Sarrafzadeh, M. | bioRxiv Science

bioRxiv2021

Free for all, or free-for-all? A content analysis of Australian university open access policies

Recent research demonstrates that Australia lags in providing open access to research outputs. In Australia, while the two major research funding bodies require open access of outputs from projects they fund, these bodies only fund a small proportion of research conducted. The major source of research and experimental development funding in Australian higher education is general university, or institutional, funding, and such funds are not subject to national funder open access policies. Thus, institutional policies and other institutional supports for open access are important in understanding Australias OA position. The purpose of this paper is, therefore, to understand the characteristics of Australian institutional open access policies and to explore the extent they represent a coherent and unified approach to delivering and promoting open access in Australia. Open access policies were located using a systematic web search approach and then their contents were analysed. Only half of Australian universities were found to have an open access policy. There was a wide variation in language used, expressed intent of the policy and expectations of researchers. Few policies mention monitoring or compliance and only three mention consequences for non-compliance. While it is understandable that institutions develop their own policies, when language is used which does not reflect national and international understandings, when requirements are not clear and with consequences, policies are unlikely to contribute to understanding of open access, to uptake of the policy, or to ease of transferring understanding and practices between institutions. A more unified approach to open access is recommended.

scientific communication and education↗

bioRxiv2018

Predicting the effects of epidural stimulation to improve hand function in patients with spinal cord injury: An active learning-based solution using dynamic sample weighting

In patients with chronic spinal cord injury (SCI), few therapies are available to improve neurological function. Neuromodulation of the spinal cord with epidural stimulation (EDS) has shown promise enabling the voluntary activation of motor pools caudal to the level of the injury. EDS is performed with multiple electrode arrays in which several stimulation variables such as the frequency, amplitude, and location of the stimulation significantly affect the type and amplitude of motor responses. This paper presents a novel technique to predict the final functionality of a patient with SCI after cervical EDS within a deep learning framework. Additionally, we suggest a committee-based active learning method to reduce the number of clinical experiments required to optimize EDS stimulation variables by exploring the stimulation configuration space more efficiently. We also developed a novel method to dynamically weight the results of different experiments using neural networks to create an optimal estimate of the quantity of interest. The essence of our approach was to use machine learning methods to predict the hand contraction force in a patient with chronic SCI based on different EDS parameters. The accuracy of the prediction of stimulation outcomes was evaluated based on three measurements: mean absolute error, standard deviation, and correlation coefficient. The results show that the proposed method can be used to reliably predict the outcome of cervical EDS on maximum voluntary contraction force of the hand with a prediction error of approximately 15%. This model could allow scientists to establish stimulation parameters more efficiently for SCI patients to produce enhanced motor responses in this novel application.\n\nAuthor SummarySpinal cord injury (SCI) can lead to permanent sensorimotor deficits that have a major impact on quality of life. In patients with a motor complete injury, there is no therapy available to reliably improve motor function. Recently, neuromodulation of the spinal cord with epidural stimulation (EDS) has allowed patients with motor-complete SCI regain voluntary movement below the level of injury in the cervical and thoracic spine. EDS is performed using multi-electrode arrays placed in the dorsal epidural space spanning several spinal segments. There are numerous stimulation parameters that can be modified to produce different effects on motor function. Previously, defining these parameters was based on observation and empiric testing, which are time-consuming and inefficient processes. There is a need for an automated method to predict motor and sensory function based on a given combination of EDS settings. We developed a novel method to predict the gripping function of a patient with SCI undergoing cervical EDS based on a set of stimulation parameters within a deep learning framework. We also addressed a limiting factor in machine learning methods in EDS, which is a general lack of training measurements for the learning model. We proposed a novel active learning method to minimize the number of training measurements required. The model for predicting responses to EDS could be used by scientists and clinicians to efficiently determine a set of stimulation parameters that produce a desired effect on motor function.

neuroscience↗