Cluster Population Research Articles

The New Healthcare: AI-Driven Population Stratification, Proactive Medicine, and Predictive Analysis

Abstract Background The Covid-19 pandemic has highlighted structural and functional weakness of local health services. Italy carried out its reorganization with DM 77/2022, financed by Mission 6 PNRR of the CE. The model adopted aims to move from a reactive to proactive medicine, to be implemented close to the places where citizens live. The priority objective is to know data on the progress of chronic pathologies and non self-sufficiency using advanced digitalization tools. Methods 14 types of administrative data from 2022 to 2023, concerning 180000 (ISTAT data) people in the Lecce Social Health District (DSS Lecce), were analyzed with AI (Chat GPT). Results Thanks to algorithms, 6 categories of clinical complexity were identified. The analysis showed that the population assisted is 198696, for temporary presences, with the most represented age group being 50-55 years. Births decreased by 36% in 10 years (from 1681 to 1076), with 2197 deaths in 2023. 42,38% of the population (average age 38,58 years) did not use healthcare resources, while 57,62% (average age 54,19 years) required them. 48646 patients have cardiovascular disease, but only 10% with heart failure were actively monitored. 25047 have metabolic disease, 11195 are diabetics, 5929 have chronic respiratory diseases, 8167 chronic neurological diseases and 907 are cancer patients. Increasing age leads to polymorbidity and show signs of social, economic and health fragility. Conclusions the strategic objective of the Lecce DSS was management of chronicity according to specific care pathways, PDTA. Create healthcare teams capable of being case managers depending on the level of complexity (family doctors, nurses, etc). With the 2024 data there will be 3 consecutive years of analysis which will allow the development of predictive systems to identify population clusters at risk of increasing complexity and in need of proactive medical interventions, use of telemedicine with the support of a Territorial Operations Center. Key messages • AI-driven stratification supports personalized health plans to improve healthcare delivery, patient outcomes, and optimize resource allocation. • A new model of Population Health Management supported by Artificial Intelligence.

Epidemiological Characteristics and Spatiotemporal Clustering of Symptomatic Hepatitis E Virus Reinfection in Zhejiang Province, 2005–2023

Hepatitis E virus (HEV) reinfection is prevalent among the population, posing a significant burden on prevention and control efforts. In this study, we conducted a comprehensive analysis of data from China’s Disease Prevention and Control Information System’s infectious disease surveillance system to identify the epidemiological characteristics, spatiotemporal clustering, and high-risk populations of HEV reinfection. From 2005 to 2023, HEV reinfection in Zhejiang Province exhibited a fluctuating trend, peaking in 2020, with a 3–5-year lag compared to the pattern of HEV incidence. The Cox model indicated that individuals aged 40–50 and females are at higher risk of reinfection. Spatial autocorrelation was observed in reinfection cases from 2011 to 2016, with high–high clustering areas concentrated in downtown Hangzhou. Additionally, spatiotemporal scanning revealed that the clustering of reinfection cases has shifted from Hangzhou to coastal areas in recent years. Our findings suggest that targeted prevention and control measures for HEV rein fection should be implemented based on the characteristics of high-risk populations and spatiotemporal clustering patterns.

Population genomics of a specialized insect, Tetraopes texanus (Coleoptera: Cerambycidae), across a fragmented grassland system

AbstractSpecialist insects are especially susceptible to loss of genetic diversity in the face of habitat destruction and fragmentation. Implementing effective conservation practices for specialist insects will benefit from knowledge of population structure and genetic diversity. Because insects are hyper-diverse, characterizing the population structure of all species within the insect community is untenable, even if focused within a particular habitat type. Thus, concentrating on a single species specialized to a particular habitat type is needed to infer general trends. Here, we investigate the range-wide population genetics of Tetraopes texanus Horn 1878 (Coleoptera: Cerambycidae), which provides a useful model of grassland insects due to its’ habitat specificity and unique biology. Tetraopes texanus occurs primarily in Texas and Oklahoma, into Northern Mexico, and possibly into eastern New Mexico but also occurs in Black Belt prairies of Mississippi and Alabama. Mitochondrial and nuclear DNA (RAD-seq) analysis identified two distinct population clusters of T. texanus corresponding to the Texas and Oklahoma population and the Mississippi and Alabama population. Demographic models indicate ongoing, though incomplete, isolation of the two populations, with estimated dates of divergence in the mid-Pleistocene, coinciding with the end of a glacial period and a shift in glacial interval. These results can inform conservation of grassland adapted insects and offers insight to the biogeography of the Gulf Coastal Plain.

Disulfidptosis-related subtype and prognostic signature in prostate cancer

BackgroundDisulfidptosis refers to cell death caused by the accumulation and bonding of disulfide in the cytoskeleton protein of SLC7A11-high level cells under glucose deprivation. However, the role of disulfidptosis-related genes (DRGs) in prostate cancer (PCa) classification and regulation of the tumor microenvironment remains unclear.MethodsFirstly, we analyzed the expression and mutation landscape of DRGs in PCa. We observed the expression levels of SLC7A11 in PCa cells through in vitro experiments and assessed the inhibitory effect of the glucose transporter inhibitor BAY-876 on SLC7A11-high cells using CCK-8 assay. Subsequently, we performed unsupervised clustering of the PCa population and analyzed the differentially expressed genes (DEGs) between clusters. Using machine learning techniques to select a minimal gene set and developed disulfidoptosis-related risk signatures for PCa. We analyzed the tumor immune microenvironment and the sensitivity to immunotherapy in different risk groups. Finally, we validated the accuracy of the prognostic signatures genes using single-cell sequencing, qPCR, and western blot.ResultsAlthough SLC7A11 can increase the migration ability of tumor cells, BAY-876 effectively suppressed the viability of prostate cancer cells, particularly those with high SLC7A11 expression. Based on the DRGs, PCa patients were categorized into two clusters (A and B). The risk label, consisting of a minimal gene set derived from DEGs, included four genes. The expression levels of these genes in PCa were initially validated through in vitro experiments, and the accuracy of the risk label was confirmed in an external dataset. Cluster-B exhibited higher expression levels of DRG, representing lower risk, better prognosis, higher immune cell infiltration, and greater sensitivity to immune checkpoint blockade, whereas Cluster A showed the opposite results. These findings suggest that DRGs may serve as targets for PCa classification and treatment. Additionally, we constructed a nomogram that incorporates DRGs and clinical pathological features, providing clinicians with a quantitative method to assess the prognosis of PCa patients.ConclusionThis study analyzed the potential connection between disulfidptosis and PCa, and established a prognostic model related to disulfidptosis, which holds promise as a valuable tool for the management and treatment of PCa patients.

Validating Circular End-of-Life Strategies for Domestic Post-Consumer Materials in the Latin American Region: A Life Cycle Assessment Approach

This study examines the domestic solid waste management system in the LATAM region, using the city of Guayaquil in Ecuador as a case study. Through the life cycle assessment (LCA) methodology, the study compares domestic and external recycling processes, evaluating their effects on global warming potential, fossil resource scarcity, and terrestrial ecotoxicity. The results reveal that increasing recycling rates significantly reduces environmental impacts, with domestic recycling offering slightly higher environmental benefits than external options. A demographic analysis using machine learning techniques identifies distinct patterns of waste generation across different population clusters, highlighting the need for tailored waste management strategies. The study also emphasizes the importance of accurate local data and the integration of recycling initiatives with market realities, particularly in the light of policies mandating recycled content in products like PET bottles. A sensitivity analysis of the waste recovery indicator (WRI) demonstrates the potential for substantial environmental and economic benefits with higher recycling rates. The findings suggest that, to advance towards a circular economy, Latin American cities like Guayaquil must enhance their recycling infrastructure, refine waste management policies, and focus on demographic-specific strategies. This research contributes to the broader understanding of sustainable waste management in developing regions, offering insights for future policy and infrastructure development.

Population clustering of structural brain aging and its association with brain development

Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the ‘last in, first out’ mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.

Population clustering of structural brain aging and its association with brain development.

Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the 'last in, first out' mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.

Host population structure and species resolution reveal prophage transmission dynamics.

Much knowledge about bacteriophages has been obtained via genomics and metagenomics over the last decades. However, most studies dealing with prophage diversity have rarely conducted phage species delimitation (aspect 1) and have hardly integrated the population structure of the host (aspect 2). Yet, these two aspects are essential in assessing phage diversity. Here, we implemented an operational definition of phage species (clustering at 95% identity, 90% coverage) and integrated the host's population structure to understand prophage diversity better. Gathering the most extensive data set of Acinetobacter baumannii phages, we show that most prophage species have four or fewer prophages per species, and just five prophage species have more than 100 prophages. Most prophage species have a narrow host range and are geographically restricted; yet, very few have a broad host range being well spread in distant lineages of A. baumannii. These few broad host range prophage species are cosmopolitan and the most abundant species. Prophages in the same bacterial genome are very divergent, and prophages can easily be gained and lost within the bacterial lineages. Finally, even with this extensive data set, the prophage diversity has not been fully grasped. This study shows how integrating the host population structure and clustering at the species level allows us to better appreciate phage diversity and its transmission dynamics.

On Combining Ratio and Product Type Estimators For Estimation of Finite Population Mean In Adaptive Cluster Sampling Design

This article introduces a novel class of estimators and several new novel member estimators, combining the ratio and product forms, within the framework of Adaptive Cluster Sampling (ACS) design for estimating finite population mean. Specifically designed for rare or hidden clustered populations, the new novel estimators developed from the proposed class offer enhanced efficiency in estimation. To study the proposed class comprehensively, we derive expressions for the bias and Mean Squared Error (MSE) up to the first order of approximation. Through comprehensive simulation studies, we demonstrate the superior efficiency of the new developed estimators over several existing alternatives considered in this study.

A Statistical, Photometric, and SED Analysis to Characterize the BSS Populations in Old Open Cluster: Berkeley 39

ABSTRACT We present a statistical, photometric, and spectral energy distribution (SED) analysis to characterize the blue straggler stars (BSSs) populations in the Galactic old open cluster Berkeley 39. Berkeley 39 is a 6.16 Gyr old open cluster located at a distance of 3.99 Kpc.Gaia DR3 astrometry data have been used to estimate the membership probabilities using ensemble-based unsupervised machine learning techniques. We identified 21 BSS candidates on the colour–magnitude diagram, with 19 of them being detected in the Swift/UVOT UVW2 filter. We analysed the radial surface density profile and examined the cluster dynamical states and mass segregation effect. The SEDs of 19 BSSs are constructed using multiwavelength data covering UV to IR wavelengths. A single-component SED is fitted successfully for 14 BSS candidates. We discovered hot companions in five BSS candidates. These hot companions have temperatures of approximately 14 000 to 23 000 K, radii ranging from 0.04 to 0.13 R$_{\odot }$, and luminosities ranging from 0.16 to 2.91 L$_{\odot }$. Among these, three are most likely extremely low-mass white dwarfs (WDs) with masses around 0.17 to 0.18 M$_{\odot }$, and two are low-mass WDs with masses around 0.18 to 0.39 M$_{\odot }$. This confirms that they are post-mass transfer (Case A or Case B) systems. We also investigated the variable characteristics of BSSs by analysing their light curves using data from TESS. Our analysis confirms that two BSSs identified as eclipsing binaries in Gaia DR3 are indeed eclipsing binaries. Additionally, one of the two eclipsing binary BSSs shows evidence of having hot companions, as indicated by the multiwavelength SEDs.

Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure

Reduced functional connectivity of physiological systems is associated with poor prognosis in critically ill patients. However, physiological network analysis is not commonly used in clinical practice and awaits quantitative evidence. Acute liver failure (ALF) is associated with multiorgan failure and mortality. Prognostication in ALF is highly important for clinical management but is currently dependent on models that do not consider the interaction between organ systems. This study aims to examine whether physiological network analysis can predict survival in patients with ALF. Data from 640 adult patients admitted to the ICU for paracetamol-induced ALF were extracted from the MIMIC-III database. Parenclitic network analysis was performed on the routine biomarkers using 28-day survivors as reference population and network clusters were identified for survivors and non-survivors using k-clique percolation method. Network analysis showed that liver function biomarkers were more clustered in survivors than in non-survivors. Arterial pH was also found to cluster with serum creatinine and bicarbonate in survivors compared with non-survivors, where it clustered with respiratory nodes indicating physiologically distinctive compensatory mechanism. Deviation along the pH-bicarbonate and pH-creatinine axes significantly predicts mortality independent of current prognostic indicators. These results demonstrate that network analysis can provide pathophysiologic insight and predict survival in critically ill patients with ALF.

Double white dwarf binary population in MOCCA star clusters

There could be a significant population of double white dwarf binaries (DWDs) inside globular clusters (GCs); however, these binaries are often too faint to be individually observed. We have utilized a large number GC models evolved with the Monte Carlo Cluster Simulator (MOCCA) code to create a large statistical dataset of DWDs. These models include multiple-stellar populations, resulting in two distinct initial populations: one dense and the other less dense. Due to the lower density of one population, a large number of objects escape during the early GC evolution, leading to a high mass-loss rate. In this dataset we have analyzed three main groups of DWDs, namely in-cluster binaries, escaped binaries, and binaries formed from the isolated evolution of primordial binaries. We compared the properties of these groups to observations of close and wide binaries. We find that the number of escaping DWDs is significantly larger than the number of in-cluster binaries and those that form via the isolated evolution of all primordial binaries in our GC models. This suggests that dynamics play an important role in the formation of DWDs. For close binaries, we found a good agreement in the separations of escaped binaries and isolated binaries, but in-cluster binaries showed slight differences. We could not reproduce the observed extremely low mass WDs due to the limitations of our stellar and binary evolution prescriptions. For wide binaries, we also found a good agreement in the separations and masses, after accounting for observational selection effects. Even though the current observational samples of DWDs are extremely biased and incomplete, we conclude that our results compare reasonably well with observations.

Discrepancies between spectroscopy and HST photometry in tagging multiple stellar populations in 22 globular clusters

Multiple populations (MPs) in globular clusters (GCs) are stars that are distinct for their abundances of light elements. The MPs can be directly separated by measuring abundances of C, N, O, Na, Al, and Mg with spectroscopy or indirectly from photometric sequences created by the impact of different chemistry on band passes of particular filters, such as the HST pseudo-colours in the ultraviolet. An attempt to link HST pseudo-colour maps (PCMs) and spectroscopy was made by Marino et al. (2019, MNRAS, 487, 3815), using abundances mostly from our FLAMES survey. However, we found that an incomplete census of stars in common was used in their population tagging. We corrected the situation by building our own PCMs and matching them with our abundances in 20 GCs, plus two GCs from other sources, doubling the sample with spectroscopic abundances available. We found that the pseudo-colour (magF275W − 2 × magF336W + magF438) does not have a monotonic trend with Na abundances, enhanced by proton-capture reactions in MPs. Moreover, on average about 16% of stars with spectroscopic Na abundances show a discrepant tagging of MPs with respect to the HST photometry. Stars with second generation (SG) chemistry are mistaken for first generation (FG) objects according to HST photometry and vice versa. In general, photometric indices tend to overestimate the fraction of FG stars, in particular in low mass GCs. We offer a simple explanation for these findings. Finally, we publish all our PCMs (with more than 31 800 stars in 22 GCs) with star ID and coordinates; this is done to ensure easy verification and reproduction, as should be the case in scientific papers.

Testing the sources of the peculiar abundances in globular clusters

This work aims to analyze some of the polluters proposed in the self-enrichment scenarios put forward to explain the multiple populations in globular clusters (GCs), extending previous studies. Three scenarios with different polluter stars were tested: asymptotic giant branch stars (AGBs), high-mass interacting binaries (IBs), and fast rotating massive stars (FRMSs). With abundance data available from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey and ΔY estimates from precise Hubble Space Telescope (HST) photometry, twenty-six clusters were studied, increasing the number of clusters in previous studies by more than a factor of three. We also included the study of the abundances of N, C, Mg, and Al, extending previous studies that mainly focused on the abundances of He, O, and Na. In addition, we constructed an empirical model to test whether one could explain the chemical signatures of the “enriched” population of GC stars with a fixed source and dilution process based on empirical data. In agreement with work by other authors, we found that the proposed polluters can generally predict the qualitative abundance patterns in GC stars and in some cases quantitatively predict some elements, but in most cases when we compare the model yields with the observations, we find that they cannot explain the entire set of observed abundance patterns. The empirical model succeeds in reproducing the abundances of Al for a given ΔY (and vice versa), showing that there is a direct relationship between Al and He, with one increasing proportionally to the other. However, the empirical model fails to reproduce the observed abundances of Na and N, in agreement with the results of previous works. The observed decoupling between the maximum abundances of CNO-cycle elements such as N and Na with those of Al and He provides new information and constraints for future models and could take us a step closer to understanding the origin of the peculiar abundance variations of GC stars.

GOALS-JWST: Constraining the Emergence Timescale for Massive Star Clusters in NGC 3256

We present the results of a James Webb Space Telescope NIRCam and NIRSpec investigation into the young massive star cluster (YMC) population of NGC 3256, the most cluster-rich luminous infrared galaxy in the Great Observatories All Sky LIRG Survey. We detect 3061 compact YMC candidates with a signal-to-noise ratio ≥3 at F150W, F200W, and F335M. Based on yggdrasil stellar population models, we identify 116/3061 sources with F150W – F200W > 0.47 and F200W – F355M > −1.37 colors, suggesting that they are young (t ≤ 5 Myr), dusty (A V = 5−15), and massive (M ⊙ > 105). This increases the sample of dust-enshrouded YMCs detected in this system by an order of magnitude relative to previous Hubble Space Telescope studies. With NIRSpec integral field unit pointings centered on the northern and southern nucleus, we extract the Paα and 3.3 μm polycyclic aromatic hydrocarbon (PAH) equivalent widths for eight bright and isolated YMCs. Variations in both the F200W – F335M color and 3.3 μm PAH emission with the Paα line strength suggest a rapid dust clearing (<3−4 Myr) for the emerging YMCs in the nuclei of NGC 3256. Finally, with both the age and dust emission accurately measured, we use yggdrasil to derive the color excess (E(B − V)) for all eight YMCs. We demonstrate that YMCs with strong 3.3 μm PAH emission (F200W – F335M > 0) correspond to sources with E(B − V) > 3, which are typically missed in UV-optical studies. This underscores the importance of deep near-infrared imaging for finding and characterizing these very young and dust-embedded sources.

Dwarf galaxies in the MATLAS Survey: Hubble Space Telescope observations of the globular cluster systems of 74 ultra-diffuse galaxies

Ultra-diffuse galaxies (UDGs), characterised by their low surface brightness and large physical size, constitute a subclass of dwarf galaxies that challenge our current understanding of galaxy formation and evolution. In this paper, we probe the properties of 74 UDGs, identified in the MATLAS survey, based on a comprehensive study of their globular cluster (GC) populations. We obtained high-resolution HST imaging of these galaxies using the ACS F606W and F814W filters, which allowed us to select GCs on the basis of the colour and concentration index. After a background subtraction and completeness correction, we calculated an overall total of 387 GCs. The number of GCs per galaxy ranges from 0 to 38, with the majority (64%) having low counts (0 − 2 GCs). On average, the more massive UDGs tend to host a larger number of GCs. We find that our UDGs have specific frequencies (SN) ranging from 0 to 91, with a small population (9%) having SN &gt; 30. The median SN of our sample is similar to the one for the Perseus cluster UDGs, despite the fact that our UDGs are found in lower density environments. The SN measurements for individual galaxies can extend beyond those found in Perseus, but remain below the values found for UDGs in the Virgo and Coma cluster. Based on a trending analysis of the SN values with the host galaxy properties, we find trends with host galaxy size, roundness, colour, and local density. For the UDGs with sufficiently high statistics, we studied 2D density maps of the GC distributions, which display a variety of appearances: symmetric, asymmetric, off-centre, and elongated. The UDGs with disturbed density maps also show disturbed stellar light morphologies. We further quantified the distribution by modelling it with a Sérsic profile, finding Re, GC/Re, gal ∼ 1.0, which indicates that the GCs follow the stellar light of the host galaxy.

The Clearing Timescale for Infrared-selected Star Clusters in M83 with HST

We present an analysis of Hubble Space Telescope data from Wide Field Camera 3 (WFC3)/Ultraviolet Imaging Spectrometer, WFC3/IR, and the Advanced Camera for Surveys, investigating the young stellar cluster (YSC) population in the face-on spiral galaxy M83. Within the field of view of the IR pointings, we identify 454 sources with compact F814W continuum and Paβ line emission with a S/N ≥ 3 as possible YSC candidates embedded in dust. We refine this selection to 97 candidates based on their spectral energy distributions, multiwavelength morphology, and photometric uncertainties. For sources that are detected in all bands and have mass >102.8 M ⊙ (53 sources), we find that by 2 Myr 75% of IR-selected star clusters have an A V ≤ 1 and that by 3 Myr the fraction rises to ∼82%. This evidence of early clearing implies that presupernova (pre-SN) feedback from massive stars is responsible for clearing the majority of the natal gas and dust that surround IR-selected star clusters in M83. Further, this result is consistent with previous estimates based on WFC3 observations and adds to the growing body of literature suggesting pre-SN feedback to be crucial for YSC emergence in normal star-forming galaxies. Finally, we find a weak correlation between the YSC concentration index and age over the first 10 Myr, which matches previous studies and indicates little or no change in the size of YSCs in M83 during their early evolution.

The Gaia-ESO Survey DR5.1 and Gaia DR3 GSP-Spec: a comparative analysis

Context. The third data release of Gaia, has provided stellar parameters, metallicity [M/H], [α/Fe], individual abundances, broadening parameter from its Radial Velocity Spectrograph (RVS) spectra for about 5.6 million objects thanks to the GSP-Spec module, implemented in the Gaia pipeline. The catalogue also publishes the radial velocity of 33 million sources. In recent years, many spectroscopic surveys with ground-based telescopes have been undertaken, including the public survey Gaia-ESO, designed to be complementary to Gaia, in particular towards faint stars. Aims. We took advantage of the intersections between Gaia RVS and Gaia-ESO to compare their stellar parameters, abundances and radial and rotational velocities. We aimed at verifying the overall agreement between the two datasets, considering the various calibrations and the quality-control flag system suggested for the Gaia GSP-Spec parameters. Methods. For the targets in common between Gaia RVS and Gaia-ESO, we performed several statistical checks on the distributions of their stellar parameters, abundances and velocities of targets in common. For the Gaia surface gravity and metallicity we considered both the uncalibrated and calibrated values. Results. Overall, there is a good agreement between the results of the two surveys. We find an excellent agreement between the Gaia and Gaia-ESO radial velocities given the uncertainties affecting each dataset. Less than 25 out of the ≈2100 Gaia-ESO spectroscopic binaries are flagged as non-single stars by Gaia. For the effective temperature and in the bright regime (G ≤ 11), we found a very good agreement, with an absolute residual difference of about 5 K (±90 K) for the giant stars and of about 17 K (±135 K) for the dwarf stars; in the faint regime (G ≥ 11), we found a worse agreement, with an absolute residual difference of about 107 K (±145 K) for the giant stars and of about 103 K (±258 K) for the dwarf stars. For the surface gravity, the comparison indicates that the calibrated gravity should be preferred to the uncalibrated one. For the metallicity, we observe in both the uncalibrated and calibrated cases a slight trend whereby Gaia overestimates it at low metallicity; for [M/H] and [α/Fe], a marginally better agreement is found using the calibrated Gaia results; finally for the individual abundances (Mg, Si, Ca, Ti, S, Cr, Ni, Ce) our comparison suggests to avoid results with flags indicating low quality (XUncer = 2 or higher). These remarks are in line with the ones formulated by GSP-Spec. We confirm that the Gaia vbroad parameter is loosely correlated with the Gaia-ESO v sin i for slow rotators. Finally, we note that the quality (accuracy, precision) of the GSP-Spec parameters degrades quickly for objects fainter than G ≈ 11 or GRVS ≈ 10. Conclusions. We find that the somewhat imprecise GSP-Spec abundances due to its medium-resolution spectroscopy over a short wavelength window and the faint G regime of the sample under study can be counterbalanced by working with averaged quantities. We extended our comparison to star clusters using averaged abundances, using not only the stars in common, but also the members of clusters in common between the two samples, still finding a very good agreement. Encouraged by this result, we studied some properties of the open-cluster population, using both Gaia-ESO and Gaia clusters: our combined sample traces very well the radial metallicity and [Fe/H] gradients, the age-metallicity relations in different radial regions, and allows us to place the clusters in the thin disc.

An enhanced seagull algorithm for multi-threshold image segmentation based on Kapur entropy

Abstract To address the shortcomings of the Seagull Optimization Algorithm (SOA), such as poor distribution of individuals’ positions and low population diversity leading to susceptibility to local optima after the collision avoidance phase, an Enhanced Seagull Algorithm (ESOA) is proposed. Firstly, a chaos-guided mechanism is utilized to replace the original individual positions after collision avoidance with the mean of individual optima and current positions, effectively improving the distribution of the population. Secondly, a circular-topology neighborhood structure with random weights is introduced, allowing interaction between the current individual’s position and the information from its two adjacent optimal positions, thereby improving population clustering and enhancing diversity. Thirdly, a dynamic lens mapping strategy is employed to direct the optimal seagull individuals toward their newly generated mapped points, enhancing their ability to avoid local optima. ESOA is applied to the problem of multi-threshold image segmentation based on Kapur entropy, and experimental results demonstrate its effectiveness in improving segmentation performance.

Distribution and morphology of serotonin transporter-immunoreactive type I cells in the rat carotid body.

The present study reexamined the immunolocalization of membranous serotonin transporter (SERT) in the rat carotid body, and demonstrated SERT-immunoreactive cells of unreported morphology. SERT was immunohistochemically localized in a very small population of cell clusters or single type I cells (2.8%) immunoreactive for synaptophysin, the marker of these cells. Intense SERT immunoreactivity outlined the perinuclear cytoplasm and multiple cytoplasmic processes of type I cells. Of SERT-immunoreactive type I cells, 14.6% and 32.6% were immunoreactive for tyrosine hydroxylase (TH) and dopamine beta-hydroxylase, respectively, while 75.9% were immunoreactive for serotonin (5-HT). 5-HT-immunoreactive products were localized in cell bodies rather than cytoplasmic processes. SERT-immunoreactive type I cells were composed of an oval cell body with multiple threads and spherical or elongated cytoplasmic processes. Clusters or single SERT-immunoreactive type I cells were localized between or attached to other TH-immunoreactive type I cells by cell bodies or variform cytoplasmic processes. SERT-immunoreactive type I cells mainly contained bassoon-immunoreactive products in their cell bodies rather than their variform cytoplasmic processes. These results demonstrated the characteristic morphology of SERT-immunoreactive type I cells, which extend multiple cytoplasmic processes with variform terminal parts. Their morphology might be suitable for uptake of 5-HT to control the serotonergic modulation in the carotid body.