Technical Noise Research Articles

Exploiting Nonclassical Motion of a Trapped Ion Crystal for Quantum-Enhanced Metrology of Global and Differential Spin Rotations.

We theoretically investigate prospects for the creation of nonclassical spin states in trapped ion arrays by coupling to a squeezed state of the collective motion of the ions. The correlations of the generated spin states can be tailored for quantum-enhanced sensing of global or differential rotations of subensembles of the spins by working with specific vibrational modes of the ion array. We propose a pair of protocols to utilize the generated states and demonstrate their viability even for small systems, while assessing limitations imposed by spin-motion entanglement and technical noise. Our work suggests new opportunities for the preparation of many-body states with tailored correlations for quantum-enhanced metrology in spin-boson systems.

Single-cell RNA sequencing data imputation using bi-level feature propagation.

Single-cell RNA sequencing (scRNA-seq) enables the exploration of cellular heterogeneity by analyzing gene expression profiles in complex tissues. However, scRNA-seq data often suffer from technical noise, dropout events and sparsity, hindering downstream analyses. Although existing works attempt to mitigate these issues by utilizing graph structures for data denoising, they involve the risk of propagating noise and fall short of fully leveraging the inherent data relationships, relying mainly on one of cell-cell or gene-gene associations and graphs constructed by initial noisy data. To this end, this study presents single-cell bilevel feature propagation (scBFP), two-step graph-based feature propagation method. It initially imputes zero values using non-zero values, ensuring that the imputation process does not affect the non-zero values due to dropout. Subsequently, it denoises the entire dataset by leveraging gene-gene and cell-cell relationships in the respective steps. Extensive experimental results on scRNA-seq data demonstrate the effectiveness of scBFP in various downstream tasks, uncovering valuable biological insights.

Abstract 7422: Enabling biomarkers of chromosomal instability for tumor only targeted gene panel sequencing

Abstract Capture-based targeted sequencing is routinely used for small variant detection in cancer clinical care. Alongside targeted DNA, off-target DNA is also sequenced. These off-target reads are distributed across the genome, allowing for a whole genome copy number profile to be derived without the need for a SNP backbone. Here we present CopyRight, a method that generates robust genome-wide copy number profiles, which can be used for downstream chromosomal instability (CIN) quantification, overcoming various sources of technical noise with a novel approach that only requires a single tumor sample. We analyzed a variety of capture-based NGS protocols using our novel computational method, including the TruSight Oncology 500 (TSO500) panel, and compared them to the current gold standard for genome-wide copy number profiling from FFPE tissues: shallow whole genome sequencing (sWGS). Comparable results were obtained from targeted sequencing depending on sample purity, preservation method, and read depth. Additionally, benchmarks of CopyRight against other computational algorithms show an improvement in performance without the need for a matched normal tissue, the usual drawback for other methods in the cancer field. CIN signatures are a new set of emerging biomarkers that reflect the diversity of defective pathways that have operated in a tumor, which require robust genome-wide copy number profiles. These CIN signatures can be used to predict response to cytotoxic agents and targeted therapies and could ultimately help guide therapy selection in patients. As most clinical sequencing workflows rely on targeted sequencing, CopyRight can be included in current clinical assays, enabling CIN biomarker quantification with no extra technical or experimental requirements. Citation Format: David Gómez-Sánchez, Joe Sneath Thompson, Barbara Hernando, Diego García-López, Hector de Galard, Abhipsa Roy, Amy Cullen, Laura Madrid, José Teles, Ania Piskorz, Jason Yip, Alice Cádiz, Maria Escobar-Rey, Roberto Moreno-Vellisca, Nuria Carrizo, Eva Álvarez, Miguel Quintela-Fandino, Mariano Barbacid, Javier Ramos-Paradas, Juan Manuel Coya, Irene Ferrer, Jon Zugazagoitia, Luis Paz-Ares, Geoff Macintyre. Enabling biomarkers of chromosomal instability for tumor only targeted gene panel sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7422.

Noise Differentiation and Atom Number Measurement in Optical Lattice Clocks by Analyzing Clock Stabilities with Various Parameters

We propose a method that enables the precise determination of the number of atoms in a Dick-noise-free optical lattice clock, by effectively addressing quantum projection noise. Our approach relies on conducting stability measurements at three distinct parameter sets, allowing us to differentiate between quantum projection noise, photon shot noise, and technical noise. Importantly, it enables accurate extraction of the atom number, even in the presence of photon shot noise and technical noise. We utilize numerical simulations to validate our approach, optimize the modulation parameters for minimal uncertainty, and investigate the impact of atom number fluctuations on the determinacy of our results. The numerical results show the validity of our method and demonstrate an estimated uncertainty in the atom number that is below 4% with 6.7 h measurement, provided that the standard deviation of atom number fluctuation is kept below 0.14 times the average atom number.

ScGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.

ScBiG for representation learning of single-cell gene expression data based on bipartite graph embedding.

Analyzing single-cell RNA sequencing (scRNA-seq) data remains a challenge due to its high dimensionality, sparsity and technical noise. Recognizing the benefits of dimensionality reduction in simplifying complexity and enhancing the signal-to-noise ratio, we introduce scBiG, a novel graph node embedding method designed for representation learning in scRNA-seq data. scBiG establishes a bipartite graph connecting cells and expressed genes, and then constructs a multilayer graph convolutional network to learn cell and gene embeddings. Through a series of extensive experiments, we demonstrate that scBiG surpasses commonly used dimensionality reduction techniques in various analytical tasks. Downstream tasks encompass unsupervised cell clustering, cell trajectory inference, gene expression reconstruction and gene co-expression analysis. Additionally, scBiG exhibits notable computational efficiency and scalability. In summary, scBiG offers a useful graph neural network framework for representation learning in scRNA-seq data, empowering a diverse array of downstream analyses.

Weak signal extraction in non-stationary channel with weak measurement

An emerging challenge of integrated communication and sensing is the extraction of weak sensing signals transmitted through an unknown non-stationary channel. In this work, we propose a weak signal extraction method with weak measurement. Taking advantage of time division multiplexing, we preliminarily estimate the channel via adjustable finite impulse response filter, further suppressing the interfering signal caused by background noises via spectrum shift. By subsequently using the time-varying phase estimation method via weak measurement, the real-time detection of weak signals in the non-stationary channel is achieved. We demonstrate via theoretical analysis and confirmatory experiment that our method is able to amplify the phase shift, to suppress technical noise and to improve detection resolution limit, while proving robust against light source fluctuations, initial phase differences and detector saturation. The method hence enables weak sensing signal extraction with a low signal-to-noise ratio non-stationary channel. Furthermore, we interface our measurement method to squeezed light sources, offering the possibility of surpassing standard quantum limit.

Quantifying orthogonal barcodes for sequence census assays.

Barcode-based sequence census assays utilize custom or random oligonucloetide sequences to label various biological features, such as cell-surface proteins or CRISPR perturbations. These assays all rely on barcode quantification, a task that is complicated by barcode design and technical noise. We introduce a modular approach to quantifying barcodes that achieves speed and memory improvements over existing tools. We also introduce a set of quality control metrics, and accompanying tool, for validating barcode designs. https://github.com/pachterlab/kb_python, https://github.com/pachterlab/qcbc.

JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics.

Combinatorial patterns of epigenetic features reflect transcriptional states and functions of genomic regions. While many epigenetic features have correlated relationships, most existing data normalization approaches analyze each feature independently. Such strategies may distort relationships between functionally correlated epigenetic features and hinder biological interpretation. We present a novel approach named JMnorm that simultaneously normalizes multiple epigenetic features across cell types, species, and experimental conditions by leveraging information from partially correlated epigenetic features. We demonstrate that JMnorm-normalized data can better preserve cross-epigenetic-feature correlations across different cell types and enhance consistency between biological replicates than data normalized by other methods. Additionally, we show that JMnorm-normalized data can consistently improve the performance of various downstream analyses, which include candidate cis-regulatory element clustering, cross-cell-type gene expression prediction, detection of transcription factor binding and changes upon perturbations. These findings suggest that JMnorm effectively minimizes technical noise while preserving true biologically significant relationships between epigenetic datasets. We anticipate that JMnorm will enhance integrative and comparative epigenomics.

Bridging the Gaps in Meta-Omic Analysis: Workflows and Reproducibility.

The past few years have seen significant advances in the study of complex microbial communities associated with the evolution of sequencing technologies and increasing adoption of whole genome shotgun sequencing methods over the once more traditional Amplicon-based methods. Although these advances have broadened the horizon of meta-omic analyses in planetary health, human health, and ecology from simple sample composition studies to comprehensive taxonomic and metabolic profiles, there are still significant challenges in processing these data. First, there is a widespread lack of standardization in data processing, including software choices and the ease of installing and running attendant software. This can lead to several inconsistencies, making comparing results across studies and reproducing original results difficult. We argue that these drawbacks are especially evident in metatranscriptomic analysis, with most analyses relying on ad hoc scripts instead of pipelines implemented in workflow managers. Additional challenges rely on integrating meta-omic data, since methods have to consider the biases in the library preparation and sequencing methods and the technical noise that can arise from it. Here, we critically discuss the current limitations in metagenomics and metatranscriptomics methods with a view to catalyze future innovations in the field of Planetary Health, ecology, and allied fields of life sciences. We highlight possible solutions for these constraints to bring about more standardization, with ease of installation, high performance, and reproducibility as guiding principles.

Mb-PHENIX: diffusion and supervised uniform manifold approximation for denoizing microbiota data.

Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure. We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data. Our method infers the missing information ofcount matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion withsupervised Uniform Manifold Approximation Projection (sUMAP) space as initialization. Our hybrid machine learning approach allows to denoise microbiota data, revealing differential abundance microbes among study groups where traditional abundance analysis fails. The mb-PHENIX algorithm is available at https://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub).

Single-shot vector magnetic measurements using synchronous coherent population trapping resonance in a feedback compensation system

We demonstrate a real-time, single-shot vector magnetic measurement technique using synchronous coherent population trapping (SCPT) in an apparatus consisting of a small rubidium vapor cell. Vector modality of our magnetometer is enabled by designing a feedback system based on performing a peak-lock on a particularly strong 2ΩL magnetic resonance produced in the SCPT spectrum and compensating the external magnetic field via a three-axis field coil. With its current design, this magnetometer exhibits high sensitivities of approximately 155, 129, and 57pT/Hz in measuring the magnetic field vector components along the (x,y,z) axes. Sensitivities closer to the shot-noise limit can be achieved in the future by reducing the technical noise of our equipment and by employing a differential detection and polarization rotation measurement scheme in our system.

High-sensitivity atomic magnetometer realized by weak-value-amplification effect

The weak-value-amplification (WVA) effect, which is also called weak measurement, has been developed extensively in various sensing systems. Here, we report the actual realization of the WVA effect in spin-exchange relaxation-free atomic magnetometer system, wherein the slight separation of transverse momentum of the probe light is amplified by introducing orthogonal pre- and post-selection states. A differential detector is used to obtain the transverse position of the probe light accurately in real time. The weak coupling of the magneto-optical interaction with atoms will be reflected in the differential signal. The WVA effect is observed and demonstrated directly and a high sensitivity of 8 fT/Hz is achieved. Also, we obtain the stable and distinct simulated magnetocardiography signal through our system. The present successful implementation of this probe method paves the way for further technical noise suppression and sensitivity improvement of quantum sensors.

An End-to-End Deep Hybrid Autoencoder Based Method for Single-Cell RNA-Seq Data Analysis.

Single-cell RNA sequencing technology provides powerful support for researchers to understand the complex mechanisms of cells at the single-cell level. Due to the high sparsity, technical noise, and computational complexity of single-cell transcriptome data, the existing data analysis methods are unable to effectively extract the fine-grained characteristics of scRNA-seq data, resulting in inaccurately analyze the heterogeneity of the individual cell from a great quantity of cell mixtures. To address these shortcomings, we proposed an end-to-end analysis method called dhaSCA, which integrates the Graph convolutional neural network (GCN) feature learning and downstream tasks such as classification and imputation into a unified deep learning manner. dhaSCA uses hybrid GCN-MLP deep autoencoder and to capture structural information between cells, and learn the low dimensional cell representation. It also introduces downstream tasks as constraints to guide the model to learn more accurate cell features. We conducted various experiments to evaluate the performance of dhaSCA based on eight real RNA-Seq datasets, including classification, imputation, clustering, and visualization. The results show that dhaSCA outperforms other state-of-the-art methods in these downstream tasks. Therefore, dhaSCA is able to obtain a richer representation of cells, and provides strong support for efficient analysis of single-cell data.

The impact of the change in institutional regulation on construction productivity: firm-level evidence in a developing economy

For many, the business environment is regarded as the main factor determining changes in construction productivity. However, changes in a country’s institutional regulation can contribute to the productivity changes of construction firms over the long term but are often overlooked in construction productivity research. To study the impact of institutional regulation on construction productivity, a sample of 55 public-listed construction Malaysian firms (2009–2020) is used as a case study to estimate total factor productivity (TFP) based on the Geometric-Young Index (GYI). Stochastic Frontier Analysis (SFA) is then used to decompose the TFP-GYI into the technological, environment, technical, scale-mix, and statistical noise components. This is followed by the Generalized Method Moments (GMM) to model the impact of different institutional regulations on TFP and decomposed productivity components. The results indicate that the institutional regulation framework influences construction firms’ TFP. Property rights and the rule of law (PRRL), and budget balance and change in wages (BBCW) have a positive impact on TFP, which largely influences technical efficiency. In addition to uncovering the impact of institutional regulation on construction productivity, namely capital availability and regulatory environment, it is concluded that institutional regulation plays a vital role in determining long-term construction firm productivity and needs to be considered by policymakers in formulating supporting incentives and policies.

Subspace tracking for phase noise source separation in frequency combs.

It is widely acknowledged that the phase noise of an optical frequency comb primarily stems from the common mode (carrier-envelope) and the repetition rate phase noise. However, owing to technical noise sources or other intricate intra-cavity factors, residual phase noise components, distinct from the common mode and the repetition rate phase noise, may also exist. We introduce a measurement technique that combines subspace tracking and multi-heterodyne coherent detection for the separation of different phase noise sources. This method allows us to break down the overall phase noise sources associated with a specific comb-line into distinct phase noise components associated with the common mode, the repetition rate and the residual phase noise terms. The measurement method allow us, for the first time, to identify and measure residual phase noise sources of a frequency modulated mode-locked laser.

Design of Acoustic Signal for Positioning of Smart Devices.

This paper addresses the limitations of using smartphones in innovative localization systems based on audio signal processing, particularly in the frequency range of 18-22 kHz, due to the lack of technical specifications and noise characterization. We present a comprehensive study on signal design and performance analysis for acoustic communication in air ducts, focusing on signal propagation in indoor environments considering room acoustics and signal behavior. The research aims to determine optimal parameters, including the frequency band, signal types, signal length, pause duration, and sampling frequency, for the efficient transmission and reception of acoustic signals for commercial off-the-shelf (COST) devices. Factors like inter-symbol interference (ISI) and multiple access interference (MAI) that affect signal detection accuracy are considered. The measurements help define the frequency spectrum for common devices like smartphones, speakers, and sound cards. We propose a custom signal with specific properties and reasons for their selection, setting the signal length at 50 ms and a pause time of 5 ms to minimize overlap and interference between consecutive signals. The sampling rate is fixed at 48 kHz to maintain the required resolution for distinguishing individual signals in correlation-based signal processing.

Noise resilient exceptional-point voltmeters enabled by oscillation quenching phenomena

Exceptional point degeneracies (EPD) of linear non-Hermitian systems have been recently utilized for hypersensitive sensing. This proposal exploits the sublinear response that the degenerate frequencies experience once the system is externally perturbed. The enhanced sensitivity, however, might be offset by excess (fundamental and/or technical) noise. Here, we developed a self-oscillating nonlinear platform that supports transitions between two distinct oscillation quenching mechanisms – one having a spatially symmetric steady-state, and the other with an asymmetric steady-state – and displays nonlinear EPDs (NLEPDs) that can be employed for noise-resilient sensing. The experimental setup incorporates a nonlinear electronic dimer with voltage-sensitive coupling and demonstrates two-orders signal-to-noise enhancement of voltage variation measurements near NLEPDs. Our results resolve a long-standing debate on the efficacy of EPD-sensing in active systems above self-oscillating threshold.

A MIMO approach for longitudinal sensing and control noise projections of Advanced Virgo gravitational wave detector

So far, the sensitivity of gravitational-wave (GW) detectors, in the low-frequency and mid-frequency regions of its bandwidth, has been limited by technical noises. The re-injection of sensing and control noises can be one of the main limitations. After the end of the third observing run O3, in preparation for the fourth observing run O4, an upgrade phase started among all the km-scale GW detectors, namely LIGO, Virgo and KAGRA, with the aim of improving their sensitivity. In particular, for the case of Advanced Virgo, one of the most significant upgrades is the installation of a signal recycling (SR) mirror, introducing the SR cavity. The main target of this SR mirror is to shape the sensitivity curve of the detector. The installation of a SR mirror adds an extra optical cavity and, thus, extra DoFs (longitudinal and angular), that should be controlled to keep its working point, ultimately increasing the complexity of the whole control strategy. In order to have an accurate description of the interferometer, we have implemented a multiple-input multiple-output (MIMO) model in the frequency domain. The target of this paper, after showing the Advanced Virgo configuration for the next observing run, is to describe the control scheme used for the main longitudinal degrees of freedom using a MIMO approach. In particular, we detail a useful matrix representation for the modeled system. Finally, we use the implemented model to project the sensing and control noises on the sensitivity curve. Following the obtained results, we propose noise subtraction filters to achieve the low control noise target in the low-frequency region of the sensitivity curve. Additionally, using this model, we have implemented the core of a noise budget tool, which will allow to estimate the contribution of all the known sources of noise on the measured sensitivity.

Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data

Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ Tcells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.