Search Results (120)

Background/Objectives: Fear of missing out (FOMO) refers to the pervasive experience of worrying that others may be having rewarding or meaningful experiences from which one is absent or excluded. FOMO has been linked with both sleep disturbances and poor mental health outcomes, particularly in emerging adults (ages 18–29). This study tested whether insomnia symptoms mediate the relationship between FOMO and depressive symptoms in emerging adults and whether gender moderates the links between FOMO, insomnia symptoms, and depression symptoms. Methods: We conducted a secondary analysis of cross-sectional survey data from 849 emerging adults in the United States. Participants completed validated measures of FOMO, insomnia symptoms, and depression symptoms. We tested our hypotheses using regression models in SPSS version 29 and mediation and moderation models via the PROCESS macro. Analyses included age, race/ethnicity, and education as covariates. Results: FOMO predicted greater insomnia severity and more depression symptoms, and insomnia severity partially mediated the link between FOMO and depression symptoms. The FOMO–insomnia association was moderated by gender, with a stronger link among men. Conclusions: These findings suggest that insomnia is a plausible mechanism linking FOMO to depression in emerging adults. Gender differences suggest that FOMO may disproportionately disrupt sleep in men and highlight the need for tailored prevention efforts to target both FOMO and sleep disruption among emerging adults. Full article

Knowledge Graph Reasoning (KGR) aims to deduce missing or novel knowledge by learning structured information and semantic relationships within Knowledge Graphs (KGs). Despite significant advances achieved by deep neural networks in recent years, existing models typically extract non-linear representations from explicit features in a relatively simplistic manner and fail to fully exploit semantic heterogeneity of relation types and entity co-occurrence frequencies. Consequently, these models struggle to capture critical predictive cues embedded in various entities and relations. To address these limitations, this paper proposes a relation aware spectral decoupling attention network for KGR (RASD). First, a spectral decoupling attention network module projects joint embeddings of entities and relations into the frequency domain, extracting features across different frequency bands and adaptively allocating attention at the global level to model frequency specific information. Next, a relation-aware learning module employs relation aware filters and an augmentation mechanism to preserve distinct relational properties and suppress redundant features, thereby enhancing representation of heterogeneous relations. Experimental results demonstrate that RASD achieves significant and consistent improvements over multiple leading baseline models on link prediction tasks across five public benchmark datasets. Full article

The ZC4H2 gene is the site of congenital mutations linked to neurodevelopmental and musculoskeletal pathologies collectively termed ZARD (ZC4H2-Associated Rare Disorders). ZC4H2 consists of a coiled coil and a single novel zinc finger with four cysteines and two histidines, from which the protein obtains its name. Alpha Fold 3 confidently predicts a structure for the zinc finger but also for similarly sized random sequences, providing equivocal information on its folding status. We show using synthetic peptide fragments that the zinc finger of ZC4H2 is genuine and folds upon binding a zinc ion with picomolar affinity. NMR pH titration of histidines and UV–Vis of a cobalt complex of the peptide indicate its four cysteines coordinate zinc, while two histidines do not participate in binding. The experimental NMR structure of the zinc finger has a novel structural motif similar to RANBP2 zinc fingers, in which two orthogonal hairpins each contribute two cysteines to coordinate zinc. Most of the nine ZARD mutations that occur in the ZC4H2 zinc finger are likely to perturb this structure. While the ZC4H2 zinc finger shares the folding motif and cysteine-ligand spacing of the RANBP2 family, it is missing key substrate-binding residues. Unlike the NZF branch of the RANBP2 family, the ZC4H2 zinc finger does not bind ubiquitin. Since the ZC4H2 zinc finger occurs in a single copy, it is also unlikely to bind DNA. Based on sequence homology to the VAB-23 protein, the ZC4H2 zinc finger may bind RNA of a currently undetermined sequence or have alternative functions. Full article

Background: Acute heart failure (AHF) is a major cause of hospitalizations, posing significant challenges to healthcare systems. Despite advancements in management, the rate of poor outcomes remains high globally, emphasizing the need for timely interventions. This study aimed to identify early admission-based factors predictive of poor outcomes in hospitalized AHF patients, in order to contribute to early risk stratification and optimize patient care. Methods: This retrospective single-center study analyzed routine data of adult patients hospitalized for AHF at a public university teaching hospital in Switzerland. Outcomes included in-hospital death, intensive care (ICU) treatment, and length of hospital stay (LOHS). Potential predictors were limited to routine parameters, readily available at admission. Missing predictor data was imputed and predictors were identified by means of multivariable regression analysis. Results: Data of 638 patients (median age 84 years, range 45–101 years, 50% female) were included in the study. In-hospital mortality was 7.1%, ICU admission rate 3.8%, and median LOHS was 8 days (IQR 5–12). Systolic blood pressure ≤ 100 mmHg (Odds ratio (OR) 3.8, p = 0.009), peripheral oxygen saturation ≤ 90% or oxygen supplementation (OR 5.9, p < 0.001), and peripheral edema (OR 2.7, p = 0.044) at hospital admission were identified as predictors of in-hospital death. Furthermore, a stroke or transient ischemic attack in the patient’s history (OR 3.2, p = 0.023) was associated with in-hospital death. ICU admission was associated with oxygen saturation ≤ 90% or oxygen supplementation (OR 22.9, p < 0.001). Factors linked to longer LOHS included oxygen saturation ≤ 90% or oxygen supplementation (IRR 1.2, p < 0.001), recent weight gain (IRR 1.1, p = 0.028), and concomitant chronic kidney disease (IRR 1.2, p < 0.001). Conclusions: This study validated established predictors of AHF outcomes in a Swiss cohort, highlighting the predictive value of poor perfusion status, fluid overload, and comorbidities such as chronic kidney disease. The identified predictors imply potential for developing tools to improve rapid treatment decisions. Future research should focus on the prospective external validation of the identified predictors and the design and validation of risk scores, incorporating these parameters to optimize early interventions and reduce adverse outcomes in AHF. Full article

Plant modeling uses mathematical and computational methods to simulate plant structures, physiological processes, and interactions with various environments. In precision agriculture, it enables the digital monitoring and prediction of crop growth, supporting better management and efficient resource use. Wheat, as a major global staple, is vital for food security. However, wheat stripe rust, a widespread and destructive disease, threatens yield stability. The paper proposes wheat cultivation suitability evaluation with stripe rust disease using an agriculture group consensus framework (WCSE-AGC) to tackle this issue. Assessing stripe rust severity in regions relies on wheat pathologists’ judgments based on multiple criteria, creating a multi-attribute, multi-decision-maker consensus problem. Limited regional coverage and inconsistent evaluations among wheat pathologists complicate consensus-reaching. To support wheat pathologist participation, this study employs artificial-intelligence-generated content (AIGC) techniques by using Claude 3.7 to simulate wheat pathologists’ scoring through role-playing and chain-of-thought prompting. WCSE-AGC comprises three main stages. First, a graph neural network (GNN) models trust propagation within wheat pathologists’ social networks, completing missing trust links and providing a solid foundation for weighting and clustering. This ensures reliable expert influence estimations. Second, integrating secretary bird optimization (SBO), K-means, and three-way clustering detects overlapping wheat pathologist subgroups, reducing opinion divergence and improving consensus inclusiveness and convergence. Third, a two-stage optimization balances group fairness and adjustment cost, enhancing consensus practicality and acceptance. The paper conducts experiments using publicly available real wheat stripe rust datasets from four different locations, Ethiopia, India, Turkey, and China, and validates the effectiveness and robustness of the framework through comparative and sensitivity analyses. Full article

Ambient fine particulate matter of size less than 2.5 μm in aerodynamic diameter (PM_2.5) is a key ambient air pollutant that has been linked to numerous adverse health outcomes. Reliable estimates of PM_2.5 are important for supporting epidemiological and health impact assessment studies. Precise measurements of PM_2.5 are available through networks of monitors; however, these are spatially sparse and temporally incomplete. Chemical transport model (CTM) simulations and satellite-retrieved aerosol optical depth (AOD) measurements are two data sources that have been used to develop prediction models for PM_2.5 at fine spatial resolutions with increased spatial coverage. As part of the Multi-Angle Imager for Aerosols (MAIA) project, a geostatistical regression model has been developed to bias-correct AOD, followed by Bayesian ensemble averaging to gap-fill missing AOD values with CTM simulations. Here, we present a suite of statistical software (available in the R package ensembleDownscaleR) to facilitate the adaptation of this modeling approach to other settings and air quality modeling applications. We describe the Bayesian ensemble averaging approach, model specifications, estimation methods, and evaluation via cross-validation that is implemented in the software. We also provide a case study of estimating PM_2.5 using 2018 data from the Los Angeles metropolitan area with an accompanying tutorial. All code is fully reproducible and available on GitHub, data are made on Zenodo, and the ensembleDownscaleR package is available for download on GitHub. Full article

Background and Objectives: Over the past few decades, a substantial body of evidence has linked periodontitis to systemic diseases—including hypertension—but the mechanisms underlying this association are not fully understood. This study aims to identify the factors that may mediate this relationship, including an analysis of the inflammatory biomarker NLRP3 and IL-1β levels in serum and saliva in patients with both diseases. Materials and Methods: This study included 108 individuals (mean age, 47.8 years, SD 12.8), 38.9% male and 61.1% female. The participants were divided into four groups: Group I—26 healthy participants; Group II—24 participants with periodontitis; Group III—26 participants with hypertension; and Group IV—32 participants with both periodontitis and hypertension. Clinical examinations were performed to diagnose hypertension and periodontitis, including a survey and blood tests in all patients. NLRP3 and IL-1β levels in serum and saliva were measured using ELISA. Results: Patients with periodontitis and hypertension were significantly older than those without these conditions (respectively, p < 0.001 and p < 0.001) and had more missing teeth (respectively, p < 0.001 and p = 0.037). Higher values were found in the periodontitis and hypertension group than in healthy individuals for VLDL (p = 0.001), triglycerides (p = 0.001), CRP (p = 0.003), WBC (p = 0.007), blood sugar (p = 0.002), total cholesterol (p = 0.003), and LDL (p = 0.010). Significantly higher levels of NLRP3 in saliva (p = 0.038) and serum (p = 0.021) were observed in patients with periodontitis than in those without periodontitis. Significant correlations were found between serum NLRP3 levels and the presence of hypertension (p = 0.001) and between saliva IL-1β levels and the presence of hypertension (p = 0.010). Serum NLRP3 levels demonstrated a predictive value for hypertension (AUC 0.693, 95% CI 0.590–0.796, and p = 0.001), with an established cutoff value of 0.68 ng/mL (sensitivity 0.623, specificity 0.630). Conclusions: The higher levels and correlations of pro-inflammatory markers in serum and saliva observed in patients with periodontitis and hypertension support the hypothesis of a relationship between these diseases, likely mediated by low-grade systemic inflammation. Full article

Near-UV circular dichroism (CD) spectroscopy is a widely used method that provides, among others, information about the tertiary structure of biomolecular systems such as proteins, RNA, or DNA. Experimental near-UV CD spectra of proteins reflect the CD signals averaged over the many conformations that these systems can adopt. Theoretical approaches have been developed to predict such spectroscopic properties and link modeled conformations of complex biosystems to easily accessible experimental data, without having the resort to costly structural biology techniques. However, these predictions are mostly generated on the basis of a single experimental structure, missing the dynamic information reflecting the protein conformational variability. Here, we describe a complete reformulation of the theoretical foundations behind the prediction of CD spectra. We propose a QM/MM-based automated pipeline that generates an average near-UV CD spectrum from a given MD ensemble in a fast manner based on these theoretical considerations and further test it on protein systems. This pipeline has been implemented in an open-source program called DichroProt. Full article

Fear of missing out (FoMO) creates a strong urge to stay continuously connected and informed about peers’ activities, identified as a risk factor for problematic social media use and risky behaviors. Sexting is generally defined as the exchange of sexually suggestive or explicit photos, videos, or text messages through cell phones or other technologies. Despite its social relevance, the link between FoMO and sexting remains underexplored. This study examines their relationship in young adults—an understudied group compared to adolescents—while controlling for age, sex, and sexual orientation. The study surveyed 911 Italian young adults (18–30 years, M_age = 22.3, SD_age = 2.57, 74% women, 70.4% heterosexual) through an online questionnaire. The results indicate that FoMO predicts only risky sexting behaviors (sexting under substance use and sexting for emotion regulation) while not influencing experimental sexting (sending one’s own sexts). Additionally, the link between FoMO and sexting for emotion regulation is stronger among LGB individuals. Therefore, FoMO has proven to be strongly related to the two kinds of risky sexting but not to experimental sexting. Understanding this relationship can inform prevention and intervention programs on relationships, online communication, and sexting in young adults. Full article

Dynamic network link prediction refers to the prediction of possible future links or the identification of missing links on the basis of historical information of dynamic networks. Link prediction aids people in exploring and analyzing complex change patterns in the real world and it could be applied in personalized recommendation systems, intelligence analysis, anomaly detection, and other fields. This paper aims to provide a comprehensive review of dynamic network link prediction. Firstly, dynamic networks are categorized into dynamic univariate networks and dynamic multivariate networks according to the changes in their sets. Furthermore, dynamic network link prediction algorithms are classified into regular sampling and irregular sampling by the method of network sampling. After summarizing and comparing the common datasets and evaluation indicators for dynamic network link prediction, we briefly review classic related algorithms in recent years, and classify them according to the network changes, sampling methods, underlying principles of algorithms, and other classification methods. Meanwhile, the basic ideas, advantages, and disadvantages of these algorithms are discussed in detail. The application fields and challenges in this area are also summarized. In the final summary of the paper, the future research directions such as link prediction in dynamic heterogeneous weighted networks and the security issues brought about by link prediction are discussed. Full article

With the rapid growth of internet mobile technology, recent research has increasingly focused on the mental health challenges faced by young people, particularly in relation to social media use. One significant concern is the impact of the fear of missing out (FoMO) and online social anxiety, yet the underlying mechanisms that link these factors remain largely unexplored. This study aims to address this gap by investigating the role of FoMO in predicting online social anxiety among university students, with a particular focus on understanding how irrational procrastination and media multitasking may mediate this relationship. In total, 451 university students completed a survey on demographics, FoMO, online social anxiety, irrational procrastination, and media multitasking questionnaires. After controlling for demographic variables, the findings revealed that (a) FoMO showed a significant positive correlation with online social anxiety; (b) the connection between FoMO and online social anxiety in university students was partially mediated by irrational procrastination; and (c) the connection between FoMO and online social anxiety in university students was partially mediated by media multitasking. This research contributes to the understanding of the psychological mechanisms that link FoMO to online social anxiety, offering insights that can inform interventions aimed at improving university students’ mental health in the digital age. Full article

Pedunculate oak (Quercus robur L.) is widely distributed across Europe and serves critical ecological, economic, and recreational functions. Investigating its responses to stressors such as drought, extreme temperatures, pests, and pathogens provides valuable insights into its capacity to adapt to climate change. Genetic and dendrochronological studies offer complementary perspectives on this adaptability. Tree-ring analysis (dendrochronology) reveals how Q. robur has historically responded to environmental stressors, linking growth patterns to specific conditions such as drought or temperature extremes. By examining tree-ring width, density, and dynamics, researchers can identify periods of growth suppression or enhancement and predict forest responses to future climatic events. Genetic studies further complement this by uncovering adaptive genetic diversity and inheritance patterns. Identifying genetic markers associated with stress tolerance enables forest managers to prioritize the conservation of populations with higher adaptive potential. These insights can guide reforestation efforts and support the development of climate-resilient oak populations. By integrating genetic and dendrochronological data, researchers gain a holistic understanding of Q. robur’s mechanisms of resilience. This knowledge is vital for adaptive forest management and sustainable planning in the face of environmental challenges, ultimately helping to ensure the long-term viability of oak populations and their ecosystems. The topics covered in this review are very broad. We tried to include the most relevant, important, and significant studies, but focused mainly on the relatively recent Eastern European studies because they include the most of the species’ area. However, although more than 270 published works have been cited in this review, we have, of course, missed some published studies. We apologize in advance to authors of those relevant works that have not been cited. Full article

JC polyomavirus (JCPyV) establishes a persistent, asymptomatic kidney infection in most of the population. However, JCPyV can reactivate in immunocompromised individuals and cause progressive multifocal leukoencephalopathy (PML), a fatal demyelinating disease with no approved treatment. Mutations in the hypervariable non-coding control region (NCCR) of the JCPyV genome have been linked to disease outcomes and neuropathogenesis, yet few metanalyses document these associations. Many online sequence entries, including those on NCBI databases, lack sufficient sample information, limiting large-scale analyses of NCCR sequences. Machine learning techniques, however, can augment available data for analysis. This study employs a previously compiled dataset of 989 JCPyV NCCR sequences from GenBank with associated patient PML status and viral tissue source to train multilayer perceptrons for predicting missing information within the dataset. The PML status and tissue source models were 100% and 87.8% accurate, respectively. Within the dataset, 348 samples had an unconfirmed PML status, where 259 were predicted as No PML and 89 as PML sequences. Of the 63 sequences with unconfirmed tissue sources, eight samples were predicted as urine, 13 as blood, and 42 as cerebrospinal fluid. These models can improve viral sequence identification and provide insights into viral mutations and pathogenesis. Full article

The implementation of biological advancements in agricultural production is the response to the needs of the agricultural sector in the 21st century, enabling increased production and improved food quality. Biological progress in the maize breeding and seed industries is unique in terms of their social and ecological innovation aspects. It affects agricultural productivity and the adaptation of cultivated maize varieties to market demands and changing climate conditions without compromising the environment. Modern maize resistance breeding relies on a wide range of molecular genetic research techniques. These technologies enable the identification of genomic regions associated with maize smut resistance, which is crucial for characterizing and manipulating these regions. Therefore, the aim of this study was to identify molecular markers (SilicoDArT and SNP) linked to candidate genes responsible for maize smut resistance, utilizing next-generation sequencing, as well as association and physical mapping. By using next-generation sequencing (NGS) and statistical tools, the analyzed maize genotypes were divided into heterotic groups, which enabled the prediction of the hybrid formula in heterosis crosses. In addition, Illumina sequencing identified 60,436 SilicoDArT markers and 32,178 SNP markers (92,614 in total). For association mapping, 32,900 markers (26,234 SilicoDArT and 6666 SNP) meeting the criteria (MAF > 0.25 and the number of missing observations < 10%) were used. Among the selected markers, 61 were highly statistically significant (LOD > 2.3). Among the selected 61 highly statistically significant markers (LOD > 2.3), 10 were significantly associated with plant resistance to maize smut in two locations (Smolice and Kobierzyce). Of the 10 selected markers, 3 SilicoDArT (24016548, 2504588, 4578578) and 3 SNP (4779579, 2467511, 4584208) markers were located within genes. According to literature reports, of these six genes, three (ATAD3, EDM2, and CYP97A3) are characterized proteins that may play a role in the immune response that develops in response to corn smut infection. In the case of genotypes belonging to the same origin groups, markers linked to these genes can be used to select varieties resistant to corn smut. These markers will also be tested on genotypes belonging to other maize origin groups to demonstrate their universality. Full article

Healthcare researchers are increasingly utilizing smartphone sensor data as a scalable and cost-effective approach to studying individualized health-related behaviors in real-world settings. However, to develop reliable and robust digital behavioral signatures that may help in the early prediction of the individualized disease trajectory and future prognosis, there is a critical need to quantify the potential variability that may be present in the underlying sensor data due to variations in the smartphone hardware and software used by large population. Using sensor data collected in real-world settings from 3000 participants’ smartphones for up to 84 days, we compared differences in the completeness, correctness, and consistency of the three most common smartphone sensors—the accelerometer, gyroscope, and GPS— within and across Android and iOS devices. Our findings show considerable variation in sensor data quality within and across Android and iOS devices. Sensor data from iOS devices showed significantly lower levels of anomalous point density (APD) compared to Android across all sensors (p  <  1 × 10⁻⁴). iOS devices showed a considerably lower missing data ratio (MDR) for the accelerometer compared to the GPS data (p  <  1 × 10⁻⁴). Notably, the quality features derived from raw sensor data across devices alone could predict the device type (Android vs. iOS) with an up to 0.98 accuracy 95% CI [0.977, 0.982]. Such significant differences in sensor data quantity and quality gathered from iOS and Android platforms could lead to considerable variation in health-related inference derived from heterogenous consumer-owned smartphones. Our research highlights the importance of assessing, measuring, and adjusting for such critical differences in smartphone sensor-based assessments. Understanding the factors contributing to the variation in sensor data based on daily device usage will help develop reliable, standardized, inclusive, and practically applicable digital behavioral patterns that may be linked to health outcomes in real-world settings. Full article