Reopening a critical period: Young brains, compared with adult brains, are plastic. This phenomenon has given rise to the concept of critical periods, during which acquisition of certain skills is optimal. In mice, an auditory critical period is only open in early postnatal days. The youthful brain tunes circuits to sounds in its environment in a way that the adult brain does not. This facility may form the basis for childhood language acquisition in humans. Blundonet al.show that by manipulating adenosine signaling in mice, some plasticity of the adult auditory cortex can be regained (see the Perspective by Kehayas and Holmaat). Disruption of adenosine production or adenosine receptor signaling in adult mice leads to improved tone discrimination abilities.Science, this issue p.1352; see also p.1335

Background: It is well-documented that neonates can experience pain after injury. However, the contribution of individual populations of sensory neurons to neonatal pain is not clearly understood. Here we characterized the functional response properties and neurochemical phenotypes of single primary afferents after injection of carrageenan into the hairy hindpaw skin using a neonatal ex vivo recording preparation. Results: During normal development, we found that individual afferent response properties are generally unaltered. However, at the time period in which some sensory neurons switch their neurotrophic factor responsiveness, we observe a functional switch in slowly conducting, broad spiking fibers (“C”-fiber nociceptors) from mechanically sensitive and thermally insensitive (CM) to polymodal (CPM). Cutaneous inflammation induced prior to this switch (postnatal day 7) specifically altered mechanical and heat responsiveness, and heat thresholds in fast conducting, broad spiking (“A”-fiber) afferents. Furthermore, hairy skin inflammation at P7 transiently delayed the functional shift from CM to CPM. Conversely, induction of cutaneous inflammation after the functional switch (at P14) caused an increase in mechanical and thermal responsiveness exclusively in the CM and CPM neurons. Immunocytochemical analysis showed that inflammation at either time point induced TRPV1 expression in normally non-TRPV1 expressing CPMs. Realtime PCR and western blotting analyses revealed that specific receptors/channels involved in sensory transduction were differentially altered in the DRGs depending on whether inflammation was induced prior to or after the functional changes in afferent prevalence. Conclusion: These data suggest that the mechanisms of neonatal pain development may be generated by different afferent subtypes and receptors/channels in an age-related manner.

During brain development, neurons migrate from germinal zones to their final positions to assemble neural circuits. A unique saltatory cadence involving cyclical organelle movement (e.g., centrosome motility) and leading-process actomyosin enrichment prior to nucleokinesis organizes neuronal migration. While functional evidence suggests that leading-process actomyosin is essential for centrosome motility, the role of the actin-enriched leading process in globally organizing organelle transport or traction forces remains unexplored.

Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projections minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. A linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90% classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.

In order to facilitate the study of neuron migration, we propose a method for 3-D detection and tracking of centrosomes in time-lapse confocal image stacks of live neuron cells. We combine Laplacian-based blob detection, adaptive thresholding, and the extraction of scale and roundness features to find centrosome-like objects in each frame. We link these detections using the joint probabilistic data association filter (JPDAF) tracking algorithm with a Newtonian state-space model tailored to the motion characteristics of centrosomes in live neurons. We apply our algorithm to image sequences containing multiple cells, some of which had been treated with motion-inhibiting drugs. We provide qualitative results and quantitative comparisons to manual segmentation and tracking results showing that our average motion estimates agree to within 13% of those computed manually by neurobiologists.

Vehicle re-identification is a demanding and challenging task in automated surveillance systems. The goal of vehicle re-identification is to associate images of the same vehicle to identify re-occurrences of the same vehicle. Robust re-identification of individual vehicles requires reliable and discriminative features extracted from specific parts of the vehicle. In this work, we construct an efficient and robust wheel detector that precisely locates and selects vehicular wheels from vehicle images. The associated hubcap geometry can hence be utilized to extract fundamental signatures from vehicle images and exploit them for vehicle re-identification. Wheels pattern information can yield additional information about vehicles in questions. To that end, we utilized a vehicle imagery dataset that has thousands of side-view vehicle collected under different illumination conditions and elevation angles. The collected dataset was used for training and testing the wheel detector. Experiments show that our approach could detect vehicular wheels accurately for 99.41% of the vehicles in the dataset.