pwc-archive/papers-with-abstracts huggingface dataset:
Compartment model of strategy-dependent time delays in replicator dynamics
Authors: Małgorzata Fic, Frank Bastian, Jacek Miękisz, Chaitanya S. Gokhale
Date: 2024-09-02 00:00:00

Real-world processes often exhibit temporal separation between actions and reactions - a characteristic frequently ignored in many modelling frameworks. Adding temporal aspects, like time delays, introduces a higher complexity of problems and leads to models that are challenging to analyse and computationally expensive to solve. In this work, we propose an intermediate solution to resolve the issue in the framework of evolutionary game theory. Our compartment-based model includes time delays while remaining relatively simple and straightforward to analyse. We show that this model yields qualitatively comparable results with models incorporating explicit delays. Particularly, we focus on the case of delays between parents' interaction and an offspring joining the population, with the magnitude of the delay depending on the parents' strategy. We analyse Stag-Hunt, Snowdrift, and the Prisoner's Dilemma game and show that strategy-dependent delays are detrimental to affected strategies. Additionally, we present how including delays may change the effective games played in the population, subsequently emphasising the importance of considering the studied systems' temporal aspects to model them accurately.
Trophic Cascades and Habitat Suitability in Udanti Sitnadi Tiger Reserve: Impacts of Prey Depletion and Climate Change on Predator Prey Dynamics
Authors: Krishnendu Basak, Chiranjib Chaudhuri, M Suraj, Moiz Ahmed
Date: 2024-08-30 00:00:00

This study investigates the trophic cascades and habitat suitability in Udanti Sitnadi Tiger Reserve (USTR), highlighting the roles of apex predators, subordinate predators, and prey species in maintaining ecosystem balance. Using the Trophic Species Distribution Model (SDM), we explored prey-predator interactions and habitat suitability, revealing that tigers, due to prey depletion, increasingly rely on cattle, while leopards adapt by preying on smaller species. The study emphasizes the need for prey augmentation and habitat restoration to support apex predators. Additionally, climate change projections for 2021-2040 and 2081-2100 under CMIP6 scenarios SSP245 and SSP585 indicate significant regional habitat shifts, necessitating adaptive management strategies. Kuladighat is projected to face habitat contraction, while Sitanadi may experience habitat expansion. Effective conservation efforts such as habitat restoration, prey augmentation and predator recovery are the most important steps needed to maintain the purpose of a Tiger reserve and conservation potential of Udanti-Sonabeda Tiger Conservation Unit (TCU). To achieve these dynamics, focusing on community participation, anti-poaching measures, and scientific recommendations are the most crucial components to focus on. This comprehensive analysis underscores the critical role of targeted conservation activities in prey-depleted landscapes to ensure the long-term survival of tigers and the overall health of forest ecosystems, enhancing biodiversity and mitigating human-wildlife conflicts in USTR.
Dogs on forest trails; Understanding ecology of Striped Hyena and wild Canids in the presence of free-ranging dogs in Udanti-Sitanadi Tiger Reserve, Central India using Joint Distribution and Deep Neural Networks
Authors: Chiranjib Chaudhuri, Krishnendu Basak, M Suraj, Moiz Ahmed, Amit Kumar
Date: 2024-08-30 00:00:00

This study uses Joint Species Distribution Models (JSDMs) and Deep Neural Networks (DNNs) to explore how wild carnivores and free-ranging dogs interact in the Udanti-Sitanadi Tiger Reserve (USTR) in Central India. The research focuses on key species like the Striped Hyena, Grey Wolf, Golden Jackal, and Indian Fox, revealing significant overlaps in habitat with free-ranging dogs, especially in densely populated areas like the Sitanadi region of the tiger reserve. These overlaps pose serious risks to wildlife through competition for resources, predation, and the spread of diseases. The study shows that the Striped Hyena prefers gentle slopes and forested areas, while the Grey Wolf tends to avoid cropland and thrives in regions with higher rainfall that supports a stable prey base. The Golden Jackal, more adaptable than the others, favors west-facing slopes and stable temperatures, whereas the Indian Fox is mainly found in the less disturbed, mountainous Kuladighat region. Additionally, the study highlights the potential impacts of climate change, predicting that the Grey Wolf could face habitat extinction under more severe scenarios. These findings underscore the urgent need for conservation strategies tailored to address both dog wild carnivore interactions and the growing challenges posed by climate change, focusing on protecting the critical habitats of vulnerable species like the Striped Hyena and Grey Wolf.
Crown-Like Structures in Breast Adipose Tissue: Finding a 'Needle-in-a-Haystack' using Artificial Intelligence and Collaborative Active Learning on the Web
Authors: Praphulla MS Bhawsar, Cody Ramin, Petra Lenz, Máire A Duggan, Alexandra R Harris, Brittany Jenkins, Renata Cora, Mustapha Abubakar, Gretchen Gierach, Joel Saltz, Jonas S Almeida
Date: 2024-09-12 00:00:00
Tasks: Active Learning, Diagnostic, whole slide images

Crown-like structures (CLS) in breast adipose tissue are formed as a result of macrophages clustering around necrotic adipocytes in specific patterns. As a histologic marker of local inflammation, CLS could have potential diagnostic utility as a biomarker for breast cancer risk. However, given the scale of whole slide images and the rarity of CLS (a few cells in an entire tissue sample), microscope-based manual identification is a challenge for the pathologist. In this report, we describe an artificial intelligence pipeline to solve this needle-in-a-haystack problem. We developed a zero-cost, zero-footprint web platform to enable remote operation on digital whole slide imaging data directly in the web browser, supporting collaborative annotation of the data by multiple experts. The annotated images then allow for incremental training and fine tuning of deep neural networks via active learning. The platform is reusable and requires no backend or installations, thus ensuring the data remains secure and private under the governance of the end user. Using this platform, we iteratively trained a CLS identification model, evaluating the performance after each round and adding examples to the training data to overcome failure cases. The resulting model, with an AUC of 0.90, shows promise as a first-pass screening tool to detect CLS in breast adipose tissue, considerably reducing the workload of the pathologist. Platform available at: https://episphere.github.io/path
Investigation of Electrical Conductivity Changes during Brain Functional Activity in 3T MRI
Authors: Kyu-Jin Jung, Chuanjiang Cui, Soo-Hyung Lee, Chan-Hee Park, Ji-Won Chun, Dong-Hyun Kim
Date: 2024-09-12 00:00:00

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is widely used to visualize brain activation regions by detecting hemodynamic responses associated with increased metabolic demand. While alternative MRI methods have been employed to monitor functional activities, the investigation of in-vivo electrical property changes during brain function remains limited. In this study, we explored the relationship between fMRI signals and electrical conductivity (measured at the Larmor frequency) changes using phase-based electrical properties tomography (EPT). Our results revealed consistent patterns: conductivity changes showed negative correlations, with conductivity decreasing in the functionally active regions whereas B1 phase mapping exhibited positive correlations around activation regions. These observations were consistent across both motor and visual cortex activations. To further substantiate these findings, we conducted electromagnetic radio-frequency simulations that modeled activation states with varying conductivity, which demonstrated trends similar to our in-vivo results for both B1 phase and conductivity. These findings suggest that in-vivo electrical conductivity changes can indeed be measured during brain activity. However, further investigation is needed to fully understand the underlying mechanisms driving these measurements.
Effective management of white rust disease in red amaranth: a field study in Dhaka, Bangladesh
Authors: Abu Noman Faruq Ahmmed, MD. Zahidul Islam, Raihan Ferdous
Date: 2024-09-11 00:00:00
Tasks: Management

This study aimed to evaluate the effective management strategies of Albugo candida, a pathogen of white rust disease in red amaranth (Amaranthus tricolor L.), accountable for the reduction of seed production. The study was performed during the Rabi season of 2018 and the Kharif season of 2019 at Sher-e-Bangla Agricultural University in Bangladesh. Eight treatments, including chemical, botanical, and biopesticide treatments such as Ridomil Gold 68 WG, Autostin 50 WP, Dithane M 45, Goldton 50 WP, the Bordeaux mixture, G-Derma, Garlic bulb extract, and Allamanda leaf extract, were evaluated. Four foliar sprays were applied at seven-day intervals after disease symptom onset. The field experiments followed a randomized complete block design with three replications. A microscopic study confirmed that Albugo candida was the causal organism. In both seasons, Ridomil Gold demonstrated superior efficacy in reducing disease incidence in plants, disease incidence in leaves, and disease severity, which were 63.07%, 62.78.5, and 84.31%, respectively, in Rabi and 69.73%, 65.71%, and 88.41%, respectively, in the Kharif season. Allamanda leaf extract also had statistically similar results, while Autostin exhibited promising effectiveness. Furthermore, compared with the other treatments, the combination of Ridomil Gold and Allamanda leaf extract significantly enhanced the growth parameters and seed yield in both seasons. Assessing the collective effectiveness of the treatments, Ridomil Gold demonstrated the most efficient control of white rust disease. Consequently, Ridomil Gold holds promise for application in red amaranth seed production. Additionally, the use of Allamanda leaf extract is an environmentally friendly approach to white rust disease management and promotes safer crop production practices. This study offers significant guidance to practitioners seeking optimal disease management strategies.
The microbiome science of composting and human excrement composting: a review
Authors: Jeff Meilander, J. Gregory Caporaso
Date: 2024-09-11 00:00:00
Tasks: Management

Linear waste management systems are unsustainable and contribute to environmental degradation, economic inequity, and health disparities. Among the array of environmental challenges stemming from anthropogenic impacts, the management of human excrement (human feces and urine) stands as a significant concern. Over two billion people do not have access to adequate sanitation resulting in a global public health crisis. Composting is the microbial biotechnology aimed at cycling organic waste, including human excrement, for improved public health, agricultural productivity and safety, and environmental sustainability. Applications of modern microbiome-omics and related technologies have vast capacity to support continued advances in composting science and praxis. In this article, we review literature focused on applications of microbiome technologies to study composting systems and reactions. The studies we survey generally fall into the categories of animal manure composting, food and landscaping waste composting, biosolids composting, and human excrement composting. We review experiments utilizing microbiome technologies to investigate strategies for enhancing pathogen suppression and accelerating the biodegradation of organic matter. Additionally, we explore studies focused on the bioengineering potential of microbes as inoculants to facilitate degradation of toxins such as pharmaceuticals or per- and polyfluoroalkyl substances (PFAS). The findings from these studies underscore the importance of advancing our understanding of composting processes through the integration of emerging microbiome-omics technologies. We conclude that work to-date has demonstrated exciting basic and applied science potential from studying compost microbiomes, with promising implications for enhancing global environmental sustainability and public health.
Modelling the effect of antibody depletion on dose-response behavior for common immunostaining protocols
Authors: Dominik Tschimmel, Steffen Waldherr, Tim Hucho
Date: 2024-09-10 00:00:00

Antibody binding properties for immunostaining applications are often characterized by dose-response curves, which describe the amount of bound antibodies as a function of the antibody concentration applied at the beginning of the experiment. A common model for the dose-response curve is the Langmuir isotherm, which assumes an equilibrium between the binding and unbinding of antibodies. However, for common immunostaining protocols, the equilibrium assumption is violated, and the dose-response behavior is governed by an accumulation of permanently bound antibodies. Assuming a constant antibody concentration, the resulting accumulation model can easily be solved analytically. However, in many experimental setups the overall amount of antibodies is fixed, such that antibody binding reduces the concentration of free antibodies. Solving the corresponding depletion accumulation model is more difficult and seems to be impossible for heterogeneous epitope landscapes. In this paper, we first solve the depletion-free accumulation model analytically for a homogeneous epitope landscape. From the obtained solution, we derive inequalities between the depletion-free accumulation model, the depletion accumulation model, and the Langmuir isotherm. This allows us to characterize the depletion effect for homogeneous epitope landscapes. Next, we generalize the problem to heterogeneous epitope landscapes, where we prove the existence and uniqueness of a solution that behaves as expected from the experimental setting. These natural properties define bounds for the depletion accumulation model. We conclude this paper by applying the bounds to characterize the depletion effect for heterogeneous epitope landscapes.
Multilevel testing of constraints induced by structural equation modeling in fMRI effective connectivity analysis: A proof of concept
Authors: G. Marrelec, A. Giron
Date: 2024-09-09 00:00:00

In functional MRI (fMRI), effective connectivity analysis aims at inferring the causal influences that brain regions exert on one another. A common method for this type of analysis is structural equation modeling (SEM). We here propose a novel method to test the validity of a given model of structural equation. Given a structural model in the form of a directed graph, the method extracts the set of all constraints of conditional independence induced by the absence of links between pairs of regions in the model and tests for their validity in a Bayesian framework, either individually (constraint by constraint), jointly (e.g., by gathering all constraints associated with a given missing link), or globally (i.e., all constraints associated with the structural model). This approach has two main advantages. First, it only tests what is testable from observational data and does allow for false causal interpretation. Second, it makes it possible to test each constraint (or group of constraints) separately and, therefore, quantify in what measure each constraint (or, e..g., missing link) is respected in the data. We validate our approach using a simulation study and illustrate its potential benefits through the reanalysis of published data.
signDNE: A python package for ariaDNE and its sign-oriented extension
Authors: Felix Risbro Hjerrild, Shan Shan, Doug M Boyer, Ingrid Daubechies
Date: 2024-09-09 00:00:00

A key challenge in evolutionary biology is to develop robust computational tools that can accurately analyze shape variations across diverse anatomical structures. The Dirichlet Normal Energy (DNE) is a shape complexity metric that addresses this by summarizing the local curvature of surfaces, particularly aiding the analytical studies and providing insights into evolutionary and functional adaptations. Building on the DNE concept, we introduce a Python-based implementation, designed to compute both the original DNE and a newly developed sign-oriented DNE metric. This Python package includes a user-friendly command line interface (CLI) and built-in visualization tools to facilitate the interpretation of the surface's local curvature properties. The addition of signDNE, which integrates the convexity and concavity of surfaces, enhances the tool's ability to identify fine-scale features across a broad range of biological structures. We validate the robustness of our method by comparing its performance with standard implementations on a dataset of triangular meshes with varying discrete representations. Additionally, we demonstrate its potential applications through visualization of the local curvature field (i.e., local curvature value over the surface) on various biological specimens, showing how it effectively captures complex biological features. In this paper, we offer a brief overview of the Python CLI for ease of use. Alongside the Python implementation, we have also updated the original MATLAB package to ensure consistent and accurate DNE computation across platforms. These improvements enhance the tool's flexibility, reduce sensitivity to sampling density and mesh quality, and support a more accurate interpretation of biological surface topography.
Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent
Authors: Jeremy D. Goldhaber-Fiebert, Hawre Jalal, Fernando Alarid Escudero
Date: 2024-09-08 00:00:00

Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Prior studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of their estimates of decision uncertainty and VOI are uncharacterized. We compare such iSTMs-produced estimates to corresponding cSTMs. For a 2-alternative decision and normally distributed incremental costs and benefits, we derive analytical expressions for the probability of being cost-effective and the expected value of perfect information (EVPI) for cSTMs and iSTMs, accounting for correlations in incremental outcomes at the population and individual levels. Numerical simulations illustrate our findings and explore relaxation of normality assumptions or having >2 decision alternatives. iSTM estimates of decision uncertainty and VOI are biased but asymptotically consistent (i.e., bias->0 as number of microsimulated individuals->infinity). Decision uncertainty depends on one tail of the INMB distribution (e.g., P(INMB<=0)) which depends on estimated variance (larger with iSTMs given first-order noise). While iSTMs overestimate EVPI, their direction of bias for the probability of being cost-effective is ambiguous. Bias is larger when uncertainties in incremental costs and effects are negatively correlated. While more samples at the population uncertainty level are interchangeable with more microsimulations for estimating EINMB, minimizing iSTM bias in estimating decision uncertainty and VOI depends on sufficient microsimulations. Analysts should account for this when allocating their computational budgets and, at minimum, characterize such bias in their reported results.
Focusing Viral Risk Ranking Tool on Prediction
Authors: Katherine Budeski, Marc Lipsitch
Date: 2024-09-07 00:00:00
Tasks: Prediction

Preparing to rapidly respond to emerging infectious diseases is becoming ever more critical. "SpillOver: Viral Risk Ranking" is an open-source tool developed to evaluate novel wildlife-origin viruses for their risk of spillover from animals to humans and their risk of spreading in human populations. However, several of the factors used in the risk assessment are dependent on evidence of previous zoonotic spillover and/or sustained transmission in humans. Therefore, we performed a reanalysis of the "Ranking Comparison" after removing eight factors that require post-spillover knowledge and compared the adjusted risk rankings to the originals. The top 10 viruses as ranked by their adjusted scores also had very high original scores. However, the predictive power of the tool for whether a virus was a human virus or not as classified in the Spillover database deteriorated when these eight factors were removed. The area under the receiver operating characteristic curves (AUROC) for the original score, 0.94, decreased to 0.73 for the adjusted scores. Furthermore, we compared the mean and standard deviation of the human and non-human viruses at the factor level. Most of the excluded spillover-dependent factors had dissimilar means between the human and non-human virus groups compared to the non-spillover dependent factors, which frequently demonstrated similar means between the two groups with some exceptions. We concluded that the original formulation of the tool depended heavily on spillover-dependent factors to "predict" the risk of zoonotic spillover for a novel virus. Future iterations of the tool should take into consideration other non-spillover dependent factors and omit those that are spillover-dependent to ensure the tool is fit for purpose.
How hosts and pathogens choose the strengths of defense and counter-defense. A game-theoretical view
Authors: Shalu Dwivedi, Ravindra Garde, Stefan Schuster
Date: 2024-09-06 00:00:00

Host-pathogen interactions consist of an attack by the pathogen, frequently a defense by the host and possibly a counter-defense by the pathogen. Here, we present a game-theoretical approach to describing such interactions. We consider a game where the host and pathogen are players and they can choose between the strategies of defense (or counter-defense) and no response. Specifically, they may or may not produce a toxin and an enzyme degrading the toxin, respectively. We consider that the host and pathogen must also incur a cost for toxin or enzyme production. We highlight both the sequential and non-sequential versions of the game and determine the Nash equilibria. Further, we resolve a paradox occurring in that interplay. If the inactivating enzyme is very efficient, producing the toxin becomes useless, leading to the enzyme being no longer required. Then, production of the defense becomes useful again. In game theory, such situations can be described by a generalized matching pennies game. As a novel result, we find under which conditions the defense cycle leads to a steady state or to an oscillation. We obtain, for saturating dose-response kinetics and considering monotonic cost functions, 'partial (counter-)defense' strategies as pure Nash equilibria. This implies that producing a moderate amount of toxin and enzyme is the best choice.