Sensitively detecting hazardous and suspected bioaerosols is crucial for safeguarding public health. The potential impact of pollen on identifying bacterial species through fluorescence spectra should not be overlooked. Before the analysis, the spectrum underwent preprocessing steps, including normalization, multivariate scattering correction, and Savitzky-Golay smoothing. Additionally, the spectrum was transformed using difference, standard normal variable, and fast Fourier transform techniques. A random forest algorithm was employed for the classification and identification of 31 different types of samples. The fast Fourier transform improved the classification accuracy of the sample excitation-emission matrix fluorescence spectrum data by 9.2%, resulting in an accuracy of 89.24%. The harmful substances, including Staphylococcus aureus, ricin, beta-bungarotoxin, and Staphylococcal enterotoxin B, were clearly distinguished. The spectral data transformation and classification algorithm effectively eliminated the interference of pollen on other components. Furthermore, a classification and recognition model based on spectral feature transformation was established, demonstrating excellent application potential in detecting hazardous substances and protecting public health. This study provided a solid foundation for the application of rapid detection methods for harmful bioaerosols.

In the present work, both direct and inverse problems are considered for a Fisher-type fractional diffusion equation, which is proposed to describe the phenomenon of cell migration. For the direct problem, a solution is given via the Fourier method and the Laplace transform. On the other hand, we solved the inverse problem from a Bayesian statistical framework using a set of data that are the result of a cell migration experiment on a wound closure assay. We estimated the parameters of the mathematical model via Markov Chain Monte Carlo methods.

CNN has demonstrated remarkable performance in EEG signal detection, yet it still faces limitations in terms of global perception. Additionally, due to individual differences in EEG signals, the generalization ability of epilepsy detection models is week. To address this issue, this paper presents a cross-patient epilepsy detection method utilizing a multi-head self-attention mechanism. This method first utilizes Short-Time Fourier Transform (STFT) to transform the original EEG signals into time-frequency features, then models local information using Convolutional Neural Network (CNN), subsequently captures global dependency relationships between features using the multi-head self-attention mechanism of Transformer, and finally performs epilepsy detection using these features. Meanwhile, this model employs a light multi-head attention mechanism module with an alternating structure, which can comprehensively extract multi-scale features while significantly reducing computational costs. Experimental results on the CHB-MIT dataset show that the proposed model achieves accuracy, sensitivity, specificity, F1 score, and AUC of 92.89%, 96.17%, 92.99%, 94.41%, and 96.77%, respectively. Compared to the existing methods, the method proposed in this paper obtains better performance along with better generalization.

Children with attention-deficit/hyperactivity disorder show deficits in processing speed, as well as aberrant neural oscillations, including both periodic (oscillatory) and aperiodic (1/f-like) activity, reflecting the pattern of power across frequencies. Both components were suggested as underlying neural mechanisms of cognitive dysfunctions in attention-deficit/hyperactivity disorder. Here, we examined differences in processing speed and resting-state-Electroencephalogram neural oscillations and their associations between 6- and 12-year-old children with (n = 33) and without (n = 33) attention-deficit/hyperactivity disorder. Spectral analyses of the resting-state EEG signal using fast Fourier transform revealed increased power in fronto-central theta and beta oscillations for the attention-deficit/hyperactivity disorder group, but no differences in the theta/beta ratio. Using the parameterization method, we found a higher aperiodic exponent, which has been suggested to reflect lower neuronal excitation-inhibition, in the attention-deficit/hyperactivity disorder group. While fast Fourier transform-based theta power correlated with clinical symptoms for the attention-deficit/hyperactivity disorder group only, the aperiodic exponent was negatively correlated with processing speed across the entire sample. Finally, the aperiodic exponent was correlated with fast Fourier transform-based beta power. These results highlight the different and complementary contribution of periodic and aperiodic components of the neural spectrum as metrics for evaluation of processing speed in attention-deficit/hyperactivity disorder. Future studies should further clarify the roles of periodic and aperiodic components in additional cognitive functions and in relation to clinical status.

The identification of drug-induced cardiotoxicity remains a pressing challenge with far-reaching clinical and economic ramifications, often leading to patient harm and resource-intensive drug recalls. Current methodologies, including in vivo and in vitro models, have severe limitations in accurate identification of cardiotoxic substances. Pioneering a paradigm shift from these conventional techniques, our study presents two deep learning-based frameworks, STFT-CNN and SST-CNN, to assess cardiotoxicity with markedly improved accuracy and reliability. Leveraging the power of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) as a more human-relevant cell model, we record mechanical beating signals through impedance measurements. These temporal signals were converted into enriched two-dimensional representations through advanced transformation techniques, specifically short-time Fourier transform (STFT) and synchro-squeezing transform (SST). These transformed data are fed into the proposed frameworks for comprehensive analysis, including drug type classification, concentration classification, cardiotoxicity classification, and new drug identification. Compared to traditional models like recurrent neural network (RNN) and 1-dimensional convolutional neural network (1D-CNN), SST-CNN delivered an impressive test accuracy of 98.55% in drug type classification and 99% in distinguishing cardiotoxic and noncardiotoxic drugs. Its feasibility is further highlighted with a stellar 98.5% average accuracy for classification of various concentrations, and the superiority of our proposed frameworks underscores their promise in revolutionizing drug safety assessments. With a potential for scalability, they represent a major leap in drug safety assessments, offering a pathway to more robust, efficient, and human-relevant cardiotoxicity evaluations.

UNASSIGNED: This study focuses on enhancing the precision of epidemic time series data prediction by integrating Gated Recurrent Unit (GRU) into a Graph Neural Network (GNN), forming the GRGNN. The accuracy of the GNN (Graph Neural Network) network with introduced GRU (Gated Recurrent Units) is validated by comparing it with seven commonly used prediction methods.
UNASSIGNED: The GRGNN methodology involves multivariate time series prediction using a GNN (Graph Neural Network) network improved by the integration of GRU (Gated Recurrent Units). Additionally, Graphical Fourier Transform (GFT) and Discrete Fourier Transform (DFT) are introduced. GFT captures inter-sequence correlations in the spectral domain, while DFT transforms data from the time domain to the frequency domain, revealing temporal node correlations. Following GFT and DFT, outbreak data are predicted through one-dimensional convolution and gated linear regression in the frequency domain, graph convolution in the spectral domain, and GRU (Gated Recurrent Units) in the time domain. The inverse transformation of GFT and DFT is employed, and final predictions are obtained after passing through a fully connected layer. Evaluation is conducted on three datasets: the COVID-19 datasets of 38 African countries and 42 European countries from worldometers, and the chickenpox dataset of 20 Hungarian regions from Kaggle. Metrics include Average Root Mean Square Error (ARMSE) and Average Mean Absolute Error (AMAE).
UNASSIGNED: For African COVID-19 dataset and Hungarian Chickenpox dataset, GRGNN consistently outperforms other methods in ARMSE and AMAE across various prediction step lengths. Optimal results are achieved even at extended prediction steps, highlighting the model\'s robustness.
UNASSIGNED: GRGNN proves effective in predicting epidemic time series data with high accuracy, demonstrating its potential in epidemic surveillance and early warning applications. However, further discussions and studies are warranted to refine its application and judgment methods, emphasizing the ongoing need for exploration and research in this domain.

For cryo-electron tomography (cryo-ET) of beam-sensitive biological specimens, a planar sample geometry is typically used. As the sample is tilted, the effective thickness of the sample along the direction of the electron beam increases and the signal-to-noise ratio concomitantly decreases, limiting the transfer of information at high tilt angles. In addition, the tilt range where data can be collected is limited by a combination of various sample-environment constraints, including the limited space in the objective lens pole piece and the possible use of fixed conductive braids to cool the specimen. Consequently, most tilt series are limited to a maximum of ±70°, leading to the presence of a missing wedge in Fourier space. The acquisition of cryo-ET data without a missing wedge, for example using a cylindrical sample geometry, is hence attractive for volumetric analysis of low-symmetry structures such as organelles or vesicles, lysis events, pore formation or filaments for which the missing information cannot be compensated by averaging techniques. Irrespective of the geometry, electron-beam damage to the specimen is an issue and the first images acquired will transfer more high-resolution information than those acquired last. There is also an inherent trade-off between higher sampling in Fourier space and avoiding beam damage to the sample. Finally, the necessity of using a sufficient electron fluence to align the tilt images means that this fluence needs to be fractionated across a small number of images; therefore, the order of data acquisition is also a factor to consider. Here, an n-helix tilt scheme is described and simulated which uses overlapping and interleaved tilt series to maximize the use of a pillar geometry, allowing the entire pillar volume to be reconstructed as a single unit. Three related tilt schemes are also evaluated that extend the continuous and classic dose-symmetric tilt schemes for cryo-ET to pillar samples to enable the collection of isotropic information across all spatial frequencies. A fourfold dose-symmetric scheme is proposed which provides a practical compromise between uniform information transfer and complexity of data acquisition.

Background.The MTF has difficulties being determined (according to the provisions of the IEC standards) in the hospital setting due to the lack of resources.Purpose.The objective of this work is to propose a quantitative method for obtaining the point spread function (PSF) and the modulation transfer function (MTF) of a digital mammography system from an image of a bar pattern.Methods.The method is based on the measurement of the contrast transfer function (CTF) of the system over the image of the bar pattern. In addition, a theoretical model for thePSFis proposed, from which the theoreticalCTFof the system is obtained by means of convolution with a square wave (mathematical simulation of the bar pattern). Through an iterative process, the free parameters of thePSFmodel are varied until the experimentalCTFcoincides with the one calculated by convolution. Once thePSFof the system is obtained, we calculate theMTFby means of its Fourier transform. TheMTFcalculated from the modelPSFhave been compared with those calculated from an image of a 65μm diameter gold wire using an oversampling process.Results.TheCTFhas been calculated for three digital mammographic systems (DMS 1, DMS 2 and DMS 3), no differences of more than 5 % were found with the CTF obtained with the PSF model. The comparison of theMTFshows us the goodness of thePSFmodel.Conclusions.The proposed method for obtainingPSFandMTFis a simple and accessible method, which does not require a complex configuration or the use of phantoms that are difficult to access in the hospital world. In addition, it can be used to calculate other magnitudes of interest such as the normalized noise power spectrum (NNPS) and the detection quantum efficiency (DQE).

Fourier harmonic analysis (FHA) is a robust method for identification of minute changes in sperm nuclear shape that are indicative of reduced fertility. The current study was designed to develop a fertility prediction model for Nili-Ravi buffalo bulls through FHA of sperm. In experiment I, FHA technique was standardized, average sperm nuclear perimeter was measured and sperm nuclear shape plot of buffalo bull was constructed. Sperm of buffalo bulls (n = 10) were stained with YOYO-1 and Hoechst-33342 to differentiate live and dead, and digital images were captured using phase contrast and fluorescent microscopy. The images were analyzed by ImageJ software and 100 sperm/bull were evaluated. The results are described as mean ± SEM values of mean harmonic amplitude (mharm), skewness harmonic amplitude (skharm), kurtosis harmonic amplitude (kurharm) and variance harmonic amplitude (varharm) at Fourier frequencies 0-5 along with the cartesian and polar coordinate plots of buffalo bull sperm. In experiment II, a fertility prediction model was developed based on FHA of buffalo bull sperm. Semen samples of low (n = 6), medium (n = 3) and high (n = 8) fertility bulls were investigated for FHA of sperm and harmonic amplitudes (HA) were generated. Firstly, to determine if live and dead sperm population have unique nuclear shape distribution; the mean, skewness, kurtosis and variance HA 0-5 of 1700 live and 1294 dead spermatozoa of 17 bulls were evaluated. T-test signified a difference in the mharm0 (2.363 ± 0.01 vs. 2.439 ± 0.02), skharm0 (-0.0002 ± 0.07 vs. -0.266 ± 0.09), kurharm0 (-0.156 ± 0.07 vs. 0.260 ± 0.18), kurharm2 (0.142 ± 0.11 vs. 1.031 ± 0.32) and varharm4 (0.109 ± 0.00 vs. 0.082 ± 0.00) of live vs. dead sperm population (p < 0.05). Therefore, 100 live sperm/bull were further evaluated for mean, skewness, kurtosis and variance HA 0-5 values among high (n = 6) and low-fertility (n = 6) groups. Results of T-test showed higher values of mharm2 (0.739 ± 0.01 vs. 0.686 ± 0.00), mharm4 (0.105 ± 0.001 vs. 0.007 ± 0.001), and skharm0 (0.214 ± 0.109 vs. -0.244 ± 0.097) in high vs. low-fertility group (p < 0.05). In next step, five significantly different combinations of discriminant measures between high and low-fertility groups were obtained by discriminant analysis. In conclusion, mharm4, skharm0 and varharm2 correctly identified 91.7 % of bulls into their respective fertility groups, and upon cross validation the value of the canonical correlation was 0.928.

High-resolution mass spectrometry (HRMS) is a powerful technique for the characterization and quantitation of complex biological mixtures, with several applications including clinical monitoring and tissue imaging. However, these medical and pharmaceutical applications are pushing the analytical limits of modern HRMS techniques, requiring either further development in instrumentation or data processing methods. Here, we demonstrate new developments in the interactive Fourier-transform analysis for mass spectrometry (iFAMS) software including the first application of Gábor transform (GT) to protein quantitation. Newly added automation tools detect signals from minimal user input and apply thresholds for signal selection, deconvolution, and baseline correction to improve the objectivity and reproducibility of deconvolution. Additional tools were added to improve the deconvolution of highly complex or congested mass spectra and are demonstrated here for the first time. The \"Gábor Slicer\" enables the user to explore trends in the Gábor spectrogram with instantaneous ion mass estimates accurate to 10 Da. The charge adjuster allows for easy visual confirmation of accurate charge state assignments and quick adjustment if necessary. Deconvolution refinement utilizes a second GT of isotopically resolved data to remove common deconvolution artifacts. To assess the quality of deconvolution from iFAMS, several comparisons are made to deconvolutions using other algorithms such as UniDec and an implementation of MaxEnt in Agilent MassHunter BioConfirm. Lastly, the newly added batch processing and quantitation capabilities of iFAMS are demonstrated and compared to a common extracted ion chromatogram approach.