publications

2025

1. Direct‐Print 3D Electrodes for Large‐Scale, High‐Density, and Customizable Neural Interfaces

   Pingyu Wang, Eric G. Wu, Hasan Uluşan, and 11 more authors

   Advanced Science, Jan 2025

   Abs DOI

   Abstract Silicon‐based microelectronics can scalably record and modulate neural activity at high spatiotemporal resolution, but their planar form factor poses challenges in targeting 3D neural structures. A method for fabricating tissue‐penetrating 3D microelectrodes directly onto planar microelectronics using high‐resolution 3D printing via 2‐photon polymerization and scalable microfabrication technologies are presented. This approach enables customizable electrode shape, height, and positioning for precise targeting of neuron populations distributed in 3D. The effectiveness of this approach is demonstrated in tackling the critical challenge of interfacing with the retina—specifically, selectively targeting retinal ganglion cell (RGC) somas while avoiding the axon bundle layer. 6,600‐microelectrode, 35 µm pitch, tissue‐penetrating arrays are fabricated to obtain high‐fidelity, high‐resolution, and large‐scale retinal recording that reveals little axonal interference, a capability previously undemonstrated. Confocal microscopy further confirms the precise placement of the microelectrodes. This technology can be a versatile solution for interfacing silicon microelectronics with neural structures at a large scale and cellular resolution.

2. Decomposition of retinal ganglion cell electrical images for cell type and functional inference

   Eric G Wu, Andra M Rudzite, Martin O Bohlen, and 12 more authors

   Journal of Neural Engineering, Aug 2025

   Abs DOI

   Abstract Objective. Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision. Approach. The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose EI into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments. Main results. The decomposition was evaluated using large-scale multi-electrode recordings from the macaque retina. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells) substantially more accurately than previous approaches. Significance. These findings contribute to more accurate inference of RGC types and their original light responses based purely on their electrical features, with potential implications for vision restoration technology.

2024

1. Fixational eye movements enhance the precision of visual information transmitted by the primate retina

   Eric G. Wu, Nora Brackbill, Colleen Rhoades, and 7 more authors

   Nature Communications, Sep 2024

   Abs DOI

   Abstract Fixational eye movements alter the number and timing of spikes transmitted from the retina to the brain, but whether these changes enhance or degrade the retinal signal is unclear. To quantify this, we developed a Bayesian method for reconstructing natural images from the recorded spikes of hundreds of retinal ganglion cells (RGCs) in the macaque retina (male), combining a likelihood model for RGC light responses with the natural image prior implicitly embedded in an artificial neural network optimized for denoising. The method matched or surpassed the performance of previous reconstruction algorithms, and provides an interpretable framework for characterizing the retinal signal. Reconstructions were improved with artificial stimulus jitter that emulated fixational eye movements, even when the eye movement trajectory was assumed to be unknown and had to be inferred from retinal spikes. Reconstructions were degraded by small artificial perturbations of spike times, revealing more precise temporal encoding than suggested by previous studies. Finally, reconstructions were substantially degraded when derived from a model that ignored cell-to-cell interactions, indicating the importance of stimulus-evoked correlations. Thus, fixational eye movements enhance the precision of the retinal representation.

2. Modeling responses of macaque and human retinal ganglion cells to natural images using a convolutional neural network

   Alex R. Gogliettino, Sam Cooler, Ramandeep S. Vilkhu, and 7 more authors

   Mar 2024

   Abs DOI

   Abstract Linear-nonlinear (LN) cascade models provide a simple way to capture retinal ganglion cell (RGC) responses to artificial stimuli such as white noise, but their ability to model responses to natural images is limited. Recently, convolutional neural network (CNN) models have been shown to produce light response predictions that were substantially more accurate than those of a LN model. However, this modeling approach has not yet been applied to responses of macaque or human RGCs to natural images. Here, we train and test a CNN model on responses to natural images of the four numerically dominant RGC types in the macaque and human retina – ON parasol, OFF parasol, ON midget and OFF midget cells. Compared with the LN model, the CNN model provided substantially more accurate response predictions. Linear reconstructions of the visual stimulus were more accurate for CNN compared to LN model-generated responses, relative to reconstructions obtained from the recorded data. These findings demonstrate the effectiveness of a CNN model in capturing light responses of major RGC types in the macaque and human retinas in natural conditions.

3. Diverse functional properties of polyaxonal amacrine cells in the primate retina

   Alexandra Kling, Benjamin Hofflich, Sam Cooler, and 6 more authors

   Investigative Ophthalmology & Visual Science, Jun 2024

   Abs

   Amacrine cells, the most diverse cell class in the retina, shape the light response properties of retinal ganglion cells (RGCs). Yet the functions of the many amacrine cell types are little understood, especially in the primate retina. Here, we characterize the functional properties of several novel macaque polyaxonal amacrine cell (PAC) types, including their gap junction coupling to RGCs, receptive field (RF) properties, and responses to naturalistic scenes. 512-electrode recordings were performed from the peripheral macaque retina ex vivo. Cells were identified as PAC or RGC based on their electrical images (average spatiotemporal voltage pattern of their spikes): PACs have multiple axons and slower spike propagation, while RGCs have one axon heading towards the optic disc. To summarize the spatiotemporal and chromatic properties of each cell, the spike-triggered average was calculated from responses to spatiotemporal noise. Cells were clustered into types based on these properties. Cross-correlations of spike trains were used to assess electrical coupling. In most recordings, the well-known A1 PAC type was identified based on its spatial and temporal properties. In some recordings, multiple novel PAC types were identified. Most had larger RFs than A1 cells, with more spatial structure and irregular outlines. Several PAC types had a spatially segregated mixture of ON and OFF subregions in their RFs. One type displayed ON sensitivity in the center with several radiating OFF branches. Most PAC types exhibited larger RF coverage than A1s. At least one PAC type (in addition to the A1) appeared to form an electrically coupled network. One ON PAC type was apparently coupled to ON parasol RGCs, with some cells exhibiting weak bidirectional coupling and others strong unidirectional coupling RGC->PAC. These cells are the likely substrate of a coupled network that performs distant lateral inhibition previously identified indirectly in ON parasol cell recordings. Finally, distinct PAC types with similar RFs had distinct patterns of response to naturalistic movies. Multiple novel PAC types in the primate retina were identified, with unusual and diverse functional properties. Cross-correlation of spike trains revealed electrical connectivity within some PAC types and between specific PAC and RGC types. Early results suggest distinct roles of different PAC types in natural image signaling. This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.

2023

1. Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures

   Moosa Zaidi, Gorish Aggarwal, Nishal P Shah, and 10 more authors

   Journal of Neural Engineering, Aug 2023

   Abs DOI

   Abstract Objective . Retinal implants are designed to stimulate retinal ganglion cells (RGCs) in a way that restores sight to individuals blinded by photoreceptor degeneration. Reproducing high-acuity vision with these devices will likely require inferring the natural light responses of diverse RGC types in the implanted retina, without being able to measure them directly. Here we demonstrate an inference approach that exploits intrinsic electrophysiological features of primate RGCs. Approach. First, ON-parasol and OFF-parasol RGC types were identified using their intrinsic electrical features in large-scale multi-electrode recordings from macaque retina. Then, the electrically inferred somatic location, inferred cell type, and average linear-nonlinear-Poisson model parameters of each cell type were used to infer a light response model for each cell. The accuracy of the cell type classification and of reproducing measured light responses with the model were evaluated. Main results. A cell-type classifier trained on 246 large-scale multi-electrode recordings from 148 retinas achieved 95% mean accuracy on 29 test retinas. In five retinas tested, the inferred models achieved an average correlation with measured firing rates of 0.49 for white noise visual stimuli and 0.50 for natural scenes stimuli, compared to 0.65 and 0.58 respectively for models fitted to recorded light responses (an upper bound). Linear decoding of natural images from predicted RGC activity in one retina showed a mean correlation of 0.55 between decoded and true images, compared to an upper bound of 0.81 using models fitted to light response data. Significance. These results suggest that inference of RGC light response properties from intrinsic features of their electrical activity may be a useful approach for high-fidelity sight restoration. The overall strategy of first inferring cell type from electrical features and then exploiting cell type to help infer natural cell function may also prove broadly useful to neural interfaces.

2022

1. Maximum \textita posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors

   Eric G. Wu, Nora Brackbill, Alexander Sher, and 3 more authors

   In Advances in Neural Information Processing Systems 35, May 2022

   Abs DOI

   Visual information arriving at the retina is transmitted to the brain by signals in the optic nerve, and the brain must rely solely on these signals to make inferences about the visual world. Previous work has probed the content of these signals by directly reconstructing images from retinal activity using linear regression or nonlinear regression with neural networks. Maximum a posteriori (MAP) reconstruction using retinal encoding models and separately-trained natural image priors offers a more general and principled approach. We develop a novel method for approximate MAP reconstruction that combines a generalized linear model for retinal responses to light, including their dependence on spike history and spikes of neighboring cells, with the image prior implicitly embedded in a deep convolutional neural network trained for image denoising. We use this method to reconstruct natural images from ex vivo simultaneously-recorded spikes of hundreds of retinal ganglion cells uniformly sampling a region of the retina. The method produces reconstructions that match or exceed the state-of-the-art in perceptual similarity and exhibit additional fine detail, while using substantially fewer model parameters than previous approaches. The use of more rudimentary encoding models (a linear-nonlinear-Poisson cascade) or image priors (a 1/f spectral model) significantly reduces reconstruction performance, indicating the essential role of both components in achieving high-quality reconstructed images from the retinal signal.

2. Unusual properties of novel ganglion cell and amacrine cell types in macaque and human retina

   Alexandra Kling, Eric Wu, Sam Cooler, and 5 more authors

   Investigative Ophthalmology & Visual Science, Jun 2022

   Abs

   The primate retina contains ~20 anatomically identified retinal ganglion cell (RGC) types and >10 polyaxonal amacrine cell (PAC) types. However, only 7 RGC types and 1 PAC type have been studied functionally in detail. Our goal is to understand the diversity of visual processing in the RGC and PAC types of the macaque and human retina. Large-scale multi-electrode recordings were performed from the peripheral retina. The spike-triggered average of each cell was calculated from responses to flickering checkerboards and used to summarize its spatial, temporal and chromatic properties. Cell types were identified by clustering their functional properties. Substantial errors due to spike sorting were ruled out by stereotypical properties of cells across the electrode array, refractory periods, spatiotemporal structure of electrical footprints, and mosaic organization of receptive fields (RFs) within each type. In every recording, the well-known ON & OFF parasol, ON & OFF midget, ON & OFF smooth monostratified, and small bistratified RGCs, and A1 PACs, were identified. In 12 macaque retina recordings, at least 15 new RGC and 12 new PAC types with distinct RF properties were identified (Fig. 1A). Each new cell type was observed in several retinas. Most of the novel cell types exhibited one or more unusual properties: 1) inhomogeneous spatial RFs composed of multiple spots or branches; 2) spatially segregated ON and OFF inputs, driven by L/M cones or S cones, not in a center-surround arrangement (Fig. 1B); and 3) sparse spiking in response to checkerboards and naturalistic movies. In 4 human retina recordings, ~20 putative homologs of macaque RGC and PAC types were identified (Fig. 1B). One human PAC type exhibited double the spatial density of its macaque homolog, and one had no obvious homolog. Most novel RGC and PAC types in the primate retina exhibited unusual spatial and spectral RF properties and firing patterns, suggesting specific functions in visual processing. While most human cell types appear to have macaque homologs, a few cells and properties may be unique to the human retina. This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually. Figure 1. A. Clusters of visual response parameters represented by t-SNE, for 472 RGCs and 132 PACs in a single recording from macaque retina, manually labeled. Insets: expanded view of novel OFF PACs and RGCs clusters. B. Examples of spatial RFs of novel cell types. Scale bars 0.5 mm.

3. Automated identification of ganglion and amacrine cell types in a large primate retina dataset

   Sam Cooler, Alexandra Kling, Eric Wu, and 5 more authors

   Investigative Ophthalmology & Visual Science, Jun 2022

   Abs

   Recent advances in the analysis of large-scale 512-electrode array recordings have revealed 10+ novel retinal ganglion cell (RGC) and amacrine cell types in the primate retina. To better understand the function of these cell types and the way they vary across retinas and experimental conditions, we are developing methods to automatically identify both the known and novel cell types in a large archival dataset. We analyzed 182 ex vivo recordings (89k cells) from peripheral macaque retina. The spatial, temporal, and spectral properties of each cell’s visual response were parameterized from the spike triggered average obtained with random checkerboard visual stimuli (Fig. 1A). To classify novel cell types with atypically sparse spiking and inhomogeneous receptive fields (RFs), we used a multi-stage process, iteratively identifying known and then novel types. We compensated for the variation of light response properties across the visual field and across retinas by using the parameters of known RGC types as benchmarks. The automated classification methods reliably identified the ON & OFF parasol and ON & OFF midget RGC types, achieving 0.96±0.06 accuracy (mean±SD), with 49% of experiments reaching >0.98 accuracy (≈1 error/type/recording) relative to manual supervised labels. Normalizing for spatial and temporal variability using ON & OFF parasol RGCs increased classification accuracy from 0.70±0.13 (Fig. 1B) by reducing across-dataset parameter variance (Fig. 1C). Measures of RF properties, such as the RF solidity (fill fraction of convex hull), enabled classification of a novel type with 0.92±0.12 accuracy in 8 recordings (6k cells; Fig. 1D). Automated classification applied to a large primate retina dataset, using iterative analysis and variability normalization, provides a broad, objective overview of the functional properties of numerous known and novel cell types. This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually. Figure 1: A) Receptive field (RF) and electrical profiles for two known and one novel cell type, scale bars are 500 µm. B) Functional properties (represented by t-SNE) of cells from three datasets showing clusters by type (marker color), from different retinas (symbol, numeric labels). C) RF area mean by recording for OFF parasol and OFF midget RGCs, and the ratio between the two, which has lower variance. D) Parameter “RF solidity” separates a novel RGC type where temporal parameters alone failed.

4. Focal electrical stimulation of human retinal ganglion cells for vision restoration

   Sasidhar S Madugula, Alex R Gogliettino, Moosa Zaidi, and 14 more authors

   Journal of Neural Engineering, Dec 2022

   Abs DOI

   Abstract Objective . Vision restoration with retinal implants is limited by indiscriminate simultaneous activation of many cells and cell types, which is incompatible with reproducing the neural code of the retina. Recent work has shown that primate retinal ganglion cells (RGCs), which transmit visual information to the brain, can be directly electrically activated with single-cell, single-spike, cell-type precision – however, this possibility has never been tested in the human retina. In this study we aim to characterize, for the first time, direct in situ extracellular electrical stimulation of individual human RGCs. Approach . Extracellular electrical stimulation of individual human RGCs was conducted in three human retinas ex vivo using a custom large-scale, multi-electrode array capable of simultaneous recording and stimulation. Measured activation properties were compared directly to extensive results from macaque. Main results . Precise activation was in many cases possible without activating overlying axon bundles, at low stimulation current levels similar to those used in macaque. The major RGC types could be identified and targeted based on their distinctive electrical signatures. The measured electrical activation properties of RGCs, combined with a dynamic stimulation algorithm, was sufficient to produce an evoked visual signal that was nearly optimal given the constraints of the interface. Significance . These results suggest the possibility of high-fidelity vision restoration in humans using bi-directional epiretinal implants.

2021

1. Inferring retinal ganglion cell light response properties from intrinsic electrical feature

   Moosa Zaidi, Gorish Aggarwal, Nishal P. Shah, and 10 more authors

   Investigative Ophthalmology & Visual Science, Jun 2021

   Abs

   An ideal epiretinal implant for treating vision loss would stimulate each retinal ganglion cell (RGC) of each distinct type according to its natural function, to reproduce the neural code of the retina. However, no methods exist to determine the normal, healthy light response properties of each RGC of each type in a region of retina that is no longer light-responsive. This study presents a method to infer a model of RGC light responses using only intrinsic features of recorded electrical activity. Analysis was performed on 283 recordings from populations of ON and OFF parasol RGCs in isolated macaque retina using 512-electrode arrays. The type of each recorded RGC was determined from its light responses. To emulate interfacing to a blind retina, a classifier was used to infer the type (ON or OFF) of each cell using two features of recorded electrical activity, without reference to the light stimulus: the average electrical image (EI) of a spike over the electrode array, and the autocorrelation function (ACF) of spike times. EIs were also used to infer receptive field locations. A linear-nonlinear Poisson (LNP) light response model was then inferred for each cell using its true cell label, its estimated location, and the average spatiotemporal filter and nonlinearity in the population. The prediction accuracy of inferred models and models fitted to measured responses was compared, for white noise and natural scenes stimuli (Fig. 1). K-means (k=2) clustering of the top 2 principal components of ACFs reliably separated ON and OFF parasol cells in individual retinas. However, cluster labels (ON, OFF) could not be reliably determined from ACFs, due to inter-retina variability. Instead, ACF clusters were labeled using “votes” from a cell-by-cell linear classifier of selected EI features. This approach achieved 94% mean accuracy on 25 test retinas. Across six retinas, the RGC light response models produced a mean correlation between inferred models and data of 0.48 and 0.51, and between fitted models and data of 0.63 and 0.57 (an upper bound), for white noise and natural scenes stimuli respectively. Intrinsic features of electrical activity can be used to infer RGC light response models. This is a 2021 ARVO Annual Meeting abstract. Figure 1. Example rasters of light responses measured (data), predicted from an inferred light response model (infer), or predicted from a light response model fitted to recorded data (fit).

2020

1. Functional Organization of Midget and Parasol Ganglion Cells in the Human Retina

   Alexandra Kling, Alex R. Gogliettino, Nishal P. Shah, and 6 more authors

   Aug 2020

   Abs DOI

   ABSTRACT The functional organization of diverse retinal ganglion cell (RGC) types, which shapes the visual signal transmitted to the brain, has been examined in many species. The unique spatial, temporal, and chromatic properties of the numerically dominant RGC types in macaque monkey retina are presumed to most accurately model human vision. However, the functional similarity between RGCs in macaques and humans has only begun to be tested, and recent work suggests possible differences. Here, the properties of the numerically dominant human RGC types were examined using large-scale multi-electrode recordings with fine-grained visual stimulation in isolated retina, and compared to results from dozens of recordings from macaque retina using the same experimental methods and conditions. The properties of four major human RGC types – ON-parasol, OFF-parasol, ON-midget, and OFF-midget – closely paralleled those of the same macaque RGC types, including the spatial and temporal light sensitivity, precisely coordinated mosaic organization of receptive fields, ON-OFF asymmetries, spatial response nonlinearity, and sampling of photoreceptor inputs over space. Putative smooth monostratified cells and polyaxonal amacrine cells were also identified based on similarities to cell types previously identified in macaque retina. The results suggest that recently proposed differences between human and macaque RGCs probably reflect experimental differences, and that the macaque model provides an accurate picture of human RGC function.

2018

1. Methylxanthine Drug Monitoring with Wearable Sweat Sensors

   Li‐Chia Tai, Wei Gao, Minghan Chao, and 13 more authors

   Advanced Materials, Jun 2018

   DOI

2017

1. Autonomous sweat extraction and analysis applied to cystic fibrosis and glucose monitoring using a fully integrated wearable platform

   Sam Emaminejad, Wei Gao, Eric Wu, and 11 more authors

   Proceedings of the National Academy of Sciences, May 2017

   Abs DOI

   Significance The inherent inaccessibility of sweat in sedentary individuals in large volume (≥10 µL) for on-demand and in situ analysis has limited our ability to capitalize on this noninvasive and rich source of information. Through devising an electrochemically enhanced, programmable, and miniaturized iontophoresis interface, integrated in a wearable sensing platform, we demonstrated a method for periodic sweat extraction and in situ analysis. The system can be programmed to induce sweat with various secretion profiles, which in combination with the in situ analysis capability allow us to gain real-time insight into the sweat-secretion and gland physiology. To demonstrate the clinical value of our platform, human subject studies were performed in the context of the cystic fibrosis diagnosis and preliminary investigation of the blood/sweat glucose correlation. , Perspiration-based wearable biosensors facilitate continuous monitoring of individuals’ health states with real-time and molecular-level insight. The inherent inaccessibility of sweat in sedentary individuals in large volume (≥10 µL) for on-demand and in situ analysis has limited our ability to capitalize on this noninvasive and rich source of information. A wearable and miniaturized iontophoresis interface is an excellent solution to overcome this barrier. The iontophoresis process involves delivery of stimulating agonists to the sweat glands with the aid of an electrical current. The challenge remains in devising an iontophoresis interface that can extract sufficient amount of sweat for robust sensing, without electrode corrosion and burning/causing discomfort in subjects. Here, we overcame this challenge through realizing an electrochemically enhanced iontophoresis interface, integrated in a wearable sweat analysis platform. This interface can be programmed to induce sweat with various secretion profiles for real-time analysis, a capability which can be exploited to advance our knowledge of the sweat gland physiology and the secretion process. To demonstrate the clinical value of our platform, human subject studies were performed in the context of the cystic fibrosis diagnosis and preliminary investigation of the blood/sweat glucose correlation. With our platform, we detected the elevated sweat electrolyte content of cystic fibrosis patients compared with that of healthy control subjects. Furthermore, our results indicate that oral glucose consumption in the fasting state is followed by increased glucose levels in both sweat and blood. Our solution opens the possibility for a broad range of noninvasive diagnostic and general population health monitoring applications.

2. 3D Printed “Earable” Smart Devices for Real-Time Detection of Core Body Temperature

   Hiroki Ota, Minghan Chao, Yuji Gao, and 10 more authors

   ACS Sensors, Jul 2017

   Abs DOI

   Real-time detection of basic physiological parameters such as blood pressure and heart rate is an important target in wearable smart devices for healthcare. Among these, the core body temperature is one of the most important basic medical indicators of fever, insomnia, fatigue, metabolic functionality, and depression. However, traditional wearable temperature sensors are based upon the measurement of skin temperature, which can vary dramatically from the true core body temperature. Here, we demonstrate a three-dimensional (3D) printed wearable “earable” smart device that is designed to be worn on the ear to track core body temperature from the tympanic membrane (i.e., ear drum) based on an infrared sensor. The device is fully integrated with data processing circuits and a wireless module for standalone functionality. Using this smart earable device, we demonstrate that the core body temperature can be accurately monitored regardless of the environment and activity of the user. In addition, a microphone and actuator are also integrated so that the device can also function as a bone conduction hearing aid. Using 3D printing as the fabrication method enables the device to be customized for the wearer for more personalized healthcare. This smart device provides an important advance in realizing personalized health care by enabling real-time monitoring of one of the most important medical parameters, core body temperature, employed in preliminary medical screening tests.

2016

1. Application of 3D Printing for Smart Objects with Embedded Electronic Sensors and Systems

   Hiroki Ota, Sam Emaminejad, Yuji Gao, and 14 more authors

   Advanced Materials Technologies, Apr 2016

   DOI
2. Wearable Microsensor Array for Multiplexed Heavy Metal Monitoring of Body Fluids

   Wei Gao, Hnin Y. Y. Nyein, Ziba Shahpar, and 11 more authors

   ACS Sensors, Jul 2016

   Abs DOI

   A flexible and wearable microsensor array is described for simultaneous multiplexed monitoring of heavy metals in human body fluids. Zn, Cd, Pb, Cu, and Hg ions are chosen as target analytes for detection via electrochemical square wave anodic stripping voltammetry (SWASV) on Au and Bi microelectrodes. The oxidation peaks of these metals are calibrated and compensated by incorporating a skin temperature sensor. High selectivity, repeatability, and flexibility of the sensor arrays are presented. Human sweat and urine samples are collected for heavy metal analysis, and measured results from the microsensors are validated through inductively coupled plasma mass spectrometry (ICP-MS). Real-time on-body evaluation of heavy metal (e.g., zinc and copper) levels in sweat of human subjects by cycling is performed to examine the change in concentrations with time. This platform is anticipated to provide insightful information about an individual’s health state such as heavy metal exposure and aid the related clinical investigations.

3. Wearable sweat biosensors

   Wei Gao, Hnin Y. Y. Nyein, Ziba Shahpar, and 7 more authors

   In 2016 IEEE International Electron Devices Meeting (IEDM), Dec 2016

   Abs DOI

   Wearable perspiration biosensors enable real-time analysis of the sweat composition and can provide insightful information about health conditions. In this review, we discuss the recent developments in wearable sweat sensing platforms and detection techniques. Specifically, on-body monitoring of a wide spectrum of sweat biomarkers are illustrated. Opportunities and challenges in the field are discussed. Although still in an early research stage, wearable sweat biosensors may enable a wide range of personalized diagnostic and physiological monitoring applications.

2014

1. Transcriptome Sequencing of a Large Human Family Identifies the Impact of Rare Noncoding Variants

   Xin Li, Alexis Battle, Konrad J. Karczewski, and 7 more authors

   The American Journal of Human Genetics, Sep 2014

   DOI
2. SplicePlot: a utility for visualizing splicing quantitative trait loci

   E. Wu, T. Nance, and S. B. Montgomery

   Bioinformatics, Apr 2014

   Abs DOI

   Summary: RNA sequencing has provided unprecedented resolution of alternative splicing and splicing quantitative trait loci (sQTL). However, there are few tools available for visualizing the genotypedependent effects of splicing at a population level. SplicePlot is a simple command line utility that produces intuitive visualization of sQTLs and their effects. SplicePlot takes mapped RNA sequencing reads in BAM format and genotype data in VCF format as input and outputs publication-quality Sashimi plots, hive plots and structure plots, enabling better investigation and understanding of the role of genetics on alternative splicing and transcript structure.