these molecules. AT enables the exploration of the assembly space of natural products, demonstrating how the principles of the selfish gene apply to these complex chemical structures, selecting vastly improbable and complex molecules from a vast space of possibilities. By comparing natural products with a broader molecular database, we can assess the degree of evolutionary contingency, providing insight into how molecular novelty emerges and persists. This approach not only quantifies evolutionary selection at the molecular level but also offers a new avenue for drug discovery by exploring the molecular assembly spaces of natural products. Our method provides a fresh perspective on measuring the evolutionary processes both, shaping and being read out, by the molecular imprint of selection.
Stratified dispersal explains mountain pine beetle's range expansion in Alberta
Authors: Evan C. Johnson, Micah Brush, Mark A. Lewis
Date: 2024-09-09 00:00:00

The mountain pine beetle (MPB), a destructive pest native to Western North America, has recently extended its range into Alberta, Canada. Predicting the dispersal of MPB is challenging due to their small size and complex dispersal behavior. Because of these challenges, estimates of MPB's typical dispersal distances have varied widely, ranging from 10 meters to 18 kilometers. Here, we use high-quality data from helicopter and field-crew surveys to parameterize a large number of dispersal kernels. We find that fat-tailed kernels -- those which allow for a small number of long-distance dispersal events -- consistently provide the best fit to the data. Specifically, the radially-symmetric Student's t-distribution with parameters {\nu} = 0.012 and {\rho} = 1.45 stands out as parsimonious and user-friendly; this model predicts a median dispersal distance of 60 meters, but with the 95th percentile of dispersers travelling nearly 5 kilometers. The best-fitting mathematical models have biological interpretations. The Student's t-distribution, derivable as a mixture of diffusive processes with varying settling times, is consistent with observations that most beetles fly short distances while few travel far; early-emerging beetles fly farther; and larger beetles from larger trees exhibit greater variance in flight distance. Finally, we explain why other studies have found such a wide variation in the length scale in MPB dispersal, and we demonstrate that long-distance dispersal events are critical for modelling MPB range expansion.
Determinism vs. stochasticity in competitive flour beetle communities
Authors: Evan C. Johnson, Tad Dallas, Alan Hastings
Date: 2024-09-09 00:00:00

As ecologists increasingly adopt stochastic models over deterministic ones, the question arises: when is this a positive development and when is this an unnecessary complication? While deterministic models -- like the Lotka-Volterra model -- provide straightforward predictions about competitive outcomes, they are often unrealistic. Stochastic models are more realistic, but their complexity can limit their usefulness in explaining coexistence. Here, we investigate the relative importance of deterministic and stochastic processes in competition between two flour beetle species, Tribolium castaneum and Tribolium confusum. Specifically, we use highly-replicated one-generation experiments (784 microcosms) to parameterize a mechanistic model. Both the full stochastic model and the underlying deterministic skeleton exhibit priority effects, where one species excludes the other, but the identity of the winning species depends on initial abundances. Stochasticity makes the identity of the winner less predictable, but deterministic dynamics still make reliable predictions (94% accuracy across a range of reasonable initial abundances). We conclude that deterministic population dynamics are sufficient to account for patterns of coexistence (or lack thereof), a potentially general finding that is supported by recent field studies. Additionally, we resolve longstanding issues in flour beetle research by identifying selective egg predation as the mechanism for priority effects, demonstrating the primacy of demographic stochasticity (compared to environmental stochasticity), and reinterpreting classic competition experiments to show that apparent coexistence often represents long-term transient dynamics.
Bayesian estimation of transmission networks for infectious diseases
Authors: Jianing Xu, Huimin Hu, Gregory Ellison, Lili Yu, Christopher Whalen, Liang Liu
Date: 2024-09-08 00:00:00

Reconstructing transmission networks is essential for identifying key factors like superspreaders and high-risk locations, which are critical for developing effective pandemic prevention strategies. In this study, we developed a Bayesian framework that integrates genomic and temporal data to reconstruct transmission networks for infectious diseases. The Bayesian transmission model accounts for the latent period and differentiates between symptom onset and actual infection time, enhancing the accuracy of transmission dynamics and epidemiological models. Additionally, the model allows for the transmission of multiple pathogen lineages, reflecting the complexity of real-world transmission events more accurately than models that assume a single lineage transmission. Simulation results show that the Bayesian model reliably estimates both the model parameters and the transmission network. Moreover, hypothesis testing effectively identifies direct transmission events. This approach highlights the crucial role of genetic data in reconstructing transmission networks and understanding the origins and transmission dynamics of infectious diseases.
Shear and transport in a flow environment determine spatial patterns and population dynamics in a model of nonlocal ecological competition
Authors: Nathan O. Silvano, João Valeriano, Emilio Hernández-García, Cristóbal López, Ricardo Martinez-Garcia
Date: 2024-09-06 00:00:00
Tasks: Diversity

Populations very often self-organize into regular spatial patterns with important ecological and evolutionary consequences. Yet, most existing models neglect the effect that external biophysical drivers might have both on pattern formation and the spatiotemporal population dynamics once patterns form. Here, we investigate the effect of environmental flows on pattern formation and population dynamics using a spatially nonlocal logistic model (or Fisher-Kolmogorov equation) coupled to a simple shear and a Rankine vortex flow. We find that, whereas population abundance generally decreases with increasing flow intensity, the effect of the flow on the pattern instability depends on the spatial structure of the flow velocity field. This result shows that the velocity field interacts with the spatial feedbacks responsible for pattern formation in non-trivial ways, leading to a variety of spatiotemporal population dynamics regimes in which the total population abundance can exhibit either regular oscillations with a characteristic frequency or more erratic dynamics without a well-defined period. More generally, the diversity of spatiotemporal population dynamics caused by the interplay between self-organizing feedbacks and environmental flows highlights the importance of incorporating environmental and biophysical processes when studying both ecological pattern formation and its consequences.
Noise-free comparison of stochastic agent-based simulations using common random numbers
Authors: Daniel J. Klein, Romesh G. Abeysuriya, Robyn M. Stuart, Cliff C. Kerr
Date: 2024-09-03 00:00:00

Random numbers are at the heart of every agent-based model (ABM) of health and disease. By representing each individual in a synthetic population, agent-based models enable detailed analysis of intervention impact and parameter sensitivity. Yet agent-based modeling has a fundamental signal-to-noise problem, in which small changes between simulations cannot be reliably differentiated from stochastic noise resulting from misaligned random number realizations. We introduce a novel methodology that eliminates noise due to misaligned random numbers, a first for agent-based modeling. Our approach enables meaningful individual-level analysis between ABM scenarios because all differences are driven by mechanistic effects rather than random number noise. We demonstrate the benefits of our approach on three disparate examples. Results consistently show reductions in the number of simulations required to achieve a given standard error with levels exceeding 10-fold for some applications.
A graphical exploration of the relationship between parasite aggregation indices
Authors: R. McVinish, R. J. G. Lester
Date: 2024-09-05 00:00:00

The level of aggregation in parasite populations is frequently incorporated into ecological studies. It is measured in various ways including variance-to-mean ratio, mean crowding, the $k$ parameter of the negative binomial distribution and indices based on the Lorenz curve such as the Gini index (Poulin's D) and the Hoover index. Assuming the frequency distributions follow a negative binomial, we use contour plots to clarify the relationships between aggregation indices, mean abundance and prevalence. The contour plots highlight the nonlinear nature of the relationships between these measures and suggest that correlations are not a suitable summary of these relationships.
Numerical Study of Interaction Network Structures in Competitive Ecosystems
Authors: David A. Kessler, Nadav M. Shnerb
Date: 2024-09-03 00:00:00

We present a numerical analysis of local community assembly through weak migration from a regional species pool. At equilibrium, the local community consists of a subset ("clique") of species from the regional community. Our analysis reveals that the interaction networks of these cliques exhibit nontrivial architectures. Specifically, we demonstrate the pronounced nested structure of the clique interaction matrix in the case of symmetric interactions and the hyperuniform structure seen in asymmetric communities.
Mathematical model of CAR-T-cell therapy for a B-cell Lymphoma lymph node
Authors: Soukaina Sabir, Odelaisy León-Triana, Sergio Serrano, Roberto Barrio, Victor M. Pérez-García
Date: 2024-09-02 00:00:00

CAR-T cell therapies have demonstrated significant success in treating B-cell leukemia in children and young adults. However, their effectiveness in treating B-cell lymphomas has been limited. Unlike leukemia, lymphoma often manifests as solid masses of cancer cells in lymph nodes, glands, or organs, making these tumors harder to access thus hindering treatment response. In this paper we present a mathematical model that elucidates the dynamics of diffuse large B-cell lymphoma and CAR-T cells in a lymph node. The mathematical model aids in understanding the complex interplay between the cell populations involved and proposes ways to identify potential underlying dynamical causes of treatment failure. We also study the phenomenon of immunosuppression induced by tumor cells and theoretically demonstrate its impact on cell dynamics. Through the examination of various response scenarios, we underscore the significance of product characteristics in treatment outcomes.