Element Abundances Research Articles

Unveiling the formation channels of stellar halos through their chemical fingerprints

Stellar halos around galaxies contain key information about their formation and assembly history. Using simulations, we can trace the origins of different stellar populations in these halos, contributing to our understanding of galaxy evolution. We aim to investigate the assembly of stellar halos and their chemical abundances in 28 galaxies from cosmological hydrodynamical zoom-in simulations, spanning a broad range of stellar masses, $ Stellar halos were identified using the AM-E method, focusing on the outer regions between the 1.5 optical radius and the virial radius. We divided the stellar populations based on their formation channel: ex-situ, endo-debris, and in-situ, and analyzed their chemical abundances, ages, and spatial distributions. Additionally, we explored correlations between halo mass, metallicity, and alpha-element enrichment. simulations reveal that stellar halos are predominantly composed of accreted material, including both ex-situ and endo-debris stars, in agreement with previous works. The mass fraction of these populations is independent of stellar halo mass, though their metallicities scale linearly with it. Ex-situ stars tend to dominate the outskirts and be more alpha -rich and older, while endo-debris stars are more prevalent at lower radii and tend to be less alpha -rich and slightly younger. Massive stellar halos ($ require a median of five additional satellites to build 90<!PCT!> of their mass, compared to lower-mass halos, which typically need fewer (median of 2.5) and lower-mass satellites and are assembled earlier. The diversity of accreted satellite histories results in well-defined stellar halo mass-metallicity and alpha /Fe Fe/H relations, offering a detailed view of the chemical evolution and assembly history of stellar halos. We find that the alpha /Fe Fe/H is more sensitive to the characteristics and star formation history of the contributing satellites than the stellar halo mass-metallicity relationship.

Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone.

The spatial distribution of organics in geological samples can be used to determine when and how these organics were incorporated into the host rock. Mass spectrometry (MS) imaging can rapidly collect a large amount of data, but ions produced are mixed without discrimination, resulting in complex mass spectra that can be difficult to interpret. Here, we apply unsupervised and supervised machine learning (ML) to help interpret spectra from time-of-flight-secondary ion mass spectrometry (ToF-SIMS) of an organic-carbon-rich mudstone of the Middle Jurassic of England (UK). It was previously shown that the presence of sterane molecular biomarkers in this sample can be detected via ToF-SIMS (Pasterski, M. J. et al., Astrobiology 2023, 23, 936). We use unsupervised ML on scanning electron microscopy-electron dispersive spectroscopy (SEM-EDS) measurements to define compositional categories based on differences in elemental abundances. We then test the ability of four ML algorithms─k-nearest neighbors (KNN), recursive partitioning and regressive trees (RPART), eXtreme gradient boost (XGBoost), and random forest (RF)─to classify the ToF-SIM spectra using (1) the categories assigned via SEM-EDS, (2) organic and inorganic labels assigned via SEM-EDS, and (3) the presence or absence of detectable steranes in ToF-SIMS spectra. In terms of predictive accuracy and balanced accuracy, KNN was the best performing model and RPART the worst. The feature importance, or the specific features of the ToF-SIM spectra used by the models to make classifications, cannot be determined for KNN, preventing posthoc model interpretation. Nevertheless, the feature importance extracted from the other models was useful for interpreting spectra. We determined that some of the organic ions used to classify biomarker containing spectra may be fragment ions derived from kerogen which is abundant in this mudstone sample.

Stellar atmospheric parameters and chemical abundances of ~ 5 million stars from S-PLUS multiband photometry

The APOGEE, GALAH, and LAMOST spectroscopic surveys have substantially contributed to our understanding of the Milky Way by providing a wide range of stellar parameters and chemical abundances. Complementing these efforts, photometric surveys that include narrowband and medium-band filters, such as Southern Photometric Local Universe Survey (S-PLUS), provide a unique opportunity to estimate the atmospheric parameters and elemental abundances for a much larger number of sources, compared to spectroscopic surveys. Our aim is to establish methodologies for extracting stellar atmospheric parameters and selected chemical abundances from S-PLUS photometric data, which cover approximately $3000$ square degrees, by applying seven narrowband and five broadband filters. We used all 66 S-PLUS colors to estimate parameters based on three different training samples from the LAMOST, APOGEE, and GALAH surveys, applying cost-sensitive neural network (NN) and random forest (RF) algorithms. We kept the stellar abundances that lacked corresponding absorption features in the S-PLUS filters to test for spurious correlations in our method. Furthermore, we evaluated the effectiveness of the NN and RF algorithms by using estimated $T_ eff $ and \( g\) values as the input features to determine other stellar parameters and abundances. The NN approach consistently outperforms the RF technique on all parameters tested. Moreover, incorporating $T_ eff $ and \( g\) leads to an improvement in the estimation accuracy by approximately $3<!PCT!>$. We kept only parameters with a goodness-of-fit higher than $50<!PCT!>$. Our methodology allowed us to obtain reliable estimates for fundamental stellar parameters ($T_ eff g\), and Fe/H ) and elemental abundance ratios such as alpha /Fe Al/Fe C/Fe Li/Fe and Mg/Fe for approximately five million stars across the Milky Way, with a goodness-of-fit above $60<!PCT!>$. We also obtained additional abundance ratios, including Cu/Fe O/Fe and Si/Fe . However, these ratios should be used cautiously due to their low accuracy or lack of a clear relationship with the S-PLUS filters. Validation of our estimations and methods was performed using star clusters, Transiting Exoplanet Survey Satellite (TESS) data and Javalambre Photometric Local Universe Survey (J-PLUS) photometry, further demonstrating the robustness and accuracy of our approach. By leveraging S-PLUS photometric data and advanced machine learning techniques, we have established a robust framework for extracting fundamental stellar parameters and chemical abundances from medium-band and narrowband photometric observations. This approach offers a cost-effective alternative to high-resolution spectroscopy. The estimated parameters hold significant potential for future studies, particularly when classifying objects within our Milky Way or gaining insights into its various stellar populations.

Tracing Back a Second-generation Star Stripped from Terzan 5 by the Galactic Bar

Abstract The Galactic bulge hosts the Milky Way’s oldest stars, possibly coming from disrupted globular clusters (GCs) or the bulge’s primordial building blocks, making these stars witnesses to the Galaxy’s early chemical enrichment. The Galactic bar currently dominates the bulge’s region, altering the orbits of objects formed before its formation and complicating the trace of the field stars’ original clusters. Here, we present the discovery of a fossil record of this evolution, SOS1—a star trapped in the bar, exhibiting significant enhancements in nitrogen, sodium, and aluminum, typical of second-generation GC stars. SOS1 also shows an s-process Ce enhancement, suggesting an old age and early enrichment by fast-rotating massive stars in the Galaxy’s earliest phases. With the purpose of finding the SOS1's parent GC, we derive its precise chemodynamical properties by combining high-precision proper motions from Gaia with Apache Point Observatory Galactic Evolution Experiment–detailed chemical abundances. Our analysis suggests that SOS1 was possibly stripped from the GC Terzan 5 by the Galactic bar’s gravitational influence approximately 350 Myr ago. We also found chemical similarities suggesting that SOS1 belonged to the most metal-poor, ancient, and peripheral stellar population of Terzan 5. These results not only support the hypothesis that Terzan 5 is a remnant of a primordial building block of the Galactic bulge but also suggest this cluster continues losing stars to the bar. Our method highlights how powerful the use of chemodynamical properties in the Gaia era is for tracing the Galaxy’s evolutionary history.

Biochar Reduced the Risks of Human Bacterial Pathogens in Soil via Disturbing Quorum Sensing Mediated by Persistent Free Radicals.

Biochar has great potential in reducing the abundance of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) from soil. However, its efficiency in removing other biological pollutants, such as human bacterial pathogens (HBPs) and virulence factor genes (VFGs), is rarely studied. Herein, by pyrolyzing rice straw (RS) and pine wood (PW) at 350 and 700 °C, we prepared a series of biochar (RS350, RS700, PW350, and PW700) and investigated their impacts on the abundance and pathogenicity of HBPs. Compared with PW biochar, RS biochar effectively reduced the abundance of HBPs by 6.3-40.1%, as well as their pathogenicity, evidenced by an 8.2-10.1% reduction in the abundance of VFGs. Mechanistically, more persistent free radicals (PFRs) were formed in RS biochar than that of PW biochar during pyrolysis, and PFRs triggered the degradation of N-butyryl-l-homoserine lactone (C4-HSL) from 1.05 to 0.68 ng/kg, thereby disturbing the quorum sensing (QS) of HBPs. Once the QS was disturbed, the communications among HBPs were hindered, and their virulence factors were reduced, which ultimately lowered the abundance and pathogenicity of HBPs. Collectively, our study provides insights into the role of biochar in decreasing the risks of HBPs, which is significant in the development of biochar-based technologies for soil remediation.

A flavoromics approach to investigate the effect of Saskatoon berry powder on the sensory attributes, acceptability, volatile components, and electronic nose responses of a low-fat frozen yogurt

IntroductionSaskatoon berries are grown in Canada and some northwestern states in the United States, and are notable for containing abundant antioxidant polyphenols, vitamins, metal elements, and fiber. To increase consumer interest in and accessibility to Saskatoon berries, some producers have begun to develop processes for refining Saskatoon berries into a powder with an extended shelf life that can be incorporated into a variety of value-added food products. To assess the desirability of this approach, this study sought to determine how the sensory attributes, consumer acceptability, and volatile and non-volatile composition of a plain, Greek-style frozen yogurt (PY) changed when fortified with 16% Saskatoon berry powder (SBP). Greek-style frozen yogurt was chosen as the food to be fortified for this study due to its low fat and relatively high calcium and protein content as well as its popularity among consumers.ResultsDescriptive analysis of the two yogurt formulations by 11 participants determined that SBY was higher in berry aroma, berry flavor, and sweetness, and lower in cream aroma, dairy aroma, and sourness compared to PY. SBY was lower in iciness and degree of smoothness and higher in viscosity and mouth coating compared to PY. Untrained participants (n = 112), found no significant differences in color, flavor, and overall acceptability between SBY and PY. However, SBY was significantly less acceptable than PY for texture and aroma. Iciness was the most influential variable related to texture acceptability. For aroma acceptability, berry flavor (negatively related) and berry aroma (positively related) were the most influential attributes. The exposure of Saskatoon berry powder (SBP), PY, and SBY to e-nose sensors showed consistencies in replicate analysis (n = 25 measurements/sample), and cross validation of the PCA showed that the model could sort samples into the correct class with 98.7% accuracy. Key volatile organic compounds (VOCs) responsible for berry and fruity aroma in SBP were also found to be retained in the SBY. Several key phenolic compounds with therapeutic effects such as baicalein, chlorogenate, gallic acid, p-coumaric acid, and syringic acid were also identified in both SBP and SBY samples, potentially indicating that the SBY may retain some of the health benefits associated with the consumption of raw Saskatoon berries.

Chemical enrichment in LINERs from MaNGA. I. Tracing the nuclear abundances of oxygen and nitrogen in LINERs with varied ionizing sources

The chemical enrichment in low-ionization nuclear emission-line regions (LINERs) is still an issue with spatial resolution spectroscopic data because we lack studies and because the nature of their ionizing source is uncertain, although they are the most abundant type of active galaxies in the nearby Universe. Considering different scenarios for the ionizing source (hot old stellar populations, active galactic nuclei, or inefficient accretion disks), we analyze the implications of these assumptions to constrain the chemical content of the gas-phase interstellar medium. We used a sample of 105 galaxies from the survey called Mapping Nearby Galaxies at Apache Point Observatory (MaNGA), whose nuclear central spaxels show LINER-like emission. For each scenario we considered, we built a grid of photoionization models (4928 models for each considered ionizing source) that were later used in the open-source code HII-CHI-Mistry . This allowed us to estimate chemical abundance ratios such as 12+log(O/H) or log(N/O) and to constrain the ionization parameters that characterize the ionized interstellar medium in these galaxies. The oxygen abundances in the nuclear region of LINER-like galaxies are spread over a wide range 8.08 $<$ 12+log(O/H) $<$ 8.89, with a median solar value (in agreement with previous studies) when models for active galactic nuclei are considered. Nevertheless, the nitrogen-to-oxygen ratio we derived is much less affected by the assumptions on the ionizing source and indicates suprasolar values (log(N/O) = -0.69). By comparing the different scenarios, we show that if hot old stellar populations caused the ionization of the interstellar medium, a complex picture (e.g., outflows and/or inflows that scale with the galaxy chemical abundance) would be needed to explain the chemical enrichment history, whereas the assumption of active galactic nucleus activity is compatible with the standard scenario that is found in most galaxies.

Impact of nuclear mass models on r-process nucleosynthesis and heavy element abundances in r-process-enhanced metal-poor stars

Because we lack experimental data on extremely neutron-rich nuclei, theoretical values derived from nuclear physics models are essential for the rapid neutron-capture process ($r$-process). Metal-poor stars enriched by the $r$-process offer valuable cases for studying the impact of nuclear physics models on $r$-process nucleosynthesis. This study analyzes four widely used nuclear physics models in detail: the finite-range droplet model, the Hartree-Fock-Bogoliubov, the Duflo-Zuker, and the Weizs$ a $cker-Skyrme model. Theoretical values predicted by the Weizsäcker-Skyrme model are found to agree well with experimental data, and the deviations are significantly smaller than those predicted by other models. The heavy element abundances observed in $r$-process-enhanced metal-poor stars can be accurately reproduced by $r$-process nucleosynthesis simulations using the Weizsäcker-Skyrme model, particularly for the rare-earth elements. This suggests that nuclear data provided by a nuclear physics model such as that of Weizsäcker-Skyrme are both essential and crucial for $r$-process nucleosynthesis studies.

Insight on AGB Mass-Loss and Dust Production from PNe

The asymptotic giant branch (AGB) phase, experienced by low- and intermediate-mass stars (LIMSs), plays a crucial role in galaxies due to its significant dust production. Planetary nebulae (PNe) offer a novel perspective, providing valuable insights into the dust production mechanisms and the evolutionary history of LIMSs. We selected a sample of nine PNe from the Large Magellanic Cloud (LMC), likely originating from single stars. By modeling their spectral energy distributions (SEDs) with photoionization techniques, we successfully reproduced the observed photometric data, spectra, and chemical abundances. This approach enabled us to constrain key characteristics of the central stars (CSs), dust, and gaseous nebulae, which were then compared with predictions from stellar evolution models. By integrating observational data across ultraviolet (UV) to infrared (IR) wavelengths, we achieved a comprehensive understanding of the structure of the PNe in our sample. The results of the SED analysis are consistent with evolutionary models and previous studies that focus on individual components of the PN, such as dust or the gaseous nebula. Our analysis enabled us to determine the metallicity, the progenitor mass of the CSs, and the amount of dust and gas surrounding the CSs, linking these properties to the previous AGB phase. The PN phase provides critical insights into the physical processes active during earlier evolutionary stages. Additionally, we found that higher progenitor masses are associated with greater amounts of dust in the surrounding nebulae but lower amounts of gaseous material compared to sources with lower progenitor masses.

Impact of silicon fertilization on crop growth, productivity and nutrients enhancement in rice: A review

Silicon (Si), constituting around 27.7 % of the Earth's crust by weight, is the second most abundant element after oxygen (47 %). While not considered essential, silicon is beneficial for crop growth, especially for Poaceae family crops. Intensive cultivation or continuous mono-cropping of cereals like rice depletes soil silicon levels, which may lead to decreased rice yields. Rice can absorb and accumulate silicon metabolically, a trait not common in many upland crops. Beyond boosting rice yields, silicon offers multiple benefits, such as enhancing nutrient availability (N, P, K, Ca, Mg, S, Zn), reducing nutrient toxicity (Fe, Mn, P, Al) and mitigating biotic and abiotic stress in rice. Sufficient silicon also stabilizes rice plant culms, reducing lodging risks. Thus, silicon is crucial for plant growth and improving rice productivity at the agronomic level. This review focuses on the relationship between silicon and rice crops, their interactions with other elements and strategies for managing silicon in soils and plants to sustain rice productivity.

Therapeutic Potential of Silicon Supplementation in Age-Related Diseases: A Comprehensive Review

Silicon, the second most abundant element in nature, plays a crucial role in human health. Despite its understated presence, silicon's multifaceted contributions to various physiological processes make it a promising option for therapeutic intervention. Through a comprehensive analysis of recent scientific literature, this review explores the impact of silicon supplementation on bone health, cardiovascular function, metabolic regulation, and neuroprotection. Review methods involved the analysis of scientific publications found in databases such as PubMed and scientific journals, including meta-analyses, randomized trials, and systematic reviews, concerning the scope of medical and nutritional problems, excluding case reports. Key findings suggest that silicon supplementation may positively influence bone mineral density, bone regeneration, and collagen synthesis, offering potential benefits for individuals at risk of osteoporosis and musculoskeletal disorders. Furthermore, evidence highlights silicon's role in regulating cardiovascular health, indicating its potential to mitigate atherosclerosis risk and improve lipid profiles, thereby offering promise in managing conditions such as diabetes and dyslipidemia. Additionally, emerging research underscores silicon's neuroprotective properties, hinting at its potential in combating neurodegenerative diseases like Alzheimer's. This review emphasizes the promising prospect of silicon supplementation as a complementary strategy for enhancing overall health and addressing age-related diseases.

On covering number of C*-algebras

We introduce two new invariants for C*-algebras: the approximate covering number, ACov(A, m), and the unit approximate covering number, UACov(A, m). Additionally, we define three tracial covering numbers: the tracial covering number, the tracial approximate covering number, and the tracial unit approximate covering number. We establish equivalent conditions for a number to be the approximate covering number or the unit approximate covering number. For unital Z-stable C*-algebras and simple, unital, separable AF-algebras, covering numbers are shown to be at most 2. Furthermore, we demonstrate that if a C*-algebra A is weakly purely infinite and ACov(A, m) = 1 for all m ≥ 2, then A is purely infinite. If the approximate covering number or unit approximate covering number of A is 1, then A has an abundance of soft elements, and (Cu(A), ΣA) is ideal-filtered precisely when A has an abundance of soft elements and Cu(A) is ideal-filtered. A similar result holds for unital A with covering number 1. We prove that if Λ is a collection of unital nuclear C*-algebras for which the tracial covering number does not exceed m, then any simple unital C*-algebra A that falls within the corresponding collection of weakly tracially approximated C*-algebras will also possess a tracial covering number of at most m. Similar conclusions are derived for the tracial approximate covering number and the tracial unit approximate covering number, and finally, we explore the relationships among these three types of tracial covering numbers.

Strong Chemical Tagging in FIRE: Intra- and Intercluster Chemical Homogeneity in Open Clusters in Milky Way–like Galaxy Simulations

Open-star clusters are the essential building blocks of the Galactic disk; “strong chemical tagging”—the premise that all star clusters can be reconstructed given chemistry information alone—is a driving force behind many current and upcoming large Galactic spectroscopic surveys. In this work, we characterize the abundance patterns for nine elements (C, N, O, Ne, Mg, Si, S, Ca, and Fe) in open clusters (OCs) in three galaxies (m12i, m12f, and m12m) from the Latte suite of FIRE-2 simulations, to investigate the feasibility of strong chemical tagging in these simulations. We select young massive (≥104.6 M ⊙) OCs formed in the last ∼100 Myr and calculate the intra- and intercluster abundance scatter for these clusters. We compare these results with analogous calculations drawn from observations of OCs in the Milky Way. We find the intracluster scatter of the observations and simulations to be comparable. While the abundance scatter within each cluster is minimal (≲0.020 dex), the mean abundance patterns of different clusters are not unique. We also calculate the chemical difference in intra- and intercluster star pairs and find it, in general, to be so small that it is difficult to distinguish between stars drawn from the same OC or from different OCs. Despite tracing three distinct nucleosynthetic families (core-collapse supernovae, white dwarf supernovae, and stellar winds), we conclude that these elemental abundances do not provide enough discriminating information to use strong chemical tagging for reliable OC membership.

Exoplanets across galactic stellar populations with PLATO

Context. The vast majority of exoplanet discoveries to date have occurred around stars in the solar neighbourhood, with chemical compositions comparable to that of the Sun. However, models suggest that planetary systems in different Galactic environments, with varying dynamical histories and chemical abundances, may exhibit distinct characteristics, which can help improve our understanding of planet formation processes. Aims. This study aims to assess the potential of the upcoming PLATO mission to investigate exoplanet populations around stars in diverse Galactic environments, specifically focusing on the Milky Way thin disk, thick disk, and stellar halo. We aim to quantify PLATO’s ability to detect planets in each environment and determine how these observations could constrain planet formation models. Methods. Beginning with the all-sky PLATO Input Catalogue, we kinematically classified the 2.4 million FGK stars into their respective Galactic components. For the sub-sample of stars in the long-observation LOPS2 and LOPN1 PLATO fields, we estimated planet occurrence rates using the New Generation Planet Population Synthesis dataset. Combining these estimates with a PLATO detection efficiency model, we predicted the expected planet yields for each Galactic environment during a nominal 2+2 year mission. Results. Based on our analysis, PLATO is likely to detect at least 400 exoplanets around the α-enriched thick disk stars. The majority of those planets are expected to be super-Earths and sub-Neptunes with radii between 2 and 10 R⊕ and orbital periods between 2 and 50 days, which is ideal for studying the link between the radius valley and stellar chemistry. For the metal-poor halo, PLATO is likely to detect between 1 and 80 planets with periods between 10 and 50 days, depending on the potential existence of a metallicity threshold for planet formation. The PLATO fields contain more than 3400 potential target stars with [Fe/H] < −0.6, which will help improve our understanding of planets around metal-poor stars. We identified a specific target list of 47 (kinematically classified) halo stars in the high-priority, high-signal-to-noise PLATO P1 sample, offering prime opportunities in the search for planets in metal-poor environments. Conclusions. PLATO’s unique capabilities and large field of view position it as a valuable tool for studying planet formation across the diverse Galactic environments of the Milky Way. By probing exoplanet populations around stars with a varying chemical composition, PLATO will provide helpful insights into the connection between stellar chemistry and planet formation.

Bayesian Model Reconstruction Based on Spectral Line Observations

Spectral line observations encode a wealth of information. A key challenge, therefore, lies in the interpretation of these observations in terms of models to derive the physical and chemical properties of the astronomical environments from which they arise. In this paper, we present pomme, an open-source Python package that allows users to retrieve 1D or 3D models of physical properties, such as chemical abundance, velocity, and temperature distributions of (optically thin) astrophysical media, based on spectral line observations. We discuss how prior knowledge, for instance, in the form of a steady-state hydrodynamics model, can be used to guide the retrieval process, and we demonstrate our methods on both synthetic and real observations of cool stellar winds.

The Advancement of Silicon as A Photovoltaic Material: Historical Accomplishments, Present Efficiency and Future Development

In today's society where energy and environmental issues are increasingly gaining attention, research, development, and utilization of renewable and clean energy continuously attract the interest of academia, environmental protection, and business alike. Its application has already entered people's lives and is gradually expanding its market share. As a result, there is a growing demand for more efficiently designed, environmentally friendly and less costly new energy devices. Among various energy resources such as tidal, wind or geothermal, solar energy stands out as the most widely used clean and renewable resource. Given that solar energy is virtually limitless for humans, the function of solar cells in converting solar energy to electricity demonstrates significant social, environmental, and economic benefits. For the development of solar energy and new research, photovoltaic materials are crucial. Non-toxic and with excellent photovoltaic properties, silicon is the second most abundant element in the Earth's crust and the most abundant in water. Since crystalline silicon solar cells produced for the first time in 1954, silicon has held the most important material position in photovoltaic cell manufacturing and maintained market dominance. The importance of silicon solar cell research, its development and current status are briefly outlined in this paper. It reviews the advantages and disadvantages of existing silicon-based photovoltaic materials and potential future developments. As a photovoltaic material, silicon holds significant advantages compared to others, suggesting a broad scope for both research and commercial prospects in the long term.

Insight into the mechanism of alkyl polyglucose biosurfactants enhancing antibiotics resistance genes removal during biotransformation of waste activated sludge into medium-chain fatty acids

Enhancing the biodegradability of waste activated sludge (WAS) based on environmentally friendly biosurfactants alkyl polyglucose (APG) to promote the recovery of medium-chain fatty acids (MCFAs) is a promising strategy. However, the effect and underlying mechanism of APG on antibiotic resistance genes (ARGs) removal during MCFAs production by WAS anaerobic fermentation are still unclear. This study found that APG significantly enhanced the removal of ARGs during the anaerobic fermentation to produce MCFAs, and the maximum removal efficiency of total ARGs was 95.64 % in 0.3 g APG/g TSS group, which was 46.31 % higher than the control group. The increase in extracellular lactate dehydrogenase, 16S rDNA and ARGs indicated that APG strengthened the lysis of ARGs hosts, thus suppressing the vertical transfer of ARGs caused by host proliferation. Furthermore, APG facilitated the decline in the abundance of mobile gene elements and two-component system genes, which was beneficial to reduce the horizontal transfer of ARGs. Microbial community and network analysis revealed that APG enriched functional microorganism and reduced potential ARGs hosts. Finally, partial least-squares path model analysis further demonstrated that the effectiveness of APG in ARGs removal was related to the restriction of ARGs transfer and change of bacterial community.

Source composition or melting effect: New evidence from Archean komatiites concerning the origin of low highly siderophile element abundances in Earth’s mantle

Source composition or melting effect: New evidence from Archean komatiites concerning the origin of low highly siderophile element abundances in Earth’s mantle

An analysis of the structural changes of the oxygen evolving complex of Photosystem II in the S1 and S3 states revealed by serial femtosecond crystallography.

Photosystem II (PSII) is a unique natural catalyst that converts solar energy into chemical energy using earth abundant elements in water at physiological pH. Understanding the reaction mechanism will aid the design of biomimetic artificial catalysts for efficient solar energy conversion. The Mn4O5Ca cluster cycles through five increasingly oxidized intermediates before oxidizing two water molecules into O2 and releasing protons to the lumen and electrons to drive PSII reactions. The Mn coordination and OEC electronic structure changes through these intermediates. Thus, obtaining a high-resolution structure of each catalytic intermediate would help reveal the reaction mechanism. While valuable structural information was obtained from conventional X-ray crystallography, time-resolution of conventional X-ray crystallography limits the analysis of shorted-lived reaction intermediates. Serial Femtosecond X-ray crystallography (SFX), which overcomes the radiation damage by using ultra short laser pulse for imaging, has been used extensively to study the water splitting intermediates in PSII. Here, we review the state of the art and our understanding of the water splitting reaction before and after the advent of SFX. Furthermore, we analyze the likely Mn coordination in multiple XFEL structures prepared in the dark-adapted S1 state and those following two-flashes which are poised in the penultimate S3 oxidation state based on Mn coordination chemistry. Finally, we summarize the major contributions of the SFX to our understanding of the structures of the S1 and S3 states.

Multi-omics analysis unveils the role of inflammatory cancer-associated fibroblasts in chordoma progression.

Cancer-associated fibroblasts (CAFs) constitute the primary cellular component of the stroma in chordomas, characterized by an abundance of mucinous stromal elements, potentially facilitating their initiation and progression; however, this inference has yet to be fully confirmed. In this study, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), bulk RNA-seq, multiplexed quantitative immunofluorescence (QIF), and in vivo and in vitro experiments were performed to determine the heterogeneity, spatial distribution, and clinical significance of CAFs in chordoma. ScRNA-seq was performed on 87,693 single cells derived from seven tumor samples and four control nucleus pulposus samples. A distinct CAF cluster distinguished by the upregulated expression of inflammatory genes and enriched functionality in activating inflammation-associated cells was identified. Pseudotime trajectory and cell communication analyses suggested that this inflammatory CAF (iCAF) subset originated from normal fibroblasts and interacted extensively with tumors and various other cell types. By integrating the scRNA-seq results with ST, the presence of iCAF in chordoma tissue was further confirmed, indicating their positioning at a distance from the tumor cells. Bulk RNA-seq data analysis from 126 patients revealed a correlation between iCAF signature scores, chordoma invasiveness, and poor prognosis. QIF validation involving an additional 116 patients found that although iCAFs were not in close proximity to tumor cells compared with other CAF subsets, their density correlated with malignant tumor phenotypes and adverse outcomes. In vivo and in vitro experiments further confirmed that iCAFs accelerate the malignant progression of chordomas. These findings could provide insights into the development of novel therapeutic strategies. © 2024 The Pathological Society of Great Britain and Ireland.