Predictable Patterns Research Articles

Cognitive factors underlying mathematical skills: A systematic review and meta-analysis.

In understanding the nature of mathematical skills, the most influential theories suggest that mathematical cognition draws on different systems: numerical, linguistic, spatial, and general cognitive skills. Studies show that skills in these areas are highly predictive of outcomes in mathematics. Nonetheless, the strength of these relations with mathematical achievement varies, and little is known about the moderators or relative importance of each predictor. Based on 269 concurrent and 174 longitudinal studies comprising 2,696 correlations, this meta-analysis summarizes the evidence on cognitive predictors of mathematical skills in children and adolescents. The results showed that nonsymbolic number skills (often labeled approximate number sense) correlate significantly less with mathematical achievement than symbolic number skills and that various aspects of language relate differently to mathematical outcomes. We observed differential predictive patterns for arithmetic and word problems, and these patterns only partly supported the theory of three pathways-quantitative, linguistic, and spatial-for mathematical skills. Concurrently, nonsymbolic number and phonological skills were weak but exclusive predictors of arithmetic skills, whereas nonverbal intelligence quotient (IQ) predicted word problems only. Only symbolic number skills predicted both arithmetic and word problems concurrently. Longitudinally, symbolic number skills, spatial ability, and nonverbal IQ predicted both arithmetic and word problems, whereas language comprehension was important for word problem solving only. As in the concurrent data, nonsymbolic number skill was a weak longitudinal predictor of arithmetic skills. We conclude that the candidates to target in intervention studies are symbolic number skills and language comprehension. It is uncertain whether the two other important predictors, nonverbal IQ and spatial skills, are actually malleable. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dynamic Emerging Pathways in Entrance and Exit Detection: Integrating Deep Learning and Mathematical Modeling

Entrance and exit event detection in dynamic environments has a lot of real-world applications in security, crowd management, and retail analytics. Traditional methods used for this problem, namely Line Partition and Bounding Box Diameter methods often struggle in complex scenarios that contain less predictable movement patterns of individuals. This paper proposes a model that integrates deep learning-based object detection and tracking techniques with linear regression to enhance the overall performance of enter and exit detection in static and dynamic environments. This approach captures the movement patterns using advanced object detection and tracking algorithms, enabling the extraction of y-coordinate variations from bounding box centers which are used to calculate the tangent of the linear regression equation and determine if the event is entrance or exit. Experimentations were conducted on 132 video sequences and show the superiority of our approach over the traditional methods achieving an overall accuracy of 86.36% and an F1-score of 0.86. These results demonstrate the high efficiency of this approach to accurately detect entrance and exit events, making it highly reliable and applicable to this problem. This research contributes to computer vision by integrating object detection and tracking algorithms with linear regression offering a solution for enhancing entrance and exit events detection in dynamic environments.

Uncovering Key Predictive Channels and Clinical Variables in the Gamma Band Auditory Steady-State Response in Early Stage Psychosis - a Longitudinal Study.

Psychotic disorders are characterized by abnormalities in the synchronization of neuronal responses. A 40 Hz gamma band deficit during auditory steady-state response (ASSR) measured by electroencephalogram (EEG) is a robust observation in psychosis and is associated with symptoms and functional deficits. However, the majority of ASSR studies focus on specific electrode sites, while whole scalp analysis using all channels, and the association with clinical symptoms, are rare. In this study, we use whole-scalp 40 Hz ASSR EEG measurements-power and phase locking factor-to establish deficits in early-stage psychosis (ESP) subjects, classify ESP status using an ensemble of machine learning techniques, identify correlates with principal components obtained from clinical/demographic/functioning variables, and correlate functional outcome after a short-term follow-up. We identified significant spatially-distributed group level differences for power and phase locking. The performance of different machine learning techniques and interpretation of the extracted feature importance indicate that phase locking has a more predictive and parsimonious pattern than power. Phase locking is also associated with principal components composed of measures of cognitive processes. Short-term functional outcome is associated with baseline 40 Hz ASSR signals from the FCz and other channels in both phase locking and power. This whole scalp EEG study provides additional evidence to link deficits in 40 Hz ASSRs with cognition and functioning in ESP, and corroborates with prior studies of phase locking from a subset of EEG channels. Confirming 40 Hz ASSR deficits serves as a candidate phenotype to identify circuit dysfunctions and a biomarker for clinical outcomes in psychosis.

Real-Time Acoustic Measurement System for Cutting-Tool Analysis During Stainless Steel Machining

This study presents a sound-based tool-wear monitoring system designed to overcome the limitations of conventional methods that focus solely on gradual and predictable wear patterns. The proposed system employs low-cost, high-frequency microphones and advanced signal processing—featuring analog/digital filtering, oversampling, signal conditioning, PLL-based synchronization, and feature extraction (ZCR, RMS)—to capture acoustic emissions during machining. Key innovations include optimized microphone placement, a custom PCB, and real-time data transfer via WiFi to MATLAB for analysis. Using the TreeBagger machine-learning algorithm, the system accurately predicts tool wear, detecting both gradual and abrupt wear patterns. Tested on EN 1.4307 (AISI/ASTM 304L) stainless steel, the system demonstrated robust performance in real-time tool-condition assessment. Its scalable and cost-effective design allows for the integration of additional sensors and features, providing a non-invasive and adaptive solution to enhance machining efficiency and reduce operational costs.

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.

Interparental and Parent-Teen Relationships during Adolescence as Predictors of Intra- and Interpersonal Emotion Regulation in Young Adulthood.

Parents' contributions to their children's emotion regulation during adolescence has been a relatively understudied interpersonal context of development, even though parents' roles as sources of social and emotional learning persist from childhood into adolescence and the complexity of teens' lives grows during this time. This study aims to investigate the differential predictive utility of qualities and behaviors in interparental and parent-teen relationships during adolescence for predicting youths' development of intra- and interpersonal emotion regulation over a 13-year period. To assess these hypotheses, data were obtained from a longitudinal, multi-method, multi-informant study of 184 adolescents (107 Caucasian, 53 African American, and 24 mixed/other ethnicity; median family income of USD 40,000-60,000/year in 1999, equivalent to about USD 75,000-112,000/year when accounting for inflation) and their parents. The results provide support for a differential pattern of prediction; qualities of interparental relationships in early adolescence were significant predictors of young adult interpersonal emotion regulation, whereas behaviors in interparental and parent-teen relationships in late adolescence were significant predictors of both young adult positive intra- and interpersonal emotion regulation. Notably, some father-reported relationship predictors during late adolescence had unexpected relations with later intrapersonal emotion regulation. The results are discussed in terms of the helpfulness of these specific relationship factors during each part of adolescence for supporting positive intra- and interpersonal emotional regulation development.

Self-Assembly of Colloidal Dumbbell Isomers and Plasmonic Properties for Optical Metamaterials.

In this study, we explore the self-assembly of various colloidal symmetric dumbbell (DB) isomers, including dipole Janus, cis-Janus, trans-Janus, apolar-inward and polar-inward perpendicular Janus, and alternating perpendicular Janus DBs. Using dissipative particle dynamics (DPD) simulations under conditions mimicking experimental setups, we investigate cluster formation driven by emulsion droplet evaporation. Our findings reveal a diverse set of cluster structures, which are in good agreement with experimental and simulation results reported in the literature while also predicting the formation of novel cluster configurations. These structures, characterized by well-defined and predictable patterns, are potentially applicable to creating colloidal molecules and crystals. Furthermore, we examine the dynamics of cluster formation to gain insight into the mechanisms guiding the self-assembly of these diverse colloidal DBs. The study highlights the impact of particle isomerism on the resulting assembly structures. We further select a set of typical nanoclusters obtained, including a tetrahedral cluster, which is the simplest, to study its plasmonic properties. Our findings indicate that increasing the nanoparticle (NP) radius or decreasing the gap between NPs leads to a red shift in the plasmonic resonance wavelength and enhances the resonance strength. We identify critical parameter regions where the electric-dipole and magnetic-dipole resonances can be engineered to achieve negative dielectric permittivity and magnetic permeability, which are essential for developing negative-index metamaterials.

KGDA: A Knowledge Graph Driven Decomposition Approach for Cellular Traffic Prediction

Understanding and accurately predicting cellular traffic data is vital for communication operators and device users, as it facilitates efficient resource allocation and ensures superior service quality. However, large-scale cellular traffic data forecasting remains challenging due to intricate temporal variations and complex spatial relationships. This article proposes a Knowledge Graph Driven Decomposition Approach (KGDA) for precise cellular traffic prediction. The KGDA breaks down the impact of static environmental factors and dynamic autocorrelations of cellular traffic time series, enabling the capture of overall traffic changes and understanding of traffic dependence on past values. Specifically, we propose an urban knowledge graph to capture the static environmental context of base stations, mapping these entities into the same latent space while retaining static environmental knowledge. The cellular traffic is divided into a regular pattern and fluctuating residual components, with the KGDA comprising four modules: a Knowledge Graph Representation Learning model, a traffic regular pattern prediction module, a traffic residual dynamic prediction module, and an attentional fusion module. The first leverages graph neural networks to extract spatial contexts and predict regular patterns, the second utilizes the Bi-directional Long Short-Term Memory (Bi-LSTM) model to capture autocorrelations of traffic time series, and the final module integrates the patterns and residuals to produce the final prediction result. Comprehensive experiments demonstrate that our proposed model outperforms state-of-the-art models by more than 10% in forecasting cellular traffic.

Real-world epilepsy monitoring with ultra long-term subcutaneous EEG: a 15-month prospective study.

Novel subcutaneous electroencephalography (sqEEG) systems enable prolonged, near-continuous cerebral monitoring in real-world conditions. Nevertheless, the feasibility, acceptability and overall clinical utility of these systems remains unclear. We report on the longest observational study using ultra long-term sqEEG to date. We conducted a 15-month prospective, observational study including ten adult people with treatment-resistant epilepsy. After device implantation, patients were asked to record sqEEG, to use an electronic seizure diary and to complete acceptability and usability questionnaires. sqEEG seizures were annotated visually, aided by automated detection. Seizure clustering was assessed via Fano Factor analysis and seizure periodicity at multiple timescales was investigated through circular statistics. Over a median duration of 438 days, ten patients recorded a median 18.8 hours/day, totalling 71,984 hours of real-world sqEEG data. Adherence and acceptability remained high throughout the study. While 754 sqEEG seizures were recorded across patients, over half (52%) of these were not reported in the patient diary. Of the 140 (27%) diary reports not associated with an identifiable sqEEG seizure, the majority (68%) were reported as seizures with preserved awareness. The sqEEG to diary F1 agreement score was highly variable, ranging from 0.06 to 0.97. Patient-specific patterns of seizure clustering and seizure periodicity were observed at multiple (circadian and multidien) timescales. We demonstrate feasibility and high acceptability of ultra long-term (months-years) sqEEG monitoring. These systems help provide real-world, more objective seizure counting compared to patient diaries. It is possible to monitor individual temporal fluctuations of seizure occurrence, including seizure cycles. [I do not have a X/Twitter handle]People with epilepsy may suffer from severe injuries or sudden death due to uncontrolled seizures. Many seizures remain unnoticed by people and unreported to doctors, despite these potentially catastrophic consequences. There is an urgent need to detect seizures more objectively. Our study used a novel device placed under the skin to monitor brainwave activity continuously, at home, for many months, in people with treatment-resistant epilepsy. By monitoring epilepsy from home, we detected many seizures unreported by patients, and we also found predictable patterns (cycles) of seizure occurrence over time. In the future, this type of monitoring could revolutionize epilepsy care, by improving safety, treatment management and reducing uncertainty in patients' daily lives.

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.

Geographic Gradients in Species Interactions: From Latitudinal Patterns to Ecological Mechanisms

The idea that species interactions are more ecologically and evolutionarily important toward lower latitudes underpins seminal theories in ecology and evolution. Recent global studies have found the predicted latitudinal gradients in interactions, particularly predation. However, latitudinal patterns alone do not reveal why interactions vary geographically and so do not provide strong predictions in space (e.g., for specific ecosystems) or time (e.g., forecasting responses to global change). Here, I review theory to identify a clearer, mechanistic, and testable framework for predicting geographic variation in the importance of species interactions. I review competing metrics of importance, proximate mechanisms that can increase interaction importance, and environmental gradients that could generate predictable geographic patterns (climate extremes and stability, warmer temperatures, productivity, and biodiversity). Strong empirical tests are accumulating thanks to the rise of global experiments and datasets; renewed focus on testing why interactions vary spatially will help move the field from identifying latitudinal patterns to understanding broader mechanisms.

Estimating the predictability limit of meteorological variables using permutation entropy

Understanding and quantifying the predictability of various meteorological variables remains a significant challenge in atmospheric sciences, particularly in regions with complex climates. This study aims to assess the predictability limit of wind speed, pressure, temperature, and relative humidity using permutation entropy (PermEn) analysis. By analyzing the statistical properties of PermEn values associated with each variable, our results revealed distinctive patterns of complexity and predictability. It shows clear evidence that pressure generally exhibits the lowest PermEn value, while wind speed tends to have the largest PermEn. However, the variation of PermEn values remains largely stable across different terrain conditions, which is primarily attribute to the use of daily observational data. The study also discusses the impact of window length on PermEn calculations, highlighting that longer window lengths are more likely to capture a broader range of dynamics, including more intricate and diverse temporal structures. This expanded pattern diversity leads to increased complexity of the time series, resulting in higher PermEn values. Additionally, the seasonal variation patterns of permutation entropy were found to differ among the meteorological variables considered. These findings provide valuable insight into the relative predictability of various meteorological variables, which can have important implications for atmospheric modeling, weather forecasting, and risk management.

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.

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.