Alignment Of Clusters Research Articles

In-silico identification and comparison of transcription factor binding sites cluster in anterior-posterior patterning genes in Drosophila melanogaster and Tribolium castaneum.

The cis-regulatory data that help in transcriptional regulation is arranged into modular pieces of a few hundred base pairs called CRMs (cis-regulatory modules) and numerous binding sites for multiple transcription factors are prominent characteristics of these cis-regulatory modules. The present study was designed to localize transcription factor binding site (TFBS) clusters on twelve Anterior-posterior (A-P) genes in Tribolium castaneum and compare them to their orthologous gene enhancers in Drosophila melanogaster. Out of the twelve A-P patterning genes, six were gap genes (Kruppel, Knirps, Tailless, Hunchback, Giant, and Caudal) and six were pair rule genes (Hairy, Runt, Even-skipped, Fushi-tarazu, Paired, and Odd-skipped). The genes along with 20 kb upstream and downstream regions were scanned for TFBS clusters using the Motif Cluster Alignment Search Tool (MCAST), a bioinformatics tool that looks for set of nucleotide sequences for statistically significant clusters of non-overlapping occurrence of a given set of motifs. The motifs used in the current study were Hunchback, Caudal, Giant, Kruppel, Knirps, and Even-skipped. The results of the MCAST analysis revealed the maximum number of TFBS for Hunchback, Knirps, Caudal, and Kruppel in both D. melanogaster and T. castaneum, while Bicoid TFBS clusters were found only in D. melanogaster. The size of all the predicted TFBS clusters was less than 1kb in both insect species. These sequences revealed more transversional sites (Tv) than transitional sites (Ti) and the average Ti/Tv ratio was 0.75.

Unsupervised domain adaptation with and without access to source data for estimating occupancy and recognizing activities in smart buildings

Energy-efficient buildings have gained increasing interest in the last decades as they provide optimal energy management. With the emergence of smart homes, many smart tools have been developed to optimize energy efficiency such as activities recognition (AR) and occupancy estimation (OE). The creation of these smart building tools may be disrupted by the scarcity of labeled data. Indeed, data labeling is a tedious and time-consuming task and, it can be very expensive for building objectives. However, labeled data scarcity can be solved by sharing knowledge from different domains using unsupervised domain adaptation techniques. Also, privacy issues can emerge and prevent the use of certain types of data that share residents’ attitudes. However, data privacy can be preserved by considering approaches that do not have direct access to original data. In this research, we provide a comparative analysis between unsupervised domain adaptation (UDA) methods, applied to the tasks of AR and OE, with and without direct access to source domain data. We have 6 adapted approaches for UDA with access to labeled source data: domain separation networks (DSN), cluster alignment with a teacher (CAT), CAT+ gradient reversal (RevGrad), CAT + robust RevGrad (rRevGrad), Auxiliary Target Domain-Oriented Classifier (ATDOC) with nearest centroid classifier (NC), and ATDOC with neighborhood aggregation (NA). Also, we have 6 adapted methods for UDA without labeled source data: confidence score weighting adaptation using joint model data structure (CoWA-JMDS), CoWA-JMDS without weights mixup, divide and contrast (DaC), attracting and dispersing (AaD), source hypothesis transfer with information maximization (SHOT-IM), and source hypothesis transfer with self-supervised pseudo-labeling (SHOT-Pseudo-labeling). All the considered methods have been tested on AR and OE datasets collected using ambient sensors. The comparative analysis made in this work has shown us impressive findings and has given great ideas about the type of approaches (with access or without access to the source data) that we should consider for real-world smart building applications.

Molecular dynamics simulation of Fe-Si alloys using a neural network machine learning potential

Interatomic potential development using machine learning (ML) approaches has attracted a lot of attention in recent years because these potentials can effectively describe the structural and dynamical properties of complex materials at the atomistic level. In this work, we present the development of a neural network (NN) deep ML interatomic potential for Fe-Si alloys, and we demonstrate the effectiveness of the NN-ML potential in predicting the structures and energies of liquid and crystalline phases of Fe-Si alloys in comparison with the results from ab initio molecular dynamics simulations or experimental data. The developed NN-ML potential is also used to perform molecular dynamics simulations to study the structures of Fe-Si alloys with various compositions under rapid solidification conditions. The short-ranged orders in the rapidly solidified Fe-Si alloys are also analyzed by a cluster alignment method.

Radiomic phenotyping of the lung parenchyma in a lung cancer screening cohort

High-throughput extraction of radiomic features from low-dose CT scans can characterize the heterogeneity of the lung parenchyma and potentially aid in identifying subpopulations that may have higher risk of lung diseases, such as COPD, and lung cancer due to inflammation or obstruction of the airways. We aim to determine the feasibility of a lung radiomics phenotyping approach in a lung cancer screening cohort, while quantifying the effect of different CT reconstruction algorithms on phenotype robustness. We identified low-dose CT scans (n = 308) acquired with Siemens Healthineers scanners from patients who completed low-dose CT within our lung cancer screening program between 2015 and 2018 and had two different sets of image reconstructions kernel available (i.e., medium (I30f.), sharp (I50f.)) for the same acquisition. Following segmentation of the lung field, a total of 26 radiomic features were extracted from the entire 3D lung-field using a previously validated fully-automated lattice-based software pipeline, adapted for low-dose CT scans. The lattice in-house software was used to extract features including gray-level histogram, co-occurrence, and run-length descriptors. The lattice approach uses non-overlapping windows for traversing along pixels of images and calculates different features. Each feature was averaged for each scan within a range of lattice window sizes (W) of 4, 8 and 20 mm. The extracted imaging features from both datasets were harmonized to correct for differences in image acquisition parameters. Subsequently, unsupervised hierarchical clustering was applied on the extracted features to identify distinct phenotypic patterns of the lung parenchyma, where consensus clustering was used to identify the optimal number of clusters (K = 2). Differences between phenotypes for demographic and clinical covariates including sex, age, BMI, pack-years of smoking, Lung-RADS and cancer diagnosis were assessed for each phenotype cluster, and then compared across clusters for the two different CT reconstruction algorithms using the cluster entanglement metric, where a lower entanglement coefficient corresponds to good cluster alignment. Furthermore, an independent set of low-dose CT scans (n = 88) from patients with available pulmonary function data on lung obstruction were analyzed using the identified optimal clusters to assess associations to lung obstruction and validate the lung phenotyping paradigm. Heatmaps generated by radiomic features identified two distinct lung parenchymal phenotype patterns across different feature extraction window sizes, for both reconstruction algorithms (P < 0.05 with K = 2). Associations of radiomic-based clusters with clinical covariates showed significant differences for BMI and pack-years of smoking (P < 0.05) for both reconstruction kernels. Radiomic phenotype patterns were more similar across the two reconstructed kernels, when smaller window sizes (W = 4 and 8 mm) were used for radiomic feature extraction, as deemed by their entanglement coefficient. Validation of clustering approaches using cluster mapping for the independent sample with lung obstruction also showed two statistically significant phenotypes (P < 0.05) with significant difference for BMI and smoking pack-years. Radiomic analysis can be used to characterize lung parenchymal phenotypes from low-dose CT scans, which appear reproducible for different reconstruction kernels. Further work should seek to evaluate the effect of additional CT acquisition parameters and validate these phenotypes in characterizing lung cancer screening populations, to potentially better stratify disease patterns and cancer risk.

Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.

A Dirichlet Model of Alignment Cost in Mixed-Membership Unsupervised Clustering

Mixed-membership unsupervised clustering is widely used to extract informative patterns from data in many application areas. For a shared dataset, the stochasticity and unsupervised nature of clustering algorithms can cause difficulties in comparing clustering results produced by different algorithms, or even multiple runs of the same algorithm, as outcomes can differ owing to permutation of the cluster labels or genuine differences in clustering results. Here, with a focus on inference of individual genetic ancestry in population-genetic studies, we study the cost of misalignment of mixed-membership unsupervised clustering replicates under a theoretical model of cluster memberships. Using Dirichlet distributions to model membership coefficient vectors, we provide theoretical results quantifying the alignment cost as a function of the Dirichlet parameters and the Hamming permutation difference between replicates. For fixed Dirichlet parameters, the alignment cost is seen to increase with the Hamming distance between permutations. Datasets with low variance across individuals of membership coefficients for specific clusters generally produce high misalignment costs—so that a single optimal permutation has far lower cost than suboptimal permutations. Higher variability in data, as represented by greater variance of membership coefficients, generally results in alignment costs that are similar between the optimal permutation and suboptimal permutations. We demonstrate the application of the theoretical results to data simulated under the Dirichlet model, as well as to membership estimates from inference of human-genetic ancestry. The results can contribute to improving cluster alignment algorithms that seek to find optimal permutations of replicates. Supplementary materials for this article are available online.

AIscEA: unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency.

The integrative analysis of single-cell gene expression and chromatin accessibility measurements is essential for revealing gene regulation, but it is one of the key challenges in computational biology. Gene expression and chromatin accessibility are measurements from different modalities, and no common features can be directly used to guide integration. Current state-of-the-art methods lack practical solutions for finding heterogeneous clusters. However, previous methods might not generate reliable results when cluster heterogeneity exists. More importantly, current methods lack an effective way to select hyper-parameters under an unsupervised setting. Therefore, applying computational methods to integrate single-cell gene expression and chromatin accessibility measurements remains difficult. We introduce AIscEA-Alignment-based Integration of single-cell gene Expression and chromatin Accessibility-a computational method that integrates single-cell gene expression and chromatin accessibility measurements using their biological consistency. AIscEA first defines a ranked similarity score to quantify the biological consistency between cell clusters across measurements. AIscEA then uses the ranked similarity score and a novel permutation test to identify cluster alignment across measurements. AIscEA further utilizes graph alignment for the aligned cell clusters to align the cells across measurements. We compared AIscEA with the competing methods on several benchmark datasets and demonstrated that AIscEA is highly robust to the choice of hyper-parameters and can better handle the cluster heterogeneity problem. Furthermore, AIscEA significantly outperforms the state-of-the-art methods when integrating real-world SNARE-seq and scMultiome-seq datasets in terms of integration accuracy. AIscEA is available at https://figshare.com/articles/software/AIscEA_zip/21291135 on FigShare as well as {https://github.com/elhaam/AIscEA} onGitHub. Supplementary data are available at Bioinformatics online.

On the fully coupled dynamics of flexible fibres dispersed in modulated turbulence

The present work investigates the mechanical behaviour of finite-size, elastic and inertial fibres freely moving in a homogeneous and isotropic turbulent flow at moderate Reynolds number. Four-way coupled, direct numerical simulations, based on a finite difference discretisation and the immersed boundary method, are performed to mutually couple the dynamics of fibres and fluid turbulence, allowing us to account for the backreaction of the dispersed phase to the carrier flow. An extensive parametric study is carried out in zero-gravity condition over the characteristic properties of the suspension, i.e. fibre's linear density (from iso-dense to denser-than-the-fluid fibres), length (from short fibres comparable with the dissipative scale to long fibres comparable with the integral scale) and bending stiffness (from highly flexible to almost rigid fibres), as well as the concentration (from dilute to non-dilute suspensions). Results reveal the existence of a robust turbulence modulation mechanism that is primarily controlled by the mass fraction of the suspension (with only a minor influence of the fibre's bending stiffness), which is characterised in detail by means of a scale-by-scale analysis in Fourier space. Despite such alteration with respect to the single-phase case due to the non-negligible backreaction, fibres experience only two possible flapping states (previously identified in the very dilute condition) while being transported and deformed by the flow. In addition, we show that the maximum curvature obeys different scaling laws that can be derived from the fibre dynamical equation. Finally, we explore the clustering and preferential alignment of fibres within the flow, highlighting the peculiar role of inertia and elasticity.

Structure and motifs of iron oxides from 1 to 3 TPa

Iron oxides are fundamental components of planet-forming materials. Understanding the Fe-O system's behavior and properties under high pressure can help us identify many new phases and states possible in exoplanetary interiors, especially terrestrial ones. Using the adaptive genetic algorithm (AGA), we investigate the structure of iron oxides for a wide range of stoichiometries ($0.25\leq x_O \leq 0.8$) at 1, 2, and 3 TPa. Five unreported ground-state structures with Fe$_2$O, FeO, Fe$_3$O$_5$, FeO$_2$, and FeO$_4$ compositions are identified. The calculated density of states (DOS) suggests that, except for FeO$_4$, all phases are metallic, but their carrier densities decrease with increasing pressure and oxygen content. The cluster alignment analysis of stable and metastable phases shows that several motifs may co-exist in a structure of iron oxides with low O content. In contrast, most iron oxides with high O content adopt a simple BCC motif at TPa pressures. Our results provide a crystal structure database of iron oxides for modeling and understanding the interiors of exoplanets.

Cluster Alignment With Target Knowledge Mining for Unsupervised Domain Adaptation Semantic Segmentation

Unsupervised domain adaptation (UDA) carries out knowledge transfer from the labeled source domain to the unlabeled target domain. Existing feature alignment methods in UDA semantic segmentation achieve this goal by aligning the feature distribution between domains. However, these feature alignment methods ignore the domain-specific knowledge of the target domain. In consequence, 1) the correlation among pixels of the target domain is not explored; and 2) the classifier is not explicitly designed for the target domain distribution. To conquer these obstacles, we propose a novel cluster alignment framework, which mines the domain-specific knowledge when performing the alignment. Specifically, we design a multi-prototype clustering strategy to make the pixel features within the same class tightly distributed for the target domain. Subsequently, a contrastive strategy is developed to align the distributions between domains, with the clustered structure maintained. After that, a novel affinity-based normalized cut loss is devised to learn task-specific decision boundaries. Our method enhances the model's adaptability in the target domain, and can be used as a pre-adaptation for self-training to boost its performance. Sufficient experiments prove the effectiveness of our method against existing state-of-the-art methods on representative UDA benchmarks.

Deep Learning Approaches for Overcoming Nonorthogonal Multiple Access Challenges in 5G Networks: A Review

Nonorthogonal multiple access (NOMA) is a promising multiple access scheme for 5G wireless networks. However, NOMA faces several challenges that still need to be solved optimally. Deep learning algorithms have been proposed as a potential solution to address these challenges. This review provides an overview of the use of deep learning algorithms to optimize NOMA performance in 5G networks. An investigation is conducted on how deep learning methods are applied in NOMA systems for resource allocation, channel estimation and detection, successive interference cancellation, and user clustering.They can learn optimal user clustering, optimal allocation, and interference alignment strategies, eventually boosting the network performance. In addition, deep learning algorithms can learn the complex relationships between the transmitted symbols and the received signal, leading to accurate detection of the superimposed signals. Opportunities and challenges in NOMA can be identified based on existing research showing how applying deep learning algorithms is better than conventional approaches. The main contribution of this review is to provide insights into the potential of deep learning algorithms to remarkably improve NOMA performance in 5G networks. This article is also a valuable resource for researchers and practitioners interested in using deep learning algorithms for NOMA in 5G networks.

Deep Constraints Space via Channel Alignment for Visible-Infrared Person Re-identification

Reducing the inter-modality gap has been the core of visible-infrared person re-identification (VI-ReID). Most of the existing methods directly constrain the features to reduce the gap between the different modalities. However, the consistency of channel semantic information across different modalities is ignored. In this letter, channel alignment along with severe semantic constraints is proposed to alleviate the significant distribution differences between modalities. To obtain the optimum channel matching mode, a novel channel alignment mechanism termed Channel Instance Level Alignment (CILA) is proposed at the shallow layer, and matched channel features are constrained by the proposed Channel Instance Alignment (CIA) Loss. In the middle layer, we divide the features into multiple hierarchically-aware channel clusters and align the channel clusters by Channel Cluster Alignment (CCA) Loss. The proposed method is validated on SYSU-MM01 and RegDB, and extensive experiments show that the proposed method achieves competitive performance compared with the state of the arts.

Guest Editorial Special Issue on Distributed Learning Over Wireless Edge Networks—Part I

Analyzing massive amounts of data using complex machine learning models requires significant computational resources. The conventional approach to such problems involves centralizing training data and inference processes in the cloud, i.e., in data centers. However, with the proliferation of mobile devices and increasing application of the Internet-of-Things (IoT) paradigm, very large amounts of data are collected at the edges of wireless networks, and due to privacy constraints and limited communication resources, it is undesirable or impractical to upload this data from mobile devices to the cloud for centralized learning. This problem can be solved by distributed learning at the network edge, by which edge devices collaboratively train a shared learning model using real-time mobile data. The avoidance of raw-data uploading not only helps to preserve privacy but may also alleviate network-traffic congestion and minimize latency. With that said, distributed training still requires a substantial amount of information exchange between devices and edge servers over wireless links. In the process, wireless impairments such as noise, interference, and imperfect knowledge of channel states can significantly slow down distributed learning (e.g., convergence speed) and degrades its performance (e.g., learning accuracy). This makes it crucial to optimize wireless network performance so as to support the efficient deployment of distributed learning algorithms. On the other hand, distributed learning algorithms provide a powerful tool-set for solving complex problems in wireless communication and networking. One important framework, called federated learning (FL), enables users to collaboratively learn a shared model while helping to preserve local data privacy. The application of FL can endow edge devices with capabilities of user behavior prediction, user identification, and wireless environment analysis. As another example, distributed reinforcement learning is capable of leveraging distributed computation power and data to solve complex optimization and control problems that arise in various use cases, such as network control, user clustering, resource management, and interference alignment. To cover this paradigm of distributed learning over wireless networks, this two-part Special Issue features papers dealing with two main research challenges: a) optimization of wireless network performance for efficient implementation of distributed learning in wireless networks, and b) distributed learning for solving communication problems and optimizing network performance.

Identification and Heterologous Expression of the Kendomycin B Biosynthetic Gene Cluster from Verrucosispora sp. SCSIO 07399.

Verrucosispora sp. SCSIO 07399, a rare marine-derived actinomycete, produces a set of ansamycin-like polyketides kendomycin B–D (1–3) which possess potent antibacterial activities and moderate tumor cytotoxicity. Structurally, kendomycin B–D contain a unique aliphatic macrocyclic ansa scaffold in which the highly substituted pyran ring is connected to the quinone moiety. In this work, a type I/type III polyketide synthase (PKS) hybrid biosynthetic gene cluster coding for assembly of kendomycin B (kmy), and covering 33 open reading frames, was identified from Verrucosispora sp. SCSIO 07399. The kmy cluster was found to be essential for kendomycin B biosynthesis as verified by gene disruption and heterologous expression. Correspondingly, a biosynthetic pathway was proposed based on bioinformatics, cluster alignments, and previous research. Additionally, the role of type III PKS for generating the precursor unit 3,5-dihydroxybenzoic acid (3,5-DHBA) was demonstrated by chemical complementation, and type I PKS executed the polyketide chain elongation. The kmy cluster was found to contain a positive regulatory gene kmy4 whose regulatory effect was identified using real-time quantitative PCR (RT-qPCR). These advances shed important new insights into kendomycin B biosynthesis and help to set the foundation for further research aimed at understanding and exploiting the carbacylic ansa scaffold.

Atomic structure of liquid refractory Nb5Si3 intermetallic compound alloy based upon deep neural network potential

The knowledge of atomic structure for liquids, especially for liquid alloys with complex crystal structure and high liquidus temperatures, remains poorly understood. In this work, we have extended the development of deep neural network (DNN) potential for liquid Nb5Si3. The DNN potential captures the structural features of liquid alloys compared with ab initio results. The chemical short-range order parameter suggests that there exists strong affinity between Nb and Si atoms. The dynamic property was investigated, and the diffusion coefficient obeys the Arrhenius relationship. The atomic structure has been subsequently explored for normal and undercooled liquid Nb5Si3. Large amounts of fivefold symmetry Honeycutt–Andersen pairs have been identified in liquid Nb5Si3. However, due to the violent thermal motion in a high-temperature Nb5Si3 melt, icosahedral symmetry and distorted icosahedrons (ICOs) account for little proportion according to Voronoi polyhedron (VP) analysis. The effect of thermal motion on VPs has been discussed. Except from the well documented ⟨0,2,8,2⟩ and ⟨0,1,10,2⟩ distorted ICOs, six more quasi-ICOs (⟨0,1,9,3⟩, ⟨0,2,8,1⟩, ⟨0,2,8,4⟩, ⟨0,2,8,5⟩, ⟨0,1,10,3⟩, and ⟨0,1,10,4⟩) have been proven to deform from ICOs at high temperatures. The local environment motif obtained by the atomic cluster alignment method demonstrates the existence of dominant distorted ICOs. At last, the atomic structure during melting process is discussed by VP analysis. It is found that ⟨0,2,8,1⟩, ⟨0,2,8,2⟩, ⟨0,2,8,5⟩, and ⟨0,1,10,4⟩ prefer to form at the beginning of the melting but rapidly reduce when it is fully melted.

Optical Trapping-Induced New Polymorphism of β-Cyclodextrin in Unsaturated Solution

A new polymorph of β-cyclodextrin (β-CD) was produced by optical trapping with a 1064 nm continuous wave laser beam in D2O solution. Upon optical trapping of β-CD in unsaturated solutions, a single prism crystal or multiple plate crystals were formed at and near the laser focus. The resulting crystals differed in morphology and in the Raman spectrum. Furthermore, the solubility of the plate crystal in D2O was much lower than that of the prism crystal, implying that the former crystal is thermodynamically more stable than the latter. The single-crystal X-ray crystallographic analysis showed that the prism crystal has practically the same crystallographic parameters as the crystal reported for β-CD dodecahydrate and is thought to be the thermodynamically most stable phase. These results reveal that the plate crystal is a new polymorph characteristic of optical trapping. Crucially, this is the first success to produce a new polymorph via the laser-induced crystallization method. More interestingly, the generation probability of these two polymorphs turned out to be a critical function of the initial β-CD concentration, laser power, and laser polarization mode. In particular, the polymorphism could be entirely controlled by adjusting the initial concentration, which allowed us to obtain pure prism and plate crystals at high and low CD concentrations, respectively. The dynamics and mechanism of this unique optical trapping-induced polymorphism are discussed in terms of the pre-nucleation cluster size determined by the initial concentration, the trapping efficiency as a function of the cluster size and the laser power, and the molecular alignment in the pre-nucleation clusters steered by the laser polarization mode.

Unconventional iron-magnesium compounds at terapascal pressures

Being a lithophile element at ambient pressure, magnesium is long believed to be immiscible with iron. A recent study by Gao et al. [1] showed that pressure turns magnesium into a siderophile element and can produce unconventional Fe-Mg compounds. Here, we extend the investigation to exoplanetary pressure conditions using an adaptive genetic algorithm-based variable-composition structural prediction approach. We identify several Fe-Mg phases up to 3 TPa. Our cluster alignment analysis reveals that most of the predicted Fe-Mg compounds prefer a BCC packing motif at terapascal pressures. This study provides a more comprehensive structure database to support future investigations of the high-pressure structural behavior of Fe-Mg and ternary, quaternary, etc. compounds involving these elements.

Covid-19 response of an additive manufacturing cluster in Australia

PurposeThe sudden arrival of Covid-19 severely disrupted the supply chain of personal protective equipment (PPE) in Australia. This paper aims to examine the development of a geographical cluster, which, through the application of additive manufacturing (AM), responded to the PPE supply crisis.Design/methodology/approachThis longitudinal case study focuses on an AM cluster, which was developed to supply PPE in a responsive and flexible manner from 2019/2020. The study gathered data over three stages of cluster evolution: pre, during and post-peak Covid-19.FindingsThe type and nature of exchanges between organizations involved in the cluster established important insights into success factors for cluster creation and development. Using an established complexity framework, this study identifies the characteristics of establishing a cluster. The importance of cluster alignment created initially by a common PPE supply goal led to an emerging commercial and relational imperative to address the longer-term configuration after the disruption.Practical implicationsClusters can be a viable option for a technology-driven sector when there is a “buzz” that drives and rapidly diffuses knowledge to support cluster formation. This research identifies the structural, socio-political and emergent dimensions, which need to be considered by stakeholders when aiming at improving competitiveness using clusters.Originality/valueCovid-19 has rapidly and unexpectedly disrupted the supply chain for many industries. Responding to challenges, businesses will investigate different pathways to improve the overall resilience including on-/near-shoring. The results provide insights into how clusters are formed, grow and develop and the differentiating factors that result in successful impacts of clusters on local economies.

Feasibility of multi-particle collision dynamics for rod-like particles and its application to a change in the orientational regime of a hematite particle suspension

We have verified that the method of multi-particle collision dynamics (MPCD) is able to activate a physically reasonable level of random motion in the medium of virtual fluid particles and activate the translational, ordinary rotational and spin rotational Brownian motion of the magnetic rod-like particles. MPCD was then applied to a quasi-2D system of rod-like hematite particles. The particle Brownian motion was found slightly over-activated, but this tendency decreased with smaller simulation time steps. The deviation from the theoretical value in the present results may be regarded to be acceptable as a first approximation since the Brownian motion was activated without the addition of any special technique. The applied magnetic field strength is found to play a significant role to enhance the order of the alignment of the raft-like clusters along the direction of the applied magnetic field. A similar orientational regime change along the applied magnetic field may be accomplished by an increase in the volumetric fraction of magnetic particles. We have therefore concluded that both an applied magnetic field and the particle volumetric fraction may be feasible parameters for inducing a regime change in the ordering of the orientation of rod-like hematite particles.

Cloning, expression, purification, and biochemical characterization of CpxR protein from pectobacterium carotovorum.

The cpxR gene, encoding a new cytoplasmic response regulator, which effects virulence, biofilm formation, chemotaxis, resistance to antimicrobials, and controls soft rot, was amplified by the polymerase chain reaction, cloned into the prokaryotic expression vector pET-15b, and expressed through the induction of isopropyl-β-d-thiogalactoside in Escherichia coli BL21 (DE3). Then, highly purified and stable CpxR protein was produced by nickel affinity chromatography and fast protein liquid chromatography, digested by thrombin and identified by Western blotting. Furthermore, the structure of the CpxR protein was estimated by circular dichroism spectroscopy and SWISS-MODEL. The CpxR protein was a functional part in signal transduction and bacterial resistance for Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The resear ch of the protein stability indicated the CpxR protein had excellent thermal stability and was suitable for crystallization. Then the small crystals of CpxR protein were found in the crystallizing tank. The latest 34 cpxR sequences from the public database were selected and analyzed by molecular clustering and multisequence alignment. These cpxR sequences were roughly divided into four categories. Theseresultslaidan importantfoundationforthe furtherstructural studyoftheCpxR protein.