arXiv subjects

Yingqi Zhao

Publications and source records attributed to Yingqi Zhao.

At least 19 recordsLinked to original sources

arXiv2026

Physics-Aligned Deep Learning Enables SERS Resolving and Sequencing of Dynamic Single-Molecule DNA Oligomers in Plasmonic Nanocavity

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) captures dynamic molecular behavior with ultrahigh sensitivity, but its biopolymer analysis is hindered by strong spectral heterogeneity, transient hotspot sampling, and background interference. Here, we develop a physics-aligned deep learning framework integrating contrastive attention-based multiple-instance learning (CAMIL), a tri-channel multi-kernel CNN classifier, and trajectory-level transition-guided sequence reconstruction to decode single-molecule DNA oligomer dynamics in a plasmonic nanocavity. In this work, CAMIL mines informative spectra enriched with chain-embedded nucleotide and dinucleotide signatures from contrastive positive and negative DNA trajectory bags, generating a single-molecule DNA-segment spectral-states library. This library trains a 12-class CNN to assign query time-resolved DNA SM-SERS frames to high-confidence DNA-segment states, enabling trajectory-level analysis of state composition, dwell length, entropy, switching frequency, and transition matrix. The transition matrix is further converted into state-transition-edge evidence for candidate sequence scoring, enabling asymmetric DNA sequences inference from confined stochastic sampling dynamics. This framework transforms SM-SERS heterogeneity into quantitative analytical information, advancing dynamic single-molecule decoding.

physics.optics↗

arXiv2026

SERS study of single-live-cell electrical permeabilization dynamics via plasmonic nanotubes

There is a growing demand for minimally invasive methods to analyze intracellular processes and signaling activities in individual living cells, including the identification of tumorigenic cell subpopulations. However, most conventional analytical methods require cell lysis, precluding repeated measurements in the same cell over time, or rely on exogenous labels and reporters that may perturb cellular function. Various applications based on vertical nanotubes have been developed that enable live cell monitoring and analysis by electroporation with low voltages. However, the extent and duration of membrane permeability and kinetics of membrane repair remain elusive. Here, we built a plasmonic platform with the capacity of surface enhanced Raman spectroscopy (SERS) to monitor the electroporation-induced membrane permeability dynamics in individual live cells attached onto 100-nm diameter nanotubes of 2 um height. Fibronectin was employed as extracellular matrix (ECM)-coating to facilitate cell attachment onto nanotubes. Using fluorescent-dye delivery as an independent validation method, we show that the fabricated nanostructures induce localized electrical permeabilization of the plasma membrane and enable monitoring of its subsequent recovery. We further use SERS to track molecular changes at the membrane during permeabilization and resealing. The SERS spectra provide molecular-level insight into changes in membrane-associated components and the ECM during electroporation and subsequent membrane recovery. Real time monitoring of pulse induced molecular changes holds great promise for characterizing intracellular signaling, cellular states, and cellular heterogeneity at the single cell level, including the identification of tumorigenic subpopulations. This capacity could facilitate the development of novel biosensing assay.

physics.app-ph↗

arXiv2026

LoopsBench: From Harness Engineering to Loop Engineering in Coding Agent Evaluation

Coding agent infrastructure is shifting from harness engineering toward loop engineering as coding agents are deployed for sustained long-horizon software development. Existing benchmarks often center on localized tasks or end-state outcomes, offering limited insight into sustained execution. We introduce LOOPSBENCH, a long-horizon benchmark for loop engineering in coding agent evaluation. Each task is a dependency DAG over separately testable development units with source-evidenced prerequisite edges. LOOPSBENCH comprises 112 tasks from authentic sources spanning 8 programming languages and 9 domains. Its flow-aware runtime releases tests along the ready frontier and retains completed nodes as regression obligations. We evaluate frontier coding agents paired with widely used loop implementations. The strongest configuration, Opus-4.7 with Claude Code and outer continuation, resolves 25.00% of tasks. Recorded plans recover only part of the source-recovered prerequisite DAG, and regression events remain visible across the evaluated loop profiles. We open source the benchmark data and code, including all tasks, more than 5,300 development units, and executable tests, at microsoft/Loopsbench.

cs.SE↗

arXiv2026

Label-free SERS Discrimination of Native Proline Hydroxylation at Single-molecule peptide by Deep Learning-assisted plasmonic nanopore

Post-translational modifications (PTMs) play essential roles in regulating protein structure, function, and cellular signalling. However, peptide level discrimination of hydroxylation at the single-molecule level remains difficult. Here, we report a particle-in-pore single-molecule surface-enhanced Raman spectroscopy (SERS) platform combined with peak occurrence frequency (POF) analysis and a one-dimensional convolutional neural network (1D-CNN) for discriminating hydroxylated and non-hydroxylated HIF peptide fragments. Three peptide pairs containing the Pro-564 hydroxylation site, with lengths of 7, 9, and 15 amino acids (AAs), were investigated. POF analysis revealed reproducible hydroxylation-dependent spectral changes in the 7AA and 9AA peptide pairs, which were attributed to changes in adsorption conformation and surface interactions. CNN-based classification achieved post-evaluation accuracies of 72.98%, 78.55%, and 89.74% for the 7AA, 9AA, and 15AA peptide pairs, respectively, with AUC values above 0.80 for all the pairs, indicating a reliable discrimination. Gradient-weighted feature visualization further showed that CNN-sensitive regions overlapped with recurrent POF features, supporting the chemical relevance of the learned classification patterns. Notably, for the 15AA peptide pair, the enhanced citrate-associated band suggests that hydroxylation can substantially alter peptide-gold nanoparticle adsorption behaviour. This adsorption-mediated effect may amplify hydroxylation-induced spectral differences and contribute to the improved discrimination accuracy despite the increased structural complexity. These results demonstrate that the particle-in-pore sensor, assisted by deep learning, can capture hydroxylation-induced spectral and adsorption changes in peptide fragments, providing a promising strategy for ultrasensitive analysis of weak PTM signatures in peptides.

physics.bio-ph↗

arXiv2026

Fairness-Aware Retrieval Optimization for Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) improves reliability of large language models by incorporating external knowledge, but the retrieval process can introduce bias that propagates to generated outputs. This issue is particularly challenging in top-k settings, where multiple documents jointly influence generation. We propose a fairness-aware retrieval framework that models and controls this bias. Our approach combines controlled bias injection via reranking, a position-aware model of bias propagation, and an optimization formulation that balances relevance and fairness. We further introduce a scalable solution based on Quadratic Fairness via Dual Hyperplane Approximation (FARO), which enables efficient optimization through problem decomposition. Experimental results show that our method effectively mitigates generation bias while preserving relevance. This work provides a principled approach for fairness-aware retrieval in RAG systems.

cs.DB↗

arXiv2026

Resolving Single-Peptide Phosphorylation Dynamics in Plasmonic Nanopores using Physics-Informed Bi-Path Model

Protein phosphorylation provides a dynamic readout of cellular signaling yet remains difficult to detect at low abundance and stoichiometry. Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) using particle-in-pore plasmonic nanopores offers label-free molecular detection with submolecular sensitivity. However, reliable identification of subtle post-translational modifications (PTMs) is hindered by the stochastic nature of SM-SERS signals, partial excitation of peptide residues within the plasmonic hotspot, and background interference. Here, we introduce a physics-informed deep learning framework to decode complex SM-SERS dynamics and identify single-peptide PTMs. The model integrates multiple-instance learning with a temporal encoder combining temporal convolutional networks and bidirectional gated recurrent units to capture both local spectral variability and long-range blinking dynamics. To address diffusion-driven spectral heterogeneity, long spectral trajectories are segmented using Pearson-correlation, enabling weakly supervised training under label ambiguity. This framework robustly distinguishes single peptide phosphorylation despite strong background interference and stochastic signal fluctuations. By coupling nanoplasmonic confinement with spatiotemporal deep learning, our approach enables high-fidelity detection of single-molecule phosphorylation events and advances ultrasensitive phosphoproteomic analysis.

cond-mat.mes-hall↗

arXiv2024

Label-free SERS Discrimination of Proline from Hydroxylated Proline at Single-molecule Level Assisted by a Deep Learning Model

Discriminating the low-abundance hydroxylated proline from hydroxylated proline is crucial for monitoring diseases and eval-uating therapeutic outcomes that require single-molecule sensors. While the plasmonic nanopore sensor can detect the hydrox-ylation with single-molecule sensitivity by surface enhanced Raman spectroscopy (SERS), it suffers from intrinsic fluctuations of single-molecule signals as well as strong interference from citrates. Here, we used the occurrence frequency histogram of the single-molecule SERS peaks to extract overall dataset spectral features, overcome the signal fluctuations and investigate the citrate-replaced plasmonic nanopore sensors for clean and distinguishable signals of proline and hydroxylated proline. By ligand exchange of the citrates by analyte molecules, the representative peaks of citrates decreased with incubation time, prov-ing occupation of the plasmonic hot spot by the analytes. As a result, the discrimination of the single-molecule SERS signals of proline and hydroxylated proline was possible with the convolutional neural network model with 96.6% accuracy.

physics.chem-ph↗

arXiv2024

Deep learning assisted SERS detection of prolines and hydroxylated prolines using nitrilotriacetic acid functionalized gold nanopillars

Proline (Pro) is one kind of proteinogenic amino acid and an important signaling molecule in the process of metabolism. Hydroxyproline (Hyp) is a product on Pro oxygen sensing post-translational modification (PTM), which is efficiently modulated tumor cells for angiogenesis. Distinguishing between Pro and Hyp is crucial for diagnosing connective tissue disorders, as elevated levels of Hyp can indicate abnormal collagen metabolism, often associated with diseases like osteogenesis imperfecta or fibrosis. However, there is a very small difference between molecular structures of Pro and Hyp, which is a big challenge for current detection technologies to distinguish them. For surface-enhanced Raman scattering (SERS) sensors, the similar molecule structure leads to similar Raman spectra that are difficult to distinguish. Furthermore, another problem is the weak affinity between amino acids sample and SERS-active substrates by physical adsorption. The selecting capturing of Pro and Hyp in the mixture of amino acids is not easy to achieve. In this work, we designed a new method for Pro and Hyp specifical detection and recognition by using gold nanopillars as the SERS substrate and combing nitrilotriacetic acid (NTA) with nickel (Ni) to form NTA-Ni structure as a specifical affinity agent. One side of NTA-Ni was attached to gold nanopillars through thiol binding. Another side captured the amino acids using reversible binding by receptor-ligand interaction between Ni and amino acids. Because of the different binding time with NTA-Ni and amino acids, the sensor can recognize Pro and Hyp from amino acids mixture. Then we used automatic peak assignment program for data analysis and machine learning model to distinguish between Pro and Hyp. The label-free SERS detection of amino acids PTM using gold nanopillars provides a potential method to further biomolecule detection and specifical capture.

q-bio.BM↗

arXiv2024

A novel particle-in-well technology for single-molecule sequencing by surface-enhanced Raman spectroscopy

Single-molecule surface-enhanced Raman spectroscopy based on a particle trapped in a plasmonic nanopores provides a unique method for continued and controlled detection of peptide and DNA oligonucleotides in liquid medium. However, the Brownian motion of the particle and the molecule diffusion acting on the particle hinder single-molecule sequencing. In this study, we developed a method for trapping a gold nanoparticle in an air-filled gold nanowell (particle-in-well) to stabilize the particle and provide a powerful platform for continuous single molecule readout. The unlimited resident time of the particle-in-well device with single-molecule level sensitivity elevates nucleobase detection to a new level. We present a technique capable of detecting and monitoring solid-phase molecule diffusion within the plasmonic hotspot. Furthermore, the measured spectra were employed as input data for the validation of the plasmonic hotspot size and, consequently, the distance between the particle and the well. The obtained results form the statistical and experimental base for molecular translocation and DNA sequencing technologies.

physics.app-ph↗

arXiv2024

Estimating optimal tailored active surveillance strategy under interval censoring

Active surveillance (AS) using repeated biopsies to monitor disease progression has been a popular alternative to immediate surgical intervention in cancer care. However, a biopsy procedure is invasive and sometimes leads to severe side effects of infection and bleeding. To reduce the burden of repeated surveillance biopsies, biomarker-assistant decision rules are sought to replace the fix-for-all regimen with tailored biopsy intensity for individual patients. Constructing or evaluating such decision rules is challenging. The key AS outcome is often ascertained subject to interval censoring. Furthermore, patients will discontinue their participation in the AS study once they receive a positive surveillance biopsy. Thus, patient dropout is affected by the outcomes of these biopsies. In this work, we propose a nonparametric kernel-based method to estimate the true positive rates (TPRs) and true negative rates (TNRs) of a tailored AS strategy, accounting for interval censoring and immediate dropouts. Based on these estimates, we develop a weighted classification framework to estimate the optimal tailored AS strategy and further incorporate the cost-benefit ratio for cost-effectiveness in medical decision-making. Theoretically, we provide a uniform generalization error bound of the derived AS strategy accommodating all possible trade-offs between TPRs and TNRs. Simulation and application to a prostate cancer surveillance study show the superiority of the proposed method.

stat.ME↗

arXiv2024

SAPNet: a deep learning model for identification of single-molecule peptide post-translational modifications with surface enhanced Raman spectroscopy

Nanopore resistive pulse sensors are emerging technologies for single-molecule protein sequencing. But they can hardly detect small post-translational modifications (PTMs) such as hydroxylation in single-molecule level. While a combination of surface enhanced Raman spectroscopy (SERS) with plasmonic nanopores can detect the small PTMs, the blinking Raman peaks in the single-molecule SERS spectra leads to a big challenge in data analysis and PTM identification. Herein, we developed and validated a one-dimensional convolutional neural network (1D-CNN) for amino acids and peptides identification from their PTMs including hydroxylation and phosphorylation by their single-molecule SERS spectra, named Single Amino acid and Peptide Network (SAPNet). Our work combines cutting-edge plasmonic nanopore technology for SERS signal acquisition and deep learning for fully automated extraction of information from the SERS signals. The SAPNet model achieved an overall accuracy of 99.66% for the identification of amino acids from their modification, and 98.38% for the identification of peptides from their PTM translation. We also evaluated the model with out-of-sample examples with good performance. Our work can be beneficial for early detection of diseases such as cancers and Alzheimer's disease.

q-bio.QM↗

arXiv2022

Nanofluidic trapping and enhanced Raman detection of single biomolecules in plasmonic bowl-shaped nanopore

Solid-state nanopores are emerging platforms for single-molecule protein sequencing due to their tolerance to hash physiology environment and compatibility with different electrical and optical detection methods. However, they suffer from poor molecular manipulations that were twisted with and thus limited by the detection methods. Here, we report a bowl-shaped plasmonic gold nanopore on silicon nitride with hydrogel to demonstrate near-field nanofluidic manipulation of DNA translocation for plasmon-enhanced Raman spectroscopic detection. The hydrogel linearized the DNA, and the linear DNA was trapped in the nanopore for tens of seconds due assumably to bipolar effect of the nanopore that generate electroosmotic sheath flow and bipolar surface charge distribution. Their combination led to a near-field confinement of the DNA in the nanopore hot spot to allow stable Raman detection. We envision that a combination of Raman spectroscopy with the bowl-shaped nanopores can succeed in single-molecule protein sequencing in a label-free way

physics.app-ph↗

arXiv2021

Magnetic Circular Dichroism in Hyperbolic Metamaterial Nanoparticles

The optical properties of some nanomaterials can be controlled by an external magnetic field, providing active functionalities for a wide range of applications, from single-molecule sensing to nanoscale nonreciprocal optical isolation. Materials with broadband tunable magneto-optical response are therefore highly desired for various components in next-generation integrated photonic nanodevices. Concurrently, hyperbolic metamaterials received a lot of attention in the past decade since they exhibit unusual properties that are rarely observed in nature and provide an ideal platform to control the optical response at the nanoscale via careful design of the effective permittivity tensor, surpassing the possibilities of conventional systems. Here, we experimentally study magnetic circular dichroism in a metasurface made of type-II hyperbolic nanoparticles on a transparent substrate. Numerical simulations confirm the experimental findings, and an analytical model is established to explain the physical origin of the observed magneto-optical effects, which can be described in terms of the coupling of fundamental electric and magnetic dipole modes with an external magnetic field. Our system paves the way for the development of nanophotonic active devices combining the benefits of sub-wavelength light manipulation in hyperbolic metamaterials supporting a large density of optical states with the ability to freely tune the magneto-optical response via control over the anisotropic permittivity of the system.

physics.optics↗

arXiv2020

Hyperbolic Metamaterial Nanoparticles for Efficient Hyperthermia in the II and III Near-Infrared Windows

The use of gold nanoparticles for hyperthermia therapy in near infrared (NIR) spectral regions has catalysed substantial research efforts due to the potential impact in clinical therapy applications. However, the photoscattering effect scaling with the square of the nanoparticle volume leads to a low absorption efficiency, which has hindered the utility of gold nanoparticles in NIR II regions above 1000 nm. Here, we conquer this limit by introducing hyperbolic metamaterial nanoparticles that are made of multi-layered gold/dielectric nanodisks and exhibit >70% absorption efficiency in the NIR II and III regions. Their high light-to-heat conversion is demonstrated by a much larger temperature increase than that of gold nanodisks with the same amount of gold. Efficient in vitro hyperthermia of living cells with negligible cytotoxicity shows the potential of our approach for next-generation bio-medical applications.

physics.app-ph↗

arXiv2020

lambda DNA through a plasmonic nanopore What can be detected by means of Surface Enhanced Raman Scattering?

Engineered electromagnetic fields in plasmonic nanopores enable enhanced optical detection and their use in single molecule sequencing. Here, a plasmonic nanopore prepared in a thick nanoporous film is used to investigate the interaction between the metal and a long-chain double strand DNA molecule. We discuss how the matrix of nanoporous metal can interact with the molecule thanks to: i) transient aspecific interactions between the porous surface and DNA and ii) optical forces exerted by the localized field in a metallic nanostructure. A duration of interaction up to tens of milliseconds enables to collect high signal-to-noise Raman vibrations allowing an easy label-free reading of information from the DNA molecule. Moreover, in order to further increase the event of detection rate, we tested a polymeric porous hydrogel placed beneath the solid-state membrane. This approach enables a slowdown of the molecule diffusion, thus increasing the number of detected interactions by a factor of about 20.

physics.app-ph↗

arXiv2019

SERS discrimination of single amino acid residue in single peptide by plasmonic nanocavities

Surface-enhanced Raman spectroscopy (SERS) is a sensitive label-free optical method that can provide fingerprint Raman spectra of biomolecules such as DNA, amino acids and proteins. While SERS of single DNA molecule has been recently demonstrated, Raman analysis of single protein sequence was not possible because the SERS spectra of proteins are usually dominated by signals of aromatic amino acid residues. Here, we used electroplasmonic approach to trap single gold nanoparticle in a nanohole for generating a plasmonic nanocavity between the trapped nanoparticle and the nanopore wall. The giant field generated in the nanocavity was so sensitive and localized that it enables SERS discrimination of 10 distinct amino acids at single-molecule level. The obtained spectra are used to analyze the spectra of 2 biomarkers (Vasopressin and Oxytocin) made of a short sequence of 9 amino-acids. Significantly, we demonstrated identification of single non-aromatic amino acid residues in a single short peptide chain as well as discrimination between two peptides with sequences distinguishable in 2 specific amino-acids. Our result demonstrate the high sensitivity of our method to identify single amino acid residue in a protein chain and a potential for further applications in proteomics and single-protein sequencing.

q-bio.QM↗

arXiv2019

Single-molecule DNA Bases Discrimination in Oligonucleotides by Controllable Trapping in Plasmonic Nanoholes

Surface-enhanced Raman spectroscopy (SERS) sensing of DNA sequences by plasmonic nanopores could pave a way to new generation single-molecule sequencing platforms. The SERS discrimination of single DNA bases depends critically on the time that a DNA strand resides within the plasmonic hot spot. However, DNA molecules flow through the nanopores so rapidly that the SERS signals collected are not sufficient for single-molecule analysis. In this work, we report an approach to control the time that molecules reside in the hot spot by physically adsorbing them onto a gold nanoparticle and then trapping the single nanoparticle in a plasmonic nanohole. By trapping the nanoparticle for up to minutes, we demonstrate single-molecule SERS detection of all 4 DNA bases as well as discrimination of single nucleobases in a single oligonucleotide. Our method can be extended easily to label-free sensing of single-molecule amino acids and proteins.

physics.optics↗

arXiv2018

Controlled Intracellular Delivery of Single Particles in Single Cells by 3D Hollow Nanoelectrodes

We present an electrophoretic platform based on 3D hollow nanoelectrodes capable of controlling and quantifying the intracellular delivery of single nanoparticles in single selected cells by surface-enhanced Raman spectroscopy (SERS). The gold-coated hollow nanoelectrode has a sub-femtoliter inner volume that allows the confinement and enhancement of electromagnetic fields upon laser illumination to distinguish the SERS signals of a single nanoparticle flowing through the nanoelectrode. The tight wrapping of cell membranes around the nanoelectrodes enables effective membrane electroporation such that single gold nanorods are delivered into a living cell with a delivery rate subject to the applied bias voltage. The capability of the 3D hollow nanoelectrodes to porate cells and reveal single emitters from the background under live flow is promising for the analysis of both intracellular delivery and sampling.

physics.app-ph↗