Chickensplash! Exploring the health concerns of washing raw chicken.

The Food and Drug Administration recommends against washing raw chicken due to the risk of transferring dangerous food-borne pathogens through splashed drops of water. Many cooks continue to wash raw chicken despite this warning, however, and there is a lack of scientific research assessing the extent of microbial transmission in splashed droplets. Here, we use large agar plates to confirm that bacteria can be transferred from the surface of raw chicken through splashing. We also identify and create a phylogenetic tree of the bacteria present on the chicken and the bacteria transferred during splashing. While no food-borne pathogens were identified, we note that organisms in the same genera as pathogens were transferred from the chicken surface through these droplets. Additionally, we show that faucet height, flow type, and surface stiffness play a role in splash height and distance. Using high-speed imaging to explore splashing causes, we find that increasing faucet height leads to a flow instability that can increase splashing. Furthermore, splashing from soft materials such as chicken can create a divot in the surface, leading to splashing under flow conditions that would not splash on a curved, hard surface. Thus, we conclude that washing raw chicken does risk pathogen transfer and cross-contamination through droplet ejection, and that changing washing conditions can increase or decrease the risk of splashing.

Advising caution in studying seasonal oscillations in crime rates.

Most types of crime are known to exhibit seasonal oscillations, yet the annual variations in the amplitude of this seasonality and their causes are still uncertain. Using a large collection of data from the Houston and Los Angeles Metropolitan areas, we extract and study the seasonal variations in aggravated assault, break in and theft from vehicles, burglary, grand theft auto, rape, robbery, theft, and vandalism for many years from the raw daily data. Our approach allows us to see various long term and seasonal trends and aberrations in crime rates that have not been reported before. We then apply an ecologically motivated stochastic differential equation to reproduce the data. Our model relies only on social interaction terms, and not on any exigent factors, to reproduce both the seasonality, and the seasonal aberrations observed in our data set. Furthermore, the stochasticity in the system is sufficient to reproduce the variations seen in the seasonal oscillations from year to year. Researchers should be very careful about trying to correlate these oscillations with external factors.

Sparse model selection via integral terms.

Model selection and parameter estimation are important for the effective integration of experimental data, scientific theory, and precise simulations. In this work, we develop a learning approach for the selection and identification of a dynamical system directly from noisy data. The learning is performed by extracting a small subset of important features from an overdetermined set of possible features using a nonconvex sparse regression model. The sparse regression model is constructed to fit the noisy data to the trajectory of the dynamical system while using the smallest number of active terms. Computational experiments detail the model's stability, robustness to noise, and recovery accuracy. Examples include nonlinear equations, population dynamics, chaotic systems, and fast-slow systems.

Modelling radicalization: how small violent fringe sects develop into large indoctrinated societies.

We model radicalization in a society consisting of two competing religious, ethnic or political groups. Each of the 'sects' is divided into moderate and radical factions, with intra-group transitions occurring either spontaneously or through indoctrination. We also include the possibility of one group violently attacking the other. The intra-group transition rates of one group are modelled to explicitly depend on the actions and characteristics of the other, including violent episodes, effectively coupling the dynamics of the two sects. We use a game theoretic framework and assume that radical factions may tune 'strategic' parameters to optimize given utility functions aimed at maximizing their ranks while minimizing the damage inflicted by their rivals. Constraints include limited overall resources that must be optimally allocated between indoctrination and external attacks on the other group. Various scenarios are considered, from symmetric sects whose behaviours mirror each other, to totally asymmetric ones where one sect may have a larger population or a superior resource availability. We discuss under what conditions sects preferentially employ indoctrination or violence, and how allowing sects to readjust their strategies allows for small, violent sects to grow into large, indoctrinated communities.

Fronts under arrest: Nonlocal boundary dynamics in biology.

We introduce a minimal geometric partial differential equation framework to understand pattern formation from interacting, counterpropagating fronts. Our approach concentrates on the interfaces between different states in a system, and relies on both nonlocal interactions and mean-curvature flow to track their evolution. As an illustration, we use this approach to describe a phenomenon in bacterial colony formation wherein sibling colonies can arrest each other's growth. This arrested motion leads to static separations between healthy, growing colonies. As our minimal model faithfully recovers the geometry of these competing colonies, it captures and elucidates the key leading-order mechanisms responsible for such patterned growth.