Search Results (261)

Background/Objectives: Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent condition etiologically related to suboptimal levels of dopamine (DA) and norepinephrine (NE) that is typically treated with psychostimulant medication. In individuals with ADHD, divergent thinking abilities have been shown to improve with the use of psychostimulants. Furthermore, psychostimulants affect autonomic nervous system (ANS) functioning, which can impact creative cognition. However, it is not known how DA and NE affect creative cognition in this setting and how this effect is related to autonomic activity in ADHD. Therefore, our objective was to elucidate ANS function and its relationship with divergent creativity gains related to psychostimulant treatment in ADHD. Method: Seventeen individuals diagnosed with ADHD (age 27.9 ± 6.7 sd) participated in two counterbalanced sessions—one while on their prescribed stimulant medication and another after abstaining for at least 24 h. During each session, participants completed convergent (anagrams) and divergent (Torrance Test of Creative Thinking) thinking tasks. An 8 min electrocardiogram prior to cognitive testing was taken to measure heart rate variability (HRV), which is an index of ANS functioning. Results: The hypothesized baseline pNN50 HRV measure was not predictive of enhanced creativity gains on convergent anagrams or divergent creativity on the Torrance when taking stimulants. Conclusions: In this pilot study, the relationship between baseline HRV and the impact of stimulants on anagram performance suggests the noradrenergic system may not play a role in the effect of stimulants on convergent or divergent creativity. The lack of a relationship between baseline HRV and stimulant-related changes in TTCT and anagram scores lends some support to the hypothesis that dopaminergic effects may be the predominant factor in the effect of stimulants on creativity in ADHD. Future research should further investigate the interaction between hypoactive neurotransmitter systems, particularly dopamine in divergent and norepinephrine in convergent creativity, using neuroimaging techniques to assess neurotransmitter dynamics during creativity-based tasks. Full article

Background/Objectives: Cirsium setosum (commonly known as thistle) is a traditional Chinese medicinal plant with significant therapeutic potential, exhibiting hemostatic, antioxidant, and wound-healing properties. Electrospinning offers a versatile platform for fabricating nanoscale scaffolds with tunable functionality, making them ideal for drug delivery and tissue engineering. Methods: In this study, a bioactive extract from thistle was obtained and incorporated into a thermosensitive triblock copolymer (PNNS) and polycaprolactone (PCL) to develop a multifunctional nanofibrous scaffold for enhanced wound healing. The prepared nanofibers were thoroughly characterized using Fourier-transform infrared spectroscopy (FTIR), contact angle measurements, thermogravimetric analysis (TGA), and tensile fracture testing to assess their physicochemical properties. Results: Notably, the inclusion of PNNS imparted temperature-responsive behavior to the scaffold, enabling controlled deformation in response to thermal stimuli—a feature that may facilitate wound contraction and improve scar remodeling. Specifically, the scaffold demonstrated rapid shrinkage at a physiological temperature (38 °C) within minutes while maintaining structural integrity at ambient conditions (20 °C). In vitro studies confirmed the thistle extract’s potent antioxidant activity, while in vivo experiments revealed their effective hemostatic performance in a liver bleeding model when delivered via the composite nanofibers. Thistle extract and skin temperature-responsive contraction reduced the inflammatory outbreak at the wound site and promoted collagen deposition, resulting in an ideal wound-healing rate of above 95% within 14 days. Conclusions: The integrated strategy that combines mechanical signals, natural extracts, and electrospinning nanotechnology offers a feasible design approach and significant technological advantages with enhanced therapeutic efficacy. Full article

The reaction of PtCl₂ with a PNN type ligand dppmaphen (N-(diphenylphosphanylmethyl)-2-amino-1,10-phenanthroline) yielded a new Pt(II) complex [Pt(dppmaphen)Cl]Cl·H₂O (1). Upon excitation at 370 nm, compound 1 emits yellow phosphorescence at 539 and 576 nm at room temperature. Exposure of compound 1 to MeOH vapor induces a shift in its emission to 645 nm, which can be attributed to the substitution of MeOH molecules for H₂O, resulting in the disruption and reorganization of weak interactions in 1. This response is selective for MeOH and, to a lesser extent, EtOH, the orange photoluminescence recovered in air. The emission change of 1 was reversible and visible to the naked eye. Full article

Background/Objectives: Cognitive decline has been increasingly linked to cardiac autonomic regulation; however, its specific associations with cognitive domains, such as information processing speed and executive function, remain unclear. This preliminary study examined the relationship between cardiac autonomic modulation and cognitive performance in older adults. Methods: A cross-sectional study was conducted with 101 older adults (aged ≥60 years) attending a university hospital outpatient clinic. Participants were classified as without cognitive impairment (WCI) or cognitively impaired and not demented (CIND) based on neuropsychological assessments. Heart rate variability (HRV) was measured at rest, focusing on the time-domain parameters (SDNN, rMSSD, and pNN50). Trail making test parts A and B (TMT-A and TMT-B) were used to assess information processing speed and executive function, respectively. Analyses of covariance (ANCOVAs) were performed, adjusting for confounding variables including age, sex, and comorbidities. Results: Participants in the CIND group had significantly lower HRV indices than those in the WCI group (SDNN, p < 0.05, d = 0.44; rMSSD, p < 0.05, d = 0.39; pNN50, p < 0.05, d = 0.40), indicating reduced parasympathetic modulation. Higher HRV values were observed in individuals with preserved processing speed and executive function. Specifically, pNN50 was significantly associated with processing speed (p = 0.04), and SDNN was significantly correlated with executive function (p = 0.02). These associations persisted even after adjusting for confounding factors. Conclusions: Reduced cardiac autonomic modulation, especially lower parasympathetic activity, is significantly associated with cognitive impairment in older adults. Lower pNN50 values were correlated with slower information processing speed, and lower SDNN was associated with poorer executive function. These findings support the potential use of HRV as a physiological biomarker to detect cognitive changes during ageing. Full article

In this paper, a fault diagnosis method for permanent magnet synchronous motors is proposed, combining wavelet packet transform (WPT) energy feature extraction and a genetic algorithm (GA)-optimized back propagation (BP) neural network. Firstly, for the common types of motor faults (turn-to-turn short-circuit, phase-to-phase short-circuit, loss of magnetism, inverter open-circuit, rotor eccentricity), a corresponding motor fault model is established. The stator current signals during motor operation are analyzed using wavelet packet transform, and energy features are extracted from them as feature vectors for fault diagnosis. Then, a BP neural network is constructed, and a genetic algorithm is used to optimize its initial weights and thresholds, thereby improving the network’s classification accuracy. The results show that the GA-BP model outperforms the SSA-PNN diagnostic model in terms of fault classification accuracy. In particular, for the diagnosis of normal operation, inverter open-circuit, and demagnetization faults, the accuracy rate reaches 100%. This method demonstrates high diagnostic accuracy and practical application value. Full article

Canine monocytic ehrlichiosis (CME) is a globally prevalent disease transmitted by the tick Rhipicephalus sanguineus and caused by the Gram-negative bacterium Ehrlichia spp. Following an incubation period, the infection is categorized based on the progression of the disease into acute, subclinical, and chronic stages. Besides hematological alterations, the cardiovascular system is significantly impacted by the hemodynamic effects of the disease, as persistent anemia can lead to myocardial hypoxia and the activation of inflammatory processes, potentially causing myocarditis. It is known that in dogs infected with Ehrlichia canis, there is a higher occurrence of arrhythmias and a predominance of sympathetic activity. This study assessed arrhythmogenic parameters, including P wave dispersion (Pd), QT dispersion (QTd), and QT instability, along with heart rate variability (HRV) analysis from 24 h Holter monitoring in naturally infected dogs during the acute phase (n = 10) and chronic phase (n = 10) compared to a control group (n = 10). The Pd and QTd values were higher in the infect group, confirming the arrhythmogenic character. Instability parameters (TI, LTI, and STI) were higher in sick animals, but no worsening was observed in the chronic phase. All HRV metrics in the time domain were higher in the control group, indicating a balanced sympathovagal activity throughout the day in healthy dogs. Additionally, parameters linked to parasympathetic activity (rMSSD and pNN50) were reduced in the sick groups, confirming the dominance of sympathetic activity. These findings indicate a decrease in HRV in sick individuals and reinforce this useful marker for assessing the influence of the autonomic nervous system on the cardiovascular system. In conclusion, CME exhibits arrhythmogenic activity characterized by the deterioration of predictive parameters for ventricular arrhythmias and increased activity of the sympathetic autonomic nervous system in the heart. This is likely secondary to myocarditis, myocardial hypoxia, and structural damage to cardiomyocytes. Full article

Perineuronal nets (PNNs) are specialized extracellular matrix structures that predominantly surround inhibitory neurons in the central nervous system (CNS). They have been identified as crucial regulators of synaptic plasticity and neuronal excitability. This literature review aims to summarize the current state of knowledge about PNNs, their molecular composition and structure, as well as their functional roles and involvement in neurological diseases. Furthermore, future directions in PNN research are proposed, and the therapeutic potential of targeting PNNs to develop novel treatment options for various neurological disorders is explored. This review emphasizes the importance of PNNs in CNS physiology and pathology and underscores the need for further research in this area. Full article

Polyploidy, characterized by an increase in the number of whole sets of chromosomes in an organism, offers a promising avenue for cannabis improvement. Polyploid cannabis plants often exhibit altered morphological, physiological, and biochemical characteristics with a number of potential benefits compared to their diploid counterparts. The optimization of polyploidy induction, such as the level of antimitotic agents and exposure duration, is essential for successful polyploidization to maximize survival and tetraploid rates while minimizing the number of chimeric mixoploids. In this study, three classification-based machine learning algorithms—probabilistic neural network (PNN), support vector classification (SVC), and k-nearest neighbors (KNNs)—were used to model ploidy levels based on oryzalin concentration and exposure time. The results indicated that PNN outperformed both KNNs and SVC. Subsequently, PNN was combined with a genetic algorithm (GA) to optimize oryzalin concentration and exposure time to maximize tetraploid induction rates. The PNN-GA results predicted that the optimal conditions were a concentration of 32.98 µM of oryzalin for 17.92 h. A validation study testing these conditions confirmed the accuracy of the PNN-GA model, resulting in 93.75% tetraploid induction, with the remaining 6.25% identified as mixoploids. Additionally, the evaluation of morphological traits showed that tetraploid plants were more vigorous and had larger leaf sizes compared to diploid or mixoploid plants in vitro. Full article

Background/Objectives: Corona virus disease 2019 (COVID-19) poses a threat to global public health. The early identification of critical cases is crucial to providing timely treatment to patients. Here, we investigated whether the neutrophil levels could predict COVID-19 complications. Methods: We performed a retrospective study of patients with COVID-19, admitted to the Cheikh Khalifa International University Hospital, Casablanca, Morocco. Laboratory test results collected upon admission and during hospitalization were analyzed based on clinical information. Results: Our study revealed that a rise in neutrophil “PNN” levels was associated with respiratory deterioration and intubation. They were positively correlated with the procalcitonin and C-reactive protein levels. Interestingly, PNN (polynuclear neutrophil) levels on day 5 proved to be a better predictor of intubation, acute respiratory distress syndrome (ARDS), and mortality than the initial PNN counts, C-reactive protein, or procalcitonin. Moreover, binary logistic regression with stratified PNN-day 5 data revealed that a PNN level on day 5 > 7.7 (109/L) was an independent risk factor for mortality and ARDS. Finally, the PNN levels on day 5 and proinflammatory cytokine IL-6 were positively correlated. Conclusions: Our data showed that neutrophilia proved to be an excellent predictor of complications and mortality during hospitalization and could be used to improve the management of patients with COVID-19. Full article

Results change depending on the water quality evaluation methods used, and within good-quality water, many results still have parameters with concentrations exceeding the World Health Organization (WHO) desirable limits or national threshold values (TVs). Furthermore, there are few methods to classify the severity degree of contaminated water; most methods have problems in the parameter threshold boundary and in assigning weights. Aiming to solve the above problems, a water quality evaluation framework based on the single-indicator evaluation method (SIE), Weber–Fechner (W-F) law and Probabilistic Neural Network (PNN) is presented, named SIE&W-F&PNN. Forty-three confined water samples were collected for this research in Xi’an in September 2015. The SIE, water quality index (WQI) with three different weights (method weight, entropy weight and equal weight), comprehensive evaluation method (CEM) and SIE&W-F&PNN method were used, and the evaluation criteria for contaminated water were proposed based on the W-F law. The results of these methods were compared. The reasons for confined water pollution in Xi’an were analyzed. The results show that TC, NH₄-N, NO₂-N, β, As, Mn, F⁻, TH, Fe²⁺ and Turb were the contaminating parameters of the 43 confined water samples. In order, the results for the number or ratio of ‘Poor’ and even worse water samples by method are as follows: SIE-WHO (30, 69.77%) > SIE-GB = CEM (24, 55.81%) > WQI (entropy weight) (12, 27.91%) > WQI (method weight) (10, 23.26%) > WQI (equal weight) (9, 20.93%). These discrepancies highlight the influence of evaluation methods on the results. For this study, a water sample was classified as ‘contaminated (bad) water’ if any parameter exceeded either the national TV or the WHO’s desirable limit, prioritizing drinking water safety. The SIE&W-F&PNN results show that there were 10 excellent water samples and 33 bad water samples (among which 4 water samples were rated as VL (very lightly polluted), 14 as L (lightly polluted), 14 as M (moderately polluted) and 1 as H (heavily polluted)). The SIE&W-F&PNN method ensures that no parameters in ‘excellent’ or ‘good’ water samples exceed the WHO’s desirable limits or national TVs; can be used to classify the severity of contamination of contaminated water without assigning weights, avoiding the rate mutation near the threshold boundary; and can include any number of parameters and be applied to lakes, rivers, air, soil, etc. (i.e., it is not unique to groundwater). The primary causes of confined water pollution in Xi’an include historical pollution, contemporary anthropogenic activities, geological factors, excessive groundwater extraction, and the infiltration of contaminated surface and phreatic water. Full article

Background/Objectives: We aimed to examine the acute effects of deep breathing exercise and transcutaneous auricular vagus nerve stimulation (taVNS) on autonomic nervous system activation and the characteristics of certain muscle groups and to compare these two methods. Methods: 60 healthy adults between the ages of 18 and 45 were randomly divided into two groups to receive a single session of taVNS and deep breathing exercises. Acute measurements of pulse, blood pressure, perceived stress scale, autonomic activity, and muscle properties were performed before and after the application. Results: A significant decrease was detected in the findings regarding the perceived stress scale, pulse, and blood pressure values as a result of a single session application in both groups (p < 0.05). In addition, it was determined that the findings regarding autonomic measurement values increased in favor of the parasympathetic nervous system in both groups (p < 0.05). In measurements of the structural properties of the muscle, the stiffness values of the muscles examined in both groups decreased (p < 0.05), while the findings regarding relaxation increased (p < 0.05), except for the masseter in the deep breathing (DB) group. As a result of the comparative statistical evaluation between the groups, the increase in parasympathetic activity was found to be greater in the DB group according to root mean square of differences in successive RR intervals (RMSSD), the percent of differences in adjacent RR intervals > 50 ms (pNN50), and stress index parameters (p < 0.05). In the measurements made with the Myoton^®PRO device, the increase in the relaxation value was higher in the gastrocnemius muscle of the VNS group (p < 0.05). Conclusions: It has been observed that both methods can increase parasympathetic activity and muscle relaxation in healthy people in a single session. However, DB appears to be slightly superior in increasing parasympathetic activity, and VNS appears to be slightly superior in increasing relaxation. Full article

Time series classification is a significant and complex issue in data mining, it is prevalent across various fields and holds substantial research value. However, enhancing the classification rate of time series data remains a formidable challenge. Traditional time series classification methods often face difficulties related to insufficient feature extraction or excessive model complexity. In this study, we propose a self-optimizing polynomial neural network with a temporal feature enhancement, which is referred to as OPNN-T. Existing classifiers based on polynomial neural networks (PNNs) struggle to achieve high-quality performances when dealing with time series data, primarily due to their inability to extract temporal information effectively. The goal of the proposed classifier is to enhance the nonlinear modeling capability for time series data, thereby improving the classification rate in practical applications. The key features of the proposed OPNN-T include the following: (1) A temporal feature module is employed to capture the dependencies in time series data, providing adaptability and flexibility in handling complex temporal patterns. (2) A polynomial neural network (PNN) is constructed using sub-datasets combined with three types of polynomial neurons, which enhances its nonlinear modeling capabilities across diverse scenarios. (3) A self-optimization mechanism is integrated into iteratively optimized sub-datasets, features, and polynomial types, resulting in significant improvements in the classification rate. The experimental results demonstrate that the proposed method achieves superior performances across multiple standard time series datasets, exhibiting higher classification accuracy and greater robustness than the existing classification models. Our research offers an effective solution for time series classification, and highlights the potential of polynomial neural networks in this field. Full article

Semaphorin 3A (Sema3A) is an axon guidance molecule, which is also abundant in the adult central nervous system (CNS), particularly in perineuronal nets (PNNs). PNNs are extracellular matrix structures that restrict plasticity. The cellular sources of Sema3A in PNNs are unknown. Most Sema3A-bearing neurons do not express Sema3A mRNA, suggesting that Sema3A may be released from other neurons. Another potential source of Sema3A is the choroid plexus. To identify sources of PNN-associated Sema3A, we focused on the cerebellar nuclei, which contain Sema3A⁺ PNNs. Cerebellar nuclei neurons receive prominent input from Purkinje cells (PCs), which express high levels of Sema3A mRNA. By using a non-invasive viral vector approach, we overexpressed Cre in PCs, the choroid plexus, or throughout the CNS of Sema3A^fl/fl mice. Knocking out Sema3A in PCs or the choroid plexus was not sufficient to decrease the amount of PNN-associated Sema3A. Alternatively, knocking out Sema3A throughout the CNS induced a decrease in PNN-associated Sema3A. However, motor deficits, microgliosis, and neurodegeneration were observed, which were due to Cre toxicity. Our study represents the first attempt to unravel cellular sources of PNN-associated Sema3A and shows that non-invasive viral-mediated Cre expression throughout the CNS could lead to toxicity, complicating the interpretation of Cre-mediated Sema3A knock-out. Full article

Noise profoundly affects the quality of electromagnetic data, and selecting the appropriate hyperparameters for machine learning models poses a significant challenge. Consequently, the current machine learning denoising techniques fall short in delivering precise processing of Wide Field Electromagnetic Method (WFEM) data. To eliminate the noise, this paper presents an electromagnetic data denoising approach based on the improved dung beetle optimized (IDBO) gated recurrent unit (GRU) and its application. Firstly, Spatial Pyramid Matching (SPM) chaotic mapping, variable spiral strategy, Levy flight mechanism, and adaptive T-distribution variation perturbation strategy were utilized to enhance the DBO algorithm. Subsequently, the mean square error is employed as the fitness of the IDBO algorithm to achieve the hyperparameter optimization of the GRU algorithm. Finally, the IDBO-GRU method is applied to the denoising processing of WFEM data. Experiments demonstrate that the optimization capacity of the IDBO algorithm is conspicuously superior to other intelligent optimization algorithms, and the IDBO-GRU algorithm surpasses the probabilistic neural network (PNN) and the GRU algorithm in the denoising accuracy of WFEM data. Moreover, the time domain of the processed WFEM data is more in line with periodic signal characteristics, its overall data quality is significantly enhanced, and the electric field curve is more stable. Therefore, the IDBO-GRU is more adept at processing the time domain sequence, and the application results also validate that the proposed method can offer technical support for electromagnetic inversion interpretation. Full article

The photoelectric Factor (PEF) log is a powerful tool for distinguishing between siliciclastic and carbonate lithofacies in well-log analysis and 2D correlations. However, its application in complex reservoirs has some challenges due to well spacing. We present a workflow to extend its capabilities into a 3D environment to characterize the Pennsylvanian Strawn and Canyon reef complex in the Salt Creek field, Kent County, West Texas. The productive zones within this reservoir are composed of porous oolitic grainstones and skeletal packstones. However, there are some porous shale beds within the reef complex that are indistinguishable from the porous limestone zones on the neutron porosity log that have posed major challenges to hydrocarbon production. To address these problems, we used a machine-learning procedure involving multiattribute analysis and probabilistic neural network (PNN) to predict photoelectric factor (PEF) volume to characterize the reservoir and identify the shale beds. By combining neutron porosity, gamma ray, and the predicted PEF logs, we found that (1) these shale beds, hereby referred to as shale-influenced carbonates, are characterized by photoelectric factor values ranging from 4 to 4.26 B/E. (2) Based on the PEF values, the least porous interval is the Canyon System, having <1% porosity and characterized by PEF values of >4.78 B/E; while the most porous interval is the Strawn System, composed mostly of zones with porosity ranging from 3% to 28%, characterized by PEF values varying from 4.26 to 4.78 B/E. Full article