Recovery of forearm and fine digit function after chronic spinal cord injury by simultaneous blockade of inhibitory matrix CSPG production and the receptor PTPσ

Spinal cord injuries (SCI), for which there are limited effective treatments, result in enduring paralysis and hypoesthesia, in part because of the inhibitory microenvironment that develops and limits regeneration/sprouting, especially during chronic stages. Recently, we discovered that targeted enzymatic removal of the inhibitory chondroitin sulfate proteoglycan (CSPG) component of the extracellular and perineuronal net (PNN) matrix via Chondroitinase ABC (ChABC) rapidly restored robust respiratory function to the previously paralyzed hemi-diaphragm after remarkably long times post-injury (up to 1.5 years) following a cervical level 2 lateral hemi-transection. Importantly, ChABC treatment at cervical level 4 in this chronic model also elicited improvements in gross upper arm function. In the present study, we focused on arm and hand function, seeking to highlight and optimize crude as well as fine motor control of the forearm and digits at lengthy chronic stages post-injury. However, instead of using ChABC, we utilized a novel and more clinically relevant systemic combinatorial treatment strategy designed to simultaneously reduce and overcome inhibitory CSPGs. Following a 3-month upper cervical spinal hemi-lesion using adult female Sprague Dawley rats, we show that the combined treatment had a profound effect on functional recovery of the chronically paralyzed forelimb and paw, as well as on precision movements of the digits. The regenerative and immune system related events that we describe deepen our basic understanding of the crucial role of CSPG-mediated inhibition via the PTPσ receptor in constraining functional synaptic plasticity at lengthy time points following SCI, hopefully leading to clinically relevant translational benefits

Impact of Stranger Violence and Intimate Partner Violence on the Grades of American Indian/Alaska Native Undergraduate College Students

Stranger and intimate partner violence are pervasive public health problems that have a range of negative effects, with exceptionally high prevalence among ethno–racial minority youth. This study assesses the prevalence of these types of violence among American Indian/Alaska (AI/AN) students and examines the impact of victimization on academic performance AI/AN and non-AI/AN student populations using self-reported college health survey data. Results found that students who identified fully or partially as AI/AN reported markedly higher rates of all types of violence/abuse than did other students, and students who had experienced violence/abuse had lower GPAs those who had not. The interaction effect of female and violence type on GPA was significant for AI/AN students. Recommendations for future research and direct practice with AI/AN students are discussed

Peak Speeds of Professional Football Players During Bouts of Non-curved, Manual Treadmill Sprints

Purpose: Speed training and short distance sprints have become an essential component of preparation for professional football players. Current trends in speed training have included the application of non-curved, manual treadmills, as they may enhance peak speeds with less biomechanical stress. A lack of data currently exists in regards to the effectiveness of different settings and peak speed response. Therefore, we proposed to compare peak speeds during different settings of non-curved, manual treadmills. It was hypothesized that as resistance/incline increased, peak sprinting speeds would decrease and vice versa. Methods: Fourteen male professional football players (27.14 ± 3.11 yrs., 183.93 ± 8.52 cm, 100.36 ± 15.60 kg) sprinted at peak speeds during four different incline/resistance bouts. Paired samples T-tests examined differences between bouts, and significance was set at p ≤ 0.008. Results: A significant difference (p \u3c 0.001) existed for peak speeds between each incline/resistance bout (i.e. INC15R8, INC15R5, INC20R3, INC20R1). Conclusions: The observed data differences existed between all bouts, indicating that as resistance and/or incline increased, peak speed decreased. This also indicated that as resistance and/or incline decreased, peak speed increased during sprint bouts in professional football players

Interscale mixing microscopy: far-field imaging beyond the diffraction limit

Optical microscopy is widely used to analyze the properties of materials and structures, to identify and classify these structures, and to understand and control their responses to external stimuli. The extent of available applications is determined largely by the resolution offered by a particular microscopy technique. Here we present an analytic description and an experimental realization of interscale mixing microscopy, a diffraction-based imaging technique that is capable of detecting and characterizing wavelength/10 objects in far-field measurements with both coherent and incoherent broadband light. This technique is aimed at analyzing subwavelength objects based on far-field measurements of the interference created by the objects and a finite diffraction grating. A single measurement, analyzing the multiple diffraction orders, is often sufficient to determine the parameters of the object. The presented formalism opens opportunities for spectroscopy of nanoscale objects in the far field

Privacy Risks of Securing Machine Learning Models against Adversarial Examples

The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and the privacy domain have typically been considered separately. It is thus unclear whether the defense methods in one domain will have any unexpected impact on the other domain. In this paper, we take a step towards resolving this limitation by combining the two domains. In particular, we measure the success of membership inference attacks against six state-of-the-art defense methods that mitigate the risk of adversarial examples (i.e., evasion attacks). Membership inference attacks determine whether or not an individual data record has been part of a model's training set. The accuracy of such attacks reflects the information leakage of training algorithms about individual members of the training set. Adversarial defense methods against adversarial examples influence the model's decision boundaries such that model predictions remain unchanged for a small area around each input. However, this objective is optimized on training data. Thus, individual data records in the training set have a significant influence on robust models. This makes the models more vulnerable to inference attacks. To perform the membership inference attacks, we leverage the existing inference methods that exploit model predictions. We also propose two new inference methods that exploit structural properties of robust models on adversarially perturbed data. Our experimental evaluation demonstrates that compared with the natural training (undefended) approach, adversarial defense methods can indeed increase the target model's risk against membership inference attacks.Comment: ACM CCS 2019, code is available at https://github.com/inspire-group/privacy-vs-robustnes

Cell death delineates axon pathways in the hindlimb and does so independently of neurite outgrowth

We wished to know whether the cell death and phagocytosis seen near the outgrowing nerve front in the hindlimb delineate axon pathways and, if so, whether the cells died only in the presence of growth cones. We unilaterally deleted the lumbosacral neural tube and reconstructed the patterns of neurite outgrowth and phagocytes during the stage when neurites first begin to colonize the thigh. In the control limbs, sensory and motor nerve pathways coincided with sites of phagocytosis, including those pathways that had yet to be colonized by growth cones. For instance, phagocytes were clustered at foci within the muscle masses where muscle nerves form a day later. However, they were not seen in adjacent, nonpathway regions such as posterior sclerotome or dorsal and ventral to the region of the plexus in which axons extend only posteriorly. Phagocytes were also seen in defined regions that are probably inaccessible to growth cones because they are too distant from pathways (i.e., subjacent to the apical ectodermal ridge) or express substances that are typical of precartilagenous tissues which may prohibit axon advance. In the experimental limbs, we conservatively estimated that neurite outgrowth was reduced to less than one-tenth (neurites were visible only with electron microscopy) or less than one-third of normal. Outgrowth extended less far distally and, in half the cases, motor innervation was completely abolished. Despite the extensive reduction in neurite outgrowth, the distribution of phagocytes was indistinguishable from that of the control side. Furthermore, the number of phagocytes did not differ significantly. We conclude that cell death delineates axon pathways remarkably well and does so without an interaction with growth cones; it is an independent characteristic of the axonal pathways and may be directly or indirectly important to axonal pathfinding. This is the first identification of a feature that characterizes prospective nerve pathways in the hindlimb.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27050/1/0000040.pd

Human-Agent Interaction Model Learning based on Crowdsourcing

Missions involving humans interacting with automated systems become increasingly common. Due to the non-deterministic behavior of the human and possibly high risk of failing due to human factors, such an integrated system should react smartly by adapting its behavior when necessary. A promise avenue to design an efficient interaction-driven system is the mixed-initiative paradigm. In this context, this paper proposes a method to learn the model of a mixed-initiative human-robot mission. The first step to set up a reliable model is to acquire enough data. For this aim a crowdsourcing campaign was conducted and learning algorithms were trained on the collected data in order to model the human-robot mission and to optimize a supervision policy with a Markov Decision Process (MDP). This model takes into account the actions of the human operator during the interaction as well as the state of the robot and the mission. Once such a model has been learned, the supervision strategy can be optimized according to a criterion representing the goal of the mission. In this paper, the supervision strategy concerns the robot’s operating mode. Simulations based on the MDP model show that planning under uncertainty solvers can be used to adapt robot’s mode according to the state of the human-robot system. The optimization of the robot’s operation mode seems to be able to improve the team’s performance. The dataset that comes from crowdsourcing is therefore a material that can be useful for research in human-machine interaction, that is why it has been made available on our website