Individual Frequency Research Articles

Traffic Origin–Destination Flow Prediction Considering Individual Travel Frequency: A Classification-Based Approach

Traffic Origin–Destination Flow Prediction Considering Individual Travel Frequency: A Classification-Based Approach

Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment

This study focuses on the diagnostic analysis of cartilage damage in the knee joint based on acoustic signals generated by the joint. The research utilizes a combination of advanced signal processing techniques, specifically empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA), alongside convolutional neural networks (CNNs) for classification and detection tasks. Acoustic signals, often reflecting the mechanical behavior of the joint during movement, serve as a non-invasive diagnostic tool for assessing the cartilage condition. EEMD is applied to decompose the signals into intrinsic mode functions (IMFs), which are then analyzed using DFA to quantify the scaling properties and detect irregularities indicative of cartilage damage. The separation of individual frequency components allows for multi-scale analysis of the signals, with each of the functions resulting from the analysis reflecting local variations in the amplitude and frequency over time and allowing for effective removal of noise present in the signal. The CNN model is trained on features extracted from these signals to accurately classify different stages of cartilage degeneration. The proposed method demonstrates the potential for early detection of knee joint pathology, providing a valuable tool for preventive healthcare and reducing the need for invasive diagnostic procedures. The results suggest that the combination of EEMD-DFA for feature extraction and CNN for classification offers a promising approach for the non-invasive assessment of cartilage damage.

Resting-state electroencephalographic rhythms depend on sex in patients with dementia due to Parkinson's and Lewy Body diseases: An exploratory study.

Parkinson's disease with dementia (PDD) and dementia with Lewy bodies (DLB) are more prevalent in males than females. Furthermore, they typically showed abnormally high delta (< 4 Hz) and low alpha (8-10 Hz) rhythms from resting-state electroencephalographic (rsEEG) activity. Here, we hypothesized that those abnormalities may depend on the patient's sex. An international database provided clinical-demographic-rsEEG datasets for cognitively unimpaired older (Healthy; N = 49; 24 females), PDD (N = 39; 13 females), and DLB (N = 38; 15 females) participants. Each group was stratified into matched female and male subgroups. The rsEEG rhythms were investigated across the individual rsEEG delta, theta, and alpha frequency bands based on the individual alpha frequency peak. The eLORETA freeware was used to estimate cortical rsEEG sources. In the Healthy group, widespread rsEEG alpha source activities were greater in the females than in the males. In the PDD group, widespread rsEEG delta source activities were lower and widespread rsEEG alpha source activities were greater in the females than in the males. In the DLB group, central-parietal rsEEG delta source activities were lower, and posterior rsEEG alpha source activities were greater in the females than in the males. These results suggest sex-dependent hormonal modulation of neuroprotective-compensatory neurophysiological mechanisms in PDD and DLB patients underlying the generation of rsEEG delta and alpha rhythms, which should be considered in the treatment of vigilance dysregulation in those patients.

No significant relationship found between spontaneous motor tempo, heartbeat, and individual alpha frequency: an analysis of internal tempos

Many theories of time perception propose the existence of an internal pacemaker, and studies across behavioral, physiological, and neuroscience fields have explored this concept. Specifically, Spontaneous Motor Tempo (SMT), the most comfortable and natural tapping tempo for each individual, is thought to reflect this internal pacemaker’s tempo. Changes in heart rate are also linked to time estimation, while Individual Alpha Frequency (IAF), the peak in the alpha range (8–13 Hz) observed in EEG, is reported to reflect the brain’s temporal processing. Despite the associations of SMT, heart rate, and IAF with intrinsic tempo, their interrelations remain unexplored. In this pre-registered study, we measured SMT, IAF, and heart rate in 32 healthy university students aged 18–21 and examined how these variables relate to each other. During the experiment, participants sat with their eyes closed for 5 min while we recorded their EEG and heart rate. They then tapped the space key with their index finger at their most comfortable tempo, which we used to determine SMT. Participants also completed a questionnaire about their age, chronotype, and musical experience. Our results showed no significant correlations among SMT, heart rate, and IAF. Regression analysis further confirmed that SMT is not influenced by heart rate or IAF. Therefore, no significant relationships among behavioral, physiological, and neuroscience-related tempo were uncovered. Our contribution lies in measuring SMT, heart rate, and IAF within the same experimental context. All procedures, including scripts used for the analyses, were pre-registered before data collection, and we report these null results to mitigate publication bias.

QEEG Neuromarkers of Complex Childhood Trauma in Adolescents.

Introduction. Complex childhood trauma (CCT) involves prolonged exposure to severe interpersonal stressors, leading to deficits in executive functioning and self-regulation during adolescence, a critical period for neurodevelopment. While qEEG parameters, particularly alpha oscillations, have been proposed as potential biomarkers for trauma, empirical documentation in developmental samples is limited. Aim. This preregistered study investigated whether adolescents with CCT exhibit qEEG patterns similar to those reported for PTSD, such as reduced posterior alpha power, increased individual alpha peak frequency (iAPF), right-lateralized alpha frequencies, and lower total EEG power (RMS) compared to controls. Materials and Methods. EEG data from 26 trauma-exposed adolescents and 28 controls, sourced from an open database, underwent similar preprocessing. qEEG features, including alpha power, iAPF, alpha asymmetry, and RMS, were extracted from eyes-open and eyes-closed conditions and analyzed using mixed ANOVAs. Results. Significant group differences were found in total EEG power, with trauma-exposed adolescents showing lower RMS than controls. No significant differences were found in posterior absolute alpha power, iAPF, or alpha asymmetry. However, we observed that posterior relative alpha power was higher in the trauma group, though the difference was not statistically significant but showing a small to medium effect size. Additionally, a negative correlation between CPTSD severity and EEG power in the EO condition was observed, suggesting trauma-related cortical hypoactivation. Conclusion. Reduced total EEG power and modified alpha dynamics may serve as candidate neuromarkers of CCT. These findings underscore the need for further research to validate qEEG biomarkers for understanding and diagnosing trauma-related disorders in developmental populations.

Emergence of Neuroendocrine Tumors in Patients Treated with Androgen Receptor Pathway Inhibitors for Metastatic Prostate Cancer: A Systematic Review and Meta-analysis.

It has been shown that androgen receptor pathway inhibitor (ARPIs) treatment for metastatic castration-resistant prostate cancer (mCRPC) improves overall survival rates, but ARPIs appear to be associated with a higher frequency of treatment-related neuroendocrine prostate cancer (t-NEPC). Our aim was to quantify the proportion of prostate adenocarcinoma cases that transition to t-NEPC following ARPI therapy. We conducted a comprehensive search of the literature on t-NEPC using databases including MEDLINE and Scopus. Eligible studies reported outcome data for NEPC in patients with prior mCRPC treated with an ARPI. To determine the pooled frequency of neuroendocrine transformation, the Freeman-Tukey variance-stabilizing arcsine transformation was applied to individual frequencies. Among the 938 patients in eight eligible studies, t-NEPC diagnosis was confirmed in 171 patients, predominantly via pathology. Baseline biopsy verification to ensure the absence of NEPC was performed in most cases. The definition of t-NEPC varied among the studies. Five studies used a morphological definition based on histopathology, and three studies used NEPC biomarker detection on circulating tumor cells. A meta-analysis of aggregate data revealed an overall NEPC frequency following ARPI therapy of 16% (95% confidence interval 9-24%). ARPI-related NEPC represents a frequently underdiagnosed late complication of mCRPC. Given the absence of biomarkers for diagnosis, routine repeat biopsy at the mCRPC stage should be considered to diagnose t-NEPC transitions.

Typological Approaches to Recognizing Genus and Subgenus of Coronaviruses by Structural and Non-Structural Genes

Owing to rapid growth of data on viral genomes in the result of metagenomic researches, bioinformatics and virology are increasingly interacting. There is even the term viral informatics, implying the existence of a whole complex of the databases, knowledge databases about the viruses and software tools for working with them. Among the problems of bioinformatics in virology, it was earlier pointed out to annotation of viral genomes. In the present work on the example of recognizing of subgenus and genus of the coronaviruses a fairly simple and effective typological approach to virus annotation is proposed which uses frequency characteristics of the codons in individual genes. Typological approach is characterized by averaging known data, in particular, such codon frequency characteristics, to determine the similarity or resemblance with them of analogical characteristics for object under consideration. Recognition of subgenus and genus is based on statistics that reveals deviation of coronavirus gene considered from corresponding gene of viral genome with known genus or subgenus. The work compares recognition based on structural genes encoding virion proteins (nucleocapsid protein N and spike protein S) and genes of non-structural proteins combined into a single reading frame ORF1ab. Four typological approaches were discussed in the article. In the first two averaging of all available data and data on prototypical strains only was done over the genera. In the third approach original data on prototype strains were averaged over the subgenera. The fourth approach was based on individual frequency characteristics of prototype strains of the subgenera. Three of the four typological approaches revealed high efficiency in recognizing genus and subgenus of the coronaviruses while using N-gene. The fourth approach proved to be the most effective for identifying genus and subgenus of the coronaviruses. In addition, it has made it possible to reduce the number of codons considered in N-gene of the coronaviruses and to increase recognition efficiency to almost 100 %.

Resting-State EEG Alpha Rhythms Are Related to CSF Tau Biomarkers in Prodromal Alzheimer's Disease.

Patients with mild cognitive impairment due to Alzheimer's disease (ADMCI) typically show abnormally high delta (<4 Hz) and low alpha (8-12 Hz) rhythms measured from resting-state eyes-closed electroencephalographic (rsEEG) activity. Here, we hypothesized that the abnormalities in rsEEG activity may be greater in ADMCI patients than in those with MCI not due to AD (noADMCI). Furthermore, they may be associated with the diagnostic cerebrospinal fluid (CSF) amyloid-tau biomarkers in ADMCI patients. An international database provided clinical-demographic-rsEEG datasets for cognitively unimpaired older (Healthy; N = 45), ADMCI (N = 70), and noADMCI (N = 45) participants. The rsEEG rhythms spanned individual delta, theta, and alpha frequency bands. The eLORETA freeware estimated cortical rsEEG sources. Posterior rsEEG alpha source activities were reduced in the ADMCI group compared not only to the Healthy group but also to the noADMCI group (p < 0.001). Negative associations between the CSF phospho-tau and total tau levels and posterior rsEEG alpha source activities were observed in the ADMCI group (p < 0.001), whereas those with CSF amyloid beta 42 levels were marginal. These results suggest that neurophysiological brain neural oscillatory synchronization mechanisms regulating cortical arousal and vigilance through rsEEG alpha rhythms are mainly affected by brain tauopathy in ADMCI patients.

Investigating Neurometabolite Changes in Response to Median Nerve Stimulation.

Rhythmic median nerve stimulation (MNS) at 10Hz has been shown to cause a substantial reduction in tic frequency in individuals with Tourette syndrome. The mechanism of action is currently unknown but is hypothesized to involve entrainment of oscillations within the sensorimotor cortex. We used functional magnetic resonance spectroscopy (fMRS) to explore the dynamic effects of MNS on neurometabolite concentrations. Here, we investigated the effects of rhythmic and arrhythmic 10Hz MNS on glutamate (Glu) and GABA concentrations in the contralateral sensorimotor cortex in 15 healthy controls, using a blocked fMRS design. We used a Mescher-Garwood-semi-localized by adiabatic selective refocusing (MEGA-sLASER) sequence at 7 T. Our results show no difference in the difference-from-baseline measures between the two stimulation conditions. Looking at the effect of MNS over both conditions there is a trend for an initial increase in Glu/tCr (total creatine) followed by a decrease over time, whereas GABA/tCr decreased during each stimulation block. These results suggest that despite entrainment of oscillations during rhythmic MNS, there are no significant differences in the tonic neuromodulatory effects of rhythmic and arrhythmic stimulation. The reduction in Glu over the course of stimulation may reflect a decrease in the glutamatergic firing due to adaptation. This may make it less likely that an involuntary movement is generated during continuous stimulation.

Thrips dynamics in Allium crops: Unraveling the role of reproductive mode and weather variables in Thrips tabaci population development.

Thrips tabaci is the main thrips species affecting onion and related species. It is a cryptic species complex comprising three phylogenetic groups characterized by different reproductive modes (thelytoky or arrhenotoky) and host plant specialization. Thrips tabaci populations vary widely in genetic diversity, raising questions about the factor(s) that drive this diversity. We investigated the genetic diversity, reproductive mode, and heteroplasmy frequency in T. tabaci populations from different Allium spp fields in six locations in the Netherlands in 2021 and at two locations from the North and South of Uruguay over three years to unravel how the diversity is structured in the populations and if changes can be associated with weather variables. The thrips populations from each location studied were characterized by sequencing 33 individuals per sampling using the cytochrome oxidase subunit I (COI) gene. The reproductive mode was determined using specific primers and a phylogenetic analysis. Daily data for the weather variables was obtained from agrometeorological experimental stations located in the Uruguayan sampled crop fields. The diversity, reproductive mode, and heteroplasmy of T. tabaci populations in onion crops exhibited significant year-to-year variation depending on the location. Changes in the relative frequency of each reproductive mode in populations are associated with weather variables: precipitation, temperature, number of frosts, and relative humidity. Heteroplasmy frequency was associated with the same weather variables except temperature. In Uruguay and the Netherlands, T. tabaci thelytokous haplotype H1 was the most common, showing different heteroplasmy levels within and between the populations. In the field populations, a high frequency of heteroplasmic H1 individuals was associated with low precipitation, while all H1 individuals were also associated with high temperature and high relative humidity. In Uruguay, heteroplasmy was associated with arrhenotokous T. tabaci haplotypes, which were highly common in the North, pointing at specific adaptations leading to a faster population build-up. All this information may be instrumental for designing more precise integrated pest management techniques in both conventional and organic production.

Parietal alpha stimulation causally enhances attentional information coding in evoked and oscillatory activity.

Selective attention is a fundamental cognitive mechanism that allows people to prioritise task-relevant information while ignoring irrelevant information. Previous research has suggested key roles of parietal event-related potentials (ERPs) and alpha oscillatory responses in attention tasks. However, the informational content of these signals is less clear, and their causal effects on the coding of multiple task elements are yet unresolved. To test the causal roles of alpha oscillations and ERPs in coding different types of attentional information (where to attend, what to attend to, and visual stimulus). We first used EEG to examine the temporal dynamics of alpha oscillations and ERPs in coding attentional information. Then, we applied rhythmic-TMS (rh-TMS) at individual alpha frequency over the right intraparietal sulcus (IPS), while concurrently measuring EEG, to causally manipulate parietal alpha power and ERPs and investigate their roles in coding multiple task features in a selective attention task. EEG-only data suggested that ERPs coded all three types of task-relevant information with distinct temporal dynamics, while alpha oscillations carried information regarding both where to attend and what to attend to. TMS-EEG results indicated that, compared to arrhythmic-TMS, alpha rh-TMS increased alpha power and inter-trial phase coherence and yielded more negative posterior-contralateral ERPs. Moreover, alpha rh-TMS specifically and causally improved the multivariate decoding of the information about where to attend (but not what to attend to or feature information) during task performance, with decoding improvements predicting changes in behavioural performance. These findings illuminate the dynamics with which the complementary aspects of a selective attention task are encoded in evoked and oscillatory brain activity. Moreover, they reveal a specific and causal role of IPS-controlled evoked and oscillatory activity in carrying behaviour-driving information exclusively about where to focus attention.

Ultra-compact quintuple-band terahertz metamaterial biosensor for enhanced blood cancer diagnostics.

Cancer and its diverse variations pose one of the most significant threats to human health and well-being. One of the most aggressive forms is blood cancer, originating from bone marrow cells and disrupting the production of normal blood cells. The incidence of blood cancer is steadily increasing, driven by both genetic and environmental factors. Therefore, early detection is crucial as it enhances treatment outcomes and improves success rates. However, accurate diagnosis is challenging due to the inherent similarities between normal and cancerous cells. Although various techniques are available for blood cancer identification, high-frequency imaging techniques have recently shown promise, particularly for real-time monitoring. Notably, terahertz (THz) frequencies offer unique advantages for biomedical applications. This research proposes an innovative terahertz metamaterial-based biosensor for high-efficacy blood cancer detection. The proposed structure is ultra-compact and operates across five bands within the range of 0.6 to 1.2 THz. It is constructed using a polyethylene terephthalate (PET) dielectric layer and two aluminum (Al) layers, with the top layer serving as a base for the THz-range resonator. Careful design, architectural arrangement, and optimization of the geometry parameters allow for achieving nearly perfect absorption rates (>95%) across all operating bands. The properties of the proposed sensor are extensively evaluated through full-wave electromagnetic (EM) analysis, which includes assessing the refractive index and the distribution of the electric field at individual working frequencies. The suitability for blood cancer diagnosis has been validated by integrating the sensor into a microwave imaging (MWI) system and conducting comprehensive simulation studies. These studies underscore the device's capability to detect abnormalities, particularly in distinguishing between healthy and cancerous cells. Benchmarking against state-of-the-art biosensors in recent literature indicates that the proposed sensor is highly competitive in terms of major performance indicators while maintaining a compact size.

Biomechanical analysis and application of image processing technology based on deep learning in the indoor evaluation system of high-rise buildings

In the context of biomechanics and the built environment, understanding the relationship between the indoor space layout of high-rise buildings and human biomechanical responses is crucial. To enhance the quality of the indoor space layout of buildings with respect to human biomechanical comfort and functionality, this paper proposes a method for extracting the characteristics of the indoor space layout of high-rise buildings based on deep-learning image processing. The indoor space layout parameters are not only related to architectural aesthetics but also have implications for human movement and biomechanical behavior. For example, the dimensions and configurations of rooms and corridors can affect gait patterns, body postures, and muscle activations during walking and other activities. According to the extracted indoor space layout parameters, their edge sequences are determined. The image processing algorithm based on deep learning is then used to control the convergence of the characteristic parameters. This process is not only for architectural feature extraction but also to analyze how these features interact with human biomechanics. For instance, the angles and lengths of corridors can influence the turning radii and step frequencies of individuals, which are key biomechanical factors. By extracting the characteristics of the indoor space layout of high-rise buildings, we can evaluate how well the space accommodates human movement and biomechanical needs. The results reveal that the proposed method can effectively extract features relevant to both architecture and biomechanics, and the accuracy in assessing biomechanically relevant features is always higher than 90%. This high accuracy indicates the potential of this method to contribute to the design of indoor spaces that are more conducive to human biomechanical well-being and efficient movement.

Investigation of the Prevalence of Depressive Illness in Vitiligo Patients: A Cross-sectional Study

Background Vitiligo, as a progressive skin disease, is associated with the public's negative attitudes toward this condition, as well as numerous social and familial problems that can lead to psychiatric disorders. Aim The present study was conducted to determine the frequency of depressive illness in patients with vitiligo. Methods This is a cross-sectional descriptive study conducted using census sampling on 178 patients with vitiligo who were referred to the skin clinic of Imam Khomeini Hospital (RA) in Jiroft in 2023. Data were collected face-to-face using the Beck Depression Questionnaire and analyzed using descriptive and inferential statistical tests in SPSS-22 statistical software, with a significance level of p less than 0.05. Results The average age of the examined patients was 31.4 ± 6.8 years. Of the participants, 53.3% were women and 46.7% were men. The frequency of individuals exhibiting moderate and severe depressive symptoms was 17.9% and 28.8%, respectively. The prevalence of depressive illness was higher among married women compared to single women. Conclusion The high prevalence of depressive illness symptoms in these patients underscores the urgent need for effective interventions and treatment strategies to address this mental health concern. The findings of this study highlight the necessity for continuous surveillance and monitoring of depressive illness prevalence and its risk factors, which will inform the development of effective interventions and policies aimed at mitigating this mental health issue.

Assessing Contrast Sensitivity Function in CRB1-Retinopathies: Exploring Child-Friendly Measures of Visual Function.

Mutations affecting the CRB1 gene can result in a range of retinal phenotypes, including early onset severe retinal dystrophy/Leber congenital amaurosis (EOSRD/LCA), retinitis pigmentosa, cone-rod dystrophy (CORD), and macular dystrophy (MD). As research into treatment strategies advances towards clinical translation, there is a need to establish reliable outcome metrics. This study explores the contrast sensitivity function (CSF) across different spatial frequencies in individuals with CRB1-retinopathies using the child-friendly PopCSF test, an iPad-based "gamified" assessment. Prospective cross-sectional study of 20 patients with molecularly confirmed biallelic CRB1 pathogenic variants from Moorfields Eye Hospital, London, UK, was conducted. Best-corrected visual acuity (BCVA), contrast sensitivity using the Pelli-Robson chart, and the PopCSF test were performed. Of the 20 CRB1 patients, seven had EOSRD/LCA, three had CORD, and 10 had MD. There was no statistically significant difference between the mean BCVA between phenotypes (P = 0.066). However, a significant difference was found between groups in the mean letter log contrast sensitivity (logCS) and area under the contrast sensitivity function (AUCSF) with P = 0.047 and P < 0.001, respectively. A moderate positive correlation was observed between Pelli-Robson and PopCSF (r = 0.53, P = 0.020). The CRB1 cohort had significantly lower CSF at both low and high spatial frequencies compared to controls. Among the CRB1 phenotypes, patients with EOSRD/LCA, exhibited the lowest CSF. This study is the first to examine CSF across spatial frequencies in patients with CRB1-retinopathies using the novel PopCSF test. The CSF holds promise as a potential functional vision trial endpoint.

Using Hierarchical Bayesian Modeling to Enhance Statistical Inference on Contrast Sensitivity.

The purpose of this study is to introduce a nonparametric hierarchical Bayesian model (HBM) that enables advanced statistical inference on contrast sensitivity (CS) both at individual spatial frequencies (SFs) and across multiple SFs in clinical trials, where CS measurements are crucial for assessing safety and efficacy. The HBM computes the joint posterior distribution of CS at six Food and Drug Administration-designated SFs across the population, individual, and test levels. It incorporates covariances at both population and individual levels to capture the relationship between CSs across SFs. A Bayesian inference procedure (BIP) is also used to estimate the posterior distribution of CS at each SF independently. Both methods are applied to a quantitative CSF (qCSF) dataset of 112 subjects and compared in terms of precision, test-retest reliability of CS estimates, sensitivity, accuracy, and statistical power in detecting CS changes. The HBM reveals correlations between CSs in pairs of SFs and provides significantly more precise estimates and higher test-retest reliability compared to the BIP. Additionally, it improves the average sensitivity and accuracy in detecting CS changes for individual subjects, as well as statistical power for detecting group-level CS changes at individual and combinations of multiple SFs between luminance conditions. The HBM establishes a comprehensive framework to enhance sensitivity, accuracy, and statistical power for detecting CS changes in hierarchical experimental designs. The HBM presents a valuable tool for advancing CS assessments in the clinic and clinical trials, potentially improving the evaluation of treatment efficacy and patient outcomes.

Underlying mechanisms for altered reactivity of posterior cortical electroencephalographic alpha rhythms in DLB and PDD

AbstractBackgroundThe amplitude of resting‐state electroencephalographic (rsEEG) rhythms is a promising neurophysiological biomarker to investigate the abnormalities of oscillatory neurophysiological thalamocortical mechanisms related to the general cortical arousal and vigilance in wakefulness in patients with dementia due to neurodegenerative diseases as Alzheimer’s disease (ADD), Parkinson’s disease (PDD) and Lewy Body disease (DLB). Here, we tested the hypothesis that the reactivity of posterior rsEEG alpha (about 8‐12 Hz) rhythms during the transition from eyes‐closed to ‐open condition may be lower in PDD patients than in DLB patients.MethodsA Eurasian database provided clinical‐demographic‐rsEEG datasets in 35 ADD patients, 65 PDD patients, 30 DLB patients, and 25 matched cognitively unimpaired (Healthy) persons. The rsEEG rhythms were investigated at individual delta, theta, and alpha frequency bands, as well as fixed beta (14‐30 Hz) and gamma (30‐40 Hz) bands. The eLORETA freeware was used to estimate cortical rsEEG sources.ResultsResults showed substantial (i.e., greater than ‐10%) reduction (reactivity) in the posterior alpha source activities from the eyes‐closed to the eyes‐open condition in 88% of the Healthy seniors, 53% of ADD patients, 53% of the DLB patients, and only 37% of the PDD patients (Figure 1). In these alpha‐reactive participants, there was lower reactivity in the posterior alpha source activities in the three neurodegenerative (i.e., ADD, PDD, and DLB) groups than in the Healthy group. Furthermore, the reduction of the occipital alpha reactivity was stronger in the PDD and DLB groups than in the ADD group. Finally, the reduction of the parietal alpha reactivity was stronger in the PDD group than in the ADD and DLB groups (Figure 2).ConclusionThese results suggest that ADD, PDD, and DLB patients may be characterized by very poor reactivity in the posterior cortical mechanisms desynchronizing rsEEG alpha rhythms in relation to increased vigilance levels as an interesting neurophysiological biomarker. This biomarker may be used as a primary endpoint for interventions with drugs or brain electromagnetic stimulations to improve vigilance regulation and quality of life in neurodegenerative patients, allowing them to follow TV programs and interactions in their social environment.

Searching for similarities in the accretion flow of Seyfert 1 galaxies and cataclysmic variables based on the flare profiles of IRAS 13224–3809, 1H 0707–495, Mrk 766, and MV Lyr

Aims. We studied the fast variability of three selected active galactic nuclei (AGNs), IRAS 13224−3809, 1H 0707−495, and Mrk 766, and the cataclysmic variable MV Lyr, which were observed by the XMM-Newton and Kepler spacecrafts, respectively. Our goal is to search for the common origin of the variability and to test the so-called sandwich model, in which a geometrically thick corona surrounds a geometrically thin disc. Context. We studied the substructures of the averaged flare profiles. The flare profile method identifies individual flares in the light curve, and averages them. The direct fitting of the profile substructures identified individual characteristic frequencies that are seen in standard power density spectra (PDS) as a break frequency or quasi-periodic oscillation. The credibility of the flare profile substructures is demonstrated by comparison with the autocorrelation function. Results. We found that the flare profiles of AGNs are similar to those of a cataclysmic variable in the low state. We explain this as a consequence of a truncated inner disc in a sandwich model. The same scenario is also able to explain the characteristic break frequencies in X-ray PDS, which are not seen in the optical. We also searched for substructures in the flare profile of IRAS 13224−3809. In addition to a permanently present main flare, we found that a transient side-lobe appears before the main flare and is only seen in a high-flux period. The complex flare profile of this AGN suggests that an additional source of X-rays appears during the high-flux period. We propose a scenario in which an accretion flow fluctuation enters the sandwich corona and propagates further to some very central part of the accretion disc.

The Effect of FoMO, Uncertainty Avoidance and Impulsiveness on Financial Investment Decisions of Individual Investors

The increase in the use of internet, mobile devices and social media tools causes an increase in the factors affecting the investment decisions of individual investors. Especially social media platforms can trigger individual investors' fear of missing out (FoMO), uncertainty avoidance and impulsive buying emotions, which can direct investors' behavior and directly affect their investment decisions. In the literature, FoMO, uncertainty avoidance and impulsiveness, which have been studied in marketing, communication, and psychology, have been addressed separately in a few studies in finance. The most important point that distinguishes this study from other studies is that the relationship between FoMO, uncertainty avoidance and impulsiveness in the investment decisions of individual investors is studied together. The data used in the study was collected through a survey to measure uncertainty avoidance, impulsivity and FoMO levels from people over the age of 18, who have income and have made at least one financial investment. In the study, factor analysis was conducted on the participants' investment preferences, uncertainty avoidance, impulsivity and FoMO scales, Cronbach Alpha values of the factors were examined, and then cluster analysis was conducted for each scale. In the study, by examining individual investors' investment preferences and investment frequency separately, it was concluded that investors who invest in the same investment type exhibit different behaviors according to their investment frequency. This shows that investors who invest in the same investment instruments have different levels of FoMO, impulsiveness and uncertainty avoidance. The relationships between the frequency of investment in each type of investment and FoMO, uncertainty avoidance and impulsiveness are presented in detail in the relevant sections of the study.

Pesticide presence in stream water, suspended sediment and biofilm is strongly linked to upstream catchment land use and crop type

Pesticide pollution can present high ecological risks to aquatic ecosystems. Small streams are particularly susceptible. There is a need for reproducible and readily available methods to identify aquatic regions at risk of pesticide contamination. There is currently a limited understanding of the relationship between upstream catchment land use and the presence of pesticides in multiple aquatic matrices. The aim of this study was to develop empirical relationships between different land uses and the levels of pesticides detected in multiple aquatic matrices. The inclusion of biofilm and suspended sediment as monitoring matrices has recently been proven effective for the characterization of pesticide exposure in stream ecosystems. Ten streams in Ontario, Canada with a variety of upstream catchment land uses were sampled in 2021 and 2022. Water, suspended sediment and biofilm were collected and analyzed from each site for the presence of approximately 500 different pesticides. Each of the three matrices exhibited distinctive pesticide exposure profiles. We found a significant relationship between the percentage of agriculture and urban land use and the detection of multiple pesticides in water, sediment and biofilm (logistic regressions, P<0.05). Statistically significant probabilistic models capable of predicting pesticide detections based on upstream catchment land use were developed. High-resolution cover crop maps identified soybeans, corn and other agriculture (e.g., vegetables, berries, canola) as the key variables associated with individual pesticide detection frequencies in each of the three matrices (linear regressions, P<0.05). Soybean land use was also the strongest predictor of site-wide pesticide pollution. This modelling approach using upstream catchment land use variables has the potential to be a powerful tool to identify streams at risk of pesticide pollution.