Low-density Clusters Research Articles

A deep learning-based method for estimating the main stem length of sweet potato seedlings

The length of sweet potato seedling main stems is critical for harvest decisions, but accurately measuring it is challenging due to leaf occlusion and self-curvature. This study proposes a method for stem reconstruction and length estimation using a two-stage classifier, feature extraction, and semantic segmentation to obtain stem masks. Image processing separates clustered skeletons into singular lines for detailed analysis. Object detection identifies roots, stem-leaf intersections, and establishes a search relationship among segments. Stems are reconstructed based on directional, distance, and curvature constraints, then divided into four parts for length estimation using stereo depth information. Experimental results demonstrate effectiveness under various conditions: un-occluded, partially occluded, curved stems, and low-density clusters. Median length estimation error is 1.7 cm, with maximum and minimum errors of 3.2 cm and 0.3 cm respectively. This automated method provides a reliable solution for sweet potato seedling harvest and grading.

Density peaks clustering based on density voting and neighborhood diffusion

Density Peaks Clustering (DPC) is a well-known clustering technique in the data mining field with fewer parameters as well as no iteration. However, when dealing with datasets containing multiple peaks, DPC may subjectively choose the wrong cluster centers through the decision graph. Additionally, DPC requires a considerable amount of time to estimate density and relative distance. Moreover, DPC is sensitive to the value of cut-off distance. To overcome these issues, a density peaks clustering algorithm based on density voting and neighborhood diffusion (DPC-DVND) is proposed. Firstly, the proposed algorithm utilizes the k nearest neighbors and KD-tree to enhance the efficiency of computing local density and relative distance. Secondly, this study selects the potential cluster centers by density voting and applies the number of votes instead of density to calculate the feasibility of each potential center becoming a cluster center, so that the centers of low-density clusters can be better distinguished. Finally, two neighborhood density diffusion rules are designed to propagate labels and form the core structure of clusters. Experiments on synthetic, real, and image datasets are performed to compare different methods. Results show that DPC-DVND outperforms other state-of-the-art algorithms in terms of effectiveness and efficiency.

Constrained Density Peak Clustering

Clustering is a commonly used tool for discovering knowledge in data mining. Density peak clustering (DPC) has recently gained attention for its ability to detect clusters with various shapes and noise, using just one parameter. DPC has shown advantages over other methods, such as DBSCAN and K-means, but it struggles with datasets that have both high and low-density clusters. To overcome this limitation, the paper introduces a new semi-supervised DPC method that improves clustering results with a small set of constraints expressed as must-link and cannot-link. The proposed method combines constraints and a k-nearest neighbor graph to filter out peaks and find the center for each cluster. Constraints are also used to support label assignment during the clustering procedure. The efficacy of this method is demonstrated through experiments on well-known data sets from UCI and benchmarked against contemporary semi-supervised clustering techniques.

Photometric determination of the mass accretion rates of pre-main-sequence stars

We studied the properties of the young stellar populations in the NGC 299 cluster in the Small Magellanic Cloud using observations obtained with the Hubble Space Telescope in the V, I, and Hα bands. We identified 250 stars with Hα excess exceeding 5σ and an equivalent width of the Hα emission line of at least 20 Å, which indicates that these stars are still undergoing active accretion and therefore represent bona fide pre-main-sequence (PMS) objects. For 240 of them, we derived physical stellar parameters such as the mass, age, and mass accretion rate by comparing the observed photometry with theoretical models. We find evidence that suggests the existence of two populations of PMS stars, one with a median age of around 25 Myr and the other about 50 Myr old. These ages are consistent with previously determined ages for the main population of the cluster. The average mass accretion rate for these PMS stars is ∼5 × 10−9 M⊙ yr−1, which is comparable to the values found with the same method in other low-metallicity, low-density clusters in the Magellanic Clouds, but is about a factor of three lower than those measured for stars of similar mass and age in denser Magellanic Cloud stellar regions. Our findings support the hypothesis that both the metallicity and density of the forming environment can affect the mass accretion rate and thus the star formation process in a region. A study of the spatial distribution of both massive stars and (low-mass) PMS objects reveals that the former are clustered near the nominal centre of NGC 299, whereas the PMS stars are rather uniformly distributed over the field. Although it is possible that the PMS stars formed in situ in a more diffuse manner than massive stars, it is also plausible that the PMS stars formed initially in a more compact structure together with the massive stars and were later dispersed due to two-body relaxation. To explore this possibility, we studied the cluster’s stellar density profile. We find a core radius rc ≃ 0.6 pc and a tidal radius rt ≃ 5.5 pc, with an implied concentration parameter c ≃ 1, suggesting that the cluster could be dispersing into the field.

Intermittent Fault Recognition of Analog Circuits in the Presence of Outliers via Density Peak Clustering With Adaptive Weighted Distance

Intermittent faults (IF) are the main cause of analog circuit failures, which are short lived and repeatable. Since IF sample labels are difficult to obtain, unsupervised cluster recognition is an important method to help experts analyze potential classes of IFs. However, outliers generated by noise can affect the recognition of IFs. In this paper, an improved density peak clustering method is proposed, which enhances the fault recognition performance in terms of both distance and outlier detection. First, an adaptive weighted distance is proposed, which can give different distance weights according to the similarity between two samples. Thus, the outliers have a greater distance from the IF samples and reduce the influence of the outliers on the IF recognition. Second, a new outlier detection strategy is used to improve the density peak clustering (DPC) algorithm. The new strategy enhances outlier detection and prevents low-density clusters from being misdetected as outliers, thus further reducing the impact of outliers on IF recognition. Finally, the proposed method is applied to two typical analog filter circuits. The results show that this method can effectively recognize IFs in analog circuits.

A density connection weight-based clustering approach for dataset with density-sparse region

Density-Based clustering, an important clustering analysis approach, has several advantages of discovering clusters with arbitrary shapes and identifying noise samples etc. However, existing density-based clustering approaches cannot effectively identify clusters in density-sparse region. In this paper, we propose a density-based clustering approach for dataset with density-sparse region by using density connection weight. Firstly, we define a series of new concepts, such as density connection weight and average weight. And data objects are divided into core objects, boundary objects and noise objects according to average weight, which can effectively distinguish core objects and boundary objects in density-sparse region. Secondly, a density clustering assignment strategy is constructed based on density connection weight. A density clustering algorithm, together with the dimensionality reduction using t-SNE, judiciously handles the low-density clusters in multi-density datasets and the minimal clusters in extremely imbalanced datasets. In the end, the experimental results on the UCI and synthetic datasets validate that our algorithm exhibits prominent clustering performance, especially while being adapted in diverse clustering tasks, including the multi-density, imbalanced and uniform distribution datasets.

The shape of water – How cluster formation provides a unifying explanation of water’s anomalous properties

Water, essential to life on Earth, has many anomalous properties, that are not yet fully understood. We used density functional theory, the COSMO-RS implicit solvent model and statistical thermodynamics to calculate the dynamic equilibrium of formation of water clusters in water. Clusters of certain shape are predicted to be thermodynamically stable at ambient down to supercooled conditions and their presence almost quantitatively explains water’s anomalous properties as a function of temperature and pressure. The dodecahedron is the dominant cluster below ∼235 K and below ∼150 MPa and forms a miscibility gap with water below a critical point at ∼230 K. The hexagonal torus is the dominant cluster above ∼235 K and below ∼75 MPa. Both the dodecahedron and the torus are low-density clusters and their presence explains the density maximum of pure water at 277 K (+4 °C). The waters in these clusters are tetrahedrally coordinated, their structures are consistent with experimental data and their presence explains the observed tetrahedral fraction as a function of temperature. A significant part of the overall thermodynamic stability comes from the configurational entropy of the hydrogen bond network in the clusters, so dynamic cluster formation and reformation would be observed as fluctuations in the water structure, rather than stable clusters. At pressures above ∼100 MPa, many of water’s anomalous properties diminish or vanish, which is also the pressure above which high-density water clusters become more stable and prevalent. Our simple unifying 3-cluster theory reproduces water’s anomalous properties from supercooled to ambient temperatures and from ambient pressure up to at least 400 MPa, including density anomalies, two-liquid behavior, compressibility, and heat capacity.

Low-density star cluster formation: discovery of a young faint fuzzy on the outskirts of the low-mass spiral galaxy NGC 247

ABSTRACT The classical globular clusters found in all galaxy types have half-light radii of rh ∼ 2–4 pc, which have been tied to formation in the dense cores of giant molecular clouds. Some old star clusters have larger sizes, and it is unclear if these represent a fundamentally different mode of low-density star cluster formation. We report the discovery of a rare, young ‘faint fuzzy’ star cluster, NGC 247-SC1, on the outskirts of the low-mass spiral galaxy NGC 247 in the nearby Sculptor group, and measure its radial velocity using Keck spectroscopy. We use Hubble Space Telescope imaging to measure the cluster half-light radius of rh ≃ 12 pc and a luminosity of LV ≃ 4 × 105L⊙. We produce a colour–magnitude diagram of cluster stars and compare to theoretical isochrones, finding an age of ≃300 Myr, a metallicity of [Z/H] ∼ −0.6 and an inferred mass of M⋆ ≃ 9 × 104M⊙. The narrow width of blue-loop star magnitudes implies an age spread of ≲50 Myr, while no old red-giant branch stars are found, so SC1 is consistent with hosting a single stellar population, modulo several unexplained bright ‘red straggler’ stars. SC1 appears to be surrounded by tidal debris, at the end of an ∼2 kpc long stellar filament that also hosts two low-mass, low-density clusters of a similar age. We explore a link between the formation of these unusual clusters and an external perturbation of their host galaxy, illuminating a possible channel by which some clusters are born with large sizes.

Structural changes in the cruise network by ship size in Northeast Asia

Several ports want cruise ships to call at their ports for economic benefits. However, the main cause of concern is the complexity of port selection behavior for cruise lines. Tracking cruise ship movement data may help them understand the port selection behavior of cruise lines. This study aimed to examine the structural changes in the cruise network by ship size in Northeast Asia from 2014 to 2019 using network science methods with automatic identification system data. We identified five key findings. First, the number of nodes and edges in the mega- and small-size ship networks was growing rapidly. Second, the small-size network was growing with unique characteristics of low density and average clustering coefficient, and high average shortest path length and diameter. Third, the hub ports of Shanghai in the mega- and Hiroshima and Kobe in the small-size had gained more degrees over time. Fourth, owing to the deployment of new mega-ships in Shanghai, existing large- and mid-ships were shifting to different ports. Finally, modularity of all sizes increased over time, and the community structure became clearer.

Uniform modelling of the stellar density of thirteen tidal streams within the Galactic halo

ABSTRACT We present the results of fitting a flexible stellar stream density model to a collection of thirteen streams around the Milky Way, using photometric data from DES, DECaLS, and Pan-STARRS. We construct density maps for each stream and characterize their tracks on the sky, width, and distance modulus curves along the length of each stream. We use these measurements to compute lengths and total luminosities of streams and identify substructures. Several streams show prominent substructures, such as stream broadening, gaps, large deviations of stream tracks, and sharp changes in stream densities. Examining the group of streams as a population, as expected we find that streams with globular cluster progenitors are typically narrower than those with dwarf galaxy progenitors, with streams around 100 pc wide showing overlap between the two populations. We also note the average luminosity of globular cluster streams is significantly lower than the typical luminosity of intact globular clusters. The likely explanation is that observed globular cluster streams preferentially come from lower luminosity and lower density clusters. The stream measurements done in a uniform manner presented here will be helpful for more detailed stream studies such as identifying candidate stream members for spectroscopic follow up and stellar stream dynamical modelling.

The strong Gauss–Lucas theorem and analyticity of correlation functions via the Lee–Yang theorem

We provide a simple mechanism for going from Lee–Yang type theorems to analyticity of correlation functions by exploiting under-appreciated inequalities of Newman. We also describe a Lee–Yang approach that recovers the consequences of a low density cluster expansion for spin S models without any combinatorics.

Involvement of LDL and ox-LDL in Cancer Development and Its Therapeutical Potential.

Lipid metabolism disorder is related to an increased risk of tumorigenesis and is involved in the rapid growth of cancer cells as well as the formation of metastatic lesions. Epidemiological studies have demonstrated that low-density lipoprotein (LDL) and oxidized low-density lipoprotein (ox-LDL) are closely associated with breast cancer, colorectal cancer, pancreatic cancer, and other malignancies, suggesting that LDL and ox-LDL play important roles during the occurrence and development of cancers. LDL can deliver cholesterol into cancer cells after binding to LDL receptor (LDLR). Activation of PI3K/Akt/mTOR signaling pathway induces transcription of the sterol regulatory element-binding proteins (SREBPs), which subsequently promotes cholesterol uptake and synthesis to meet the demand of cancer cells. Ox-LDL binds to the lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1) and cluster of differentiation 36 (CD36) to induce mutations, resulting in inflammation, cell proliferation, and metastasis of cancer. Classic lipid-lowering drugs, statins, have been shown to reduce LDL levels in certain types of cancer. As LDL and ox-LDL play complicated roles in cancers, the potential therapeutic effect of targeting lipid metabolism in cancer therapy warrants more investigation.

Determining target populations for leprosy prophylactic interventions: a hotspot analysis in Indonesia

BackgroundLeprosy incidence remained at around 200,000 new cases globally for the last decade. Current strategies to reduce the number of new patients include early detection and providing post-exposure prophylaxis (PEP) to at-risk populations. Because leprosy is distributed unevenly, it is crucial to identify high-risk clusters of leprosy cases for targeting interventions. Geographic Information Systems (GIS) methodology can be used to optimize leprosy control activities by identifying clustering of leprosy cases and determining optimal target populations for PEP.MethodsThe geolocations of leprosy cases registered from 2014 to 2018 in Pasuruan and Pamekasan (Indonesia) were collected and tested for spatial autocorrelation with the Moran’s I statistic. We did a hotspot analysis using the Heatmap tool of QGIS to identify clusters of leprosy cases in both areas. Fifteen cluster settings were compared, varying the heatmap radius (i.e., 500 m, 1000 m, 1500 m, 2000 m, or 2500 m) and the density of clustering (low, moderate, and high). For each cluster setting, we calculated the number of cases in clusters, the size of the cluster (km2), and the total population targeted for PEP under various strategies.ResultsThe distribution of cases was more focused in Pasuruan (Moran’s I = 0.44) than in Pamekasan (0.27). The proportion of total cases within identified clusters increased with heatmap radius and ranged from 3% to almost 100% in both areas. The proportion of the population in clusters targeted for PEP decreased with heatmap radius from > 100% to 5% in high and from 88 to 3% in moderate and low density clusters. We have developed an example of a practical guideline to determine optimal cluster settings based on a given PEP strategy, distribution of cases, resources available, and proportion of population targeted for PEP.ConclusionPolicy and operational decisions related to leprosy control programs can be guided by a hotspot analysis which aid in identifying high-risk clusters and estimating the number of people targeted for prophylactic interventions.

Spatial Distribution of Lepidopteran Stem Borers in Louisiana Rice Fields.

Louisiana rice is attacked by a complex of lepidopteran stem borers that injure the vegetative and reproductive stages of rice, reducing tillering and grain output. Currently, insecticidal seed treatments are used in Louisiana as a preemptive means to control stem borers in rice. Our objective was to better understand the spatial distribution of stem borers in Louisiana rice fields. Thirteen nontreated commercial rice fields in Louisiana were mapped using GPS software and surveyed for stem borer damage in 2017 and 2018. Damage was recorded by counting the number of blank panicles (whiteheads) within quadrats located at different areas in each field. An ANOVA, spatial interpolation, hotspot analysis, and negative binomial regression were used to determine where stem borer injury was concentrated and whether the distribution of these pests was influenced by rice stand count, field composition, and site location. The results show that stem borers are typically aggregated along field edges, with population density decreasing toward the center of rice fields. Stem borer damage was 4.2- and 3.9-fold greater along field edges than in field centers in 2017 and 2018, respectively. Hotspot spatial analysis revealed 41.7 and 52.1% of low-density clusters occurred at or near field centers, respectively. Negative binomial regression revealed stem borer injury declines with increasing distance from field edges, with regional differences in infestation levels. This suggests stem borer control efforts could be spatially targeted to improve the efficiency of pest management.

Optimization of energy resources in various building cluster archetypes

The current work presents the development of building cluster archetypes, and the optimization of their energy resources mix. Twelve archetypes of building clusters are developed to reflect current North American practices. These archetypes range from low-density, all residential clusters, to high-density mixed-use clusters. A methodology is developed to explore near-optimal mixtures of renewable and alternative energy resources and their interaction with the existing energy grid. The methodology as well includes a sub-routine that examines energy sharing potential among clusters. The application of the optimization methodology to the selected archetypes shows that the optimal mix of energy sources varies according to the type of the cluster and its density. For instance, while roof-integrated PV panels can supply all the energy needs of low-density residential clusters, backup energy sources are required for higher density residential clusters to fulfill the thermal loads. The electrical energy demand of high-density mixed-use clusters can be met by involving alternative sources of energy, such as waste to energy. In the mixed-use case presented in this study, the waste required to satisfy the total electric load is as high as 10:1 as compared to the waste disposal. Sharing of energy resources among various clusters allows reducing the waste needed by about 37%.

Anomalous Noble Gas Solubility in Liquid Cloud Water: Possible Implications for Noble Gas Temperatures and Cloud Physics

AbstractThe noble gas temperature climate proxy is an established tool that has previously been applied to determine the source of groundwater recharge, however, unanswered questions remain. In fractured media (e.g., volcanic islands) recharge can be so rapid that groundwater is significantly depleted in heavy noble gases, indicating that the water has retained noble gas concentrations from higher elevations. Previous studies of rain samples have confirmed a match to patterns seen in fractured‐rock groundwater for heavy noble gases along with a significant helium excess. Snow has been shown to be a credible source for both the helium excess and the observed heavy noble gas pattern. Here, liquid cloud water samples were collected at two mountainous sites and analyzed for noble gas concentrations. A pattern like that of rainwater was found. However, an analysis of diffusive uptake of noble gases into cloud water demonstrates that droplets of 1 mm diameter and smaller should be in constant solubility equilibrium with the atmosphere. To explain this, we present a novel hypothesis that relies on the assumption that liquid water consists of two types of water molecule clusters bounded by hydrogen bonds: a low‐density ice‐like structure and a high‐density condensed structure. In this model, the pressure gradient near the surface of a droplet resulting from surface tension could allow for the formation of a surface layer that is rich in ice‐like low density clusters. This can explain both the helium excess and the heavy noble gas depletion seen in the samples.

Hot Jupiter formation in dense clusters: secular chaos in multiplanetary systems

ABSTRACT Exoplanetary observations reveal that the occurrence rate of hot Jupiters is correlated with star clustering. In star clusters, interactions between planetary systems and close flyby stars can significantly change the architecture of primordially coplanar, circular planetary systems. Flybys can impact hot Jupiter formation via activation of high-eccentricity excitation mechanisms such as the Zeipel–Lidov–Kozai (ZLK) effect and planet–planet scattering. Previous studies have shown that, for a two-planet system, close flybys, especially at high incidence angles, can efficiently activate the ZLK mechanism, thus triggering high-eccentricity tidal migration and ultimately form hot Jupiters. Here, we extend our previous study with a multiplanet (triple) system. We perform high-precision, high-accuracy few-body simulations of stellar flybys and subsequent planetary migration within the perturbed planetary systems using the code spacehub. Our simulations demonstrate that a single close flyby on a multiplanet system can activate secular chaos and ultimately lead to hot Jupiter formation via high-eccentricity migration. We find that the hot Jupiter formation rate per system increases with both the size of the planetary system and the mass of the outer planet, and we quantify the relative formation fractions for a range of parameters. Hot Jupiters formed via secular chaos are expected to be accompanied by massive companions with very long periods. Our study further shows that flyby-induced secular chaos is preferred in low-density clusters where multiplanet systems are more likely to survive, and that it contributes a significant fraction of hot Jupiter formation in star clusters compared to the flyby-induced ZLK mechanism.

Spatial Distribution of Lissorhoptrus oryzophilus (Coleoptera: Curculionidae) in Rice.

Rice water weevil, Lissorhoptrus oryzophilus Kuschel (Coleoptera: Curculionidae), is a destructive insect pest of rice in the United States. The pruning of rice roots by L. oryzophilus larvae can cause up to 25% yield loss. Currently, insecticidal seed treatments are used in the Louisiana rice industry as a pre-emptive means of controlling this weevil species. Our objective was to gain a better understanding of the spatial distribution of L. oryzophilus in Louisiana rice fields. Thirteen untreated commercial rice fields in Louisiana were mapped using GPS software, and surveyed for L. oryzophilus larvae over two years (2017 and 2018). An ANOVA, spatial interpolation, hotspot analysis, and multiple linear regression were used to determine where populations of L. oryzophilus were concentrated and whether distributions of the pest were related to edge effects. The results showed that L. oryzophilus larvae are typically aggregated along the edges of rice fields, with populations decreasing toward the center of rice fields. Lissorhoptrus oryzophilus densities were 3.3- and 2.2- fold greater along field edges than in field centers in 2017 and 2018, respectively. Hotspot spatial analysis revealed 59% and 32% of low-density clusters occurred at or near field centers, respectively. Multiple linear regression revealed larval densities decline with increasing distance from overwintering sites and alternative habitats (such as tree lines and levees containing bunch grasses and other alternative noncrop plants). These results suggest potential for insecticidal seed treatments to be applied selectively within rice fields to control this pest.

Matrix metalloproteinase-9 (MMP-9) and tissue inhibitor of metalloproteinases 1 (TIMP-1) are localized in the nucleus of retinal Müller glial cells and modulated by cytokines and oxidative stress.

Matrix metalloproteinases (MMPs) are involved in the pathology of numerous inflammatory retinal degenerations, including retinitis pigmentosa (RP). Our previous work revealed that intravitreal injections with tissue inhibitor of metalloproteinases 1 (TIMP-1) reduce the progression of rod cell death and inhibit cone cell remodeling that involves reactive gliosis in retinal Müller glial cells (MGCs) in rodent models. The underlying cellular and molecular mechanisms of how TIMP-1 functions in the retina remain to be resolved; however, MGCs are involved in structural homeostasis, neuronal cell survival and death. In the present study, MMP-9 and TIMP-1 expression patterns were investigated in a human MGC line (MIO-M1) under inflammatory cytokine (IL-1β and TNF-α) and oxidative stress (H2O2) conditions. First, both IL-1β and TNF-α, but not H2O2, have a mild in vitro pro-survival effect on MIO-M1 cells. Treatment with either cytokine results in the imbalanced secretion of MMP-9 and TIMP-1. H2O2 treatment has little effect on their secretion. The investigation of their intracellular expression led to interesting observations. MMP-9 and TIMP-1 are both expressed, not only in the cytoplasm, but also inside the nucleus. None of the treatments alters the MMP-9 intracellular distribution pattern. In contrast to MMP-9, TIMP-1 is detected as speckles. Intracellular TIMP-1 aggregation forms in the cytoplasmic area with IL-1β treatment. With H2O2 treatments, the cell morphology changes from cobbles to spindle shapes and the nuclei become larger with increases in TIMP-1 speckles in an H2O2 dose-dependent manner. Two TIMP-1 cell surface receptors, low density lipoprotein receptor-related protein-1 (LRP-1) and cluster of differentiation 82 (CD82), are expressed within the nucleus of MIO-M1 cells. Overall, these observations suggest that intracellular TIMP-1 is a target of proinflammatory and oxidative insults in the MGCs. Given the importance of the roles for MGCs in the retina, the functional implication of nuclear TIMP-1 and MMP-9 in MGCs is discussed.

Order in the chaos

Black hole (BH) triples represent one of the astrophysical pathways for BH mergers in the Universe detectable by LIGO and VIRGO. We study the formation of BH triples via binary–binary encounters in dense clusters, showing that up to two-thirds of the triples formed through this channel are hierarchical, whereas the remaining one-third are in a non-hierarchical, unstable configuration. We built a database of 32 000 N-body simulations to investigate the evolution of BH triples focusing on mildly hierarchical and non-hierarchical unstable configurations. Varying the mutual orbital inclination, the three BH masses and the inner and outer eccentricities, we show that retrograde, nearly planar configurations lead to a significant shrinkage of the inner binary. We find a universal trend of triple systems, namely that they tend to evolve toward prograde configurations and that the orbital flip, driven by the torque exerted on the inner BH binary (BHB) by the outer BH, leads in general to tighter inner orbits. In some cases, the resulting BHB undergoes coalescence within a Hubble time, releasing gravitational waves. A large fraction of merging BHBs with an initial separation of 1 AU enter the 10−3 − 10−1 Hz frequency band with large eccentricities, thus representing potential eccentric LISA sources. Mergers originating from an initially tighter BHB (a ∼ 0.01 AU), instead often have eccentricities above 0.7 in the 1 Hz band. We find that the mass distribution of the mergers in this astrophysical channel maps the original BH binary spectrum. This might have interesting consequences in light of the growing population of BH mergers detected by LIGO and VIRGO, namely that eccentric sources detected in high-frequency detectors are most likely connected with a high-velocity dispersion stellar environment, whereas eccentric sources detected in low-frequency detectors are likely to develop in low-density clusters.