Spectral features related to the auditory perception of twang-like voices

BACKGROUND

To the best of our knowledge, studies on the relationship between spectral energy distribution and the degree of perceived twang-like voices are still sparse. Through an auditory-perceptual test we aimed to explore the spectral features that may relate with the auditory-perception of twang-like voices.

METHODS

Ten judges who were blind to the test's tasks and stimuli rated the amount of twang perceived on seventy-six audio samples. The stimuli consisted of twenty voices recorded from eight CCM singers who sustained the vowel [a:] in different pitches, with and without a twang-like voice. Also, forty filtered and sixteen synthesized-manipulated stimuli were included.

RESULTS AND CONCLUSIONS

Based on the intra-rater reliability scores, four judges were identified as suitable to be included in the analyses. Results showed that the frequency of F1 and F2 correlated strongly with the auditory-perception of twang-like voices (0.90 and 0.74, respectively), whereas F3 showed a moderate negative correlation (-0.52). The frequency difference between F1 and F3 showed a strong negative correlation (-0.82). The mean energy between 1-2 kHz and 2-3 kHz correlated moderately (0.51 and 0.42, respectively). The frequency of F4 and F5, and the energy above 3 kHz showed weak correlations. Since the spectral changes under 2 kHz have been associated with the jaw, lips, and tongue adjustments (i.e. vowel articulation) and a higher vertical laryngeal position might affect the frequency of all formants (including F1 and F2), our results suggest that vowel articulation and the laryngeal height may be relevant when performing twang-like voices.

Spectral changes in skin blood flow during pressure manipulations or sympathetic stimulation

Skin blood flow is commonly determined by laser Doppler flowmetry (LDF). It has been suggested that pathophysiological conditions can be assessed by analysis of specific frequency domains of the LDF signals. We tested whether physiological stimuli that activate myogenic and neurogenic mechanisms would affect relevant portions of the laser Doppler spectrum. LDF sensors were placed on the right forearm of 14 healthy volunteers for myogenic (six females) and 13 for neurogenic challenge (five females). Myogenic responses were tested by positioning the arm ∼50° above/below heart level. Neurogenic responses were tested by immersing the left hand into an ice slurry with and without topical application of local anaesthetic. Short-time Fourier analyses were computed over the range of 0.06 to 0.15 Hz for myogenic and 0.02 to 0.06 Hz for neurogenic. No significant differences in spectral density were observed (P = 0.40) in the myogenic range with arm above (7 ± 54 × 10-4 dB) and below heart (7 ± 14 × 10-4 dB). Neurogenic spectral density showed no significant increase from baseline to cold pressor test (0.0017 ± 0.0013 and 0.0038 ± 0.0039 dB; P = 0.087, effect size 0.47). After application of anaesthetic, neurogenic spectral density was unchanged between the baseline and cold pressor test (0.0014 ± 0.0025 and 0.0006 ± 0.0005 dB; P = 0.173). These results suggest that changes in the myogenic and neurogenic spectral density of LDF signals did not fully reflect the skin vascular function activated by pressure manipulation and sympathetic stimulation. Therefore, LDF myogenic and neurogenic spectral density data should be interpreted with caution.

Altered neural oscillations in classical galactosaemia during sentence production

Classical galactosaemia (CG) is a hereditary disease in galactose metabolism that despite dietary treatment is characterized by a wide range of cognitive deficits, among which is language production. CG brain functioning has been studied with several neuroimaging techniques, which revealed both structural and functional atypicalities. In the present study, for the first time, we compared the oscillatory dynamics, especially the power spectrum and time-frequency representations (TFR), in the electroencephalography (EEG) of CG patients and healthy controls while they were performing a language production task. Twenty-one CG patients and 19 healthy controls described animated scenes, either in full sentences or in words, indicating two levels of complexity in syntactic planning. Based on previous work on the P300 event related potential (ERP) and its relation with theta frequency, we hypothesized that the oscillatory activity of patients and controls would differ in theta power and TFR. With regard to behavior, reaction times showed that patients are slower, reflecting the language deficit. In the power spectrum, we observed significant higher power in patients in delta (1-3 Hz), theta (4-7 Hz), beta (15-30 Hz) and gamma (30-70 Hz) frequencies, but not in alpha (8-12 Hz), suggesting an atypical oscillatory profile. The time-frequency analysis revealed significantly weaker event-related theta synchronization (ERS) and alpha desynchronization (ERD) in patients in the sentence condition. The data support the hypothesis that CG language difficulties relate to theta-alpha brain oscillations.

Modelling of Musical Perception using Spectral Knowledge Representation

We present a novel approach to representing perceptual and cognitive knowledge, spectral knowledge representation, that is focused on the oscillatory behaviour of the brain. The model is presented in the context of a larger hypothetical cognitive architecture. The model uses literal representations of waves to describe the dynamics of neural assemblies as they process perceived input. We show how the model can be applied to representations of sound, and usefully model music perception, specifically harmonic distance. We demonstrate that the model naturally captures both pitch and chord/key distance as empirically measured by Krumhansl and Kessler, thereby providing an underlying mechanism from which their toroidal model might arise. We evaluate our model with respect to those of Milne and others.

Synthesis, characterization, and biological study of new synthetic opioid hemorphin-4 peptides containing sterically restricted nonnatural amino acids

Some new hemorphin-4 analogs with structures of Xxx-Pro-Trp-Thr-NH2 and Tyr-Yyy-Trp-Thr-NH2, where Xxx is 2-amino-3-(4-hydroxy-2,6-dimethylphenyl)propanoic acid or 2-amino-3-(4-dibenzylamino-2,6-dimethylphenyl)propanoic acid, and Yyy is (2S,4S)-4-amino-pyrrolidine-2-carboxylic acid, were synthesized and characterized by electrochemical and spectral analyses. In vivo anticonvulsant and antinociceptive activities of peptide derivatives were studied after intracerebroventricular injection in mice. The therapeutic effects of the modified peptides on seizures and pain in mice were evaluated to provide valuable insights into the potential applications of the novel compounds. Electrochemical characterization showed that the compounds behave as weak protolytes and that they are in a soluble, stable molecular form at physiological pH values. The antioxidant activity of the peptides was evaluated with voltammetric analyses, which were confirmed by applying the 2,2-Diphenyl-1-picrylhydrazyl method. The compounds showed satisfactory results regarding their structural stability, reaching the desired centers for the manifestation of biological activity without hydrolysis processes at 37°C and physiological pH. Dm-H4 and H4-P1 exhibited 100% and 83% potency to suppress the psychomotor seizures in the 6-Hz test compared to 67% activity of H4. Notably, only the H4-P1 had efficacy in blocking the tonic component in the maximal electroshock test with a potency comparable to H4. All investigated peptides containing unnatural conformationally restricted amino acids showed antinociceptive effects. The analogs Db-H4 and H4-P1 showed the most pronounced and long-lasting effect in both experimental models of pain induced by thermal and chemical stimuli. Dm-H4 produced a dose-dependent thermal antinociception and H4-P2 inhibited only formalin-induced pain behavior.

Computer-Aided Multiphoton Microscopy Diagnosis of 5 Different Primary Architecture Subtypes of Meningiomas

Meningiomas rank among the most common intracranial tumors, and surgery stands as the primary treatment modality for meningiomas. The precise subtyping and diagnosis of meningiomas, both before and during surgery, play a pivotal role in enabling neurosurgeons choose the optimal surgical program. In this study, we utilized multiphoton microscopy (MPM) based on 2-photon excited fluorescence and second-harmonic generation to identify 5 common meningioma subtypes. The morphological features of these subtypes were depicted using the MPM multichannel mode. Additionally, we developed 2 distinct programs to quantify collagen content and blood vessel density. Furthermore, the lambda mode of the MPM characterized architectural and spectral features, from which 3 quantitative indicators were extracted. Moreover, we employed machine learning to differentiate meningioma subtypes automatically, achieving high classification accuracy. These findings demonstrate the potential of MPM as a noninvasive diagnostic tool for meningioma subtyping and diagnosis, offering improved accuracy and resolution compared with traditional methods.

Online Detection of Laser Welding Penetration Depth Based on Multi-Sensor Features

The accurate online detection of laser welding penetration depth has been a critical problem to which the industry has paid the most attention. Aiming at the laser welding process of TC4 titanium alloy, a multi-sensor monitoring system that obtained the keyhole/molten pool images and laser-induced plasma spectrum was built. The influences of laser power on the keyhole/molten pool morphologies and plasma thermo-mechanical characteristics were investigated. The results showed that there were significant correlations among the variations of the keyhole-molten pool, plasma spectrum, and penetration depth. The image features and spectral features were extracted by image processing and dimension-reduction methods, respectively. Moreover, several penetration depth prediction models based on single-sensor features and multi-sensor features were established. The mean square error of the neural network model built by multi-sensor features was 0.0162, which was smaller than that of the model built by single-sensor features. The established high-precision model provided a theoretical basis for real-time feedback control of the penetration depth in the laser welding process.

Cross-frequency cortex-muscle interactions are abnormal in young people with dystonia

Sensory processing and sensorimotor integration are abnormal in dystonia, including impaired modulation of beta-corticomuscular coherence. However, cortex-muscle interactions in either direction are rarely described, with reports limited predominantly to investigation of linear coupling, using corticomuscular coherence or Granger causality. Information-theoretic tools such as transfer entropy detect both linear and non-linear interactions between processes. This observational case-control study applies transfer entropy to determine intra- and cross-frequency cortex-muscle coupling in young people with dystonia/dystonic cerebral palsy. Fifteen children with dystonia/dystonic cerebral palsy and 13 controls, aged 12-18 years, performed a grasp task with their dominant hand. Mechanical perturbations were provided by an electromechanical tapper. Bipolar scalp EEG over contralateral sensorimotor cortex and surface EMG over first dorsal interosseous were recorded. Multi-scale wavelet transfer entropy was applied to decompose signals into functional frequency bands of oscillatory activity and to quantify intra- and cross-frequency coupling between brain and muscle. Statistical significance against the null hypothesis of zero transfer entropy was established, setting individual 95% confidence thresholds. The proportion of individuals in each group showing significant transfer entropy for each frequency combination/direction was compared using Fisher's exact test, correcting for multiple comparisons. Intra-frequency transfer entropy was detected in all participants bidirectionally in the beta (16-32 Hz) range and in most participants from EEG to EMG in the alpha (8-16 Hz) range. Cross-frequency transfer entropy across multiple frequency bands was largely similar between groups, but a specific coupling from low-frequency EMG to beta EEG was significantly reduced in dystonia [P = 0.0061 (corrected)]. The demonstration of bidirectional cortex-muscle communication in dystonia emphasizes the value of transfer entropy for exploring neural communications in neurological disorders. The novel finding of diminished coupling from low-frequency EMG to beta EEG in dystonia suggests impaired cortical feedback of proprioceptive information with a specific frequency signature that could be relevant to the origin of the excessive low-frequency drive to muscle.

Minimum Minutes Machine-Learning Microfluidic Microbe Monitoring Method (M7)

Frequent outbreaks of viral diseases have brought substantial negative impacts on society and the economy, and they are very difficult to detect, as the concentration of viral aerosols in the air is low and the composition is complex. The traditional detection method is manually collection and re-detection, being cumbersome and time-consuming. Here we propose a virus aerosol detection method based on microfluidic inertial separation and spectroscopic analysis technology to rapidly and accurately detect aerosol particles in the air. The microfluidic chip is designed based on the principles of inertial separation and laminar flow characteristics, resulting in an average separation efficiency of 95.99% for 2 μm particles. We build a microfluidic chip composite spectrometer detection platform to capture the spectral information on aerosol particles dynamically. By employing machine-learning techniques, we can accurately classify different types of aerosol particles. The entire experiment took less than 30 min as compared with hours by PCR detection. Furthermore, our model achieves an accuracy of 97.87% in identifying virus aerosols, which is comparable to the results obtained from PCR detection.

Does the brain sleep differently depending on intellectual abilities?

AIMS

To compare the children's sleep electroencephalogram according to their intellectual profile.

METHODS

Children were grouped according to their Wechsler Intelligence Scale for Children (WISC) scores (17 with normal intelligence quotient [IQ, NIQ] and 24 with high IQ [HIQ]). Comparisons of spectral power between groups and its relationship with WISC scores were assessed using analyses of variance and linear regression models, adjusted for age and sex.

RESULTS

Children with HIQ had more rapid eye movement (REM) sleep, especially late at night, and more power in slow-frequency bands during REM sleep than those with NIQ. There were also positive associations between the processing speed index and the spectral power in β bands in NREM sleep, and with the spectral power in α, σ, β, and γ bands in REM sleep, with different associations between groups.

CONCLUSION

The enhanced power in slow bands during REM sleep in children with HIQ overlaps with that of typical REM sleep oscillations thought to be involved in emotional memory consolidation. The dissimilar relationships between spectral power and WISC scores in NIQ and HIQ groups may underlie functional differences in brain activity related to cognitive efficiency, questioning the direction of the relationship between sleep and cognitive functioning.

Spectral analysis of non-contrast fluoroscopy for evaluation of pulmonary perfusion: Feasibility and sensitivity testing with a phantom

BACKGROUND

Oscillating x-ray attenuation in the lungs provides an opportunity to evaluate pulmonary perfusion without contrast. Recent intensity-based methods have been compared to pulmonary scintigraphy and CT angiography but lack rigorous phantom studies.

PURPOSE

A new method to quantify the periodic signal amplitude was employed using spectral analysis. Performance was characterized using a water phantom capable of creating an oscillating x-ray attenuation at physiologic amplitudes. Feasibility in detecting abnormal perfusion was performed on a volunteer with pulmonary vascular disease and compared to pulmonary angiography, the clinical gold standard.

METHODS

For each fluoroscopic acquisition, the normalized temporal signal from each pixel was decomposed into its frequency components using Fourier transformation, and the spectral amplitude, defined as the x-ray pulsatility index (XPI), was determined at the desired frequency using a band-pass filter. XPI was displayed as a pixel-wise parametric colormap. Based on XPI maps generated using two human volunteers, a water bath phantom was constructed with a fluctuating fluid height and a 1 cm diameter pulsatility defect. Contrast-to-noise (CNR) of the defect was measured using fluoroscopy images acquired at variable fluid height fluctuation (0.1-1.9 mm) and oscillation frequency (30-60 bpm). Various sampling frame rates (3-30 fps) and acquisition durations (1.8-8 s) using truncated datasets were reconstructed from full datasets. Fluoroscopic images were obtained in a patient just prior to pulmonary angiography in the same projection.

RESULTS

XPI maps in human subjects showed high signal to background contrast with high central XPI measuring up to 0.5. Phantom experiments revealed CNR was linearly correlated to fluid height change (r2  = 0.998). CNR is proportional to increasing sampling frame rate and increasing acquisition duration as expected with Fourier analysis. XPI map displayed multifocal perfusion defects in good agreement with pulmonary angiography.

CONCLUSION

Spectral analysis is an accurate and sensitive method to detect small changes in periodic x-ray attenuation using a short fluoroscopic acquisition. This method demonstrated good agreement to pulmonary angiography and shows promise for clinical imaging of pulmonary perfusion using standard fluoroscopic methods.

Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm

The quality of cocoa beans is crucial in influencing the taste, aroma, and texture of chocolate and consumer satisfaction. High-quality cocoa beans are valued on the international market, benefiting Ivorian producers. Our study uses advanced techniques to evaluate and classify cocoa beans by analyzing spectral measurements, integrating machine learning algorithms, and optimizing parameters through genetic algorithms. The results highlight the critical importance of parameter optimization for optimal performance. Logistic regression, support vector machines (SVM), and random forest algorithms demonstrate a consistent performance. XGBoost shows improvements in the second generation, followed by a slight decrease in the fifth. On the other hand, the performance of AdaBoost is not satisfactory in generations two and five. The results are presented on three levels: first, using all parameters reveals that logistic regression obtains the best performance with a precision of 83.78%. Then, the results of the parameters selected in the second generation still show the logistic regression with the best precision of 84.71%. Finally, the results of the parameters chosen in the second generation place random forest in the lead with a score of 74.12%.

Corrigendum: EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training

[This corrects the article DOI: 10.3389/fnins.2023.1254423.].

Stability Analysis of Metal Active-Gas Welding Short-Circuiting Transfer Based on Input Pulsating Energy

In this study, a platform for a welding experiment, used to collect input and output electrical signals, was constructed, and the algorithm for the input pulsating energy interpolation line (IPEI) was given. Experiments with MAG surface straight line welding were conducted at various voltages. Analysis of the IPEI in relation to the welding current was performed while combining real-world welding occurrences with high-speed camera images of droplet transfer. It was established that the IPEI can be employed as a characteristic parameter to assess the stability of the short-circuiting transfer process in MAG welding. The three criteria for assessing the stability were the spectrum, approximation entropy, and coefficient of variation. A comparative analysis was conducted on each of these approaches. It was determined that the most effective technique is approximation entropy. The approximation entropy of the welding current and IPEI are also highly consistent, with a correlation coefficient as high as 0.9889.

Rapid discrimination of urine specific gravity using spectroscopy and a modified combination method based on SPA and spectral index

To achieve high-accuracy urine specific gravity discrimination and guide the design of four-waveband multispectral sensors. A modified combination strategy was attempted to be proposed based on the successive projections algorithm (SPA) and the spectral index (SI) in the present study. First, the SPA was used to select four spectral variables in the full spectra. Second, the four spectral variables were mathematically transformed by SI to obtain SI values. Then, SPA gradually fusions the SI values and establishes models to identify USG. The results showed that the SPA can screen out the four characteristic wavelengths related to the measured sample attributes. SIs can be used to improve the performance of constructed prediction models. The best model only involves four spectral variables and 1 SI value, with high accuracy (91.62%), sensitivity (0.9051), and specificity (0.9667). The results reveal that m-SPA-SI can effectively distinguish USG and provide design guidance for 4-wavelength multispectral sensors.

A pilot study to assess the origin of the spectral power increases of heart rate variability associated with transient changes in the R-R interval

Spectral analysis of heart rate variability (HRV) is used to assess cardiovascular autonomic function. In the power density spectrum calculated from a time series of the R-R interval (RRI), three main components are distinguished: very-low-frequency (VLF; 0.003-0.04 Hz), low-frequency (LF; 0.04-0.15 Hz), and high-frequency (HF; 0.15-0.4 Hz) components. However, the physiological correlates of these frequency components have yet to be determined. In this study, we conducted spectral analysis of data segments of various lengths (5, 30, 100, and 200 s) of the RRI time series during active standing. Because of the trade-off relationship between time and frequency resolution, the analysis of the RRI data segment shorter than 30 s was needed to identify the temporal relationships between individual transient increases in RRI and the resulting spectral power changes. In contrast, the segment of 200 s was needed to properly evaluate the magnitude of the increase in the VLF power. The results showed that a transient increase in the RRI was tightly associated with simultaneous increases in the powers of the VLF, LF, and HF components. We further found that the simultaneous power increases in these three components were caused by the arterial baroreceptor reflex responding to rapid blood pressure rise.

Statistical inference for dependence networks in topological data analysis

Topological data analysis (TDA) provide tools that are becoming increasingly popular for analyzing multivariate time series data. One key aspect in analyzing multivariate time series is dependence between components. One application is on brain signal analysis. In particular, various dependence patterns in brain networks may be linked to specific tasks and cognitive processes. These dependence patterns may be altered by various neurological and cognitive impairments such as Alzheimer's and Parkinson's diseases, as well as attention deficit hyperactivity disorder (ADHD). Because there is no ground-truth with known dependence patterns in real brain signals, testing new TDA methods on multivariate time series is still a challenge. Our goal here is to develop novel statistical inference procedures via simulations. Simulations are useful for generating some null distributions of a test statistic (for hypothesis testing), forming confidence regions, and for evaluating the performance of proposed TDA methods. To the best of our knowledge, there are no methods that simulate multivariate time series data with potentially complex user-specified connectivity patterns. In this paper we present a novel approach to simulate multivariate time series with specific number of cycles/holes in its dependence network. Furthermore, we also provide a procedure for generating higher dimensional topological features.

EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training

Tailor-made notched music training (TMNMT) is a promising therapy for tinnitus. Residual inhibition (RI) is one of the few interventions that can temporarily inhibit tinnitus, which is a useful technique that can be applied to tinnitus research and explore tinnitus mechanisms. In this study, RI effect of TMNMT in tinnitus was investigated mainly using behavioral tests, EEG spectral and microstate analysis. To our knowledge, this study is the first to investigate RI effect of TMNMT. A total of 44 participants with tinnitus were divided into TMNMT group (22 participants; ECnm, NMnm, RInm represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by TMNMT music, respectively) and Placebo control group (22 participants; ECpb, PBpb, RIpb represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by Placebo music, respectively) in a single-blind manner. Behavioral tests, EEG spectral analysis (covering delta, theta, alpha, beta, gamma frequency bands) and microstate analysis (involving four microstate classes, A to D) were employed to evaluate RI effect of TMNMT. The results of the study showed that TMNMT had a stronger inhibition ability and longer inhibition time according to the behavioral tests compared to Placebo. Spectral analysis showed that RI effect of TMNMT increased significantly the power spectral density (PSD) of delta, theta bands and decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of TMNMT had shorter duration (microstate B, microstate C), higher Occurrence (microstate A, microstate C, microstate D), Coverage (microstate A) and transition probabilities (microstate A to microstate B, microstate A to microstate D and microstate D to microstate A). Meanwhile, RI effect of Placebo decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of Placebo had shorter duration (microstate C, microstate D), higher occurrence (microstate B, microstate C), lower coverage (microstate C, microstate D), higher transition probabilities (microstate A to microstate B, microstate B to microstate A). It was also found that the intensity of tinnitus symptoms was significant positively correlated with the duration of microstate B in five subgroups (ECnm, NMnm, RInm, ECpb, PBpb). Our study provided valuable experimental evidence and practical applications for the effectiveness of TMNMT as a novel music therapy for tinnitus. The observed stronger residual inhibition (RI) ability of TMNMT supported its potential applications in tinnitus treatment. Furthermore, the temporal dynamics of EEG microstates serve as novel functional and trait markers of synchronous brain activity that contribute to a deep understanding of the neural mechanism underlying TMNMT treatment for tinnitus.

Fully Integrated Ultrathin Solid Immersion Grating Microspectrometer for Handheld Visible and Near-Infrared Spectroscopic Applications

Despite advances in microfabrication, compact spectrometers still face challenges in shrinking their size without sacrificing optical performance. Here,  the solid immersion grating microspectrometer (SIG-µSPEC) for high spectral resolution in a broad operational wavelength range is reported. The spectroscopic module incorporates a silicon microslit, index-matched lens, plane mirrors, solid immersion grating (SIG), and a CMOS line sensor within a small form factor. The SIG facilitates high angular dispersion of light on a planar focal plane, resulting in an average spectral resolution of 5.8 nm, with over 76% maximum sensitivity from 400 to 800 nm. SIG-µSPEC measures the spectral reflectance of fruits at different ripening stages, clearly revealing changes in the chlorophyll absorption band. The measured spectrum is further utilized for the precise prediction of the soluble solid content (SSC) levels, achieving a high correlation (R2 = 0.91) and a ratio of prediction-to-deviation of 2.36. This compact microspectrometer holds the potential for precise and non-invasive spectral analysis across point-of-care fields.

Interaction of narcissoside with α-amylase from Bacillus subtilis and Porcine pancreatic by multi-spectral analysis and molecular dynamics simulation

In this work, interaction mechanism of narcissoside with two α-amylase from Bacillus subtilis (BSA) and Porcine pancreatic (PPA) are comparatively studied by multi-spectral analysis, molecular docking and molecular dynamics simulation. The results prove that narcissoside can statically quench fluorescence of BSA/PPA. Two complexes are mainly formed by hydrogen bond and van der Waals force. With the increase of temperature, the two complexes formed by narcissoside and two enzymes become unstable. At the same experimental temperature, the binding force of narcissoside to PPA is higher than that of BSA. The binding of narcissoside to PPA/BSA increases the hydrophobicity of microenvironment. Moreover, the secondary structure of PPA/BSA is mainly changed by decreasing the α-helix. The optimal binding modes of narcissoside with BSA/PPA are predicted by molecular docking, and the stability of the two complexes is evaluated by molecular dynamics simulations.

Synthesis, characterization, biological activity and computation-based efficacy of cobalt(II) complexes of biphenyl-2-ol against SARS-CoV-2 virus

Cobalt(II) complexes of biphenyl-2-ol of composition, CoCl2-n(OC6H4C6H5-2)n(H2O)4 (where n = 1 or 2), were prepared by reacting cobaltous(II) chloride with equi- and bimolar ratios of sodium salt of biphenyl-2-ol. The structural characterization of the synthesized complexes was accomplished by NMR, FTIR, thermogravimetry (TGA), high resolution mass spectroscopy (HRMS), electronic spectroscopic techniques coupled with density functional theory (DFT). The stability of the complexes in different pH media of solvent was studied. Chemical reactivity parameters of the newly synthesized complexes, computed using DFT, indicated greater reactivity of complex 2 over complex 1 and free ligand as indicated by its low HOMO-LUMO energy gap corresponding to 1.71 eV. Molecular docking (MD) studies were carried out in order to study the binding affinities between amino acid residues of DNA duplex (PDB ID: 1BNA) and SARS-CoV-2 (PDB ID: 7T9K) with newly synthesized complexes. Complex 2 has shown promising antivirus behaviour with an inhibition constant value of 0.0423 µmol-1 with amino acid residues of SARS-CoV-2 virus. Toxicity of the complexes was predicted using ProTox-II online server. Antibacterial studies have indicated the complexes to exhibit greater efficacy than the free ligand, while the antioxidant activities have suggested them to display enhanced antioxidant behaviour as compared to reference compounds.Communicated by Ramaswamy H. Sarma.

Can IR Images of the Water Surface Be Used to Quantify the Energy Spectrum and the Turbulent Kinetic Energy Dissipation Rate?

Near-surface oceanic turbulence plays an important role in the exchange of mass, momentum, and energy between the atmosphere and the ocean. The climate modifying the air-sea CO2 transfer rate varies linearly with the surface turbulent kinetic energy dissipation rate to the 1/4 power in a range of systems with different types of forcing, such as coastal oceans, river estuaries, large tidal freshwater rivers, and oceans. In the first part of this paper, we present a numerical study of the near-surface turbulent kinetic energy spectra deduced from a direct numerical simulation (DNS) compared to turbulent kinetic energy spectra deduced from idealized infrared (IR) images. The DNS temperature fields served as a surrogate for IR images from which we have calculated the underlying kinetic energy spectra. Despite the near-surface flow region being highly anisotropic, we demonstrated that modeled isotropic and homogeneous turbulence spectra can serve as an approximation to observed near-surface spectra within the inertial and dissipation ranges. The second part of this paper validates our numerical observations in a laboratory experiment. In this experiment, we compared the turbulent kinetic energy spectra near the surface, as measured using a submerged shear sensor with the spectra derived from infrared images collected from above the surface. The energy dissipation measured by the shear sensor was found to be within 20% of the dissipation value derived from the IR images. Numerically and experimentally, we have demonstrated that IR-based and remote measurement techniques of the aquatic near surface offer a potentially accurate and non-invasive way to measure near-surface turbulence, which is needed by the community to improve models of oceanic air-sea heat, momentum, and gas fluxes.

Comparison of comprehensive quantitative EEG metrics between typically developing boys and girls in resting state eyes-open and eyes-closed conditions

Introduction

A majority of published studies comparing quantitative EEG (qEEG) in typically developing (TD) children and children with neurodevelopmental or psychiatric disorders have used a control group (e.g., TD children) that combines boys and girls. This suggests a widespread supposition that typically developing boys and girls have similar brain activity at all locations and frequencies, allowing the data from TD boys and girls to be aggregated in a single group.

Methods

In this study, we have rigorously challenged this assumption by performing a comprehensive qEEG analysis on EEG recoding of TD boys (n = 84) and girls (n = 62), during resting state eyes-open and eyes-closed conditions (EEG recordings from Child Mind Institute's Healthy Brain Network (HBN) initiative). Our qEEG analysis was performed over narrow-band frequencies (e.g., separating low α from high α, etc.), included sex, age, and head size as covariates in the analysis, and encompassed computation of a wide range of qEEG metrics that included both absolute and relative spectral power levels, regional hemispheric asymmetry, and inter- and intra-hemispheric magnitude coherences as well as phase coherency among cortical regions. We have also introduced a novel compact yet comprehensive visual presentation of the results that allows comparison of the qEEG metrics of boys and girls for the entire EEG locations, pairs, and frequencies in a single graph.

Results

Our results show there are wide-spread EEG locations and frequencies where TD boys and girls exhibit differences in their absolute and relative spectral powers, hemispheric power asymmetry, and magnitude coherence and phase synchrony.

Discussion

These findings strongly support the necessity of including sex, age, and head size as covariates in the analysis of qEEG of children, and argue against combining data from boys and girls. Our analysis also supports the utility of narrow-band frequencies, e.g., dividing α, β, and γ band into finer sub-scales. The results of this study can serve as a comprehensive normative qEEG database for resting state studies in children containing both eyes open and eyes closed paradigms.

Spectral-based thickness profiling of the corpus callosum enhances anomaly detection in fetal alcohol spectrum disorders

Introduction

Fetal alcohol spectrum disorders (FASD) range from fetal alcohol syndrome (FAS) to non-syndromic forms (NS-FASD). The neuroanatomical consequences of prenatal alcohol exposure are mainly the reduction in brain size, but also focal abnormalities such as those of the corpus callosum (CC). We previously showed a narrowing of the CC for brain size, using manual measurement and its usefulness to improve diagnostic certainty. Our aim was to automate these measurements of the CC and identify more recurrent abnormalities in FAS subjects, independently of brain size reduction.

Methods

We developed a fast, automated, and normalization-free method based on spectral analysis to generate thicknesses of the CC continuously and at singular points (genu, body, isthmus, and splenium), and its length (LCC). We applied it on midsagittal section of the CC extracted from T1-anatomical brain MRI of 89 subjects with FASD (52 FAS, 37 NS-FASD) and 126 with typically development (6-20 y-o). After adjusting for batch effect, we compared the mean profiles and thicknesses of the singular points across the 3 groups. For each parameter, we established variations with age (growth charts) and brain size in the control group (scaling charts), then identified participants with abnormal measurements (<10th percentile).

Results

We confirmed the slimming of the posterior half of the CC in both FASD groups, and of the genu section in the FAS group, compared to the control group. We found a significant group effect for the LCC, genu, median body, isthmus, and splenium thicknesses (p < 0.05). We described a body hump whose morphology did not differ between groups. According to the growth charts, there was an excess of FASD subjects with abnormal LCC and isthmus, and of FAS subjects with abnormal genu and splenium. According to the scaling charts, this excess remained only for LCC, isthmus and splenium, undersized for brain size.

Conclusion

We characterized size-independent anomalies of the posterior part of the CC in FASD, with an automated method, confirming and extending our previous study. Our new tool brings the use of a neuroanatomical criterion including CC damage closer to clinical practice. Our results suggest that an FAS signature identified in NS-FASD, could improve diagnosis specificity.

Visualization of Subcutaneous Blood Vessels Based on Hyperspectral Imaging and Three-Wavelength Index Images

Blood vessel visualization technology allows nursing staff to transition from traditional palpation or touch to locate the subcutaneous blood vessels to visualized localization by providing a clear visual aid for performing various medical procedures accurately and efficiently involving blood vessels; this can further improve the first-attempt puncture success rate for nursing staff and reduce the pain of patients. We propose a novel technique for hyperspectral visualization of blood vessels in human skin. An experiment with six participants with different skin types, race, and nationality backgrounds is described. A mere separation of spectral layers for different skin types is shown to be insufficient. The use of three-wavelength indices in imaging has shown a significant improvement in the quality of results compared to using only two-wavelength indices. This improvement can be attributed to an increase in the contrast ratio, which can be as high as 25%. We propose and implement a technique for finding new index formulae based on an exhaustive search and a binary blood-vessel image obtained through an expert assessment. As a result of the search, a novel index formula was deduced, allowing high-contrast blood vessel images to be generated for any skin type.

Unraveling Autonomic Dysfunction in GBA-Related Parkinson's Disease

Background

Patients with Parkinson's disease (PD) and GBA gene mutations (GBA-PD) develop nonmotor complications more frequently than noncarriers. However, an objective characterization of both cardiovascular and sudomotor autonomic dysfunction using extensive clinical and instrumental measures has never been provided so far. Survival is reduced in GBA-PD regardless of age and dementia, suggesting that other hitherto unrecognized factors are involved.

Objectives

To provide instrumental measures of pattern and severity of autonomic dysfunction in GBA-PD and explore their correlation with other non-motor symptoms and implications for clinical practice.

Methods

In this cross-sectional study, 21 GBA-PD and 24 matched PD noncarriers underwent extensive assessment of motor and non-motor features, including neuropsychological testing. Cardiovascular autonomic function was explored through a comprehensive battery of indexes, including power spectral analysis of the R-R intervals and blood pressure short-term variability during resting state and active maneuvers. Dynamic Sweat Test was used to assess post-ganglionic sudomotor dysfunction.

Results

Despite minimal or absent clinical correlates, cardiovagal and sympathetic indexes, heart rate variability parameters and sudomotor postganglionic function were more severely impaired in GBA-PD than noncarriers (overcoming relatively preserved compensatory peripheral sympathetic function), suggesting more prominent cardiac sympatho-vagal demodulation, efferent baroreflex failure and peripheral sympathetic dysfunction in GBA-PD. Cardiovascular dysautonomia showed marginal correlations with cognitive impairment.

Conclusions

Compared to PD noncarriers, GBA-PD display more severe instrumental autonomic abnormalities, which may be underestimated by purely clinical measures, despite their relevance on morbidity and mortality. This supports the necessity of implementing instrumental autonomic assessment in all GBA-PD, regardless of clinically overt symptoms.

Assessing the cardiac autonomic response to bicycle exercise in Olympic athletes with different loads of endurance training: new insights from statistical indicators based on multilevel exploratory factor analysis

Aim: The mechanisms governing the organism's response to exercise are complex and difficult to study. Spectral analysis of heart rate variability (HRV) could represent a convenient methodology for studying humans' autonomic nervous system (ANS). However, difficulties in interpreting the multitude of correlated HRV-derived indices, mainly when computed over different time segments, may represent a barrier to its usage. This preliminary investigation addressed to elite athletes proposes a novel method describing the cardiac autonomic response to exercise based on multilevel exploratory factor analysis (MEFA), which reduces the multitude of HRV-derived indices to fewer uncorrelated ANS indicators capable of accounting for their interrelationships and overcoming the above difficulties. Methods: The study involved 30 Italian Olympic athletes, divided into 15 cyclists (prevalent high-intensity endurance training) and 15 shooters (prevalent technical training with low-intensity endurance component). All athletes underwent a complete test of a dynamic protocol, constituted by a rest-stand test followed by a stepwise bicycle stress test subdivided into a single bout of progressive endurance (from aerobic to anaerobic) exercise and recovery. Then, by spectral analysis, values of 12 ANS proxies were computed at each time segment (9 epochs in all) of the complete test. Results: We obtained two global ANS indicators (amplitude and frequency), expressing the athletes' overall autonomic response to the complete test, and three dynamic ANS indicators (amplitude, signal self-similarity, and oscillatory), describing the principal dynamics over time of the variability of RR interval (RRV). Globally, cyclists have significantly higher amplitude levels (median ± MAD: cyclists 69.9 ± 20.5; shooters 37.2 ± 19.4) and lower frequency levels (median ± MAD: cyclists 37.4 ± 14.8; shooters 78.2 ± 10.2) than shooters, i.e., a parasympathetic predominance compared to shooters. Regarding the RRV dynamics, the signal self-similarity and oscillatory indicators have the strongest sensitivity in detecting the rest-stand change; the amplitude indicator is highly effective in detecting the athletes' autonomic changes in the exercise fraction; the amplitude and oscillatory indicators present significant differences between cyclists and shooters in specific test epochs. Conclusion: This MEFA application permits a more straightforward representation of the complexity characterizing ANS modulation during exercise, simplifying the interpretation of the HRV-derived indices and facilitating the possible real-life use of this non-invasive methodology.

Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns

A typical absence seizure is a generalized epileptic event characterized by a sudden, brief alteration of consciousness that serves as a hallmark for various generalized epilepsy syndromes. Distinguishing between similar interictal and ictal electroencephalographic (EEG) epileptiform patterns poses a challenge. However, quantitative EEG, particularly spectral analysis focused on EEG rhythms, shows potential for differentiation. This study was designed to investigate discernible differences in EEG spectral dynamics and entropy patterns during the pre-ictal and post-ictal periods compared to the interictal state. We analyzed 20 EEG ictal patterns from 11 patients with confirmed typical absence seizures, and assessed recordings made during the pre-ictal, post-ictal, and interictal intervals. Power spectral density (PSD) was used for the quantitative analysis that focused on the delta, theta, alpha, and beta bands. In addition, we measured EEG signal regularity using approximate (ApEn) and multi-scale sample entropy (MSE). Findings demonstrate a significant increase in delta and theta power in the pre-ictal and post-ictal intervals compared to the interictal interval, especially in the posterior brain region. We also observed a notable decrease in entropy in the pre-ictal and post-ictal intervals, with a more pronounced effect in anterior brain regions. These results provide valuable information that can potentially aid in differentiating epileptiform patterns in typical absence seizures. The implications of our findings are promising for precision medicine approaches to epilepsy diagnoses and patient management. In conclusion, our quantitative analysis of EEG data suggests that PSD and entropy measures hold promise as potential biomarkers for distinguishing ictal from interictal epileptiform patterns in patients with confirmed or suspected typical absence seizures.

Using Distributed Temperature Sensing for Long-Term Monitoring of Pockmark Activity in the Gulf of Patras (Greece): Data Processing Hints and Preliminary Findings

Patras Gulf pockmark field (Western Greece) is a tectonically controlled field that has been activated at least twice by strong earthquakes (M5.4, 14 July 1993 and M6.4, 8 June 2008), and episodic gas seepages have been recorded in the past using geophysical means. A distributed temperature sensor (DTS) system was deployed inside a shallow pockmark and along an active fault at the northern end of the field. This ongoing experiment represents the first long-term monitoring ever conducted on gas-bearing pockmarks and active faults by the DTS system. For now, we have acquired and analyzed data regarding about 1.56 years. One of the primary objectives of this study is to establish methodological queues for data processing and analysis, including spectral analysis and incomplete data treatment techniques, to be standardized for use in further stages of the experiment. Spectral analysis was proven capable of separating the temperature footprint of background environmental components, such as sea-atmosphere heat flux, tides, and winds/waves, from high-frequency temperature residuals. Those residuals represent unusual events that might be correlated to seismicity. Monitoring the causal relationship between seismic activity and seabed water temperature changes in the field was thus attempted. No significant local earthquakes occurred during the monitoring period. Although the relation between seismicity and irregular seabed water temperature events was not systematic, we postulate that four thermal events have a causative link with the local seismicity. The DTS system constitutes a low-cost monitoring system, and the promising preliminary results of this experiment suggest that it is worth testing for a longer period.

Relationship between Eccentricity and Volume Determined by Spectral Algorithms Applied to Spatially Registered Bi-Parametric MRI and Prostate Tumor Aggressiveness: A Pilot Study

(1) Background: Non-invasive prostate cancer assessments using multi-parametric MRI are essential to the reliable detection of lesions and proper management of patients. While current guidelines call for the administration of Gadolinium-containing intravenous contrast injections, eliminating such injections would simplify scanning and reduce patient risk and costs. However, augmented image analysis is necessary to extract important diagnostic information from MRIs. Purpose: This study aims to extend previous work on the signal to clutter ratio and test whether prostate tumor eccentricity and volume are indicators of tumor aggressiveness using bi-parametric (BP)-MRI. (2) Methods: This study retrospectively processed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRIs (apparent diffusion coefficient, high b-value, and T2 images) were resized, translated, cropped, and stitched to form spatially registered BP-MRIs. The International Society of Urological Pathology (ISUP) grade was used to judge cases of prostate cancer as either clinically significant prostate cancer (CsPCa) (ISUP ≥ 2) or clinically insignificant prostate cancer (CiPCa) (ISUP < 2). The Adaptive Cosine Estimator (ACE) algorithm was applied to the BP-MRIs, followed by thresholding, and then eccentricity and volume computations, from the labeled and blobbed detection maps. Then, univariate and multivariate linear regression fittings of eccentricity and volume were applied to the ISUP grade. The fits were quantitatively evaluated by computing correlation coefficients (R) and p-values. Area under the curve (AUC) and receiver operator characteristic (ROC) curve scores were used to assess the logistic fitting to CsPCa/CiPCa. (3) Results: Modest correlation coefficients (R) (>0.35) and AUC scores (0.70) for the linear and/or logistic fits from the processed prostate tumor eccentricity and volume computations for the spatially registered BP-MRIs exceeded fits using the parameters of prostate serum antigen, prostate volume, and patient age (R~0.17). (4) Conclusions: This is the first study that applied spectral approaches to BP-MRIs to generate tumor eccentricity and volume metrics to assess tumor aggressiveness. This study found significant values of R and AUC (albeit below those from multi-parametric MRI) to fit and relate the metrics to the ISUP grade and CsPCA/CiPCA, respectively.

Display of WEDM Quality Indicators of Heat-Resistant Alloy Processing in Acoustic Emission Parameters

The widespread nature of heat-resistant alloys is associated with the difficulties in their mechanical machining. It forces the use of the wire electrical discharge machining to be wider. The productivity, roughness, and dimensions of the modified layer of the machined surfaces are indicators of the machining quality. The search for new diagnostic parameters that can expand the information content of the operational monitoring/diagnostics of wire electrical discharge machining and accompany the currently used electrical parameters' data is an urgent research task. The article presents the studies of the relationship between the parameters of acoustic emission signals accompanying wire electrical discharge machining of heat-resistant alloys, process quality indicators, and characteristics of discharge pulses. The results are presented as mathematical expressions and graphs demonstrating the experimentally obtained dependencies. The research focuses on the formed white layer during wire electrical discharge machining. Pictures of thin cross-sections of the machined surfaces with traces of the modified layer are provided. The issues of crack formation in the modified layer and base materials are considered.

An Artificial Neural Network to Eliminate the Detrimental Spectral Shift on Mid-Infrared Gas Spectroscopy

We demonstrate the successful implementation of an artificial neural network (ANN) to eliminate detrimental spectral shifts imposed in the measurement of laser absorption spectrometers (LASs). Since LASs rely on the analysis of the spectral characteristics of biological and chemical molecules, their accuracy and precision is especially prone to the presence of unwanted spectral shift in the measured molecular absorption spectrum over the reference spectrum. In this paper, an ANN was applied to a scanning grating-based mid-infrared trace gas sensing system, which suffers from temperature-induced spectral shifts. Using the HITRAN database, we generated synthetic gas absorbance spectra with random spectral shifts for training and validation. The ANN was trained with these synthetic spectra to identify the occurrence of spectral shifts. Our experimental verification unambiguously proves that such an ANN can be an excellent tool to accurately retrieve the gas concentration from imprecise or distorted spectra of gas absorption. Due to the global shift of the measured gas absorption spectrum, the accuracy of the retrieved gas concentration using a typical least-mean-squares fitting algorithm was considerably degraded by 40.3%. However, when the gas concentration of the same measurement dataset was predicted by the proposed multilayer perceptron network, the sensing accuracy significantly improved by reducing the error to less than ±1% while preserving the sensing sensitivity.

Preparation and Molecular Structural Characterization of Fulvic Acid Extracted from Different Types of Peat

Humic acid is a type of polymeric, organic weak acid mixture with a core aromatic structure and main-component oxygen-containing functional group. Fulvic acid is a type of humic substance that can be dissolved in acid, alkali, or water. This study discusses the influence of different peptides on the molecular structure of fulvic acid, which was extracted from herbaceous, woody, and mossy peats using alkaline dissolution and acid precipitation methods. Analyses using infrared, UV-Vis, 13C-NMR, and X-ray photoelectron spectroscopies, as well as X-ray diffraction (XRD), were conducted to compare the effects of different peat types on the content and molecular structure of fulvic acid. The woody peat fulvic acid content was the highest among all peat fulvic acids (0.38%). However, the yield of fulvic acid from herbaceous peat was the highest (2.53%). Herbaceous peat fulvic acid contains significant quantities of carbonyl, amino, methylene, carboxyl, and phenolic hydroxyl groups and ether bonds. Woody peat fulvic acid contains carbonyl and methoxy groups, benzenes, aromatic carbons, aromatic ethers, and phenols. The degree of aromatization of woody peat fulvic acid was the highest. Mossy peat fulvic acid contains high levels of hydroxy, methyl, methylene, and phenol groups and aromatic ethers. The structural differences in fulvic acids in the different types of peat were primarily manifested in the content of functional groups, with little influence from the types of functional groups. XRD analysis of the different peats revealed that their structures all comprised benzene rings. However, mossy peat contained more C=O and -COOH groups, whereas herbaceous peat contained more C-O groups.

Analysis of Thermal Aging Influence on Selected Physical and Mechanical Characteristics of Polyaddition and Polycondensation Poly(dimethylsiloxane)

The aim of the study was to determine the effect of accelerated thermal aging on the properties of selected poly(dimethylsiloxanes) (PDMS) differing in viscosity and hardness. This was related to the potential application for specialist casting molds with complex geometry. Four polyaddition silicones and two polycondensation ones were selected. As part of the work, tensile strength, hardness, density, roughness, and Dynamic Mechanical Analysis (DMA) and Fourier Transform Infrared Spectroscopy (FTIR) were tested, which allowed us to determine the degree of degradation of the analyzed materials subjected to thermal aging at a temperature of 150 ± 2 °C. The aging temperature was conditioned by the parameters of the materials that can be cast into molds made of poly(dimethylsiloxanes) e.g., with polymer resins, for which the exothermic peak ranges from 100 to 200 °C depending on the volume. It was observed that the initial Shore A hardness value affects parameters such as tensile strength or the amount of value change (its increase or decrease) after thermal aging. It can also be concluded that for polyaddition PDMS, the viscosity of the material has an effect on the size of the relative elongation value after thermal aging.

Detection of Fungal Diseases in Lettuce by VIR-NIR Spectroscopy in Aquaponics

One of the main challenges facing the development of aquaponics is disease control, due on one hand to the fact that plants cannot be treated with chemicals because they can lead to mortality in cultured fish. The aim of this study was to apply the visible-near-infrared spectroscopy and vegetation index approach to test aquaponically cultivated lettuce (Lactuca sativa L.) infected with different fungal pathogens (Aspergillus niger, Fusarium oxysporum, and Alternaria alternata). The lettuces on the third leaf formation were placed in tanks (with dimensions 1 m/0.50 m/0.35 m) filled up with water from the aquaponics system every second day. In this study, we included reference fungal strains Aspergillus niger NBIMCC 3252, Fusarium oxysporum NBIMCC 125, and Alternaria alternata NBIMCC 109. Diffuse reflectance spectra of the leaves of lettuce were measured directly on the plants using a USB4000 spectrometer in the 450-1100 nm wavelength range. In near-infrared spectral range, the reflectance values of infected leaves are lower than those of the control, which indicates that some changes in cell structures occurred as a result of the fungal infection. All three investigated pathogens had a statistically significant effect on leaf water content and water band index. Vegetative indices such as Chlorophyll Absorption in Reflectance Index (CARI), Modified chlorophyll absorption in reflectance index (MCARI), Plant Senescence Reflectance Index (PSRI), Red Edge Index (REI2), Red Edge Index (REI3), and Water band index (WBI) were found to be effective in distinguishing infected plants from healthy ones, with WBI demonstrating the greatest reliability.

Data for characterization of the optical properties of Atlantic salmon (Salmo salar) blood

Photoplethysmography is a recent addition to physio-logging in Atlantic salmon which can be used for pulse oximetry provided that the properties for light propagation in salmon tissues are known. In this work, optical properties of three constituents of Atlantic salmon blood have been characterized using a photo spectrometer in the VIS-NIR range (450-920 nm). Furthermore, Atlantic salmon blood cell size has been measured using a Coulter counter as part of light scattering property evaluations. Results indicate that plasma contributes little to scattering and absorption for wavelengths typically used in pulse oximetry as opposed to blood cells which are highly scattering. Extinction spectra for oxygenated and deoxygenated hemoglobin indicate that Atlantic salmon hemoglobin is similar to that in humans. Pulse oximetry sensors originally intended for human applications may thus be used to estimate blood oxygenation levels for this species.

Comparative study of the interaction mechanism of astilbin, isoastilbin, and neoastilbin with CYP3A4

The interactions of human CYP3A4 with three selected isomer flavonoids, such as astilbin, isoastilbin and neoastilbin, were clarified using spectral analysis, molecular docking, and molecular dynamics simulation. During binding with the three flavonoids, the intrinsic fluorescence of CYP3A4 was statically quenched in static mode with nonradiative energy conversion. The fluorescence and ultraviolet/visible (UV/vis) data revealed that the three flavonoids had a moderate and stronger binding affinity with CYP3A4 due to the order of the Ka1 and Ka2 values ranging from 104 to 105  L·mol-1 . In addition, astilbin had the highest affinity with CYP3A4, then isoastilbin and neoastilbin, at the three experimental temperatures. Multispectral analysis confirmed that binding of the three flavonoids resulted in clear changes in the secondary structure of CYP3A4. It was found from fluorescence, UV/vis and molecular docking analyses that these three flavonoids strongly bound to CYP3A4 by means of hydrogen bonds and van der Waals forces. The key amino acids around the binding site were also elucidated. Furthermore, the stabilities of the three CYP3A4 complexes were evaluated using molecular dynamics simulation.

Ganglionated Plexus Ablation Procedures to Treat Vasovagal Syncope

Vasovagal syncope (VVS) refers to a heterogeneous group of conditions whereby the cardiovascular reflexes normally controlling the circulation are interrupted irregularly in response to a trigger, resulting in vasodilation, bradycardia, or both. VVS affects one-third of the population at least once in their lifetime or by the age of 60, reduces the quality of life, and may cause disability affecting certain routines. It poses a considerable economic burden on society, and, despite its prevalence, there is currently no proven pharmacological treatment for preventing VVS. The novel procedure of ganglionated plexus (GP) ablation has emerged rapidly in the past two decades, and has been proven successful in treating syncope. Several parameters influence the success rate of GP ablation, including specific ablation sites, localization and surgical techniques, method of access, and the integration of other interventions. This review aims to provide an overview of the existing literature on the physiological aspects and clinical effectiveness of GP ablation in the treatment of VVS. Specifically, we explore the association between GPs and VVS and examine the impact of GP ablation procedures as reported in human clinical trials. Our objective is to shed light on the therapeutic significance of GP ablation in eliminating VVS and restoring normal sinus rhythm, particularly among young adults affected by this condition.

Unsupervised Multitaper Spectral Method for Identifying REM Sleep in Intracranial EEG Recordings Lacking EOG/EMG Data

A large number of human intracranial EEG (iEEG) recordings have been collected for clinical purposes, in institutions all over the world, but the vast majority of these are unaccompanied by EOG and EMG recordings which are required to separate Wake episodes from REM sleep using accepted methods. In order to make full use of this extremely valuable data, an accurate method of classifying sleep from iEEG recordings alone is required. Existing methods of sleep scoring using only iEEG recordings accurately classify all stages of sleep, with the exception that wake (W) and rapid-eye movement (REM) sleep are not well distinguished. A novel multitaper (Wake vs. REM) alpha-rhythm classifier is developed by generalizing K-means clustering for use with multitaper spectral eigencoefficients. The performance of this unsupervised method is assessed on eight subjects exhibiting normal sleep architecture in a hold-out analysis and is compared against a classical power detector. The proposed multitaper classifier correctly identifies 36±6 min of REM in one night of recorded sleep, while incorrectly labeling less than 10% of all labeled 30 s epochs for all but one subject (human rater reliability is estimated to be near 80%), and outperforms the equivalent statistical-power classical test. Hold-out analysis indicates that when using one night's worth of data, an accurate generalization of the method on new data is likely. For the purpose of studying sleep, the introduced multitaper alpha-rhythm classifier further paves the way to making available a large quantity of otherwise unusable IEEG data.

Protein and lipid content estimation in soybeans using Raman hyperspectral imaging

Unlike standard chemical analysis methods involving time-consuming, labor-intensive, and invasive pretreatment procedures, Raman hyperspectral imaging (HSI) can rapidly and non-destructively detect components without professional supervision. Generally, the Kjeldahl methods and Soxhlet extraction are used to chemically determine the protein and lipid content of soybeans. This study is aimed at developing a high-performance model for estimating soybean protein and lipid content using a non-destructive Raman HSI. Partial least squares regression (PLSR) techniques were used to develop the model using a calibration model based on 70% spectral data, and the remaining 30% of the data were used for validation. The results indicate that the Raman HSI, combined with PLSR, resulted in a protein and lipid model Rp2 of 0.90 and 0.82 with Root Mean Squared Error Prediction (RMSEP) 1.27 and 0.79, respectively. Additionally, this study successfully used the Raman HSI approach to create a prediction image showing the distribution of the targeted components, and could predict protein and lipid based on a single seeds.

Assessing craniofacial growth and form without landmarks: A new automatic approach based on spectral methods

We present a novel method for the morphometric analysis of series of 3D shapes, and demonstrate its relevance for the detection and quantification of two craniofacial anomalies: trigonocephaly and metopic ridges, using CT-scans of young children. Our approach is fully automatic, and does not rely on manual landmark placement and annotations. Our approach furthermore allows to differentiate shape classes, enabling successful differential diagnosis between trigonocephaly and metopic ridges, two related conditions characterized by triangular foreheads. These results were obtained using recent developments in automatic nonrigid 3D shape correspondence methods and specifically spectral approaches based on the functional map framework. Our method can capture local changes in geometric structure, in contrast to methods based, for instance, on global shape descriptors. As such, our approach allows to perform automatic shape classification and provides visual feedback on shape regions associated with different classes of deformations. The flexibility and generality of our approach paves the way for the application of spectral methods in quantitative medicine.

The 'Postural Rhythm' of the Ground Reaction Force during Upright Stance and Its Conversion to Body Sway-The Effect of Vision, Support Surface and Adaptation to Repeated Trials

The ground reaction force (GRF) recorded by a platform when a person stands upright lies at the interface between the neural networks controlling stance and the body sway deduced from centre of pressure (CoP) displacement. It can be decomposed into vertical (VGRF) and horizontal (HGRF) vectors. Few studies have addressed the modulation of the GRFs by the sensory conditions and their relationship with body sway. We reconsidered the features of the GRFs oscillations in healthy young subjects (n = 24) standing for 90 s, with the aim of characterising the possible effects of vision, support surface and adaptation to repeated trials, and the correspondence between HGRF and CoP time-series. We compared the frequency spectra of these variables with eyes open or closed on solid support surface (EOS, ECS) and on foam (EOF, ECF). All stance trials were repeated in a sequence of eight. Conditions were randomised across different days. The oscillations of the VGRF, HGRF and CoP differed between each other, as per the dominant frequency of their spectra (around 4 Hz, 0.8 Hz and <0.4 Hz, respectively) featuring a low-pass filter effect from VGRF to HGRF to CoP. GRF frequencies hardly changed as a function of the experimental conditions, including adaptation. CoP frequencies diminished to <0.2 Hz when vision was available on hard support surface. Amplitudes of both GRFs and CoP oscillations decreased in the order ECF > EOF > ECS ≈ EOS. Adaptation had no effect except in ECF condition. Specific rhythms of the GRFs do not transfer to the CoP frequency, whereas the magnitude of the forces acting on the ground ultimately determines body sway. The discrepancies in the time-series of the HGRF and CoP oscillations confirm that the body's oscillation mode cannot be dictated by the inverted pendulum model in any experimental conditions. The findings emphasise the robustness of the VGRF "postural rhythm" and its correspondence with the cortical theta rhythm, shed new insight on current principles of balance control and on understanding of upright stance in healthy and elderly people as well as on injury prevention and rehabilitation.

Vespa: Integrated applications for RF pulse design, spectral simulation and MRS data analysis

PURPOSE

The Vespa package (Versatile Simulation, Pulses, and Analysis) is described and demonstrated. It provides workflows for developing and optimizing linear combination modeling (LCM) fitting for ¹ H MRS data using intuitive graphical user interface interfaces for RF pulse design, spectral simulation, and MRS data analysis. Command line interfaces for embedding workflows in MR manufacturer platforms and utilities for synthetic dataset creation are included. Complete provenance is maintained for all steps in workflows.

THEORY AND METHODS

Vespa is written in Python for compatibility across operating systems. It embeds the PyGAMMA spectral simulation library for spectral simulation. Multiprocessing methods accelerate processing and visualization. Applications use the Vespa database for results storage and cross-application access. Three projects demonstrate pulse, sequence, simulation, and data analysis workflows: (1) short TE semi-LASER single-voxel spectroscopy (SVS) LCM fitting, (2) optimizing MEGA-PRESS (MEscher-GArwood Point RESolved Spectroscopy) flip angle and LCM fitting, and (3) creating a synthetic short TE dataset.

RESULTS

The LCM workflows for in vivo basis set creation and spectral analysis showed reasonable results for both the short TE semi-LASER and MEGA-PRESS. Examples of pulses, simulations, and data fitting are shown in Vespa application interfaces for various steps to demonstrate the interactive workflow.

CONCLUSION

Vespa provides an efficient and extensible platform for characterizing RF pulses, pulse design, spectral simulation optimization, and automated LCM fitting via an interactive platform. Modular design and command line interface make it easy to embed in other platforms. As open source, it is free to the MRS community for use and extension. Vespa source code and documentation are available through GitHub.

Removal of refractory organics from landfill leachate by in situ electrogenerated H2O2 combined with an Fe0 Fenton-like process

Landfill leachate contains a large amount of refractory organic matter, which will cause harm to the environment if not appropriately treated. In this study, the refractory organic matter in landfill leachate has been treated by in situ electrogenerated H₂O₂ combined with an Fe⁰ Fenton-like process, aiming to explore a cleaner and more efficient process for leachate treatment. The results showed that the current, initial pH and oxygen flow rate have significant influences on H₂O₂ production. The current and oxygen flow rate are positively correlated with H₂O₂ production, and neutral conditions are more favourable. Under the conditions of a current of 200 mA, an initial pH of 7.0 and an oxygen flow rate of 0.3 L/min, H₂O₂ production reached 2.81 mM, the current efficiency was close to 80% and the highest removal efficiency of organic matter reached 40.70%. The absorbance at 280 nm (E₂₈₀) decreased from 0.1669 to 0.1180, and the ratios E₂₄₀/E₄₂₀, E₂₅₀/E₃₆₅ and E₃₀₀/E₄₀₀ in the UV and visible regions changed from 0.7825, 5.4492 and 0.2422 to 1.3135, 7.3745 and 0.2966, respectively. The maximum fluorescence intensities due to humic-like acid and fulvic-like acid substances decreased from 1275 and 1246 to 595.9 and 711.0, respectively. Spectral analysis further showed that the complex structure of refractory organic matter in the landfill leachate was obviously destroyed, and the relative content of humus decreased significantly. This study may provide a theoretical basis for the effective treatment of refractory organic matter in landfill leachate by in situ electrogenerated H₂O₂ combined with a Fenton-like process.

Multiscale Deepspectra Network: Detection of Pyrethroid Pesticide Residues on the Hami Melon

The problem of pyrethroid residues has become a topical issue, posing a potential food safety concern. Pyrethroid pesticides are widely used to prevent and combat pests in Hami melon cultivation. Due to its high sensitivity and accuracy, gas chromatography (GC) is used most frequently for detecting pyrethroid pesticide residues. However, GC has a high cost and complex operation. This study proposed a deep-learning approach based on the one-dimensional convolutional neural network (1D-CNN), named Deepspectra network, to detect pesticide residues on the Hami melon based on visible/near-infrared (380-1140 nm) spectroscopy. Three combinations of convolution kernels were compared in the single-scale Deepspectra network. The convolution group of "5 × 1" and "3 × 1" kernels obtained a better overall performance. The multiscale Deepspectra network was compared to three single-scale Deepspectra networks on the preprocessing spectral data and obtained better results. The coefficient of determination (R2) for lambda-cyhalothrin and beta-cypermethrin was 0.758 and 0.835, respectively. The residual predictive deviation (RPD) for lambda-cyhalothrin and beta-cypermethrin was 2.033 and 2.460, respectively. The Deepspectra networks were compared with two conventional regression models: partial least square regression (PLSR) and support vector regression (SVR). The results showed that the multiscale Deepspectra network outperformed the other models. It was found that the multiscale Deepspectra network could be a novel approach for the quantitative estimation of pyrethroid pesticide residues on the Hami melon. These findings can also provide an effective strategy for spectral analysis.

Using Arterial Pulse and Laser Doppler Analyses to Discriminate between the Cardiovascular Effects of Different Running Levels

BACKGROUND AND AIMS

Running can induce advantageous cardiovascular effects such as improved arterial stiffness and blood-supply perfusion. However, the differences between the vascular and blood-flow perfusion conditions under different levels of endurance-running performance remains unclear. The present study aimed to assess the vascular and blood-flow perfusion conditions among 3 groups (44 male volunteers) according to the time taken to run 3 km: Level 1, Level 2, and Level 3.

METHODS

The radial blood pressure waveform (BPW), finger photoplethygraphy (PPG), and skin-surface laser-Doppler flowmetry (LDF) signals of the subjects were measured. Frequency-domain analysis was applied to BPW and PPG signals; time- and frequency-domain analyses were applied to LDF signals.

RESULTS

Pulse waveform and LDF indices differed significantly among the three groups. These could be used to evaluate the advantageous cardiovascular effects provided by long-term endurance-running training, such as vessel relaxation (pulse waveform indices), improvement in blood supply perfusion (LDF indices), and changes in cardiovascular regulation activities (pulse and LDF variability indices). Using the relative changes in pulse-effect indices, we achieved almost perfect discrimination between Level 3 and Level 2 (AUC = 0.878). Furthermore, the present pulse waveform analysis could also be used to discriminate between the Level-1 and Level-2 groups.

CONCLUSIONS

The present findings contribute to the development of a noninvasive, easy-to-use, and objective evaluation technique for the cardiovascular benefits of prolonged endurance-running training.

Smartphone Recordings are Comparable to "Gold Standard" Recordings for Acoustic Measurements of Voice

PURPOSE

The purpose of this study was to assess the relationship and comparability of cepstral and spectral measures of voice obtained from a high-cost "flat" microphone and precision sound level meter (SLM) vs. high-end and entry level models of commonly and currently used smartphones (iPhone i12 and iSE; Samsung s21 and s9 smartphones). Device comparisons were also conducted in different settings (sound-treated booth vs. typical "quiet" office room) and at different mouth-to-microphone distances (15 and 30 cm).

METHODS

The SLM and smartphone devices were used to record a series of speech and vowel samples from a prerecorded diverse set of 24 speakers representing a wide range of sex, age, fundamental frequency (F0), and voice quality types. Recordings were analyzed for the following measures: smoothed cepstral peak prominence (CPP in dB); the low vs high spectral ratio (L/H Ratio in dB); and the Cepstral Spectral Index of Dysphonia (CSID).

RESULTS

A strong device effect was observed for L/H Ratio (dB) in both vowel and sentence contexts and for CSID in the sentence context. In contrast, device had a weak effect on CPP (dB), regardless of context. Recording distance was observed to have a small-to-moderate effect on measures of CPP and CSID but had a negligible effect on L/H Ratio. With the exception of L/H Ratio in the vowel context, setting was observed to have a strong effect on all three measures. While these aforementioned effects resulted in significant differences between measures obtained with SLM vs. smartphone devices, the intercorrelations of the measurements were extremely strong (r's > 0.90), indicating that all devices were able to capture the range of voice characteristics represented in the voice sample corpus. Regression modeling showed that acoustic measurements obtained from smartphone recordings could be successfully converted to comparable measurements obtained by a "gold standard" (precision SLM recordings conducted in a sound-treated booth at 15 cm) with small degrees of error.

CONCLUSIONS

These findings indicate that a variety of commonly available modern smartphones can be used to collect high quality voice recordings usable for informative acoustic analysis. While device, setting, and distance can have significant effects on acoustic measurements, these effects are predictable and can be accounted for using regression modeling.

Biogenic Synthesis of Cu-Mn Bimetallic Nanoparticles Using Pumpkin Seeds Extract and Their Characterization and Anticancer Efficacy

BACKGROUND

Cancer is a chronic, heterogeneous illness that progresses through a spectrum of devastating clinical manifestations and remains the 2nd leading contributor to global mortality. Current cancer therapeutics display various drawbacks that result in inefficient management. The present study is intended to evaluate the anticancer potential of Cu-Mn bimetallic NPs (CMBNPs) synthesized from pumpkin seed extract against colon adenocarcinoma cancer cell line (HT-29).

METHODS

The CMBNPs were biosynthesized by continuously stirring an aqueous solution of pumpkin seed extract with CuSO4 and manganese (II) acetate tetrahydrate until a dark green solution was obtained. The characteristic features of biogenic CMBNPs were assessed by UV-visible spectrophotometry (UV-vis), X-ray powder diffraction (XRD), energy-dispersive X-ray (EDX), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). A battery of biological assays, viz. neutral red uptake (NRU) assay, in vitro scratch assay, and comet assay, were performed for anticancer efficacy evaluation.

RESULTS

The formation of spherical monodispersed bimetallic nanoparticles with an average size of 50 nm was recorded using TEM. We observed dose-dependent cytotoxicity of CMBNPs in the HT-29 cell line with an IC₅₀ dose of 115.2 µg/mL. On the other hand, CMBNPs did not show significant cytotoxicity against normal cell lines (Vero cells). Furthermore, the treatment of CMBNPs inhibited the migration of cancer cells and caused DNA damage with a significant increase in comet tail length.

CONCLUSIONS

The results showed substantial anticancer efficacy of CMBNPs against the studied cancer cell line. However, it is advocated that the current work be expanded to different in vitro cancer models so that an in vivo validation could be carried out in the most appropriate cancer model.

Micro Spectrometers Based on Materials Nanoarchitectonics

Spectral analysis is an important tool that is widely used in scientific research and industry. Although the performance of benchtop spectrometers is very high, miniaturization and portability are more important indicators in some applications, such as on-site detection and real-time monitoring. Since the 1990s, micro spectrometers have emerged and developed. Meanwhile, with the development of nanotechnology, nanomaterials have been applied in the design of various micro spectrometers in recent years, further reducing the size of the spectrometers. In this paper, we review the research progress of micro spectrometers based on nanomaterials. We also discuss the main limitations and perspectives on micro spectrometers.

Multiscale phenological niches of seed fall in diverse Amazonian plant communities

Phenology has long been hypothesized as an avenue for niche partitioning or interspecific facilitation, both promoting species coexistence. Tropical plant communities exhibit striking diversity in reproductive phenology, but many are also noted for large synchronous reproductive events. Here we study whether the phenology of seed fall in such communities is non-random, what are the temporal scales of phenological patterns, and ecological factors that drive reproductive phenology. We applied multivariate wavelet analyses to test for phenological synchrony versus compensatory dynamics (i.e. anti-synchronous patterns where one species' decline is compensated by the rise of another) among species and across temporal scales. We used data from long-term seed rain monitoring of hyperdiverse plant communities in the western Amazon. We found significant synchronous whole-community phenology at multiple time scales, consistent with shared environmental responses or positive interactions among species. We also observed both compensatory and synchronous phenology within groups of species (confamilials) likely to share traits and seed dispersal mechanisms. Wind-dispersed species exhibited significant synchrony at ~6 mo scales, suggesting these species might share phenological niches to match seasonality of wind. Our results suggest that community phenology is shaped by shared environmental responses but that the diversity of tropical plant phenology may partly result from temporal niche partitioning. The scale-specificity and time-localized nature of community phenology patterns highlights the importance of multiple and shifting drivers of phenology.