Comparing the clinical applicability of wavefront phase imaging in keratoconus versus normal eyes.

The aim of this work is to quantitatively assess the wavefront phase of keratoconic eyes measured by the ocular aberrometer t·eyede (based on WaveFront Phase Imaging Sensor), characterized by a lateral resolution of 8.6 µm without requiring any optical element to sample the wavefront information. We evaluated the parameters: root mean square error, Peak-to-Valley, and amplitude of the predominant frequency (Fourier Transform analysis) of a section of the High-Pass filter map in keratoconic and healthy cohorts. Furthermore, we have analyzed keratoconic eyes that presented dark-light bands in this map to assess their period and orientation with the Fourier Transform. There are significant statistical differences (p value < 0.001) between healthy and keratoconic eyes in the three parameters, demonstrating a tendency to increase with the severity of the disease. Otherwise, the quantification of the bands reveals that the width is independent of eye laterality and keratoconic stage as orientation, which tends to be oblique. In conclusion, the quantitative results obtained with t·eyede could help to diagnose and monitor the progression of keratoconus.

Nonlinear Fourier classification of 663 rogue waves measured in the Philippine Sea.

Rogue waves are sudden and extreme occurrences, with heights that exceed twice the significant wave height of their neighboring waves. The formation of rogue waves has been attributed to several possible mechanisms such as linear superposition of random waves, dispersive focusing, and modulational instability. Recently, nonlinear Fourier transforms (NFTs), which generalize the usual Fourier transform, have been leveraged to analyze oceanic rogue waves. Next to the usual linear Fourier modes, NFTs can additionally uncover nonlinear Fourier modes in time series that are usually hidden. However, so far only individual oceanic rogue waves have been analyzed using NFTs in the literature. Moreover, the completely different types of nonlinear Fourier modes have been observed in these studies. Exploiting twelve years of field measurement data from an ocean buoy, we apply the nonlinear Fourier transform (NFT) for the nonlinear Schrödinger equation (NLSE) (referred to NLSE-NFT) to a large dataset of measured rogue waves. While the NLSE-NFT has been used to analyze rogue waves before, this is the first time that it is systematically applied to a large real-world dataset of deep-water rogue waves. We categorize the measured rogue waves into four types based on the characteristics of the largest nonlinear mode: stable, small breather, large breather and (envelope) soliton. We find that all types can occur at a single site, and investigate which conditions are dominated by a single type at the measurement site. The one and two-dimensional Benjamin-Feir indices (BFIs) are employed to examine the four types of nonlinear spectra. Furthermore, we verify on a part of the data set that for the localized types, the largest nonlinear Fourier mode can be attributed directly to the rogue wave, and investigate the relation between the height of the rogue waves and that of the dominant nonlinear Fourier mode. While the dominant nonlinear Fourier mode in general only contributes a small fraction of the rogue wave, we find that soliton modes can contribute up to half of the rogue wave. Since the NLSE does not account for directional spreading, the classification is repeated for the first quartile with the lowest directional spreading for each type. Similar results are obtained.

Quality evaluation of natural monomer flavors for Chinese tobacco industry based on dual mode combined Fourier transform ion cyclotron resonance mass spectrometry and isotopic fine structures.

RATIONALE: Natural monomer flavors can modify the taste of cigarettes. However, no report was published to establish the quality control method for their chemical compositions. METHODS: In this study, licorice, a traditional natural monomer flavor used in tobacco aroma processing, was selected, and the fingerprint was developed by high-performance liquid chromatography (HPLC). Next, the chemical markers of samples from different places of origin were discovered by multivariate statistical analysis. Then, its chemical constituents were identified by combination of HPLC-Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), direct infusion FT-ICR-MS (DI-FT-ICR-MS), and the technology of isotopic fine structures (IFSs). Moreover, its characteristic constituents were quantitatively analyzed using HPLC. RESULTS: The 14 common peaks were assigned in the fingerprint, and 8 of them were considered as qualitative markers by multivariate statistical analysis. A total of 42 chemical constituents were detected using HPLC-FT-ICR-MS, and 13 of them were unambiguously identified by references. Meanwhile, the elemental compositions of other eight unknown chemical components were decisively determined using IFSs. Subsequently, the contents of five characteristic constituents in 11 batches of samples were determined. CONCLUSIONS: The integration strategy established here can discover and quantify the chemical markers for improving the quality control standard of natural monomer flavor of licorice. It is expected that the strategy will be valuable for further quality control of other natural monomer flavors in Chinese tobacco industry.

Photophysical image analysis: Unsupervised probabilistic thresholding for images from electron-multiplying charge-coupled devices.

We introduce the concept photophysical image analysis (PIA) and an associated pipeline for unsupervised probabilistic image thresholding for images recorded by electron-multiplying charge-coupled device (EMCCD) cameras. We base our approach on a closed-form analytic expression for the characteristic function (Fourier-transform of the probability mass function) for the image counts recorded in an EMCCD camera, which takes into account both stochasticity in the arrival of photons at the imaging camera and subsequent noise induced by the detection system of the camera. The only assumption in our method is that the background photon arrival to the imaging system is described by a stationary Poisson process (we make no assumption about the photon statistics for the signal). We estimate the background photon statistics parameter, λbg, from an image which contains both background and signal pixels by use of a novel truncated fit procedure with an automatically determined image count threshold. Prior to this, the camera noise model parameters are estimated using a calibration step. Utilizing the estimates for the camera parameters and λbg, we then introduce a probabilistic thresholding method, where, for the first time, the fraction of misclassified pixels can be determined a priori for a general image in an unsupervised way. We use synthetic images to validate our a priori estimates and to benchmark against the Otsu method, which is a popular unsupervised non-probabilistic image thresholding method (no a priori estimates for the error rates are provided). For completeness, we lastly present a simple heuristic general-purpose segmentation method based on the thresholding results, which we apply to segmentation of synthetic images and experimental images of fluorescent beads and lung cell nuclei. Our publicly available software opens up for fully automated, unsupervised, probabilistic photophysical image analysis.

Adding Color to Mass Spectra of Biopolymers: Charge Determination Analysis (CHARDA) Assigns Charge State to Every Ion Peak.

Traditionally, mass spectrometry (MS) output is the ion abundance plotted versus the ionic mass-to-charge ratio m/z. While employing only commercially available equipment, Charge Determination Analysis (CHARDA) adds a third dimension to MS, estimating for individual peaks their charge states z starting from z = 1 and color coding z in m/z spectra. CHARDA combines the analysis of ion signal decay rates in the time-domain data (transients) in Fourier transform (FT) MS with the interrogation of mass defects (fractional mass) of biopolymers. Being applied to individual isotopic peaks in a complex protein tandem (MS/MS) data set, CHARDA aids peptide mass spectra interpretation by facilitating charge-state deconvolution of large ionic species in crowded regions, estimating z even in the absence of an isotopic distribution (e.g., for monoisotopic mass spectra). CHARDA is fast, robust, and consistent with conventional FTMS and FTMS/MS data acquisition procedures. An effective charge-state resolution Rz ≥ 6 is obtained with the potential for further improvements.

From the grapevine to the glass: A wine metabolomics tale by FT-ICR-MS.

Wine is one of the most consumed beverages around the world. Its unique characteristics arise from numerous processes, from the selection of grapevine varieties and grapes, the effect of the terroir and geographical origin, through the biochemical process of fermentation by microorganisms, until its aging. All molecules found in wine define its chemical fingerprint and can be used to tell the story of its origin, production, authenticity and quality. Wine's chemical composition can be characterized using an untargeted metabolomics approach based on extreme resolution mass spectrometry. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is currently the most powerful analytical technique to analyse such complex sample, providing the most comprehensive analysis of the chemical fingerprint of wine.

Tissue equivalent conversion of silicon-based microdosemeters in hadron therapy.

Silicon has been developed as a microdosemeter, as it can provide sensitive volumes at submicrometric levels, does not need a gas supply, has a fast response, and has low power consumption. However, since the energy response in silicon is not the same as that in tissue, a spectral conversion from silicon to tissue is necessary to obtain the probability distribution of energy deposition in tissue. In this work, we present a method for microdosimetric spectra conversion from silicon to tissue based on the scaled Fourier transformation and the geometric scaling factor, which shows relatively good results in the spectral conversion from diamond to tissue. The results illustrate that the method can convert the energy deposition spectra from silicon to tissue with proper accuracy. Meanwhile, the inconsistency between the converted and actual spectra due to the inherent difference was also observed. Whereas, the reasons for the disagreement are different. For the plateau part of the Bragg curve, the discrepancy between the converted and actual spectra is due to the poor tissue equivalent of silicon. For the proximal part of the Bragg curve, the spectral difference is attributed to the different shapes of the energy deposition spectra obtained in silicon and water, which is the same as that in the diamond. In summary, this method can be employed in the tissue equivalent conversion of silicon microdosemeter, but the poor tissue equivalent of silicon limited the accuracy of this method. In addition, the correction for the deviation between the converted and calculated spectra due to the difference in spectral shapes is required to improve the practicality of this mod.

Using the Hilbert Transform to Evaluate the Effects of Functional Foods on Autonomic Nervous System Activity: A Comparison with the Fast Fourier Transform.

Evaluating the autonomic nervous system (ANS) via heart rate variability (HRV) to investigate the effects of food on human health has attracted attention. However, using a conventional HRV analysis via the fast Fourier transform (FFT), it is difficult to remove artifacts such as body movements and/or abnormal physiological responses (unexpected events) from the HRV analysis results. In this study, an analysis combining bandpass filters and the Hilbert transform was applied to HRV data on functional food intake to compare with FFT analysis. HRV data were obtained from six males by recording electrocardiograms on functional food, γ-aminobutyric acid, intake. HRV indices were calculated by both analysis. In the Hilbert analysis, all HRV indices were obtained for the same number of sampling points as the HRV data. The standard errors of all HRV indices tended to be smaller in the Hilbert analysis than in the FFT analysis. In conclusion, the Hilbert analysis was more suitable than FFT analysis for evaluating ANS via HRV on functional foods intake.

Digital dual test syphilis/HIV detection based on Fourier Descriptors of Cyclic Voltammetry curves.

BACKGROUND: Effective and timely detection is vital for mitigating the severe impacts of Sexually Transmitted Infections (STI), including syphilis and HIV. Cyclic Voltammetry (CV) sensors have shown promise as diagnostic tools for these STI, offering a pathway towards cost-effective solutions in primary health care settings. OBJECTIVE: This study aims to pioneer the use of Fourier Descriptors (FDs) in analyzing CV curves as 2D closed contours, targeting the simultaneous detection of syphilis and HIV. METHODS: Raw CV signals are filtered, resampled, and transformed into 2D closed contours for FD extraction. Essential shape characteristics are captured through selected coefficients. A complementary geometrical analysis further extracts features like curve areas and principal axes lengths from CV curves. A Mahalanobis Distance Classifier is employed for differentiation between patient and control groups. RESULTS: The evaluation of the proposed method revealed promising results with classification performance metrics such as Accuracy and F1-Score consistently achieving values rounded to 0.95 for syphilis and 0.90 for HIV. These results underscore the potential efficacy of the proposed approach in differentiating between patient and control samples for STI detection. CONCLUSION: By integrating principles from biosensors, signal processing, image processing, machine learning, and medical diagnostics, this study presents a comprehensive approach to enhance the detection of both syphilis and HIV. This setts the stage for advanced and accessible STI diagnostic solutions.

Imaging Mass Spectrometry of Isotopically Resolved Intact Proteins on a Trapped Ion-Mobility Quadrupole Time-of-Flight Mass Spectrometer.

In this work, we demonstrate rapid, high spatial, and high spectral resolution imaging of intact proteins by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) on a hybrid quadrupole-reflectron time-of-flight (qTOF) mass spectrometer equipped with trapped ion mobility spectrometry (TIMS). Historically, untargeted MALDI IMS of proteins has been performed on TOF mass spectrometers. While advances in TOF instrumentation have enabled rapid, high spatial resolution IMS of intact proteins, TOF mass spectrometers generate relatively low-resolution mass spectra with limited mass accuracy. Conversely, the implementation of MALDI sources on high-resolving power Fourier transform (FT) mass spectrometers has allowed IMS experiments to be conducted with high spectral resolution with the caveat of increasingly long data acquisition times. As illustrated here, qTOF mass spectrometers enable protein imaging with the combined advantages of TOF and FT mass spectrometers. Protein isotope distributions were resolved for both a protein standard mixture and proteins detected from a whole-body mouse pup tissue section. Rapid (∼10 pixels/s) 10 µm lateral spatial resolution IMS was performed on a rat brain tissue section while maintaining isotopic spectral resolution. Lastly, proof-of-concept MALDI-TIMS data was acquired from a protein mixture to demonstrate the ability to differentiate charge states by ion mobility. These experiments highlight the advantages of qTOF and timsTOF platforms for resolving and interpreting complex protein spectra generated from tissue by IMS.

Utilising Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to track the oxidation of lignin by an alkaliphilic laccase.

Lignin is a complex heteroaromatic polymer which is one of the most abundant and diverse biopolymers on the planet. It comprises approximately one third of all woody plant matter, making it an attractive candidate as an alternative, renewable feedstock to petrochemicals to produce fine chemicals. However, the inherent complexity of lignin makes it difficult to analyse and characterise using common analytical techniques, proving a hindrance to the utilisation of lignin as a green chemical feedstock. Herein we outline the tracking of lignin degradation by an alkaliphilic laccase in a semi-quantitative manner using a combined chemical analysis approach using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to characterise shifts in chemical diversity and relative abundance of ions, and NMR to highlight changes in the structure of lignin. Specifically, an alkaliphilic laccase was used to degrade an industrially relevant lignin, with compounds such as syringaresinol being almost wholly removed (95%) after 24 hours of treatment. Structural analyses reinforced these findings, indicating a >50% loss of NMR signal relating to ß-ß linkages, of which syringaresinol is representative. Ultimately, this work underlines a combined analytical approach that can be used to gain a broader semi-quantitative understanding of the enzymatic activity of laccases within a complex, non-model mixture.

Classification of asbestos and their nonasbestiform analogues using FTIR and multivariate data analysis.

This study presents a possible application of Fourier transform infrared (FTIR) spectrometry and multivariate data analysis, principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA) for classifying asbestos and their nonasbestiform analogues. The objectives of the study are: 1) to classify six regulated asbestos types and 2) to classify between asbestos types and their nonasbestiform analogues. The respirable fraction of six regulated asbestos types and their nonasbestiform analogues were prepared in potassium bromide pellets and collected on polyvinyl chloride membrane filters for FTIR measurement. Both PCA and PLS-DA classified asbestos types and their nonasbestiform analogues on the score plots showed a very distinct clustering of samples between the serpentine (chrysotile) and amphibole groups. The PLS-DA model provided ∼95% correct prediction with a single asbestos type in the sample, although it did not provide all correct predictions for all the challenge samples due to their inherent complexity and the limited sample number. Further studies are necessary for a better prediction level in real samples and standardization of sampling and analysis procedures.

Clustering honey samples with unsupervised machine learning methods using FTIR data.

This study utilizes Fourier transform infrared (FTIR) data from honey samples to cluster and categorize them based on their spectral characteristics. The aim is to group similar samples together, revealing patterns and aiding in classification. The process begins by determining the number of clusters using the elbow method, resulting in five distinct clusters. Principal Component Analysis (PCA) is then applied to reduce the dataset's dimensionality by capturing its significant variances. Hierarchical Cluster Analysis (HCA) further refines the sample clusters. 20% of the data, representing identified clusters, is randomly selected for testing, while the remainder serves as training data for a deep learning algorithm employing a multilayer perceptron (MLP). Following training, the test data are evaluated, revealing an impressive 96.15% accuracy. Accuracy measures the machine learning model's ability to predict class labels for new data accurately. This approach offers reliable honey sample clustering without necessitating extensive preprocessing. Moreover, its swiftness and cost-effectiveness enhance its practicality. Ultimately, by leveraging FTIR spectral data, this method successfully identifies similarities among honey samples, enabling efficient categorization and demonstrating promise in the field of spectral analysis in food science.

Fourier analysis of corneal irregular astigmatism after small-incision lenticule extraction and transepithelial photorefractive keratectomy: A comparative study.

To compare changes in the spherical component, regular astigmatism, and irregular astigmatism of the anterior surface of the cornea after small-incision lenticule extraction (SMILE) and transepithelial photorefractive keratectomy (TransPRK). Fifty-six patients underwent SMILE in 56 eyes, and 68 patients underwet TransPRK in 68 eyes. The right eye was chosen to enter the group. Six months after the procedure, Scheimpflug images were acquired, and Fourier analysis of the anterior surface of patients' corneas was performed using the Pentacam built-in software. Fourier parameters encompass various measurements such as the steepest radius of the curvature and average eccentricity of the spherical components (SphRmin and SphEcc), maximum decentration (MaxDec), central and peripheral regular astigmatism (regular astigmatism at the center [AstC] and regular astigmatism at the periphery [AstP]), and irregularity (Irr). At 6 months postoperatively, SphEcc decreased significantly (P < .001), MaxDec increased significantly (P < .001), and Irr increased insignificantly (P = .254) in the SMILE group. SphEcc decreased significantly (P < .001) and MaxDec and Irr increased significantly (P < .001) in the TransPRK group. TransPRK caused greater changes in SphEcc, MaxDec, and Irr on the anterior corneal surface than SMILE (P < .05). The amount of MaxDec-induced changes in SMILE and TransPRK was significantly correlated with the amount of higher-order aberrations and spherical aberration changes (P < .05). SMILE and TransPRK increase overall irregular astigmatism on the anterior surface of the cornea, more so with TransPRK, where changes in decentration are associated with with increased higher-order aberrations.

Long-term, non-invasive FTIR detection of low-dose ionizing radiation exposure.

Non-invasive methods of detecting radiation exposure show promise to improve upon current approaches to biological dosimetry in ease, speed, and accuracy. Here we developed a pipeline that employs Fourier transform infrared (FTIR) spectroscopy in the mid-infrared spectrum to identify a signature of low dose ionizing radiation exposure in mouse ear pinnae over time. Mice exposed to 0.1 to 2 Gy total body irradiation were repeatedly measured by FTIR at the stratum corneum of the ear pinnae. We found significant discriminative power for all doses and time-points out to 90 days after exposure. Classification accuracy was maximized when testing 14 days after exposure (specificity > 0.9 with a sensitivity threshold of 0.9) and dropped by roughly 30% sensitivity at 90 days. Infrared frequencies point towards biological changes in DNA conformation, lipid oxidation and accumulation and shifts in protein secondary structure. Since only hundreds of samples were used to learn the highly discriminative signature, developing human-relevant diagnostic capabilities is likely feasible and this non-invasive procedure points toward rapid, non-invasive, and reagent-free biodosimetry applications at population scales.

Quantitative or qualitative biomolecular changes in blood serum composition induced by childhood obesity: A Fourier transform infrared examination.

Childhood obesity (CO) negatively affects one in three children and stands as the fourth most common risk factor of health and well-being. Clarifying the molecular and structural modifications that transpire during the development of obesity is crucial for understanding its progression and devising effective therapies. The study was indeed conducted as part of an ongoing CO treatment trial, where data were collected from children diagnosed with CO before the initiation of non-drug treatment interventions. Our primary aim was to analyze the biochemical changes associated with childhood obesity, specifically focusing on concentrations of lipids, lipoproteins, insulin, and glucose. By comparing these parameters between the CO group (n = 60) and a control group of healthy children (n = 43), we sought to elucidate the metabolic differences present in individuals with CO. Our biochemical analyses unveiled lower LDL (low-density lipoproteins) levels and higher HDL (high-density lipoproteins), cholesterol, triglycerides, insulin, and glucose levels in CO individuals compared to controls. To scrutinize these changes in more detail, we employed Fourier transform infrared (FTIR) spectroscopy on the serum samples. Our results indicated elevated levels of lipids and proteins in the serum of CO, compared to controls. Additionally, we noted structural changes in the vibrations of glucose, ß-sheet, and lipids in CO group. The FTIR technique, coupled with principal component analysis (PCA), demonstrated a marked differentiation between CO and controls, particularly in the FTIR region corresponding to amide and lipids. The Pearson test revealed a stronger correlation between biochemical data and FTIR spectra than between 2nd derivative FTIR spectra. Overall, our study provides valuable insights into the molecular and structural changes occurring in CO.

The processing of spatial frequencies through time in visual word recognition.

This study examined the temporal profile of spatial frequency processing in a word reading task in 16 normal adult readers. They had to report the word presented in a 200 ms display using a four-alternative forced-choice task (4AFC). The stimuli were made of an additive combination of the signal (i.e. the target word) and of a visual white noise patch wherein the signal-to-noise ratio varied randomly across stimulus duration. Four spatial frequency conditions were defined for the signal component of the stimulus (bandpass Butterworth filters with center frequencies of 1.2, 2.4, 4.8 and 9.6 cycles per degree). In contrast to the coarse-to-fine theory of visual recognition, the results show that the highest spatial frequency range dominates early processing, with a shift toward lower spatial frequencies at later points during stimulus exposure. This pattern interacted in a complex way with the temporal frequency content of signal-to-noise oscillations. The outcome of individual data patterns classification by a machine learning algorithm according to the corresponding spatial frequency band further shows that the most salient spatial frequency signature is obtained when the time dimension within data patterns is recoded into its Fourier transform.

A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition.

Lanxangia tsaoko's accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensional and three-dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT-NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS-DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.

Fourier Ptychographic Microscopy 10 Years on: A Review.

Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.