Alignment of angular velocity sensors for a vestibular prosthesis

Vestibular prosthetics transmit angular velocities to the nervous system via electrical stimulation. Head-fixed gyroscopes measure angular motion, but the gyroscope coordinate system will not be coincident with the sensory organs the prosthetic replaces. Here we show a simple calibration method to align gyroscope measurements with the anatomical coordinate system. We benchmarked the method with simulated movements and obtain proof-of-concept with one healthy subject. The method was robust to misalignment, required little data, and minimal processing

Cyber-Workstation for Computational Neuroscience

A Cyber-Workstation (CW) to study in vivo, real-time interactions between computational models and large-scale brain subsystems during behavioral experiments has been designed and implemented. The design philosophy seeks to directly link the in vivo neurophysiology laboratory with scalable computing resources to enable more sophisticated computational neuroscience investigation. The architecture designed here allows scientists to develop new models and integrate them with existing models (e.g. recursive least-squares regressor) by specifying appropriate connections in a block-diagram. Then, adaptive middleware transparently implements these user specifications using the full power of remote grid-computing hardware. In effect, the middleware deploys an on-demand and flexible neuroscience research test-bed to provide the neurophysiology laboratory extensive computational power from an outside source. The CW consolidates distributed software and hardware resources to support time-critical and/or resource-demanding computing during data collection from behaving animals. This power and flexibility is important as experimental and theoretical neuroscience evolves based on insights gained from data-intensive experiments, new technologies and engineering methodologies. This paper describes briefly the computational infrastructure and its most relevant components. Each component is discussed within a systematic process of setting up an in vivo, neuroscience experiment. Furthermore, a co-adaptive brain machine interface is implemented on the CW to illustrate how this integrated computational and experimental platform can be used to study systems neurophysiology and learning in a behavior task. We believe this implementation is also the first remote execution and adaptation of a brain-machine interface

Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics

Evaluating Nutraceuticals for Selective Toxicity Toward Leukemia Stem Cells

Targeting leukemia stem cells (LSCs) is critical to improving the poor outcome of acute myeloid leukemia (AML) patients. Nutraceuticals (i.e., food derived bioactive compounds) provide a wealthy resource for novel anti-cancer, and specifically anti-AML drug discovery. With the advent of novel LSC cell lines, preliminary screening of these compounds against LSC-like cells can be achieved rapidly. To identify potential novel anti-LSC therapeutics, we created and screened a unique library consisting of 288 nutraceuticals in an MTS assay against TEX leukemia cells, a surrogate LSC line and K562, a control cell line which does not possess LSC activity. Here, we identified diosmetin, a flavonoid found in citrus fruits and various green plants, as a novel anti- LSC agent (EC50: 6.0 ± 1.7μM). To confirm its activity, diosmetin (10μM) reduced clonogenic growth of primary AML cells (n = 4) with no effect on normal CD34 positive bone marrow derived stem cells (n = 3) observed in colony forming cell assays. A dose-response and time course analysis performed via the Annexin/PI assay and flow cytometry revealed that diosmetin induced apoptosis, as evidenced by the accumulation of ANN+/PI- cells. Apoptosis was further confirmed by a subG1 peak after performing cell cycle analysis. Utilizing the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool, we determined that the estrogen receptor (ER) was a potential molecular target for diosmetin’s anti-leukemia activity. To assess the role of estrogen receptors, we measured ERα and ERβ protein levels in diosmetin sensitive and insensitive cell lines. Interestingly, diosmetin sensitive cell lines display significantly elevated ERβ protein levels compared to diosmetin insensitive cells. However, this pattern was not observed for ERα. Similar results were observed through quantitative PCR measures, as TEX cells displayed levels of ESR2 (ERβ) mRNA, with no observed levels of ESR1 (ERα) mRNA levels. The opposite results were observed in K562 cells. Through ER reporter assays, it was demonstrated that diosmetin acts as a partial agonist in ERβ reporter cells, increasing luciferase activity with increasing doses of diosmetin in ERβ reporter cells. Moreover, we find that caspase 8 but not caspase 9 is elevated following diosmetin treatment, consistent with the extrinsic pathway of apoptosis and our observed increased in TNF-α, similar to previous reports highlighting the link between ERβ agonists and cancer cell death. In summary, these studies highlight that estrogen receptors, specifically ERβ, is a novel LSC therapeutic target, and the potential role of nutraceuticals as promising compounds for future drug discovery endeavours

PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery.

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI\u27s Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials

Exploiting co-adaptation for the design of symbiotic neuroprosthetic assistants

The success of brain-machine interfaces (BMI) is enabled by the remarkable ability of the brain to incorporate the artificial neuroprosthetic 'tool' into its own cognitive space and use it as an extension of the user's body. Unlike other tools, neuroprosthetics create a shared space that seamlessly spans the user's internal goal representation of the world and the external physical environment enabling a much deeper human-tool symbiosis. A key factor in the transformation of 'simple tools' into 'intelligent tools' is the concept of co-adaptation where the tool becomes functionally involved in the extraction and definition of the user's goals. Recent advancements in the neuroscience and engineering of neuroprosthetics are providing a blueprint for how new co-adaptive designs based on reinforcement learning change the nature of a user's ability to accomplish tasks that were not possible using conventional methodologies. By designing adaptive controls and artificial intelligence into the neural interface, tools can become active assistants in goal-directed behavior and further enhance human performance in particular for the disabled population. This paper presents recent advances in computational and neural systems supporting the development of symbiotic neuroprosthetic assistants

Exploiting co-adaptation for the design of symbiotic neuroprosthetic assistants

The success of brain–machine interfaces (BMI) is enabled by the remarkable ability of the brain to incorporate the artificial neuroprosthetic ‘tool’ into its own cognitive space and use it as an extension of the user’s body. Unlike other tools, neuroprosthetics create a shared space that seamlessly spans the user’s internal goal representation of the world and the external physical environment enabling a much deeper human–tool symbiosis. A key factor in the transformation of ‘simple tools’ into ‘intelligent tools’ is the concept of co-adaptation where the tool becomes functionally involved in the extraction and definition of the user’s goals. Recent advancements in the neuroscience and engineering of neuroprosthetics are providing a blueprint for how new co-adaptive designs based on reinforcement learning change the nature of a user’s ability to accomplish tasks that were not possible using conventional methodologies. By designing adaptive controls and artificial intelligence into the neural interface, tools can become active assistants in goal-directed behavior and further enhance human performance in particular for the disabled population. This paper presents recent advances in computational and neural systems supporting the development of symbiotic neuroprosthetic assistants

Investigating Ocular Movements and Vestibular Evoked Potentials for a Vestibular Neuroprosthesis: Response to Pulse Trains and Baseline Stimulation

No adequate treatment currently exists for bilateral vestibulopathy, which can result in significant decreases of social and physical functioning. To improve patients' quality of life, vestibular neuroprostheses are being developed. Efficacy of current prototypes is evaluated by recording reflexive eye movements (vestibular ocular reflex, VOR). Vestibular Evoked Potentials (VEPs) provide real-time feedback about peripheral efficacy that could be used to adapt a closed-loop neuroprosthesis to improve performance (e.g., eye movement magnitude and direction). A key building block is the prediction of VOR with VEP. In earlier work, we correlated both in response to single stimulation pulses. While impulse responses are interesting, they do not reflect a typical operating mode. To learn more about VEP at expected modulations, we studied the impact of pulse trains and baseline stimulation on VEP here. At 250 pulses per second, VEP did neither change significantly for pulse trains nor over the course of 30-minute baseline stimulation. VOR, on the other hand, changed with the number of pulses, and was also influenced by baseline stimulation