Intrinsic Clustering Research Articles

Abstract 4420: Carbohydrate-CD11b engagement enhances differentiation of tumor-associated myeloid cells in immunotherapy of solid cancers

Abstract Background: Methods to perturb tumor-associated myeloid cell function are being actively pursued to overcome these challenges in immunotherapy and search for a cure. CD11b is a potential therapeutic target to modulate suppressive myeloid derived cells and induce tumor-reactive T-cell responses. However, CD11b can bind to multiple carbohydrate ligands that trigger a variety of myeloid cell functions like adhesion, migration, and even phagocytosis and proliferation. This has created a major challenge to understand the molecular basis by which CD11b converts differences in receptor-ligand binding into subsequent immune signaling responses, and to use this knowledge for therapeutic development. Methods: This study investigated a previously reported carbohydrate ligand, BG34-200, that showed anti-tumor effect through modulating CD11b+ cells. Applying modern approaches of peptide microarray, multi-parameter FACS analysis, cellular/molecular immunological technology, advanced microscopic imaging, and transgenic mouse models of solid cancers, we studied the BG34-200-CD11b carbohydrate-protein engagement and immunological changes it triggered in the context of solid cancers including osteosarcoma, melanoma and pancreatic cancer. Results: Our results show that BG34-200 can mediate direct, specific, multi-site and multivalent binding to integrin CD11b to trigger cellular differentiation in a suppressive tumor-associated myeloid cell subset. In melanoma, osteosarcoma, and pancreatic cancer, we detected this rare myeloid cell subset in blood, which displayed an inflammatory monocyte phenotype and exhibited immunosuppressive behavior. BG34-200-CD11b engagement could induce differentiation of these tumor-associated inflammatory monocytes into dendritic cells by inducing phagocytosis that effectively trigger F-actin cytoskeletal rearrangement and intrinsic ICAM-1 clustering important for antigen presentation and effector T cell stimulation. Conclusions: Our findings improve understanding of molecular basis by which CD11b converts difference in carbohydrate ligands into subsequent immune signaling responses, may lead to development of safe and novel therapies modulating myeloid derived cell function for immunotherapy of solid cancers. Citation Format: Mei Zhang. Carbohydrate-CD11b engagement enhances differentiation of tumor-associated myeloid cells in immunotherapy of solid cancers. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4420.

UNIONS: The impact of systematic errors on weak-lensing peak counts

Context. The Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) is an ongoing deep photometric multiband survey of the northern sky. As part of UNIONS, the Canada-France Imaging Survey (CFIS) provides r-band data, which we use to study weak-lensing peak counts for cosmological inference. Aims. We assess systematic effects for weak-lensing peak counts and their impact on cosmological parameters for the UNIONS survey. In particular, we present results on local calibration, metacalibration shear bias, baryonic feedback, the source galaxy redshift estimate, intrinsic alignment, and cluster member dilution. Methods. For each uncertainty and systematic effect, we describe our mitigation scheme and the impact on cosmological parameter constraints. We obtain constraints on cosmological parameters from Monte Carlo Markov chains using CFIS data and MassiveNuS N-body simulations as a model for peak counts statistics. Results. Depending on the calibration (local versus global, and the inclusion or not of the residual multiplicative shear bias), the mean matter density parameter, Ωm, can shift by up to −0.024 (−0.5σ). We also see that including baryonic corrections can shift Ωm by +0.027 (+0.5σ) with respect to the dark-matter-only simulations. Reducing the impact of the intrinsic alignment and cluster member dilution through signal-to-noise cuts leads to larger constraints. Finally, with a mean redshift uncertainty of Δz̄ = 0.03, we see that the shift in Ωm (+0.001, which corresponds to +0.02σ) is not significant. Conclusions. This paper investigates, for the first time with UNIONS weak-lensing data and peak counts, the impact of systematic effects. The value of Ωm is the most impacted and can shift by up to ∼0.03, which corresponds to 0.5σ depending on the choices for each systematics. We expect constraints to become more reliable with future (larger) data catalogs, for which the current pipeline will provide a starting point. The code used to obtain the results is available on GitHub.

Abstract P659: Cluster Analysis of County-Level Cardiovascular Disease Mortality in the United States

Introduction: In the United States, cardiovascular disease (CVD) mortality has been declining since 1980s. However, the decline in CVD mortality is not uniform across regions and may show different trajectories that are driven by place-based social determinants of health (SDOH) such as physical and built environment, social-cultural environment, and health care system. We conducted a cluster analysis of county-level CVD mortality and SDOH indicators from 2009 to 2018. Hypothesis: We test the hypothesis that CVD mortality in US counties show different trajectories that can be grouped by clusters, and within each cluster, there are different SDOH variables that predict CVD mortality. Methods: We linked the Centers for Disease Control and Prevention's CVD mortality data with the Agency for Healthcare Research and Quality's public database on SDOH using county identifiers. CVD mortality rate was calculated as the number of deaths per 100,000 people in each county. There were 179 SDOH variables included in the model, classified into five different domains: the biological, behavioral, built and physical environment, health care system, and sociocultural environment domain. We employed a linear mixed-effects model fitted with a doubly penalized likelihood to induce sparsity for parameter estimation and effect selection. Results: We identified three clusters among a total of 3,136 counties. Overall, 1,182 counties were identified as cluster 1 that showed a consistent decline of CVD mortality over the past decade. The most important driving factor of CVD mortality in cluster 1 was population distribution - the percentage of population aged 15-17 (coefficient=7.032). Secondly, 1,116 counties were identified as cluster 2 that showed a decline between 2009 and 2012, and then a steady increase in CVD mortality after 2012. Within cluster 2, the number of mild drought months was the most predictive factor of CVD mortality (coefficient= -1.065), and the percentage of adults reporting no leisure-time physical activity was the second important predictor (coefficient= 0.646). Finally, 838 counties were identified as cluster 3 that showed a flattening trend in CVD mortality. Within cluster 3, the total number of obstetrics and gynecology, generalist, and primary care hospital residents plays the most important role (coefficient = -25.537). Each cluster has unique pattern and distinctive associations between certain SDOH variables and CVD mortality. Conclusions: In conclusion, we identified three intrinsic clusters that showed distinguishing trajectories of CVD mortality during the past decade. The SDOH indicators most predictive of CVD mortality were different within each cluster. This study suggests that place based SDOH drive substantial differences in CVD outcomes across regions.

뷰티 전공 청소년의 학습동기, 스마트 미디어 활용의 학업적응 간의 집단별 군집분석

Objectives This study clustered the perceived benefits, perceived sacrifices, perceived values, and academic adjustment of smart media based on academic motivation, growth mindset, academic expectations, and value-based acceptance model of beauty majoring youth. and identified significant differences in academic adaptation.
 Methods The sample for this study was 581 middle school and high school male and female students majoring in beauty in Seoul were sampled. Individuals of similar types among research factors were grouped into categories, and cluster analysis of the overall approach for various attributes was performed, and F-test of difference analysis on academic adaptation between clusters was performed. Cluster analysis was conducted based on the collected research data.
 Results First, the classification of group types was examined. As a result, the groups were classified into 4 types, and cluster 1 was classified into high multiple groups, cluster 2 high sacrifice, cluster 3 high, intrinsic cluster 4, and multiple bottom groups. Second, the average value of ANOVA for each cluster was. Significant differences between groups were confirmed according to perceived value and academic adjustment.
 Conclusions This study confirmed the difference between clustering for academic adaptation through cluster analysis of learners' learning motivation and smart media in order to improve the academic adaptation of beauty major youth. Additionally, it was confirmed that the improvement of the learning environment had a positive effect on academic adaptation, and it was confirmed that a strategic approach according to the difference between groups was necessary. In future studies, it is suggested that a two-way longitudinal study of differences between groups and between individuals be conducted.

Enhanced tensor low-rank representation learning for multi-view clustering

Multi-view subspace clustering (MSC), assuming the multi-view data are generated from a latent subspace, has attracted considerable attention in multi-view clustering. To recover the underlying subspace structure, a successful approach adopted recently is subspace clustering based on tensor nuclear norm (TNN). But there are some limitations to this approach that the existing TNN-based methods usually fail to exploit the intrinsic cluster structure and high-order correlations well, which leads to limited clustering performance. To address this problem, the main purpose of this paper is to propose a novel tensor low-rank representation (TLRR) learning method to perform multi-view clustering. First, we construct a 3rd-order tensor by organizing the features from all views, and then use the t-product in the tensor space to obtain the self-representation tensor of the tensorial data. Second, we use the ℓ1,2 norm to constrain the self-representation tensor to make it capture the class-specificity distribution, that is important for depicting the intrinsic cluster structure. And simultaneously, we rotate the self-representation tensor, and use the tensor singular value decomposition-based weighted TNN as a tighter tensor rank approximation to constrain the rotated tensor. For the challenged mathematical optimization problem, we present an effective optimization algorithm with a theoretical convergence guarantee and relatively low computation complexity. The constructed convergent sequence to the Karush–Kuhn–Tucker (KKT) critical point solution is mathematically validated in detail. We perform extensive experiments on four datasets and demonstrate that TLRR outperforms state-of-the-art multi-view subspace clustering methods.

Molecular-level degradation pathways of black phosphorus revealed by mass spectrometry fingerprinting.

Understanding the molecular mechanism of material transformation raises a great challenge for material characterization techniques. As a promising 2D material, the application potential of black phosphorus (BP) is seriously compromised by its environmental instability. However, until now, the degradation mechanism of BP remains ambiguous. Here we show that by using laser desorption ionization mass spectrometry (LDI-MS) fingerprinting it is possible to unravel the degradation pathways of BP at the molecular level without any chemical labeling. We found that BP-based materials can generate intrinsic phosphorus cluster (Pn+ or Pn-) fingerprint peaks in LDI-MS in both positive-ion and negative-ion modes, which allows the degradation processes of BP materials to be monitored by providing abundant mass information about intermediates and products with the sample-to-sample RSDs in the range of 1.0-28.4%. The stability of BP or cerium-encapsulated BP was monitored under ambient and increased temperature conditions for up to 20 or 180 days. Notably, by using LDI-MS fingerprinting, we reveal an unreported BP degradation pathway, i.e., nitrogen (N2)-addition oxidation, in addition to the direct oxidation pathway. Our results not only enable an in-depth understanding of the chemical instability of BP, but also, importantly, demonstrate a new powerful platform for monitoring and characterization of material transformation.

ClusterFL: A Clustering-based Federated Learning System for Human Activity Recognition

Federated Learning (FL) has recently received significant interest, thanks to its capability of protecting data privacy. However, existing FL paradigms yield unsatisfactory performance for a wide class of human activity recognition (HAR) applications, since they are oblivious to the intrinsic relationship between data of different users. We propose ClusterFL, a clustering-based federated learning system that can provide high model accuracy and low communication overhead for HAR applications. ClusterFL features a novel clustered multi-task federated learning framework that minimizes the empirical training loss of multiple learned models while automatically capturing the intrinsic clustering relationship among the nodes. We theoretically prove the convergence of proposed FL framework for non-convex and strongly convex models and provide the guidance on selection of hyper-parameters for achieving such convergence. Based on the learned cluster relationship, ClusterFL can efficiently drop the nodes that converge slower or have little correlations with others in each cluster, significantly speeding up the convergence while maintaining the accuracy performance. We evaluate the performance of ClusterFL on an NVIDIA edge testbed using four new HAR datasets collected from 145 users. The results show that ClusterFL outperforms several state-of-the-art FL paradigms in terms of overall accuracy and can save more than 50% communication overhead.

Two‐Step Cooling Strategy for Synergistic Control of CuZn and SnZn Defects Enabling 12.87% Efficiency (Ag,Cu)2ZnSn(S,Se)4 Solar Cells

AbstractAbundant intrinsic defects and defect clusters in Cu2ZnSn(S,Se)4 (CZTSSe) solar cells lead to severe nonradiative recombination and limited photoelectric performance. Therefore, developing effective method to suppress the detrimental defects is the key to achieve high‐efficiency solar cell. Herein, a convenient two‐step cooling strategy in selenization process is reported to suppress the CuZn and SnZn defects and defect clusters synergistically. The results show that rapid cooling during section from selenization temperature to turning temperature can inhibit the volatilization of Sn and restrain the corresponding Sn‐related defects, while slow cooling during the subsequent temperature section can reduce the degree of Cu‐Zn disorder. Benefitting from the synergistic effect of two‐step cooling, a significantly lowered concentration of SnZn and CuZn defect and their defect clusters [2CuZn+SnZn] in absorber is observed, meanwhile, a reduced band tailing effect and promoted carrier collection efficiency of the photovoltaic device is obtained. Finally, a device with improved open‐circuit voltage (Voc) of 505.5 mV and efficiency of 12.87% is achieved. This study demonstrates the impact of cooling process on defects controlling for the first time and provides a simple and effective new strategy for intrinsic defect control, which may be universal in other inorganic thin film solar cells.

Doping of Sb into Cu2ZnSn(S,Se)4 absorber layer via Se&Sb2Se3 co-selenization strategy for enhancing open-circuit voltage of kesterite solar cells.

Kesterite-structured Cu2ZnSn(S,Se)4 (CZTSSe) thin film photovoltaics have attracted considerable attention in recent years because of its low-cost and eco-friendly raw material, as well as high theoretical conversion efficiency. However, its photovoltaic performance is hindered by large open-circuit voltage (V OC ) deficiency due to the presence of intrinsic defects and defect clusters in the bulk of CZTSSe absorber films. The doping of extrinsic cation to the CZTSSe matrix was adopted as an effective strategy to ameliorate defect properties of the solar cell absorbers. Herein, a novel Se&Sb2Se3 co-selenization process was employed to introduce Sb into CZTSSe crystal lattice. The results reveal that Sb-doping plays an active role in the crystallization and grain growth of CZTSSe absorber layer. More importantly, one of the most seriously detrimental SnZn deep defect is effectively passivated, resulting in significantly reduced deep-level traps and band-tail states compared to Sb free devices. As a result, the power conversion efficiency of CZTSSe solar cell is increased significantly from 9.17% to 11.75%, with a V OC especially enlarged to 505 mV from 449 mV. This insight provides a deeper understanding for engineering the harmful Sn-related deep defects for future high-efficiency CZTSSe photovoltaic devices.

Cronos: A Machine Learning Pipeline for Description and Predictive Modeling of Microbial Communities Over Time.

Microbial time-series analysis, typically, examines the abundances of individual taxa over time and attempts to assign etiology to observed patterns. This approach assumes homogeneous groups in terms of profiles and response to external effectors. These assumptions are not always fulfilled, especially in complex natural systems, like the microbiome of the human gut. It is actually established that humans with otherwise the same demographic or dietary backgrounds can have distinct microbial profiles. We suggest an alternative approach to the analysis of microbial time-series, based on the following premises: 1) microbial communities are organized in distinct clusters of similar composition at any time point, 2) these intrinsic subsets of communities could have different responses to the same external effects, and 3) the fate of the communities is largely deterministic given the same external conditions. Therefore, tracking the transition of communities, rather than individual taxa, across these states, can enhance our understanding of the ecological processes and allow the prediction of future states, by incorporating applied effects. We implement these ideas into Cronos, an analytical pipeline written in R. Cronos’ inputs are a microbial composition table (e.g., OTU table), their phylogenetic relations as a tree, and the associated metadata. Cronos detects the intrinsic microbial profile clusters on all time points, describes them in terms of composition, and records the transitions between them. Cluster assignments, combined with the provided metadata, are used to model the transitions and predict samples’ fate under various effects. We applied Cronos to available data from growing infants’ gut microbiomes, and we observe two distinct trajectories corresponding to breastfed and formula-fed infants that eventually converge to profiles resembling those of mature individuals. Cronos is freely available at https://github.com/Lagkouvardos/Cronos.

(Digital Presentation) Fabrication of Monolayer-Protected Gold Cluster Thin Films: Electrochemical and Optical Properties

Quantum-sized monolayer-protected gold clusters have captivated the imagination of scientists over the last two decades and show exciting optical, electrochemical and catalytic properties when compared to larger-sized plasmonic gold nanoparticles, making them useful for myriad new applications in photonics, catalysis and sensors. One interesting property is the photoluminescence (PL) of gold clusters making them suitable as biological imaging labels. However, the low PL Quantum Yields (QY) are a concern for these clusters and research is focused on improving the PL QYs. One strategy, our research group has worked on is the shell-gold rigidification via the use of surfactants and proteins. Shell ligand rigidification was achieved by ion pairing with bulky tertiary alkyl ammonium groups and aromatic chromophores. Another strategy to reduce the non-radiative deactivation pathways is by making the gold cluster thin films. In a recent study, it was shown that solid state films of clusters not only have manifold increase in PL quantum yield but also their luminescent lifetimes and two photon absorption cross-sections are greatly enhanced in comparison to their solution analogues. Researchers have worked on various strategies to make gold cluster thin films for device applications. One problem with thin films of gold clusters is that the clusters lose their interesting optical and electrochemical properties that were observed in solution phase. For making the clusters useful by enhancing PL for photonic device applications, it is important for the clusters to retain their properties in solid state, which is the main objective of the study carried out here. One problem is that drying intrinsic gold clusters results in crystallization whose optical properties are difficult to characterize. In this study, we have used Nafion and polyvinylpyrrolidone (PVP) to make thin films of water-soluble glutathione-protected gold clusters. Water soluble cluster Au22(SG)18 with reported PLQY of 8% were synthesized and characterized by UV-VIS absorption and PL measurements and thin films of these clusters were made using water soluble PVP and Nafion polymers via drop casting. Enhanced PL and electrochemical properties were observed for clusters in solid state. Similarly, non-polar gold cluster films are fabricated with Polystyrene and optical absorption and PL properties were retained in the solid state.Although drop casting with polymers was found to retain the optical properties, the uniformity of films is a problem. The uniformity of the film is dependent on solvent’s surface tension thus diminishing the quality of the film. Therefore, there is a need of depositing films of gold clusters in a controlled manner which governs their thickness and uniformity. Electropolymerization technique is known to make films during the polymerization of monomers possessing functional groups whose oxidation or reduction via anti-bonding or non-boding electrons on a heteroatom. In this study, we have used electropolymerization strategy to fabricate thin films of gold clusters on conducting substrates. The film is deposited at the working electrode during cyclic voltammetric (CV) sweep when passed through the required reduction or oxidation potential of the monomer under suitable conditions. This technique makes a uniform film throughout the exposed area of the working electrode active surface and the thickness of the film is often proportional to the number of sweep cycles employed. Therefore, introduction of gold clusters in this process with monomers which are previously known to bond with each other will incorporate clusters in the film during the polymerization process and will ease their film formation with uniform thickness. Amongst various size gold clusters, the one which shows good stability as a result of following the magic number series is Au25(SR)18 - . The stability of clustersis one of the important parameter required for practical applications. Aniline and other amine based monomers are used as precursors to make thin films of Au25 on conducting substrate such as Indium tin oxide. By solubilizing the gold clusters with the monomers they are deposited along with the polymers.

Low-rank tensor approximation with local structure for multi-view intrinsic subspace clustering

Among various multi-view clustering approaches, tensor-based multi-view subspace clustering methods aim to explore the high-order correlations across varying views and have achieved encouraging effects. Nevertheless, there are still some demerits in them: (1) View-specific information hinders the mining of global consensus. (2) The local structure inside individual view lacks consideration. (3) Clustering results are not utilized to reversely guide the low-rank tensor optimization. In order to tackle these drawbacks, we propose a unified model termed as Low-rank Tensor Approximation with Local Structure for Multi-view Intrinsic Subspace Clustering. Specifically, the proposed model learns multiple intrinsic subspace representations via the rank preserving decomposition, which is to mitigate the impact of view-specific information on enhancing the global consistency. Then, these intrinsic subspace representations are assembled into a 3-order target tensor with tensor nuclear norm constraint. To preserve the consistent locality, we adopt the manifold regularization to constrain each view when mapping into the intrinsic subspace. Furthermore, since the learned label indicator matrix implicitly characterizes the cluster structure, which is used to guide the optimization of the low-rank tensor representation. Finally, abundant experiments on six real-word datasets demonstrate that the proposed method is superior over other state-of-the-art clustering methods.

Creating jackknife and bootstrap estimates of the covariance matrix for the two-point correlation function

ABSTRACT We present correction terms that allow delete-one Jackknife and Bootstrap methods to be used to recover unbiased estimates of the data covariance matrix of the two-point correlation function $\xi \left(\mathbf {r}\right)$. We demonstrate the accuracy and precision of this new method using a large set of 1000 QUIJOTE simulations that each cover a comoving volume of $1\rm {\left[h^{-1}Gpc\right]^3}$. The corrected resampling techniques recover the correct amplitude and structure of the data covariance matrix as represented by its principal components to within ∼10 per cent, the level of error achievable with the size of the sample of simulations used for the test. Our corrections for the internal resampling methods are shown to be robust against the intrinsic clustering of the cosmological tracers both in real- and redshift space using two snapshots at z = 0 and z = 1 that mimic two samples with significantly different clustering. We also analyse two different slicing of the simulation volume into $\, n_{\rm sv}\, =64$ or 125 sub-samples and show that the main impact of different $\, n_{\rm sv}\,$ is on the structure of the covariance matrix due to the limited number of independent internal realizations that can be made given a fixed $\, n_{\rm sv}\,$.

Orthogonal multi-view tensor-based learning for clustering

Multi-view spectral clustering aims to improve the performance of spectral clustering through multi-view data. Many multi-view spectral clustering methods have been proposed recently and achieved promising performance. Among these methods, most of them are designed to pursue numerical consistency in multi-view similarity matrices. However, each similarity matrix has its unique statistic distribution, which makes it not appropriate to seek numerical consistency in multi-view similarity matrices or directly average the multi-view similarity matrices. To overcome the aforementioned problem, we propose a novel Orthogonal Multi-view Tensor-based Learning for clustering, abbreviated as OMTL. Specifically, OMTL introduces an orthogonal matrix factorization to eliminate the view-specific statistic distribution and preserve the intrinsic clustering structure of each view, which fully considers the consensus information contained in multiple views to boost multi-view spectral clustering performance. Further, we employ a low-rank tensor constraint to explore the high order correlations among multiple views. By designing an alternating direction method of multipliers (ADMM) based optimization algorithm, the intrinsic similarity matrix of multi-view data can be efficiently learned for spectral clustering. Extensive experiments on several benchmark datasets have illustrated the superior clustering performance of the proposed method compared to several state-of-the-art multi-view clustering methods.

Unsupervised query-adaptive implicit subtopic discovery for diverse image retrieval based on intrinsic cluster quality

Unsupervised query-adaptive implicit subtopic discovery for diverse image retrieval based on intrinsic cluster quality

Dirichlet process mixture models for single-cell RNA-seq clustering.

ABSTRACTClustering of cells based on gene expression is one of the major steps in single-cell RNA-sequencing (scRNA-seq) data analysis. One key challenge in cluster analysis is the unknown number of clusters and, for this issue, there is still no comprehensive solution. To enhance the process of defining meaningful cluster resolution, we compare Bayesian latent Dirichlet allocation (LDA) method to its non-parametric counterpart, hierarchical Dirichlet process (HDP) in the context of clustering scRNA-seq data. A potential main advantage of HDP is that it does not require the number of clusters as an input parameter from the user. While LDA has been used in single-cell data analysis, it has not been compared in detail with HDP. Here, we compare the cell clustering performance of LDA and HDP using four scRNA-seq datasets (immune cells, kidney, pancreas and decidua/placenta), with a specific focus on cluster numbers. Using both intrinsic (DB-index) and extrinsic (ARI) cluster quality measures, we show that the performance of LDA and HDP is dataset dependent. We describe a case where HDP produced a more appropriate clustering compared to the best performer from a series of LDA clusterings with different numbers of clusters. However, we also observed cases where the best performing LDA cluster numbers appropriately capture the main biological features while HDP tended to inflate the number of clusters. Overall, our study highlights the importance of carefully assessing the number of clusters when analyzing scRNA-seq data.

Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection.

ABSTRACTMycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.IMPORTANCE Mycobacterium tuberculosis H37Rv is one of the world's most impactful pathogens, and a large part of the success of the organism relies on the differential expression of its genes to adapt to its environment. The expression of the organism's genes is driven primarily by its transcriptional regulatory network, and most research on the TRN focuses on identifying and quantifying clusters of coregulated genes known as regulons. While previous studies have relied on molecular measurements, in the manuscript we utilized an alternative technique that performs machine learning to a large data set of transcriptomic data. This approach is less reliant on hypotheses about the role of specific regulatory systems and allows for the discovery of new biological findings for already collected data. A better understanding of the structure of the M. tuberculosis TRN will have important implications in the design of improved therapeutic approaches.

IMPLEMENTATION OF THE SUSTAINABLE DEVELOPMENT PRIORITIES OF TOURISM AND HOTEL AND RESTAURANT CLUSTERS IN REGIONS OF UKRAINE

The presentation of priorities for the operation of the tourism, hotel, and restaurant clusters in regional development planning should take place within the framework of the translation of the regional objectives into an inherent process deriving from the intrinsic cluster nature. This process is carried out based on a systematic description of the purposeful actions of the cluster actors' associations outside the territory of cities, villages, and towns, which is an element in the search for the best solution for a specific situation. Consequently, the study defined the specifics of implementing the sustainable development of the tourism, hotel, and restaurant clusters priorities. In particular, the content of the process of finding a better solution for the specific sustainable development situation of the cluster is defined and the elements of the search for a better solution to the specific sustainable development situation of the tourism, hotel, and restaurant cluster are presented. The process of finding the best solution for the specific sustainability situation of the tourism, hotel, and restaurant clusters define as: in a plane of organizational stability; in a plane of technological stability; in a plane of financial stability. These elements are essential in ensuring the functionality of cluster members and methods of finding their paths development. This methodology should contain an algorithm for finding patterns in information arrays and meaningful interpretation of the obtained facts determines the order of action to approximate cluster actors to optimality parameters. Thus, the search for the best solution in terms of organizational sustainability should ensure a rhythmic process of providing tourist or hotel and restaurant cluster products. This process is linked to the dimensions of technological and financial sustainability. The search for the best solution in terms of technological stability should determine whether the business of each of the cluster actors uses its production capacity to the fullest extent. The search for the best solution in terms of financial stability focuses on determining the security of business in sub-areas of profitability, creditworthiness, and solvency.

First-principles identification of VI + Cui defect cluster in cuprous iodide: origin of red light photoluminescence

The γ-phase cuprous iodide (CuI) emerges as a promising transparent p-type semiconductor for next-generation display technology because of its wide direct band gap, intrinsic p-type conductivity, and high carrier mobility. Two main peaks are observed in its photoluminescence (PL). One is short wavelength (410–430 nm) emission, which is well attributed to the electronic transitions at Cu vacancy, whereas the other long wavelength emission (680–720 nm) has not been fully understood. In this paper, through first-principles simulations, we investigate the formation energies and emission line shapes for various defects, and discover that the intrinsic point defect cluster is the source of the long wavelength emission. Our finding is further supported by the prediction that the defect concentration decreases dramatically as the chemical condition changes from Cu-rich to I-rich, explaining the significant reduction in the red light emission if CuI is annealed in abundant I environment.

Kinetics and nucleation dynamics in ion-seeded atomic clusters

The time-dependent kinetics of formation and evolution of nanosize atomic clusters is investigated and illustrated with the nucleation dynamics of ion-seed ${\mathrm{Ar}}_{n}{\mathrm{H}}^{+}$ particles. The rates of growth and degradation of Ar-atomic shells around the seed ion are inferred from molecular dynamics (MD) simulations. Simulations of cluster formation have been performed with accurate quantum-mechanical binary interaction potentials. Both the nonequilibrium and equilibrium growth of ${\mathrm{Ar}}_{n}{\mathrm{H}}^{+}$ are investigated at different temperature and densities of the atomic gas and seed ions. Formation of ${\mathrm{Ar}}_{n\ensuremath{\le}40}$ shells is the main mechanism which regulates the kinetics of nanocluster growth and the diffusive fluctuations of the cluster size distribution. The time evolution of the cluster intrinsic energy and cluster size distributions are analyzed at the nonthermal, quasiequilibrium, and thermal equilibrium stages of ${\mathrm{Ar}}_{n}{\mathrm{H}}^{+}$ formation. We have determined the self-consistent model parameters for the temporal fluctuations of the cluster size and found coefficients of the diffusive growth mechanism describing the equilibrium distribution of nanoclusters. Nucleation of haze and nanodust particles in astrophysical and atmospheric ionized gases is discussed.