Cluster Population Research Articles

Clusters, Clumps, Dust, and Gas (CCDG) in NGC 1614: Benchmarking Cluster Demographics in Extreme Systems

Abstract Observations of young star clusters in a variety of galaxies have been used to constrain basic properties related to star formation, such as the fraction of stars found in clusters (Γ) and the shape of the cluster mass function (CMF). However, the results can depend heavily on the reliability of the cluster age-dating process and other assumptions. One of the biggest challenges for successful age-dating lies in breaking the age–reddening degeneracy, where older, dust-free clusters and young, reddened clusters can have similar broadband colors. While this degeneracy affects cluster populations in all galaxies, it is particularly challenging in systems with dusty, extreme star-forming environments. We study the cluster demographics in the luminous infrared galaxy NGC 1614 using Hubble Space Telescope imaging taken in eight optical–near-infrared passbands. For age-dating, we adopt a spectral energy distribution fitting process that limits the maximum allowed reddening by region and includes Hα photometry directly. We find that without these assumptions essentially all clusters in the dust-free UV-bright arm that should have ages ≈50–250 Myr are incorrectly assigned ages younger than 10 Myr. We find that this method greatly reduces the number of clusters in the youngest (τ < 10 Myr) age bin and shows a fairly uniform distribution of massive clusters, the most massive being ≈few × 107 M ⊙. A maximum likelihood fit shows that the CMF is well fitted by a power law with an index of approximately −1.8, with no statistically significant high-mass cutoff. We calculate the fraction of stars born in clusters to be Γ1−10 = 22.4% ± 5.7%. The fraction of stars in clusters decreases quickly over time, with Γ10−100 = 4.5% ± 1.1% and Γ100−400 = 1.7% ± 0.4%, suggesting that clusters dissolve rapidly over the first ∼0.5 Gyr. The decreasing fraction of stars in clusters is consistent with the declining shape observed for the cluster age distribution.

Spectro-photometry and radial distribution of multiple stellar populations in globular clusters from gaia XP spectra

Abstract Understanding the formation of multiple populations in globular clusters (GCs) represents a challenge for stellar population studies. Nevertheless, the outermost cluster regions, likely to hold clues about the initial configuration of GC stars, remain underexplored. We use synthetic spectra reflecting the chemical compositions of first- and second-population (1P, 2P) stars in 47 Tucanae to identify spectral regions sensitive to these populations. This led us to define new photometric bands that effectively distinguish 1P and 2P giant stars using Gaia XP spectra. Testing these filters, we constructed the pseudo two-color diagrams dubbed chromosome maps (ChMs) and, for the first time, identified 1P and 2P stars in the cluster’s outermost regions and beyond its tidal radius. We constructed similar diagrams for NGC 3201, NGC 6121, NGC 6752, and NGC 6397, thus exploring GCs with different metallicities. The ChMs effectively distinguished multiple populations in the outer regions of all clusters, except for the metal-poor NGC 6397. Our findings, together with literature results from more-internal regions, show that the 2P stars of 47 Tucanae are more-centrally concentrated than the 1P. A similar pattern is seen for 2P stars with extreme chemical composition of NGC 3201. The multiple populations of NGC 6121, and NGC 6752 share the same radial distributions. These radial behaviors are consistent with the GC formation scenarios where 2P stars originate in the central regions. Noticeably, results on NGC 3201 are in tension with the conclusion from recent work that its 1P is more centrally concentrated than the 2P and might form with more central concentration.

Irradiation damage evolution dependence on misorientaton angle for ∑ 5 grain boundary of Nb: An atomistic simulation-based study

Abstract Niobium as refractory element holds ability to arrest the primary radiation damage in reactor's fissionable conditions and can withstand high temperature applications. We have inclined the investigation of irradiated Nb ∑ 5 symmetric tilt angled-grain boundary (STGB) models at two high-angled grain boundary misorientation: 53.13 ° (∑ 5(2-10)/ (120)) and 36.87 ° (∑ 5(3-10)/ (130)) respectively. A hybrid of Ziegler-Biersack-Littmark (ZBL) and embedded atom method (EAM) potentials were superimposed to simulate radiation damage. Statistical averaging of the displacement cascades were conducted to study dynamic evolution of the point defects and interstitial clusters at varying magnitudes of primary knock-on atom (PKA) energies, irradiation temperatures and PKA directions respectively. The irradiated GB models were compared with a irradiated bulk Nb specimen and the results of the study indicate that the irradiated Nb system with greater misorientation angle i.e., Nb ∑ 5 (θ = 53.13 ° ) survived with lower number Frenkel pair defects as well as the population small-sized interstitial clusters. The cluster analysis indicated the highest population of interstitial clusters survived in Nb STGB models were irradiated along <1 3 5> PKA direction and 100 keV recoil energies respectively.

The cosmological analysis of X-ray cluster surveys. VI. Inference based on analytically simulated observable diagrams

The number density of galaxy clusters across mass and redshift has been established as a powerful cosmological probe, yielding important information on the matter components of the Universe. Cosmological analyses with galaxy clusters traditionally employ scaling relations, which are empirical relationships between cluster masses and their observable properties. However, many challenges arise from this approach as the scaling relations are highly scattered, maybe ill-calibrated, depend on the cosmology, and contain many nuisance parameters with low physical significance. For this paper, we used a simulation-based inference method utilizing artificial neural networks to optimally extract cosmological information from a shallow X-ray survey, solely using count rates, hardness ratios, and redshifts. This procedure enabled us to conduct likelihood-free inference of cosmological parameters $ m $ and $ To achieve this, we analytically generated several datasets of 70\,000 cluster samples with totally random combinations of cosmological and scaling relation parameters. Each sample in our simulation is represented by its galaxy cluster distribution in a count rate (CR) and hardness ratio (HR) space in multiple redshift bins. We trained convolutional neural networks (CNNs) to retrieve the cosmological parameters from these distributions. We then used neural density estimation (NDE) neural networks to predict the posterior probability distribution of $ m $ and $ given an input galaxy cluster sample. Using the survey area as a proxy for the number of clusters detected for fixed cosmological and astrophysical parameters, and hence of the Poissonian noise, we analyze various survey sizes. The 1 sigma errors of our density estimator on one of the target testing simulations are 1\,000\,deg$^2$, 15.2<!PCT!> for $ m $ and 10.0<!PCT!> for $ and 10\,000\,deg$^2$, 9.6<!PCT!> for $ m $ and 5.6<!PCT!> for $ We also compare our results with a traditional Fisher analysis and explore the effect of an additional constraint on the redshift distribution of the simulated samples. We demonstrate, as a proof of concept, that it is possible to calculate cosmological predictions of $ m $ and $ from a galaxy cluster population without explicitly computing cluster masses and even the scaling relation coefficients, thus avoiding potential biases resulting from such a procedure.

Probing Populations of Dark Stellar Remnants in the Globular Clusters 47 Tuc and Terzan 5 Using Pulsar Timing

We present a new method to combine multimass equilibrium dynamical models and pulsar timing data to constrain the mass distribution and remnant populations of Milky Way globular clusters (GCs). We first apply this method to 47 Tuc, a cluster for which there exists an abundance of stellar kinematic data and which is also host to a large population of millisecond pulsars. We demonstrate that the pulsar timing data allow us to place strong constraints on the overall mass distribution and remnant populations even without fitting on stellar kinematics. Our models favor a small population of stellar-mass black holes (BHs) in this cluster (with a total mass of 446−72+75M⊙ ), arguing against the need for a large (>2000 M ⊙) central intermediate-mass BH. We then apply the method to Terzan 5, a heavily obscured bulge cluster that hosts the largest population of millisecond pulsars of any Milky Way GC and for which the collection of conventional stellar kinematic data is very limited. We improve existing constraints on the mass distribution and structural parameters of this cluster and place stringent constraints on its black hole content, finding an upper limit on the mass in BHs of ∼4000 M ⊙. This method allows us to probe the central dynamics of GCs even in the absence of stellar kinematic data and can be easily applied to other GCs with pulsar timing data, for which data sets will continue to grow with the next generation of radio telescopes.

A Systematic Analysis of Star Cluster Disruption by Tidal Shocks. II. Predicting Star Cluster Dissolution Rates from a Time-series Analysis of Their Tidal Histories

Most of the dynamical mass loss from star clusters is thought to be caused by the time variability of the tidal field (“tidal shocks”). Systematic studies of tidal shocks have been hampered by the fact that each tidal history is unique, implying both a reproducibility and a generalization problem. Here we address these issues by investigating how star cluster evolution depends on the statistical properties of its tidal history. We run a large suite of direct N-body simulations of clusters with tidal histories generated from power spectra of a given slope and with different normalizations, which determine the timescales and amplitudes of the shocks, respectively. At fixed normalization (i.e., the same median tidal field strength), the dissolution timescale is nearly independent of the power spectrum slope. However, the dispersion in dissolution timescales, obtained by repeating simulations for different realizations of statistically identical tidal histories, increases with the power spectrum slope. This result means that clusters experiencing high-frequency shocks have more similar mass-loss histories than clusters experiencing low-frequency shocks. The density–mass relationship of the simulated clusters follows a power law with slope between 1.08 and 1.45, except for the lowest normalizations (for which clusters effectively evolve in a static tidal field). Our findings suggest that star cluster evolution can be described statistically from a time-series analysis of its tidal history, which is an important simplification for describing the evolution of the star cluster population during galaxy formation and evolution.

Contrasting the role of historic factors in phylogeograpic patterns in the native Johnny darter (Etheostoma nigrum) and invasive round goby (Neogobius melanostomus) in lower michigan.

Round goby (Neogobius melanostomus) is an invasive fish present in all five Great Lakes and is becoming increasingly common in their tributaries. Johnny darter (Etheostoma nigrum) is a native species that often coexists with N. melanostomus. In this work, historic factors are addressed as a source of genomic variation in study populations of these species. To do this, patterns of variation in the mitochondrial gene NADH dehydrogenase subunit 2 (ND2) were characterized for both species throughout Lower Michigan. Populations of N. melanostomus and E. nigrum were sampled from 17 localities representing both eastern and western basins of Lower Michigan to test the hypothesis that populations differ between the eastern and western basins of the Great Lakes. Neogobius melanostomus populations were largely homogenous with no significant differences detected among populations or between the eastern and western basins. Additionally, N. melanostomus exhibited no evidence of overarching historical genetic structure, consistent with the recent invasion and rapid expansion of this species. Etheostoma nigrum exhibited significant differentiation among local populations; however, similarity among mtDNA haplotypes indicated that differences among populations are recent, suggesting that local forces are a more important factor in shaping patterns of variation than historical factors. Contrary to predictions, there were no significant differences detected between the eastern and western basins of the Great Lakes; however, construction of a neighbor-joining tree with F ST estimates revealed clustering of populations by basin with some anomalies. These anomalies may be the result of recent stream capture events facilitating gene flow between the two basins.

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.