Time profiles of energy balance in dairy cows in association with metabolic status, inflammatory status, and disease.

The early lactation period in dairy cows is characterized by complex interactions among energy balance (EB), disease, and alterations in metabolic and inflammatory status. The objective of this study was to cluster cows based on EB time profiles in early lactation and investigate the association between EB clusters and inflammatory status, metabolic status, oxidative stress, and disease. Holstein-Friesian dairy cows (n = 153) were selected and monitored for disease treatments during wk 1 to 6 in lactation. Weekly EB was calculated based on energy intake and energy requirements for maintenance and milk yield in wk 1 to 6 in lactation. Weekly plasma samples were analyzed for metabolic variables in wk 1 to 6, and inflammatory and oxidative stress variables in wk 1, 2, and 4 in lactation. Liver activity index (LAI) was computed from plasma albumin, cholesterol, and retino-binding protein concentration. First, cows were clustered based on time profiles of EB, resulting in 4 clusters (SP: stable positive; MN: mild negative; IN: intermediate negative; SN: severe negative). Cows in the SN cluster had higher plasma nonesterified fatty acids and ß-hydroxybutyrate concentrations, compared with cows in the SP cluster, with the MN and IN cluster being intermediate. Cows in the SN cluster had a higher milk yield, lower dry matter intake in wk 1, lower insulin concentration compared with cows in the SP cluster, and lower glucose and IGF-1 concentration compared with cows in the SP and MN clusters. Energy balance clusters were not related with plasma haptoglobin, cholesterol, albumin, paraoxonase, and liver activity index (LAI). Second, cows were grouped based on health status [IHP: cows with treatment for inflammatory health problem (endometritis, fever, clinical mastitis, vaginal discharge or retained placenta); OHP: cows with no IHP but treatment for other health problem (milk fever, cystic ovaries, claw, and leg problems, rumen and intestine problems or other diseases); NHP: cows with no treatments, in the first 6 weeks after calving]. Energy balance was not different among health status groups. The IHP cows had lower nonesterified fatty acids and greater insulin concentration in plasma compared with OHP. The IHP cows had lower plasma albumin concentration, lower LAI and higher haptoglobin concentration compared with OHP and NHP. Overall, EB time profiles were associated with the metabolic status of dairy cows in early lactation, but were only limitedly related with markers of inflammation and oxidative stress status. Inflammatory and metabolic status were related to disease events in early lactation and caused prolonged effects on liver metabolism.

Geospatial truck parking locations data for Europe.

This data article introduces a comprehensive dataset of real-world truck parking locations across Europe. The dataset comprises N = 19,713 designated parking sites classified according to public accessibility and suitability for heavy-duty trucks (HDTs). More specifically, core information comprises the truck stop category, latitude and longitude information, area size, and country assignment. Furthermore, additional information such as truck traffic flow volumes, proximity to the highway network, and land use information provide supplemental data on ambient conditions and thus enhance the contextual relevance of those locations. The dataset was systematically generated using OpenStreetMap (OSM) data, focusing on parking areas, rest areas, and fueling stations as predominant public truck parking sites. These locations were evaluated and filtered for truck accessibility and suitability and then complemented and validated using commercial truck routing / geocoding software. Further refinement was achieved by Mean-Shift clustering. The further integration of supplementary datasets increased the information level, and all clustered locations were labeled into four archetypal categories. Finally, filtering retained only confidently classified publicly accessible and truck-certified parking and service facilities. This dataset assists in finding real-world stop options for HDTs during national or international operations and identifying suitable and most attractive sites for deploying alternative charging or refueling infrastructures along the European transport network. Accordingly, it can serve as a valuable resource for research in traffic science, future energy systems, and alternative truck powertrains. Its added value extends to diverse stakeholders like Charge Point Operators (CPOs), truck manufacturers, logistics companies, and public authorities.

Identification of disease phenotypes in acetylcholine receptor-antibody myasthenia gravis using proteomics-based consensus clustering.

BACKGROUND: The clinical heterogeneity of myasthenia gravis (MG), an autoimmune disease defined by antibodies (Ab) directed against the postsynaptic membrane, constitutes a challenge for patient stratification and treatment decision making. Novel strategies are needed to classify patients based on their biological phenotypes aiming to improve patient selection and treatment outcomes. METHODS: For this purpose, we assessed the serum proteome of a cohort of 140 patients with anti-acetylcholine receptor-Ab-positive MG and utilised consensus clustering as an unsupervised tool to assign patients to biological profiles. For in-depth analysis, we used immunogenomic sequencing to study the B cell repertoire of a subgroup of patients and an in vitro assay using primary human muscle cells to interrogate serum-induced complement formation. FINDINGS: This strategy identified four distinct patient phenotypes based on their proteomic patterns in their serum. Notably, one patient phenotype, here named PS3, was characterised by high disease severity and complement activation as defining features. Assessing a subgroup of patients, hyperexpanded antibody clones were present in the B cell repertoire of the PS3 group and effectively activated complement as compared to other patients. In line with their disease phenotype, PS3 patients were more likely to benefit from complement-inhibiting therapies. These findings were validated in a prospective cohort of 18 patients using a cell-based assay. INTERPRETATION: Collectively, this study suggests proteomics-based clustering as a gateway to assign patients to a biological signature likely to benefit from complement inhibition and provides a stratification strategy for clinical practice. FUNDING: CN and CBS were supported by the Forschungskommission of the Medical Faculty of the Heinrich Heine University Düsseldorf. CN was supported by the Else Kröner-Fresenius-Stiftung (EKEA.38). CBS was supported by the Deutsche Forschungsgemeinschaft (DFG-German Research Foundation) with a Walter Benjamin fellowship (project 539363086). The project was supported by the Ministry of Culture and Science of North Rhine-Westphalia (MODS, "Profilbildung 2020" [grant no. PROFILNRW-2020-107-A]).

A New Classification and Laparoscopic Treatment of Extrahepatic Choledochal Cyst.

BACKGROUND: Prior typing methods fail to provide predictive insights into surgical complexities for extrahepatic choledochal cyst (ECC). This study aims to establish a new classification system for ECC through clustering of imaging results. Additionally, it seeks to compare the differences among the identified ECC types and assess the levels of surgical difficulty. METHODS: The imaging data of 124 patients were automatically grouped through a K-means clustering analysis. According to the characteristics of the new grouping, corrections and interventions were carried out to establish a new classification. Demographic data, clinical presentations, surgical parameters, complications, reoperation, and prognostic indicators were analyzed according to different types. Factors contributing to prolonged surgical time were also evaluated. RESULTS: A new classification system of ECC: Type A (upper segment), Type B (middle segment), Type C (lower segment), and Type D (entire bile duct). The incidences of comorbidities (calculus or infection) were significantly different (P=0.000, P=0.002). Additionally, variations in the incidence of postoperative cholangitis were statistically significant (P=0.046). The operative time was significantly different between groups (P=0.001). Age, BMI > 30, classification, and the presence of combined stones exhibit a significant association with prolonged operative time (P=0.002, P=0.000, P=0.011, P=0.011). CONCLUSION: In conclusion, our utilization of machine learning-driven cluster analysis has enabled the creation of a novel extrahepatic biliary dilatation typology. This classification, in conjunction with factors like age, combined stone occurrence, and obesity, significantly influences the complexity of laparoscopic choledochal cyst surgery, offering valuable insights for improved surgical treatment.

Exploring sex differences in blood-based biomarkers following exhaustive exercise using bioinformatics analysis.

This study examined the acute effects of exercise testing on immunology markers, established blood-based biomarkers, and questionnaires in endurance athletes, with a focus on biological sex differences. Twenty-four healthy endurance-trained participants (16 men, age: 29.2± 7.6 years, maximal oxygen uptake ( V ˙ O 2 max ): 59.4 ± 7.5 ml · min-1 · kg-1; 8 women, age: 26.8 ± 6.1 years, V ˙ O 2 max : 52.9 ± 3.1 ml · min-1 · kg-1) completed an incremental submaximal exercise test and a ramp test. The study employed exploratory bioinformatics analysis: mixed ANOVA, k-means clustering, and uniform manifold approximation and projection, to assess the effects of exhaustive exercise on biomarkers and questionnaires. Significant increases in biomarkers (lymphocytes, platelets, procalcitonin, hemoglobin, hematocrit, red blood cells, cell-free DNA (cfDNA)) and fatigue were observed post-exercise. Furthermore, differences pre- to post-exercise were observed in cytokines, cfDNA, and other blood biomarkers between male and female participants. Three distinct groups of athletes with differing proportions of females (Cluster 1: 100% female, Cluster 2: 85% male, Cluster 3: 37.5% female and 65.5% male) were identified with k-means clustering. Specific biomarkers (e.g., interleukin-2 (IL-2), IL-10, and IL-13, as well as cfDNA) served as primary markers for each cluster, potentially informing individualized exercise responses. In conclusion, our study identified exercise-sensitive biomarkers and provides valuable insights into the relationships between biological sex and biomarker responses.

Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics data.

Imaging-based spatial transcriptomics techniques generate data in the form of spatial points belonging to different mRNA classes. A crucial part of analyzing the data involves the identification of regions with similar composition of mRNA classes. These biologically interesting regions can manifest at different spatial scales. For example, the composition of mRNA classes on a cellular scale corresponds to cell types, whereas compositions on a millimeter scale correspond to tissue-level structures. Traditional techniques for identifying such regions often rely on complementary data, such as pre-segmented cells, or lengthy optimization. This limits their applicability to tasks on a particular scale, restricting their capabilities in exploratory analysis. This article introduces "Points2Regions," a computational tool for identifying regions with similar mRNA compositions. The tool's novelty lies in its rapid feature extraction by rasterizing points (representing mRNAs) onto a pyramidal grid and its efficient clustering using a combination of hierarchical and k $$ k $$ -means clustering. This enables fast and efficient region discovery across multiple scales without relying on additional data, making it a valuable resource for exploratory analysis. Points2Regions has demonstrated performance similar to state-of-the-art methods on two simulated datasets, without relying on segmented cells, while being several times faster. Experiments on real-world datasets show that regions identified by Points2Regions are similar to those identified in other studies, confirming that Points2Regions can be used to extract biologically relevant regions. The tool is shared as a Python package integrated into TissUUmaps and a Napari plugin, offering interactive clustering and visualization, significantly enhancing user experience in data exploration.

Assessing the relationship between malaria incidence levels and meteorological factors using cluster-integrated regression.

This paper introduces a novel approach to modeling malaria incidence in Nigeria by integrating clustering strategies with regression modeling and leveraging meteorological data. By decomposing the datasets into multiple subsets using clustering techniques, we increase the number of explanatory variables and elucidate the role of weather in predicting different ranges of incidence data. Our clustering-integrated regression models, accompanied by optimal barriers, provide insights into the complex relationship between malaria incidence and well-established influencing weather factors such as rainfall and temperature.We explore two models. The first model incorporates lagged incidence and individual-specific effects. The second model focuses solely on weather components. Selection of a model depends on decision-makers priorities. The model one is recommended for higher predictive accuracy. Moreover, our findings reveal significant variability in malaria incidence, specific to certain geographic clusters and beyond what can be explained by observed weather variables alone.Notably, rainfall and temperature exhibit varying marginal effects across incidence clusters, indicating their differential impact on malaria transmission. High rainfall correlates with lower incidence, possibly due to its role in flushing mosquito breeding sites. On the other hand, temperature could not predict high-incidence cases, suggesting that other factors other than temperature contribute to high cases.Our study addresses the demand for comprehensive modeling of malaria incidence, particularly in regions like Nigeria where the disease remains prevalent. By integrating clustering techniques with regression analysis, we offer a nuanced understanding of how predetermined weather factors influence malaria transmission. This approach aids public health authorities in implementing targeted interventions. Our research underscores the importance of considering local contextual factors in malaria control efforts and highlights the potential of weather-based forecasting for proactive disease management.

Exploring the influence of slaughterhouse type and slaughtering steps on Campylobacter jejuni contamination in chicken meat: A cluster analysis approach.

Campylobacter jejuni (C. jejuni), a foodborne pathogen, poses notable hazards to human health and has significant economic implications for poultry production. This study aimed to assess C. jejuni contamination levels in chicken carcasses from both backyard and commercial slaughterhouses in Chiang Mai province, Thailand. It also sought to examine the effects of different slaughtering practices on contamination levels and to offer evidence-based recommendations for reducing C. jejuni contamination. Through the sampling of 105 chicken carcasses and subsequent enumeration of C. jejuni, the study captured the impact of various slaughtering practices. Utilizing k-modes clustering on the observational and bacterial count data, the research identified distinct patterns of contamination, revealing higher levels in backyard operations compared to commercial ones. The application of k-modes clustering highlighted the impact of critical slaughtering practices, particularly chilling, on contamination levels. Notably, samples with the lowest bacterial counts were typically from the chilling step, a practice predominantly found in commercial facilities. This observation underpins the recommendation for backyard slaughterhouses to incorporate ice in their post-evisceration soaking process. Mimicking commercial practices, this chilling method aims to inhibit C. jejuni growth by reducing carcass temperature, thereby enhancing food safety. Furthermore, the study suggests backyard operations adopt additional measures observed in commercial settings, like segregating equipment for each slaughtering step and implementing regular cleaning protocols. These strategic interventions are pivotal in reducing contamination risks, advancing microbiological safety in poultry processing, and aligning with global food safety enhancement efforts.

Creating a bionic scaffold via light-curing liquid crystal ink to reveal the role of osteoid-like microenvironment in osteogenesis.

Osteoid plays a crucial role in directing cell behavior and osteogenesis through its unique characteristics, including viscoelasticity and liquid crystal (LC) state. Thus, integrating osteoid-like features into 3D printing scaffolds proves to be a promising approach for personalized bone repair. Despite extensive research on viscoelasticity, the role of LC state in bone repair has been largely overlooked due to the scarcity of suitable LC materials. Moreover, the intricate interplay between LC state and viscoelasticity in osteogenesis remains poorly understood. Here, we developed innovative hydrogel scaffolds with osteoid-like LC state and viscoelasticity using digital light processing with a custom LC ink. By utilizing these LC scaffolds as 3D research models, we discovered that LC state mediates high protein clustering to expose accessible RGD motifs to trigger cell-protein interactions and osteogenic differentiation, while viscoelasticity operates via mechanotransduction pathways. Additionally, our investigation revealed a synergistic effect between LC state and viscoelasticity, amplifying cell-protein interactions and osteogenic mechanotransduction processes. Furthermore, the interesting mechanochromic response observed in the LC hydrogel scaffolds suggests their potential application in mechanosensing. Our findings shed light on the mechanisms and synergistic effects of LC state and viscoelasticity in osteoid on osteogenesis, offering valuable insights for the biomimetic design of bone repair scaffolds.

A role for synapsin tetramerization in synaptic vesicle clustering.

Although synapsins have long been proposed to be key regulators of synaptic vesicle (SV) clustering, their mechanism of action has remained mysterious and somewhat controversial. Here, we review synapsins and their associations with each other and with SVs. We highlight the recent hypothesis that synapsin tetramerization is a mechanism for SV clustering. This hypothesis, which aligns with numerous experimental results, suggests that the larger size of synapsin tetramers, in comparison to dimers, allows tetramers to form optimal bridges between SVs that overcome the repulsive force associated with the negatively charged membrane of SVs and allow synapsins to form a reserve pool of SVs within presynaptic terminals.

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems.

Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome's functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute's Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data. IMPORTANCE: Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome's metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

scGAAC: A graph attention autoencoder for clustering single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNA-seq) enables the investigation of intricate mechanisms governing cell heterogeneity and diversity. Clustering analysis remains a pivotal tool in scRNA-seq for discerning cell types. However, persistent challenges arise from noise, high dimensionality, and dropout in single-cell data. Despite the proliferation of scRNA-seq clustering methods, these often focus on extracting representations from individual cell expression data, neglecting potential intercellular relationships. To overcome this limitation, we introduce scGAAC, a novel clustering method based on an attention-based graph convolutional autoencoder. By leveraging structural information between cells through a graph attention autoencoder, scGAAC uncovers latent relationships while extracting representation information from single-cell gene expression patterns. An attention fusion module amalgamates the learned features of the graph attention autoencoder and the autoencoder through attention weights. Ultimately, a self-supervised learning policy guides model optimization. scGAAC, a hypothesis-free framework, performs better on four real scRNA-seq datasets than most state-of-the-art methods. The scGAAC implementation is publicly available on Github at: https://github.com/labiip/scGAAC.

RIBAP: a comprehensive bacterial core genome annotation pipeline for pangenome calculation beyond the species level.

Microbial pangenome analysis identifies present or absent genes in prokaryotic genomes. However, current tools are limited when analyzing species with higher sequence diversity or higher taxonomic orders such as genera or families. The Roary ILP Bacterial core Annotation Pipeline (RIBAP) uses an integer linear programming approach to refine gene clusters predicted by Roary for identifying core genes. RIBAP successfully handles the complexity and diversity of Chlamydia, Klebsiella, Brucella, and Enterococcus genomes, outperforming other established and recent pangenome tools for identifying all-encompassing core genes at the genus level. RIBAP is a freely available Nextflow pipeline at github.com/hoelzer-lab/ribap and zenodo.org/doi/10.5281/zenodo.10890871.

A Comparison of Measures for Assessing Profile Similarity in Dyads.

Profile similarity measures are used to quantify the similarity of two sets of ratings on multiple variables. Yet, it remains unclear how different measures are distinct or overlap and what type of information they precisely convey, making it unclear what measures are best applied under varying circumstances. With this study, we aim to provide clarity with respect to how existing measures interrelate and provide recommendations for their use by comparing a wide range of profile similarity measures. We have taken four steps. First, we reviewed 88 similarity measures by applying them to multiple cross-sectional and intensive longitudinal data sets on emotional experience and retained 43 useful profile similarity measures after eliminating duplicates, complements, or measures that were unsuitable for the intended purpose. Second, we have clustered these 43 measures into similarly behaving groups, and found three general clusters: one cluster with difference measures, one cluster with product measures that could be split into four more nuanced groups and one miscellaneous cluster that could be split into two more nuanced groups. Third, we have interpreted what unifies these groups and their subgroups and what information they convey based on theory and formulas. Last, based on our findings, we discuss recommendations with respect to the choice of measure, propose to avoid using the Pearson correlation, and suggest to center profile items when stereotypical patterns threaten to confound the computation of similarity.

A novel clustered-based detection method for shilling attack in private environments.

The topic of privacy-preserving collaborative filtering is gaining more and more attention. Nevertheless, privacy-preserving collaborative filtering techniques are vulnerable to shilling or profile injection assaults. Hence, it is crucial to identify counterfeit profiles in order to achieve total success. Various techniques have been devised to identify and prevent intrusion patterns from infiltrating the system. Nevertheless, these strategies are specifically designed for collaborative filtering algorithms that do not prioritize privacy. There is a scarcity of research on identifying shilling attacks in recommender systems that prioritize privacy. This work presents a novel technique for identifying shilling assaults in privacy-preserving collaborative filtering systems. We employ an ant colony clustering detection method to effectively identify and eliminate fake profiles that are created by six widely recognized shilling attacks on compromised data. The objective of the study is to categorize the fraudulent profiles into a specific cluster and separate this cluster from the system. Empirical experiments are conducted with actual data. The empirical findings demonstrate that the strategy derived from the study effectively eliminates fraudulent profiles in privacy-preserving collaborative filtering.

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.

Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.

Integrative gene expression analysis and animal model reveal immune- and autophagy-related biomarkers in osteomyelitis.

BACKGROUND: Osteomyelitis (OM) is recognized as a significant challenge in orthopedics due to its complex immune and inflammatory responses. The prognosis heavily depends on timely diagnosis, accurate classification, and assessment of severity. Thus, the identification of diagnostic and classification-related genes from an immunological standpoint is crucial for the early detection and tailored treatment of OM. METHODS: Transcriptomic data for OM was sourced from the Gene Expression Omnibus (GEO) database, leading to the identification of autophagy- and immune-related differentially expressed genes (AIR-DEGs) through differential expression analysis. Diagnostic and classification models were subsequently developed. The CIBERSORT algorithm was utilized to examine immune cell infiltration in OM, and the relationship between OM clusters and various immune cells was explored. Key AIR-DEGs were further validated through the creation of OM animal models. RESULTS: Analysis of the transcriptomic data revealed three AIR-DEGs that played a significant role in immune responses and pathways. Nomogram and receiver operating characteristic curve analyses were performed, demonstrating excellent diagnostic capability for differentiating between OM patients and healthy individuals, with an area under the curve of 0.814. An unsupervised clustering analysis discerned two unique patterns of autophagy- and immune-related genes, as well as gene patterns. Further exploration into immune infiltration exhibited notable variances across different subtypes, especially between OM cluster 1 and gene cluster A, highlighting their potential role in mitigating inflammatory responses by regulating immune activities. Moreover, the mRNA and protein expression levels of three AIR-DEGs in the animal model were aligned with those in the training and validation data sets. CONCLUSIONS: From an immunological perspective, a diagnostic model was successfully developed, and two distinct clustering patterns were identified. These contributions offer a significant resource for the early detection and personalized immunotherapy of patients with OM.

Directly selecting cell-type marker genes for single-cell clustering analyses.

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ T cell types and potential prognostic marker genes.

Geotemporal analysis of melanoma incidence and mortality identifies distinct clusters of trends within the United States between 2001-2019.

The extent to which the geographic diversity of the U.S. plays a significant role in melanoma incidence and mortality over time has not been precisely characterized. We obtained age-adjusted melanoma data for the 50 states between the years 2001-2019 from the SEER registry and performed hierarchical clustering (complete linkage, Euclidean space) to uncover geotemporal trend groups over 2 decades. While there was a global increase in incidence during this time (b1=+0.41, p<0.0001), there were 6 distinct clusters (by absolute and Z-score) with significantly different temporal trends (ANCOVA p<0.0001). Cluster 2 (C2) states had the sharpest increase in incidence with b1=+0.66, p<0.0001. For mortality, the global rate decreased (b1=-0.03, p=.0003) with 3 and 6 clusters by absolute and Z scores, respectively (ANCOVA p<0.05). Cluster 1 (C1) states exhibited the smallest decline in mortality (b1=-0.017, p=0.008). Mortality to incidence ratios (MIRs) declined (b1=-0.0037, p<0.0001) and harbored 4 and 6 clusters by absolute and Z-score analysis, respectively (ANCOVA p<0.0001). Cluster 4 (C4) states had the lowest rate of MIR decline (b1=-0.003, p<0.0001). These results provide an unprecedented higher dimensional view of melanoma behavior over space and time. With more refined analyses, geospatial studies can uncover local trends which can inform public health agencies to more properly allocate resources.