Hasil untuk "Neurophysiology and neuropsychology"

Carissa D’Aniello-Heyda, Rachel R. Tambling, Sarah A. Kapeli et al.

College students commonly experience mental health concerns but may not seek treatment. Mental health literacy (MHL) may help to explain the perspectives college students hold and their help seeking behaviors. However, there are challenges with the measures used to assess MHL. We investigated college student perception of mental health, mental health challenges, and mental health treatments in preparation for measurement development. A sample of 127 college students was recruited from a four-year institution in the Southeastern United States. Participants completed survey measures of depression and anxiety; questions about the knowledge, importance and conceptualizations of MHL; symptoms of a variety of mental health conditions (including mania and schizophrenia); preferences for treatment; awareness of resources; and willingness to seek help. High levels of depression and anxiety were found in this sample, who also believed they had adequate levels of MHL (59.8 on a scale of 1–100). Findings indicated positive conceptualizations of MHL and a willingness to seek support from a variety of professional and non-professional sources. However, the results showed that college students lacked knowledge about conditions beyond depression and anxiety, and treatments available. Future development of an instrument used to measure MHL requires a nuanced approach. Results suggest that college students are lacking in information about symptoms, treatments, and supportive care for a variety of mental health conditions. Additionally, results suggest that MHL is limited to understanding of conditions such as depression and anxiety. These results indicate that more education around MHL for college students is necessary.

Feng T, Shan G, Hu Y et al.

Tong Feng,1 Guangliang Shan,2 Yaoda Hu,2 Huijing He,2 Guo Pei,1 Ruohan Zhou,1 Qiong Ou1 1Sleep Center, Department of Geriatric Respiratory, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People’s Republic of China; 2Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, People’s Republic of ChinaCorrespondence: Qiong Ou, Sleep Center, Department of Geriatric Respiratory, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Road, Yuexiu District, Guangzhou City, Guangdong Province, People’s Republic of China, Tel +86 13609717251, Email ouqiong2776@hotmail.comPurpose: Approximately 30% of patients with sleep-disordered breathing (SDB) present with masked hypertension, primarily characterized by elevated nighttime blood pressure. This study aimed to develop a hypertension prediction model tailored for primary care physicians, utilizing simple, readily available predictors derived from type IV sleep monitoring devices.Patients and Methods: Participants were recruited from communities in Guangdong Province, China, between April and May 2021. Data collection included demographic information, clinical indicators, and results from type IV sleep monitors, which recorded oxygen desaturation index (ODI), average nocturnal oxygen saturation (MeanSpO2), and lowest recorded oxygen saturation (MinSpO2). Hypertension was diagnosed using blood pressure monitoring or self-reported antihypertensive medication use. A nomogram was constructed using multivariate logistic regression after Least Absolute Shrinkage and Selection Operator (LASSO) regression identified six predictors: waist circumference, age, ODI, diabetes status, family history of hypertension, and apnea. Model performance was evaluated using area under the curve (AUC), calibration plots, and decision curve analysis (DCA).Results: The model, developed in a cohort of 680 participants and validated in 401 participants, achieved an AUC of 0.775 (95% CI: 0.730– 0.820) in validation set. Calibration plots demonstrated excellent agreement between predictions and outcomes, while DCA confirmed significant clinical utility.Conclusion: This hypertension prediction model leverages easily accessible indicators, including oximetry data from type IV sleep monitors, enabling effective screening during community-based SDB assessments. It provides a cost-effective and practical tool for prioritizing early intervention and management strategies in both primary care and clinical settings.Keywords: sleep-disordered breathing, prediction model, hypertension, risk predictors

Y. Estaremi, M. S. Al Ghafri, and S. Shamsigamchi

In this paper we define $λ$-hyponormal operators on an infinite dimensional Hilbert space $\mathcal{H}$ and find a class of $λ$-hyponormal operators that can not be hypercyclic. Also, we study closedness of range and $λ$-hyponormality of weighted composition operators on the Hilbert space $\mathcal{H}=L^2(μ)$. Moreover, we apply the hypercyclicity results to $λ$-hyponormal weighted composition operators. Finally, we provide some examples to illustrate our main results.

Mahmoud Galal Ahmed, Nour Shaheen, Ahmed Shaheen et al.

Objective: The aim of this study is to compare the clinical outcomes, safety, and workflow of patients who underwent endovascular treatment (EVT) alone or in combination with intravenous thrombolysis (IVT), against a control group who only received EVT. Methods: A retrospective analysis from May 2018 to September 2021 was conducted on 50 patients exhibiting acute ischemic stroke symptoms. 35 patients received EVT alone, while 15 received EVT and IVT. The data collected included demographic information, comorbid diseases, symptom onset time, duration from admission to puncture, and clinical outcomes via the National Institutes of Health Stroke Scale score and modified Rankin scale score at admission and 90 days post-procedure. Results: No significant differences were observed in the 90-day modified Rankin scale scores between the EVT + IVT and EVT alone groups (p > 0.05). NIHSS scores were also similar between the groups (p > 0.05). The EVT + IVT group demonstrated higher intracranial hemorrhage and symptomatic intracranial hemorrhage rates than the EVT alone group, but the difference became insignificant after adjusting for age and sex (p < 0.01). Mortality outcomes showed no significant difference (p > 0.05). Conclusion: The combined EVT and IVT treatment's safety outcomes are not inferior, aligning with existing literature.

Le Gao, Zhimin Wang, Yun Long et al.

Abstract Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying ASD patients based on resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising diagnostic tool, but challenging due to the complex and unclear etiology of autism. And it is difficult to effectively identify ASD patients with a single data source (single task). Therefore, to address this challenge, we propose a novel multi-task learning framework for ASD identification based on rs-fMRI data, which can leverage useful information from multiple related tasks to improve the generalization performance of the model. Meanwhile, we adopt an attention mechanism to extract ASD-related features from each rs-fMRI dataset, which can enhance the feature representation and interpretability of the model. The results show that our method outperforms state-of-the-art methods in terms of accuracy, sensitivity and specificity. This work provides a new perspective and solution for ASD identification based on rs-fMRI data using multi-task learning. It also demonstrates the potential and value of machine learning for advancing neuroscience research and clinical practice.

Benedikt Hilmes, Nick Rossenbach, and Ralf Schlüter

In this work we evaluate the utility of synthetic data for training automatic speech recognition (ASR). We use the ASR training data to train a text-to-speech (TTS) system similar to FastSpeech-2. With this TTS we reproduce the original training data, training ASR systems solely on synthetic data. For ASR, we use three different architectures, attention-based encoder-decoder, hybrid deep neural network hidden Markov model and a Gaussian mixture hidden Markov model, showing the different sensitivity of the models to synthetic data generation. In order to extend previous work, we present a number of ablation studies on the effectiveness of synthetic vs. real training data for ASR. In particular we focus on how the gap between training on synthetic and real data changes by varying the speaker embedding or by scaling the model size. For the latter we show that the TTS models generalize well, even when training scores indicate overfitting.

Ali Solhpour, Siddharth Kumar, Matthew J. Koch et al.

For years, there have been many discussions about the optimal/beneficial threshold for transfusion of blood products in subarachnoid hemorrhage (SAH), and it remains to be established. Over the period spent by patients who are recuperating from such acute intracranial bleeding, they often become anemic. This is a rationale why these patients are considered candidates for transfusion to restore normal hemoglobin levels and optimal arterial oxygen content. After a comprehensive review of multidisciplinary studies, it becomes evident that the benefits of blood transfusion may vary greatly depending on the situation. The objective here is to summarize the reported outcomes following administration of blood products, i.e., platelets, tranexamic acid, prothrombin complex concentrate, red blood cells, and colloids/crystalloids for optimal oxygenation and to minimize rebleeding. These treatments are reviewed in the context of how they interact with the brain during the early brain injury, the vasospasm, microthrombus formation, inflammation, brain edema, and the delayed cerebral ischemic phases. In severe SAH, cardiac dysfunction and hyponatremia are not uncommon, and the transfusion-associated circulatory overload should be monitored. Thus, continuous hemodynamic monitoring is necessary to prevent pulmonary edema, along with the maintenance of euvolemia. The paper also highlights conditions when transfusion is contraindicated. Patient blood management programs should be promoted to develop clearer hospital transfusion guidelines to strive for optimization of patient hemoglobin and iron stores, and to train for more restrictive RBC policy. The results reported thus far need to be critically reviewed by a panel of experts, along with the need to design novel rigorous prospective parallel-group studies to establish SAH-specific guidelines.

Moritz Köster, Marlene Meyer

Developmental research on action processing in the motor cortex relies on a key neural marker – a decrease in 6–12 Hz activity (coined mu suppression). However, recent evidence points towards an increase in mu power, specific for the observation of others’ actions. Complementing the findings on mu suppression, this raises the critical question for the functional role of the mu rhythm in the developing motor system. We here discuss a potential solution to this seeming controversy by suggesting a gating function of the mu rhythm: A decrease in mu power may index the facilitation, while an increase may index the inhibition of motor processes, which are critical during action observation. This account may advance our conception of action understanding in early brain development and points towards critical directions for future research.

Cheng Guo, Leidong Fan, Ziyu Xue et al.

In media industry, the demand of SDR-to-HDRTV up-conversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range). The research community has started tackling this low-level vision task by learning-based approaches. When applied to real SDR, yet, current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience. Different from other network-oriented methods, we attribute such deficiency to training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a luminance-segmented network (LSN) consisting of a global mapping trunk, and two Transformer branches on bright and dark luminance range. We also update assessment criteria by tailored metrics and subjective experiment. Finally, ablation studies are conducted to prove the effectiveness. Our work is available at: https://github.com/AndreGuo/HDRTVDM.

Sauvik Chatterjee, Sougata Biswas, and Arunava Chakrabarti

Flat, non-dispersive bands and topological phase transition in multiple Su-Schrieffer-Heeger (SSH) chains, cross-linked via periodically arranged nodal points are explored within a tight binding framework. We give analytic prescription, based on a real space decimation scheme, that extracts the energy eigenvalues corresponding to the flat bands along with their degeneracy. The topological phase transition is confirmed through the existence of quantized Zak phase for all the Bloch bands, and the edge states that are protected by chiral symmetry, consistent with the bulk-boundary correspondence. In addition to the edge states, the entangled systems are shown to give rise to clusters of localized eigenstates in the bulk of the system, in contrast to a purely one dimensional SSH system.

Du CQ, Zhang DX, Chen J et al.

Cong-Qi Du,1 Dong-Xue Zhang,1 Jing Chen,1,2 Qiu-Fen He,1,2 Wen-Qin Lin1 1Reproductive Medicine Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, People’s Republic of China; 2Department of Embryo Laboratory, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, People’s Republic of ChinaCorrespondence: Wen-Qin Lin, Tel +86-0571-87235031, Email wzlwq70@126.comBackground: Poor sleep quality has been linked to lower semen quality, but it is unclear whether this result in decreased fertility. To address this question, we retrospectively evaluated the relationship between men’s sleep quality and treatment outcomes in subfertile couples receiving assisted reproductive technology (ART).Patient Enrollment and Methods: From September 2017 to November 2019, 282 subfertile couples referred to a Chinese fertility clinic and eligible for ART procedures were enrolled in our study. Sociodemographic characteristics, life habits, and sleep habits in the year prior to ART were recorded. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI). We first divided the patients into two groups based on sleep quality (good sleep: PSQI < 5 and poor sleep: PSQI ≥ 5). Then, the ART outcomes (fertilization rate, good quality embryo rate, implantation rate, positive pregnancy rate, clinical pregnancy rate, live birth rate, miscarriage rate, and birth weight) of each group were analyzed. Finally, multivariate linear and logistic regression analysis were used to examine the relationship between sleep quality (discrete variable or dichotomous variable) and ART outcomes.Results: The participants in the poor sleep group showed a lower fertilization rate of 60.13% (543/903) when compared with 67.36% for the good sleep group (902/1339), P < 0.001. The global PSQI score had a significant influence on birth weight (β, − 63.81; 95% CI, − 119.91- − 8.52; P = 0.047), and live birth rate (OR, 0.88; 95% CI, 0.78- 0.99; P = 0.047) after adjusting for the interfering factors. Men’s sleep quality was unrelated to good quality embryos rate, implantation rate, positive pregnancy rate, clinical pregnancy rate, or miscarriage rate.Conclusion: Men’s sleep quality was positively associated with fertilization rate, birth weight, and live birth rate among couples undergoing ART.Keywords: sleep quality, PSQI, fertility, male reproduction, in vitro fertilization, intracytoplasmic sperm injection

Shenglong Zhou, and Geoffrey Ye Li

Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics. Popular optimization algorithms are based on the frameworks of the (stochastic) gradient descent methods or the alternating direction method of multipliers. In this paper, we deploy an exact penalty method to deal with federated learning and propose an algorithm, FedEPM, that enables to tackle four critical issues in federated learning: communication efficiency, computational complexity, stragglers' effect, and data privacy. Moreover, it is proven to be convergent and testified to have high numerical performance.

Dan Wang, Ao Li, Keyi Dong et al.

Social stress, a common stressor, causes multiple forms of physical and mental dysfunction. Prolonged exposure to social stress is associated with a higher risk of psychological disorders, including anxiety disorders and major depressive disorder (MDD). The orexinergic system is involved in the regulation of multiple motivated behaviors. The current study examined the regulatory effect of orexinergic projections from the lateral hypothalamic area (LHA) to the lateral habenula (LHb) in depression- and anxiety-like behaviors after chronic social defeat stress. When mice were defeated during social interaction, both orexinergic neurons in the LHA and glutamatergic neurons in the LHb were strongly activated, as indicated by the FosTRAP strategy. Infusion of orexin in the LHb significantly alleviated social avoidance and depression-like behaviors induced by chronic social defeat stress. Administration of an orexin receptor 2 antagonist in the LHb further aggravated the depressive phenotype. Photoactivation of orexinergic cell bodies in the LHA or terminals in the LHb relieved anxiety-like behaviors induced by chronic social defeat stress. Collectively, we identified the antidepressant and anxiolytic effects of the circuit from LHA orexinergic neurons to the LHb in response to chronic social stress, providing new evidence of the antidepressant properties of LHA orexin circuits.

Haseeb Ur Rehman, Faouzi Bellili, Amine Mezghani et al.

This paper tackles the problem of joint active and passive beamforming optimization for an intelligent reflective surface (IRS)-assisted multi-user downlink multiple-input multiple-output (MIMO) communication system. We aim to maximize spectral efficiency of the users by minimizing the mean square error (MSE) of the received symbol. For this, a joint optimization problem is formulated under the minimum mean square error (MMSE) criterion. First, block coordinate descent (BCD) is used to decouple the joint optimization into two sub-optimization problems to separately find the optimal active precoder at the base station (BS) and the optimal matrix of phase shifters for the IRS. While the MMSE active precoder is obtained in a closed form, the optimal phase shifters are found iteratively using a modified version (also introduced in this paper) of the vector approximate message passing (VAMP) algorithm. We solve the joint optimization problem for two different models for IRS phase shifts. First, we determine the optimal phase matrix under a unimodular constraint on the reflection coefficients, and then under the constraint when the IRS reflection coefficients are modeled by a reactive load, thereby validating the robustness of the proposed solution. Numerical results are presented to illustrate the performance of the proposed method using multiple channel configurations. The results validate the superiority of the proposed solution as it achieves higher throughput compared to state-of-the-art techniques.

Brennan LC, Kirkham FJ, Gavlak JC

Lucy Charlotte Brennan,1 Fenella Jane Kirkham,1&ndash; 3 Johanna Cristine Gavlak2 1Developmental Neurosciences Section, UCL Great Ormond Street Institute of Child Health, London, UK; 2Department of Child Health, University Hospital Southampton NHS Foundation Trust, Southampton, UK; 3Clinical and Experimental Sciences, University of Southampton, Southampton, UKCorrespondence: Fenella Jane KirkhamDevelopmental Neurosciences Section, UCL Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UKTel +44 207 905 4114Email Fenella.Kirkham@ucl.ac.ukAbstract: Obstructive sleep-disordered breathing (SDB), which includes primary snoring through to obstructive sleep apnea syndrome (OSAS), may cause compromise of respiratory gas exchange during sleep, related to transient upper airway narrowing disrupting ventilation, and causing oxyhemoglobin desaturation and poor sleep quality. SDB is common in chronic disorders and has significant implications for health. With prevalence rates globally increasing, this condition is causing a substantial burden on health care costs. Certain populations, including people with sickle cell disease (SCD), exhibit a greater prevalence of OSAS. A review of the literature provides the available normal polysomnography and oximetry data for reference and documents the structural upper airway differences between those with and without OSAS, as well as between ethnicities and disease states. There may be differences in craniofacial development due to atypical growth trajectories&nbsp;or extramedullary hematopoiesis in anemias such as SCD. Studies involving MRI of the upper airway illustrated that OSAS populations tend to have a greater amount of lymphoid tissue, smaller airways, and smaller lower facial skeletons from measurements of the mandible and linear mental spine to clivus. Understanding the potential relationship between these anatomical landmarks and OSAS could help to stratify treatments, guiding choice towards those which most effectively resolve the obstruction. OSAS is relatively common in SCD populations, with hypoxia as a key manifestation, and sequelae including increased risk of stroke. Combatting any structural defects with appropriate interventions could reduce hypoxic exposure and consequently reduce the risk of comorbidities in those with SDB, warranting early treatment interventions.Keywords: obstructive sleep apnea, sickle cell, polysomnography, desaturation, MRI, airway, adenoids

Bernardo Monechi, Miguel Ibáñez-Berganza, and Vittorio Loreto

The ongoing rapid urbanization phenomena make the understanding of the evolution of urban environments of utmost importance to improve the well-being and steer societies towards better futures. Many studies have focused on the emerging properties of cities, leading to the discovery of scaling laws mirroring, for instance, the dependence of socio-economic indicators on city sizes. Though scaling laws allow for the definition of city-size independent socio-economic indicators, only a few efforts have been devoted to the modeling of the dynamical evolution of cities as mirrored through socio-economic variables and their mutual influence. In this work, we propose a Maximum Entropy (ME), non-linear, generative model of cities. We write in particular a Hamiltonian function in terms of a few macro-economic variables, whose coupling parameters we infer from real data corresponding to French towns. We first discover that non-linear dependencies among different indicators are needed for a complete statistical description of the non-Gaussian correlations among them. Furthermore, though the dynamics of individual cities are far from being stationary, we show that the coupling parameters corresponding to different years turn out to be quite robust. The quasi time-invariance of the Hamiltonian model allows proposing an analytic model for the evolution in time of the macro-economic variables, based on the Langevin equation. Despite no temporal information about the evolution of cities has been used to derive this model, its forecast accuracy of the temporal evolution of the system is compatible to that of a model inferred using explicitly such information.

Essam A. Rashed, and Mohamed G. Awad

A supervised diagnosis system for digital mammogram is developed. The diagnosis processes are done by transforming the data of the images into a feature vector using wavelets multilevel decomposition. This vector is used as the feature tailored toward separating different mammogram classes. The suggested model consists of artificial neural networks designed for classifying mammograms according to tumor type and risk level. Results are enhanced from our previous study by extracting feature vectors using multilevel decompositions instead of one level of decomposition. Radiologist-labeled images were used to evaluate the diagnosis system. Results are very promising and show possible guide for future work.

Chengshuai Wu, Ilya Kanevskiy, and Michael Margaliot

A dynamical system is called contractive if any two solutions approach one another at an exponential rate. More precisely, the dynamics contracts lines at an exponential rate. This property implies highly ordered asymptotic behavior including entrainment to time-varying periodic vector fields and, in particular, global asymptotic stability for time-invariant vector fields. Contraction theory has found numerous applications in systems and control theory because there exist easy to verify sufficient conditions, based on matrix measures, guaranteeing contraction. Here, we provide a geometric generalization of contraction theory called k-order contraction. A dynamical system is called k-order contractive if the dynamics contracts k-parallelotopes at an exponential rate. For k=1 this reduces to standard contraction. We describe easy to verify sufficient conditions for k-order contractivity based on matrix measures and the kth additive compound of the Jacobian of the vector field. We also describe applications of the seminal work of Muldowney and Li, that can be interpreted in the framework of 2-order contraction, to systems and control theory.

Breanna N. Harris, Zachary P. Hohman, Callie M. Campbell et al.

The neurophysiological underpinnings involved in susceptibility to and maintenance of anxiety are not entirely known. However, two stress-responsive systems, the hypothalamic-pituitary-adrenal axis and the endocannabinoid system, may interact in anxiety. Here, we examine the relationship between FAAH genotype, CRFR1 genotype, baseline cortisol, and state anxiety in a rural adult population using data from Project FRONTIER. We predicted that FAAH A (AA and AC vs CC; rs324420) and three CRFR1 SNP minor alleles (rs7209436 C→ T [minor allele]; rs110402, G → A [minor]; and rs242924 G→ T [minor]), would interact to predict low baseline cortisol and low state anxiety scores. We found partial support for our prediction. In CRFR1 minor carriers, the FAAH AA or AC (vs. CC) genotype was associated with higher cortisol and with lower anxiety. In CRFR1 non-minors, those with FAAH AA or AC (vs. CC) showed decreased cortisol and higher anxiety. These results suggest that FAAH CC genotype only conveys risk for anxiety in individuals who are also carriers of the CRFR1 minor combination. FAAH genotype was significantly associated with baseline cortisol but was not independently associated with anxiety. Contrary to our predictions, baseline cortisol was negatively associated with anxiety. Lastly, we did not find any independent relationships between any of our SNPs and baseline cortisol or anxiety. These data suggest FAAH and cortisol interact to predict state anxiety, but that the relationship depends on CRFR1 genotype. The Project FRONTIER dataset is supported by Texas Tech University Health Sciences Center Garrison Institute on Aging. Keywords: FAAH, CRFR1, Anxiety, Cortisol, Project FRONTIER

Jessica D. Richardson, Julius Fridriksson