Electron Tomography and Machine Learning for Understanding the Highly Ordered Structure of Leafhopper Brochosomes.

Insects known as leafhoppers (Hemiptera: Cicadellidae) produce hierarchically structured nanoparticles known as brochosomes that are exuded and applied to the insect cuticle, thereby providing camouflage and anti-wetting properties to aid insect survival. Although the physical properties of brochosomes are thought to depend on the leafhopper species, the structure-function relationships governing brochosome behavior are not fully understood. Brochosomes have complex hierarchical structures and morphological heterogeneity across species, due to which a multimodal characterization approach is required to effectively elucidate their nanoscale structure and properties. In this work, we study the structural and mechanical properties of brochosomes using a combination of atomic force microscopy (AFM), electron microscopy (EM), electron tomography, and machine learning (ML)-based quantification of large and complex scanning electron microscopy (SEM) image data sets. This suite of techniques allows for the characterization of internal and external brochosome structures, and ML-based image analysis methods of large data sets reveal correlations in the structure across several leafhopper species. Our results show that brochosomes are relatively rigid hollow spheres with characteristic dimensions and morphologies that depend on leafhopper species. Nanomechanical mapping AFM is used to determine a characteristic compression modulus for brochosomes on the order of 1-3 GPa, which is consistent with crystalline proteins. Overall, this work provides an improved understanding of the structural and mechanical properties of leafhopper brochosomes using a new set of ML-based image classification tools that can be broadly applied to nanostructured biological materials.

Food addiction: A common neurobiological mechanism with drug abuse.

Drugs and food both exert a rewarding effect through the firing of dopamine neurons in the ventral tegmental area, resulting in the release of dopamine into the nucleus accumbens and effects on the mesolimbic pathway. Here, we review the neuroimaging literature to consider the validity of food addiction and the common neurobiological mechanisms that overlap in food and drug addiction. This review paper focuses on findings from Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI) and structural imaging studies, as well as evidence from neuroimaging studies of bariatric surgery and pharmacological interventions on obese individuals. We examine not only functional and structural changes in the mesolimbic pathways, but also in other frontal areas shown to be involved in drug addiction, including the prefrontal cortex, orbitofrontal cortex and anterior cingulate cortex, as well as changes in neurotransmitter systems beyond dopaminergic systems.

Design and preliminary evaluation of the FINGER rehabilitation robot: controlling challenge and quantifying finger individuation during musical computer game play.

BACKGROUND: This paper describes the design and preliminary testing of FINGER (Finger Individuating Grasp Exercise Robot), a device for assisting in finger rehabilitation after neurologic injury. We developed FINGER to assist stroke patients in moving their fingers individually in a naturalistic curling motion while playing a game similar to Guitar Hero. The goal was to make FINGER capable of assisting with motions where precise timing is important. METHODS: FINGER consists of a pair of stacked single degree-of-freedom 8-bar mechanisms, one for the index and one for the middle finger. Each 8-bar mechanism was designed to control the angle and position of the proximal phalanx and the position of the middle phalanx. Target positions for the mechanism optimization were determined from trajectory data collected from 7 healthy subjects using color-based motion capture. The resulting robotic device was built to accommodate multiple finger sizes and finger-to-finger widths. For initial evaluation, we asked individuals with a stroke (n = 16) and without impairment (n = 4) to play a game similar to Guitar Hero while connected to FINGER. RESULTS: Precision design, low friction bearings, and separate high speed linear actuators allowed FINGER to individually actuate the fingers with a high bandwidth of control (-3 dB at approximately 8 Hz). During the tests, we were able to modulate the subject's success rate at the game by automatically adjusting the controller gains of FINGER. We also used FINGER to measure subjects' effort and finger individuation while playing the game. CONCLUSIONS: Test results demonstrate the ability of FINGER to motivate subjects with an engaging game environment that challenges individuated control of the fingers, automatically control assistance levels, and quantify finger individuation after stroke.