Cluster Tool Research Articles

101. Patients with Carbapenem-resistant Klebsiella aerogenes and Enterobacter cloacae complex Colonization or Infection Have Different Baseline Characteristics but Similar Mortality and Clinical Outcomes

Abstract Background Recently, comparative phylogenetics dictated the formerly named Enterobacter aerogenes be separated from the genus Enterobacter and renamed Klebsiella aerogenes (KA); however, it remains unclear whether the genotypic differences responsible for the reclassification translate into clinical differences. We aimed to evaluate clinical characteristics and outcomes of patients colonized or infected with carbapenem-resistant (CR) Enterobacter cloacae complex (ECC) or CR KA. Methods We conducted a nested, case-control study of patients hospitalized from May 9th, 2016 until November 11th, 2019 and enrolled in The Consortium on Resistance Against Carbapenems in Klebsiella and other Enterobacterales II (CRACKLE-2). Cases were patients with an index culture with CR KA and controls were patients with an index culture positive for CR ECC, matched to each case by: 1) country, 2) anatomical source, 3) infection vs. colonization, and 4) availability/result of whole genome sequencing for the isolate. Where WGS was available the pan-genome Ortholog Clustering Tool was used to identify flexible genomic islands (FGI) associated with KA (n=65) versus ECC (n=66). Results Cases with CR KA (n=95) were matched with 95 controls with CR ECC. 49% of cultures met infection criteria. When compared with control patients, cases with KA were less likely to have liver disease (6% vs. 19%,p=0.009) but more likely to have been admitted for longer at the time of culture (median [IQR] 16[1,32] vs. 7[1,23]), have a Pitt score ≥ 4 (53% vs. 38%, p=0.041), and to have been in an intensive care unit (ICU) at the time of culture (54% vs. 45%, p=0.246) (Table 1). 30-day mortality (21% vs. 25%, p=0.492), 90-day mortality (26% vs. 35% p=0.208), and 30-day DOOR outcomes (p=0.727) did not differ between cases and controls for all patients (Figure 1, 2). There were 94 unique FGI containing 216 putative virulence factors for KA versus 77 and132 for ECC. Conclusion Despite significant differences in baseline characteristics and in the putative virulence genes present, clinical outcomes are similar in patients with CR KA and CR ECC colonization and infection (alive without events 37% vs. 36%). Disclosures Michael J. Satlin, MD, AbbVie: DSMB participant|bioMerieux: Grant/Research Support|Merck: Grant/Research Support|Selux Diagnostics: Grant/Research Support|SNIPRBiome: Grant/Research Support Keith S. Kaye, MD, MPH, Allecra: Advisor/Consultant|CARB-X: Advisor/Consultant|GSK: Advisor/Consultant|Merck: Advisor/Consultant|Shionogi: Advisor/Consultant|Spero: Advisor/Consultant Carol Hill, PhD, Glaxo SmithKline: Retirement Health, Cash Balance Plan|Glaxo SmithKline: Stocks/Bonds (Public Company) David van Duin, MD, PhD, Merck: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Advisor/Consultant|Qpex: Advisor/Consultant|Roche: Advisor/Consultant|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support

Can Bipartite Graph Be Effective Tool for Clustering: A Self Organizing Map based Automated Plant Disease Detection

Can Bipartite Graph Be Effective Tool for Clustering: A Self Organizing Map based Automated Plant Disease Detection

Generalized Optimal Scheduling of Cluster Tools With Reentrance and Residency Time Constraints

Generalized Optimal Scheduling of Cluster Tools With Reentrance and Residency Time Constraints

Scheduling Time-constrained Cluster Tools with Non-identical Parallel Processing Chambers

Scheduling Time-constrained Cluster Tools with Non-identical Parallel Processing Chambers

Cyclic scheduling of multi-type wafers concurrent processing in single-arm cluster tools with residency time constraints

Cyclic scheduling of multi-type wafers concurrent processing in single-arm cluster tools with residency time constraints

Концептуальные подходы, методические и экономические инструменты оценки функционирования и развития рынка услуг городского туризма

In the modern world, tourism, as an industry of interconnected sectors, influences the development of specific sectors of the economy and performs social and cultural functions. The article presents the author’s position, based on summaries of existing approaches and considering the paradigms of urbanization theory, that urban spaces today serve as a unifying sphere for various types of tourism, which are viewed as structural components of urban tourism. Based on the research fi ndings, the author provides a unique interpretation of the concepts ‘urban tourism’ and ‘urban tourism services market’, characterizing the urban tourism services market of Novosibirsk by highlighting various types of tourism resources – cultural-historical, architectural, infrastructural, etc.; the necessity for theoretical exploration of specific aspects of the urban tourism services market is argued. In particular, the possibility and necessity of implementing a conceptual economic model for assessing the current state and forecasting the development of the urban tourism services market based on the identification of basic factors of its functioning – demand potential and supply potential – are justified. The role of investment policy in the development of the urban tourism services market and the possibility of using cluster tools to activate demand and stimulate the sale of urban tourism services in the metropolis are outlined.

Identification of Priority Areas for Ecological Restoration Based on Ecosystem Service Bundles and Human Activity Footprint in Western Jilin, China

As a typical ecologically fragile region in the north of China, ecosystems in western Jilin Province have been severely damaged by a combination of natural factors and human activities. Ecological restoration sites need to be identified and viable strategies need to be developed to maximize the restoration of ecosystem functions and enhance human well-being. This study used the InVEST model, K-means clustering, and spatial statistical tools to identify priority sites for ecological restoration in western Jilin Province based on the change in ecosystem service bundles and in the human activity footprint. The results showed that provisioning services continued to increase and other services decreased and then increased during the study period. The provisioning service bundles and provisioning-regulating bundles increased continuously, the regulating service bundles, cultural service bundles and the service synergy bundles decreased continuously, and the supporting service bundles first increased and then decreased. Out of 48,005 evaluation units, 10,203 were prioritized for ecological restoration, accounting for 21.25% of the total. This study provides a scientific basis for restoring regional ecosystems and improving the supply of ecosystem services.

Quantitative Modelling of Residual Molecular Contamination in Low-Pressure Processing Environments

During the fabrication, micro-chips circuits are very sensitive to all kinds of contamination present in the environments fluid in contact with the wafers.A previous study [1] describes the issue of wafer molecular surface contamination as a result of exposure to clean room air. If needed clean room air can be purified to the desired level using appropriate chemical filters.A lot of state-of-the-art processing steps occur in low pressure environments, or so called vacuum cluster tools comprising a wafer handler equipped with different chambers. All of these chambers have a finite residual base pressure (typically on the order of 1 Pa).A major part of the residual gas may consist of rather harmless inert gas such as N2 or Ar. A fraction of the background ambient may consist of uncontrolled residual gas that could originate from different sources (e.g. desorption from reactor walls or wafer backside, back diffusion from vacuum pumps, )Such uncontrolled molecules could adhere to the wafer surface and leave some surface contaminants that can negatively impact the subsequent process step (e.g.).The current study quantitatively describes this issue.In a gas ambient the ideal gas law provides eq.1Where: C i is the volume concentration of impurity ih i : the fraction of the number of impurity molecules, i, over the total number of all molecules p is the total pressure of all residual gas. In the current study we assume the sticking coefficient to be 1. This is a safe worst-case scenario and also the scenario for the impurity molecules that stick the best and are thus most detrimental and of major concern.The thermal contamination build-up on the surface, si, is determined by the mean thermal velocity. The surface concentration of contaminant i building up on the surface, normalized to its volume concentration in the gas phase, is given by the average thermal bombardment velocity on the surface (= arrival velocity) multiplied with the exposure time. The thermal velocity is roughly independent of pressure and is primarily determined by the temperature and the mass of the molecules: for room temperature air it is as high as 500 m/s. this implies that molecules travel very fast. The normalized concentration is then given by eq.2This normalized concentration has the dimension of length (expressed in units of m).One can compare this length to the characteristic space available in the vacuum chamber perpendicular to the wafer surface. For most systems this is expected to be on the order of 1 to 10 cm.3 numerical examples are provided in Table I.These examples show that significant amounts of contamination (1e11 to 1e13 at/cm2) can readily build-up on the surface.As can be seen, on Figure 1 the normalized deposition reaches already 10 cm in as little 1 msec implying that all available contamination gas inside the reactor would have deposited on the wafer surface in only 1 msec.In real cases there is typically a continuous supply of impurity molecules to the gas phase (from desorption from other surfaces etc..) with the surface with the highest sticking coefficient ultimately collecting the most contaminants.Figure 1 shows the surface concentration of surface contamination normalized to the impurity volume concentration in the ambient as a function of time.References P.W. Mertens, ECSTransactions 92, p.237, (2019); Figure 1

Well-connectedness and community detection

Community detection methods help reveal the meso-scale structure of complex networks. Integral to detecting communities is the expectation that communities in a network are edge-dense and “well-connected”. Surprisingly, we find that five different community detection methods–the Leiden algorithm optimizing the Constant Potts Model, the Leiden algorithm optimizing modularity, Infomap, Markov Cluster (MCL), and Iterative k-core (IKC)–identify communities that fail even a mild requirement for well-connectedness. To address this issue, we have developed the Connectivity Modifier (CM), which iteratively removes small edge cuts and re-clusters until communities are well-connected according to a user-specified criterion. We tested CM on real-world networks ranging in size from approximately 35,000 to 75,000,000 nodes. Post-processing of the output of community detection methods by CM resulted in a reduction in node coverage. Results on synthetic networks show that the CM algorithm generally maintains or improves accuracy in recovering true communities. This study underscores the importance of network clusterability–the fraction of a network that exhibits community structure–and the need for more models of community structure where networks contain nodes that are not assigned to communities. In summary, we address well-connectedness as an important aspect of clustering and present a scalable open-source tool for well-connected clusters.

High-Dimensional Bayesian Semiparametric Models for Small Samples: A Principled Approach to the Analysis of Cytokine Expression Data.

In laboratory medicine, due to the lack of sample availability and resources, measurements of many quantities of interest are commonly collected over a few samples, making statistical inference particularly challenging. In this context, several hypotheses can be tested, and studies are not often powered accordingly. We present a semiparametric Bayesian approach to effectively test multiple hypotheses applied to an experiment that aims to identify cytokines involved in Crohn's disease (CD) infection that may be ongoing in multiple tissues. We assume that the positive correlation commonly observed between cytokines is caused by latent groups of effects, which in turn result from a common cause. These clusters are effectively modeled through a Dirichlet Process (DP) that is one of the most popular choices as nonparametric prior in Bayesian statistics and has been proven to be a powerful tool for model-based clustering. We use a spike-slab distribution as the base measure of the DP. The nonparametric part has been included in an additive model whose parametric component is a Bayesian hierarchical model. We include simulations that empirically demonstrate the effectiveness of the proposed testing procedure in settings that mimic our application's sample size and data structure. Our CD data analysis shows strong evidence of a cytokine gradient in the external intestinaltissue.

A Clustering Approach to Unveil User Similarities in 6-DoF Extended Reality Applications

The advent in our daily life of Extended Reality (XR) technologies, such as Virtual and Augmented Reality, has led to the rise of user-centric systems, offering higher level of interaction and presence in virtual environments. In this context, understanding the actual interactivity of users is still an open challenge and a key step to enabling user-centric system. In this work, our goal is to construct an efficient clustering tool for 6 Degree-of-Freedom (DoF) navigation trajectories by extending the applicability of existing behavioural tool. Specifically, we first compare the navigation in 6-DoF with its 3-DoF counterpart, highlighting the main differences and novelties. Then, we investigate new metrics aimed at better modelling behavioural similarities between users in a 6-DoF system. More concretely, we define and compare 11 similarity metrics which are based on different distance features ( i.e. , user positions in the 3D space, user viewing directions) and distance measurements ( i.e. , Euclidean, Geodesic, angular distance). Our solutions are validated and tested on real navigation paths of users interacting with dynamic volumetric media in both 6-DoF Virtual Reality and Augmented Reality conditions. Results show that metrics based on both user position and viewing direction better perform in detecting user similarity while navigating in a 6-DoF system. Such easy-to-use but robust metrics allow us to answer a fundamental question for user-centric systems: “how do we detect if users look at the same content in 6-DoF?”, opening the gate to new solutions based on users interactivity, such as viewport prediction, live streaming services optimised based on users behaviour but also for user-based quality assessment methods.

Computational algorithm based on health and lifestyle traits to categorize lifemetabotypes in the NUTRiMDEA cohort

Classifying individuals based on metabotypes and lifestyle phenotypes using exploratory factor analyses, cluster definition, and machine-learning algorithms is promising for precision chronic disease prevention and management. This study analyzed data from the NUTRiMDEA online cohort (baseline: n = 17332 and 62 questions) to develop a clustering tool based on 32 accessible questions using machine-learning strategies. Participants ranged from 18 to over 70 years old, with 64.1% female and 35.5% male. Five clusters were identified, combining metabolic, lifestyle, and personal data: Cluster 1 (“Westernized Millennial”, n = 967) included healthy young individuals with fair lifestyle habits; Cluster 2 (“Healthy”, n = 10616) consisted of healthy adults; Cluster 3 (“Mediterranean Young Adult”, n = 2013) represented healthy young adults with a healthy lifestyle and showed the highest adherence to the Mediterranean diet; Cluster 4 (“Pre-morbid”, n = 600) was characterized by healthy adults with declined mood; Cluster 5 (“Pro-morbid”, n = 312) comprised older individuals (47% >55 years) with poorer lifestyle habits, worse health, and a lower health-related quality of life. A computational algorithm was elicited, which allowed quick cluster assignment based on responses (“lifemetabotypes”). This machine-learning approach facilitates personalized interventions and precision lifestyle recommendations, supporting online methods for targeted health maintenance and chronic disease prevention.

Inferring ancestry with the hierarchical soft clustering approach tangleGen.

Understanding the genetic ancestry of populations is central to numerous scientific and societal fields. It contributes to a better understanding of human evolutionary history, advances personalized medicine, aids in forensic identification, and allows individuals to connect to their genealogical roots. Existing methods, such as ADMIXTURE, have significantly improved our ability to infer ancestries. However, these methods typically work with a fixed number of independent ancestral populations. As a result, they provide insight into genetic admixture, but do not include a hierarchical interpretation. In particular, the intricate ancestral population structures remain difficult to unravel. Alternative methods with a consistent inheritance structure, such as hierarchical clustering, may offer benefits in terms of interpreting the inferred ancestries. Here, we present tangleGen, a soft clustering tool that transfers the hierarchical machine learning framework Tangles, which leverages graph theoretical concepts, to the field of population genetics. The hierarchical perspective of tangleGen on the composition and structure of populations improves the interpretability of the inferred ancestral relationships. Moreover, tangleGen adds a new layer of explainability, as it allows identifying the single-nucleotide polymorphisms that are responsible for the clustering structure. We demonstrate the capabilities and benefits of tangleGen for the inference of ancestral relationships, using both simulated data and data from the 1000 Genomes Project.

Scheduling Cluster Tools with Multi-Space Process Modules and a Multi-Finger-Arm Robot in Wafer Fabrication Subject to Wafer Residency Time Constraints

Scheduling Cluster Tools with Multi-Space Process Modules and a Multi-Finger-Arm Robot in Wafer Fabrication Subject to Wafer Residency Time Constraints

Applying rearrangement distances to enable plasmid epidemiology with pling.

Plasmids are a key vector of antibiotic resistance, but the current bioinformatics toolkit is not well suited to tracking them. The rapid structural changes seen in plasmid genomes present considerable challenges to evolutionary and epidemiological analysis. Typical approaches are either low resolution (replicon typing) or use shared k-mer content to define a genetic distance. However, this distance can both overestimate plasmid relatedness by ignoring rearrangements, and underestimate by over-penalizing gene gain/loss. Therefore a model is needed which captures the key components of how plasmid genomes evolve structurally - through gene/block gain or loss, and rearrangement. A secondary requirement is to prevent promiscuous transposable elements (TEs) leading to over-clustering of unrelated plasmids. We choose the 'Double Cut and Join Indel' (DCJ-Indel) model, in which plasmids are studied at a coarse level, as a sequence of signed integers (representing genes or aligned blocks), and the distance between two plasmids is the minimum number of rearrangement events or indels needed to transform one into the other. We show how this gives much more meaningful distances between plasmids. We introduce a software workflow pling (https://github.com/iqbal-lab-org/pling), which uses the DCJ-Indel model, to calculate distances between plasmids and then cluster them. In our approach, we combine containment distances and DCJ-Indel distances to build a TE-aware plasmid network. We demonstrate superior performance and interpretability to other plasmid clustering tools on the 'Russian Doll' dataset and a hospital transmission dataset.

AptamerRunner: An accessible aptamer structure prediction and clustering algorithm for visualization of selected aptamers

Aptamers are short single-stranded DNA or RNA molecules with high affinity and specificity for targets and are generated using the iterative Systematic Evolution of Ligands by EXponential enrichment (SELEX) process. Next-generation sequencing (NGS) revolutionized aptamer selections by allowing a more comprehensive analysis of SELEX-enriched aptamers as compared to Sanger sequencing. The current challenge with aptamer NGS datasets is identifying a diverse cohort of candidate aptamers with the highest likelihood of successful experimental validation. Herein we present AptamerRunner, an aptamer sequence and/or structure clustering algorithm that synergistically integrates computational analysis with visualization and expertise-directed decision making. The visual integration of networked aptamers with ranking data, such as fold enrichment or scoring algorithm results, represents a significant advancement over existing clustering tools by providing a natural context to depict groups of aptamers from which ranked or scored candidates can be chosen for experimental validation. The inherent flexibility, user-friendly design, and prospects for future enhancements with AptamerRunner has broad-reaching implications for aptamer researchers across a wide range of disciplines.

Collaborative Scheduling for Single-Arm Cluster Tools With an Equipment Front-End Module Subject to Chamber Cleaning Requirements

Collaborative Scheduling for Single-Arm Cluster Tools With an Equipment Front-End Module Subject to Chamber Cleaning Requirements

A clustering tool for generating biological geometries for computational modeling in radiobiology.

Objective.To develop a computational tool that converts biological images into geometries compatible with computational software dedicated to the Monte Carlo simulation of radiation transport (TOPAS), and subsequent biological tissue responses (CompuCell3D). The depiction of individual biological entities from segmentation images is essential in computational radiobiological modeling for two reasons: image pixels or voxels representing a biological structure, like a cell, should behave as a single entity when simulating biological processes, and the action of radiation in tissues is described by the association of biological endpoints to physical quantities, as radiation dose, scored the entire group of voxels assembling a cell.Approach.The tool is capable of cropping and resizing the images and performing clustering of image voxels to create independent entities (clusters) by assigning a unique identifier to these voxels conforming to the same cluster. The clustering algorithm is based on the adjacency of voxels with image values above an intensity threshold to others already assigned to a cluster. The performance of the tool to generate geometries that reproduced original images was evaluated by the dice similarity coefficient (DSC), and by the number of individual entities in both geometries. A set of tests consisting of segmentation images of cultured neuroblastoma cells, two cell nucleus populations, and the vasculature of a mouse brain were used.Main results.The DSC was 1.0 in all images, indicating that original and generated geometries were identical, and the number of individual entities in both geometries agreed, proving the ability of the tool to cluster voxels effectively following user-defined specifications. The potential of this tool in computational radiobiological modeling, was shown by evaluating the spatial distribution of DNA double-strand-breaks after microbeam irradiation in a segmentation image of a cell culture.Significance.This tool enables the use of realistic biological geometries in computational radiobiological studies.

Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis.

Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in the analysis of scRNA-seq data to identify the groups of cells with similar expression profiles. However, cell clustering is technically challenging, as raw scRNA-seq data have various analytical issues, including high dimensionality and dropout values. Existing research has developed deep learning models, such as graph machine learning models and contrastive learning-based models, for cell clustering using scRNA-seq data and has summarized the unsupervised learning of cell clustering into a human-interpretable format. While advances in cell clustering have been profound, we are no closer to finding a simple yet effective framework for learning high-quality representations necessary for robust clustering. In this study, we propose scSimGCL, a novel framework based on the graph contrastive learning paradigm for self-supervised pretraining of graph neural networks. This framework facilitates the generation of high-quality representations crucial for cell clustering. Our scSimGCL incorporates cell-cell graph structure and contrastive learning to enhance the performance of cell clustering. Extensive experimental results on simulated and real scRNA-seq datasets suggest the superiority of the proposed scSimGCL. Moreover, clustering assignment analysis confirms the general applicability of scSimGCL, including state-of-the-art clustering algorithms. Further, ablation study and hyperparameter analysis suggest the efficacy of our network architecture with the robustness of decisions in the self-supervised learning setting. The proposed scSimGCL can serve as a robust framework for practitioners developing tools for cell clustering. The source code of scSimGCL is publicly available at https://github.com/zhangzh1328/scSimGCL.

Periodic Scheduling Optimization for Dual-Arm Cluster Tools with Arm Task and Residency Time Constraints via Petri Net Model

In order to improve quality assurance in wafer manufacturing, there are strict process requirements. Besides the well-known residency time constraints (RTCs), a dual-arm cluster tool also requires each robot arm to execute a specific set of tasks. We call such a tool an arm task-constrained dual-arm cluster tool (ATC-DACT). To do this, one of the arms is identified as the dirty one and the other as the clean one. The dirty one can deal with raw wafers, while the clean one can deal with processed wafers. This requirement raises a new problem for scheduling a cluster tool. This paper discusses the scheduling problem of ATC-DACTs with RTCs. Due to the arm task constraints, the proven, effective swap strategy is no longer applicable to ATC-DACTs, making the scheduling problem difficult. To address this problem, we explicitly describe the robot waiting as an event and build a Petri net (PN) model. Then, we propose a hybrid task sequence (HTS) as an operation strategy by combining the swap and backward strategies. Based on the HTS, the necessary and sufficient conditions for schedulability are established; also, a linear programming model is developed. We then develop an algorithm using these results to optimally schedule the system. Industrial case studies demonstrate the application of this method.