High-resolution Signal Research Articles

Combining PEDOT:PSS Polymer Coating with Metallic 3D Nanowires Electrodes to Achieve High Electrochemical Performances for Neuronal Interfacing Applications.

This study presents a novel approach to improve the performance of microelectrode arrays (MEAs) used for electrophysiological studies of neuronal networks. The integration of 3D nanowires (NWs) with MEAs increases the surface-to-volume ratio, which enables subcellular interactions and high-resolution neuronal signal recording. However, these devices suffer from high initial interface impedance and limited charge transfer capacity due to their small effective area. To overcome these limitations, the integration of conductive polymer coatings, poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) is investigated as a mean of improving the charge transfer capacity and biocompatibility of MEAs. The study combines platinum silicide-based metallic 3D nanowires electrodes with electrodeposited PEDOT:PSS coatings to deposit ultra-thin (<50 nm) layers of conductive polymer onto metallic electrodes with very high selectivity. The polymer-coated electrodeswerefully characterized electrochemically and morphologically to establish a direct relationship between synthesis conditions, morphology, and conductive features. Results show that PEDOT-coated electrodes exhibit thickness-dependent improved stimulation and recording performances, offering new perspectives for neuronal interfacing with optimal cell engulfment to enable the study of neuronal activity with acute spatial and signal resolution at the sub-cellular level.

A Pneumatic Low-Pass Filter for High-Fidelity Cuff-Based Pulse Waveform Acquisition.

Cuff-based pulse waveform acquisition (CBPWA) devices are reliable solutions for non-invasive cardiovascular diagnostics. However, poor signal resolution has limited clinical applications. This study aims to demonstrate the improved signal quality of CBPWA devices by implementing passive pneumatic low-pass filters (pLPF). Conventionally, pressure sensor output resolution is a percentage of the operating range. Therefore, measurement of small pressure changes in a large range must sacrifice signal resolution to accommodate for the large mean pressures. We design a pLPF to obtain the running mean pressure and combine it with a high-resolution differential pressure sensor for isolating the signal's pulsatile component. Thirty-one volunteers participated in a device proof-of-concept study at Caltech. Volunteers were measured at rest in the supine position on the left arm. The filtering behavior is mathematically modeled and experimentally verified, showing good agreement between measured and predicted cutoff frequencies. In the human study, the device successfully captured high-fidelity pulse waveform measurements for all volunteers: a blood pressure (BP) reading was followed by inflate-and-hold acquisition in diastolic BP (DBP), mean arterial pressure (MAP), and supra systolic BP (sSBP). The study demonstrated the reliability and high signal resolution of pLPF for CBPWA. Considering the widespread use of the brachial cuff, a system for high-resolution CBPWA motivates the clinical implementation of non-invasive pulse waveform analysis (PWA).

Multivariate time-varying Kalman filter approach for cycle-based maximum queue length estimation

Real-time queue length is a crucial information, increasingly needed for signal operation and signal optimization purposes. This paper proposes a novel multivariate time-varying Kalman filter approach to estimate the cycle-based maximum queue lengths in real-time by only using high-resolution vehicle loop detector data and signal timing data. The modeling framework builds on state changing points that can be identified at detector site and shockwave theory that models the propagation of traffic states in the space–time domain. The times at which traffic states change at the detector site, known as break points (e.g., free flow to jam condition, jam to saturation condition, etc.) are identified using high resolution detector data and the end of the queue is estimated using linear models that build on the shockwave theory. Nevertheless, this deterministic approach, which is previously proposed in the literature, does not account for measurements errors associated with traffic state changing points or the assumptions in the shockwave theory. Therefore, we propose a multivariate time-varying Kalman filter approach to produce a robust estimate for the cycle-based maximum queue length in both undersaturated and oversaturated conditions. Further, the developed algorithm can estimate the residual queue length and inform its occurrence in case of oversaturation. The proposed methodology can easily be adapted in practice, as it relies solely on measurements from a single loop detector that may be located close to the stop line.

Super-resolution reconstruction method of ground penetrating radar signals based on wavelet theory and its application in reverse time migration

The finite difference time domain (FDTD) method was used to solve the Maxwell’s equation to obtain the reverse time migration (RTM) of ground penetrating radar (GPR) signals, namely, the FDTD-RTM. In order to ensure that the correct numerical solution of iterative calculation was achieved, it was necessary to obtain high-resolution signals, which greatly limits the applicability of FDTD-RTM in engineering. Based on the characteristics of wavelet multi-resolution analysis, this study proposed a super-resolution signal reconstruction method to improve signal resolution, with the view to completely solving the problem of FDTD-RTM limitation caused by insufficient signal sampling using GPR. The results of electromagnetic simulation showed that the signals reconstructed by the above method were highly similar to the signals sampled with the same resolution. On this basis, reverse time migration electromagnetic simulation and physical model tests were designed. The results of both experiments showed that the under-sampled GPR signals could achieve FDTD-RTM following super-resolution reconstruction, and that the migration imaging results of the target were basically consistent with the design scheme. The signal super-resolution reconstruction method based on the wavelet theory was thus shown to successfully achieve the overall application of FDTD-RTM in GPR signal analysis.

Room-Temperature Ferromagnetism in Layered Mn-Substituted MoS2

Intrinsically two-dimensional (2D) ferromagnetic materials normally exhibit a low transition temperature, above which they would lose their magnetic properties. This makes it difficult to develop them for practical applications. Substituting transition metal atoms into 2D systems provides a straightforward way for achieving room-temperature ferromagnetism. Here, we propose a one-step chemical vapor deposition method to prepare Mn-substituted MoS2 monolayers, which exhibit robust magnetism, as confirmed by a combined study of physical property measurement systems and magneto-optical measurements. High-resolution transmission electron microscopy, Raman signals, and X-ray photoelectron spectroscopy analysis have all shown that the manganese atoms in MoS2 act as a substitute for the molybdenum sites. The microscopic origin of magnetism in Mn-MoS2 is further revealed on the basis of first-principles calculations, which corroborate the experimental results obtained. Our study demonstrates a simple route to creating 2D ferroelectric materials with broad prospects in spintronic devices and next-generation memory components.

Joint denoising method of seismic velocity signal and acceleration signals based on independent component analysis

The signal-to-noise ratio (SNR) of seismic data is the key to seismic data processing, and it also directly affects interpretation of seismic data results. The conventional denoising method, independent variable analysis, uses adjacent traces for processing. However, this method has problems, such as the destruction of effective signals. The widespread use of velocity and acceleration geophones in seismic exploration makes it possible to obtain different types of signals from the same geological target, which is fundamental to the joint denoising of these two types of signals. In this study, we propose a joint denoising method using seismic velocity and acceleration signals. This method selects the same trace of velocity and acceleration signal for Independent Component Analysis (ICA) to obtain the independent initial effective signal and separation noise. Subsequently, the obtained effective signal and noise are used as the prior information for a Kalman filter, and the final joint denoising results are obtained. This method combines the advantages of low-frequency seismic velocity signals and high-frequency and high-resolution acceleration signals. Simultaneously, this method overcomes the problem of inconsistent stratigraphic reflection caused by the large spacing between adjacent traces, and improves the SNR of the seismic data. In a model data test and in field data from a work area in the Shengli Oilfield, the method increases the dominate frequency of the signal from 20 to 40 Hz. The time resolution was increased from 8.5 to 6.8 ms. The test results showed that the joint denoising method based on seismic velocity and acceleration signals can better improve the dominate frequency and time resolution of actual seismic data.

Investigation of the 6s6p3P2-6s7s3S1 transition of ytterbium atoms with modulation-transfer spectroscopy

We perform the precise measurement of the 6s6p3P2-6s7s3S1 transition frequencies of the neutral ytterbium (Yb) atoms by using the modulation transfer spectroscopy. High-resolution dispersive signals of all isotopes except low-abundance 168Yb (F = 2-F = 1) and 171Yb (F = 5/2-F = 3/2) are obtained. The frequencies of locked laser are measured using an optical comb, and the obtained results are traced to the absolute transition frequency of the 171Yb 1S0-3P0 clock. We measure the isotope shifts relative to 174Yb and evaluate the systematic shifts. The absolute frequency of the 6s6p3P2-6s7s3S1 transition in 174Yb is determined to be 389260605.8 ± 3.2 MHz, which has a huge deviation from the existing data in the studies. Based on the obtained transition frequencies, we calculate the hyperfine constants of the 3P2 and 3S1 states. This research will provide valuable data for the ytterbium-related experiments and improve the reported transition frequencies with higher precision.

Reducing Red Light Running (RLR) with Adaptive Signal Control: A Case Study

Traffic accidents are a leading cause of premature death for citizens, with millions of injuries and fatalities occurring annually. Due to the fact that a large proportion of accidents are caused by red light running, reduction of the frequency of red light running (RLR) has been extensively researched in recent years. However, most of the previous studies have focused on reducing RLR frequency through driver education or warning sign design, with little attention paid to the relationship between RLR behavior and traffic signal control. Considering RLR is significantly affected by the number of vehicles arriving during yellow, it is possible to identify RLR behaviors in advance by analyzing data on yellow-arriving vehicles. Meanwhile, based on the strong correlation between yellow arriving and RLR frequency, it is possible to reduce RLR by traffic signal control. In this paper, we propose a quantitative model of correlation between RLR frequency and yellow light arrival based on high-resolution traffic and signal event data from Twin Cities, Minnesota. On this basis, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is implemented to find trade-offs between minimizing the RLR frequency and the traffic delay. A case study of a 6-intersection arterial road reveals that in unsaturation, saturation, and supersaturation flow, our approach can converge to a Pareto optimal front in 30–50 iterations, which shows that is possible to simultaneously reduce RLR frequency and enhance traffic efficiency safety, which is conducive to ensuring the life safety of traffic participants.

An Optimized Two-Dimensional Quantitative Nuclear Magnetic Resonance Strategy for the Rapid Quantitation of Diester-Type C19-Diterpenoid Alkaloids from Aconitum carmichaelii.

With the development of nuclear magnetic resonance (NMR) spectrometers and probes, two-dimensional quantitative nuclear magnetic resonance (2D qNMR) technology with a high signal resolution and great application potential has become increasingly accessible for the quantitation of complex mixtures. However, the requirement that the relaxation recovery time be equal to at least five times T1 (longitudinal relaxation time) makes it difficult for 2D qNMR to simultaneously achieve high quantitative accuracy and high data acquisition efficiency. By comprehensively using relaxation optimization and nonuniform sampling, we successfully established an optimized 2D qNMR strategy for HSQC experiments at the half-hour level and then accurately quantified the diester-type C19-diterpenoid alkaloids in Aconitum carmichaelii. The optimized strategy had the advantages of high efficiency, high accuracy, good reproducibility, and low cost and thus could serve as a reference to optimize 2D qNMR experiments for quantitative analysis of natural products, metabolites, and other complex mixtures.

Ocular motor nerve palsy in patients with diabetes: High-resolution MR imaging of nerve enhancement

To evaluate the extent of signal abnormality in impaired ocular motor nerves using high signal and spatial resolution MRI sequences and to discuss the involvement of inflammatory or microvascular impairment in patients with diabetic ophthalmoplegia. We conducted a retrospective study of 10 patients referred for acute ocular motor nerve palsy in the context of diabetes mellitus from September 15th, 2021 to April 24th, 2022. 3T MRI evaluation included diffusion, 3D TOF, FLAIR, coronal STIR and post-injection 3D T1 SPACE DANTE sequences. Ten patients were included: 9 males and 1 female aged from 46 to 79 years. Five patients presented with cranial nerve (CN) III palsy, and 5 presented with CN VI palsy. Third nerve palsy was pupil-sparing in 4 patients and pupil-involved in 1 patient. Pain was associated in all patients with CN III deficiencies and in 2 patients CN VI deficiencies. In all patients, MRI sequences ruled out mass effect and vascular pathology, such as acute stroke or aneurysm. Eight patients presented with STIR hypersignals, some with enlargement of the involved nerve. The diagnosis was confirmed through a post-injection 3D T1 SPACE DANTE sequence, which showed extended enhancement along the abnormal portion of the nerve. High-resolution MRI evaluation of diplopia in diabetic patients is used to rule out a diagnosis of acute stroke and contributes to the positive diagnosis of ocular motor nerve impairment, possibly combining the influences of inflammatory and microvascular phenomena. Dedicated MR imaging should be included in the initial diagnosis and longitudinal follow-up of patients with diabetic ophthalmoplegia.

Quantifying uncertainty of marine water quality forecasts for environmental management using a dynamic multi-factor analysis and multi-resolution ensemble approach

Unpredictable climate change and human activities pose enormous challenges to assessing the water quality components in the marine environment. Accurately quantifying the uncertainty of water quality forecasts can help decision-makers implement more scientific water pollution management strategies. This work introduces a new method of uncertainty quantification driven by point prediction for solving the engineering problem of water quality forecasting under the influence of complex environmental factors. The constructed multi-factor correlation analysis system can dynamically adjust the combined weight of environmental indicators according to the performance, thereby increasing the interpretability of data fusion. The designed singular spectrum analysis is utilized to reduce the volatility of the original water quality data. The real-time decomposition technique cleverly avoids the problem of data leakage. The multi-resolution-multi-objective optimization ensemble method is adopted to absorb the characteristics of different resolution data, so as to mine deeper potential information. Experimental studies are conducted using 6 actual water quality high-resolution signals with 21,600 sampling points from the Pacific islands and corresponding low-resolution signals with 900 sampling points, including temperature, salinity, turbidity, chlorophyll, dissolved oxygen, and oxygen saturation. The results illustrate that the model is superior to the existing model in quantifying the uncertainty of water quality prediction.

Developing High-Resolution Metastasis Signatures for Improved Cancer Prognosis and Drug Sensitivity Prediction using Single-Cell RNA Sequencing Data: A Case Study in Lung Adenocarcinoma

Metastasis remains the reason for chemoresistance and high cancer mortality. It is hence a valuable predictive factor in cancer prognosis and drug sensitivity. Single-cell RNA sequencing (scRNA-seq) can reveal cellular heterogeneity in metastasis microenvironment and capture high-resolution signatures for improved cancer prediction. As a case study, an integrated analysis framework was designed for metastatic lung adenocarcinoma (LUAD) scRNA-seq profiles and we identified nine key prognostic genes (KPGs) that were trained and validated in 407 internal and external patient cohorts using machine learning and other methods. Correlation analysis revealed the strong association between KPGs signatures and several clinical characteristics such as gender, [Formula: see text]-stage and [Formula: see text]-stage. We incorporated these risk clinical variables into a KPGs nomogram model with superior accuracy for overall survival (OS) prediction. We also found that high risk group with high nomogram scores had poorer prognosis accompanied by a higher tumor mutation burden (TMB) and was more sensitive to chemotherapy and targeted agents, which was associated with the upregulation of DNA replication, ECM receptor interaction, P53 signaling pathway, spliceosome and proteasome pathway. Collectively, we proposed a simple and feasible strategy to mine single-cell resolution metastasis signatures from scRNA-seq data for improved cancer prognosis and drug sensitivity prediction, which will be a useful tool in risk gene discovery and targeted therapy in metastatic cancers.

High-Accuracy and High-Tolerance Laser Encoder With a Grating-Pyramid Configuration

Laser encoders is promising positioning sensors for nanometer measurement. It is a key challenge for laser encoders to offer high accuracy while ensuring high tolerance in service. In this paper, we present a laser encoder employing a grating-pyramid configuration. This configuration realizes double diffraction via a single retro-reflector, which offers the following two advantages: (i) providing high-resolution output signals due to optical quadruple frequency; (ii) improving alignment tolerance due to retro-reflection of the single pyramid. Its operating principle is described in detail, and the performance are evaluated. Simulation result presents that stand-off error and pitch error are the critical head-to-scale tolerance for the developed laser encoder. Experiment result shows that the laser encoder offers a stand-off tolerance of ±0.68 mm and a pitch tolerance of ±1.67°. Furthermore, an interpolation accuracy of 3 nm is achieved, which indicates that the laser encoder would achieve nanometer-level resolution. Both simulation and experimental results demonstrate its high-accuracy and high-tolerance.

Dynamic dilemma zone protection system for high-speed signalized intersections: A comprehensive safety -operational assessment

Dynamic dilemma zone protection (DZP) system with continuous vehicle tracking via radar sensors has been implemented at multiple locations in Alabama to promote the safety and operational efficiency of high-speed signalized intersections. The present study discusses the main features of the DZP system, and its comprehensive assessment in three important aspects: changes in (1) vehicle arrival distributions in different signal intervals (green, yellow, and red), (2) dilemma zone length and location, and (3) dilemma zone conflicts before and after the DZP system implementation. For the system assessment, an extensive amount of data collection and analysis efforts were performed with radar sensor-based vehicle attribute data and high-resolution signal controller data collected from seven intersection approaches where a DZP system has been recently implemented. The analysis results showed that vehicle arrivals on green intervals were significantly increased after the DZP system implementation, while vehicle arrivals on the yellow and red intervals were both reduced significantly despite no change in total vehicle arrivals before and after the system implementation. The chance of dilemma zone conflicts (i.e., red-light running and abrupt stops) was also significantly reduced after the DZP system implementation. Results also showed that the dilemma zone length became shorter, and its location was closer to the intersection stop bar after its implementation. Thus, the present study claims that the DZP system promotes intersection operational efficiency by effectively utilizing available green time as well as improving intersection safety by reducing the number of vehicles trapped within the dilemma zone.

Ultrasound of the neck

Ultrasound examination of the neck organs enables an assessment that in many cases is superior to that of magnetic resonance imaging and computed tomography. Ultrasound is therefore not only afirst line or point of care imaging modality but can provide imaging for the concluding diagnosis in cases. Because of the good sonographic accessibility of the majority of the structures of the neck, many technical advances, in particular high-resolution ultrasound and signal post-processing have amajor influence on the possibilities of ultrasound. Lymph nodes and salivary glands are the main focus in clinical applications, although other diseases and swellings of the neck can also be clarified with ultrasound. Special applications are ultrasound-guided interventions, e.g., biopsies or the sonographic assessment of peripheral nerves. As in any imaging modality, a comprehensive clinical knowledge is necessary for the diagnostic assessment. Because of constant assessment and thus continuous modification of the examination, ultrasound examinations may only be performed adequately with the appropriate clinical knowledge.

Online adaptive group-wise sparse Penalized Recursive Exponentially Weighted N-way Partial Least Square for epidural intracranial BCI.

Motor Brain-Computer Interfaces (BCIs) create new communication pathways between the brain and external effectors for patients with severe motor impairments. Control of complex effectors such as robotic arms or exoskeletons is generally based on the real-time decoding of high-resolution neural signals. However, high-dimensional and noisy brain signals pose challenges, such as limitations in the generalization ability of the decoding model and increased computational demands. The use of sparse decoders may offer a way to address these challenges. A sparsity-promoting penalization is a common approach to obtaining a sparse solution. BCI features are naturally structured and grouped according to spatial (electrodes), frequency, and temporal dimensions. Applying group-wise sparsity, where the coefficients of a group are set to zero simultaneously, has the potential to decrease computational time and memory usage, as well as simplify data transfer. Additionally, online closed-loop decoder adaptation (CLDA) is known to be an efficient procedure for BCI decoder training, taking into account neuronal feedback. In this study, we propose a new algorithm for online closed-loop training of group-wise sparse multilinear decoders using L p -Penalized Recursive Exponentially Weighted N-way Partial Least Square (PREW-NPLS). Three types of sparsity-promoting penalization were explored using L p with p = 0., 0.5, and 1. The algorithms were tested offline in a pseudo-online manner for features grouped by spatial dimension. A comparison study was conducted using an epidural ECoG dataset recorded from a tetraplegic individual during long-term BCI experiments for controlling a virtual avatar (left/right-hand 3D translation). Novel algorithms showed comparable or better decoding performance than conventional REW-NPLS, which was achieved with sparse models. The proposed algorithms are compatible with real-time CLDA. The proposed algorithm demonstrated good performance while drastically reducing the computational load and the memory consumption. However, the current study is limited to offline computation on data recorded with a single patient, with penalization restricted to the spatial domain only.

A flexible carbon nanotube electrode array for acute in vivo EMG recordings.

Executing complex behaviors requires precise control of muscle activity. Our understanding of how the nervous system learns and controls motor skills relies on recording electromyographic (EMG) signals from multiple muscles that are engaged in the motor task. Despite recent advances in tools for monitoring and manipulating neural activity, methods for recording in situ spiking activity in muscle fibers have changed little in recent decades. Here, we introduce a novel experimental approach to recording high-resolution EMG signals using parylene-coated carbon nanotube fibers (CNTFs). These fibers are fabricated via a wet spinning process and twisted together to create a bipolar electrode. Single CNTFs are strong, extremely flexible, small in diameter (14-24 µm), and have low interface impedance. We present two designs to build bipolar electrode arrays that, due to the small size of CNTF, lead to high spatial resolution EMG recordings. To test the EMG arrays, we recorded the activity of small (4 mm length) vocal muscles in songbirds in an acute setting. CNTF arrays were more flexible and yielded multiunit/bulk EMG recordings with higher SNR compared with stainless steel wire electrodes. Furthermore, we were able to record single-unit recordings not previously reported in these small muscles. CNTF electrodes are therefore well-suited for high-resolution EMG recording in acute settings, and we present both opportunities and challenges for their application in long-term chronic recordings.NEW & NOTEWORTHY We introduce a novel approach to record high-resolution EMG signals in small muscles using extremely strong and flexible carbon nanotube fibers (CNTFs). We test their functionality in songbird vocal muscles. Acute EMG recordings successfully yielded multiunit recordings with high SNR. Furthermore, they successfully isolated single-unit spike trains from CNTF recordings. CNTF electrodes have great potential for chronic EMG studies of small, deep muscles that demand high electrode flexibility and strength.

Using optimal controlled singlet spin order to accurately target molecular signal in MRI and MRS

Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) have made great successes in clinical diagnosis, medical research, and neurological science. MRI provides high resolution anatomical images of tissues/organs, and MRS provides information of the functional molecules related to a specific tissue/organ. However, it is difficult for classic MRI/MRS to selectively image/probe a specific metabolite molecule other than the water or fat in tissues/organs. This greatly limits their applications on the study of the molecular mechanism(s) of metabolism and disease. Herein, we report a series of molecularly targeted MRI/MRS methods to target specific molecules. The optimal control method was used to efficiently prepare the singlet spin orders of varied multi-spin systems and in turn greatly expand the choice of the targeted molecules in the molecularly targeted MRI/MRS. Several molecules, such as N-acetyl-l-aspartic acid (NAA), dopamine (DA), and a tripeptide (alanine-glycine-glycine, AGG), have been used as targeted molecules for molecularly targeted MRI and MRS. We show in vivo NAA-targeted 1H MRS spectrum of a human brain. The high-resolution signal of NAA suggests a promising way to study important issues in molecular biology at the molecular level, e.g., measuring the local pH value of tissue in vivo, demonstrating the high potential of such methods in medicine.

Multiscale Integrated Temperature/Flow Velocity Sensor Patches for Microfluidic Applications

Embedded sensors in microfluidic systems play a big role in different chemical and biological analyses. Versatile metal electrodes in microfluidic chips have been designed to detect the physical and chemical parameters of the fluid, but in situ monitoring by flexible and transparent embedded sensors still remains a significant challenge. Here, such a challenge is tackled for the first time, by simple fabrication of multiapplication sensing patches into a microfluidic chip. The sensing patches are made with polydimethylsiloxane (PDMS) and fluorescent dye. Different strategies of assembling sensing patches could be used to realize both local and dynamic sensing for multiscale detection. In Strategy 1, one sensing patch is utilized to measure the temperature of microheaters below 100 °C and its detecting resolution is 1.25 °C. In Strategy 2, four sensing patches are placed in four channels to monitor the real-time temperature change (23 °C–50 °C) in different microchannels. In Strategy 3, two sensing patches are fabricated in a microchannel, which has a high signal resolution to monitor the flow rate of less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10 ~\mu \text{L}$ </tex-math></inline-formula> /min. The simple fabrication process and different combinatorial strategies facilitate the development of miniaturization and functionalization of microfluidic sensors, so as to realize the monitoring of more parameters and play a significant role in the fields of biological and chemical analysis.

Versatile nanogold tagging system applied to the identification of heterochromatin through cryo-FIB-ET.

Cryo-Focused Ion Beam-Electron Tomography (cryo-FIB-ET) is swiftly becoming a powerful tool for investigating structure-function relationships in native environments, enabling the discovery of new biology. However, features which can be identified are those which are sufficiently large that they can be unambiguously identified visually (whole organelles, ribosomes, the nuclear pore complex, proteasomes), or through the matching of high resolution signatures (2D template matching) of sufficient molecular weight. The need for additional tagging methods is paramount, in order to identify macromolecules and expand our scientific inquiries. We have developed a delivery and tagging system utilizing a gold nanoparticle that specifically targets heterochromatin to be visualized by cryo-FIB-ET and resolved on a nucleosome basis by subtomogram analysis. There has been enormous progress in determining chromatin chain topology in vitro and in situ through the use of electron microscopy on a variety of conditions: frozen-hydrated samples, resin embedded samples, and cryo-conditions. As such, a useful tool for researchers would be the ability to distinguish between heterochromatin and euchromatin in native contexts, such as areas of active vs inactive gene expression. In general, the labeling, delivery, and identification strategy presented here would facilitate future progress for distinguishing and identifying various macromolecules endogenously, with minimal perturbations, imaged within the cell at high spatial resolution.