Lack of evidence for ectopic sprouting of genetically labeled Aβ touch afferents in inflammatory and neuropathic trigeminal pain.

BACKGROUND: Mechanical and in particular tactile allodynia is a hallmark of chronic pain in which innocuous touch becomes painful. Previous cholera toxin B (CTB)-based neural tracing experiments and electrophysiology studies had suggested that aberrant axon sprouting from touch sensory afferents into pain-processing laminae after injury is a possible anatomical substrate underlying mechanical allodynia. This hypothesis was later challenged by experiments using intra-axonal labeling of A-fiber neurons, as well as single-neuron labeling of electrophysiologically identified sensory neurons. However, no studies have used genetically labeled neurons to examine this issue, and most studies were performed on spinal but not trigeminal sensory neurons which are the relevant neurons for orofacial pain, where allodynia oftentimes plays a dominant clinical role. FINDINGS: We recently discovered that parvalbumin::Cre (Pv::Cre) labels two types of Aβ touch neurons in trigeminal ganglion. Using a Pv::CreER driver and a Cre-dependent reporter mouse, we specifically labeled these Aβ trigeminal touch afferents by timed taxomifen injection prior to inflammation or infraorbital nerve injury (ION transection). We then examined the peripheral and central projections of labeled axons into the brainstem caudalis nucleus after injuries vs controls. We found no evidence for ectopic sprouting of Pv::CreER labeled trigeminal Aβ axons into the superficial trigeminal noci-receptive laminae. Furthermore, there was also no evidence for peripheral sprouting. CONCLUSIONS: CreER-based labeling prior to injury precluded the issue of phenotypic changes of neurons after injury. Our results suggest that touch allodynia in chronic orofacial pain is unlikely caused by ectopic sprouting of Aβ trigeminal afferents.

Piezopotential gated nanowire--nanotube hybrid field-effect transistor.

We report the first piezoelectric potential gated hybrid field-effect transistors based on nanotubes and nanowires. The device consists of single-walled carbon nanotubes (SWNTs) on the bottom and crossed ZnO piezoelectric fine wire (PFW) on the top with an insulating layer between. Here, SWNTs serve as a carrier transport channel, and a single-crystal ZnO PFW acts as the power-free, contact-free gate or even an energy-harvesting component later on. The piezopotential created by an external force in the ZnO PFW is demonstrated to control the charge transport in the SWNT channel located underneath. The magnitude of the piezopotential in the PFW at a tensile strain of 0.05% is measured to be 0.4-0.6 V. The device is a unique coupling between the piezoelectric property of the ZnO PFW and the semiconductor performance of the SWNT with a full utilization of its mobility. The newly demonstrated device has potential applications as a strain sensor, force/pressure monitor, security trigger, and analog-signal touch screen.

Gauging possibilities for action based on friction underfoot.

Standing and walking generate information about friction underfoot. Five experiments examined whether walkers use such perceptual information for prospective control of locomotion. In particular, do walkers integrate information about friction underfoot with visual cues for sloping ground ahead to make adaptive locomotor decisions? Participants stood on low-, medium-, and high-friction surfaces on a flat platform and made perceptual judgments for possibilities for locomotion over upcoming slopes. Perceptual judgments did not match locomotor abilities: Participants tended to overestimate their abilities on low-friction slopes and underestimate on high-friction slopes (Experiments 1-4). Accuracy improved only for judgments made while participants were in direct contact with the slope (Experiment 5), highlighting the difficulty of incorporating information about friction underfoot into a plan for future actions.

Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography

Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.