Imaging and finite element analysis: a methodology for non-invasive characterization of aortic tissue.

Characterization of the mechanical properties of arterial tissues usually involves an invasive procedure requiring tissue removal. In this work we propose a non-invasive method to perform a biomechanical analysis of cardiovascular aortic tissue. This method is based on combining medical imaging and finite element analysis (FEA).

A jump persistent turning walker to model zebrafish locomotion.

Zebrafish are gaining momentum as a laboratory animal species for the investigation of several functional and dysfunctional biological processes. Mathematical models of zebrafish behaviour are expected to considerably aid in the design of hypothesis-driven studies by enabling preliminary in silico tests that can be used to infer possible experimental outcomes without the use of zebrafish. This study is motivated by observations of sudden, drastic changes in zebrafish locomotion in the form of large deviations in turn rate.

Local and global epidemic outbreaks in populations moving in inhomogeneous environments.

We study disease spreading in a system of agents moving in a space where the force of infection is not homogeneous. Agents are random walkers that additionally execute long-distance jumps, and the plane in which they move is divided into two regions where the force of infection takes different values. We show the onset of a local epidemic threshold and a global one and explain them in terms of mean-field approximations.

Effect of individual behavior on epidemic spreading in activity-driven networks.

In this work we study the effect of behavioral changes of individuals on the propagation of epidemic diseases. Specifically, we consider a susceptible-infected-susceptible model over a network of contacts that evolves in a time scale that is comparable to the individual disease dynamics. The phenomenon is modeled in the context of activity-driven networks, in which contacts occur on the basis of activity potentials.

Data-driven stochastic modelling of zebrafish locomotion.

In this work, we develop a data-driven modelling framework to reproduce the locomotion of fish in a confined environment. Specifically, we highlight the primary characteristics of the motion of individual zebrafish (Danio rerio), and study how these can be suitably encapsulated within a mathematical framework utilising a limited number of calibrated model parameters. Using data captured from individual zebrafish via automated visual tracking, we develop a model using stochastic differential equations and describe fish as a self propelled particle moving in a plane.

Classification of collective behavior: a comparison of tracking and machine learning methods to study the effect of ambient light on fish shoaling.

Traditional approaches for the analysis of collective behavior entail digitizing the position of each individual, followed by evaluation of pertinent group observables, such as cohesion and polarization. Machine learning may enable considerable advancements in this area by affording the classification of these observables directly from images. While such methods have been successfully implemented in the classification of individual behavior, their potential in the study collective behavior is largely untested.

Influence of robotic shoal size, configuration, and activity on zebrafish behavior in a free-swimming environment.

In animal studies, robots have been recently used as a valid tool for testing a wide spectrum of hypotheses. These robots often exploit visual or auditory cues to modulate animal behavior. The propensity of zebrafish, a model organism in biological studies, toward fish with similar color patterns and shape has been leveraged to design biologically inspired robots that successfully attract zebrafish in preference tests.

Male motion coordination in anopheline mating swarms.

The Anopheles gambiae species complex comprises the primary vectors of malaria in much of sub-Saharan Africa. Most of the mating in these species occurs in swarms composed almost entirely of males. Intermittent, organized patterns in such swarms have been observed, but a detailed description of male-male interactions has not previously been available.

Classification algorithms using multiple MRI features in mild traumatic brain injury.

Objective: The purpose of this study was to develop an algorithm incorporating MRI metrics to classify patients with mild traumatic brain injury (mTBI) and controls.

Methods: This was an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant prospective study. We recruited patients with mTBI and healthy controls through the emergency department and general population.

Improved stability and half-life of fluorinated phosphotriesterase using Rosetta.

Recently we demonstrated that incorporating p-fluorophenylalanine (pFF) into phosphotriesterase dramatically improved folding, thereby leading to enhanced stability and function at elevated temperatures. To further improve the stability of the fluorinated enzyme, Rosetta was used to identify multiple potential stabilizing mutations. One such variant, pFF-F104A, exhibited enhanced activity at elevated temperature and maintained activity over many days in solution at room temperature.

Gene delivery from supercharged coiled-coil protein and cationic lipid hybrid complex.

A lipoproteoplex comprised of an engineered supercharged coiled-coil protein (CSP) bearing multiple arginines and the cationic lipid formulation FuGENE HD (FG) was developed for effective condensation and delivery of nucleic acids. The CSP was able to maintain helical structure and self-assembly properties while exhibiting binding to plasmid DNA. The ternary CSP·DNA(8:1)·FG lipoproteoplex complex demonstrated enhanced transfection of β-galactosidase DNA into MC3T3-E1 mouse preosteoblasts.

Sociality modulates the effects of ethanol in zebra fish.

Background: The complex social behavior exhibited by zebra fish is often leveraged in preclinical studies to investigate whether and how psychoactive compounds modulate inter individual interactions. Due to theoretical and methodological constraints, previous studies on the effects of ethanol (EtOH) on social behavior focused on homogeneous groups in which all individuals were treated, thereby limiting the possibility of isolating all the intervening variables.

Methods: To identify how a social group affects the individual response to EtOH, we quantified the behavior of a single treated individual (acute 0.

Encoding range minima and range top-2 queries.

We consider the problem of encoding range minimum queries (RMQs): given an array A[1..n] of distinct totally ordered values, to pre-process A and create a data structure that can answer the query RMQ(i,j), which returns the index containing the smallest element in A[i.

Unraveling flow patterns through nonlinear manifold learning.

From climatology to biofluidics, the characterization of complex flows relies on computationally expensive kinematic and kinetic measurements. In addition, such big data are difficult to handle in real time, thereby hampering advancements in the area of flow control and distributed sensing. Here, we propose a novel framework for unsupervised characterization of flow patterns through nonlinear manifold learning.

Targeting Neural Synchrony Deficits is Sufficient to Improve Cognition in a Schizophrenia-Related Neurodevelopmental Model.

Cognitive symptoms are core features of mental disorders but procognitive treatments are limited. We have proposed a "discoordination" hypothesis that cognitive impairment results from aberrant coordination of neural activity. We reported that neonatal ventral hippocampus lesion (NVHL) rats, an established neurodevelopmental model of schizophrenia, have abnormal neural synchrony and cognitive deficits in the active place avoidance task.