Current Signature Research Articles

Controlled Translocation of Proteins through a Biological Nanopore for Single-Protein Fingerprint Identification.

After the successful sequencing of nucleic acids, nanopore technology has now been applied to proteins. Recently, it has been demonstrated that an electro-osmotic flow can be used to induce the transport of unraveled polypeptides across nanopores. Polypeptide translocation, however, is too fast for accurate reading its amino acid compositions. Here, we show that the introduction of hydrophobic residues into the lumen of the nanopore reduces the protein translocation speed. Importantly, the introduction of a tyrosine at the entry of the nanopore and an isoleucine at the entry of the β-barrel of the nanopore reduced the speed of translocation to ∼10 amino acids/millisecond while keeping a relatively large ionic current, a crucial component for protein identification. These nanopores showed unique features within their current signatures, which may pave the way toward protein fingerprinting using nanopores.

Gene signatures for cancer research: A 25-year retrospective and future avenues.

Over the past two decades, extensive studies, particularly in cancer analysis through large datasets like The Cancer Genome Atlas (TCGA), have aimed at improving patient therapies and precision medicine. However, limited overlap and inconsistencies among gene signatures across different cohorts pose challenges. The dynamic nature of the transcriptome, encompassing diverse RNA species and functional complexities at gene and isoform levels, introduces intricacies, and current gene signatures face reproducibility issues due to the unique transcriptomic landscape of each patient. In this context, discrepancies arising from diverse sequencing technologies, data analysis algorithms, and software tools further hinder consistency. While careful experimental design, analytical strategies, and standardized protocols could enhance reproducibility, future prospects lie in multiomics data integration, machine learning techniques, open science practices, and collaborative efforts. Standardized metrics, quality control measures, and advancements in single-cell RNA-seq will contribute to unbiased gene signature identification. In this perspective article, we outline some thoughts and insights addressing challenges, standardized practices, and advanced methodologies enhancing the reliability of gene signatures in disease transcriptomic research.

Bearing fault detection in adjustable speed drives via self-organized operational neural networks

AbstractAdjustable speed drives (ASDs) are widely used in industry for controlling electric motors in applications such as rolling mills, compressors, fans, and pumps. Condition monitoring of ASD-fed induction machines is very critical for preventing failures. Motor current signature analysis offers a non-invasive approach to assess motor condition. Application of conventional convolutional neural networks provides good results in detecting and classifying fault types for utility line-fed motors, but the accuracy drops considerably in the case of ASD-fed motors. This work introduces the use of self-organized operational neural networks to enhance the accuracy of detecting and classifying bearing faults in ASD-fed induction machines. Our approach leverages the nonlinear neurons and self-organizing capabilities of self-organized operational neural networks to better handle the non-stationary nature of ASD operations, providing more reliable fault detection and classification with minimal preprocessing and low complexity, using raw motor current data.

Signature of the western boundary currents in local climate variability.

The western boundary currents are characterized by narrow, intense ocean jets and are among the most energetic phenomena in the world ocean. The importance of the western boundary currents to the mean climate is well established: they transport vast quantities of heat from the subtropics to the midlatitudes1, and they govern the structure of the climatological mean surface winds2-6, precipitation4-6 and extratropical storm tracks7-13. Their importance to climate variability is much less clear, as the tropospheric response to extratropical sea surface temperature (SST) variability is generally modest relative to the internal variability in the midlatitude atmosphere12-14. Here we exploit novel local analyses based on high-spatial-resolution data to demonstrate that SST variability in the western boundary currents has a more robust signature in climate variability than has been indicated in previous work. Our results indicate that warm SST anomalies in the major boundary currents of both hemispheres are associated with a distinct signature of locally enhanced precipitation and rising motion anomalies that extend throughout the depth of the troposphere. The tropospheric signature closely mirrors that of ocean dynamical processes in the boundary currents. Thus, the findings indicate a distinct and robust pathway through which extratropical ocean dynamical processes influence local climate variability. The observational relationships are also reproducible in Earth system model simulations but only when the simulations are run at high spatial resolution.

The genetic consequences of historic climate change on the contemporary population structure of a widespread temperate North American songbird

Studies of widely distributed species can inform our understanding of how past demographic events tied to historic glaciation and ongoing population genetic processes interact to shape contemporaneous patterns of biodiversity at a continental scale. In this study, we used whole-genome resequencing to investigate the current population structure and genetic signatures of past demographic events in the widespread migratory American goldfinch (Spinus tristis). Phylogenetic relationships inferred from whole mitochondrial genomes were poorly resolved. In contrast, a genome-wide panel of > 4.5 million single nucleotide polymorphisms (SNPs) strongly supported the existence of eastern and western populations separated by western mountain ranges and additional population structuring within the western clade. Demographic modeling estimated that the eastern and western populations diverged approximately one million years ago, and both populations experienced subsequent population bottlenecks during the last glacial period. Species distribution models showed a severe contraction of suitable habitat for the American goldfinch during this period, with predicted discontinuities that are consistent with multiple, isolated glacial refugia that coincide with present-day population structure. Low overall genetic differentiation between the eastern and western populations (FST ∼ 0.01) suggests ongoing gene flow accompanied divergence, and individuals with admixed genomic signatures were sampled along a potential contact zone. Nevertheless, outlier SNPs were identified near genes associated with feather color, song, and migratory behavior and provide strong candidates for further study of the mechanisms underlying reproductive isolation and speciation in birds.

Defect Reconstruction Using Multilayer Perceptron for Regression and Classification Tasks Based On Eddy Current Signatures

Defect Reconstruction Using Multilayer Perceptron for Regression and Classification Tasks Based On Eddy Current Signatures

Identifying Ionospheric Small‐Scale Currents: A Spatial Correlation Study Using Closely‐Spaced Pairs of Ground Magnetometers

AbstractThe occurrence of small‐scale and intense ionospheric currents that can contribute to geomagnetically induced currents have recently been discovered. A difficulty in their characterization is that their signatures are often only observed at single widely spaced (typically 200–400 km) ground geomagnetic stations. These small‐scale structures motivate the examination of the maximum station separation required to fully characterize these small‐scale signatures. We analyze distributions of correlation coefficients between closely spaced mid‐latitude and auroral zone ground magnetometer stations spanning day to month long intervals to assess the separation distance at which geomagnetic signatures appear in only one station. Distributions were analyzed using periods that included low and high geomagnetic activity. We used data from pairs of magnetometer stations across North America within 200 km of each other, all of which were separated primarily latitudinally. Results show that while measurements remain largely similar up to separations of 200 km, large and frequent differences appear starting at around 130 km separation. Larger differences and lower correlations are observed during high geomagnetic activity, while low geomagnetic activity leads to frequent high correlation even past 200 km separation. Small but identifiable differences can appear in magnetometer data from stations as close as 35 km during high geomagnetic activity. We recommend future magnetometer array deployment in the auroral and sub‐auroral zone to have separations of 100–150 km. This enables the monitoring of large scale effects of geomagnetic storms, better temporal and spatial resolution of substorms, and observations of small‐scale current signatures.

A Modified EMD Technique for Broken Rotor Bar Fault Detection in Induction Machines.

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally.

Understanding coaxial photodiode-based multispectral pyrometer measurements at the overhang regions in laser powder bed fusion for part qualification

Coaxial photodiode monitoring sensors provide a digital signature at the melt pool level in laser powder bed fusion (L-PBF), facilitating faster part qualification. However, current signatures are plagued by significant noise and signal variation and show counterintuitive trends such as a decrease in measured temperature near overhang surfaces. This paper investigates the behavior of coaxial photodiode-based melt pool monitoring (PD-MPM) thermal measurements at overhang regions by conducting a combination of experiments and multiphysics simulations. High-speed in situ synchrotron X-ray imaging is coupled with a coaxial photodiode system to enable comparison of the observed melt pool phenomena and monitoring signals during double-track AlSi10Mg experiments. A multiphysics model is developed to simulate the melt pool dynamics and concurrent sensor signals throughout the process. We propose a surrogate model that clarifies the correlation between sensor signals and melt pool temperature. Both experimental and simulation results emphasize the significant impacts of laser energy and keyhole formation on solid-liquid interface discontinuities and PD-MPM signals. Results reveal that a boiling region with a relatively smaller projected area, as near an overhang, can lead to a decrease in the measured melt pool temperature, even when the true peak temperature remains constant, meaning that PD-MPM temperature measurements cannot be used to estimate absolute melt pool temperature. Consequently, in overhang regions, interaction between the melt pool and underlying powder results in an abruptly deforming melt pool and a pronounced decrease in both individual photodiode intensities and overall measured melt pool temperature. This work illustrates how to correctly interpret the coaxial photodiode monitoring signals in L-PBF by uncovering the intricate dynamics within the melt pool, particularly in challenging overhang regions, and pave the foundation for data-driven part qualification.

Turbulence Embedded Into the Ionosphere by Electromagnetic Waves

AbstractWhen charged particles are accelerated from Earth's magnetosphere and precipitate into the atmosphere, their impact with neutral gas creates the aurora. Structured electric fields drive the acceleration processes but they are also passed down to the ionosphere, meaning that turbulence can in part be embedded into the ionosphere rather than emerge through instability processes locally. Applying a point‐cloud analysis technique adapted from observational cosmology, we show how observed turbulence in the ionosphere matches electrical current signatures in the pulsating aurora in a series of conjunctions between space‐ and ground‐based instruments. We propose that the temporal spectrum of pulsations in the pulsating aurora is the driver of a clearly observed energy injection into the ionosphere's unstable bottomside. Precipitating electrons produce electric fields through charge deposition, and we observe wave characteristics that are present in this pattern. Next, the relative electron‐ion drifts excite the Farley‐Buneman instability, the distribution of whose waves are organized according to the local electric field. It is the temporal characteristics of chorus wave interactions in the magnetosphere that is imparted, via precipitating electrons, to the pulsating aurora, and so we propose that chorus wave interactions are capable of embedding turbulent structure into the ionosphere. This structure (now pressure gradients) dissipate energy in the E‐region through turbulent processes, observed by the icebear coherent scatter radar.

A Critical Analysis of Design for Reduction in Vibrations of Centrifugal Impellers

Centrifugal pumps are widely used for various applications. Numerous technical practices use centrifugal pumps, particularly when consistent and adaptable pump performance is required. Still, these can have several issues, including low efficiency when operated under off-design conditions and poor capitalization performance. These frequent transport mixtures, such as liquid and gas, and pure liquids. It is a well-known fact that several problems encountered by pumps in a pumping station are related to structural instability or impeller/blade failure. Vibrations can cause catastrophic failures in the impeller, so these must be kept to a minimum while designing the impeller. Vibration, cavitation, rough running, lower-than-expected efficiency, and shorter pump life can all be traced back to unfavorable process flow conditions. Although there are some design guidelines for pump configuration, the effects of fluctuating discharge on structural stability have yet to be investigated. An additional pressing problem is the identification of time cracks. Failure can occur due to cracks much before the design loads. In this work, we aim to investigate and analyze the centrifugal impeller design consideration. Using different methods for designing and modeling, we compared significant results and found the optimal solutions in which a centrifugal impellor can be designed. This work enables us to determine the best techniques and methods to overcome the problem of vibrations produced in the centrifugal impeller. This also helps us to understand the centrifugal pump's behavior and performance to improve its efficiency. Centrifugal impellors are compared across conditions, including free, forced, damped, and undamped systems. Three successful methods for designing and modeling a centrifugal impellor are proposed using these parameters. The methods of design and analysis provide predictions of the flow fields that are highly reliable. Regarding fluid distribution and pump efficiency, computational fluid dynamics provides various solutions that may be applied to impeller design. Using fluent software, we can better comprehend the pump's resonant operation. Blade mistuning is a severe problem that can be handled using the blade tip timing and strain gauge technique. Vibration and motor current signature analysis (MCSA) is also mentioned to investigate vibrational problems with the centrifugal impeller. Several strategies are discussed to lessen or eliminate the issue of vibrations in impellers. The limitations and disadvantages of using these techniques are discussed. The summary of results provides significant design and improvement in centrifugal pumps in the recent past.

A non-intrusive technique for estimating the efficiency of low voltage three-phase induction motors

This paper introduces a non-intrusive technique based on the air–gap torque (AGT) method to estimate the efficiency of induction motors (IMs). The technique uses a 3rd-degree equation to calculate the sum of windage and friction losses (FWL) and rotor stray load losses (SLLr), considering the number of IM poles. IM speed is estimated using motor current signature analysis, and a new procedure to estimate stator resistance is presented. Efficiency is then determined by applying the estimated rotor speed, stator resistance, and the sum of FWL and SLLr in the AGT equations. This method requires only nameplate information, IM line voltages, and currents. Simulations and experimental tests for balanced, unbalanced, and harmonic feeding modes show the proposed technique’s estimated efficiency values are very close to measured values, unlike the AGT method. This demonstrates the proposed technique’s high accuracy, making it suitable for non-intrusive efficiency estimation of in-service IMs in industrial programs.

A Robust Online Diagnostic Strategy of Inverter Open-Circuit Faults for Robotic Joint BLDC Motors

As robots are increasingly used in remote, safety-critical, and hazardous applications, the reliability of robots is becoming more important than ever before. Robotic arm joint motor-drive systems are vulnerable to hardware failures due to harsh operating environment in many scenarios, which may yield various joint failures and result in significant downtime costs. Targeting the most common robotic joint brushless DC (BLDC) motor-drive systems, this paper proposes a robust online diagnostic method for semiconductor faults for BLDC motor drives. The proposed fault diagnostic technique is based on the stator current signature analysis. Specifically, this paper investigates the performance of the BLDC joint motors under open-circuit faults of the inverter switches using finite element co-simulation tools. Furthermore, the proposed methodology is not only capable of detecting any open-circuit faults but also identifying faulty switches based on a knowledge table by considering various fault conditions. The robustness of the proposed technique was verified through extensive simulations under different speed and load conditions. Moreover, simulations have been carried out on a Kinova Gen-3 robot arm to verify the theoretical findings, highlighting the impacts of locked joints on the robot’s end-effector locations. Finally, experimental results are presented to corroborate the performance of the proposed fault diagnostic strategy.

Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma.

We aim to improve the prediction of response or resistance to immunotherapies in patients with melanoma. This goal is based on the hypothesis that current gene signatures predicting immunotherapy outcomes show only modest accuracy due to the lack of spatial information about cellular functions and molecular processes within tumors and their microenvironment. We collected gene expression data spatially from three cellular compartments defined by CD68+ macrophages, CD45+ leukocytes, and S100B+ tumor cells in 55 immunotherapy-treated melanoma specimens using Digital Spatial Profiling-Whole Transcriptome Atlas. We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification. We achieved robust performance of compartment-specific signatures in predicting the outcome of immune checkpoint inhibitors in the discovery cohort. Of the three signatures, the S100B signature showed the best performance in the validation cohort (N = 45). We also compared our compartment-specific signatures with published bulk signatures and found the S100B tumor spatial signature outperformed previous signatures. Within the eight-gene S100B signature, five genes (PSMB8, TAX1BP3, NOTCH3, LCP2, and NQO1) with positive coefficients predict the response, and three genes (KMT2C, OVCA2, and MGRN1) with negative coefficients predict the resistance to treatment. We conclude that the spatially defined compartment signatures utilize tumor and tumor microenvironment-specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical assessment.

Blade imbalance fault identification in doubly fed induction generator through current signature analysis using wavelet transform

Using wind turbines (WTs) equipped with doubly fed induction generators (DFIG) is a popular technology for generating renewable energy. To ensure safe operation, prompt maintenance, and better operational reliability, the induction generator used in wind energy must be monitored. In this paper, an analysis is carried out on stator currents of the DFIG machine in a wind farm to identify any blade imbalances in the wind farm. A fault characteristics extraction analysis is carried out on the machine stator currents to detect the fault in the system. Firstly, the mathematical equation of the DFIG blade unbalanced stator current is generated using the DFIG model. Secondly, Park's Transformation is used to modify the stator's 3-phase current. Further, by evaluating the feature frequency amplitude variation in the squared signal by doing a spectral analysis on the stator current vector's squared signal. Lastly, a Simulink model for the DFIG is developed. The suggested approach analyses the fault signal of the imbalanced blade fault at various wind velocities. The outcomes show that the suggested method for diagnosing impeller imbalance faults can successfully locate the fault.

An intelligent fault diagnosis method for rolling bearing using motor stator current signals

Abstract In the diagnosis of rolling bearing faults, the Motor Current Signature Analysis (MCSA) method offers advantages such as low cost, simplicity, and convenience compared to using vibration signals, temperature information, and other diagnostic objects. However, owing to the interference of high-frequency noise, power frequency, and its harmonics in current signals, which can severely affect the accuracy of bearing fault diagnosis, it is extremely challenging to use the original current signals during bearing faults directly for diagnostic purposes. Therefore, this paper proposes an intelligent fault diagnosis method based on the feature reconstruction (FR) method and convolutional neural networks (CNN). This method can achieve high-precision fault diagnosis using single-phase stator current signals from motors as the diagnostic objects. First, the FR method effectively removes the impact of high-frequency noise, supply frequency, and its harmonics from the current signals, while also highlighting subtle fault feature signals to a certain extent. Second, a CNN suitable for learning the characteristics of the current signals was constructed. Through feature extraction, learning, and classification of the current signal samples processed by the FR method, a diagnostic method with a high classification accuracy was obtained. Visualization techniques were used to present the final diagnosis results intuitively. The experimental results demonstrated the highest diagnostic accuracy and average diagnostic accuracy of the proposed method in diagnosing rolling bearing fault types, with an average diagnostic accuracy of approximately 99% for actual faulty bearing samples.

Fault Diagnosis of Induction Machine for Rotor Cage Damage Using MCSA for Industrial Application

The induction machine plays a vital role in industries. Induction machine failure causes significant process delays, higher maintenance expenses, and income loss. The machine’s predictive maintenance becomes crucial to find the primary cause of the defect. This paper focuses on the experimental results of induction machines used in the cement industry. Motor Current Signature Analysis is used to analyze the condition of rotor bars due to distorted input supply causing harmonics. The significance of this test is to determine the primary cause of the unexpected failure. The experimental results of the machine reveal distorted input with harmonics resulting in torque ripples. This might lead to broken rotor bars in the rotor cage with sidebands resulting in poor efficiency of the machine. Motor Current Signature Analysis predicts the lifespan of the machine to avoid unexpected failure. The Finite Element Method is used to design the machine for conditions one, two adjacent, and two non-adjacent broken rotor bars to observe the dynamics of rotor bar currents with numerical results. After a substantial overhaul of the machine, the proposed method is again performed, resulting in improved machine efficiency of 88.83%.

Current understanding of ELF4 deficiency: a novel inborn error of immunity.

ELF4 deficiency has been recently recognized as a novel disorder within the spectrum of inborn errors of immunity (IEIs), specifically categorized as a "disease of immune dysregulation." Cases of this condition, reported by our team and others, are very limited worldwide. As such, our current knowledge of this new disease remains preliminary.This review aims to provide a brief overview of the clinical manifestations, pathogenesis, and treatment strategies for this novel IEI. A comprehensive review was conducted after an extensive literature search in the PubMed/Medline database and websites concerning transcriptional factor ELF4 and reports concerning patients with ELF4 deficiency. Our search strategy was "ELF4 OR ETS-related transcription factor Elf-4 OR EL4-like factor 4 OR myeloid Elf-1-like factor" as of the time of manuscript submission. The current signature manifestations of ELF4 deficiency disorder are recurrent and prolonged oral ulcer, abdominal pain, and diarrhea in pediatric males. In some cases, immunodeficiency and autoimmunity can also be prominent. Targeted Sanger sequencing or whole exome sequencing can be used to detect variation in ELF4 gene. Western blotting for ELF4 expression of the patient's cells can confirm the pathogenic effect of the variant. To fully confirm the pathogenicity of the variant, further functional test is strongly advised. Glucocorticoid and biologics are the mainstream management of ELF4 deficiency disorder. Pediatric males presenting with recurring ulcerations in digestive tract epithelium with or without recurrent fever should be suspected of DEX. When atypical presentations are prominent, variations in ELF4 gene should be carefully evaluated functionally due to the complex nature of ELF4 function. Experience of treating DEX includes use of glucocorticoid and biologics and more precise treatment needs more patients to identify and further mechanistic study.

MCSA-based fault diagnosis for rotary parts in servo motion system: Approach and experimental study

Motor current signature analysis (MCSA), as a non-invasive diagnostic method, is robust to environmental noise and of less sensor cost than vibration-based monitoring. Although large amount of progress has been achieved, little attention is paid to the MCSA-based diagnosis task in the Servo motion systems (SMS). An approach using Park vector demodulation, comb filtering and the ensemble empirical mode decomposition (EEMD) is proposed for cyclic fault event detection in this paper. Experiment studies reveal that the MCSA approach could extract the fault signature under extremely low-speed or noisy working condition, which has potential in the scenarios where the accelerometers placement is limited or affected by interferences.

Non‐Geophysical Interhemispheric Asymmetries in Large Magnetic Field Residuals Between Swarm Observations and Earth Magnetic Field Models During Moderate to Quiet Geomagnetic Conditions

AbstractWe present a statistical study of large magnetic field vector residuals between Swarm observations and the 13th generation International Geomagnetic Reference Field (IGRF‐13) model under quiet to moderate geomagnetic conditions. Since the vast majority of data are taken during these conditions, statistics of these large residuals are important for satellite operation when using IGRF‐13 as reference, as well as for magnetosphere‐ionosphere‐thermosphere studies. Residuals with magnitude of vector differences between observations and model estimations larger than 300 nT under Dst > −50 nT are studied from 2014 to 2020. All large residuals appear in the high‐latitude auroral zone region peaking around 70° (−70°) magnetic latitude (MLAT) in the northern hemisphere (southern hemisphere) with also a secondary occurrence peak just below 80° (−80°) MLAT. However, the two hemispheres show clear asymmetries in the magnetic longitude and magnetic local time distribution where both hemispheres show high concentration of large residuals around the geographic poles. Since polar satellite's orbits give rise to highly biased number of observations around the geographic poles, it results in a decrease in occurrence rate with respect to the total number of measurements. We suggest that the asymmetries of large residuals in the two hemispheres around the geographic pole are not geophysical, but due to satellite orbit bias around the geographic poles and difference between IGRF and observations due to model error and geophysical processes not included in the model. Identifying these biases is helpful to better separate out geophysical electric current signatures from non‐geophysical ones.