K-means Research Articles

The Relationship Between Horizontal Jumping and Sprinting Ability Across Sexes in Young Active Adults

Purpose: The purpose of this research was to investigate the relationship between horizontal jump distance, 10 m time, and 30–40 m time in multi-sport athletes separated by sex and sprint speed. Methods: A total of 1352 athletes (742 males and 610 females) performed 40 m sprints, standing broad jumps (SBJs), and standing triple jumps (STJs). Data were separated by sex and then grouped as fast, average, and slow using the K-Means algorithm in three conditions (acceleration, max speed, and combined). Results: Regression models explained 84.01% of the variance (F(7,757) = 574.5, p < 0.001) for the 10 m times with mass, speed group, and sex as significant predictors and 88.51% of the variance (F = (7,757 = 841.6, p < 0.001) for the 30–40 m times with SBJ, STJ, speed group, sex, and the interaction of sex and group as significant predictors. Conclusions: These results suggest that when examining general athlete physical performance, horizontal jump tests and max speed sprint times can be used equivalently to stratify athletes. However, it is important to group athletes by speed and sex before being able to predict sprint ability from horizontal jump tests. Further, athlete mass is a significant factor in the prediction of acceleration ability but not maximum speed, and horizontal jumps were significant factors in the prediction of max speed but not acceleration.

AI-Powered Geotechnics: Enhancing Rock Mass Classification for Safer Engineering Practices

AbstractRock mass classification is fundamental for evaluating rock mass quality, essential for stability analysis and geotechnical design. Traditional classification methods are limited by joint observation technology, which typically gathers joint information from one-dimensional or two-dimensional perspectives, failing to comprehensively capture three-dimensional joint occurrences. This often necessitates empirical formulas for joint distribution, resulting in less precise joint parameter calculations. This paper reviews 44 seminal articles on rock engineering classification in construction and subterranean projects, tracing the evolution from foundational methods like Rock Quality Designation, Rock Mass Rating, Q-system, Basic Quality, and Hydropower Classification to contemporary techniques. It highlights the transformative impact of data science, particularly artificial intelligence, on rock engineering. The analysis reveals 73 distinct algorithms used 162 times in literature, with Support Vector Machines Support, Vector Regression, K-means clustering, K-Nearest Neighbors, Artificial Neural Networks and Random Forest being the most successful. This paper examines each method's advantage and limitations, discussing the challenges of algorithm deployment in the scientific community. The findings underscore the integration of machine learning and meta-heuristic optimization methods in rock engineering classification, offering valuable insights for future research and applications.

A mileage correction method of pipeline inertial measurement for dam internal deformation monitoring

Internal deformation monitoring is of great significance to the safe operation and maintenance of dams. Some scholars have carried out dam internal deformation monitoring by pipeline inertial measurement with a pipeline measurement robot, which has improved the monitoring accuracy and efficiency considerably, compared with the traditional monitoring methods. However, there are still shortcomings in this method. Because the odometer on the pipeline measurement robot will slip or slide, resulting in errors in the mileage of the multiple measurement routes, which hinders the development of the accuracy of pipeline inertial measurement. This paper proposes a mileage correction method based on junction detection to improve the accuracy of pipeline inertial measurement for better dam internal deformation monitoring. The mileage correction method establishes a reference model of pipeline junction location, detects the locations of pipeline junction welds on the measurement routes using two k-mean clustering, and corrects the odometer data based on the difference between the detected and reference locations. We evaluate the enhancement of this mileage correction method on the internal accuracy and deviation values compared with other method for pipeline inertial measurement. The proposed method is validated using measured data from the internal pipeline of Tianchi Dam. The results show that compared with the results directly fused without detection–correction, the method proposed in this paper reduces the root-mean-square error of the results by an average of 30.30% and improves the deviation compared with the reference deformation by an average of 2.3 mm. Therefore, the method has good results and practical applications.

Profiling feedback antecedents in higher education students: a person-oriented perspective

The ability to give, receive and process feedback is essential for higher education students not only during their studies, but also for their future work life. Despite the extensive amount of research on feedback in education, there is limited research on feedback skills as collaborative skills and on what might influence these skills. Through surveying a large sample of 2,907 university students who worked in self-managed project teams, this study explores how individual characteristics as antecedents relate to students’ perceived feedback skills. We use a person-oriented approach to examine how these antecedents relate within students and how these profiles relate to perceived feedback skills. In addition to reliability, confirmatory factor analysis provided evidence for the structural validity of a newly developed feedback skills instrument. Five scales were used from existing feedback instruments as antecedents of feedback skills, and these were also found to be valid and reliable. Through a person-oriented approach, we applied a hierarchical and a k-means cluster analysis to create student profiles or groups based on feedback antecedents. We identified five distinct groups of students with common feedback antecedents. The results indicate that the five groups also had different levels of perceived feedback skills. The study contributes to the limited research on the dynamics of giving and receiving feedback from the perspective of students in the context of collaborative learning. It has implications for researchers and practitioners to better understand individual differences and to consider these differences when designing collaborative learning activities and facilitating student teams.

A data-driven approach to traditional village colors: K-Means clustering of online images

ABSTRACT This study aims to quantify the color characteristics of traditional villages, explore the key aspects of color that attract tourist attention, and propose solutions to enhance the readability and legibility of these villages. The findings are intended to provide practical guidance for the restoration and use of village colors. The research focuses on how tourists perceive the color characteristics of traditional villages. The objective is to conduct color clustering analysis to identify the composition and characteristics of village colors, and further analyze the underlying causes to offer suggestions for optimization and enhancement. It employs a methodology based on urban imagery theory, using clustering and secondary extraction of color data from online images. The results reveal distinct contrasts and harmonies in the colors of the five elements providing a basis for improving spatial readability and visual guidance. These findings not only offer practical design references for optimizing traditional village colors but also provide valuable insights for the protection and restoration of other traditional sites. In particular, the contrast between the colors of boundaries and landmarks can enhance visual recognition, while the complementary color designs of paths and boundaries can improve spatial readability, contributing to the sustainable development of traditional villages.

Beyond the Hip: Clinical Phenotypes of Hip Osteoarthritis Across the Biopsychosocial Spectrum

Background/Objectives: To identify clinical phenotypes of hip osteoarthritis (OA) within a biopsychosocial framework. Methods: A cross-sectional analysis of 143 individuals with hip OA awaiting total hip arthroplasty (THA) was performed. Phenotyping features included sociodemographic and biomedical information, pain-related cognitions and emotions, mental disorders, traumatic experiences, self-efficacy, social support, perceived stress, and somatosensory function. Outcome measures included the hip disability and osteoarthritis outcome score and the numeric pain-rating scale. Decision tree learning was used to select the most important phenotyping features. K-means clustering analyses were performed to identify clinical phenotypes and a decision tree algorithm was trained to classify individuals in the identified clinical phenotypes. Results: Selected phenotyping features associated with pain and disability included a combination of biomedical, psychological, and social variables. Two distinct clinical phenotypes were identified. Individuals within the maladaptive phenotype (34%) reported more comorbidities, less self-efficacy and higher levels of anxiety, depression, pain-related fear-avoidance, and feelings of injustice. No differences were found regarding social support and somatosensory function. Regarding the outcome measures, individuals within the maladaptive phenotype reported higher levels of pain and disability. Finally, based on the Fear-Avoidance Components Scale (FACS) and the anxiety subscale of the Hospital Anxiety and Depression Scale (HADS-A), individuals could be classified into the clinical phenotypes with 87.8% accuracy. Conclusions: Two clinical phenotypes, an adaptive and a maladaptive phenotype, can be identified in individuals with hip OA using the FACS and HADS-A. The identification of these clinical phenotypes represents a crucial step toward precision medicine, enabling the development of targeted treatment pathways tailored to the distinct biomedical and psychological features of each phenotype.

A Multi-Robot Task Allocation Method Based on the Synergy of the K-Means++ Algorithm and the Particle Swarm Algorithm

Addressing challenges in the traditional K-means algorithm, such as the challenge of selecting initial clustering center points and the lack of a maximum limit on the number of clusters, and where the set of tasks in the clusters is not reasonably sorted after the task assignment, which makes the cooperative operation of multiple robots inefficient, this paper puts forward a multi-robot task assignment method based on the synergy of the K-means++ algorithm and the particle swarm optimization (PSO) algorithm. According to the processing capability of the robots, the K-means++ algorithm that limits the maximum number of clusters is used to cluster the target points of the task. The clustering results are assigned to the multi-robot system using the PSO algorithm based on the distances between the robots and the centers of the clusters, which divides the multi-robot task assignment problem into a multiple traveling salesmen problem. Then, the PSO algorithm is used to optimize the ordering of the task sets in each cluster for the multiple traveling salesmen problem. An experimental verification platform is established by building a simulation and physical experiment platform utilizing the Robot Operating System (ROS). The findings indicate that the proposed algorithm outperforms both the clustering-based market auction algorithm and the non-clustering particle swarm algorithm, enhancing the efficiency of collaborative operations among multiple robots.

Abstract 4125691: A machine learning approach to examining the associations of minority stressors and physical activity among sexual and gender minority adults

Introduction: Physical activity (PA) is an important modifiable factor for cardiovascular disease (CVD). Sexual and gender minority (SGM; e.g., lesbian/gay, bisexual, transgender) adults face higher risks of inadequate PA and CVD due to increased exposure to minority stressors, such as experienced and anticipated discrimination, which may influence SGM adults’ willingness to engage in PA. To our knowledge, researchers have not investigated the impact of minority stressors on objectively-measured PA among SGM adults. Goal: Using k-means clustering, we sought to identify whether clusters characterized by greater exposure to minority stressors were associated with lower moderate and vigorous PA (MVPA) and higher sedentary behavior among SGM adults. Methods: This daily diary study included an online sample of SGM adults living in the United States. Participants completed daily surveys about personally experienced discrimination and anticipated discrimination (i.e., expectation of experiencing discrimination) and wore wrist accelerometers for 28 days to objectively measure PA. We used k-means clustering to identify clusters based on reports of experienced and anticipated discrimination. We first determined the optimal number of clusters using established partition criteria. Next, we ran linear regression models (adjusted for demographic factors) to examine the associations of minority stress clusters with MVPA and sedentary time per week. Results: Among 42 SGM adults (mean age 27.0±7.7 years) with 1133 person-days of accelerometry data (~3% missing data), we identified four minority stress clusters: low anticipated/low experienced (LALE; reference group); low anticipated/high experienced (LAHE); high anticipated/low experienced (HALE); and high anticipated/high experienced discrimination (HAHE). Participants in the HALE cluster (n=12) engaged in 202 fewer minutes of MVPA than those in the LALE cluster (n=7). Participants in the LAHE cluster (n=10) had 123 fewer minutes of vigorous PA than those in the LALE cluster. No differences were identified for sedentary time. Conclusions: This is the first study to examine the association of minority stressors with objective PA among SGM adults. Participants in the HALE and LAHE clusters engaged in significantly lower PA than those with low levels of both experienced and anticipated discrimination. Findings underscore the importance of assessing minority stressors and the need for interventions to improve PA among SGM adults.

Distinct causes underlie double-peaked trilobite morphological disparity in cephalic shape

AbstractTrilobite cephalic shape disparity varied through geological time and was integral to their ecological niche diversity, and so is widely used for taxonomic assignments. To fully appreciate trilobite cephalic evolution, we must understand how this disparity varies and the factors responsible. We explore trilobite cephalic disparity using a dataset of 983 cephalon outlines of c. 520 species, analysing the associations between cephalic morphometry and taxonomic assignment and geological Period. Elliptical Fourier transformation visualised as a Principal Components Analysis suggests significant differences in morphospace occupation and in disparity measures between the groups. Cephalic shape disparity peaks in the Ordovician and Devonian. The Cambrian–Ordovician expansion of morphospace occupation reflects radiations to new niches, with all trilobite orders established by the late Ordovician. In comparison, the Silurian–Devonian expansion seems solely a result of within-niche diversification. Linear Discriminant Analyses cross-validation, average cephalon shapes, and centroid distances demonstrate that, except for Harpida and the Cambrian and Ordovician Periods, order and geological Period cannot be robustly predicted for an unknown trilobite. Further, k-means clustering analyses suggest the total dataset naturally subdivides into only seven groups that do not correspond with taxonomy, though k-means clusters do decrease in number through the Palaeozoic, aligning with findings of decreasing disparity.

Abstract 4123872: Unsupervised clustering analysis of large-scale proteomics data identifies molecular phenogroups of heart failure in a population-based cohort

Background: Heart failure (HF) is a heterogeneous syndrome with high mortality. Previous work characterizing HF subgroups primarily relied on clinical data to cluster patients, an approach constrained by the boundaries of documented clinical features. Clustering based on biomarker data, such as proteomics, may provide insights into underlying molecular processes. However, few studies have used this approach, and all used targeted cardiovascular proteomics panels and a restricted HF ejection fraction group. Hypothesis: We used unsupervised clustering analysis to characterize molecular phenogroups in a population-based HF cohort using a large-scale array of circulating plasma proteins. We hypothesized that distinct proteomic-defined phenogroups would exhibit differences in clinical characteristics, mortality, and protein expression. Methods: We measured 7151 SOMAmers (Slow off-rate modified aptamers) targeting human proteins via the SomaLogic 7K SomaScan assay in 1351 HF patients. After feature selection, 831 SOMAmers were used in k-means clustering of patients via R package “ConsensusClusterPlus.” Phenogroups were compared for clinical characteristics, all-cause mortality, and protein expression. Results: Three phenogroups were identified, exhibiting different clinical characteristics ( Table 1 ) and survival probabilities (5-year survival probability in phenogroup 1: 65% [61%, 68%], phenogroup 2: 45% [40%, 51%], and phenogroup 3: 26% [22%, 30%]). In covariate-adjusted Cox models, membership in phenogroups 2 and 3 was significantly associated with increased rate of mortality compared to phenogroup 1. Phenogroups did not differ by ejection fraction or New York Heart Association class. Pathway overrepresentation analysis of differentially expressed SOMAmers identified pathways associated with inflammatory response and immune cell trafficking. Conclusions: Unsupervised clustering analysis using large-scale proteomics data identified three HF phenogroups with different proteomic signatures, clinical characteristics, and outcomes. Future work is necessary to elucidate how molecular differences contribute to heterogeneity in HF to improve risk stratification beyond current recommended clinical guidelines.

Energy Associated With Dynamic Network Changes in Patients With Multiple Sclerosis and Cognitive Impairment.

Patients with multiple sclerosis (MS) often experience cognitive impairment, and this is related to structural disconnection and subsequent functional reorganization. It is unclear how specific patterns of functional reorganization might make it harder for cognitively impaired (CI) patients with MS to dynamically adapt how brain regions communicate, which is crucial for normal cognition. We aimed to identify dynamic functional network patterns that are relevant to cognitive impairment in MS and investigate whether these patterns can be explained by altered energy costs. Resting-state functional and diffusion MRI was acquired in a cross-sectional design, as part of the Amsterdam MS cohort. Patients with clinically definitive MS (relapse-free) were classified as CI (≥2/7 domains Z < -2), mildly CI (MCI) (≥2/7 domains Z < -1.5), or cognitively preserved (CP) based on an expanded Brief Repeatable Battery of Neuropsychological Tests. Functional connectivity states were determined using k-means clustering of moment-to-moment cofluctuations (i.e., edge time series), and the resulting state sequence was used to characterize the frequency of transitions. Control energy of the state transitions was calculated using the structural network with network control theory. Imaging and cognitive data were available for 95 controls and 330 patients (disease duration: 15 years; 179 CP, 65 MCI, and 86 CI). We identified a "visual network state," "sensorimotor network state," "ventral attention network state," and "default mode network state." CI patients transitioned less frequently between connectivity states compared with CP (β = -5.78; p = 0.038). Relative to the time spent in a state, CI patients transitioned less from a "default mode network state" to a "visual network state" (β = -0.02; p = 0.004). The CI patients required more control energy to transition between states (β = 0.32; p = 0.007), particularly for the same transition (β = 0.34; p = 0.049). This study showed that it costs more energy for MS patients with cognitive impairment to dynamically change the functional network, possibly explaining why these transitions occur less frequently. In particular, transitions from a default mode network state to a visual network state were relevant for cognition in these patients. To further study the order of events leading to these network disturbances, future work should include longitudinal data across different disease stages.

Abstract 4129223: Large Language Modeling-Enabled Analysis of Atrial Fibrillation Topics and Sentiments on Social Media

Purpose: We utilized large language modeling (LLM) and automated topic modeling techniques to describe public perceptions regarding AF on Reddit, a social media platform. Methods: In this qualitative study, we curated all AF-related discussions on Reddit from 2006 to 2023 by searching for all posts and comments containing AF-related terms (“atrial fibrillation”, “afib”, and “atrial flutter”). Discussions were embedded using a state-of-the-art embedding model (BAAI/bge-base-en-v1.5). Embeddings are reduced in dimensionality using Uniform Manifold Approximation and Project and clustered using K-Means Clustering to find topics and groups. Topic and group labels are enhanced using large language models (Llama2 and GPT4, respectively). Results: We identified a total of 86,323 discussions related to AF from 38,183 unique users between April 14, 2006 and November 20, 2023. Discussions around wearables and ablations increased over time, while discussions around anticoagulation had periodic spikes. The topic modeling pipeline automatically identified 64 topics and 9 groups (Figure 1). Groups emphasized the following themes: lifestyle factors and triggers associated with AF (groups 1, 5, 7); patient experiences with management strategies including anticoagulation, rate control, pharmacological rhythm control, and ablation (groups 2, 4, 5); experiences with wearable devices (group 8); psychological burden of living with AF (group 6); and online advertisements for AF-related medications (groups 3, 9). Conclusions: Our AI-enabled topic modeling analysis revealed public perceptions about AF including triggers, experiences with different management strategies, impact of wearables, and psychological burden of the diagnosis, which may help guide potential strategies for improving shared decision making around the AF patient journey.

Attitudes, perceptions, and intentions toward sports betting legalization: a cluster analysis of California residents

ABSTRACT In the United States, the potential legalization of sports betting in non legal states presents a complex landscape of opportunities and challenges. This study examines California voters’ attitudes toward sports betting, the perceived impacts of its legalization, and the factors influencing their voting intentions. Utilizing the Theory of Reasoned Action (TRA), the research explores how attitudes and subjective norms shape voter behavior. A K-Means clustering analysis identified three distinct voter segments: supporters, neutrals, and opponents. The analysis revealed significant differences in voting intentions, attitudes, and perceived impacts across these clusters. These findings highlight the importance of robust policy and tailored marketing strategies that address the specific concerns and motivations of each voter segment. By providing insights into voter preferences and behaviors, this research offers valuable guidance for policymakers, operators, and stakeholders involved in the sports betting industry, supporting informed decision-making and responsible policy and messaging strategy.

Calculation Method Based on Flower Petal Area and Other Plant Leaf Spot Area

In order to calculate the area of irregular shapes such as plant leaves and diseased spots or flower petals, this paper presents a new method to calculate them by using flood fill algorithm, HSV color space, improved k-means algorithm and morphological operation. First, 501 butterfly petal images and pathological leaf images of Bauhinia and phyllotaxus were collected, and then flood was used Fill algorithm selects the disease-free area and records the selected pixel value. HSV color space conversion is applied to the image to facilitate the segmentation of leaves. Then, the improved k-means algorithm is used to extract the binary image of leaves and record the pixel value of the outer contour with morphological closed operation. Finally, the proportion and truth of the disease spots of plant leaves are obtained by calculating the pixel value and the real value of the rectangle in the sampling area Real area. Compared with the results of artificial labeling, the average accuracy of petal area and lesion area of Phalaenopsis was 96.3% and 96.61%, respectively. It can be seen that the program can calculate the area of irregular shape of plant surface accurately. In conclusion, this method can replace the artificial grid method to calculate the information of plant leaf area and disease proportion, and effectively reduce the work intensity of experimental personnel.

QLTI-07. COLLABORATIVE RESEARCH SUITE FOR GENOMIC ANALYSIS, DATA REPORTING AND WORKFLOW CUSTOMISATION

Abstract The genomic analysis pipeline involves multiple stages, including data extraction, transformation, and loading (ETL), exploratory data analysis (EDA), machine learning (ML) modeling, and reporting. The ETL stage involves data profiling and quality control measures to ensure data integrity and consistency. The EDA stage employs statistical and visualization methods to understand data distribution, identify patterns, and detect anomalies. Machine learning modelling involves supervised classification techniques, such as logistic regression and support vector machines (SVMs), to predict tumour classification and develop gene signatures. Unsupervised classification techniques, such as K-means clustering and Hierarchical clustering, identify patterns and relationships in genomic data. Quality metrics evaluate data completeness, accuracy, and consistency, ensuring data reliability and validity. The integration of data science techniques into a comprehensive framework can be achieved with the development of Application Dashboards that provide real-time insights into genomic data across every stage of the analysis pipeline. This enables clinical researchers and bioinformaticians to make informed decisions quickly, improving turn-around time and accelerating the discovery of new insights. In contrast to conventional methods of using stand-alone scripts, these dashboards offer a reusable and scalable solution, allowing for easy modification and adaptation to new datasets and analysis pipelines, thereby reducing development effort and increasing productivity. The dashboards constitute a centralized platform for data visualization, exploration, and analysis, facilitating collaboration and communication among research team members. With customizable and interactive visualizations, users can explore genomic data from multiple dimensions and the integration of multiple sources, including genomic and transcriptomic data. The technologies used to power this Research suite include Streamlit for Web App development, Python for data analysis, Opensearch for data storage and other utility libraries for reporting.

Advancing hyperspectral imaging techniques for root systems: a new pipeline for macro- and microscale image acquisition and classification

Abstract Background Understanding the environmental impacts on root growth and root health is essential for effective agricultural and environmental management. Hyperspectral imaging (HSI) technology provides a non-destructive method for detailed analysis and monitoring of plant tissues and organ development, but unfortunately examples for its application to root systems and the root-soil interface are very scarce. There is also a notable lack of standardized guidelines for image acquisition and data analysis pipelines. Methods This study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale, using the imec VNIR SNAPSCAN camera. Focusing on three graminoid species with different root architectures allowed us to evaluate the influence of key image acquisition parameters and data processing techniques on the differentiation of root, soil, and root-soil interface/rhizosheath spectral signatures. We compared two image classification methods, Spectral Angle Mapper (SAM) and K-Means clustering, and two machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to assess their efficiency in automating root system image classification. Results Our study demonstrated that training a RF model using SAM classifications, coupled with wavelength reduction using the second derivative spectra with Savitzky-Golay (SG) smoothing, provided reliable classification between root, soil, and the root-soil interface, achieving 88–91% accuracy across all configurations and scales. Although the root-soil interface was not clearly resolved, it helped to improve the distinction between root and soil classes. This approach effectively highlighted spectral differences resulting from the different configurations, image acquisition settings, and among the three species. Utilizing this classification method can facilitate the monitoring of root biomass and future work investigating root adaptations to harsh environmental conditions. Conclusions Our study addressed the key challenges in HSI acquisition and data processing for root system analysis and lays the groundwork for further exploration of VNIR HSI application across various scales of root system studies. This work provides a full data analysis pipeline that can be utilized as an online Python-based tool for the semi-automated analysis of root-soil HSI data.

SURG-03. GENE EXPRESSION CHANGES ASSOCIATED WITH RECURRENCE AFTER GROSS TOTAL RESECTION OF NEWLY DIAGNOSED WHO GRADE 1 MENINGIOMA

Abstract Patients who undergo gross total resection (GTR) of Central Nervous System World Health Organization (WHO) grade 1 meningioma constitute a “low risk” group, but some low risk meningiomas can recur despite reassuring clinical and histological features. In this study, gene expression in newly diagnosed WHO grade 1 meningiomas that had undergone GTR were evaluated for their association with recurrence. This was a retrospective, international, multi-center cohort study that included WHO grade 1 meningiomas that underwent GTR as first treatment. Normalized gene expression values from a previously validated 34-gene panel were evaluated for their association with recurrence. Kaplan-Meier, multivariable Cox proportional hazard analyses, and K-means clustering were performed to assess the association of genes of interest with recurrence and identify molecular subgroups among clinically and histologically low risk meningiomas. 442 patients with WHO grade 1 meningiomas that underwent GTR and had available gene expression profiling data were included in the study. Median follow-up was 5.0 years (interquartile range 2.6-7.7 years), local recurrence occurred in 36 patients (8.1%), 5-year local freedom from recurrence (LFFR) was 90.5%, and median time to recurrence of 2.9 years (range 0.5-10.7 years). Eleven genes were associated with local recurrence, including lower expression of ARID1B, ESR1, LINC02593, PGR, and TMEM30B and higher expression of CDK6, CDKN2C, CKS2, KIF20A, PGK1, and TAGLN. K-means clustering based on these 11 genes distinguished 2 molecular groups of clinically and histologically low risk meningiomas with significant differences in LFFR (HR 2.5, 95% CI 1.2-5.1, p=0.016). In conclusion, gene expression profiling may help to identify newly diagnosed WHO grade 1 meningiomas that have an elevated risk of recurrence despite GTR.

Global maternal health country typologies: A framework to guide policy

Maternal mortality remains a large challenge in global health. Learning from the experience of similar countries can help to accelerate progress. In this analysis we develop a typology of country groupings for maternal health and provide guidance on how policy implications vary by country typology. We used estimates from the Global Maternal Health (GMatH) microsimulation model, which was empirically calibrated to a range of fertility, process, and mortality indicators and provides estimates for 200 countries and territories. We used the 2022 estimates of the maternal mortality ratio (MMR) and lifetime risk of maternal death (LTR) and used a k-means clustering algorithm to define groups of countries based on these indicators. We estimated the means of other maternal indicators for each group, as well as the mean impact of different policy interventions. We identified 7 groups (A-G) of country typologies with different salient features. High burden countries (A-B) generally have MMRs above 500 and LTRs above 2%, and account for nearly 25% of global maternal deaths. Countries in these groups are estimated to benefit most from improving access to family planning and increasing facility births. Middle burden countries (C-E) generally have MMRs between 100–500 and LTRs between 0.5%-3%. Countries in these groups account for 55% of global maternal deaths and would benefit most from increasing facility births and improving quality of care. Low burden countries (F-G) generally have MMRs below 100 and LTRs below 0.5%, account for 20% of global maternal deaths, and would benefit most from improving access to family planning and community-based interventions and linkages to care. Indicators vary widely across groups, but also within groups, highlighting the importance of considering multiple indicators when assessing progress in maternal health. Policy impacts also differ by country typology, providing policymakers with information to help prioritize interventions.

NIMG-44. ELEVATED TRANSLOCATOR PROTEIN 18KDA BINDING IN LOW MEAN DIFFUSIVITY CLUSTERS IS A FEATURE OF THE TRANSFORMING GLIOMA AND IDH MUTANT GLIOMA MICROENVIRONMENT

Abstract Around 95% of low-grade gliomas undergo anaplastic de-differentiation into high-grade gliomas, a process only partially understood from in vivo clinical imaging. 11 patients with suspected transforming gliomas underwent a combined magnetic resonance imaging (MRI) and positron emission tomography (PET) protocol, incorporating post-contrast T1-weighted/FLAIR acquisition for tumour delineation and diffusion tensor imaging. The PET radiotracer [11C]-(R)-PK11195 assessed translocator protein 18kDa (TSPO) distribution, whose upregulation is associated with inflammation/glioma progression. [11C]-(R)-PK11195 binding potential (BPND) maps were generated using the simplified reference tissue model, with the grey-matter cerebellar time-activity curve serving as the reference tissue input function. KMeans clustering of mean diffusivity (MD) intensity values defined spatially distinct “habitats” of high/low MD voxels within the glioma microenvironment. BPND values within high/low MD clustering habitats were evaluated, across glioma grades and isocitrate dehydrogenase (IDH) mutational status. Across all grade 2 and 3 gliomas (n= 6), mean TSPO BPND values in low MD habitats (0.0383 ±0.102) were significantly greater than high MD habitats (-0.045 ±0.142) (p= 0.003). In grade 4 gliomas (n= 5), no significant difference was observed in mean BPND values between low (0.514 ±0.176) and high (0.465 ±0.296) MD habitats (p= 0.543). IDH mutant gliomas (n= 7) exhibited significantly higher mean BPND in low MD habitats (0.116 ±0.225), relative to high habitats (0.052 ±0.287) (p= 0.021). IDH wildtype gliomas (n= 4) showed no significant differences between BPND values in low (0.498 ±0.199) and high MD habitats (0.424 ±0.325) (p= 0.467). Our novel findings demonstrate spatial heterogeneity in TSPO expression within 2/3/IDH mutant gliomas, a characteristic absent from grade 4/IDH wildtype varieties. Regions of restricted diffusion with high TSPO signal in grade 2/3/IDH mutant tumours indicate the heterogenous distribution of active TSPO-expressing immune/neoplastic cell populations, contrasting the homogenous distribution in grade 4/IDH wildtype gliomas, highlighting differences between their inflammatory profile and pathological features.

Temporal patterns of energy intake and physical activity and cross-sectional associations with body weight status in children and adolescents: results from the Portuguese National Food, Nutrition and Physical Activity Survey 2015–2016

ABSTRACT Temporal energy intake (EI) and physical activity (PA) patterns may be associated with obesity. We aimed to derive and characterise temporal EI and PA patterns and assess their cross-sectional association with weight status in participants aged 6–14 years from the Portuguese National Food, Nutrition and Physical Activity Survey 2015-2016. We extracted times and EI of all eating occasions from two 1-day food diaries/24-hour recalls, while types and times of PA from 4-day PA diaries. We derived EI patterns (n=714) and PA patterns (n=595), using, respectively, a hierarchical and K-means cluster analysis, considering the average proportion of total daily EI (%TEI) and PA intensity (%TPA), within each 2-hour interval across the 24-hour day. We assessed the association between patterns and Overweight or obesity (Body Mass Index z-score ≥+1 Standard Deviation) using adjusted logistic regressions [Odds ratio (95% Confidence Interval)]. Three EI patterns were identified and labelled based on the 2-h interval of %TEI peaks: 1–“Early afternoon &amp; Early evening”; 2–“Early afternoon &amp; Late evening”, and 3–"Late morning, Early and Mid-afternoon &amp; Late evening”. EI Pattern 3 vs. 1 was negatively associated with overweight or obesity [0.49 (0.26,0.92)]. PA pattern 1–“Late Morning, Mid-afternoon &amp; Early evening” vs. Pattern 2–“Late afternoon”, was not associated with weight status [0.95 (0.65,1.38)]. A daily EI pattern characterised by more and even %TEI peaks at earlier daytime periods, instead of less, may be negatively associated with overweight or obesity amongst Portuguese 6-to-14-year-olds, whereas the identified PA patterns might have no relationship.