Definition Of Clusters Research Articles

Characteristics of Patients With Behçet Disease From the Van Province, Eastern Turkey: Definition of Disease Clusters in a Tertiary Referral Center.

Behçet disease (BD) is a chronic inflammatory systemic disease that affects skin mucosa, joints, eyes, and blood vessels. Behçet disease shows some clinical differences in terms of disease manifestations and prognosis among the Silk Road countries, as well as various ethnicities even in the same country. In this study, we aimed to evaluate the clinical features and disease course of BD using cluster analysis in Van province, Eastern Turkey. This study was carried out in a tertiary referral center in Van province, by reviewing medical records. Seven disease manifestations were included to the cluster analysis as follows: mucocutaneous findings (oral ulcer, genital ulcer, erythema nodosum-like lesions, pseudofolliculitis), uveitis, superficial thrombophlebitis, musculoskeletal involvement, gastrointestinal system involvement, vascular involvement, and parenchymal central nervous system involvement. We identified 467 patients. After the exclusion of 6 patients who had missing data and 17 patients who did not live in Van, 444 patients (59.2% male) included into the study. Meeting the International Study Group and the International Criteria for Behçet Disease criteria were 91.6% and 96%, respectively, and 91.3% (n = 379/415) of these patients met both criteria. Four clusters were identified in the analyses: 132 patients (31.2%) in vascular (C1), 66 patients (15.6%) in ocular (C2), 60 patients (14.2%) in musculoskeletal (C3), and 165 patients (39%) in mucocutaneous (C4) clusters. Male gender ( p = 0.002; odds ratio [OR], 6.5; 95% confidence interval [CI], 2-21.4), superficial thrombophlebitis ( p = 0.001; OR, 4.7; 95% CI, 1.9-11.4), and uveitis ( p = 0.01; OR, 3.6; 95% CI, 1.3-9.9) were associated with vascular involvement in multivariate analysis. In our study, 4 clusters were detected in patients with BD from Van province. The prevalence of severe manifestations of BD may be related to genetic or environmental factors, such as differences in ethnicity and/or geographical differences. Despite the higher proportion of patients with a more severe disease, a favorable outcome was observed in our cohort.

Cluster percolation in the two-dimensional Ising spin glass.

Suitable cluster definitions have allowed researchers to describe many ordering transitions in spin systems as geometric phenomena related to percolation. For spin glasses and some other systems with quenched disorder, however, such a connection has not been fully established, and the numerical evidence remains incomplete. Here we use Monte Carlo simulations to study the percolation properties of several classes of clusters occurring in the Edwards-Anderson Ising spin-glass model in two dimensions. The Fortuin-Kasteleyn-Coniglio-Klein clusters originally defined for the ferromagnetic problem do percolate at a temperature that remains nonzero in the thermodynamic limit. On the Nishimori line, this location is accurately predicted by an argument due to Yamaguchi. More relevant for the spin-glass transition are clusters defined on the basis of the overlap of several replicas. We show that various such cluster types have percolation thresholds that shift to lower temperatures by increasing the system size, in agreement with the zero-temperature spin-glass transition in two dimensions. The overlap is linked to the difference in density of the two largest clusters, thus supporting a picture where the spin-glass transition corresponds to an emergent density difference of the two largest clusters inside the percolating phase.

Fuzzy Cluster Analysis: Pseudometrics and Fuzzy Clusters

Introduction. Clustering problems arise in various spheres of human activity. In cases where there are no initial data sufficient for statistical analysis or information obtained from experts is used, fuzzy models are proposed that take into account different types of uncertainty and more argumentatively reflect real situations that model systems of different purposes. Particular attention is drawn to invariance in problems with different types of data measured in different scales according to the classification of S. Stevens. It is known that when solving cluster analysis problems using the transitive closure operation with respect to the equivalence that is obtained, such connections between objects as similarity and dissimilarity are changed. Therefore, it is necessary to take into account the problem of adequacy when developing models and algorithms for solving problems of fuzzy cluster analysis. The purpose of the paper is an analyzing the problem of adequacy of the results of fuzzy cluster analysis on the introduction of metrics and pseudometrics on fuzzy sets in the presence of several qualitative and quantitative characteristics of objects. Propose an approach that ensures the adequacy of pseudometrics, that is, provides invariance with respect to permissible transformations of the values of fuzzy features, and also ensures the division of objects into equivalence classes without distorting the distance between them. Results. Axiomatic definitions of a fuzzy cluster and a fuzzy α level cluster are proposed, which are introduced as fuzzy sets of elements similar to certain elements of a given set, if the condition is met: the dissimilarity ratio must be an invariant pseudometric. This condition is ensured by the use of the linguistic correlation coefficient when calculating fuzzy relations of similarity and dissimilarity. Based on the definition of a fuzzy cluster of α level and threshold conorm, the distance between fuzzy clusters of α level is determined. Conclusions. The proposed approach can be the basis for the development of algorithms for solving cluster analysis problems. This provides a meaningful interpretation of the obtained clusters, and the possibility of clarifying the results in further studies of their structure. Keywords: fuzzy set, conorm, metric, pseudometric, fuzzy similarity relation, fuzzy cluster.

From components to communities: bringing network science to clustering for molecular epidemiology.

Defining clusters of epidemiologically related infections is a common problem in the surveillance of infectious disease. A popular method for generating clusters is pairwise distance clustering, which assigns pairs of sequences to the same cluster if their genetic distance falls below some threshold. The result is often represented as a network or graph of nodes. A connected component is a set of interconnected nodes in a graph that are not connected to any other node. The prevailing approach to pairwise clustering is to map clusters to the connected components of the graph on a one-to-one basis. We propose that this definition of clusters is unnecessarily rigid. For instance, the connected components can collapse into one cluster by the addition of a single sequence that bridges nodes in the respective components. Moreover, the distance thresholds typically used for viruses like HIV-1 tend to exclude a large proportion of new sequences, making it difficult to train models for predicting cluster growth. These issues may be resolved by revisiting how we define clusters from genetic distances. Community detection is a promising class of clustering methods from the field of network science. A community is a set of nodes that are more densely inter-connected relative to the number of their connections to external nodes. Thus, a connected component may be partitioned into two or more communities. Here we describe community detection methods in the context of genetic clustering for epidemiology, demonstrate how a popular method (Markov clustering) enables us to resolve variation in transmission rates within a giant connected component of HIV-1 sequences, and identify current challenges and directions for further work.

PD05-01 DISTINCT PHENOTYPES OF BLADDER PAIN IDENTIFIED IN THE MULTIDISCIPLINARY APPROACH TO THE STUDY OF CHRONIC PELVIC PAIN RESEARCH NETWORK DATASET

You have accessJournal of UrologyCME1 Apr 2023PD05-01 DISTINCT PHENOTYPES OF BLADDER PAIN IDENTIFIED IN THE MULTIDISCIPLINARY APPROACH TO THE STUDY OF CHRONIC PELVIC PAIN RESEARCH NETWORK DATASET Oluwarotimi Nettey, Nicholas Jackson, and A. Lenore Ackerman Oluwarotimi NetteyOluwarotimi Nettey More articles by this author , Nicholas JacksonNicholas Jackson More articles by this author , and A. Lenore AckermanA. Lenore Ackerman More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003229.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Patients with chronic urologic pelvic pain present a unique challenge as their underlying condition is largely a diagnosis of exclusion. In the present analysis, we extend symptom cluster definitions from a pilot study evaluating bladder pain among ambulatory females to a multicenter prospectively recruited dataset using unsupervised machine learning techniques to assess the stability and reproducibility of these bladder pain phenotypes. METHODS: Unsupervised machine learning techniques were applied to patient reported symptoms extracted from the female Genitourinary Pain Index and Interstitial Cystitis Symptom and Problems Indices. Responses from 130 premenopausal female subjects without current opioid use enrolled in the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) research network were analyzed. Symptom clusters generated from the MAPP multisite cohort (MC) were compared to previously published pilot data results to assess for alignment and concordance. RESULTS: Symptom clusters included a nonurological pelvic pain group with localized pain unrelated to the micturition cycle (NUPP-MC), a myofascial pelvic pain group (MFP-MC) with stranguria, sensation of incomplete bladder emptying, urinary urgency and high symptom bother/severity, and a bladder specific-pain group (BPS-MC) with pain worsened by bladder filling and relieved by bladder emptying.Cluster comparisons demonstrated concordance by subtype between local and multisite cohorts on various domains of bladder pain (Figure 1). MFP–MC patients had more pain severity (p<0.001), diffuse body pain involvement (p=0.02), pelvic floor tenderness (p=0.01), and increased number of flares (p=0.009). MFP-MC cluster assignation was associated with severe sexual dysfunction, impaired relationship scores as well as sleep and anxiety disturbances. CONCLUSIONS: Patient reported symptoms can be utilized to perform deep phenotyping and identify distinct subgroups of perceived bladder pain. Phenotypic clusters appear stable both in a single institutional pilot cohort and a larger multi-site study. Further validation of these phenotypes and their associations with response to therapy will clarify how to target treatment of bladder pain. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e148 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Oluwarotimi Nettey More articles by this author Nicholas Jackson More articles by this author A. Lenore Ackerman More articles by this author Expand All Advertisement PDF downloadLoading ...

Inferring multilocal typologies of agrosystems and farmers’ practices: A methodological basis for the setting of participatory breeding designs

Family farms play an essential role in agroecological transition in Sahelian countries and worldwide. They present diversified features in terms of socio-economic organization, agrobiodiversity management and cropping systems diversity. Decentralized participatory breeding approaches aim to sustain the diversity of varieties adapted for such smallholder farmers' contexts. However the lack of clear target population of environments limits the focus and the efficiency of these approaches given the large diversification of the local contexts. In this study, we surveyed variables linked to agrosystems, crop management options and farmers’ criteria of varietal evaluation from 254 family farms sampled along 13 locations spanning the target area of a decentralized participatory breeding program of cowpea crop (Vigna unguiculata L. Walp) in Niger. The objective of our study was to infer typologies of family farms in the study area based on relevant variables supporting the setting of target population of environments (TPEs) to be considered in the breeding program. We used factorial analysis of mixed data (FAMD) and the Discriminant Analysis of Principal Components (DAPC) to infer the clusters. Chi square test, analysis of variance and generalized linear model were used to identify key variables discriminating the clusters. These clusters were geographically mapped to analyze their multilocal distribution. So, we identified and characterized four clusters structuring the diversity of the local agrosystems (Typologie G), five clusters structuring the diversity of cowpea cropping management options (Typologie C) and five clusters structuring the diversity of criteria used by farmers to evaluate the performance of cowpea varieties in the local contexts (Typologie P). Typology G distinguished farms based on discriminating variables linked for instance to secondary activities, cultivated species, soil fertility management practices and farm resources including land and livestock. Typology C distinguished farms based on cowpea management pratices including the secondary crop intercropped with cowpea (sorghum, Guinea sorrel, sesame or groundnut) and the use of cowpea harvest products (seeds, haulms, hulls). Typology P was based on discriminating performance criteria including cycle length, insect resistance, striga resistance, drought resistance, haulm production and economic value of cowpea variety. This methodology provides a robust and replicable way for the definition of clusters capturing the diversity of farms and local contexts. We discussed the perspective of using these clusters to set target population of environments for decentralized breeding programs.

Clusters in the critical branching Brownian motion

Brownian particles that are replicated and annihilated at equal rate have strongly correlated positions, forming a few compact clusters separated by large gaps. We characterize the distribution of the particles at a given time, using a definition of clusters in terms of a coarse-graining length recently introduced by some of us. We show that, in a non-extinct realization, the average number of clusters grows as where is the Hausdorff dimension of the boundary of the super-Brownian motion (SBM), found by Mueller, Mytnik, and Perkins. We also compute the distribution of gaps between consecutive particles. We find two regimes separated by the characteristic length scale where D is the diffusion constant and β the branching rate. The average number of gaps greater than g decays as for and for . Finally, conditioned on the number of particles n, the above distributions are valid for ; the average number of gaps greater than is much less than one, and decays as , in agreement with the universal gap distribution predicted by Ramola, Majumdar, and Schehr. Our results interpolate between a dense SBM regime and a large-gap regime, unifying two previously independent approaches.

Nanopore-only assemblies for genomic surveillance of the global priority drug-resistant pathogen, Klebsiella pneumoniae.

Oxford Nanopore Technologies (ONT) sequencing has rich potential for genomic epidemiology and public health investigations of bacterial pathogens, particularly in low-resource settings and at the point of care, due to its portability and affordability. However, low base-call accuracy has limited the reliability of ONT data for critical tasks such as antimicrobial resistance (AMR) and virulence gene detection and typing, serotype prediction, and cluster identification. Thus, Illumina sequencing remains the standard for genomic surveillance despite higher capital and running costs. We tested the accuracy of ONT-only assemblies for common applied bacterial genomics tasks (genotyping and cluster detection, implemented via Kleborate, Kaptive and Pathogenwatch), using data from 54 unique Klebsiella pneumoniae isolates. ONT reads generated via MinION with R9.4.1 flowcells were basecalled using three alternative models [Fast, High-accuracy (HAC) and Super-accuracy (SUP), available within ONT's Guppy software], assembled with Flye and polished using Medaka. Accuracy of typing using ONT-only assemblies was compared with that of Illumina-only and hybrid ONT+Illumina assemblies, constructed from the same isolates as reference standards. The most resource-intensive ONT-assembly approach (SUP basecalling, with or without Medaka polishing) performed best, yielding reliable capsule (K) type calls for all strains (100 % exact or best matching locus), reliable multi-locus sequence type (MLST) assignment (98.3 % exact match or single-locus variants), and good detection of acquired AMR genes and mutations (88-100 % correct identification across the various drug classes). Distance-based trees generated from SUP+Medaka assemblies accurately reflected overall genetic relationships between isolates. The definition of outbreak clusters from ONT-only assemblies was problematic due to inflation of SNP counts by high base-call errors. However, ONT data could be reliably used to 'rule out' isolates of distinct lineages from suspected transmission clusters. HAC basecalling + Medaka polishing performed similarly to SUP basecalling without polishing. Therefore, we recommend investing compute resources into basecalling (SUP model), wherever compute resources and time allow, and note that polishing is also worthwhile for improved performance. Overall, our results show that MLST, K type and AMR determinants can be reliably identified with ONT-only R9.4.1 flowcell data. However, cluster detection remains challenging with this technology.

An Algorithm to Separate Ionospheric Turbulence Radar Echoes From Those of Meteor Trails in Large Data Sets

AbstractCoherent scatter echoes from disintegrating meteors and from the unstable ionospheric E‐region can overlap considerably between 90 and 110 km altitudes. As the physical origin of plasma irregularities produced by meteor trails differs starkly from that of E‐region auroral irregularities, this has consequences for winds as well as electrodynamic studies, thereby introducing a need to distinguish between the two types of echoes. To that goal, we have developed a novel separation algorithm to automatically sort through arbitrarily large data sets in the region of overlap. This proves very useful when the 3D location of echoes is available. The algorithm uses a definition of crowding, or clustering, in both time and space and has been developed and tested with a comprehensive data set obtained from the recently built Canadian icebear 3D radar. We discuss the characteristics belonging to the two classes of echoes, and present statistical results about the location of each type of echo as a function of conditions. Our proposed algorithm can be applied to any coherent scatter echo data with high resolution 3D location information.

An automatic density peaks clustering based on a density-distance clustering index

&lt;abstract&gt;&lt;p&gt;The density peaks clustering (DPC) algorithm plays an important role in data mining by quickly identifying cluster centers using decision graphs to identify arbitrary clusters. However, the decision graph introduces uncertainty in determining the cluster centers, which can result in an incorrect number of clusters. In addition, the cut-off distance parameter relies on prior knowledge, which poses a limitation. To address these issues, we propose an improved automatic density peaks clustering (ADPC) algorithm. First, a novel clustering validity index called density-distance clustering (DDC) is introduced. The DDC index draws inspiration from the density and distance characteristics of cluster centers, which is applicable to DPC and aligns with the general definition of clustering. Based on the DDC index, the ADPC algorithm automatically selects the suitable cut-off distance and acquires the optimal number of clusters without additional parameters. Numerical experimental results validate that the introduced ADPC algorithm successfully automatically determines the optimal number of clusters and cut-off distance, significantly outperforming DPC, AP and DBSCAN algorithms.&lt;/p&gt;&lt;/abstract&gt;

A new automated national register-based surveillance system for outbreaks in long-term care facilities in Norway detected three times more severe acute respiratory coronavirus virus 2 (SARS-CoV-2) clusters than traditional methods.

To develop and test a new automated surveillance system that can detect, define and characterize infection clusters, including coronavirus disease 2019 (COVID-19), in long-term care facilities (LTCFs) in Norway by combining existing national register data. The numerous outbreaks in LTCFs during the COVID-19 pandemic highlighted the need for accurate and timely outbreak surveillance. As traditional methods were inadequate, we used severe acute respiratory coronavirus virus 2 (SARS-CoV-2) as a model to test automated surveillance. We conducted a nationwide study using data from the Norwegian preparedness register (Beredt C19) and defined the study population as an open cohort from January 2020 to December 2021. We analyzed clusters (≥3 individuals with positive SARS-CoV-2 test ≤14 days) by 4-month periods including cluster size, duration and composition, and residents' mortality associated with clusters. The study population included 173,907 individuals; 78% employees and 22% residents. Clusters were detected in 427 (43%) of 993 LTCFs. The median cluster size was 4-8 individuals (maximum, 50) by 4-month periods, with a median duration of 9-17 days. Employees represented 60%-82% of cases in clusters and were index cases in 60%-90%. In the last 4-month period of 2020, we detected 107 clusters (915 cases) versus 428 clusters (2,998 cases) in the last period of 2021. The 14-day all-cause mortality rate was higher in resident cases from the clusters. Varying the cluster definitions changed the number of clusters. Automated national surveillance for SARS-CoV-2 clusters in LTCFs is possible based on existing data sources and provides near real-time detailed information on size, duration, and composition of clusters. Thus, this system can assist in early outbreak detection and improve surveillance.

Not all clusters are equal: dynamics of molecular HIV-1 clusters in a statewide Rhode Island epidemic.

Molecular epidemiology is a powerful tool to characterize HIV epidemics and prioritize public health interventions. Typically, HIV clusters are assumed to have uniform patterns over time. We hypothesized that assessment of cluster evolution would reveal distinct cluster behavior, possibly improving molecular epidemic characterization, towards disrupting HIV transmission. Retrospective cohort. Annual phylogenies were inferred by cumulative aggregation of all available HIV-1 pol sequences of individuals with HIV-1 in Rhode Island (RI) between 1990 and 2020, representing a statewide epidemic. Molecular clusters were detected in annual phylogenies by strict and relaxed cluster definition criteria, and the impact of annual newly-diagnosed HIV-1 cases to the structure of individual clusters was examined over time. Of 2153 individuals, 31% (strict criteria) - 47% (relaxed criteria) clustered. Longitudinal tracking of individual clusters identified three cluster types: normal, semi-normal and abnormal. Normal clusters (83-87% of all identified clusters) showed predicted growing/plateauing dynamics, with approximately three-fold higher growth rates in large (15-18%) vs. small (∼5%) clusters. Semi-normal clusters (1-2% of all clusters) temporarily fluctuated in size and composition. Abnormal clusters (11-16% of all clusters) demonstrated collapses and re-arrangements over time. Borderline values of cluster-defining parameters explained dynamics of non-normal clusters. Comprehensive tracing of molecular HIV clusters over time in a statewide epidemic identified distinct cluster types, likely missed in cross-sectional analyses, demonstrating that not all clusters are equal. This knowledge challenges current perceptions of consistent cluster behavior over time and could improve molecular surveillance of local HIV epidemics to better inform public health strategies.

Koopman-Based Spectral Clustering of Directed and Time-Evolving Graphs

While spectral clustering algorithms for undirected graphs are well established and have been successfully applied to unsupervised machine learning problems ranging from image segmentation and genome sequencing to signal processing and social network analysis, clustering directed graphs remains notoriously difficult. Two of the main challenges are that the eigenvalues and eigenvectors of graph Laplacians associated with directed graphs are in general complex-valued and that there is no universally accepted definition of clusters in directed graphs. We first exploit relationships between the graph Laplacian and transfer operators and in particular between clusters in undirected graphs and metastable sets in stochastic dynamical systems and then use a generalization of the notion of metastability to derive clustering algorithms for directed and time-evolving graphs. The resulting clusters can be interpreted as coherent sets, which play an important role in the analysis of transport and mixing processes in fluid flows.Graphic

Epidemiological analysis of Legionnaires' disease in Scotland: a genomic study

Legionella pneumophila is the main cause of a severe pneumonic illness known as Legionnaires' disease and is a global public health threat. Whole-genome sequencing (WGS) can be applied to trace environmental origins of L pneumophila infections, providing information to guide appropriate interventions. We aim to explore the evolutionary and epidemiological relationships in a 36-year Scottish L pneumophila reference isolate collection. We investigated the genomic epidemiology of Legionnaires' disease over 36 years in Scotland, comparing genome sequences for all clinical L pneumophila isolates (1984-2020) with a sequence dataset of 3211 local and globally representative isolates. We used a stratified clustering approach to capture epidemiological relationships by core genome Multi-locus Sequence Typing, followed by high-resolution phylogenetic analysis of clusters to measure diversity and evolutionary relatedness in context with epidemiological metadata. Clustering analysis showed that 111 (57·5 %) of 193 of L pneumophila infections in Scotland were caused by ten endemic lineages with a wide temporal and geographical distribution. Phylogenetic analysis of L pneumophila identified hospital-associated sublineages that had been detected in the hospital environment up to 19 years. Furthermore, 12 (30·0%) of 40 community-associated infections (excluding a single, large outbreak) that occurred over a 13 year period (from 2000 to 2013) were caused by a single widely distributed endemic clone (ST37), consistent with enhanced human pathogenicity. Finally, our analysis revealed clusters linked by national or international travel to distinct geographical regions, indicating several previously unrecognised travel links between closely related isolates (fewer than five single nucleotide polymorphisms) connected by geography. Our analysis reveals the existence of previously undetected endemic clones of L pneumophila that existed for many years in hospital, community, and travel-associated environments. In light of these findings, we propose that cluster and outbreak definitions should be reconsidered, and propose WGS-based surveillance as a critical public health tool for real-time identification and mitigation of clinically important endemic clones. Chief Scientist Office, Biotechnology and Biological Sciences Research Council (UK), Medical Research Council Precision Medicine Doctoral Training Programme, Wellcome Trust, and Medical Research Council (UK).

Single-cell RNA-seq reveals fate determination control of an individual fibre cell initiation in cotton (Gossypium hirsutum).

Cotton fibre is a unicellular seed trichome, and lint fibre initials per seed as a factor determines fibre yield. However, the mechanisms controlling fibre initiation from ovule epidermis are not understood well enough. Here, with single-cell RNA sequencing (scRNA-seq), a total of 14 535 cells were identified from cotton ovule outer integument of Xu142_LF line at four developmental stages (1.5, 1, 0.5 days before anthesis and the day of anthesis). Three major cell types, fibre, non-fibre epidermis and outer pigment layer were identified and then verified by RNA in situ hybridization. A comparative analysis on scRNA-seq data between Xu142 and its fibreless mutant Xu142 fl further confirmed fibre cluster definition. The developmental trajectory of fibre cell was reconstructed, and fibre cell was identified differentiated at 1 day before anthesis. Gene regulatory networks at four stages revealed the spatiotemporal pattern of core transcription factors, and MYB25-like and HOX3 were demonstrated played key roles as commanders in fibre differentiation and tip-biased diffuse growth respectively. A model for early development of a single fibre cell was proposed here, which sheds light on further deciphering mechanism of plant trichome and the improvement of cotton fibre yield.

ENERGY CLUSTERS IN LIGHT OF THE AMENDMENT TO THE ACT ON RENEWABLE ENERGY SOURCES (RES)

The aim of the article is to present the idea of ​​energy clusters and legislative changes concerning the creation of energy clusters. An attempt was made to theoretically analyze the concept of energy clusters in the light of the amendment to the Act on renewable energy sources (RES). The method of literature review was applied and the most important items concerning the functioning of energy clusters were referred to. Provisions of subsequent RES laws regarding energy clusters were presented, starting from the Act of February 20, 2015, in which the definition of an energy cluster appeared for the first time, and ending with the latest amendment to the Act of February 24, 2022. It was found that the progressing globalization encourages austerity measures, especially in the energy sector, which contributes to the development of the economy. The way to meet the requirements of the European Union regarding increasing the share of renewable energy sources in the energy mix and limiting the consumption of conventional resources may be distributed energy based on the activities of prosumers, which is undoubtedly manifested by energy clusters.

Development of formats for the implementation of the intelligent business model of trade enterprises

Common to all commercial enterprises is the problem of violating the basics of the formation of intellectual activity. The same problem of enterprises’ intellectual activities is quite relevant from a practical point of view. One of the useful tools, the use of which makes it possible to translate the issues of intellectual activities, is the formation of a business model. In the process of the study reported here, a meaningful part of the intellectual business model of an enterprise was proposed and the corresponding concept was introduced. The main groups of indicators for assessing the level of intellectual activities of the business model of an enterprise were combined into the following clusters: qualifications (Q), skills (S), possibilities (V). The level of an enterprise's intellectual activities was assessed using an empirical and graphical method since the selected assessment indicators in their content can only accept point scores. The proposed methodology for forming a radar system to determine the format of business models for intellectual activity of trading enterprises is based on the definition of clusters of indicators to assess the level of an enterprise’s intellectual activities using an integral indicator. According to the radar system, the formats of enterprises' intellectual activities have been determined: innovative, integrated, rational, content-oriented, conducting. It was investigated that the potential for increasing the level of intellectual activity of the integrated format of the business model of an enterprise is about 14 %. The conclusion about the level of the enterprise's intellectual activities is made on the basis of determining the scale of values of expert assessments. Validation of the assessment of the level of intellectual activity of an enterprise was carried out separately for each format for all proposed clusters of assessment indicators. Based on the results of testing, recommendations have been compiled for choosing the format of a business model for the studied enterprises.

The heterogeneous immune landscape between lung adenocarcinoma and squamous carcinoma revealed by single-cell RNA sequencing

A thorough interrogation of the immune landscape is crucial for immunotherapy strategy selection and prediction of clinical responses in non-small-cell lung cancer (NSCLC) patients. Single-cell RNA sequencing (scRNA-seq) techniques have prompted the opportunity to dissect the distinct immune signatures between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two major subtypes of NSCLC. Here, we performed scRNA-seq on 72,475 immune cells from 40 samples of tumor and matched adjacent normal tissues spanning 19 NSCLC patients, and drew a systematic immune cell transcriptome atlas. Joint analyses of the distinct cellular compositions, differentially expressed genes (DEGs), cell–cell interactions, pseudotime trajectory, transcriptomic factors and prognostic factors based on The Cancer Genome Atlas (TCGA), revealed the central roles of cytotoxic and effector T and NK cells and the distinct functional macrophages (Mφ) subtypes in the immune microenvironment heterogeneity between LUAD and LUSC. The dominant subtype of Mφ was FABP4-Mφ in LUAD and SPP1-Mφ in LUSC. Importantly, we identified a novel lymphocyte-related Mφ cluster, which we named SELENOP-Mφ, and further established its antitumor role in both types, especially in LUAD. Our comprehensive depiction of the immune heterogeneity and definition of Mφ clusters could help design personalized treatment for lung cancer patients in clinical practice.

Recognizing seizure clusters in the community: The path to uniformity and individualization in nomenclature and definition.

Seizure emergencies experienced by patients with epilepsy include status epilepticus and seizure clusters. Although an accepted definition of status epilepticus exists, no clear consensus definition of seizure clusters has emerged; this is further complicated by the appearance in the literature of various empirically based definitions that have been developed for clinical trial study designs. In general, patients with intractable epilepsy have been shown to have a significant risk for acute episodes of increased seizure activity called seizure clusters (also referred to as acute repetitive seizures, among other terms) that differ from their usual seizure pattern. Duration (e.g., number of hours or days) is often included in the definition of a seizure cluster; however, the duration may vary among patients, with some seizure clusters lasting ≥24 h and requiring long-acting treatment for this period. In addition to seizure cluster duration, the time between seizures and possible acceleration in seizure frequency during the cluster may be important variables. The recognition and treatment of seizure clusters require urgent action because episodes that are not quickly and appropriately treated may lead to injury or progress to status epilepticus or potentially death. Most seizure clusters occur outside a medical facility (in the community) and treatment is usually administered by nonmedical individuals; therefore, health care providers may benefit from a clear description of these potential seizure emergencies that they can then use to educate patients and caregivers on the prompt and appropriate identification of seizure clusters and administration of rescue therapy. Here we explore why greater uniformity is needed in the discussion of seizure clusters. This exploration examines epidemiologic studies of seizure clusters and status epilepticus, inconsistencies in nomenclature and definitions for seizure clusters, practical application of seizure cluster terminology, and the potential use of acute seizure action plans and patient-specific individualized definitions in the clinical setting.

Consequences: Bench to home.

Seizure clusters (also referred to as acute repetitive seizures) consist of several seizures interspersed with brief interictal periods. Seizure clusters can break down γ-aminobutyric acidergic (GABAergic) inhibition of dentate granule cells, leading to hyperactivation. Functional changes to GABAA receptors, which play a vital neuroinhibitory role, can include altered GABAA receptor subunit trafficking and cellular localization, intracellular chloride accumulation, and dysregulation of proteins critical to chloride homeostasis. A reduction in neuroinhibition and potentiation of excitatory neurotransmission in CA1 pyramidal neurons represent pathological mechanisms that underlie seizure clusters. Benzodiazepines are well-established treatments for seizure clusters; however, there remain barriers to appropriate care. At the clinical level, there is variability in seizure cluster definitions, such as the number and/or type of seizures associated with a cluster as well as the interictal duration between seizures. This can lead to delays in diagnosis and timely treatment. There are gaps in understanding between clinicians, their patients, and caregivers regarding acute treatment for seizure clusters, such as the use of rescue medications and emergency services. This lack of consensus to define seizure clusters in addition to a lack of education for appropriate treatment can affect quality of life for patients and place a greater burden on patient families and caregivers. For patients with seizure clusters, the sense of unpredictability can lead to continuous traumatic stress, during which patients and families live with a heightened level of anxiety. Clinicians can affect patient quality of life and clinical outcomes through improved seizure cluster education and treatment, such as the development and implementation of a personalized seizure action plan as well as prescriptions for suitable rescue medications indicated for seizure clusters and instructions for their proper use. In all, the combination of targeted therapy along with patient education and support can improve quality of life.