Neuromodulation: present and emerging methods.

Neuromodulation has wide ranging potential applications in replacing impaired neural function (prosthetics), as a novel form of medical treatment (therapy), and as a tool for investigating neurons and neural function (research). Voltage and current controlled electrical neural stimulation (ENS) are methods that have already been widely applied in both neuroscience and clinical practice for neuroprosthetics. However, there are numerous alternative methods of stimulating or inhibiting neurons. This paper reviews the state-of-the-art in ENS as well as alternative neuromodulation techniques-presenting the operational concepts, technical implementation and limitations-in order to inform system design choices

Reward, punishment, and prosocial behavior: Recent developments and implications

Reward and punishment change the payoff structures of social interactions and therefore can potentially play a role in promoting prosocial behavior. Yet, there are boundary conditions for them to be effective. We review recent work that addresses the conditions under which rewards and punishment can enhance prosocial behavior, the proximate and ultimate mechanisms for individuals’ rewarding and punishing decisions, and the reputational and behavioral consequences of reward and punishment under noise. The reviewed evidence points to the importance of more field research on how reward and punishment can promote prosocial behavior in real-world settings. We also highlight the need to integrate different methodologies to better examine the effects of reward and punishment on prosocial behavior

Contact of Single Asperities with Varying Adhesion: Comparing Continuum Mechanics to Atomistic Simulations

Atomistic simulations are used to test the equations of continuum contact mechanics in nanometer scale contacts. Nominally spherical tips, made by bending crystals or cutting crystalline or amorphous solids, are pressed into a flat, elastic substrate. The normal displacement, contact radius, stress distribution, friction and lateral stiffness are examined as a function of load and adhesion. The atomic scale roughness present on any tip made of discrete atoms is shown to have profound effects on the results. Contact areas, local stresses, and the work of adhesion change by factors of two to four, and the friction and lateral stiffness vary by orders of magnitude. The microscopic factors responsible for these changes are discussed. The results are also used to test methods for analyzing experimental data with continuum theory to determine information, such as contact area, that can not be measured directly in nanometer scale contacts. Even when the data appear to be fit by continuum theory, extracted quantities can differ substantially from their true values

Towards low-latency real-time detection of gravitational waves from compact binary coalescences in the era of advanced detectors

Electromagnetic (EM) follow-up observations of gravitational wave (GW) events will help shed light on the nature of the sources, and more can be learned if the EM follow-ups can start as soon as the GW event becomes observable. In this paper, we propose a computationally efficient time-domain algorithm capable of detecting gravitational waves (GWs) from coalescing binaries of compact objects with nearly zero time delay. In case when the signal is strong enough, our algorithm also has the flexibility to trigger EM observation before the merger. The key to the efficiency of our algorithm arises from the use of chains of so-called Infinite Impulse Response (IIR) filters, which filter time-series data recursively. Computational cost is further reduced by a template interpolation technique that requires filtering to be done only for a much coarser template bank than otherwise required to sufficiently recover optimal signal-to-noise ratio. Towards future detectors with sensitivity extending to lower frequencies, our algorithm's computational cost is shown to increase rather insignificantly compared to the conventional time-domain correlation method. Moreover, at latencies of less than hundreds to thousands of seconds, this method is expected to be computationally more efficient than the straightforward frequency-domain method.Comment: 19 pages, 6 figures, for PR

A fully Bayesian semi-parametric scalar-on-function regression (SoFR) with measurement error using instrumental variables

Wearable devices such as the ActiGraph are now commonly used in health studies to monitor or track physical activity. This trend aligns well with the growing need to accurately assess the effects of physical activity on health outcomes such as obesity. When accessing the association between these device-based physical activity measures with health outcomes such as body mass index, the device-based data is considered functions, while the outcome is a scalar-valued. The regression model applied in these settings is the scalar-on-function regression (SoFR). Most estimation approaches in SoFR assume that the functional covariates are precisely observed, or the measurement errors are considered random errors. Violation of this assumption can lead to both under-estimation of the model parameters and sub-optimal analysis. The literature on a measurement corrected approach in SoFR is sparse in the non-Bayesian literature and virtually non-existent in the Bayesian literature. This paper considers a fully nonparametric Bayesian measurement error corrected SoFR model that relaxes all the constraining assumptions often made in these models. Our estimation relies on an instrumental variable (IV) to identify the measurement error model. Finally, we introduce an IV quality scalar parameter that is jointly estimated along with all model parameters. Our method is easy to implement, and we demonstrate its finite sample properties through an extensive simulation. Finally, the developed methods are applied to the National Health and Examination Survey to assess the relationship between wearable-device-based measures of physical activity and body mass index among adults living in the United States

A scalable 32 channel neural recording and real-time FPGA based spike sorting system

This demo presents a scalable a 32-channel neural recording platform with real-time, on-node spike sorting ca- pability. The hardware consists of: an Intan RHD2132 neural amplifier; a low power Igloo ® nano FPGA; and an FX3 USB 3.0 controller. Graphical User Interfaces for controlling the system, displaying real-time data, and template generation with a modified form of WaveClus are demonstrated

Long non-coding RNA profiling of human lymphoid progenitor cells reveals transcriptional divergence of B cell and T cell lineages.

To elucidate the transcriptional 'landscape' that regulates human lymphoid commitment during postnatal life, we used RNA sequencing to assemble the long non-coding transcriptome across human bone marrow and thymic progenitor cells spanning the earliest stages of B lymphoid and T lymphoid specification. Over 3,000 genes encoding previously unknown long non-coding RNAs (lncRNAs) were revealed through the analysis of these rare populations. Lymphoid commitment was characterized by lncRNA expression patterns that were highly stage specific and were more lineage specific than those of protein-coding genes. Protein-coding genes co-expressed with neighboring lncRNA genes showed enrichment for ontologies related to lymphoid differentiation. The exquisite cell-type specificity of global lncRNA expression patterns independently revealed new developmental relationships among the earliest progenitor cells in the human bone marrow and thymus