Browse Articles
Discover research articles across all indexed journals
Identification and characterization of eccDNA-driven genes in humans
Extrachromosomal circular DNA (eccDNA) amplification promotes oncogene expression and cancer development. However, the global transcriptional landscape mediated by eccDNA has not yet been extensively profiled. Here we report a comprehensive analysis spanning cancer, non-cancerous disease and health by developing a new approach to catalog eccDNA-driven genes (EDGs). EDG expression is significantly higher than the average level. Our study identifies 27 common EDGs (CEDGs) existing in most cancer types. Integrated analysis of the CEDGs on gene expression, pathway and network, genetic alteration, epigenetic state, single-cell state, immune infiltration, microbiome and clinically-related features reveals their crucial roles in tumorigenesis and clinical significance. A 17-gene CEDG signature and nomogram was constructed to predict pan-cancer patients’ outcomes. By a novel eccDriver algorithm, 432 candidate eccDNA-driven drivers were identified. We show the candidate drivers regulate five major biological processes including immune system process, developmental process, metabolic process, cell cycle and division, and regulation of transport. 275 of the 432 candidate drivers are clinically actionable with approved drugs. We also demonstrate that eccDNA generation is associated with DNA methylation. Our study reveals general EDG function in humans and provides the most comprehensive discovery of eccDNA-driven driver genes in cancer and non-cancerous diseases to date for future research and application.
Performance improvement of DC motor control system using PID controller with Kookaburra and Red Panda optimization algorithm
Editorial Note: Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study
A novel framework for neutron-gamma density logging: semi-empirical modeling, directional neutron sources, and experimental benchmarking
Visual feature analysis on selective appetite in individuals with autism spectrum disorders
Background Individuals with autism spectrum disorders (ASD) experience more severe selective eating problems than their neurotypical peers. Identifying the causes of selective eating behavior poses a considerable challenge, even for caregivers. Accurate identification of the underlying causes of this behavior is essential for developing interventions aimed at overcoming dysfunctional, unbalanced diets. However, studies that meticulously identify the causes of selective eating behaviors are scarce. This investigation aims to explore the differences in preferences for sunny-side-up eggs between individuals with ASD and those with typical development (TD), focusing on the factors influencing their likes and dislikes through a systematic analysis of visual features. Method Thirty-nine individuals with ASD (mean age, 23.4 ± 4.7 years; 82% men) and fifty individuals with TD (mean age, 22.2 ± 1.3 years; 64% men) participated in this study. We used a total of 50 images of sunny-side-up eggs as visual stimuli. Using Non-negative Matrix Factorization and Decision Tree analysis, factors associated with visual preferences for sunny-side-up eggs were identified. Data and Results We could identify factors associated with visual preferences for sunny-side-up eggs. Subsequent linear regression analysis provided insight into how these visual features delineate preference boundaries between liked and disliked foods, with noteworthy distinctions emerging between the ASD and TD groups. Conclusions This study provides novel insights into the visual determinants of food preferences in individuals with ASD through systematic analysis of image features. Our findings indicated the potential to predict preferences while elucidating the causes of selective eating behaviors, thereby offering solutions for individuals with ASD.
Heavy metal synergistic pollution risk assessment in the soil-crop system of the nanyang basin
A deep dive into the coelacanth phylogeny
The discovery in 1938 of a living coelacanth, Latimeria chalumnae, triggered much research and discussion on the evolutionary history and phylogeny of these peculiar sarcopterygian fishes. Indeed, coelacanths were thought to represent the ‘missing link’ between fishes and tetrapods, a phylogenetic position which is now dismissed. Since the first analyses using a phylogenetic approach were carried out three decades ago, a relatively similar data matrix has been consistently used by researchers for running analyses, with no significant changes aside from the addition of new taxa and characters, and minor corrections to the states’ definition and scorings. Here, we investigate the phylogeny of Actinistia with an updated data matrix based on a list of partially new or modified characters. From the initial list of characters available in the most recent studies, we removed 16 characters, modified 16 other characters’ definition and added 18 new characters, resulting in a list of 112 characters. We also revised the data matrix by correcting 171 miscoding found for 37 taxa. Based on the new phylogeny, we propose a new classification of coelacanths including 46 coelacanth genera, part of them allocated within nine families and four sub-families. Most of these groups were already named but were not recognised as clades, or poorly or not diagnosed in previous phylogenetic analyses. We provide several new or emended diagnoses for each clade. For the first time, a set of Palaeozoic coelacanth genera are found gathered within a clade, namely the Diplocercidae. All Mesozoic coelacanths, including extant Latimeria, are resolved as members of the order Coelacanthiformes, a clade that arose in the Permian, with Coelacanthus diverging first. We also found that most Mesozoic coelacanths are gathered into a clade, the Latimerioidei, itself divided into the Latimeriidae and the Mawsoniidae, each of which is divided into two subfamilies. Although these important changes, the new phylogeny of the Actinistia shows no significant alteration, and it remains relatively similar compared to previous studies. This demonstrates that the coelacanth phylogeny is now rather stable despite the weak support for most nodes in the phylogeny, and despite the difficulty of defining relevant morphological characters to score in this relatively slowly evolving lineage.
Correction: A climate vulnerability assessment of the fish community in the Western Baltic Sea
The various effect of social isolation on depression risk among old population in China during covid-19 pandemic: A population based survey
Background The aim of the current study is to assess various effect of social isolation on depression risk among older adults during COVID-19 in China. Methods Data was obtained from the China Longitudinal Ageing Social Survey (CLASS) conducted in 2020. A total of 9883 participants were included. Depression status was assessed by 9-item Center for Epidemiological Studies Depression Scale (CES-D). Social isolation was assessed by Lubben Social Network Scale-6 (LSNS-6). The odds ratios of depression risk according to LSNS-6 categories were obtained using a logistic regression model with adjustment for potential confounding variables. Results The prevalence of depression was 31.2%, and the presence of social isolation was 37.9% during the COVID-19 among older population. A decrease in depression risk was observed with reduced isolation. The odds ratio for the lowest versus highest was 0.75 (95% confidence interval: 0.63, 0.89; Ptrend = 0.012). Friend support reduced depression risk more significantly than family support. The association between LSNS-6 friend subscale and depression risk was differentiated by LSNS-6 family subscale. In men, LSNS-6 friend subscale tended to be associated with depression risk inversely when their LSNS-6 family subscale was less than 6 (interaction P = 0.041). Similar associations and stratified modifications were observed among those who lived in rural areas (interaction P = 0.002), married (interaction P = 0.003), Han (interaction P = 0.01), lived with others (interaction P = 0.001), and so on. Conclusions Most depression cases were found to be strongly associated with social isolation during the pandemic. Our findings have provided empirical evidence for researchers to understand the association between social isolation and depression, which could help them evaluate and manage depression promptly. Because of the cross-sectional design, we cannot establish causal relationships between depression and isolation.
Occupational fall incidence associated with heated tobacco product use and lifestyle behaviors in Japan
Leveraging technology to probe mechanisms of psychopathology: A proof of concept study of inhibitory control
Objective Quantifying relevant behavioral mechanisms has relied on rigorous, time-consuming tools restricted to laboratory settings and inaccessible to the clinical community. Advances in technology provide an opportunity to develop more accessible platforms. Here, we developed CALM-IT, a novel mobile-application to experimentally assess inhibitory control in vivo Method In a transdiagnostic sample of 200 youth aged 8–20, we (i) apply knowledge from canonical inhibitory control tasks in the methodological design of the mobile application, (ii) establish feasibility and engagement with CALM-IT, (iii) assess test-retest reliability of CALM-IT, (iv) investigate the convergent validity of CALM-IT with behavioral and neural responses to laboratory-based tasks, and (v) probe clinical relevance via associations with clinical symptoms. Results First, we provide evidence that our novel inhibitory control mobile application, CALM-IT, was accessible, feasible, and engaging. Second, we found performance was reliable over time. Third, we found CALM-IT performance was associated with established measures of inhibitory control and activation in the bilateral inferior frontal gyrus. Associations with brain but not behavior survived after controlling for age. Finally, we found evidence linking impaired CALM-IT performance to increased levels of co-occurring anxiety, irritability, and attention deficit hyperactivity disorder (ADHD) symptoms. Conclusion Validation of this neuroscience-informed mobile application represents a critical first step in bridging precise, mechanism-driven research and community-based assessment of childhood psychopathology. The present work lays the groundwork for future research that could provide researchers and clinicians with a multifaceted tool to measure clinically-relevant behaviors in an engaging and accessible manner.
Enhanced absorption in organic solar cells via combined anti-reflection and Mie resonance effects in subwavelength ellipsoidal dielectric nanostructures
Exploring factors impacting Hispanic/Latinx individuals’ response to a type 2 diabetes digital storytelling intervention
Background Hispanic/Latinx individuals have high prevalence of type 2 diabetes and its complications yet often face barriers in accessing diabetes prevention and self-management interventions. One possible approach is to implement digital storytelling interventions, which involve narrative-driven videos made by individuals who have lived experience with particular conditions or illnesses. These stories can inspire viewers with similar life experiences to change behaviors or attitudes. Little is known about which characteristics influence how individuals respond to digital storytelling interventions with healthful behaviors and improved outcomes – information necessary to further tailor these interventions to improve type 2 diabetes outcomes. Previously, the Rochester Healthy Community Partnership used the digital storytelling process to develop Stories for Change Diabetes and tested intervention effectiveness. Methods We conducted a secondary analysis to examine the sociodemographic and disease-related factors that affected participants’ responses to the Stories for Change Diabetes intervention. Drawing on Social Cognitive Theory and Culture-Centric Health Promotion principles, we analyzed results from the 227 intervention participants stratified by whether they experienced a clinically meaningful decrease (>0.5%) in hemoglobin A1c between baseline and three-month follow-up. We then used multivariable logistic regressions to identify factors associated with change in hemoglobin A1c. Results Participants with diabetes duration <5 years and/or whose diabetes self-efficacy improved between baseline and 3-month follow-up were more likely to experience a meaningful decrease in hemoglobin A1c at three months (compared to participants without those characteristics). Conclusions These findings will provide insight into how digital storytelling interventions can be effectively tailored to Hispanic/Latinx individuals most likely to benefit. Trial registration: Not applicable
Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity
Abstract Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models like Markov models often fail to capture the complex temporal dependencies and distributional nuance inherent in eye gaze trajectories data. This study introduces a Generative Adversarial Network (GAN) framework employing Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) generators and discriminators to generate high-fidelity synthetic eye gaze velocity trajectories. We conducted a comprehensive evaluation of four GAN architectures: CNN-CNN, LSTM-CNN, CNN-LSTM, and LSTM-LSTM–trained under two conditions: using only adversarial loss ( $$L_G$$ ) and using a weighted combination of adversarial and spectral losses. Our findings reveal that the LSTM-CNN architecture trained with this new loss function exhibits the closest alignment to the real data distribution, effectively capturing both the distribution tails and the intricate temporal dependencies. The inclusion of spectral regularization significantly enhances the GANs’ ability to replicate the spectral characteristics of eye gaze movements, leading to a more stable learning process and improved data fidelity. Comparative analysis with a Hidden Markov Model (HMM) optimized to four hidden states further highlights the advantages of the LSTM-CNN GAN. Statistical metrics show that the HMM-generated data significantly diverges from the real data in terms of mean, standard deviation, skewness, and kurtosis. In contrast, the LSTM-CNN model closely matches the real data across these statistics, affirming its capacity to model the complexity of eye gaze dynamics effectively. These results position the spectrally regularized LSTM-CNN GAN as a robust tool for generating synthetic eye gaze velocity data with high fidelity. Its ability to accurately replicate both the distributional and temporal properties of real data holds significant potential for applications in simulation environments, training systems, and the development of advanced eye-tracking technologies, ultimately contributing to more naturalistic and responsive human-computer interactions.
Estimating sales transitions between competing products via optimal transport
In mature markets, where products are widely adopted, understanding how customers switch between competing products is crucial for companies to conduct effective marketing actions. However, due to privacy regulations, it is increasingly difficult to obtain point-of-sale (POS) data with individual customer identifiers (IDs). In this paper, we propose a method that estimates how sales shift between products using aggregated POS data without customer IDs. We formulate this as an optimal transport problem aimed at minimizing the total cost of brand-switching and introduce two regularization terms based on assumptions about sales transitions. We then solve the optimization problem with these regularizations using a projected gradient method. We validated our approach on proprietary POS data from the Japanese beverage industry and found that the estimated transitions aligned with real market changes. For instance, during a liquor tax reform period, customers switched from products whose tax rates increased to those with lower rates. In the coffee market, many customers moved toward a newly launched brand. Although these results suggest that our method can capture market dynamics, the proprietary data limits reproducibility. In addition, the absence of customer IDs makes it impossible to track individual customer transitions. Incorporating such identifiers in future research could offer more deeper insights into consumer behavior.
S100 A16 promotes the progression of osteosarcoma by activating the PI3 K/AKT signaling pathway through ANXA2
Abstract Osteosarcoma is a common primary malignant bone tumor. S100A16 gene was reported to highly expressed in several tumor tissues while the relationship between S100A16 and osteosarcoma remains less well-understood. This study aimed to investigate the expression characteristics of S100A16 in osteosarcoma and the mechanism by which it promotes osteosarcoma progression. Firstly, by analyzing databases and assessing mRNA and protein level, we found that the expression of S100A16 was significantly promoted in osteosarcoma, as compared with normal tissue. Then transfection techniques were employed to upregulate and downregulate S100A16 in osteosarcoma cells, the results demonstrated that S100A16 can increase osteosarcoma cell viability, migration and invasion capacities, while decline osteosarcoma cell apoptosis. GSEA (gene set enrichment analysis) revealed that increased expression of S100A16 was enriched in the PI3K/AKT pathway. Cellular experiments showed that the S100A16 promoted osteosarcoma progression by activating the PI3K/AKT signaling pathway, and upregulated expression of ANXA2, a crucial protein in occurrence and development of tumors. We also found that overexpression of ANXA2 can restore the decreased levels of p-PI3K and p-AKT induced by S100A16 inhibition, which indicated that S100A16 stimulates PI3K/AKT pathway activation via ANXA2. To sum up, S100A16 can promotes osteosarcoma progression by activating the PI3K/AKT signaling pathway through ANXA2, suggesting that the S100A16/ANXA2 axis may represent a novel therapeutic target for osteosarcoma.
The TMA team and TTP pathway improved outcomes in a cohort with Thrombotic thrombocytopenic purpura
Background Providing optimal care for patients with thrombotic thrombocytopenic purpura (TTP) is challenging because of multiple involved specialties, knowledge gaps, and a high rate of disease relapse. A thrombotic microangiopathy (TMA) Team and TTP Pathway could improve outcomes. Objectives To assess if a structured TTP Pathway, supported by a TMA Team, improved TTP care by reducing TTP relapse and TTP-related death (TTP-RRD) at a rural Appalachian medical center. Methods Prospective cohort quality improvement project using the DMAIC quality improvement framework (Define, Measure, Analyze, Improve, Control) to develop a TMA Team and TTP Pathway. Pathway care included standardized use of therapeutic plasma exchange (TPE), rituximab, caplacizumab, as well as improved coordination between medical services, and regular outpatient biochemical TTP surveillance. Outcomes were determined by retrospective chart review for patients with acute TTP treated with usual care (N = 16 episodes) and the TTP Pathway (N = 16 episodes). Results and conclusions All patients had acquired TTP. TTP-RRD at 90 days was reduced from 69% with usual care to 6% with Pathway care (95% CI 0.35 to 0.90, P = 0.0004), a relative risk reduction of 91%; TTP relapse alone at 90 days was reduced from 62% to 0% (95% CI 0.36 to 0.88, P = 0.0002) with Pathway care. The number needed to treat to prevent TTP-RRD was 1.59 at 90 days. Over the project duration usual care demonstrated a hazard ratio for TTP-RRD of 12.58 compared to Pathway care. With the intervention, the duration of TPE was increased (median 6 vs 12 sessions, P < 0.05), as was use of rituximab (31.3% vs 93.8%, 95% CI −0.36 to −0.88, P = 0.003), and caplacizumab (6.3% vs 62.5%, 95% CI −0.027 to −0.81, P = 0.001). All Pathway patients underwent biochemical surveillance, and 31% had pre-emptive rituximab to reduce possibility of clinical relapse. A structured TTP Pathway significantly reduces morbidity and aligns care with modern clinical guidelines. The TMA Team is a valuable institutional resource to improve outcomes.
Investigating the impact of meteorological parameters on daily soil temperature changes using machine learning models
Abstract Soil temperature (ST) is one of the critical parameters in agricultural meteorology and significantly influences physical, chemical, and biological activities in the soil environment. One of the major challenges in agricultural studies is the limited number of synoptic stations for measuring ST. Novel data mining methods offer effective solutions for obtaining reliable estimations while reducing costs and improving accuracy. This study estimated daily ST at depths of 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm using meteorological parameters from an American synoptic station over three years (2020–2022). Seasonal ARIMA (SARIMA), Multiple Linear Regression (MLR), and Artificial Neural Network (ANN) models were applied for ST prediction. A bivariate correlation test with a p-value less than 0.05 was conducted to determine the relationship between meteorological variables and ST. The results indicate that the ANN model outperforms SARIMA and MLR in predicting ST at all depths. For instance, at 5 cm depth, the ANN model achieved RMSE = 0.85, r = 0.98, MAE = 1, and PBIAS = 1.5%, compared to SARIMA (RMSE = 1.5, r = 0.96, MAE = 1.16, PBIAS = 2.5%) and MLR (RMSE = 1.35, r = 0.97, MAE = 1.3, PBIAS = 3%). Similarly, at 100 cm depth, the ANN model achieved RMSE = 0.65, r = 0.91, MAE = 0.55, and PBIAS = 2.2%, compared to SARIMA (RMSE = 1.3, r = 0.96, MAE = 0.58, PBIAS = 3.5%) and MLR (RMSE = 1.15, r = 0.91, MAE = 0.68, PBIAS = 3.8%). The analysis also revealed that surface temperature (Avg. Infrared) and air temperature (Avg. T) were the most influential parameters in ST prediction. Additionally, the ANN model exhibited the lowest error rates across all depths, highlighting its superior capability for estimating daily ST in agricultural soils. This study provides a comprehensive framework for accurately estimating daily ST at different depths, offering valuable insights for agricultural and environmental applications.
The application of deep learning in economic analysis and marketing strategy formulation in the tourism industry
The tourism industry is ever-evolving in nature, as it operates in a global marketplace that has become progressively global and offers great potential due to technological advances. The tourism industry faces challenges in accurately forecasting economic impacts and understanding visitor patterns that rapid global changes. Motivated by these needs, this research introduces the Tourism Variational Recurrent Neural Network (TourVaRNN), aiming to enhance the tourism industry by predicting economic impacts and visitor behaviors for effective marketing strategies through advanced Deep Learning (DL) techniques. The research applies a variational recurrent neural network for enhancing tourism demands and model the complex temporal dependencies within tourism data. The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. Marketing campaigns in the tourism sector can be fine-tuned through visitor segmentation, which seeks to comprehend and classify visitors according to their demographics, preferences, and behaviors. The model employs robust forecasting of economic impacts, visitor spending patterns, and behavior while accounting for uncertainty through variational inference. The implementation uses Python language on a tourism dataset comprising necessary attributes like visitor numbers, days, spending patterns, employment, international tourism samples over a specific region, and a diverse age group analyzed over a year. The proposed method is evaluated in terms of performance metrics such as economic impact assessment, visitor segmentation efficiency, inference time analysis, and budget allocation utilization for effective economic and marketing strategy analysis in the tourism industry. TourVaRNN’s improved segmentation efficiency of 15.7 percent allows for more targeted marketing, increasing engagement with visitors and income. Decisions may be made in real-time, improving operational efficiency in tourism management, thanks to a 17.5% reduction in inference time (to 40 ms). The most efficient use of funds is guaranteed by a 13.4% rise in budget allocation utilization, leading to maximum economic benefits.