Unwanted Signals Research Articles

Sequentially amplified integration of catalytic DNA circuits for high-performance intracellular imaging of miRNA and interpretation of mRNA-miRNA signalling pathway

The cascaded catalytic circuits are viable tools for improving the signal gain of biosensors, yet their sensing performance is still limited by the signal leakage from complex biological environment and unsatisfying reaction efficiency from inter-reactants steric hindrance. Herein, we proposed a catalytically localized DNA (CLD) circuit for the accurate and high-efficiency imaging of microRNA (miRNA) in living cells by virtue of the sequentially and successively amplified integration of catalytic DNA circuits. The compact CLD circuit was constructed by integrating two elemental catalytic circuits, cell-responsive EDR module and analyte-sensing CHA module, where CHA module was initially caged in EDR module for eliminating the unwanted off-site and off-target signal leakage. Only by cell-specific messenger RNA (mRNA)-activated EDR operation then the elemental CHA circuit could be successively connected to facilitate the highly efficient intramolecular reaction with low steric hindrance, thus leading to accelerated reaction efficiency for miRNA analyte. The multiple molecular recognition and the spatial self-confinement of the smart CLD circuit enable the accurate and high-efficiency imaging of intracellular miRNA. The interaction network of mRNA and miRNA was then investigated in situ through our CLD circuit, which provides a powerful tool for discovering the underlying signal pathways between these different RNAs in living cells.

In situ mixer calibration for superconducting quantum circuits

Mixers play a crucial role in superconducting quantum computing, primarily by facilitating frequency conversion of signals to enable precise control and readout of quantum states. However, imperfections, particularly local oscillator leakage and unwanted sideband signal, can significantly compromise control fidelity. To mitigate these defects, regular and precise mixer calibrations are indispensable, yet they pose a formidable challenge in large-scale quantum control. Here, we introduce an in situ and scalable mixer calibration scheme using superconducting qubits. Our method leverages the qubit's response to imperfect signals, allowing for calibration without modifying the wiring configuration. We experimentally validate the efficacy of this technique by benchmarking single-qubit gate error and qubit coherence time.

Design of a pHEMT RF mixer based on compact microstrip diplexer

Abstract This paper presents a new topology for a microwave active mixer using compact microstrip diplexer constituted from two dual-mode open loop resonators. The resonators are tuned to the radio frequency (RF) and local-oscillator (LO) frequencies, respectively, to effectively isolate the two input signals, while the diplexer output port is connected to the gate circuit of a commercial low cost GaAs pHEMT transistor. The drain circuit of the active device is connected to the load via an intermediate-frequency (IF)-modified Chebyshev low-pass filter to remove the unwanted RF and LO signals and reduce other spurious frequencies at the output of the mixer circuit. The circuit has been designed and constructed for an RF of 850 MHz, LO frequency of 1050 MHz, and an IF of 200 MHz. Experimental measurements show that the circuit provides a conversion gain of 18 dB at LO drive power of +2 dBm. It also operates satisfactorily over the RF range from 800 to 900 MHz with good image frequency rejection and stable operation.

Hybrid Wavelet‐Deep Learning Framework for Fluorescence Microscopy Images Enhancement

ABSTRACTFluorescence microscopy is a powerful tool for visualizing cellular structures, but it faces challenges such as noise, low contrast, and autofluorescence that can hinder accurate image analysis. To address these limitations, we propose a novel hybrid image enhancement method that combines wavelet‐based denoising, linear contrast enhancement, and convolutional neural network‐based autofluorescence correction. Our automated method employs Haar wavelet transform for noise reduction and a series of adaptive linear transformations for pixel value adjustment, effectively enhancing image quality while preserving crucial details. Furthermore, we introduce a semantic segmentation approach using CNNs to identify and correct autofluorescence in cellular aggregates, enabling targeted mitigation of unwanted background signals. We validate our method using quantitative metrics, such as signal‐to‐noise ratio (SNR) and peak signal‐to‐noise ratio (PSNR), demonstrating superior performance compared to both mathematical and deep learning‐based techniques. Our method achieves an average SNR improvement of 8.5 dB and a PSNR increase of 4.2 dB compared with the original images, outperforming state‐of‐the‐art methods such as BM3D and CLAHE. Extensive testing on diverse datasets, including publicly available human‐derived cardiosphere and fluorescence microscopy images of bovine endothelial cells stained for mitochondria and actin filaments, showcases the flexibility and robustness of our approach across various acquisition conditions and artifacts. The proposed method significantly improves fluorescence microscopy image quality, facilitating more accurate and reliable analysis of cellular structures and processes, with potential applications in biomedical research and clinical diagnostics.

IoT-Based Wireless System for Gait Kinetics Monitoring in Multi-Device Therapeutic Interventions.

This study presents an IoT-based gait analysis system employing insole pressure sensors to assess gait kinetics. The system integrates piezoresistive sensors within a left foot insole, with data acquisition managed using an ESP32 board that communicates via Wi-Fi through an MQTT IoT framework. In this initial protocol study, we conducted a comparative analysis using the Zeno system, supported by PKMAS as the gold standard, to explore the correlation and agreement of data obtained from the insole system. Four volunteers (two males and two females, aged 24-28, without gait disorders) participated by walking along a 10 m Zeno system path, equipped with pressure sensors, while wearing the insole system. Vertical ground reaction force (vGRF) data were collected over four gait cycles. The preliminary results indicated a strong positive correlation (r = 0.87) between the insole and the reference system measurements. A Bland-Altman analysis further demonstrated a mean difference of approximately (0.011) between the two systems, suggesting a minimal yet significant bias. These findings suggest that piezoresistive sensors may offer a promising and cost-effective solution for gait disorder assessment and monitoring. However, operational factors such as high temperatures and sensor placement within the footwear can introduce noise or unwanted signal activation. The communication framework proved functional and reliable during this protocol, with plans for future expansion to multi-device applications. It is important to note that additional validation studies with larger sample sizes are required to confirm the system's reliability and robustness for clinical and research applications.

Super-resolution based on frequency dependence of echo phase rotation in 3D ultrasound imaging by spatially encoded transmit/receive

Abstract Currently, we are investigating the ultrasound imaging of a single transmitter/receiver system for a puncture microscopy application. The proposed model, consisting of an encoding mask layer with irregular thickness attached to the surface of the transducer, was carried out by a finite element method simulator. To increase the number of measurements, the device was rotated for several rotations. The image for each rotation was constructed separately by numerically solving the linear equation of the image model. In this paper, we introduce a super resolution method (SCM) based on an eigenvalue analysis in order to improve the poor resolution. It was assigned as weights and multiplied by the current image. The simple averaging and coherence factor were applied to compound over a number of rotations to obtain the final image. The results show that the image has a better image resolution and also suppresses the unwanted signals as well.

Membrane filter removal in FTIR spectra through dictionary learning for exploring explainable environmental microplastic analysis

Microplastic analysis is a crucial step for locating the environmental contamination sources and controlling plastic contamination. A popular tool like Fourier transform infrared (FTIR) spectroscopy is capable of identifying plastic types and can be carried out through a variety of containers. Unfortunately, sample collection from water sources like rivers usually involves filtration so the measurements inevitably include the membrane filter that also has its own FTIR characteristic bands. Furthermore, when plastic particles are small, the membrane filter’s spectrum may overwhelm the desired plastics’ spectrum. In this study, we proposed a novel preprocessing method based on the dictionary learning technique for decomposing the variations within the acquired FTIR spectra and capturing the membrane filter’s characteristic bands for the effective removal of these unwanted signals. We break down the plastic analysis task into two subtasks — membrane filter removal and plastic classification — to increase the explainability of the method. In the experiments, our method demonstrates a 1.5-fold improvement compared with baseline, and yields comparable results compared to other state-of-the-art methods such as UNet when applied to noisy spectra with low signal-to-noise ratio (SNR), but offers explainability, a crucial quality that is missing in other state-of-the-art methods. The limitations of the method are studied by testing against generated spectra with different levels of noise, with SNR ranging from 0 to – 30dB, as well as samples collected from the lab. The components/atoms learned from the dictionary learning technique are also scrutinized to describe the explainability and demonstrate the effectiveness of our proposed method in practical applications.

Efficient and Robust Adaptive Beamforming Based on Coprime Array Interpolation

Unlike uniform linear arrays (ULAs), coprime arrays require fewer physical sensors yet provide higher degrees of freedom (DOF) and larger array apertures. However, due to the existence of “holes” in the differential co-array, the target detection performance deteriorates, especially in adaptive beamforming. To address these challenges, this paper proposes an efficient and robust adaptive beamforming algorithm leveraging coprime array interpolation. The algorithm eliminates unwanted signals and uses the Gauss–Legendre quadrature method to reconstruct an Interference-plus-Noise Covariance Matrix (INCM), thereby obtaining the beamforming coefficients. Unlike previous techniques, we utilize a virtual interpolated ULA to expand the aperture, enabling the acquisition of a high-dimensional covariance matrix. Additionally, a projection matrix is constructed to eliminate unwanted signals from the received data, greatly enhancing the accuracy of INCM reconstruction. To address the high computational complexity of integral operations used in most INCM reconstruction algorithms, we propose an approximation based on the Gauss–Legendre quadrature, which reduces the computational load while maintaining accuracy. This algorithm avoids the array aperture loss caused by using only the ULA segment in the difference co-array and improves the accuracy of INCM reconstruction. Simulation and experimental results show that the performance of the proposed algorithm is superior to the compared beamformers and is closer to the optimal beamformer in various scenarios.

Endogenous Glutathione-Activated Nucleic Acid Molecular Circuitry for Cell-Specific MicroRNA Imaging.

Sensitive and reliable microRNA imaging in living cells has significant implications for clinical diagnosis and monitoring. Catalytic DNA circuits have emerged as potent tools for tracking these intracellular biomarkers and probing the corresponding biochemical processes. However, their utility is hindered by the low resistance to external interference, leading to undesired off-site activation and consequent signal leakage. Therefore, achieving the endogenous control of the DNA circuit's activation is preferable to the reliable target analysis in living cells. In this study, we attempted to address this challenge by engineering a simple yet effective endogenous glutathione (GSH)-regulated hybridization chain reaction (HCR) circuit for acquiring high-contrast miRNA imaging. Initially, the HCR hairpin reactants were blocked by the engineered disulfide-integrated DNA duplex, thus effectively passivating their sensing function. And the precaged HCR hairpin was liberated by the cell-specific GSH molecule, thus initiating the HCR system for selectively amplified detection of microRNA-21 (miR-21). This approach prevented unwanted signal leakage before exposure into target cells, thus ensuring robust miR-21 imaging with high accuracy and reliability in specific tumor cells. Moreover, the endogenously responsive HCR circuit established a link between the small regulatory factors and miRNA, thereby enhancing the signal gain. In summary, the endogenously activatable DNA circuit represents a versatile toolbox for robust bioanalysis and exploration of potential signaling pathways in living cells.

High attenuation electromagnetic interface filter for effective processing of audio signals

Suppressing the noise or unwanted signal from the raw signal makes the system work efficiently. Electromagnetic interface (EMI) can cause the system/device to lose the data, damage the electronic equipment, interrupt the audio, or video signals, lead to poor reception, etc EMI filters are used in telecommunications, military equipment, satellite communication, etc At the industry level, EMI filters are used for home appliances, medical equipment, motor controls, test equipment, etc In this paper, active and passive EMI filters are designed to avoid or attenuate EMI or noise (unwanted EMI signal) and implemented on Xilinx Nexys 4 Artix 7 FPGA board. The filters provide a significant improvement in the attenuation of the EMI. MATLAB tool is used to design the filters, which are highly immune to undesired signals, give better stability, and perform over a wide range of frequencies. Depending on the filters’ coefficients, the frequency response will be changed. Phase response and amplitude response of the EMI filters are calculated. Insertion loss of the active filter is 1.5836, and for the passive filter, 1.9382. The gain of active and passive filters is 0.7359 and 0.912 respectively. The return loss of the active and passive EMI filters is 20 dB. HDL code is generated using MATLAB for the implementation of filter on FPGA. Using MATLAB HDL coder for implementing is new and it simplifies the design, as a result development time is reduced. Xilinx Vivado tool is used for the implementation of active filter. The total on-chip power is 0.146 W. Dynamic power is 0.049 W, and device static power is 0.097 W. The utilization of LUTs and slice registers are less, compared to the previous filter implementation, which leads to reduce the cost of hardware implementation.

Concentric Elliptical Antenna Array Synthesis using Sine Cosine Algorithm for Reducing the Sidelobe level

This paper employs the Sine Cosine Algorithm, a recent meta-heuristic method, to enhance the radiation properties of concentric elliptical antenna arrays in far-field applications, such as smart grid wireless communication and the Internet of Things. It focuses on minimizing the Sidelobe Level, a key parameter in antenna radiation, to mitigate unwanted signals and interference. The proposed method, known for its global optimization effectiveness, tackles the challenge of designing sparse multiple concentric elliptical arrays. By using the Sine Cosine Algorithm, the inter-element spacing and optimal determination of amplitude coefficients are achieved, resulting in a radiation pattern with reduced sidelobe level. To showcase the adaptability and superior performance of our proposed algorithm, we systematically evaluated its effectiveness across four distinct sets of concentric elliptical antenna arrays. These arrays comprise varying numbers of elements: (6, 12, 18 elements, 6, 12, 18, 24 elements, 6, 12, 18, 24, 30 elements and 6, 12, 18, 24, 30, 36 elements) with and without a central element. Using the sine cosine algorithm, we synthesize arrays and compare their sidelobe level and First Null Beam Width values with those from recent methods. Our findings consistently shows that the proposed algorithm consistently outperforms alternatives, yielding significantly lower sidelobe level values in all comparisons.

Effect of Sodium Ion Addition on Copper Selenide/Chitosan Film Towards Electrical and Shielding Efficiency Improvement

The operation of electronic devices can be disrupted by unwanted electromagnetic signals, affecting its operation. Deploying electromagnetic shielding is a viable solution to minimize the impact of electromagnetic interference (EMI). The conventional methods of electromagnetic shielding use metal gaskets to safeguard sensitive electronic components, which have drawbacks of cost and weight. Hence, electromagnetic shielding polymer can be an alternative to replace metal gaskets. This work investigates the effect of sodium ion (Na) addition to copper selenide/chitosan (CuSe/Ch) film for electromagnetic shielding applications. The shielding polymers were produced using solution casting methods, while the CuSe was synthesized using the chemical coprecipitation method. Impedance spectroscopy and two port waveguide methods were used to characterize the prepared polymer's electrical properties and shielding efficiency. The results indicate that Na incorporation in the CuSe/Ch film resulted in a 47 % decrease in bulk resistivity and increased DC conductivity from 6.07 × 10-6 S/cm to 3.69 × 10˗5 S/cm. The AC conductivity of films containing Na demonstrates a similar level of conductivity at lower frequencies, followed by a sharp increase at higher frequencies, indicating a more substantial influence of Na at higher frequencies. Higher absorption shielding efficiency (SEA) and lower reflection shielding efficiency (SER) were achieved by introducing Na into the CuSe chitosan film. The Na/CuSe/Ch film shows higher total shielding efficiency at an average of 20 dB, equivalent to 99 % of the EM power shield.

Engineering Chimeric Chemoreceptors and Two-Component Systems for Orthogonal and Leakless Biosensing of Extracellular γ-Aminobutyric Acid.

Two-component systems (TCSs) sensing and responding to various stimuli outside and inside cells are valuable resources for developing biosensors with synthetic biology applications. However, the use of TCS-based biosensors suffers from a limited effector spectrum, hypersensitivity, low dynamic range, and unwanted signal crosstalk. Here, we developed a tailor-made Escherichia coli whole-cell γ-aminobutyric acid (GABA) biosensor by engineering a chimeric GABA chemoreceptor PctC and TCS. By testing different TCSs, the chimeric PctC/PhoQ showed the response to GABA. Chimera-directed evolution and introduction of the insulated chimeric pair PctC/PhoQ*PhoP* produced biosensors with up to 3.50-fold dynamic range and good orthogonality. To further enhance the dynamic range and lower the basal leakage, three strategies, engineering of PhoP DNA binding sites, fine-tuning reporter expression by optimizing transcription/translation components, and a tobacco etch virus protease-controlled protein degradation, were integrated. This chimeric biosensor displayed a low basal leakage, a large dynamic range (15.8-fold), and a high threshold level (22.7 g L-1). Finally, the optimized biosensor was successfully applied in the high-throughput microdroplet screening of GABA-overproducing Corynebacterium glutamicum, demonstrating its desired properties for extracellular signal biosensing.

SAW Humidity Sensing with rr-P3HT Polymer Films.

In the present paper the humidity sensing properties of regioregular rr-P3HT (poly-3-hexylthiophene) polymer films is investigated by means of surface acoustic wave (SAW) based sensors implemented on LiNbO3 (1280 Y-X) and ST-quartz piezoelectric substrates. The polymeric layers were deposited along the SAW propagation path by spray coating method and the layers thickness was measured by atomic force microscopy (AFM) technique. The response of the SAW devices to relative humidity (rh) changes in the range ~5-60% has been investigated by measuring the SAW phase and frequency changes induced by the (rh) absorption in the rr-P3HT layer. The SAW sensor implemented onto LiNbO3 showed improved performance as the thickness of the membrane increases (from 40 to 240 nm): for 240 nm thick polymeric membrane a phase shift of about -1.2 deg and -8.2 deg was measured for the fundamental (~78 MHz operating frequency) and 3rd (~234 MHz) harmonic wave at (rh) = 60%. A thick rr-P3HT film (~600 nm) was deposited onto the quartz-based SAW sensor: the sensor showed a linear frequency shift of ~-20.5 Hz per unit (rh) changes in the ~5-~50% rh range, and a quite fast response (~5 s) even at low humidity level (~5% rh). The LiNbO3 and quartz-based sensors response was assessed by using a dual delay line system to reduce unwanted common mode signals. The simple and cheap spray coating technology for the rr-P3HT polymer films deposition, complemented with fast low level humidity detection of the tested SAW sensors (much faster than the commercially available Michell SF-52 device), highlight their potential in a low-medium range humidity sensing application.

Robust residual generator design for sensor fault detection in twin rotor aerodynamic system

The twin rotor aerodynamic system (TRAS) reflects the dynamics of vertical take-off rotor systems. TRAS considers the coupling effect and gyroscopic disturbance torque as unwanted signals. For safe and reliable TRAS operation, proper control input is vital, along with fast fault indication. This paper addresses the output sensor fault detection problem in the linear model of TRAS subjected to l2-norm bounded disturbance. A robust design of an observer is formulated as H-/H∞ optimization problem to maximize the sensitivity/robustness criterion. The approach minimizes the disturbance effect on the residual signal, which leads to successful fault detection. Furthermore, a linear quadratic regulator-based state feedback controller is proposed to meet the system's desired transient response requirement. The incorporation of an observer-based residual generator for fault detection along with the state-feedback controller differs the current work from the existing work. Successful fault detection in the output sensor of TRAS depicts the good performance of the proposed observer.

Grooving and Absorption on Substrates to Reduce the Bulk Acoustic Wave for Surface Acoustic Wave Micro-Force Sensors.

Improving measurement accuracy is the core issue with surface acoustic wave (SAW) micro-force sensors. An electrode transducer can stimulate not only the SAW but also the bulk acoustic wave (BAW). A portion of the BAW can be picked up by the receiving transducer, leading to an unwanted or spurious signal. This can harm the device's frequency response characteristics, thereby potentially reducing the precision of the micro-force sensor's measurements. This paper examines the influence of anisotropy on wave propagation, and it also performs a phase-matching analysis between interdigital transducers (IDTs) and bulk waves. Two solutions are shown to reduce the influence of BAW for SAW micro sensors, which are arranged with acoustic absorbers at the ends of the substrate and in grooving in the piezoelectric substrate. Three different types of sensors were manufactured, and the test results showed that the sidelobes of the SAW micro-force sensor could be effectively inhibited (3.32 dB), thereby enhancing the sensitivity and performance of sensor detection. The SAW micro-force sensor manufactured using the new process was tested and the following results were obtained: the center frequency was 59.83 MHz, the fractional bandwidth was 1.33%, the range was 0-1000 mN, the linearity was 1.02%, the hysteresis was 0.59%, the repeatability was 1.11%, and the accuracy was 1.34%.

Enhancement of Brain Computer Interface System Based on Artificial Intelligent Technique

With the rapid development of information technology, the EEG has become an increasingly popular method for analyzing brain signals. EEG signals greatly contribute to(BCI) brain-computer communication . BCI outputs are used to restore many functions in relation to individuals with a motor impairment, and these signals are complex random signals produced from hundreds of millions of neurons in EEG mixtures in the brain that contain many data about brain activity. its include artefacts which have a great influence on the diagnosis; Hence, these unwanted signals become the most important problem in EEG signal analysis. Therefore, used four blind source separation techniques (BSS), STONE, FICA, BEFICA and EFICA. The proposed system is using one of the new Antlion (ALO) optimization algorithms to improve the performance of the previous algorithms and find out which ones are the most responsive, by comparing them and choosing the best ones according to the (PSD) standard. With the use of real data, noticed through the results that the EFICA algorithm is the best response to the improvement and the most efficient, as the ratio of (PSD) was the least possible, as ALO worked to reduce thevariance in the distribution of the frequency spectrum because it relied on solving constrained problems using various searches.

Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter

The most significant issues for a thousand users is the unwanted or loud signals in sound files, it is impossible to eliminate or minimize these noise signals without knowledge their types and ranges. To overcome this issue, present an Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter (EIS-SNR-VBRAF) is proposed. Initially, the data is collected from Adaptive Myriad Filter using Time-Varying Noise and Signal-Dependent Parameters dataset. Then, the data is given for Pre-processing section, Variational Bayesian based Robust Adaptive Filter (VBRAF) is used to reduce the signal noise from Gaussian Noise, Traffic noise and Audio signal back ground noise. The proposed technique is implemented and efficacy of EIS-SNR-VBRAF technique is assessed by support of numerous performances such as BER, Signal-to-Noise-Ratio (SNR), Mean Squared Error, Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error, Structural Similarity Index, Cross-Correlation and Computational Complexity is analyzed. The proposed EIS-SNR-VBRAF method attains 21.18%, 23.52% and 23.65% lower RMSE, 21.52%, 21.76% and 23.24% higher PSNR, 21.19%, 21.73% and 20.48% higher Structural Similarity Index are compared with existing methods like early detection of mechanical malfunctions in vehicles utilizing sound signal processing (ED-MMV-SSP),noise reduction in infrasound signals based on mask coefficient binary weighting generalized cross correlation non-negative matrix factorization algorithm (NRID-MCWG-NMFA) and Click-event sound detection in automotive industry using machine/deep learning (CE-SDSI-DL) respectively.

Filtenna Designed with Defected Ground Structure (DGS) for Ultra-wideband Applications

In microwave imaging applications, filter and antenna are the key components as front-end devices and function independently. Antenna radiates and receives signals to or from nearby scattered objects while filter is used to suppress unwanted signals noise before and after the required bandwidth. Current antennas suffer from high loss, bandwidth limitation and impedance mismatch and deteriorate their performance near the band-edges if connected as a stand-alone device. Due to the current trend towards simplicity and size reduction, researchers are focusing on integrating the filter and antenna into a single module called integrated filter-antenna (IFA) or filtering antenna (filtenna). These would improve the noise performance of the system and pre-filtering requirements such as complexity algorithm in inverse scattering techniques This paper introduces a novel contribution in the field of UWB antenna design by incorporating Defected Ground Structure (DGS) on both the antenna and filter. Thus, the main aim of this research is to conduct detail parametric studies of the proposed integrated filter-antenna with defected ground structure (DGS) to enhance the performance of imaging system. The proposed IFA will be analysed on Rogers RO4003C dielectric substrate by using EM tool, Computer Simulation Technology (CST) and measured using R&S Vector Network Analyzer (VNA). A compact ultra-wideband (UWB) filter and UWB elliptical antenna are designed and examined in detail before combining the devices. Different types of DGS are designed and act as the ground layer of the proposed filtering antenna. The bandwidth of each design is then compared with and without the existence of DGS. The results show that the proposed IFA with DGS implementation achieve the targeted objective, with compact size, enhanced bandwidth and better performance compared to the conventional design of filter and antenna. By integrating filter and antenna into one subsystem, it can help to reduce the loss and enhancing the bandwidth of the system thus letting the antenna to operate at more different frequencies that fall within the range. Both simulated and measured results prove that by integrating filter and antenna into one module, a low loss and larger bandwidth can be accomplished. High performance compact IFA can act as microwave transceiver to improve the overall performance of the microwave imaging system, MIS.

Quantifying the impact of sample, instrument, and data processing on biological signatures in modern and fossil tissues detected with Raman spectroscopy

AbstractRaman spectroscopy is a popular tool for characterizing complex biological materials and their geological remains. Ordination methods, such as principal component analysis (PCA), use spectral variance to create a compositional space, the ChemoSpace, grouping samples based on spectroscopic manifestations reflecting different biological properties or geological processes. PCA allows to reduce the dimensionality of complex spectroscopic data and facilitates the extraction of informative features into formats suitable for downstream statistical analyses, thus representing a first step in the development of diagnostic biosignatures from complex modern and fossil tissues. For such samples, however, there is presently no systematic and accessible survey of the impact of sample, instrument, and spectral processing on the occupation of the ChemoSpace. Here, the influence of sample count, unwanted signals and different signal‐to‐noise ratios, spectrometer decalibration, baseline subtraction, and spectral normalization on ChemoSpace grouping is investigated and exemplified using synthetic spectra. Increase in sample size improves the dissociation of groups in the ChemoSpace, and our sample yields a representative and mostly stable pattern in occupation with less than 10 samples per group. The impact of systemic interference of different amplitude and frequency, periodical or random features that can be introduced by instrument or sample, on compositional biological signatures is reduced by PCA and allows to extract biological information even when spectra of differing signal‐to‐noise ratios are compared. Routine offsets ( 1 cm−1) in spectrometer calibration contribute in our sample to less than 0.1% of the total spectral variance captured in the ChemoSpace and do not obscure biological information. Standard adaptive baselining, together with normalization, increases spectral comparability and facilitates the extraction of informative features. The ChemoSpace approach to biosignatures represents a powerful tool for exploring, denoising, and integrating molecular information from modern and ancient organismal samples.