Number Of Clusters Research Articles

Improved clinical outcome prediction in depression using neurodynamics in an emotional face-matching functional MRI task.

Approximately one in six people will experience an episode of major depressive disorder (MDD) in their lifetime. Effective treatment is hindered by subjective clinical decision-making and a lack of objective prognostic biomarkers. Functional MRI (fMRI) could provide such an objective measure but the majority of MDD studies has focused on static approaches, disregarding the rapidly changing nature of the brain. In this study, we aim to predict depression severity changes at 3 and 6 months using dynamic fMRI features. For our research, we acquired a longitudinal dataset of 32 MDD patients with fMRI scans acquired at baseline and clinical follow-ups 3 and 6 months later. Several measures were derived from an emotion face-matching fMRI dataset: activity in brain regions, static and dynamic functional connectivity between functional brain networks (FBNs) and two measures from a wavelet coherence analysis approach. All fMRI features were evaluated independently, with and without demographic and clinical parameters. Patients were divided into two classes based on changes in depression severity at both follow-ups. The number of coherence clusters (nCC) between FBNs, reflecting the total number of interactions (either synchronous, anti-synchronous or causal), resulted in the highest predictive performance. The nCC-based classifier achieved 87.5% and 77.4% accuracy for the 3- and 6-months change in severity, respectively. Furthermore, regression analyses supported the potential of nCC for predicting depression severity on a continuous scale. The posterior default mode network (DMN), dorsal attention network (DAN) and two visual networks were the most important networks in the optimal nCC models. Reduced nCC was associated with a poorer depression course, suggesting deficits in sustained attention to and coping with emotion-related faces. An ensemble of classifiers with demographic, clinical and lead coherence features, a measure of dynamic causality, resulted in a 3-months clinical outcome prediction accuracy of 81.2%. The dynamic wavelet features demonstrated high accuracy in predicting individual depression severity change. Features describing brain dynamics could enhance understanding of depression and support clinical decision-making. Further studies are required to evaluate their robustness and replicability in larger cohorts.

Abstract 78: Superior resolution of intracellular markers with CyTOF reveals greater functional diversity of human T cells

Abstract Measuring functional signatures of immune cells in a comprehensive manner, spanning the inflammatory (Th1/Th17) and immunosuppressive (Th2/Treg) lineages, would provide key insights into several facets of cancer research and therapy. Determining mechanisms underlying success/failure of checkpoint blockade, defining immunosuppressive activity of the tumor-resident cell subsets, and identifying immunological biomarkers that predict clinical outcomes, could all be achievable if detection of cytokines spanning many lineages among individual cells was in reach. To achieve this goal, powerful cytometric research tools are required. Cell phenotyping panels are expanding and often now include measurement of intracellular markers of functional potential, such as cytokines, phosphorylation events, and transcription factors. Many of these readouts, including cytokines of the Th2/Treg lineages (IL-5, IL-10, and IL-13), have been notoriously difficult to detect in human cells using fluorescence-based cytometry. Large panels with multiplex intracellular markers are subject to challenges with autofluorescence and spillover spread, thus limiting fluorochrome capacity and reducing resolving power for rare subsets even with full spectrum flow cytometers. For such applications, mass cytometry may overcome such limitations and enable better signal resolution. To address this, we evaluated three small (13-plex) panels using a full spectrum flow cytometer and the CyTOF XT™ mass cytometer. Each panel was comprised of surface and intracellular analytes (cytokines, phospho-epitopes, or transcription factors) and designed to minimize potential impact of spectral overlap on the resolution of spectral flow data. PBMCs were stimulated, split, and stained with either fluorochrome- (Cytek® Aurora) or metal-conjugated antibodies (CyTOF XT). Data sets were analyzed by PhenoGraph clustering and visualized with opt-SNE to determine cellular functional diversity. Overall, data collected on the CyTOF XT demonstrated superior resolution for many intracellular readouts, including cytokines IL-5, IL-10, and IL-13. Also, when better signal/noise enabled accurate clustering, a higher number of immune cell clusters possessing distinct functional signatures resulted from mass cytometry datasets as compared with fluorescent counterparts. In summary, the CyTOF XT mass cytometry platform offers superior signal resolution compared to spectral flow for a range of intracellular targets. Use of mass cytometry translates to the identification of more diverse functional subsets and thus a clearer understanding of the immune signatures present in a sample in the context of unbiased analysis. Our findings indicate that the CyTOF XT platform could serve as a catalyst for seminal discoveries in immune profiling to drive therapeutic design and advanced disease monitoring in cancer. Citation Format: Erika L. Smith-Mahoney, Michael J. Cohen, Anna C. Belkina, Thirumahal Selvanantham, David King, Christina Loh, Amedeo J. Cappione, Jennifer E. Snyder-Cappione. Superior resolution of intracellular markers with CyTOF reveals greater functional diversity of human T cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 78.

Abstract 6864: Tumor suppressor function of the ubiquitin ligase Siah1a in melanoma is mediated by myeloid derived suppressor cells

Abstract Growing evidence supports the important role of myeloid-derived suppressor cells (MDSC) in melanoma progression and response to therapy. MDSC contributes to a pro-tumorigenic phenotype, yet the mechanistic basis for the immune suppressor properties acquired by MDSC remains largely unknown. Mammals have two homologous Siah genes, Siah1 (Siah1a and Siah1b in mice) and Siah2. In the context of melanoma, the ubiquitin ligases Siah1a and Siah2 have melanoma-intrinsic and -extrinsic (immune) functions. Correspondingly, ablation of Siah1a/2 in melanoma or its microenvironment attenuates melanoma development in a number of mouse melanoma models. Given that number of macrophage clusters infiltrated into melanoma when grown in Siah2 KO mice, we set to assess macrophage function in melanoma. Selective ablation of Siah1a or Siah2 in macrophages, using Lyz2Cre, revealed that Siah1a but not Siah2 elicits a tumor suppressor function in melanoma. Inoculation of mouse melanoma Yummer1.7 cells (Braf mutated and Pten deleted) in mice lacking Siah1a in macrophages, resulted in bigger melanoma tumors compared to melanoma grown in either WT or mice in which macrophages were ablated of Siah2. FACS analysis of melanoma tumors grown in mice harboring Siah1a mutant macrophages revealed a significant increase in MDSC, which coincided with a decrease in CD4+ and CD8+ cells, compared with WT mice. These observations suggest that a lack of Siah1a in macrophages leads to an expansion of MDSC that affects surrounding immune cells, including T cells. RNA-seq analysis of MDSC that were differentiated in vitro from bone marrow cells of WT or Siah1a ablated macrophage mice revealed increased expression of genes involved in proliferation and alternative macrophage activation in MDSC lacking Siah1a when compared to the WT genotype. Mapping mechanisms underlying Siah1a tumor suppressor function is expected to reveal novel regulatory cues in melanoma control by the MDSC population while mapping novel means for stratifications of melanoma to therapy, and possibly new means for therapy of this tumor type. Citation Format: Marzia Scortegagna, Yuanning Du, Yongmei Feng, Ze'ev Ronai. Tumor suppressor function of the ubiquitin ligase Siah1a in melanoma is mediated by myeloid derived suppressor cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6864.

Abstract 3799: Spatial biomarker prediction in papillary renal cell carcinoma using artificial intelligence-based immunofluorescent inference

Abstract Papillary renal cell carcinoma (pRCC) accounts for ~15-20% of the global burden for kidney cancers but still lacks effective mechanisms to predict patient responses to therapeutic intervention. Existing therapies targeting kidney cancers (anti-angiogenesis and immunotherapy) intimately involve the tumor microenvironment in their mechanism of action. However, the role of the physical organization of the tumor in determining therapeutic efficacy remains unknown. Previous efforts have utilized transcriptional profiling to stratify patients into treatment categories but here we propose to leverage artificial neural networks (“AI”) to infer the expression of marker genes for particular cell types of interest based on ubiquitously expressed immunofluorescence channels, unstained brightfield images, and hematoxylin and eosin (H&E) stained images. We have developed AI models that are capable of the prediction of a range of relevant cell types based on the expression profile of DAPI, type IV collagen, and integrin beta-1, including endothelial cells, T cells, macrophages and other cell types. This work applies an AI model class known as generative adversarial networks (GANs) to produce photo-realistic simulated immunofluorescence images based on the input channels. This approach may allow us to accurately predict complex multiplex immunofluorescence (mIF) images from simpler, more routine histological methods, greatly reducing the cost burden required to profile histopathology images for cell types of interest. Our previous work identified a number of interesting cell-cell association clusters correlated with patient prognosis and this extension of that previous work aims to allow for the prediction of these interactions while obviating the need to perform the original, costly multiplex immunofluorescence assay or even a more reduced immunofluorescence staining panel which may still be out of reach for many clinical pathology labs and the interpretation of which may be challenging. By simplifying and increasing the availability of predictive AI models for digital pathology, we hope to further develop and apply predictive biomarkers for patient prognosis and treatment response in papillary renal cell carcinoma. Citation Format: Jared Brewer, Joseph Vento, Scott Haake, Anupama Reddy. Spatial biomarker prediction in papillary renal cell carcinoma using artificial intelligence-based immunofluorescent inference [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3799.

Abstract 4298: Predicting tumor evolution from digital histology using AI

Abstract Diverse clinical presentations of clear cell renal cell carcinoma (ccRCC) confound clinical decision making, leading to over and undertreatment. Clonal evolution of ccRCC proceeds through distinct trajectories characterised by differing levels of genomic intratumoral heterogeneity (gITH) and chromosomal complexity (weighted genomic instability index, wGII). However, accurate evaluation of these indices requires multiregional profiling of fresh tumour; cost prohibitive and logistically challenging in the clinical setting. Clinical histopathology workflows routinely capture multiple tumour areas enabling the use artificial intelligence (AI) to predict tumour evolutionary features directly from clinical grade H&E whole slide image (WSIs). ccRCC displays profound genetic and histological ITH but the link between these entities remains unclear. We leverage the TRACERx Renal cohort, comprising 1485 WSIs from 81 tumours to predict WGII and gITH and to gain insights into the relationship between genetic and histological ITH. Critically, each WSI is associated with a wGII and gITH label derived from a closely linked fresh tumour sample. For both prediction tasks, we extracted meaningful features for each WSI using self-supervised representation learning “MoCo”. Since high wGII confers poor prognosis we focussed on predicting binary stratification label of high wGII or low wGII (relative to the cohort median). First we predicted wGII as a continuous variable using a supervised multiple instance learning regression model trained on the MoCo features, and then classified the predicted wGII into “high” or “low” achieving 0.80 AUROC. To predict gITH we postulated that the degree of gITH would correlate with histological ITH. Using an unsupervised clustering of refined MoCo features we defined 24 histological clusters. The number of computationally derived histological clusters within a single tumour positively correlated with gITH (pearson’s 0.56). We used the number of clusters to classify WSIs into prognostic binary groups of high or low gITH (relative to the cohort median) achieving an AUROC of 0.80. To understand the biological relationship between histological and genetic ITH we pathologically characterised the histological clusters: a pathologist annotated WSIs with tumour architecture and cytomorphology. Image tiles were associated with the annotations using spatial coordinates, illuminating phenotypic traits of different evolutionary trajectories and providing an interpretability framework for our AI pipelines. Since the tumour evolutionary course dictates disease progression tempo, applying evolutionary classification in clinic can fundamentally improve patient care. Here, for the first time, we provide a framework to translate fundamental evolutionary principles underpinning tumour biology and clinical progression into a prognostic computational pathology biomarker possible to clinically implement. Citation Format: Charlotte E. Spencer, Axel Camara, Auriane Riou, Lewis Au, Jose I. Lopez, Zayd Tippu, Charles Maussion, Kenneth Ho, Amy Strange, Emma Nye, Veronique Birault, Lydwine Van-praet, Kim Edmonds, Eleanor Carlyle, Steve Hazell, Sarah Rudman, James Larkin, Samra Turajlic. Predicting tumor evolution from digital histology using AI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4298.

Analysis of Factors Affecting Carbon Emissions based on Gray Correlation Analysis

In order to explore the influencing factors of carbon dioxide emissions, this paper collects data on carbon emissions and several influencing factors that may be related to its changes from 2004 to 2021, considering that too many indicators may make the data redundant and lack of representativeness, using the K-Means clustering algorithm to classify the indicators and defining the names of the corresponding first-level indicators, and analyzing the elbow diagrams for the determination of the model K-value. For the determination of the K value of the model, the optimal number of clusters was analyzed using the elbow diagram, so that the center of each cluster as the first-level indicator data was spliced with the normalized emission data to form a new dataset, and the correlation coefficients between the first-level indicators and the carbon emissions were finally calculated using the grey correlation analysis method. The results show that the ability to mitigate carbon dioxide content has the greatest impact on carbon content, and the correlation coefficient between the two reaches 0.756, which indicates that increasing the green area is very effective in achieving the goal of energy saving and emission reduction.

Covariate-Dependent Clustering of Undirected Networks with Brain-Imaging Data

This article focuses on model-based clustering of subjects based on the shared relationships of subject-specific networks and covariates in scenarios when there are differences in the relationship between networks and covariates for different groups of subjects. It is also of interest to identify the network nodes significantly associated with each covariate in each cluster of subjects. To address these methodological questions, we propose a novel nonparametric Bayesian mixture modeling framework with an undirected network response and scalar predictors. The symmetric matrix coefficients corresponding to the scalar predictors of interest in each mixture component involve low-rankness and group sparsity within the low-rank structure. While the low-rank structure in the network coefficients adds parsimony and computational efficiency, the group sparsity within the low-rank structure enables drawing inference on network nodes and cells significantly associated with each scalar predictor. Being a principled Bayesian mixture modeling framework, our approach allows model-based identification of the number of clusters, offers clustering uncertainty in terms of the co-clustering matrix and presents precise characterization of uncertainty in identifying network nodes significantly related to a predictor in each cluster. Empirical results in various simulation scenarios illustrate substantial inferential gains of the proposed framework in comparison with competitors. Analysis of a real brain connectome dataset using the proposed method provides interesting insights into the brain regions of interest (ROIs) significantly related to creative achievement in each cluster of subjects. Supplementary material shows the convergence rate for the posterior predictive density of the proposed model, additional simulation examples with model misspecification, full conditional distributions to run the Markov chain Monte Carlo (MCMC) algorithm and also presents traceplots for various model parameters to demonstrate convergence of the MCMC algorithm.

Euroscepticism and anti-establishment parties in Europe

ABSTRACT This article explores the relationship between party-based Euroscepticism and parties taking anti-establishment positions in contemporary Europe to map the relationship between these positions. The paper reports on an expert-informed qualitative comparative classification (EiQCC) of parties in 30 European countries conducted 2018–2019 to present a classification of 77 parties as Eurosceptic and anti-establishment. With this, we show that there were a number of different clusters of parties with various relationships between anti-establishment and Eurosceptic positions. The largest cluster showed a strong association between anti-establishment discourses and Euroscepticism. But there were also significant clusters where there was little or no interaction and were either anti-establishment and not Eurosceptic, or Eurosceptic without being anti-establishment. We also identify clusters where there was a weak association between anti-establishment and Eurosceptic positions and where anti-establishment parties had Eurosceptic stances but their position on European integration was either very weakly articulated or of very low salience.

Stability analysis to identify improved lines of cluster bean (Cyamopsis tetragonoloba L. Taub.)

AbstractTo select a stable and best-performing cluster bean line over seasons, an experiment was carried out using five shortlisted advanced breeding lines of vegetable cluster bean in randomized block design with four replications in Kharif and Summer seasons of 2019–20 and 2020–21 at ICAR-Indian Institute of Horticultural Research, Bengaluru. Additive mean effect and multiplicative interaction analysis of variation indicated a significant genotype and environment (G × E) interaction for all the traits. A high environment effect of 33.73% of the total sum of squares was observed for the trait pods per cluster followed by yield per hectare, single pod weight, pods per plant and clusters per plant. The first two interactive principal component axes (IPCA) cumulatively contributed 87.9, 97.3, 94.6, 98.6 and 85.6% variations for yield per hectare, number of clusters per plant, pods per plant, pods per cluster and single pod weight, respectively, leaving a small but significant amount of variation in the third IPCA. A mean versus weighted average of absolute score (WAAS) biplot indicated that genotype IIHRCB 26-2-1 is stable and best for the trait clusters per plant (>20 clusters/plant) while, IIHRCB 22-1-1 is superior and stable (WAAS mean nearly 0.00) for pods per cluster with >5 pods/cluster, pods per plant with >90 pods/plant, single pod weight with >3.0 g and yield per hectare around 24 t/ha. For all the environments, genotype IIHRCB 22-1-1 was found ‘all-time winner’ for yield per hectare and single pod weight. Based on multitrait stability index, IIHRCB 22-1-1 was found the best performer and the most stable genotype.

Extension of the Voronoi Diagram Algorithm to Orthotropic Space for Material Structural Design.

Nowadays, the interaction of additive technologies and methods for designing or optimizing porous structures has yielded good results. Construction with complex microarchitectures can be created using this approach. Varying the microarchitecture leads to changes in weight and mechanical properties. However, there are problems with geometry reconstruction when dealing with complex microarchitecture. One approach is to use Voronoi cells for geometry reconstruction. In this article, an extension of the Voronoi diagram algorithm to orthotropic space for material structural design is presented. The inputs for the method include porosity, ellipticity, and ellipticity direction fields. As an example, a beam with fixed end faces and center kinematic loading was used. To estimate robust results for different numbers of clusters, 50, 75, and 100 clusters are presented. The porosity for smoothed structures ranged from 21.5% up to 22.8%. The stress-strain state was determined for the resulting structures. The stiffness for the initial and smoothed structures was the same. However, in the case of 75 and 100 clusters, local stress factors appeared in the smoothed structure. The maximum von Mises stress decreased by 20% for all smoothed structures in the area of kinematic loading and increased by 20% for all smoothed structures in the area of end faces.

Exploring the Genetic Variability of Blackgram [Vigna mungo (L.) Hepper] Genotypes Over Environments

Background: The present investigation was carried out in 50 diverse blackgram genotypes during rabi 2021-22 in three different locations viz., Agricultural College and Research Institute, Madurai (E1), National Pulses Research Institute, Vamban (E2) and a farmer’s field at Vizhudhudayan, Ariyalur district (E3) for exploring the genetic variability of the blackgram genotypes over environments. Methods: The data on 10 quantitative traits viz., days to 50% flowering, plant height (cm), number of branches per plant, number of clusters per plant, number of pods per cluster, total number of pods per plant, pod length (cm), hundred seed weight (g) and seed yield per plant (g) were subjected to genetic variability analysis. Result: The traits viz., plant height (cm), number of branches per plant, number of clusters per plant, total number of pods per plant, hundred seed weight (g) and seed yield per plant (g) revealed a high phenotypic and genotypic coefficient of variation, heritability and genetic advance as percent of mean. Hence, these traits may be given much importance during selection due to their additive gene action. These results might render yield enhancement in future breeding programs.

Flexible multi-objective particle swarm optimization clustering with game theory to address human activity discovery fully unsupervised

Human activity recognition is a crucial field of study, but current approaches often require ground truth labels, which are not always available. We propose a new method called the Flexible Multi-Objective Particle swarm optimization clustering method based on Game theory (FMOPG), which can identify human activities without any supervision. Unlike traditional clustering methods that require an estimate of the number of clusters and are often inaccurate, FMOPG handles varying cluster numbers with an incremental technique, selecting clusters with good connectivity and separation. We enhance Particle Swarm Optimization (PSO) with mean-shift vectors for faster convergence and better handling of non-spherical clusters. Employing multi-objective optimization and Gaussian mutation, FMOPG provides a range of optimal solutions. We map the optimization problem to game theory to select the best solution based on different criteria. A smart grid-based method is proposed for population initialization, reducing variance and improving reliability. FMOPG outperforms state-of-the-art methods, improving clustering accuracy by 3.65%. Moreover, the incremental technique has improved clustering time by 71.18%.

Optimization of number and range of shunt valve performance levels in infant hydrocephalus: a machine learning analysis.

Shunt surgery is the main treatment modality for hydrocephalus, the leading cause of brain surgery in children. The efficacy of shunt surgery, particularly in infant hydrocephalus, continues to present serious challenges in achieving improved outcomes. The crucial role of correct adjustments of valve performance levels in shunt outcomes has been underscored. However, there are discrepancies in the performance levels of valves from different companies. This study aims to address this concern by optimizing both the number and range of valve performance levels for infant hydrocephalus, aiming for improved shunt surgery outcomes. We conducted a single-center cohort study encompassing infant hydrocephalus cases that underwent initial shunt surgery without subsequent failure or unimproved outcomes. An unsupervised hierarchical machine learning method was utilized for clustering and reporting the valve drainage pressure values for all patients within each identified cluster. The optimal number of clusters corresponds to the number of valve performance levels, with the valve drainage pressure ranges within each cluster indicating the pressure range for each performance level. Comparisons based on the Silhouette coefficient between 3-7 clusters revealed that this coefficient for the 4-cluster (4-performance level) was at least 28.3% higher than that of other cluster formations in terms of intra-cluster similarity. The Davies-Bouldin index for the 4-performance level was at least 37.2% lower than that of other configurations in terms of inter-cluster dissimilarity. Cluster stability, indicated by a Jaccard index of 71% for the 4-performance level valve, validated the robustness, reliability, and repeatability of our findings. Our suggested optimized drainage pressure ranges for each performance level (1.5-5.0, 5.0-9.0, 9.0-15.0, and 15.0-18.0cm H2O) may potentially assist neurosurgeons in improving clinical outcomes for patients with shunted infantile hydrocephalus.

Utilizing K-Means Clustering to Understanding Audience Interest in SEO-Optimized Media Content

This study observes k-means clustering for segmenting SEO data to understand audience interests, identifying the elbow method as crucial for determining the optimal number of clusters. It highlights notable differences in content engagement across clusters, emphasizing the need for refined SEO strategies and a deeper understanding of audience segmentation. Despite challenges like SEO's dynamic nature and data reliance, this methodology provides a strong foundation for enhancing content strategies. Future research suggestions include cross-platform data integration, longitudinal studies, sentiment analysis, content experimentation, user experience (UX) focus, and monitoring algorithm updates to develop more adaptive content and SEO strategies aligned with changing audience behaviors.

Characterizing soil hydrology in the Indo-Gangetic plain of Bihar, India: Methods and preliminary results

In the Eastern Gangetic Plain (EGP) soil hydrology is a major determinant of land use and also governs the ecosystem services derived from cropping systems, particularly greenhouse gas (GHG) emissions from rice fields. To characterize patterns of soil hydrology in these, daily field monitoring of water levels was conducted during the monsoon (kharif) season in a comparatively wet (2021) and dry (2022) year with flooding depth and drainage tracked with field water tubes across 47 (2021) and 183 (2022) locations. Fields were clustered into hydrologic response types (HRT) which can then be used for land surface modelling, land use recommendations, and to target agronomic interventions that contribute to sustainable development outcomes. Clusters based on two methods of summarizing a single information source were compared. The information source was a time-series of field water-level observations, and the two methods were (1) the original time-series and their first differences and (2) a set of derived hydrologic descriptors that are conceptually related to greenhouse gas (GHG) emissions. Clustering was (1) by k-means with an optimization of cluster numbers and (2) by hierarchical clustering with the same number of clusters as identified by k-means. Hydrologic behaviour shifted dramatically between growing seasons, and it was not possible to identify consistent HRT's across years. The clusters had only a weak relation with soil properties, almost no relation with farmer perception of relative landscape position, and no relation with rice establishment method. Clusters based on time-series were moderately well predicted in the dry year 2022 by optimized random forest models, with the most important predictors being the number of irrigations, seasonal precipitation, pre-monsoon groundwater levels, seasonal groundwater level change, and pH, this latter as a surrogate for landscape position and other soil properties. In the wet year 2021 clusters were (poorly) predicted by just seasonal precipitation and pre-monsoon groundwater levels. This shows the complex relation of soil hydrology with landscape position and land management, as well as synoptic climate. By contrast, clusters based on the descriptors were not well-matched with those from the time-series, and could not be well predicted by random forest models. This shows that different clustering criteria may result in different interpretations of the landscape hydrology and thus different heuristics for anticipating the hydrology of a given field under different management choices.

Electrolyzed Oxidizing Water in Controlling Pseudomonas syringae pv. tomato in Tomato Crops

Bacterial speck disease in tomato crops is caused by Pseudomonas syringae pv. tomato. Chemical control is mainly used for the control of phytopathogens, which carries a risk for both human health and the environment, making it necessary to search for environmentally friendly alternatives, such as the use of electrolyzed water. In the present study, preventive treatments were applied to tomato plants of the saladette variety. The treatments employed were electrolyzed oxidizing water (EOW), electrolyzed reduced water (ERW), a commercial bactericide (Kasumin), and untreated plants as the base control. During the vegetative stage, the disease severity, stem diameter, number of leaves, and number of clusters were determined. In addition, the soluble solids (°Brix), titratable acidity (TA), pH, color, polar and equatorial diameter, weight, and weight loss of the harvested fruit were determined. According to the results, the lowest severity was obtained in the plants treated with oxidizing water, achieving results similar to those achieved with the commercial bactericide Kasumin. It can be concluded that oxidizing water can be applied to tomato crops since its effect is similar to that of Kasumin, but without affecting the growth and development of the crop. Moreover, it is environmentally friendly.

An Improved Evolutionary Multi-Objective Clustering Algorithm Based on Autoencoder

Evolutionary multi-objective clustering (EMOC) algorithms have gained popularity recently, as they can obtain a set of clustering solutions in a single run by optimizing multiple objectives. Particularly, in one type of EMOC algorithm, the number of clusters k is taken as one of the multiple objectives to obtain a set of clustering solutions with different k. However, the numbers of clusters k and other objectives are not always in conflict, so it is impossible to obtain the clustering solutions with all different k in a single run. Therefore, evolutionary multi-objective k-clustering (EMO-KC) has recently been proposed to ensure this conflict. However, EMO-KC could not obtain good clustering accuracy on high-dimensional datasets. Moreover, EMO-KC’s validity is not ensured as one of its objectives (SSDexp, which is transformed from the sum of squared distances (SSD)) could not be effectively optimized and it could not avoid invalid solutions in its initialization. In this paper, an improved evolutionary multi-objective clustering algorithm based on autoencoder (AE-IEMOKC) is proposed to improve the accuracy and ensure the validity of EMO-KC. The proposed AE-IEMOKC is established by combining an autoencoder with an improved version of EMO-KC (IEMO-KC) for better accuracy, where IEMO-KC is improved based on EMO-KC by proposing a scaling factor to help effectively optimize the objective of SSDexp and introducing a valid initialization to avoid the invalid solutions. Experimental results on several datasets demonstrate the accuracy and validity of AE-IEMOKC. The results of this paper may provide some useful information for other EMOC algorithms to improve accuracy and convergence.

AutoSCAN: automatic detection of DBSCAN parameters and efficient clustering of data in overlapping density regions.

The density-based clustering method is considered a robust approach in unsupervised clustering technique due to its ability to identify outliers, form clusters of irregular shapes and automatically determine the number of clusters. These unique properties helped its pioneering algorithm, the Density-based Spatial Clustering on Applications with Noise (DBSCAN), become applicable in datasets where various number of clusters of different shapes and sizes could be detected without much interference from the user. However, the original algorithm exhibits limitations, especially towards its sensitivity on its user input parameters minPts and ɛ. Additionally, the algorithm assigned inconsistent cluster labels to data objects found in overlapping density regions of separate clusters, hence lowering its accuracy. To alleviate these specific problems and increase the clustering accuracy, we propose two methods that use the statistical data from a given dataset's k-nearest neighbor density distribution in order to determine the optimal ɛ values. Our approach removes the burden on the users, and automatically detects the clusters of a given dataset. Furthermore, a method to identify the accurate border objects of separate clusters is proposed and implemented to solve the unpredictability of the original algorithm. Finally, in our experiments, we show that our efficient re-implementation of the original algorithm to automatically cluster datasets and improve the clustering quality of adjoining cluster members provides increase in clustering accuracy and faster running times when compared to earlier approaches.

Brain magnetic resonance images segmentation via improved mixtures of factor analyzers based on dynamic co-clustering

The main goal of this paper is to attain accurate and automatic detection of brain tumors in gray magnetic resonance images using reducing the local dimensions. We propose a novel model called Improved Mixtures of Factor Analyzers based on Dynamic Co-Clustering (IMFADCC) for the detection and localization of brain tumors. After image preprocessing and enhancement, the optimal numbers of row clusters and column clusters corresponding to each row cluster, alongside other model parameters, are determined concurrently based on the size of the tumor by our model. Then, using the optimal values obtained, the image is co-clustered by simultaneously clustering rows and columns until the block containing the tumor is identified. The output image is divided into a certain number of blocks, one of which contains the tumor. For post-processing, the identified block is binarized using minimum error thresholding. The proposed model accurately detects and locates the block binarized within the original image while considering the remainder of the image as background. The effectiveness of the proposed method was assessed by evaluating its accuracy, Dice, intersection on union, geometric mean, receiver operating characteristic curve, specificity, and sensitivity criteria on the BraTS2018, BraTS2019, and BraTS2020 datasets. The results show that our method has significant performance and is highly accurate in detecting tumors of different sizes in complex and high-size images. Moreover, our method outperforms the existing diagnostic methods.

K-MEANS AND AGGLOMERATIVE HIERARCHY CLUSTERING ANALYSIS ON THE STAINLESS STEEL CORROSION PROBLEM

Stainless Steel (SS) is a material that is widely used in various fields because it is resistant to corrosion. However, if SS is exposed to heat at high temperatures for a long period of time, a sigma phase, namely the Fe-Cr compound, will form, which indicates that corrosion has begun. The appearance of this corrosion can be detected through color changes on the SS surface, ranging from light brown to dark blue. Corrosion events will be observed through the distribution of color on the sample surface at the location selected through the SS microstructure image. Cluster analysis will be used to group the colors on the surface of the SS sample through the images used. The results of cluster analysis can be used to identify SS color which indicates the appearance of corrosion in the sample. In this research, we will examine the determination of many clusters for K-Means and Agglomerative Hierarchy with Ward's Criterion, Single, Average, and Complete Linkages. In addition, the model quality measure was tested with Silhouette Coeficient. Single linkage gives the worst results because it gives the impression that only one dominant color appears so it can be said that it is unable to distribute each color to the specified cluster. Likewise with Average because the number of clusters cannot be determined with certainty. On the other hand, the K-Means results are similar to Ward's results, this is reasonable because the basic idea of both is to find the minimum distance between each object and its center, in this case the average is used as the measure of the center, while the results that are most similar to the original image are clustering uses complete linkage. These results can be used as recommendations for academics and practitioners in the fields of Statistics, Mathematics and Materials Engineering in the subsequent analysis process to solve SS corrosion problems.