Neurological diseases and trauma often cause demyelination, resulting in the disruption of axonal function and integrity. Endogenous remyelination promotes recovery, but the process is not well understood because no method exists to definitively distinguish regenerated from preexisting myelin. To date, remyelinated segments have been defined as anything abnormally short and thin, without empirical data to corroborate these morphological assumptions. To definitively identify regenerated myelin, we used a transgenic mouse with an inducible membrane-bound reporter and targeted Cre recombinase expression to a subset of glial progenitor cells after spinal cord injury, yielding remarkably clear visualization of spontaneously regenerated myelin in vivo. Early after injury, the mean length of sheaths regenerated by Schwann cells and oligodendrocytes (OLs) was significantly shorter than control, uninjured myelin, confirming past assumptions. However, OL-regenerated sheaths elongated progressively over 6 mo to approach control values. Moreover, OL-regenerated myelin thickness was not significantly different from control myelin at most time points after injury. Thus, many newly formed OL sheaths were neither thinner nor shorter than control myelin, vitiating accepted dogmas of what constitutes regenerated myelin. We conclude that remyelination, once thought to be static, is dynamic and elongates independently of axonal growth, in contrast to stretch-based mechanisms proposed in development. Further, without clear identification, past assessments have underestimated the extent and quality of regenerated myelin.regeneration | plasticity | internode N ervous system disorders including traumatic injury, stroke, and neurodegenerative diseases such as multiple sclerosis induce loss of myelin and myelinating cells, interrupting signal conduction and depriving axons of trophic support essential for survival (1-4). Postmitotic oligodendrocytes (OLs) do not readily participate in remyelination (5, 6). Instead, glial progenitors, distinguished by expression of the α-receptor for PDGF and the chondroitin sulfate proteoglycan neural/glial antigen 2 (NG2) proliferate following demyelination and differentiate into remyelinating cells within a few weeks (7-10). Regeneration of myelin membranes restores saltatory conduction and supports axonal integrity, leading to partial recovery of function (3,4,11,12). However, remyelination can fail during disease progression, and limited or abnormal myelin regeneration is thought to underlie chronic conduction deficits following trauma (11,13,14). Enhancing or substituting endogenous remyelination via pharmacological intervention or stem/progenitor cell transplantation has been a major, but unrealized, focus of clinical therapy development for decades (15)(16)(17).There is much we do not understand about spontaneous remyelination, including the rate of OL regeneration, whether remyelinating cells select specific phenotypes or morphotypes of axons to ensheathe, and whether the initial number, thicknes...

Background: Mitochondrial dysfunction occurs in many tissues during normal aging. Results: Aged neural progenitor cells (NPCs) have decreased regenerative capacity, fewer functional mitochondria, and less oxygen consumption compared with young adult NPCs. Conclusion: Coordinated changes in proteomics, subcellular structure, and physiology demonstrate an altered metabolic strategy in aged NPCs. Significance: Such alterations may explain the age-dependent responses to hypoxia encountered during tumor or stroke.

Objective. Brain-computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges. Approach. We introduce a new approach to brain-computer interfacing based on partially observable Markov decision processes (POMDPs). POMDPs provide a principled approach to handling uncertainty and achieving co-adaptation in the following manner: (1) Bayesian inference is used to compute posterior probability distributions ('beliefs') over brain and environment state, and (2) actions are selected based on entire belief distributions in order to maximize total expected reward; by employing methods from reinforcement learning, the POMDP's reward function can be updated over time to allow for co-adaptive behaviour. Main results. We illustrate our approach using a simple non-invasive BCI which optimizes the speed-accuracy trade-off for individual subjects based on the signal-to-noise characteristics of their brain signals. We additionally demonstrate that the POMDP BCI can automatically detect changes in the user's control strategy and can co-adaptively switch control strategies on-the-fly to maximize expected reward. Significance. Our results suggest that the framework of POMDPs offers a promising approach for designing BCIs that can handle uncertainty in neural signals and co-adapt with the user on an ongoing basis. The fact that the POMDP BCI maintains a probability distribution over the user's brain state allows a much more powerful form of decision making than traditional BCI approaches, which have typically been based on the output of classifiers or regression techniques. Furthermore, the co-adaptation of the system allows the BCI to make online improvements to its behaviour, adjusting itself automatically to the user's changing circumstances.

This paper describes our "breaker" submission to the 2017 EMNLP "Build It Break It" shared task on sentiment analysis. In order to cause the "builder" systems to make incorrect predictions, we edited items in the blind test data according to linguistically interpretable strategies that allow us to assess the ease with which the builder systems learn various components of linguistic structure. On the whole, our submitted pairs break all systems at a high rate (72.6%), indicating that sentiment analysis as an NLP task may still have a lot of ground to cover. Of the breaker strategies that we consider, we find our semantic and pragmatic manipulations to pose the most substantial difficulties for the builder systems.

Abstract. The performance of non-invasive electroencephalogram-based (EEG) brain-computer interfacing (BCI) has improved significantly in recent years. However, remaining challenges include the non-stationarity and the low signal-to-noise ratio (SNR) of the EEG, which limit the bandwidth and hence the available applications. In this paper, we review ongoing research in our labs and introduce novel concepts and applications. First, we present an enhancement of the 3-class self-paced Graz-BCI that allows interaction with the massive multiplayer online role playing game World of Warcraft. Second, we report on the long-term stability and robustness of detection of oscillatory components modulated by distinct mental tasks. Third, we describe a scalable, adaptive learning framework, which allows users to teach the BCI new skills on-the-fly. Using this hierarchical BCI, we successfully train and control a humanoid robot in a virtual home environment.