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Subduction zones have the greatest potential to generate large earthquakes and tsunamis. However, when undertaking Probabilistic Tsunami Hazard Assessments (PTHAs), subduction zones are a significant source of epistemic uncertainty. Therefore, understanding how the spatial distribution of elastic strain accumulation on the subduction interface influences the tsunami hazard is important for providing comprehensive hazard assessments, as well as quantifying uncertainty. This is especially important if the spatial locking distribution is undefined, and if it changes through time. Physics-based earthquake simulators allow different interpretations of the subduction interface locking distribution to be modelled, and how this influences the long-term seismicity, and the tsunami hazard, can be explored. Using three physics-based synthetic earthquake catalogues, generated by the earthquake simulator RSQSim, we analysed the tsunami hazard in Aotearoa/New Zealand. Three alternative representations of the subduction interface locking distribution along the Hikurangi Subduction Margin and the Tonga-Kermadec Subduction Zone were specified in the simulator to generate the catalogues. We modelled the tsunamis generated by |$M_W\, \gt $| 8.0 earthquakes from each of the catalogues and undertook PTHAs. These assessments show that patches of high slip-deficit, both along strike and dip of the subduction interface, increase the tsunami hazard at the coast. Locking along the shallowest segments of the subduction interface also significantly increases the tsunami hazard. Our results show that careful consideration of the locking distribution in physical models is necessary before using them for PTHAs. They also show that by analysing multiple physical models of subduction zones, uncertainty in hazard assessments caused by the unresolved interface properties can also begin to be quantified. [ABSTRACT FROM AUTHOR]

The central Apennines are notoriously subject to important seismic sequences, such as the 2009 and 2016–2017, L'Aquila, Amatrice-Visso-Norcia (AVN) sequences, respectively. Here, we examine the temporal and spatial variation of the S-wave attenuation in Central Italy over a period from 2011 to 2017, including the AVN sequence. First, we computed the S-wave attenuation (Qβ) as a function of frequency Q(f) using the coda normalization method. Then, to visualize the spatial variation of the attenuation over time, we calculated the attenuation of coda waves using a novel 2D kernel-based function over the study area. Our results showed a 13% variation in S-wave attenuation between the pre-sequence (2011–2016) and the sequence phase, with a significant 37% decrease in Q (increase in attenuation) detected during the Visso period. Spatially, a high attenuation anomaly aligns with the Monti Sibillini thrust formation, while in time, we observed a northward migration of this high attenuation during the Norcia phase. Temporal variation in the crustal S-wave attenuation and its frequency dependence may be linked to fluid movement and fracturing developed during the AVN sequence. Coda-Q mapping confirmed an increase in attenuation during the sequence within the fault plane zones. Additionally, the broader area of interest reveals a northward extension of high attenuation, following the NS direction of the Monti Sibillini thrust. [ABSTRACT FROM AUTHOR]

Tsunami is one of the deadliest natural disasters which can occur, leading to great loss of life and property. This study focuses on predicting tsunamis, using earthquake dataset from the year 1995 to 2023. The research introduces the Hybrid Quantum Neural Network (HQNN), an innovative model that combines Neural Network (NN) architecture with Parameterized Quantum Circuits (PmQC) to tackle complex machine learning (ML) problems where deep learning (DL) models struggle, aiming for higher accuracy in prediction while maintaining a compact model size. The hybrid model's performance is compared with the classical model counterpart to investigate the quantum circuit's effectivity as a layer in a DL model. The model has been implemented using 2-6 features through Principle Component Analysis (PCA) method. HQNN's quantum circuit is a combination of Pennylane's embedding (Angle Embedding (AE) and Instantaneous Quantum Polynomial (IQP) Embedding) and layer circuits (Basic Entangler Layers (BEL), Random Layers (RL), and Strongly Entangling Layers (SEL)), along with the classical layers. Results show that the proposed model achieved high performance, with a maximum accuracy up to 96.03% using 4 features with the combination of AE and SEL, superior to the DL model. Future research could explore the scalability and diverse applications of HQNN, as well as its potential to address practical ML challenges. [ABSTRACT FROM AUTHOR]

Background: This case report describes a unique presentation of sphingosine-1-phosphate lyase insufficiency syndrome (SPLIS) caused by a rare SGPL1 variant, highlighting the diagnostic and management challenges associated with this condition. Case presentation: A 2-year-old Iranian female presented with steroid-resistant nephrotic syndrome (NS), primary adrenal insufficiency (AI), growth delay, seizures, and hyperpigmentation. Laboratory evaluation revealed hypoalbuminemia, significant proteinuria, hyperkalemia, and elevated adrenocorticotropic hormone (ACTH) levels. The patient was diagnosed with SPLIS through genetic testing, revealing a c.1018 C > T variant in SGPL1. Despite supportive treatment, including corticosteroids and cyclosporine, the patient's condition deteriorated, leading to end-stage renal disease and sepsis, ultimately resulting in death. Conclusions: This case underscores the clinical heterogeneity of SPLIS and the importance of early genetic evaluation in patients with combined NS and AI. Personalized management approaches and increased awareness among clinicians are essential to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Introduction: While the fact that visual stimuli synthesized by Artificial Neural Networks (ANN) may evoke emotional reactions is documented, the precise mechanisms that connect the strength and type of such reactions with the ways of how ANNs are used to synthesize visual stimuli are yet to be discovered. Understanding these mechanisms allows for designing methods that synthesize images attenuating or enhancing selected emotional states, which may provide unobtrusive and widely-applicable treatment of mental dysfunctions and disorders. Methods: The Convolutional Neural Network (CNN), a type of ANN used in computer vision tasks which models the ways humans solve visual tasks, was applied to synthesize ("dream" or "hallucinate") images with no semantic content to maximize activations of neurons in precisely-selected layers in the CNN. The evoked emotions of 150 human subjects observing these images were self-reported on a two-dimensional scale (arousal and valence) utilizing self-assessment manikin (SAM) figures. Correlations between arousal and valence values and image visual properties (e.g., color, brightness, clutter feature congestion, and clutter sub-band entropy) as well as the position of the CNN's layers stimulated to obtain a given image were calculated. Results: Synthesized images that maximized activations of some of the CNN layers led to significantly higher or lower arousal and valence levels compared to average subject's reactions. Multiple linear regression analysis found that a small set of selected image global visual features (hue, feature congestion, and sub-band entropy) are significant predictors of the measured arousal, however no statistically significant dependencies were found between image global visual features and the measured valence. Conclusion: This study demonstrates that the specific method of synthesizing images by maximizing small and precisely-selected parts of the CNN used in this work may lead to synthesis of visual stimuli that enhance or attenuate emotional reactions. This method paves the way for developing tools that stimulate, in a non-invasive way, to support wellbeing (manage stress, enhance mood) and to assist patients with certain mental conditions by complementing traditional methods of therapeutic interventions. [ABSTRACT FROM AUTHOR]

Recognizing the action of plastic bag taking from CCTV video footage represents a highly specialized and niche challenge within the broader domain of action video classification. To address this challenge, our paper introduces a novel benchmark video dataset specifically curated for the task of identifying the action of grabbing a plastic bag. Additionally, we propose and evaluate three distinct baseline approaches. The first approach employs a combination of handcrafted feature extraction techniques and a sequential classification model to analyze motion and object-related features. The second approach leverages a multiple-frame convolutional neural network (CNN) to exploit temporal and spatial patterns in the video data. The third approach explores a 3D CNN-based deep learning model, which is capable of processing video data as volumetric inputs. To assess the performance of these methods, we conduct a comprehensive comparative study, demonstrating the strengths and limitations of each approach within this specialized domain. [ABSTRACT FROM AUTHOR]

Chromosomal abnormalities of the embryo are the most common cause of first-trimester pregnancy loss. In this single-center study, we assessed the frequency and the spectrum of chromosomal abnormalities in miscarriages for each year of maternal age from 23 to 44. Cytogenetic data were obtained by conventional karyotyping of 7118 miscarriages in women with naturally conceived pregnancies. Chromosomal abnormalities were identified in 67.25% of miscarriages. The total incidence of chromosomal abnormalities increased with maternal aging; however, its average change for a one-year increase in maternal age differed between age spans, equaling 0.704% in the span from 23 to 37 years and 2.095% in the span from 38 to 44 years. At the age of 38 years, the incidence rate surged sharply by 14.79% up to 79.01% and then increased progressively up to 94% in 44-year-old women. The spectrum of chromosomal abnormalities in miscarriages was the same for each year of maternal age from 23 to 44 years. However, the proportions of particular chromosomal abnormalities differed between karyotypically abnormal miscarriages in younger and older women. The proportions of trisomy 16, polyploidy, monosomy X, mosaic aneuploidies, and structural rearrangements decreased with increasing maternal age. In contrast, the proportions of multiple aneuploidies and regular trisomies 13, 15, 18, 21, and 22 showed an upward trend with maternal aging. To summarize, despite the increase in the total incidence of chromosomal abnormalities in miscarriages with maternal aging, the rate of change differs for younger and older women, being three times lower in the former than in the latter. Moreover, the proportion of some abnormalities in karyotypically abnormal miscarriages shows a steady growth, whereas the proportion of others becomes increasingly low with maternal aging, most probably due to the age-dependent prevalence of different molecular and cellular defects. [ABSTRACT FROM AUTHOR]

The window of implantation (WOI) is a critical phase of the menstrual cycle during which the endometrial lining becomes receptive and facilitates embryo implantation. Drawing on findings from various branches of "omics", including genomics, epigenomics, transcriptomics, proteomics, lipidomics, metabolomics, and microbiomics, this narrative review aims to (1) discuss mechanistic insights on endometrial receptivity and its implication in infertility; (2) highlight advances in investigations for endometrial receptivity; and (3) discuss novel diagnostic and therapeutic strategies that may improve reproductive outcomes. [ABSTRACT FROM AUTHOR]

Brain diseases pose significant treatment challenges due to the restrictive nature of the blood–brain barrier (BBB). Recent advances in targeting macromolecules offer promising avenues for overcoming these obstacles through receptor-mediated transcytosis (RMT). We summarize the current progress in targeting brain drug delivery with macromolecules for brain diseases. This exploration details the transport mechanisms across the BBB, focusing on RMT and its use of natural ligands for drug delivery. Furthermore, the review examines macromolecular ligands such as antibodies, peptides, and aptamers that leverage RMT for effective BBB traversal. Advancements in macromolecules-based delivery systems for brain diseases are summarized, emphasizing their therapeutic potential and limitations. Finally, emerging RMT strategies, including viral vectors, exosomes, and boron neutron capture therapy, are discussed for their precision in brain-targeted treatments. This comprehensive overview underscores the potential of RMT-based approaches to revolutionize brain disease therapy. [ABSTRACT FROM AUTHOR]

Background: Sperm samples are separated into bad and good quality samples in function of their phenotype, but this does not indicate their genetic quality. Methods: Here, we used GeneChip miRNA arrays to analyze microRNA expression in ten semen samples selected based on high-magnification morphology (score 6 vs. score 0) to identify miRNAs linked to sperm phenotype. Results: We found 86 upregulated and 21 downregulated miRNAs in good-quality sperm (score 6) compared with bad-quality sperm samples (score 0) (fold change > 2 and p-value < 0.05). MiR-34 (FC × 30, p = 8.43 × 10−8), miR-30 (FC × 12, p = 3.75 × 10−6), miR-122 (FC × 8, p = 0.0031), miR-20 (FC × 5.6, p = 0.0223), miR-182 (FC × 4.83, p = 0.0008) and miR-191 (FC × 4, p = 1.61 × 10−6) were among these upregulated miRNAs. In silico prediction algorithms predicted that miRNAs upregulated in good-quality sperm targeted 910 genes involved in key biological functions of spermatozoa, such as cell death and survival, cellular movement, molecular transport, response to stimuli, metabolism, and the regulation of oxidative stress. Genes deregulated in bad-quality sperm were involved in cell growth and proliferation. Conclusions: This study reveals that miRNA profiling may provide potential biomarkers of sperm quality. [ABSTRACT FROM AUTHOR]

A nonlinear dynamical model is developed in this paper that depicts interactions among vultures, human, animals and their carcasses. Diseases such as plague, anthrax and rabies are spreading due to the cascade impact caused by the catastrophic drop in the vulture population, particularly in Africa and Asia. The built model is theoretically studied using qualitative differential-equation theory to demonstrate the system's rich dynamical features, which are critical for maintaining the ecosystem's equilibrium. According to the qualitative findings, depending on the parameter combinations, the system not only displays stability of many equilibrium states but also experiences transcritical and Hopf bifurcations. By keeping an eye on the variables causing the decline of the vulture population, the model's outputs may assist in maintaining balance with the prevention of the spread of disease through carcasses. Hopf-bifurcation results in the bifurcation of the limit cycle through the threshold, supporting the idea that interactions between humans and vultures may also be periodic. [ABSTRACT FROM AUTHOR]

Background: Symptoms of psychological distress such as depression and anxiety as well as gender are known to be associated with sleep quality in middle-aged and older adults. However, little is known about the effect on sleep quality of interactions among these factors., Purpose: This study was developed to investigate the moderating effect of gender on the relationship between psychological distress and sleep quality in middle-aged and older adults., Methods: A cross-sectional design, secondary data analysis of data on 5,590 individuals aged ≥ 45 years from the Taiwan Biobank database (2009-2018) was used. In this database, psychological distress was assessed using the Patient Health Questionnaire-4, and sleep quality was assessed using a self-report, one-question scale. Demographic data and health-related variables were evaluated as potential confounding factors. A hierarchical regression was conducted to examine the moderating effect of gender on the relationship between psychological distress and sleep quality after adjusting for potential confounders., Results: The participants with severe psychological distress were found to have a lower mean quality of sleep than those without ( p < .01), and males returned a better mean quality of sleep score than females ( p < .01). Moreover, a significant interaction effect between psychological distress and gender on sleep quality score was found ( b = 0.123, SE = 0.022, p = .03). In terms of participants with severe psychological distress, males reported significantly poorer sleep quality than females., Conclusions: The findings provide evidence that gender moderates the relationship between sleep quality and psychological distress. Also, in terms of individuals with severe psychological distress, males reported worse sleep quality than women, indicating psychological distress impacts sleep quality in men more than women. Nurses should be aware of these findings when working with clinical professionals to tailor gender-specific education interventions to improve sleep quality and psychological health., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2025 The Authors. Published by Wolters Kluwer Health, Inc.)

Neurodegenerative diseases, particularly Alzheimer's disease and Parkinson's disease, present formidable challenges in modern medicine due to their complex pathologies and the absence of curative treatments. Despite advances in symptomatic management, early diagnosis remains essential for mitigating disease progression and improving patient outcomes. Traditional diagnostic methods, such as MRI, PET, and cerebrospinal fluid biomarker analysis, are often inadequate for the early detection of these diseases. Emerging porous materials, including metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), MXene, zeolites, and porous silicon, offer promising new approaches for the early diagnosis of neurodegenerative diseases. These materials, characterized by highly tunable physicochemical properties, have the potential to capture and concentrate disease-specific biomarkers such as amyloid-beta (Aβ), tau protein, and alpha-synuclein (α-Syn). The integration of these materials into advanced biosensors for real-time detection holds the promise of revolutionizing neurodiagnostic, enabling non-invasive, highly sensitive, and specific detection platforms. Furthermore, the incorporation of artificial intelligence (AI) and machine learning (ML) techniques into the analysis of sensor data enhances diagnostic accuracy and allows for more efficient interpretation of complex biomarker profiles. AI and ML can optimize feature selection, improve pattern recognition, and facilitate the prediction of disease progression, making them indispensable tools for personalized medicine. This review explores the potential of porous materials in neurodegenerative disease diagnostics, emphasizing their design, functionality, and the synergistic role of AI and ML in advancing clinical applications., (© 2025 Wiley‐VCH GmbH.)

Three hospitals implemented molecular point-of-care tests (POCTs) to screen patients for SARS-CoV-2 infection upon admission during the 2021/2022 influenza season, which in Belgium lasted from January to April 2022. The samples were simultaneously tested for influenza A/B. Influenza positivity at admission was examined in relation to patient characteristics and symptomatology. Influenza POCTs were performed on all patients requiring urgent hospitalization, regardless of the admission reason. A total of 9327 patients were included in the study, of which 411 (4.4%) tested positive for influenza A/B. Asymptomatic infection and mild illness accounted for respectively 11.2% (95% CI: 8.5%-14.6%), and 43.3% (95% CI: 38.6%-48.1%) of the cases. A total of 66% (95% CI: 60%-72%) of all patients in these symptom categories (asymptomatic and mild illness) showed a high viral load (cycle threshold [Ct] < 24). Only in 30 (7.3%, 95% CI: 5.2%-10.2%) of all cases and in two (4.4%, 95% CI: 1.2%-14.5%) of the asymptomatic cases, the symptomatology worsened during hospital stay. Coinfections with both influenza and SARS-CoV-2 occurred in 35 patients (8.5% of all influenza positive patients). There was no difference in symptomatology between patients with co-infections and those with an influenza mono-infection. Patients could not be reliably categorized into carriers with low versus high viral loads based on symptomatology, age, and vaccination status. More than half of the influenza-positive individuals were either asymptomatic or had mild symptoms upon admission, while often carrying high viral loads. Our results show that without screening of patients at hospital admission, a considerable number of patients with a high viral load may be incorrectly classified as being not infectious., (© 2025 Wiley Periodicals LLC.)

Epilepsy is a complex disease in the brain. Complete control of seizure has always been a challenge in epilepsy treatment. Currently, clinical management primarily involves pharmacological and surgical interventions, with the former being the preferred approach. However, antiepileptic drugs often exhibit low bioavailability due to inherent limitations such as poor water solubility and difficulty penetrating the blood-brain barrier (BBB). These issues significantly reduce the drugs' effectiveness and limit their clinical application in epilepsy treatment. Additionally, the diagnostic accuracy of current imaging techniques and electroencephalography (EEG) for epilepsy is suboptimal, often failing to precisely localize epileptogenic tissues. Accurate diagnosis is critical for the surgical management of epilepsy. Thus, there is a pressing need to enhance both the therapeutic outcomes of epilepsy medications and the diagnostic precision of the condition. In recent years, the advancement of nanotechnology in the biomedical sector has led to the development of nanomaterials as drug carriers. These materials are designed to improve drug bioavailability and targeting by leveraging their large specific surface area, facile surface modification, ability to cross the BBB, and high biocompatibility. Furthermore, nanomaterials have been utilized as contrast agents in imaging and as materials for EEG electrodes, enhancing the accuracy of epilepsy diagnoses. This review provides a comprehensive examination of current research on nanomaterials in the treatment and diagnosis of epilepsy, offering new strategies and directions for future investigation., Competing Interests: Declarations. Ethics Approval: Not applicable. Consent to Participate: Not applicable. Consent for Publication: All authors agreed with the submitted version of the manuscript. Competing Interests: The author declares no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)