Predictable Patterns Research Articles

Abstract 4125887: Aladyn Individual: Dynamic Individual Comorbidity Modeling for Genomic Discovery and Clinical Prediction

Intro: Considering comorbidity patterns for both discovery and prediction of individual disease trajectories is essential for personalized medicine. Existing methods often lack genetic integration and interpretability. We develop an approach suited for dynamic environments that combines genetics with individual and population level-trajectories for enhanced discovery and prediction. Method: Our method employs a dynamic Bayesian hierarchical latent allocation framework to learn and predict disease trajectories. Leveraging longitudinal electronic health care data from 485,698 UK Biobank participants and 287,012 All of Us Research Program participants, we aim to learn individual and population level disease trajectories for 352 distinct conditions. Genetic risks inform the model in two fundamental ways: First, we introduce a genetic warping parameter that recognizes the tendency of an individual to experience events earlier or later within these profiles while retaining the relative order of progression (1a). Second, we introduce a gaussian process to combine individual and population level trends in comorbidity profile prevalence over time (1b), whose mean is augmented by genetic predilection. Finally, we allow for profile weights to update with each new diagnoses, merging prediction and classification. Results: Our results indicate stark disparities in disease progression; individuals at high genetic risk for myocardial infarction experience events nearly a decade earlier than those at low risk (median age of onset 53.7 [53.1-53.9] vs 62.6 years [61.4-63.1]) (1b). Our model dynamically updates and predicts correct diagnoses 79.5% more frequently than existing topic-modeling approaches (1c,d). The dynamic approach adjusts disease profiles by at least 11.7% annually for 50% of individuals, with 65% experiencing over a 10% change in comorbidity profiles. Simulation results confirm the model's superior accuracy [51.4-41%], precision [50.9 vs 39.9%], and recall [48.4 vs 39.2%] compared to fixed-weight approaches. Summary: We offer a novel approach for integrating genetic risk with dynamic modeling of complex disease profiles for joint biologic discovery and personalized clinical prediction over the lifetime.

Nomenclature for human and animal fungal pathogens and diseases: a proposal for standardized terminology.

Medically important pathogenic fungi invade vertebrate tissue and are considered primary when part of their nature life cycle is associated with an animal host and are usually able to infect immunocompetent hosts. Opportunistic fungal pathogens complete their life cycle in environmental habitats or occur as commensals within or on the vertebrate body, but under certain conditions can thrive upon infecting humans. The extent of host damage in opportunistic infections largely depends on the portal and modality of entry as well as on the host's immune and metabolic status. Diseases caused by primary pathogens and common opportunists, causing the top approximately 80% of fungal diseases [D. W. Denning, Lancet Infect Dis, 24:e428-e438, 2024, https://doi.org/10.1016/S1473-3099(23)00692-8], tend to follow a predictive pattern, while those by occasional opportunists are more variable. For this reason, it is recommended that diseases caused by primary pathogens and the common opportunists are named after the etiologic agent, for example, histoplasmosis and aspergillosis, while this should not be done for occasional opportunists that should be named as [causative fungus] [clinical syndrome], for example, Alternaria alternata cutaneous infection. The addition of a descriptor that identifies the location or clinical type of infection is required, as the general name alone may cover widely different clinical syndromes, for example, "rhinocerebral mucormycosis." A list of major recommended human and animal disease entities (nomenclature) is provided in alignment with their causative agents. Fungal disease names may encompass several genera of etiologic agents, consequently being less susceptible to taxonomic changes of the causative species, for example, mucormycosis covers numerous mucormycetous molds.

Atmospheric Circulation on Other Planets: Venus, Mars, Jupiter

Abstract. This review provides a comparative analysis of atmospheric circulation across four planets in our solar system: Venus, Mars, Jupiter, and Earth. By examining the unique characteristics of each planet, including atmospheric composition, rotation rates, axial tilt, and solar radiation, the study explores how these factors influence the dynamics of their respective atmospheres. Venus, with its dense CO atmosphere and slow retrograde rotation, exhibits a super-rotating atmosphere leading to extreme and uniform climate conditions. Marss thin atmosphere and significant seasonal variations, combined with global dust storms, result in highly variable and dynamic atmospheric patterns. Jupiters rapid rotation and internal heat contribute to complex and powerful atmospheric dynamics, including strong jet streams and long-lasting storms like the Great Red Spot. In contrast, Earth's atmospheric circulation is driven by a combination of solar radiation, rotation, and the presence of oceans and continents, resulting in relatively stable and predictable weather patterns. The study highlights the diversity of atmospheric processes within our solar system, providing insights that are crucial for understanding planetary climates and assessing the potential habitability of exoplanets.

Modulation of starch-based film properties for encapsulation of microbial inoculant

Controlled release of beneficial microorganisms in agriculture by encapsulation in biopolymeric matrices can improve biofertilizer efficacy, but it requires the modulation of properties to ensure more efficient and predictable release patterns. This study investigated the effect of a starch-based system to protect and release Priestia megaterium (former Bacillus megaterium) processed as films modified with potential cell-protective additives (maltodextrin, cellulose, and bentonite). The release kinetics, physicochemical and morphological film characteristics, and their protection against UV (Ultraviolet) radiation and temperature were evaluated. The microorganism release was dependent of the film microstructure and composition, both in initial and extended-release rates. Maltodextrin incorporation increased cell release, while cellulose and bentonite delayed due to influences in the water uptake (swelling) and diffusion across polymer structure. Modified films protected the microorganisms against UV radiation and extreme temperatures, being the film with all additives (SMCB) the best protective formulation (100 % UVC survival) compared to starch matrix (< 20 % UVC survival after 40 min) and the one with the highest viability at higher (54 % survival at 45 °C) and lower (80 % survival at 15 °C) temperatures. These insights pave the way for targeted, efficient, and sustainable biological solutions to agricultural practices, aligning with evolving needs in modern agriculture.

Challenges in developing reliable phosphorus predictive models: Unpredictable release under soil redox changes

Phosphorus (P), crucial for plant nutrition, is unevenly distributed in the Earth's crust, necessitating its supplementation in agriculture through fertilizers. However, excessive use can lead to water pollution. Our research focuses on the P adsorbing complex, investigating P release due to flooding, using 12 well- characterized soils with contrasting properties. Our research measures directly the P-adsorbing complex using adsorption/desorption isotherms. We observed that the P concentration in the solution —sufficient to prevent desorption yet low enough to avoid further sorption by the soil— decreases when the soil undergoes complete reduction (anoxia). When grouped by similarity, calcareous soils exhibit higher maximum P adsorption capacities (Xmax) under alternating reducing conditions (ARC) compared to continuous reducing conditions (CRC). In slightly acidic soils, CRC leads to a wider spread in Xmax values than ARC. For acidic, organic matter-rich soils, ARC results in the highest Xmax values (123 mmol P kg-1 soil) compared to CRC, whereas in acidic, light-textured soils, CRC shows significantly higher mean Xmax values than ARC. Nevertheless, we were unable to develop a predictive model for soil P desorption based on key intrinsic properties and climate. When an environmental or anthropogenic transformation induces anoxia, the P released does not follow a predictable pattern.

Application of Human Plasma Targeted Lipidomics and Analysis of Toxic Elements to Capture the Metabolic Complexities of Hypothyroidism.

Hypothyroidism (HT) affects millions worldwide and can lead to various lipid disorders. The metabolic complexity and the influence of toxic elements in autoimmune and non-autoimmune HT subtypes are not fully understood. This study aimed to investigate the relationships between plasma lipidome, toxic elements, and clinical classifications of HT in unexposed individuals. Samples were collected from 120 adults assigned to a study group with Hashimoto's disease and non-autoimmune HT, and a healthy control group. Quantification of 145 pre-defined lipids was performed by using triple quadrupole tandem mass spectrometry (TQ MS/MS) in multiple reactions monitoring (MRM) mode via positive electrospray ionization (ESI). Levels of toxic elements were determined using inductively coupled plasma mass spectrometry (ICP-MS). Significant associations between altered levels of several components of the plasma lipidome and Al, Cd, Ni, As, and Pb with HT were found. We show metabolic differences in lysophosphatidylcholines (LPC) and phosphatidylcholines (PC) between HT and controls, with distinct predicted activation patterns for lysolecithin acyltransferase and phospholipase A2. There are significant changes in the lipidome profiles of healthy subjects compared to euthyroid HT patients treated with L-thyroxine, which are related to the type of hypothyroidism and non-occupational exposure to toxic elements.

Can mobile promotion automation decrease overall purchases? The role of promotion pattern predictability in customer habit formation

Automating mobile promotions facilitates customization through geo-location and temporal targeting; however, this can lead the geographic and temporal patterns of promotions to become predictable. We argue that this predictability enables customer habit formation as the promotion, the context in which it is received, and the purchase behavior become associated in the customer's memory through repeated co-occurrence. This results in the customer purchasing only when promotions are expected to occur, decreasing their overall purchase likelihood. Across four studies, including empirical data from two different industries in different global regions and two controlled laboratory experiments focused on different purchase scenarios, we find that customers exposed to predictable promotion patterns shift their purchases to when promotions are expected, decreasing overall purchase probability. These effects are stronger for customers with consistent past purchase patterns or low involvement with the product, and predictability of promotion patterns does not impact customer attitudes, providing evidence that they are driven by customer habit formation.

Electromagnetic frequency prediction pattern for electrical circuited shell based on FSTT theory using electromagnetic wave prediction method

This research focuses on electromagnetic analysis of electrical circuited shell to predict possible failure using wave prediction method. The equation is established considering axial and lateral hydrostatic pressure based on first order shear deformation theory of shell motion using the wave propagation approach and classic Flügge shell equations. For validation of accuracy of this research method, a comparison carried out with experimental data. With this method the effects of circuit parameters as well as different t power-law exponent on the frequencies are investigated. The results showed fantastic agreement between the present method and experimental ones.

Tumor-Associated Tractography Derived from High-Angular-Resolution Q-Space MRI May Predict Patterns of Cellular Invasion in Glioblastoma.

The invasion of glioblastoma cells beyond the visible tumor margin depicted by conventional neuroimaging is believed to mediate recurrence and predict poor survival. Radiomic biomarkers that are associated with the direction and extent of tumor infiltration are, however, non-existent. Patients from a single center with newly diagnosed glioblastoma (n = 7) underwent preoperative Q-space magnetic resonance imaging (QSI; 3T, 64 gradient directions, b = 1000 s/mm2) between 2018 and 2019. Tumors were manually segmented, and patterns of inter-voxel coherence spatially intersecting each segmentation were generated to represent tumor-associated tractography. One patient additionally underwent regional biopsy of diffusion tract- versus non-tract-associated tissue during tumor resection for RNA sequencing. Imaging data from this cohort were compared with a historical cohort of n = 66 glioblastoma patients who underwent similar QSI scans. Associations of tractography-derived metrics with survival were assessed using t-tests, linear regression, and Kaplan-Meier statistics. Patient-derived glioblastoma xenograft (PDX) mice generated with the sub-hippocampal injection of human-derived glioblastoma stem cells (GSCs) were scanned under high-field conditions (QSI, 7T, 512 gradient directions), and tumor-associated tractography was compared with the 3D microscopic reconstruction of immunostained GSCs. In the principal enrollment cohort of patients with glioblastoma, all cases displayed tractography patterns with tumor-intersecting tract bundles extending into brain parenchyma, a phenotype which was reproduced in PDX mice as well as in a larger comparison cohort of glioblastoma patients (n = 66), when applying similar methods. Reconstructed spatial patterns of GSCs in PDX mice closely mirrored tumor-associated tractography. On a Kaplan-Meier survival analysis of n = 66 patients, the calculated intra-tumoral mean diffusivity predicted the overall survival (p = 0.037), as did tractography-associated features including mean tract length (p = 0.039) and mean projecting tract length (p = 0.022). The RNA sequencing of human tissue samples (n = 13 tumor samples from a single patient) revealed the overexpression of transcripts which regulate cell motility in tract-associated samples. QSI discriminates tumor-specific patterns of inter-voxel coherence believed to represent white matter pathways which may be susceptible to glioblastoma invasion. These findings may lay the groundwork for future work on therapeutic targeting, patient stratification, and prognosis in glioblastoma.

Towards Predictive Pollution Control Through Traffic Flux Forecasting With Deep Learning: A Case Study in the City of Valencia

ABSTRACTTraffic congestion represents a significant urban challenge, with notable implications for public health and environmental well‐being. Consequently, urban decision‐makers prioritize the mitigation of congestion. This study delves into the efficacy of harnessing extensive data on urban traffic dynamics, coupled with comprehensive knowledge of road networks, to enable Artificial Intelligence (AI) in forecasting traffic flux well in advance. Such forecasts hold promise for informing emission reduction measures, particularly those aligned with Low Emission Zone policies. The investigation centers on Valencia, leveraging its robust traffic sensor infrastructure, one of the most densely deployed worldwide, encompassing approximately 3500 sensors strategically positioned across the city. Employing historical data spanning 2016 and 2017, we undertake the task of training and characterizing a Long Short‐Term Memory (LSTM) Neural Network for the prediction of temporal traffic patterns. Our findings demonstrate the LSTM's efficacy in real‐time forecasting of traffic flow evolution, facilitated by its ability to discern salient patterns within the dataset.

Performance of instrumental activities of daily living in patients with haemophilic arthropathy. A cross-sectional cohort study.

The development of haemophilic arthropathy causes joint damage that leads to functional impairment that limits the performance of activities in patients with haemophilia. The aim was to identify the best predictive model for performing instrumental activities of daily living in adult patients with haemophilia arthropathy. Cross-sectional cohort study. 102 patients were recruited. The dependent variable was the performance of instrumental activities of daily living (Lawton and Brody scale). The dependence on the performance of activities of daily living was the dependent endpoint (Barthel scale). The secondary variables were joint damage (Hemophilia Joint Health Score), pain intensity, and clinical, anthropometric, and sociodemographic variables. The degree of dependence, joint damage, pain intensity, and marital status (Cp=5.60) were the variables that best explain the variability in the performance of instrumental activities of daily living (R2 adj=0.51). Loss of predictive capacity is acceptable with good mean internal (R2 mean=0.40) and external (R2-r2=0.09) validation. According to the predictive pattern obtained, patients with haemophilia, who were married, without joint pain or damage, and independent in their day-to-day lives, had a score of 7.91 points (95% CI: 7.42; 8.39) in the performance of instrumental activities of daily living. The predictive model for the functional capacity of instrumental activities of daily living in haemophilia patients encompasses factors such as level of autonomy, joint impairment, pain severity, and marital status. Notably, despite the presence of joint damage, individuals with haemophilia exhibit a significant level of independence in carrying out both basic daily tasks and instrumental activities of daily living. Id NCT04715100.

Conceptualizing bias in EHR data: A case study in performance disparities by demographic subgroups for a pediatric obesity incidence classifier.

Electronic Health Records (EHRs) are increasingly used to develop machine learning models in predictive medicine. There has been limited research on utilizing machine learning methods to predict childhood obesity and related disparities in classifier performance among vulnerable patient subpopulations. In this work, classification models are developed to recognize pediatric obesity using temporal condition patterns obtained from patient EHR data in a U.S. study population. We trained four machine learning algorithms (Logistic Regression, Random Forest, Gradient Boosted Trees, and Neural Networks) to classify cases and controls as obesity positive or negative, and optimized hyperparameter settings through a bootstrapping methodology. To assess the classifiers for bias, we studied model performance by population subgroups then used permutation analysis to identify the most predictive features for each model and the demographic characteristics of patients with these features. Mean AUC-ROC values were consistent across classifiers, ranging from 0.72-0.80. Some evidence of bias was identified, although this was through the models performing better for minority subgroups (African Americans and patients enrolled in Medicaid). Permutation analysis revealed that patients from vulnerable population subgroups were over-represented among patients with the most predictive diagnostic patterns. We hypothesize that our models performed better on under-represented groups because the features more strongly associated with obesity were more commonly observed among minority patients. These findings highlight the complex ways that bias may arise in machine learning models and can be incorporated into future research to develop a thorough analytical approach to identify and mitigate bias that may arise from features and within EHR datasets when developing more equitable models.

Quantifying Hand Motion Complexity in Simulated Sailing Using Inertial Sensors.

The control of hand movement during sailing is important for performance. To quantify the amount of regularity and the unpredictability of hand fluctuations during the task, the mathematical algorithm Approximate Entropy (ApEn) of the hand acceleration can be used. Approximate Entropy is a mathematical algorithm that depends on the combination of two input parameters including (1) the length of the sequences to be compared (m), and (2) the tolerance threshold for accepting similar patterns between two segments (r). The aim of this study is to identify the proper combinations of 'm' and 'r' parameter values for ApEn measurement in the hand movement acceleration data during sailing. Inertial Measurement Units (IMUs) recorded acceleration data for both the mainsail (non-dominant) and tiller (dominant) hands across the X-, Y-, and Z-axes, as well as vector magnitude. ApEn values were computed for 24 parameter combinations, with 'm' ranging from 2 to 5 and 'r' from 0.10 to 0.50. The analysis revealed significant differences in acceleration ApEn regularity between the two hands, particularly along the Z-axis, where the mainsail hand exhibited higher entropy values (p = 0.000673), indicating greater acceleration complexity and unpredictability. In contrast, the tiller hand displayed more stable and predictable acceleration patterns, with lower ApEn values. ANOVA results confirmed that parameter 'm' had a significant effect on acceleration complexity for both hands, highlighting differing motor control demands between the mainsail and tiller hands. These findings demonstrate the utility of IMU sensors and ApEn in detecting nuanced variations in acceleration dynamics during sailing tasks. This research contributes to the understanding of hand-specific acceleration patterns in sailing and provides a foundation for further studies on adaptive sailing techniques and motor control strategies for both novice and expert sailors.

Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learning

This study presents an innovative approach to real-time modeling of urban drainage networks, leveraging a highly accurate coupled one- and two-dimensional hydrodynamic model to generate a training dataset for node water levels. By employing global states inferred from monitoring points as model inputs, this study overcomes the limitations imposed by the scarcity of monitoring data and the challenge of capturing all node levels. The Crossformer algorithm, which simultaneously accounts for correlation at both temporal and feature scales, is applied to enhance the precision of simultaneous water level predictions across the network. Comparative analysis of different prediction patterns reveals that extending predictions based on a high-accuracy infrastructure offers more benefits than direct modifications to the algorithm's structure. In addition, this paper pioneered the application of online continuous learning concepts to update the prediction model in real time, achieving a balanced integration of measured and simulated data. Consequently, this paper establishes a complete monitoring-predicting-updating real-time simulation system for urban drainage networks.

What makes future teachers successful? A longitudinal study on pre-service teachers

ABSTRACT This study aims to examine pre-service teachers’ academic success longitudinally. We studied in 537 pre-service students how three criteria of success (achievement, intention to persistent and actual retention) could be predicted by demographic, cognitive, personality, and motivational variables. Applying structural equation modelling, we found distinct patterns in the predictive power of the variables across the criteria. About one fourth of the variance in achievement could be explained by verbal ability and conscientiousness. Intention to persist was weakly intercorrelated with actual retention; the criteria also showed distinct prediction patterns. Intention to persist could be explained by only one predictor. Intrinsic motivation accounted for almost half of the variance in the criterion. The included predictors were less powerful in the prediction of actual retention: age, verbal ability and intrinsic motivation together accounted for about ten percent of the variance. The results underscore the complex and multifaceted nature of pre-service teachers’ academic success.

Optimizing Antidepressant Efficacy: Generalizable Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response.

Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R-squared = 0.31; placebo: R-squared = 0.22). Remarkably, the sertraline response biomarker is further tested on an independent escitalopram-medicated cohort of MDD patients, validating its generalizability (p = 0.01) and suggesting an overlap of psychopharmacological mechanisms across selective serotonin reuptake inhibitors. Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel and reliable insights into the intricate neuropsychopharmacology of antidepressant treatment, paving the way for advances in precision medicine and development of more targeted antidepressant therapeutics.

Influence of climate, weather and floral associations on pollinator community composition across an elevational gradient

Insect pollinators, which are ectothermic, are especially sensitive to abiotic conditions, which often drive predictable patterns of pollinator species turnover along environmental gradients. However, pollinator activity is also reliant on suitable biotic conditions, such as the presence of host plants. High‐elevation environments provide a useful setting to examine the relative contribution of abiotic and biotic factors in shaping species interactions as they are often characterised by strong environmental gradients over short geographic distances. Here, we examined pollination interaction networks across an elevational gradient from 930–2000 m a.s.l. in southern Australia, to determine the underlying patterns of pollinator activity and their interactions with flowers. Interaction frequency of Diptera increased at high elevations, while interaction frequency of Hymenoptera and Coleoptera decreased. We provide evidence that this elevational pattern of activity is partly driven by floral associations, with interactions dominated by Hymenoptera‐attracting plant families at lower elevations (Proteaceae, Fabaceae) and a Diptera‐attracting family at high elevation (Asteraceae). Pollinator activity was also influenced by weather conditions, with reduced activity for all three orders at lower temperatures, and Diptera active across the broadest range of temperature, humidity and wind conditions. We suggest that changes across elevation gradients in pollinator community composition are driven by both direct responses to abiotic conditions such as temperature, as well as the elevational distribution patterns of associated flowering plants. Despite these distinct shifts in composition of the pollinator assemblage with elevation, pollination network structure was stable across the elevational gradient, with moderate levels of specialisation and low levels of connectance and nestedness present across the gradient. By considering both abiotic conditions and biotic processes, our results provide insight for predicting the impacts of upslope vegetation shifts on pollinator communities in the face of climate change.

Analysis of differences in fossil fuel consumption in the world based on the fractal time series and complex network

Fossil fuels remain indispensable energy resources despite their non-renewable nature. Understanding the patterns of global fossil fuel consumption is essential for energy security and policy-making. This study employs complex network theory and fractal time series analysis to explore the underlying dynamics and patterns of fossil fuel consumption globally, with a focus on coal, oil, and gas consumption.The study applies the Hurst index to raw fossil fuel consumption data to identify fractal characteristics. Additionally, the visibility graph method is used to convert time series data into complex networks, allowing further analysis of consumption patterns. The study examines fossil fuel consumption in 38 countries to assess global trends and differences. The analysis reveals that global fossil fuel consumption follows a fractal time series pattern, with Hurst index values exceeding 0.9, indicating long-term memory characteristics. The application of the visibility graph method demonstrates variations in the Hurst index of degree distribution, enabling the differentiation of consumption patterns across regions. The method also uncovers distinct features of coal, oil, and gas consumption when viewed from a network perspective. The findings suggest that fossil fuel consumption has predictable long-term patterns, which are crucial for assessing future energy demands. The study highlights the importance of legislative measures to safeguard fossil fuel resources, especially for countries like China, where energy security and international competitiveness are paramount. Understanding these consumption patterns could guide future energy policies aimed at managing non-renewable resources more effectively.

Long Term 5G Base Station Traffic Prediction Method Based on Spatial-Temporal Correlations

In the domain of 5G network management, accurately predicting traffic volumes at base stations remains a critical yet challenging endeavor, primarily due to the complexities inherent in the spatial and temporal data interactions. Current methods often fall short in effectively harnessing long-term trends and spatial interconnections among base stations. To bridge these gaps, this paper introduces the GCformer model, a novel approach that capitalizes on both spatial relationships and temporal patterns for multi-base station traffic prediction. Spatially, the proposed model employs graph convolutional networks to integrate diverse spatial information and construct insightful adjacency matrices that includes Euclidean distances and non-Euclidean distances (area types of base station locations and similarities in traffic flow among various stations), thereby enhancing the predictability of traffic dynamics. Temporally, the application of the Transformer's attention mechanism enables better capture of long-term relational dependencies in the temporal domain of 5G base station traffic data. Additionally, a time-variant optimization module is designed to establish diurnal cycle data for each base station's traffic, replacing the traditional positional encoding with a more nuanced model that improves the learning of historical data correlations. Empirical results from exhaustive case studies confirm the superiority of the GCformer model in forecasting traffic volumes. The GCformer exhibits a 4.01% improvement in mean squared error and a 3.37% enhancement in mean absolute error compared to the best-performing baseline model, showcasing its potential to significantly improve operational strategies in 5G networks.

Early Caregiver Predictability Shapes Neural Indices of Statistical Learning Later in Infancy.

Caregivers play an outsized role in shaping early life experiences and development, but we often lack mechanistic insight into how exactly caregiver behavior scaffolds the neurodevelopment of specific learning processes. Here, we capitalized on the fact that caregivers differ in how predictable their behavior is to ask if infants' early environmental input shapes their brains' later ability to learn about predictable information. As part of an ongoing longitudinal study in South Africa, we recorded naturalistic, dyadic interactions between 103 (46 females and 57 males) infants and their primary caregivers at 3-6 months of age, from which we calculated the predictability of caregivers' behavior, following caregiver vocalization and overall. When the same infants were 6-12-months-old they participated in an auditory statistical learning task during EEG. We found evidence of learning-related change in infants' neural responses to predictable information during the statistical learning task. The magnitude of statistical learning-related change in infants' EEG responses was associated with the predictability of their caregiver's vocalizations several months earlier, such that infants with more predictable caregiver vocalization patterns showed more evidence of statistical learning later in the first year of life. These results suggest that early experiences with caregiver predictability influence learning, providing support for the hypothesis that the neurodevelopment of core learning and memory systems is closely tied to infants' experiences during key developmental windows.