Cluster Tool Research Articles

Dimensionality Reduction Hybrid U-Net for Brain Extraction in Magnetic Resonance Imaging.

In various applications, such as disease diagnosis, surgical navigation, human brain atlas analysis, and other neuroimage processing scenarios, brain extraction is typically regarded as the initial stage in MRI image processing. Whole-brain semantic segmentation algorithms, such as U-Net, have demonstrated the ability to achieve relatively satisfactory results even with a limited number of training samples. In order to enhance the precision of brain semantic segmentation, various frameworks have been developed, including 3D U-Net, slice U-Net, and auto-context U-Net. However, the processing methods employed in these models are relatively complex when applied to 3D data models. In this article, we aim to reduce the complexity of the model while maintaining appropriate performance. As an initial step to enhance segmentation accuracy, the preprocessing extraction of full-scale information from magnetic resonance images is performed with a cluster tool. Subsequently, three multi-input hybrid U-Net model frameworks are tested and compared. Finally, we propose utilizing a fusion of two-dimensional segmentation outcomes from different planes to attain improved results. The performance of the proposed framework was tested using publicly accessible benchmark datasets, namely LPBA40, in which we obtained Dice overlap coefficients of 98.05%. Improvement was achieved via our algorithm against several previous studies.

The use of cluster formation principles and tools to improve the efficiency of lending to agroindustrial complex entities

The analysis of the concepts of application of the cluster approach has been carried out. Criteria are identified that summarize the content of the considered concepts and the types of relationships in the cluster that affect the creditworthiness of its participants. Based on the selected criteria, the advantages of clustering are grouped, which should be taken into account when lending. The grouping of these advantages made it possible to identify areas of practical application of the cluster approach. To improve the efficiency of the value chain of agro-food products, the clustering tools used in European practice are disclosed. Based on the systematization of the advantages of applying the cluster approach and the European experience, clustering tools that are acceptable in the conditions of the Republic of Belarus and favorably affect the result of lending are proposed.

Allocation of Users of Mental Health Services to Needs-Based Care Clusters: An Italian Pilot Study

In Italy, despite strong community-based mental health services, needs assessment is unsatisfactory. Using the Mental Health Clustering Tool (MHCT) we adopted a multidimensional and non-diagnosis dependent approach to assign mental health services users with similar needs to groups corresponding to resources required for effective care. We tested the MHCT in nine Departments of Mental Health in four Italian regions. After a brief training, 318 professionals assessed 12,938 cases with a diagnosis of schizophrenia, depression, bipolar disorder and personality disorder through the MHCT. 53% of cases were 40–59 years, half were females, 51% had a diagnosis of schizophrenia, 48% of cases were clinically severe. Clusters included different levels of clinical severity and diagnostic groups. The largest cluster was 11 (ongoing recurrent psychosis), with 18.9% of the sample, followed by cluster 3 (non-psychotic disorders of moderate severity). The MHCT could capture a variety of problems of people with mental disorders beyond the traditional psychiatric assessment, therefore depicting service population from a different standpoint. Following a brief training, MHCT assessment proved to be feasible. The automatic allocation of cases made the attribution to clusters easy and acceptable by professionals. To what extent clustering provide a sound base for care planning will be the matter of further research.

Transformer and Graph Convolutional Network for Text Classification

Graph convolutional network (GCN) is an effective tool for feature clustering. However, in the text classification task, the traditional TextGCN (GCN for Text Classification) ignores the context word order of the text. In addition, TextGCN constructs the text graph only according to the context relationship, so it is difficult for the word nodes to learn an effective semantic representation. Based on this, this paper proposes a text classification method that combines Transformer and GCN. To improve the semantic accuracy of word node features, we add a part of speech (POS) to the word-document graph and build edges between words based on POS. In the layer-to-layer of GCN, the Transformer is used to extract the contextual and sequential information of the text. We conducted the experiment on five representative datasets. The results show that our method can effectively improve the accuracy of text classification and is better than the comparison method.

Spatiotemporal Patterns of African Swine Fever in Wild Boar in the Russian Federation (2007-2022): Using Clustering Tools for Revealing High-Risk Areas.

African swine fever (ASF) is an infectious disease that affects both domestic pigs (DPs) and wild boar (WB). The WB population plays an important role in the spread of ASF as the WB acts as a natural reservoir of the virus and transmits it to other susceptible wild and domestic pigs. Our study was aimed at revealing the areas with a high concentration of the WB population, and their potential relationships with the grouping of ASF cases in WB during the course of the ASF spread in the Russian Federation (2007-2022). We collected the annual data on WB numbers by municipalities within the regions of the most intensive ASF spread. We then conducted spatiotemporal analysis to identify clustering areas of ASF cases and compare them with the territories with a high density of WB population. We found that some of the territories with elevated ASF incidence in WB demonstrated spatial and temporal coincidence with the areas with a high WB population density. We also visualized the zones ("emerging hot spots") with a statistically significant rise in the WB population density in recent years, which may be treated as areas of paramount importance for the application of surveillance measures and WB population control.

The Impact of Processing Time Variations on Swap Sequence Performance in Dual-Armed Cluster Tools

The performance of a swap sequence is analyzed by assuming cyclic scheduling in dual-armed cluster tools with processing time variations. A dual-armed cluster tool consists of multiple processing modules (PMs), one material handling robot that can hold two wafers at the same time, and loadlocks where wafer cassettes are loaded or unloaded. The swap sequence in a dual-armed cluster tool is widely used in practice and known to be optimal with deterministic processing times when the bottleneck PM’s workload is larger than the robot workload. However, in practice, processing times on a PM can have a small variation, which leads to a different processing time of each wafer on the PM. Hence, when the processing time variation is introduced, the performance of the swap sequence needs to be analyzed. This paper first defines a fundamental cycle and analyzes its cycle time. It then proposes optimality conditions of the swap sequence and performs numerical experiments to show the effectiveness of the sequence. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —A dual-armed cluster tool used for semiconductor manufacturing processes is usually operated with a swap sequence because it is simple, easy to control, and proven to be optimal with deterministic processing times. However, studies on the performance of the swap sequence are still limited with processing times varying in PMs which often occur in practice. Hence, this study shows the effectiveness of the swap sequence with the processing time variations by analyzing cycle times, optimality conditions, and performing numerical experiments.

Choosing Data Clustering Tools For GIS-Based Visualization Of Disease Incidence In The Population

Objective — To substantiate the choice of optimal tools for clustering spatially referenced data on disease incidence for GIS-based analysis of their spatial distribution. Material and Methods — We used primary data on the incidence of malignant neoplasms, chronic alcoholism, and asthma in the population of eight administrative areas in Arkhangelsk Oblast as a constituent entity of the Arctic Zone of the Russian Federation. Disease incidence was averaged over a 5-year period from 2016 to 2020. We assessed the methods for visualizing the distribution of spatially referenced indicators using the ArcMap geoinformation system tools. Results — The study yielded differences in the outcomes of automated clustering of spatially referenced data in ArcMap, depending on the normality of the distribution in individual samples and the spread of indicator values, which was visually reflected on the resulting map. The parameter values in the samples directly affected the features of data clustering. Hence, this issue is important to consider for ensuring the correct choice of the appropriate analytical tool. Conclusion — Our study demonstrated that when using tools for automated clustering of spatially referenced incidence data in terms of their visualization in ArcGIS, it is necessary to consider the factors that directly affect the accuracy of their presentation. We consider it most appropriate to use a clustering tool based on the geometric interval method.

Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively.

Public Usage of E-Government in EU Countries: Are There Any Consequences for Tax Evasion?

Due to the processes of digitalization and automation, the e-Government has become an essential tool of public administration. The paper deals with the problem of e-Government usage by the public and its potential connection to tax evasion. The analysis is based on available secondary panel and cross-sectional data on the level of EU countries in the period 2008 to 2021. We compare the situation in the countries and classify them into homogenous clusters based on the overall public usage of e-Government. Principal component analysis and cluster analysis have been used as tools for dimension reduction and clustering. Moreover, we also found evidence of a potential relationship between the usage of e-Government and tax evasion. The provision of online information and services by the government to its citizens as well as online communication with public authorities are related to the lower level of tax evasion in the country. Hence, public authorities should support e-Government usage and e-filing systems to fight against tax evasion.

Using big data and Population Health Management to assess care and costs for patients with severe mental disorders and move toward a value-based payment system

BackgroundMental health (MH) care often exhibits uneven quality and poor coordination of physical and MH needs, especially for patients with severe mental disorders. This study tests a Population Health Management (PHM) approach to identify patients with severe mental disorders using administrative health databases in Italy and evaluate, manage and monitor care pathways and costs. A second objective explores the feasibility of changing the payment system from fee-for-service to a value-based system (e.g., increased care integration, bundled payments) to introduce performance measures and guide improvement in outcomes.MethodsSince diagnosis alone may poorly predict condition severity and needs, we conducted a retrospective observational study on a 9,019-patient cohort assessed in 2018 (30.5% of 29,570 patients with SMDs from three Italian regions) using the Mental Health Clustering Tool (MHCT), developed in the United Kingdom, to stratify patients according to severity and needs, providing a basis for payment for episode of care. Patients were linked (blinded) with retrospective (2014–2017) physical and MH databases to map resource use, care pathways, and assess costs globally and by cluster. Two regions (3,525 patients) provided data for generalized linear model regression to explore determinants of cost variation among clusters and regions.ResultsSubstantial heterogeneity was observed in care organization, resource use and costs across and within 3 Italian regions and 20 clusters. Annual mean costs per patient across regions was €3,925, ranging from €3,101 to €6,501 in the three regions. Some 70% of total costs were for MH services and medications, 37% incurred in dedicated mental health facilities, 33% for MH services and medications noted in physical healthcare databases, and 30% for other conditions. Regression analysis showed comorbidities, resident psychiatric services, and consumption noted in physical health databases have considerable impact on total costs.ConclusionsThe current MH care system in Italy lacks evidence of coordination of physical and mental health and matching services to patient needs, with high variation between regions. Using available assessment tools and administrative data, implementation of an episodic approach to funding MH could account for differences in disease phase and physical health for patients with SMDs and introduce performance measurement to improve outcomes and provide oversight.

IDeLUCS: a deep learning interactive tool for alignment-free clustering of DNA sequences.

We present an interactive Deep Learning-based software tool for Unsupervised Clustering of DNA Sequences (iDeLUCS), that detects genomic signatures and uses them to cluster DNA sequences, without the need for sequence alignment or taxonomic identifiers. iDeLUCS is scalable and user-friendly: its graphical user interface, with support for hardware acceleration, allows the practitioner to fine-tune the different hyper-parameters involved in the training process without requiring extensive knowledge of deep learning. The performance of iDeLUCS was evaluated on a diverse set of datasets: several real genomic datasets from organisms in kingdoms Animalia, Protista, Fungi, Bacteria, and Archaea, three datasets of viral genomes, a dataset of simulated metagenomic reads from microbial genomes, and multiple datasets of synthetic DNA sequences. The performance of iDeLUCS was compared to that of two classical clustering algorithms (k-means++ and GMM) and two clustering algorithms specialized in DNA sequences (MeShClust v3.0 and DeLUCS), using both intrinsic cluster evaluation metrics and external evaluation metrics. In terms of unsupervised clustering accuracy, iDeLUCS outperforms the two classical algorithms by an average of ∼20%, and the two specialized algorithms by an average of ∼12%, on the datasets of real DNA sequences analyzed. Overall, our results indicate that iDeLUCS is a robust clustering method suitable for the clustering of large and diverse datasets of unlabeled DNA sequences. iDeLUCS is available at https://github.com/Kari-Genomics-Lab/iDeLUCS under the terms of the MIT licence.

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.

A Riemannian Tool for Clustering of Geo-Spatial Multivariate Data

A Riemannian Tool for Clustering of Geo-Spatial Multivariate Data

ClustFinder: A tool for threshold-delineated clustering of microbial isolates by pairwise genomic distance

This paper presents ClustFinder, a command line tool designed to automate clustering of genomes based on genomic distance. This tool will aid researchers and public health professionals in the identification of epidemiological clusters. Here, we demonstrate the usage of ClustFinder with example datasets. ClustFinder is available at github.com/Denes-Lab/ClustFinder.

The legal concept of discrimination by association: where does it fit into the digital era?

Personal autonomy, a core human rights principle, refers to the ability to both design and conduct the course of one’s life through making choices from a range of valuable options. This plays a pivotal role in the realm of equality and non discrimination, which are relevant in the context of the European Union (EU). Through case law, the concept of discrimination by association has emerged as a crucial step in strengthening anti-discrimination, recognising that individuals who are not part of a protected group but who have a relationship with someone who is, or are in some way associated with them, may be treated unfavourably, thus requiring appropriate remedies. Currently, the field of non-discrimination increasingly warrants attention, due to the rapid advancement of technology, which harbours unique discriminatory potential. For instance, through data mining and the use of artificial intelligence tools for profiling and clustering, people may be grouped based on collective characteristics that may not accurately represent their individual features, resulting in differential treatment regardless of whether legally recognised vulnerable groups are involved. It therefore becomes crucial to question to what extent discrimination by association can effectively address this discriminatory power or whether new measures need to be developed to safeguard personal autonomy and prevent the proliferation of such phenomena in the current digital landscape.

Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks

The insulation condition of HVDC grids consisting of cable systems, GIS, and converters should be monitored by partial discharge (PD) analysers using artificial intelligence (AI) tools for efficient insulation diagnosis. Although there are many experiences of PD monitoring solutions developed for the supervision of the insulation condition of HVAC grids using PD analysers, there are no standardised requirements for their qualification available yet. The international technical specification TS IEC 62478 provides general rules for PD measurements using electromagnetic methods but does not define performance requirements for qualification tests. HVDC and HVAC PD analysers must be tested by unambiguous test procedures. This paper compiles experiences of using PD analysers with HFCT sensors in HVAC grids (cable systems, GIS, and AIS) to define a qualification procedure for HVAC systems. This procedure is applicable to HVDC grids (cable systems, GIS, AIS, and converters) because the particularities related to the insulation behaviour under HVDC voltage are also considered. Representative PD sources are discussed in HVAC and HVDC positive and negative polarity. The PD pulse trend of representative insulation defects in HVDC cable systems is quite different from that of HVAC grids. Special attention should be paid to the acquisition of PD signals in HVDC grids since few pulses appear in solid insulations, mainly during voltage changes (polarity reversals or surges), but rarely in continuous operation with constant direct voltage. A synthetic PD simulator has been developed to reproduce trains of PD pulses or noise signals, similar to those that can appear in the power network. A set of three functionality tests has been developed for qualification of the diagnostic capabilities of PD analysers working up to 30 MHz addressed to HVDC or HVAC grids: (1) PD recognition test, (2) PD clustering test, and (3) PD location test. This qualification procedure has been validated by means of a round-robin test performed by five research institutes (RISE, FFII, TUDelft, TAU, and UPM) using commercial and in-development AI PD recognition and clustering tools to demonstrate its robustness and applicability. Applying this qualification procedure, two PD methods for electrical detection and prevention of insulation defects have been approved, one for HVAC and the other for HVDC grids.

Cleaning Plan Optimization for Dual-Armed Cluster Tools With General Chamber Cleaning Periods

Cleaning Plan Optimization for Dual-Armed Cluster Tools With General Chamber Cleaning Periods

SpineTool is an open-source software for analysis of morphology of dendritic spines

Dendritic spines form most excitatory synaptic inputs in neurons and these spines are altered in many neurodevelopmental and neurodegenerative disorders. Reliable methods to assess and quantify dendritic spines morphology are needed, but most existing methods are subjective and labor intensive. To solve this problem, we developed an open-source software that allows segmentation of dendritic spines from 3D images, extraction of their key morphological features, and their classification and clustering. Instead of commonly used spine descriptors based on numerical metrics we used chord length distribution histogram (CLDH) approach. CLDH method depends on distribution of lengths of chords randomly generated within dendritic spines volume. To achieve less biased analysis, we developed a classification procedure that uses machine-learning algorithm based on experts’ consensus and machine-guided clustering tool. These approaches to unbiased and automated measurements, classification and clustering of synaptic spines that we developed should provide a useful resource for a variety of neuroscience and neurodegenerative research applications.

Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data

BackgroundHigh-throughput sequencing (HTS) offers unprecedented opportunities for the discovery of causative gene variants in multiple human disorders including cancers, and has revolutionized clinical diagnostics. However, despite more than a decade of use of HTS-based assays, extracting relevant functional information from whole-exome sequencing (WES) data remains challenging, especially for non-specialists lacking in-depth bioinformatic skills.ResultsTo address this limitation, we developed Var∣Decrypt, a web-based tool designed to greatly facilitate WES data browsing and analysis. Var∣Decrypt offers a wide range of gene and variant filtering possibilities, clustering and enrichment tools, providing an efficient way to derive patient-specific functional information and to prioritize gene variants for functional analyses. We applied Var∣Decrypt on WES datasets of 10 acute erythroid leukemia patients, a rare and aggressive form of leukemia, and recovered known disease oncogenes in addition to novel putative drivers. We additionally validated the performance of Var∣Decrypt using an independent dataset of ~ 90 multiple myeloma WES, recapitulating the identified deregulated genes and pathways, showing the general applicability and versatility of Var∣Decrypt for WES analysis.ConclusionDespite years of use of WES in human health for diagnosis and discovery of disease drivers, WES data analysis still remains a complex task requiring advanced bioinformatic skills. In that context, there is a need for user-friendly all-in-one dedicated tools for data analysis, to allow biologists and clinicians to extract relevant biological information from patient datasets. Here, we provide Var∣Decrypt (trial version accessible here: https://vardecrypt.com/app/vardecrypt), a simple and intuitive Rshiny application created to fill this gap. Source code and detailed user tutorial are available at https://gitlab.com/mohammadsalma/vardecrypt.

Machine learning and galaxy morphology: for what purpose?

ABSTRACT Classification of galaxies is traditionally associated with their morphologies through visual inspection of images. The amount of data to come render this task, inhuman and Machine Learning (mainly Deep Learning) has been called to the rescue for more than a decade. However, the results look mitigate and there seems to be a shift away from the paradigm of the traditional morphological classification of galaxies. In this paper, I want to show that the algorithms indeed are very sensitive to the features present in images, features that do not necessarily correspond to the Hubble or de Vaucouleurs vision of a galaxy. However, this does not preclude to get the correct insights into the physics of galaxies. I have applied a state-of-the-art ‘traditional’ Machine Learning clustering tool, called Fisher-EM, a latent discriminant subspace Gaussian mixture model algorithm to 4458 galaxies carefully classified into 18 types by the EFIGI project. The optimum number of clusters given by the integrated complete likelihood criterion is 47. The correspondence with the EFIGI classification is correct, but it appears that the Fisher-EM algorithm gives a great importance to the distribution of light which translates to characteristics such as the bulge to disc ratio, the inclination or the presence of foreground stars. The discrimination of some physical parameters (bulge-to-total luminosity ratio, (B–V)T, intrinsic diameter, presence of flocculence or dust, and arm strength) is very comparable in the two classifications.