Cost-optimal Seeding Strategy During a Botanical Pandemic in Domesticated Fields

Context: Botanical pandemics cause enormous economic damage and food shortage around the globe. However, since botanical pandemics are here to stay in the short-medium term, domesticated field owners can strategically seed their fields to optimize each session's economic profit. Objective: Given the pathogen's epidemiological properties, we aim to find an economically optimal grid-based seeding strategy for field owners and policymakers. Methods: We propose a novel epidemiological-economic mathematical model that describes the economic profit from a field of plants during a botanical pandemic. We describe the epidemiological dynamics using a spatio-temporal extended Susceptible-Infected-Recovered epidemiological model with a non-linear output epidemiological model. Results and Conclusions: We provide an algorithm to obtain an optimal grid-formed seeding strategy to maximize economic profit, given field and pathogen properties. In addition, we implement the proposed model in realistic settings, analyzing the sensitivity of the economic profit as a function of several epidemiological and economic properties. We show that the recovery and basic infection rates have a similar economic influence. Unintuitively, we show that in the context of a botanic pandemic, a larger farm does not promise higher economic profit. Significance: Our results demonstrate a significant benefit of using the proposed seeding strategy and shed more light on the dynamics of the botanical pandemic in domesticated fields

The Family Tree Graph as a Predictor of the Family Members' Satisfaction with One Another

Individuals' satisfaction with their nuclear and extended family plays a critical role in individuals everyday life. Thus, a better understanding of the features that determine one's satisfaction with her family can open the door to the design of better sociological policies. To this end, this study examines the relationship between the family tree graph and family members' satisfaction with their nuclear and extended family. We collected data from 486 families which included a family tree graph and family members' satisfaction with each other. We obtain a model that is able to explain 75\% of the family members' satisfaction with one another. We found three indicators for more satisfied families. First, larger families, on average, have more satisfied members. Moreover, families with kids from the same parents - i.e., without step-siblings also express more satisfaction from both their siblings and parents when the children are already adults. Lastly, the average satisfaction of the family's oldest alive generation has a positive linear and non-linear correlation with the satisfaction of the entire extended family

Temporal Graphs Anomaly Emergence Detection: Benchmarking For Social Media Interactions

Temporal graphs have become an essential tool for analyzing complex dynamic systems with multiple agents. Detecting anomalies in temporal graphs is crucial for various applications, including identifying emerging trends, monitoring network security, understanding social dynamics, tracking disease outbreaks, and understanding financial dynamics. In this paper, we present a comprehensive benchmarking study that compares 12 data-driven methods for anomaly detection in temporal graphs. We conduct experiments on two temporal graphs extracted from Twitter and Facebook, aiming to identify anomalies in group interactions. Surprisingly, our study reveals an unclear pattern regarding the best method for such tasks, highlighting the complexity and challenges involved in anomaly emergence detection in large and dynamic systems. The results underscore the need for further research and innovative approaches to effectively detect emerging anomalies in dynamic systems represented as temporal graphs

Individual Variation Affects Outbreak Magnitude and Predictability in an Extended Multi-Pathogen SIR Model of Pigeons Vising Dairy Farms

Zoonotic disease transmission between animals and humans is a growing risk and the agricultural context acts as a likely point of transition, with individual heterogeneity acting as an important contributor. Thus, understanding the dynamics of disease spread in the wildlife-livestock interface is crucial for mitigating these risks of transmission. Specifically, the interactions between pigeons and in-door cows at dairy farms can lead to significant disease transmission and economic losses for farmers; putting livestock, adjacent human populations, and other wildlife species at risk. In this paper, we propose a novel spatio-temporal multi-pathogen model with continuous spatial movement. The model expands on the Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) framework and accounts for both within-species and cross-species transmission of pathogens, as well as the exploration-exploitation movement dynamics of pigeons, which play a critical role in the spread of infection agents. In addition to model formulation, we also implement it as an agent-based simulation approach and use empirical field data to investigate different biologically realistic scenarios, evaluating the effect of various parameters on the epidemic spread. Namely, in agreement with theoretical expectations, the model predicts that the heterogeneity of the pigeons' movement dynamics can drastically affect both the magnitude and stability of outbreaks. In addition, joint infection by multiple pathogens can have an interactive effect unobservable in single-pathogen SIR models, reflecting a non-intuitive inhibition of the outbreak. Our findings highlight the impact of heterogeneity in host behavior on their pathogens and allow realistic predictions of outbreak dynamics in the multi-pathogen wildlife-livestock interface with consequences to zoonotic diseases in various systems

Can We Mathematically Spot Possible Manipulation of Results in Research Manuscripts Using Benford's Law?

The reproducibility of academic research has long been a persistent issue, contradicting one of the fundamental principles of science. What is even more concerning is the increasing number of false claims found in academic manuscripts recently, casting doubt on the validity of reported results. In this paper, we utilize an adaptive version of Benford's law, a statistical phenomenon that describes the distribution of leading digits in naturally occurring datasets, to identify potential manipulation of results in research manuscripts, solely using the aggregated data presented in those manuscripts. Our methodology applies the principles of Benford's law to commonly employed analyses in academic manuscripts, thus, reducing the need for the raw data itself. To validate our approach, we employed 100 open-source datasets and successfully predicted 79% of them accurately using our rules. Additionally, we analyzed 100 manuscripts published in the last two years across ten prominent economic journals, with ten manuscripts randomly sampled from each journal. Our analysis predicted a 3% occurrence of result manipulation with a 96% confidence level. Our findings uncover disturbing inconsistencies in recent studies and offer a semi-automatic method for their detection

High Resolution Spatio-Temporal Model for Room-Level Airborne Pandemic Spread

Airborne pandemics have caused millions of deaths worldwide, large-scale economic losses, and catastrophic sociological shifts in human history. Researchers have developed multiple mathematical models and computational frameworks to investigate and predict the pandemic spread on various levels and scales such as countries, cities, large social events, and even buildings. However, modeling attempts of airborne pandemic dynamics on the smallest scale, a single room, have been mostly neglected. As time indoors increases due to global urbanization processes, more infections occur in shared rooms. In this study, a high-resolution spatio-temporal epidemiological model with airflow dynamics to evaluate airborne pandemic spread is proposed. The model is implemented using high-resolution 3D data obtained using a light detection and ranging (LiDAR) device and computing the model based on the Computational Fluid Dynamics (CFD) model for the airflow and the Susceptible-Exposed-Infected (SEI) model for the epidemiological dynamics. The pandemic spread is evaluated in four types of rooms, showing significant differences even for a short exposure duration. We show that the room's topology and individual distribution in the room define the ability of air ventilation to reduce pandemic spread throughout breathing zone infection

Predicting Lung Cancer's Metastats' Locations Using Bioclinical Model

Lung cancer is a leading cause of cancer-related deaths worldwide. The spread of the disease from its primary site to other parts of the lungs, known as metastasis, significantly impacts the course of treatment. Early identification of metastatic lesions is crucial for prompt and effective treatment, but conventional imaging techniques have limitations in detecting small metastases. In this study, we develop a bioclinical model for predicting the spatial spread of lung cancer's metastasis using a three-dimensional computed tomography (CT) scan. We used a three-layer biological model of cancer spread to predict locations with a high probability of metastasis colonization. We validated the bioclinical model on real-world data from 10 patients, showing promising 74% accuracy in the metastasis location prediction. Our study highlights the potential of the combination of biophysical and ML models to advance the way that lung cancer is diagnosed and treated, by providing a more comprehensive understanding of the spread of the disease and informing treatment decisions

Cancer-inspired Genomics Mapper Model for the Generation of Synthetic DNA Sequences with Desired Genomics Signatures

Genome data are crucial in modern medicine, offering significant potential for diagnosis and treatment. Thanks to technological advancements, many millions of healthy and diseased genomes have already been sequenced; however, obtaining the most suitable data for a specific study, and specifically for validation studies, remains challenging with respect to scale and access. Therefore, in silico genomics sequence generators have been proposed as a possible solution. However, the current generators produce inferior data using mostly shallow (stochastic) connections, detected with limited computational complexity in the training data. This means they do not take the appropriate biological relations and constraints, that originally caused the observed connections, into consideration. To address this issue, we propose cancer-inspired genomics mapper model (CGMM), that combines genetic algorithm (GA) and deep learning (DL) methods to tackle this challenge. CGMM mimics processes that generate genetic variations and mutations to transform readily available control genomes into genomes with the desired phenotypes. We demonstrate that CGMM can generate synthetic genomes of selected phenotypes such as ancestry and cancer that are indistinguishable from real genomes of such phenotypes, based on unsupervised clustering. Our results show that CGMM outperforms four current state-of-the-art genomics generators on two different tasks, suggesting that CGMM will be suitable for a wide range of purposes in genomic medicine, especially for much-needed validation studies

The Scientometrics and Reciprocality Underlying Co-Authorship Panels in Google Scholar Profiles

Online academic profiles are used by scholars to reflect a desired image to their online audience. In Google Scholar, scholars can select a subset of co-authors for presentation in a central location on their profile using a social feature called the Co-authroship panel. In this work, we examine whether scientometrics and reciprocality can explain the observed selections. To this end, we scrape and thoroughly analyze a novel set of 120,000 Google Scholar profiles, ranging across four disciplines and various academic institutions. Our results suggest that scholars tend to favor co-authors with higher scientometrics over others for inclusion in their co-authorship panels. Interestingly, as one's own scientometrics are higher, the tendency to include co-authors with high scientometrics is diminishing. Furthermore, we find that reciprocality is central to explaining scholars' selections

Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks

In the realm of machine and deep learning regression tasks, the role of effective feature engineering (FE) is pivotal in enhancing model performance. Traditional approaches of FE often rely on domain expertise to manually design features for machine learning models. In the context of deep learning models, the FE is embedded in the neural network's architecture, making it hard for interpretation. In this study, we propose to integrate symbolic regression (SR) as an FE process before a machine learning model to improve its performance. We show, through extensive experimentation on synthetic and real-world physics-related datasets, that the incorporation of SR-derived features significantly enhances the predictive capabilities of both machine and deep learning regression models with 34-86% root mean square error (RMSE) improvement in synthetic datasets and 4-11.5% improvement in real-world datasets. In addition, as a realistic use-case, we show the proposed method improves the machine learning performance in predicting superconducting critical temperatures based on Eliashberg theory by more than 20% in terms of RMSE. These results outline the potential of SR as an FE component in data-driven models