Individual Clusters Research Articles

Controlled preparation of ordered dendritic nanoclusters at Au fractal biointerfaces

Owing to their ubiquitous use by natural systems, Au fractal structures by electrodeposition have been included in the design of new functional materials, yet a need exists for studying probe factors that influence the nature of materials produced in this process. In this investigation, we observe the presence of HCl results in a uniform structure of individual Au clusters. Also, the increase of deposition potential causes a decreasing size of Au cluster by accelerating the deposition process. Finally, as the deposition time becomes longer, significant increases occur in areas, densities, and fractal dimensions of individual Au clusters on the biointerface.

An Efficient Online Loop Closure Detection System With Local Spatial Co-Occurrence Information

As a vital component in simultaneous localization and mapping techniques, appearance-based loop closure detection (LCD) plays important roles in bounding the long-term drift errors. In this paper, an online LCD system based on the mutual co-occurrence information among visual features is proposed. First, a feature tracker module is designed to generate distinctive visual words, exploiting tracked words tool to improve efficiency. Then an incrementally built vocabulary is organized by a hierarchical navigable small world graph, where the visual words are indexed. To merge a homologous word into the existing one, the vocabulary applies an improved high dimensional online clustering method, which regards individual cluster as a normal distribution form. At the query phase, a list of candidate frames is located due to the co-occurrence constraint. Ultimately, the loop closure is specified by passing the temporal and similarity check, which avoids the memory consumption of historic image data. Validation tests based on public datasets and experimental sequence demonstrate the merit of low running time and memory cost, while the high-precision performance is retained in the system.

Are all underimmunized measles clusters equally critical?

This research develops a novel system science approach to examine the potential risk of outbreaks caused by geographical clustering of underimmunized individuals for an infectious disease like measles. We use an activity-based population network model and school immunization records to identify underimmunized clusters of zip codes in the Commonwealth of Virginia. Although Virginia has high vaccine coverage for measles at the state level, finer-scale investigation at the zip code level finds three statistically significant underimmunized clusters. This research examines why some underimmunized geographical clusters are more critical in causing outbreaks and how their criticality changes with a possible drop in overall vaccination coverage. Results show that different clusters can cause vastly different outbreaks in a region, depending on their size, location, immunization rate and network characteristics. Among the three underimmunized clusters, we find one to be critical and the other two to be benign in terms of an outbreak risk. However, when the vaccine coverage among children drops by just 5% (or 0.8% overall in the population), one of the benign clusters becomes highly critical. This work also examines the demographic and network properties of these clusters to identify factors that are responsible for affecting the criticality of the clusters. Although this work focuses on measles, the methodology is generic and can be applied to study other infectious diseases.

The characterization of toll-like receptor repertoire in Pinna nobilis after mass mortality events suggests adaptive introgression.

The fan mussel Pinna nobilis is currently on the brink of extinction due to a multifactorial disease mainly caused to the highly pathogenic parasite Haplosporidium pinnae, meaning that the selection pressure outweighs the adaptive potential of the species. Hopefully, rare individuals have been observed somehow resistant to the parasite, stretching the need to identify the traits underlying this better fitness. Among the candidate to explore at first intention are fast-evolving immune genes, of which toll-like receptor (TLR). In this study, we examined the genetic diversity at 14 TLR loci across P. nobilis, Pinna rudis and P. nobilis × P. rudis hybrid genomes, collected at four physically distant regions, that were found to be either resistant or sensitive to the parasite H. pinnae. We report a high genetic diversity, mainly observed at cell surface TLRs compared with that of endosomal TLRs. However, the endosomal TLR-7 exhibited unexpected level of diversity and haplotype phylogeny. The lack of population structure, associated with a high genetic diversity and elevated dN/dS ratio, was interpreted as balancing selection, though both directional and purifying selection were detected. Interestingly, roughly 40% of the P. nobilis identified as resistant to H. pinnae were introgressed with P. rudis TLR. Specifically, they all carried a TLR-7 of P. rudis origin, whereas sensitive P. nobilis were not introgressed, at least at TLR loci. Small contributions of TLR-6 and TLR-4 single-nucleotide polymorphisms to the clustering of resistant and susceptible individuals could be detected, but their specific role in resistance remains highly speculative. This study provides new information on the diversity of TLR genes within the P. nobilis species after MME and additional insights into adaptation to H. pinnae that should contribute to the conservation of this Mediterranean endemic species.

The association between 24-hour activity, sedentary and sleep compositions and mental health in Australian adults: a cross-sectional study

BackgroundPhysical activity, sedentary behaviour, and sleep are key components to health and well-being. Compositional data analysis of activity data overcomes the limitations of traditional statistical approaches and comprehensively assesses the association of all activities throughout a 24-hour day. Few studies have used compositional analysis to explore associations between movement behaviours and mental health. This study examined the association between 24-hour activity compositions and mental health in middle-aged Australian adults.MethodsThis study used cross-sectional data from participants (n = 322; mean age 40.4 years; 58.1% female; 47.8% university degree; 84.8% partnered) in the longitudinal Annual Rhythms in Adults’ lifestyle and health study (Adelaide, Australia). Activity composition (sleep, sedentary behaviour, light physical activity, and moderate-to-vigorous physical activity) was derived using 24-hour Fitbit accelerometry from the first month of participation (December). Mental health outcomes (depression, anxiety, and stress) were obtained from the Depression Anxiety Stress Scale 21-item short-form (DASS-21). The associations between activity composition (conveyed as isometric log ratios) and DASS-21 scores were examined using compositional multi-level linear regression models with a random intercept for clustering of individuals within families. Using the compositional time reallocation model, expected differences in mental health were examined for hypothetical time reallocations between individual activities.ResultsFavourable associations were observed when time (15 min) was reallocated to light physical activity from sleep (depression: -0.31 [95% CI=-0.57: -0.06]; anxiety: -0.20 [95% CI=-0.37: -0.03] and from sedentary behaviour (depression: -0.29 [95% CI=-0.46: -0.13]; anxiety: -0.14 [95% CI=-0.25: -0.03]; stress: -0.16 [95% CI=-0.31: -0.01]). Detrimental associations were observed when time was reallocated away from light physical activity to sleep (depression: 0.32 [95% CI = 0.07: 0.58]; anxiety: 0.20 [95% CI = 0.03: 0.37]) and to sedentary behaviour (depression: 0.30 [95% CI = 0.13: 0.48); anxiety: 0.15 [95% CI = 0.03: 0.26]; stress: 0.17 [95% CI = 0.01: 0.32]). There were no associations when time was allocated to or from moderate-to-vigorous physical activity.ConclusionThe way adults spend their time across a 24-hour day is associated with their mental health. Spending more time in light physical activity appears favourable if this time is taken from sleep and sedentary behaviour. These findings provide support for “move more, sit less” public health messages.Trial registrationThis study was prospectively registered on the Australian New Zealand Clinical Trial Registry (Trial ID: ACTRN12619001430123) on the 16/10/2019.

Multi-View Subspace Clustering by Joint Measuring of Consistency and Diversity

In multi-view subspace clustering, it is significant to find a common latent space in which the multi-view datasets are located. A number of multi-view subspace clustering methods have been proposed to explore the common latent subspace and achieved promising performance. However, previous multi-view subspace clustering algorithms seldom consider the multi-view consistency and multi-view diversity, let alone take them into consideration simultaneously. In this paper, we propose a novel multi-view subspace clustering by joint measuring the consistency and diversity, which is able to exploit these two complementary criteria seamlessly into a holistic design of clustering algorithms. The proposed model first searches a pure graph for each view by detecting the intrinsic consistent and diverse parts. A consensus graph is then obtained by fusing the multiple pure graphs. Moreover, the consensus graph is structurized to contain exactly <inline-formula><tex-math notation="LaTeX">$c$</tex-math></inline-formula> connected components where <inline-formula><tex-math notation="LaTeX">$c$</tex-math></inline-formula> is the number of clusters. In this way, the final clustering result can be obtained directly since each connected component precisely corresponds to an individual cluster. Extensive experimental studies on various datasets manifest that our model achieves comparable performance than the other state-of-the-art methods.

Social and community structure of striped dolphins in a semienclosed Mediterranean embayment

AbstractDescriptions of social structure of a species or population help to understand the processes that shaped its social system, and such knowledge can be valuable in terms of conservation planning. While striped dolphins Stenella coeruleoalba are among the most abundant cetacean species worldwide, and the most abundant in the Mediterranean Sea, their social and community structure is poorly known. This study investigates the association patterns of a population inhabiting the Gulf of Corinth, Greece. A total of 501 individuals were photo‐identified across 7 years (2011–2017). Groups were significantly larger (M = 44.7, SD = 46.3) than those studied to date in other Mediterranean areas. Association analyses in SOCPROG relied on the time and position metadata of dorsal fin photographs. Results indicated a single community characterized by nonrandom associations and a loose clustering of individuals (best division into clusters corresponded to an association index of 0.02). The high degree of social cohesion may result in part from geographic isolation within a semienclosed basin. This information can inform management action to protect a demographically isolated population classified as Endangered in the IUCN Red List, within a European Union Site of Community Importance regarded as an Important Marine Mammal Area (IMMA).

Chemical Profile and Bioactivity Evaluation of Salvia Species from Eastern Europe.

The Salvia genus comprises about 1000 species endowed with medicinal, aromatic, cosmetic, and ornamental applications. Even though the genus is one of the most-studied taxa of the Lamiaceae family, data on the chemical composition and biological properties of certain locally used Salvia species are still scarce. The present work aimed to evaluate the phytochemical profile and antimicrobial, antioxidant, and cytotoxic potential of ten Salvia species that grow in Eastern Europe (e.g., the Republic of Moldova). LC-HRMS/MS metabolite profiling allowed for the annotation of 15 phenolic and organic acids, 18 flavonoids, 19 diterpenes, 5 sesterpenes, and 2 triterpenes. Multivariate analysis (e.g., principal component analysis, hierarchical cluster analysis) revealed that S. austriaca, S. nutans, and S. officinalis formed individual clusters, whereas the remaining species had a similar composition. S. officinalis showed the highest activity against Staphylococcus aureus and Streptococcus pneumoniae (MIC = 0.625 mg/mL). As evaluated in DPPH, ABTS, and FRAP assays, S. officinalis was one of the most potent radical scavenging and metal-reducing agents (CE50 values of 25.33, 8.13, and 21.01 μg/mL, respectively), followed by S. verticillata, S. sclarea, S. kopetdaghensis, S. aethiopis, and S. tesquicola. Pearson correlation analysis revealed strong correlations with rosmarinic acid, luteolin-O-glucuronide, and hydroxybenzoic acid. When the cytotoxic activity was evaluated in human breast carcinoma MCF-7 and MDA-MB-231 cells, no significant reduction in cell viability was observed over the concentrations ranging from 25 and 100 μg/mL. The results confirm the potential use of understudied Salvia species as promising sources of antioxidant compounds for developing novel pharmaceutical, nutraceutical, or cosmeceutical products.

Exploring the relationship between perceived loneliness and subjective cognitive decline in older individuals

Objective Loneliness has been associated to a greater risk of cognitive decline and dementia in older individuals. However, evidence on whether this association also exists for older individuals who complain of cognitive problems is limited. We conducted a survey to examine the association between subjective cognitive decline in the working memory domain, perceived loneliness, depression, anxiety, and stress in older individuals with different profiles. Methods A total of 302 healthy, old individuals completed 3 questionnaires to assess subjective cognitive problems in attention, executive functions, storage, depression, anxiety, stress, and perceived loneliness. Results We conducted a cluster analysis and 3 clusters of individuals with different profiles emerged. Individuals with greater subjective cognitive problems (cluster 1) in the attention and storage domains, reported higher perceived loneliness and stress but not depression. In contrast, individuals with the least subjective cognitive problems (cluster 3) in the storage domain, reported lower perceived loneliness. Conclusions Individuals with higher subjective cognitive decline also report higher levels of perceived loneliness but not more depression than their peers. However, this correlation is present only for individuals with mild subjective cognitive decline (cluster 2). The implications for future research and interventions are discussed.

INTERPIN: A repository for intrinsic transcription termination hairpins in bacteria

The large-scale detection of putative intrinsic transcription terminators is limited to only a few bacteria currently. We discovered a group of hairpins, called cluster hairpins, present within 15 nucleotides from each other. These are expected to work in tandem to cause intrinsic transcription termination (ITT), while the single hairpin can do the same alone. Therefore, exploring these ITT sites and the hairpins across bacterial genomes becomes highly desirable. INTERPIN is the largest archived collection of in silico inferred ITT hairpins in bacteria, covering 12745 bacterial genomes and encompassing ten bacterial phyla for ∼25 million hairpins. Users can obtain details on operons, individual cluster, and single ITT hairpins that were screened therein. Integrated Genome Viewer (IGV) software interactively visualizes hairpin secondary and tertiary structures in the genomic context. We also discuss statistics for the occurrence of cluster or single hairpins and other termination alternatives while showing the validation of predicted hairpins against in vivo detected hairpins. The database is freely available at http://pallab.cds.iisc.ac.in/INTERPIN/.INTERPIN (database and software) can make predictions for both AT and GC-rich genomes, which has not been achieved by any other program so far. It can also be used to improve genome annotation as well as to get predictions to improve the understanding of the ITT pathway by further analysis.

The impact of poverty-reduction intervention on child mental health mediated by family relations: Findings from a cluster-randomized trial in Uganda

Reviews that synthesize global evidence on the impact of poverty reduction interventions on child and adolescent mental health (CAMH) report inconclusive results and highlight the need to unpack the mechanisms that connect poverty-reduction to CAMH. To address this gap, we examine the proposition that family relations is an important relational factor transmitting effect of poverty on CAMH, and test whether family relations mediate the effect of poverty-reduction intervention on depression, hopelessness, and self-concept among AIDS orphans in Uganda.We use longitudinal data collected over the course of 48 months in a cluster-randomized controlled trial conducted among N = 1410 AIDS orphans from n = 48 schools in Uganda. To examine the relationship between intervention, latent mediator (family relations and support) and CAMH outcomes (Beck Hopelessness Scale (BHS), Tennessee Self-Concept Scale (TSCS), and Depression), we ran structural equation models adjusting for clustering of individuals within schools.Relative to the control group, participants in both treatment arms reported lower levels of hopelessness and depression, and significantly higher levels of self-concept. They also report significantly higher levels of latent family relationship in all three models. In both treatment arms, the direct effect of the intervention on all three outcomes is still significant when the latent family relations mediator is included in the analyses. This suggests partial mediation. In other words, in both treatment arms, the significant positive effect of the intervention on children's depression, hopelessness, and self-concept is partially mediated by their family relationship quality.Our findings support the argument put forward by the Family Stress Model showing that the poverty-reduction program improves children's mental health functioning by improving family relationships. The implications of our study extend beyond the narrow focus of poverty reduction, suggesting that asset-building interventions have broader impacts on family dynamics and child mental health.

Geological context and human exposures to element mixtures in mining and agricultural settings in Colombia

Anthropogenic and natural sources contribute to chemical mixtures in air, water, and soil, posing potential risks to the environment and human health. To understand the interplay between element profiles in the human body, geographical location, and associated economic activities, we carried out an observational analytic cross-sectional study. The study recruited 199 participants from three municipalities, two of which had gold-mining as their primary economic activity, while the other was dedicated to agricultural and other local activities not related to mining. The concentrations of a total of 30 elements in human hair samples and 21 elements in environmental soil samples were measured using various spectrometry techniques. Unsupervised clustering analysis using Self-Organizing Maps was applied to human hair samples to analyze element concentrations. Distinct clusters of individuals were identified based on their hair element profiles, which were mapped to geographical location and economic activities. While higher levels of heavy metals (Ag, As, Hg, and Pb) were observed in individuals engaged in mining activities in certain clusters, individuals in agricultural areas show higher concentrations of elements found in pesticides (Ba and Sr). However, the elemental composition of hair is influenced not only by the anthropogenic activities but also by the inherent geological context where people live. Our findings highlight the significance of accounting for environmental factors when evaluating human health risks, as the intricate mixture of elements can yield valuable insights for targeted health interventions.

A monolayer crystalline covalent network of polyoxometalate clusters

Monolayer two-dimensional (2D) materials are of great interest because of their unique electronic structures, noticeable in-plane confinement effect, and exceptional catalytic properties. Here, we prepared 2D covalent networks of polyoxometalate clusters (CN-POM) featuring monolayer crystalline molecular sheets, formed by the covalent connection between tetragonally arranged POM clusters. The CN-POM shows a superior catalytic efficiency in the oxidation of benzyl alcohol, and the conversion rate is five times higher than that of the POM cluster units. Theoretical calculations show that in-plane electron delocalization of CN-POM contributes to easier electron transfer and increases catalytic efficiency. Moreover, the conductivity of the covalently interconnected molecular sheets was 46 times greater than that of individual POM clusters. The preparation of monolayer covalent network of POM clusters provides a strategy to synthesize advanced cluster-based 2D materials and a precise molecular model to investigate the electronic structure of crystalline covalent networks.

Humans strategically avoid connecting to others who agree and avert the emergence of network polarization in a coordination task

Clusters of like-minded individuals can impede consensus in group decision-making. We implemented an online color coordination task to investigate whether control over communication links creates clusters impeding group consensus. In 244 6-member networks, individuals were incentivized to reach a consensus by agreeing on a color, but had conflicting incentives for which color to choose. We varied (1) if communication links were static, changed randomly over time, or were player-controlled; (2) whether links determined who was observed or addressed; and (3) whether a majority existed or equally many individuals preferred each color. We found that individuals preferentially selected links to previously unobserved and disagreeing others, avoiding links with agreeing others. This prevented cluster formation, sped up consensus formation rather than impeding it, and increased the probability that the group agreed on the majority incentive. Overall, participants with a consensus goal avoided clusters by applying strategies that resolved uncertainty about others.

Predicting positions and orientations of individual kiwifruit flowers and clusters in natural environments

The positional and orientational detection of kiwifruit flower clusters in the natural environment is essential for the development of automated mechanical pollination systems. To ensure the timeliness and accuracy of this process, we propose a deep-learning-based method that predicts kiwifruit flower positions and orientations. Based on a single flower, this method effectively predicts the position and orientation of the entire cluster of flowers. First, the SOLOv2 instance segmentation model is used to locate and segment the location of the target kiwifruit flowers. Subsequently, the MobileNetV2 key point detection model is employed to detect the key points of the centre of gravity of the petal contour and the stamen part of the kiwifruit flower, and draw the orientation prediction line of the corresponding clusters. Finally, flower clusters are determined by calculating the Intersection over Union (IoU) of kiwi flowers. By taking the average of the orientation prediction line of each kiwi flower in a cluster as the entire cluster’s orientation, the method effectively generates a prediction of the said orientation. To verify the effectiveness of the proposed method, we compared and analysed the effects of SOLOv2 with the YOLOv5s, YOLOv4, and Faster-RCNN models with respect to the segmentation effect. The comparison results show that the SOLOv2 model exhibits a fast segmentation speed while maintaining a high segmentation accuracy. Verification results indicate that the SOLOv2 model has an accuracy rate of 74.2% for the detection of multi-target flowers of clustered kiwifruit from a wide perspective angle. It is therefore feasible to apply the proposed method to detect the positions and orientations of kiwi flower clusters in real time. This study can provide a technical reference for the development of robotic arms for the purpose of kiwifruit pollination.

Breakthrough SARS-CoV-2 Omicron Variant in Individuals Primed with Heterologous Vaccines Enhances Inhibition Performance of Neutralizing Antibody to BA.2 Parental Lineage.

This study aims to analyze the neutralization ability against Omicron parental variants in five clusters of individuals with different Coronavirus disease (COVID-19) immunity backgrounds, including individuals receiving a homologous or heterologous vaccine without prior infection, recovered patients with homologous or heterologous vaccination, and recovery patients without vaccination. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surrogate virus neutralization assay was performed on serum samples. Spearman correlation analysis showed that the percent inhibition against Omicron B.1.1.529 and BA.2 was significantly related to the period of serum collection (r = 0.730 and 0.787, p < 0.001, respectively). Very strong correlation between percent inhibition of neutralizing antibody against Omicron B.1.1.529 and BA.2 variants (rs = 0.973, p < 0.001) was also observed. The neutralizing activity of the sera from recovery patients receiving homologous and heterologous vaccine against the wild-type, B.1.1.529, and BA.2 Omicron variants was significantly higher (p < 0.001) than that of recovery patients without vaccination. This study robustly showed that the breakthrough SARS-CoV-2 Omicron variant in individuals who received homologous and heterologous vaccines had a high level of neutralizing activity against B.1.1.529 and BA.2 parental lineage of XBB subvariants. Therefore, the next-generation COVID-19 vaccine against emerging variants is needed to improve resilience against ongoing variants, particularly for persons who have never been infected.

Dark Matter in Fractional Gravity II: Tests in Galaxy Clusters

Recently, in Benetti et al. (Astrophys. J. 2023, 949, 65), we suggested that the dark matter (DM) component in galaxies may originate fractional gravity. In such a framework, the DM component exists, but the gravitational potential associated to its density distribution is determined by a modified Poisson equation including fractional derivatives (i.e., derivatives of noninteger type), which are meant to describe nonlocal effects; as such, this scenario is different from theories where baryonic matter emulates DM-like effects via modifications of gravity (e.g., MONDian frameworks). In Benetti et al., we showed that fractional gravity worked very well for reproducing the kinematics of disk-dominated galaxies, especially dwarfs; there is also preliminary evidence that the strength of fractional effects tends to weaken toward more massive systems. Here, we aim to test fractional gravity in galaxy clusters, with a twofold aim: (i) perform an independent sanity check that it can accurately describe such large and massive structures; (ii) derive a clear-cut trend for its strength in systems with different DM masses. To this purpose, we forward model the density and pressure distributions of the intracluster medium (ICM), working out the hydrostatic equilibrium equation in fractional gravity. Then, we perform a Bayesian analysis of the X-COP galaxy cluster sample and infer constraints on the fractional gravity parameters, for individual clusters as well as stacked clusters. We find that fractional gravity performs remarkably well in modeling the ICM profiles for the X-COP sample. We also check that the DM concentration vs. mass relation is still consistent with the expectations of N-body simulations in the standard cosmological scenario. Finally, we confirm the weakening of the fractional gravity effects toward more massive systems and derive the overall scaling of the fractional gravity parameters from dwarf galaxies to massive clusters, spanning six orders of magnitude in DM mass. Such an overall trend implies that fractional gravity can substantially alleviate the small-scale issues of the standard DM paradigm, while remaining successful on large cosmological scales.

ECG-Based Unsupervised Clustering in Coronary Artery Disease Associates with Ventricular Arrhythmia

Coronary Artery Disease (CAD) is a leading cause of life-threatening ventricular arrhythmias (LTVAs). This study aimed to identify distinct clusters of CAD individuals based on QRS morphology using a 3-nearest neighbors clustering algorithm. Cluster 1, characterized by the lowest QRS amplitudes and widest QRS complexes, was strongly associated with LTVA risk.

Connectomic insight into unique stroke patient recovery after rTMS treatment.

An improved understanding of the neuroplastic potential of the brain has allowed advancements in neuromodulatory treatments for acute stroke patients. However, there remains a poor understanding of individual differences in treatment-induced recovery. Individualized information on connectivity disturbances may help predict differences in treatment response and recovery phenotypes. We studied the medical data of 22 ischemic stroke patients who received MRI scans and started repetitive transcranial magnetic stimulation (rTMS) treatment on the same day. The functional and motor outcomes were assessed at admission day, 1 day after treatment, 30 days after treatment, and 90 days after treatment using four validated standardized stroke outcome scales. Each patient underwent detailed baseline connectivity analyses to identify structural and functional connectivity disturbances. An unsupervised machine learning (ML) agglomerative hierarchical clustering method was utilized to group patients according to outcomes at four-time points to identify individual phenotypes in recovery trajectory. Differences in connectivity features were examined between individual clusters. Patients were a median age of 64, 50% female, and had a median hospital length of stay of 9.5 days. A significant improvement between all time points was demonstrated post treatment in three of four validated stroke scales utilized. ML-based analyses identified distinct clusters representing unique patient trajectories for each scale. Quantitative differences were found to exist in structural and functional connectivity analyses of the motor network and subcortical structures between individual clusters which could explain these unique trajectories on the Barthel Index (BI) scale but not on other stroke scales. This study demonstrates for the first time the feasibility of using individualized connectivity analyses in differentiating unique phenotypes in rTMS treatment responses and recovery. This personalized connectomic approach may be utilized in the future to better understand patient recovery trajectories with neuromodulatory treatment.

JUSTIFICATION OF RISKS IN THE SYSTEM OF ECONOMIC SECURITY OF THE ENTERPRISE AND MEANS OF THEIR LEVELING

The purpose of the article is to justify risks in the economic security system of enterprises in the context of integration processes. Research materials and methods. The consequences of risks are the emergence or strengthening of negative trends in the clustering of enterprises both at the level of regions and at the level of individual clusters. Justification of risks and understanding the consequences of their impact will allow us to propose a mechanism for their neutralization and prevention. Risk is defined as a combination of the probability and possible amount of damage, or loss of resources, caused by a hazard. Taking into account possible negative phenomena and processes creates the basis for the effective functioning of clusters. The task of substantiating the risks of enterprise clustering is to establish the force of their action and the direction of influence, as well as to identify existing and possible factors of danger, and to determine methods of their neutralization. Results. The consequences of risks are the emergence or strengthening of negative trends in the processes of creation and functioning of clusters. The mathematical toolkit of probability theory is used for risk assessment, namely standard deviation, variance, mathematical expectation, and coefficient of variation. The direction and strength of internal risks are closely related to the conditions of the interaction of enterprises in the process of financial and economic activity. Understanding the essence of the risk of enterprise clustering in the context of integration processes is facilitated by a detailed study of its life cycle, which allows for determining the stages of its formation, and the vector of development, assessing the degree of change and possible consequences of the impact. Correspondence of the stages of risk development to the consequences of its influence on the clustering of enterprises in the conditions of integration processes forms the risk life cycle. In the life cycle, it is advisable to distinguish the following interrelated stages: emergence, activation, concentration, extinction, and neutralization. The risk, during the passage of certain stages, changes its strength, which is expressed in the change of pressure on the object of influence, with the help of which it is possible to judge the possible consequences on the formation and functioning of the cluster. Conclusions. The article substantiates that risk situations have a life cycle, depending on the stages of which corresponding types of consequences arise (insignificant, acceptable, significant, extreme). Correspondence of the stages of risk development to the consequences of its impact from the life cycle of the risk. The proposed life cycle of the risk situation made it possible to build matrices of correspondence of the degree of risk exposure to the significance of the consequences and impact of risks, as well as to develop a methodology for calculating the synergy effect from the interaction of existing risks.