Hierarchical Clustering Approach Research Articles

Identification of inflammatory clusters in long-COVID through analysis of plasma biomarker levels.

Mechanisms underlying long COVID remain poorly understood. Patterns of immunological responses in individuals with long COVID may provide insight into clinical phenotypes. Here we aimed to identify these immunological patterns and study the inflammatory processes ongoing in individuals with long COVID. We applied an unsupervised hierarchical clustering approach to analyze plasma levels of 42 biomarkers measured in individuals with long COVID. Logistic regression models were used to explore associations between biomarker clusters, clinical variables, and symptom phenotypes. In 101 individuals, we identified three inflammatory clusters: a limited immune activation cluster, an innate immune activation cluster, and a systemic immune activation cluster. Membership in these inflammatory clusters did not correlate with individual symptoms or symptom phenotypes, but was associated with clinical variables including age, BMI, and vaccination status. Differences in serologic responses between clusters were also observed. Our results indicate that clinical variables of individuals with long COVID are associated with their inflammatory profiles and can provide insight into the ongoing immune responses.

The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

AbstractIntroductionData clustering is an important field of machine learning that has applicability in wide areas, like, business analysis, manufacturing, energy, healthcare, traveling, and logistics. A variety of clustering applications have already been developed. Data clustering approaches based on self‐organizing map (SOM) generally use the map dimensions (of the grid) ranging from 2 × 2 to 8 × 8 (4–64 neurons [microclusters]) without any explicit reason for using the particular dimension, and therefore optimized results are not obtained. These algorithms use some secondary approaches to map these microclusters into the lower dimension (actual number of clusters), like, 2, 3, or 4, as the case may be, based on the optimum number of clusters in the specific data set. The secondary approach, observed in most of the works, is not SOM and is an algorithm, like, cut tree or the other.MethodsIn this work, the proposed approach will give an idea of how to select the most optimal higher dimension of SOM for the given data set, and this dimension is again clustered into the lower actual dimension. Primary and secondary, both utilize the SOM to cluster the data and discover that the weight matrix of the SOM is very meaningful. The optimized two‐dimensional configuration of SOM is not the same for every data set, and this work also tries to discover this configuration.ResultsThe adjusted randomized index obtained on the Iris, Wine, Wisconsin diagnostic breast cancer, New Thyroid, Seeds, A1, Imbalance, Dermatology, Ecoli, and Ionosphere is, respectively, 0.7173, 0.9134, 0.7543, 0.8041, 0.7781, 0.8907, 0.8755, 0.7543, 0.5013, and 0.1728, which outperforms all other results available on the web and when no reduction of attributes is done in this work.ConclusionsIt is found that SOM is superior to or on par with other clustering approaches, like, k‐means or the other, and could be used successfully to cluster all types of data sets. Ten benchmark data sets from diverse domains like medical, biological, and chemical are tested in this work, including the synthetic data sets.

Perceptual heterogeneity in developmental prosopagnosia is continuous, not categorical

Developmental prosopagnosia (DP) is associated with considerable perceptual heterogeneity, though the nature of this heterogeneity and whether there are discrete subgroups versus continuous deficits remains unclear. Bennetts et al. (2022) recently found that holistic versus featural processing deficits distinguished discrete DP subgroups, but their sample was relatively small (N = 37), and subgroups were defined using a single task. To characterize perceptual heterogeneity in DPs more comprehensively, we administered a broad face perception battery to a large sample of 109 DPs and 134 controls, including validated measures of face matching (Cambridge Face Perception Test – CFPT, Computerized Benton Facial Recognition Test, Same/Different Face Matching Task), holistic processing (Part-Whole Task), and feature processing (Georges Task and Part-Whole part trials). When examining face matching measures, DPs exhibited a similar distribution of performance as controls, though shifted towards impairment by an average of 1.4 SD. We next applied Bennetts (2022) hierarchical clustering approach and k-means clustering to the CFPT upright, inverted, and inversion index measures, similarly finding one group of DPs with poorer inverted face performance and another with a decreased face inversion effect (holistic processing). However, these subgroup differences failed to generalize to other measures of feature and holistic processing beyond the CFPT. We finally ran hierarchical and k-means cluster analyses on our larger battery of face matching, feature, and holistic processing measures. Results clearly showed subgroups with generally better versus worse performance across all measures, with the distinction between groups being somewhat arbitrary. Together, these findings support a continuous account of DP perceptual heterogeneity, with performance differing primarily across all aspects of face perception.

Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling

Application of machine learning in plant breeding is a recent concept, that has to be optimized for precise utilization in the breeding program of high yielding crop plants. Identification and efficient utilization of heterotic grouping pattern aided with machine learning approaches is of utmost importance in hybrid cultivar breeding as it can save time and resources required to breed a new plant hybrid/variety. In the present study, 109 genotypes of sunflower were investigated at morphological, biochemical (SDS-PAGE) and molecular levels (through micro-satellites (SSR) markers) for heterotic grouping. All the three datasets were combined, scaled, and subjected to unsupervised machine learning algorithms, i.e., Hierarchical clustering, K-means clustering and hybrid clustering algorithm (hierarchical + K-means) for assessment of efficiency and resolution power of these algorithms in practical plant breeding for heterotic grouping identification. Following the application of machine learning unsupervised clustering approach, two major groups were identified in the studied sunflower germplasm, and further classification revealed six smaller classes in each major group through hierarchical and hybrid clustering approach. Due to high resolution, obtained in hierarchical clustering, classification achieved through this algorithm was further used for selection of potential parents. One genotype from each smaller group was selected based on the maximum seed yield potential and hybridized in a line × tester mating design producing 36 F1 cross combinations. These F1s along with their parents were studied in open field conditions for validating the efficacy of identified heterotic groups in sunflowers genetic material under study. Data for 11 agronomic and qualitative traits were recorded. These 36 F1 combinations were tested for their combining ability (General/Specific), heterosis, genotypic and phenotypic correlation and path analysis. Results suggested that F1 hybrids performed better for all the traits under investigation than their respective parents. Findings of the study validated the use of machine learning approaches in practical plant breeding; however, more accurate and robust clustering algorithms need to be developed to handle the data noisiness of open field experiments.

Sex Differences in the Role of Multimorbidity on Poststroke Disability: The Taiwan Stroke Registry.

Multimorbidity is common in patients who experience stroke. Less is known about the effect of specific multimorbidity patterns on long-term disability in patients with stroke. Furthermore, given the increased poststroke disability frequently seen in female vs male patients, it is unknown whether multimorbidity has a similar association with disability in both sexes. We assessed whether specific multimorbidity clusters were associated with greater long-term poststroke disability burden overall and by sex. In the Taiwan Stroke Registry, an ongoing nationwide prospective registry, patients with first-ever ischemic stroke were enrolled; this analysis is restricted to those individuals surviving to at least 6 months poststroke. Using a hierarchical clustering approach, clusters of prestroke multimorbidity were generated based on 16 risk factors; the algorithm identified 5 distinct clusters. The association between clusters and 12-month poststroke disability, defined using the modified Rankin Scale (mRS), was determined using logistic regression models, with additional models stratified by sex. The longitudinal association between multimorbidity and functional status change was assessed using mixed-effects models. Nine-thousand eight hundred eighteen patients with first-ever ischemic stroke were included. The cluster with no risk factors was the reference, "healthier" risk group (N = 1,373). Patients with a cluster profile of diabetes, peripheral artery disease (PAD), and chronic kidney disease (CKD) (N = 1882) had significantly greater disability (mRS ≥ 3) at 1 month (OR [95% CI] = 1.36 [1.13-1.63]), 3 months (OR [95% CI] = 1.27 [1.04-1.55]), and 6 months (OR [95% CI] = 1.30 [1.06-1.59]) but not at 12 months (OR [95% CI] = 1.16 [0.95-1.42]) than patients with a healthier risk factor profile. In the sex-stratified analysis, the associations with this risk cluster remained consistent in male patients (OR [95% CI] = 1.42 [1.06-1.89]) at 12 months, who also had a higher comorbidity burden, but not in female patients (OR [95% CI] = 0.95 [0.71-1.26]), who had higher proportions of severe strokes and severe disability (p-interaction = 0.04). Taiwanese patients with multimorbidity, specifically the concurrent presence of diabetes, PAD, and CKD, had higher odds of a worse functional outcome in the first 6 months poststroke. Clusters of multimorbidity may be less informative for long-term disability in female patients. Further studies should evaluate other mechanisms for worse disability in female patients poststroke.

Trust Aware Nero Fuzzy Based Agglomerative Hierarchical Clustering with Secure Whale Optimization Routing for Enhancing Energy Efficiency in WSN

Wireless sensor networks (WSNs) comprise a network of dispersed, carefully positioned sensor nodes in their deployment environment to monitor and collect data on natural phenomena. These sensor nodes collaborate to transmit data via multi-hop communication, ultimately reaching a central base station for processing. However, WSNs face significant challenges due to the resource-constrained nature of these devices and the harsh, open environments in which they operate. Addressing energy optimization and ensuring secure communication are primary concerns in the successful operation of WSNs. This paper introduces anovelTrust aware Neuro Fuzzy Clustering head selection (TNFCH) and agglomerative hierarchical clustering approach (AHC) with Secure Whale Optimization (SWO) Algorithm Routing to enhance energy-efficient transmission in WSNs. Our proposed protocol (TNFCH-AHWO) efficiently organizes nodes by utilizing neural network and Fuzzy logic then securely transfers the data into the communication network. We employ a Trust calculation algorithm in our system to ensure Trust and data integrity, facilitating efficient lightweight operations such as key generation, encryption, decryption, and verification. This ensures hop-to-hop authentication among the nodes in WSNs. To assess the performance of our proposed protocol, we conducted simulations using the NS3 simulator. The findings of the simulation show that the suggested protocol greatly enhances various performance metrics, including energy consumption analysis, throughput, network delay, network lifetime, and packet delivery ratio when compared to existing protocols.

Clustering Based Energy Efficient Routing for Wireless Sensor Network Using Particle Swarm Optimization

The clustering strategy is the most effective and efficient way to preserve energy in the Wireless Sensor Network (WSN). However, the cluster heads in the hierarchical clustering approach use the majority of the energy that is required to carry out the operations. These operations include receiving the data from the sensor nodes, aggregating it, and then eventually transmitting it to the base station. When choosing the appropriate cluster head, you can play a significant part in reducing the amount of energy that is consumed by the WSN and, as a result, extending its lifespan. A technique for the selection of energy-efficient cluster heads that is based on the particle swarm optimization method is proposed in this study (PSO-EECH). For the method that has been proposed to measure the amount of energy used, we need to take into account the cluster distance, the distance between each sensor node and the nodes that are nearby, and the amount of residual energy that is left in sensor nodes. The aforementioned structure is also capable of doing cluster building, in which the non-cluster head node can follow its CH based on the determined weight function. The proposed PSO-EECH approach has been put through extensive testing, and the results have shown that it possesses a high degree of accuracy in every scenario. The outputs of the proposed algorithm are compared with those of other clustering-based algorithms already in existence, and the conclusions of this comparison have reported that our method outperforms the other existing methods.

Ensemble Text Summarization Model for COVID-19-Associated Datasets

The work of text summarization in question-and-answer systems has gained tremendous popularity recently and has influenced numerous real-world applications for efficient decision-making processes. In this regard, the exponential growth of COVID-19-related healthcare records has necessitated the extraction of fine-grained results to forecast or estimate the potential course of the disease. Machine learning and deep learning models are frequently used to extract relevant insights from textual data sources. However, in order to summarize the textual information relevant to coronavirus, we have concentrated on a number of natural language processing (NLP) models in this research, including Bidirectional Encoder Representations of Transformers (BERT), Sequence-to-Sequence, and Attention models. This ensemble model is built on the previously mentioned models, which primarily concentrate on the segmented context terms included in the textual input. Most crucially, this research has concentrated on two key variations: grouping-related sentences using hierarchical clustering approaches and the distributional semantics of the terms found in the COVID-19 dataset. The gist evaluation (ROUGE) score result shows a significant and respectable accuracy of 0.40 average recalls.

Social Enterprises Within the Public Sector’s Purview: A Taxonomy-Based Study on South Korea

AbstractThis study applies a hierarchical clustering approach to identify social enterprise models that have appeared in a setting of public sector-led incubation. Within such a context, a high degree of conformity ought to be apparent due to the coercive isomorphic pressures associated with public sector patronage. We nominate South Korea for our analysis, given that the rising number of social enterprises in the country is closely related to a regulatory intervention. Based on an analysis of 468 social enterprises, we find, contrary to expectations, that distinct clusters of government-certified social enterprises have emerged, namely social utility niche, job outsourcing, market opportunity, and integrated balanced models. We typologize these models according to their strategic orientation, mission focus, and institutional alignment. In doing so, we contribute to social enterprise research by illustrating how organizational pluralism may manifest when the growth of a population of social enterprises is directly linked to public sector intervention and regulation.

Deep asymmetric nonnegative matrix factorization for graph clustering

Graph clustering is a fundamental technique in machine learning that has widespread applications in various fields. Deep Nonnegative Matrix Factorization (DNMF) was recently emerged to cope with the extraction of several layers of features, and it has been demonstrated to achieve remarkable results on unsupervised tasks. While DNMF has been applied for analyzing graphs, the effectiveness of the current DNMF approaches for graph clustering is generally unsatisfactory: these methods are intrinsically data representation models, and their objective functions do not capture cluster structures, also ignores direction which is crucial in the directed graph clustering problems. To overcome these downsides, this paper proposes a graph-specific DNMF model based on the Asymmetric NMF which can handle undirected and directed graphs. Inspired by hierarchical graph clustering and graph summarization approaches, the Deep Asymmetric Nonnegative Matrix Factorization (DAsNMF) is introduced for the directed graph clustering problem. In a pseudo-hierarchical clustering setting, DAsNMF decomposes the input graph to extract low-level to high-level node representations and graph representations (summarized graphs). In addition, the asymmetric cosine and PageRank-based similarities are imposed on the proposed model to preserve the local and global graph structures. The learning process is formulated as a unified optimization problem to jointly train representation learning model and clustering model. The extensive experimental studies validate the effectiveness of the proposed method on directed graphs.

Regionalization of flow duration curves for catchments in southern India using a hierarchical cluster approach

Abstract The present study on the hydrologic regionalization was taken up to evaluate the utility of hierarchical cluster analysis for the delineation of hydrologically homogeneous regions and multiple linear regression (MLR) models for information transfer to derive flow duration curve (FDC) in ungauged basins. For this purpose, 50 catchments with largely unregulated flows located in South India were identified and a dataset of historical streamflow records and 16 catchment attributes was created. Using selected catchment attributes, three hydrologically homogenous regions were delineated using a hierarchical agglomerative cluster approach, and nine flow quantiles (10–90%) for each of the catchments in the respective clusters was derived. Regionalization approach was then adopted, whereby using step-wise regression, flow quantiles were related with readily derived basin-physical characteristics through MLR models. Cluster-wise performance analysis of the developed models indicated excellent performance with an average coefficient of determination (R2) values of 0.85, 0.97, and 0.8 for Cluster-1, -2, and -3, respectively, in comparison to poor performance when all 50 stations were considered to be in a single region. However, Jackknife cross-validation showed mixed performances with regard to the reliability of developed models with performance being good for high-flow quantiles and poor for low-flow quantiles.

Exploring the Link Between Education Length and Employment Outcomes among Youth in Europe: A Hierarchical Clustering Approach

Abstract Background As the world rapidly evolves, digitalisation continues, and artificial intelligence is used, it is crucial to understand how education systems are preparing future generations to succeed in this very dynamic environment where there is a tremendous demand for skilled workers. Objectives This paper analyses similarities and differences between European Union Member States regarding the educational attainment of young adults and their performance in the labour market. Methods/Approach Ward’s cluster method in hierarchical cluster analysis for output and outcome indicators of young adult education systems is used for two selected years: 2012 and 2021. Results Belgium, Cyprus, Denmark, France, Ireland, Latvia, Lithuania, Luxembourg, and the Netherlands have the highest average percentage of young adults with tertiary education in 2021. On the other hand, Belgium, Bulgaria, Hungary, Ireland, Lithuania, Malta, Poland, and Romania have the highest average employment and income benefits of tertiary education for young adults. Conclusions The average share of 25-to 34-year-olds with tertiary education increased significantly in the EU-27 between 2012 and 2021, partly due to higher demand for skilled workers in labour markets. Higher levels of education are generally associated with better employment opportunities and higher wages. However, there are large differences across EU economies. The results can help governments and education policymakers design future policies and provide insights into cross-country comparisons.

The limbic‐predominant hypometabolic subtype of AD is not enriched for comorbid TDP‐43 pathology or hippocampal sclerosis

AbstractBackgroundPrevious research has identified distinct subtypes of AD that differed with respect to the distribution of neurofibrillary tangles (NFT). One of them, a limbic‐predominant subtype, was enriched for comorbid TDP‐43 pathology. Our previous data‐driven analysis used FDG‐PET to identify a limbic‐predominant subtype of AD that may correspond to the limbic‐predominant NFT subtype. In the current study, we tested the expected associations between measures of neuropathology such as TDP‐43 pathology, hippocampal sclerosis, and Braak stages of NFT accumulation, and the limbic‐predominant hypometabolic subtype.MethodWe analyzed ante‐mortem FDG‐PET and neuropathological data of 74 individuals from the autopsy cohort of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including 7 cognitively normal participants, 12 participants with MCI, and 55 with dementia (as assessed at the last available clinical evaluation). Severity of TDP‐43 pathology was operationalized as the number of brain areas with TDP‐43 immunoreactive inclusions (including amygdala, hippocampus, entorhinal/inferior temporal cortex and neocortex). Hippocampal sclerosis was operationalized as a binary variable. We measured similarity of individual FDG‐PET profiles to the limbic‐predominant subtype. For that, we used Euclidean distances from each individual profile to the mean profile of the limbic‐predominant AD subtype identified previously in a larger ADNI sample using a data‐driven hierarchical clustering approach (Fig.1). Lower Euclidean distances indicated higher similarity to the subtype. Bayesian linear regressions in JASP were used to assess associations between neuropathological markers and similarity to the limbic‐predominant FDG‐PET pattern, controlling for Thal phase of amyloid accumulation, age, sex, and time delay between the FDG‐PET scan and death in the null model. We assessed Bayes factors representing evidence for a given model in comparison to the null model (BF10).ResultOur results suggest that similarity of individual FDG‐PET profiles to the limbic‐predominant FDG‐PET pattern was not related to severity of TDP‐43 pathology (BF10 = 0.449), presence of hippocampal sclerosis (BF10 = 0.544), or Braak NFT stages (BF10 = 0.525).ConclusionContrary to the expected effects, our current analysis indicates a lack of reliable associations between neuropathological markers such as TDP‐43 pathology, hippocampal sclerosis, or global NFT accumulation and the limbic‐predominant hypometabolic subtype. Future analyses will assess potential alternative contributors, such as region‐specific patterns of NFT distribution.

A novel multi document summarization with document-elements augmentation for learning materials using concept based ILP and clustering methods

Multi Document Summarization (MDS) is a technique for extracting succinct summaries from groups of related documents. The usage of MDS in the e-learning context is more appealing for providing summaries for learning materials, which helps students and teachers to focus on key concepts of the learning materials. In a learning material, availability of non-textual document-elements such as figures/diagrams, plots, graphs, tables and algorithms can be seen widely, but keeping such document-elements in the summary is not possible as they are non-textual. This proposed work incorporates the text summarization approach with document-elements augmentation to the summary to provide a detailed coverage of information without exceeding the summary length constraint. The key information in the source text is identified by important phrase features and sentence features, and the summaries are generated by selecting important sentences using the Integer Linear Programming (ILP) framework while reducing the redundancy using pre-trained sentence vectors. The relationships between the summary and document-elements are identified through document-element snippet extraction and a Hierarchical Agglomerative Clustering approach. Experimental results of the proposed summary extraction and augmentation on educational dataset (EduSumm) show better performance compared to the state-of-the-art approaches.

Leveraging transcriptomics to develop bronchopulmonary dysplasia endotypes: a concept paper

ImpactBronchopulmonary dysplasia has multiple definitions that are currently based on phenotypic characteristics. Using an unsupervised machine learning approach, we created BPD subclasses (e.g., endotypes) by clustering whole microarray data. T helper 17 cell differentiation was the most significant pathway differentiating the BPD endotypes.IntroductionBronchopulmonary dysplasia (BPD) is the most common complication of extreme prematurity. Discovery of BPD endotypes in an unbiased format, derived from the peripheral blood transcriptome, may uncover patterns underpinning this complex lung disease.MethodsAn unsupervised agglomerative hierarchical clustering approach applied to genome-wide expression of profiling from 62 children at day of life five was used to identify BPD endotypes. To identify which genes were differentially expressed across the BPD endotypes, we formulated a linear model based on least-squares minimization with empirical Bayes statistics.ResultsFour BPD endotypes (A, B,C,D) were identified using 7,319 differentially expressed genes. Across BPD endotypes, 5,850 genes had a p value < 0.05 after multiple comparison testing. Endotype A consisted of neonates with a higher gestational age and birthweight. Endotypes B-D included neonates between 25 and 26 weeks and a birthweight range of 640 to 940 g. Endotype D appeared to have a protective role against BPD compared to Endotypes B and C (36% vs. 62% vs. 60%, respectively). The most significant pathway focused on T helper 17 cell differentiation.ConclusionBioinformatic analyses can help identify BPD endotypes that associate with clinical definitions of BPD.

The aggradation of alluvial fans in response to monsoon variability over the last 400 ka in the Hajar Mountains, south-east Arabia

The preservation and interpretation of past external forcings within sedimentary archives can be complicated. Marine proxy records show that, throughout the Mid-Late Quaternary, monsoon rainfall in south-east Arabia has varied at precessional timescales (∼ 23 ka). By contrast, terrestrial environmental records from the region, such as speleothems and palaeolake sequences, generally only capture rainfall variability at eccentricity (∼ 100 ka) timescales and geomorphological archives rarely record events over multiple glacial cycles. Previous work has suggested that the alluvial fan systems of the Hajar Mountains record aggradation coincident with precessional peaks because of more northerly Indian Ocean Summer Monsoon (IOSM) rainfall, relative to present. However, the alluvial record is complex: there is a growing body of ‘anomalous’ aggradation age data from periods of precessional minima and it is spatially uneven, mainly being derived from fans at either the northern- or southern-most extents of the western side of the mountains.We present optically stimulated luminescence (OSL) ages of alluvial aggradation from new sites in the central portion of the western and eastern sides of the Hajar mountains. Sedimentary units that show evidence of more sustained flow conditions, such as coarse conglomerate deposition and the development of thick floodplain deposits, typically date to periods of higher IOSM rainfall at precessional timescales. Sites where there is evidence of deposition under more ephemeral flow conditions, like today, yield ages corresponding to precessional minima. We synthesise these data with similarly interpreted alluvial and lacustrine deposits from across the region, employing a hierarchical clustering approach to generate clusters of ages representing periods of increased hydrological activity. This objective approach to regional chronological data suggests that ten periods of more sustained hydrological activity are evidenced across the Hajar region, broadly aligning to precessional and eccentricity peaks over the last 400 ka. These clusters show that Hajar fans predominately aggrade because of increasing rainfall over their catchments, with the central ages of clusters often aligning with the onset of precessional forcing. Hajar alluvial systems preserve important records of Quaternary climate variability over long timescales in an arid region where terrestrial records are scarce due to preservation issues.

Yclon: Ultrafast clustering of B cell clones from high-throughput immunoglobulin repertoire sequencing data

MotivationThe next-generation sequencing technologies have transformed our understanding of immunoglobulin (Ig) profiles in various immune states. Clonotyping, which groups Ig sequences into B cell clones, is crucial in investigating the diversity of repertoires and changes in antigen exposure. Despite its importance, there is no widely accepted method for clonotyping, and existing methods are computationally intensive for large sequencing datasets. ResultsTo address this challenge, we introduce YClon, a fast and efficient approach for clonotyping Ig repertoire data. YClon uses a hierarchical clustering approach, similar to other methods, to group Ig sequences into B cell clones in a highly sensitive and specific manner. Notably, our approach outperforms other methods by being more than 30 to 5000 times faster in processing the repertoires analyzed. Astonishingly, YClon can effortlessly handle up to 2 million Ig sequences on a standard laptop computer. This enables in-depth analysis of large and numerous antibody repertoires. Availability and implementationYClon was implemented in Python3 and is freely available on GitHub.

Towards a personalized health care using a divisive hierarchical clustering approach for comorbidity and the prediction of conditioned group risks

The objective was to assess risk of hospitalization and mortality of comorbidities using divisive hierarchical risk clustering to advice clinical interventions. Subjects and Methods: Data from the EHR of a general population, 3799885 adults, followed by 5 years. Model were performed using Spark and Scikit-learn and accuracy for the models was analyzed. Results: The number of models generated depends in part on the number of chronic diseases included (ex testing a sample of six diseases, a total number of 397 models for all-cause mortality and 431 models for hospitalization). The estimated models offered an ordered selection for the relevant clinical variables and their estimated risk as a group and for the individual patient in the group. Accuracy was assessed according to age, sex and the cardinality of the comorbid groups. A mobile version and dashboard were developed. Conclusion: The software developed stratified hospital admission and mortality risk in clusters of chronic diseases, and for a given patient, it could advise intensifying treatment or reallocating the patient risk.

Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs.

A hierarchical clustering approach for examining the relationship between pelvis-proximal femur geometry and bone stress injury in runners

Bone stress injury (BSI) risk in runners is multifactorial and not well understood. Unsupervised machine learning approaches can potentially elucidate risk factors for BSI by identifying groups of similar runners within a population which differ in BSI incidence. Here, a hierarchical clustering approach is used to identify groups of collegiate cross country runners based on 2-dimensional frontal plane pelvis and proximal femur geometry, which was extracted from dual-energy X-ray absorptiometry scans and dimensionally reduced by principal component analysis. Seven distinct groups were identified using the cluster tree, with the initial split being highly related to female-male differences. Visual inspection revealed clear differences between groups in pelvis and proximal femur geometry, and groups were found to differ in lower body BSI incidence during the subsequent academic year (Rand index = 0.53; adjusted Rand index = 0.07). Linear models showed between-cluster differences in visually identified geometric measures. Geometric measures were aggregated into a pelvis shape factor based on trends with BSI incidence, and the resulting shape factor was significantly different between clusters (p < 0.001). Lower shape factor values, corresponding with lower pelvis height and ischial span, and greater iliac span and trochanteric span, appeared to be related to increased BSI incidence. This trend was dominated by the effect observed across clusters of male runners, indicating that geometric effects may be more relevant to BSI risk in males, or that other factors masked the relationship in females. More broadly, this work outlines a methodological approach for distilling complex geometric differences into simple metrics that relate to injury risk.