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Constraint Optimisation Approaches for Designing Group-Living Captive Breeding Programmes
No full textCaptive breeding programs play a critical role in combating the ongoing biodiversity crisis by preserving the most endangered species and supporting reintroduction efforts. Maintaining the genetic health of captive populations requires careful management to prevent inbreeding and maximize the effective population size. Decisions about which males and females should be bred together are guided by the principle of minimizing relatedness between pairs. Methods to select breeding pairs are well developed, however, some species’ ecology requires them to live in groups, and evaluating optimal groupings of multiple males and females that would be suitable to breed together is a more complex problem. Current computational tools to support the design of group-living captive breeding programs suffer from challenges of scalability and flexibility. In this paper we demonstrate the applicability of constraint programming (CP) approaches to optimize breeding groups to minimize relatedness. We present the example of the Galápagos giant tortoises as the test case used to develop our approach. Exploration of the needs of this captive breeding program has informed the development of our flexible approach to capture the constraints on viable captive breeding program design. Our findings have directly informed the implementation of new group configurations at the captive breeding centre. We further demonstrate that our approach is broadly applicable in other contexts through a second case study, providing multi-objective optimisation of a breeding program of canids. Through these case studies and an ablation study using synthetic datasets, we show that our constraint optimisation approach provides an expressive and generalizable means to support captive breeding program design, including scaling to large captive populations, which are currently intractable using current computational methods
Reliable Detection of SF6Breakdown Byproducts Using 2D Non-Hexagonal Carbon Allotrope Nanosheets
No full textSulfur hexafluoride (SF6), widely used as an insulating gas in the power industry, decomposes during long-term operation into byproducts such as H2S, SO2, SO2F2, and SOF2. Reliable detection of these compounds is essential, since their type and concentration provide diagnostic signatures of faults in gas-insulated switchgear. We employ density functional theory combined with nonequilibrium Green’s function calculations to evaluate pristine two-dimensional carbon allotropes with nonhexagonal rings, namely Graphene+, T-graphene, and Biphenylene, as potential field-effect nanosensors. To characterize the surfaces’ atomic structures, we simulated scanning tunneling microscopy images for filled states. Each surface exhibits a distinct brightness pattern that allows its identification. All interactions occur via physisorption, enabling rapid recovery and device reusability. Graphene+ uniquely identifies SO2and SOF2at a single gate voltage, while T-graphene and Biphenylene selectively detect H2S and SO2. These findings demonstrate that nonhexagonal carbon nanosheets combine high sensitivity, fast recovery, and intrinsic selectivity, underscoring their potential for real-time monitoring of SF6degradation products in power systems
Dissolved organic matter composition differs across water types in Mid-Atlantic and Great Lakes coastal regions
No full textDissolved organic matter (DOM) in coastal surface waters influences local water quality and is an important component of biogeochemical cycling in coastal systems. Many studies have highlighted transformations of DOM along longitudinal river to estuary transects; however, processes that alter DOM composition along lower reaches of rivers and nearshore estuarine waters is poorly understood. The high productivity of coastal environments and their limited representation in Earth System Models further highlights a need for better understanding of DOM transformations along coastal reaches. We leverage a spatially distributed community sampling effort that spans coastal ecosystems across two regions to identify broad spatial drivers of surface water DOM composition and identify transferable trends between regions and surface water types. Samples were collected by community members from 47 locations within the mid-Atlantic and Great Lakes coastal regions. The samples used in this study focused on surface waters from small tidal streams and rivers and nearshore estuarine/lacestuarine environments. The DOM was characterized by excitation-emission fluorescence and Fourier transform ion cyclotron resonance mass spectrometry. We observed that optically active DOM did not display systematic regional trends but instead was primarily distinguishable across water types; optically active DOM was notably lower in nearshore estuarine/lacestuarine environments compared to tidal streams and rivers. At the molecular scale, DOM also did not display systematic regional differences. However, heteroatom containing DOM (e.g., nitrogen, sulfur) distinguished water sources. We further observed strong linkages between DOM and surface water quality parameters, such as pH, that indicate potential for transferability of DOM processing across coastal domains. Collectively, our results highlight a broad similarity and transformation of terrestrial signatures that may be conserved in coastal surface waters across regional scales. Such results have important implications for making scalable predictions of coastal biogeochemical processes and their responses to future perturbations
Sustainable Water Purification Using Green-Synthesized Nanoparticles: A Comparison Between Mono- and Bimetallic Nanoparticle Systems
No full textThis review explores recent advancements in using environmentally benign monometallic nanoparticles (MMNPs) and bimetallic nanoparticles (BMNPs) for photocatalytic water purification, addressing the urgent need for sustainable solutions to global water scarcity. This study systematically analyzes how key photocatalysis variables, including nanocatalyst concentration, dye selection, and pollutant concentration, influence dye degradation outcomes. Standardized experimental conditions utilizing UV irradiation, 10 ppm, methylene blue (MB), and green synthesis routes were employed for comparative assessment. Results indicate that BMNPs, particularly Ag-Cu BMNPs composites, consistently outperform their MMNPs, achieving degradation rates between 90% and 99%, compared to 70%–85% for MMNPs. This superior performance is attributed to synergistic effects between the constituent metals. The review further highlights the advantages of plant-based synthesis methods, which offer a safer, more economical, and stable alternative to conventional chemical methods. By critically evaluating the potential of these NPs under controlled scenarios, this work underscores the transformative potential of engineering BMNPs in advancing next-generation water treatment technologies
Temperature predicts Myotis lucifugus declines and persistence after white-nose syndrome introduction
No full textNetwork construction using sparse Gaussian graphical model based on GWAS summary statistics
No full textIn genome-wide association studies (GWAS), thousands of genetic variants are tested to identify their associations with a phenotype. GWAS have identified many strongly associated genetic variants with phenotypes and have greatly enhanced our understanding of the genetic architecture of complex phenotypes and diseases. Joint analysis of multiple phenotypes can increase the overall statistical power to detect genetic associations and allows for the identification of pleiotropic loci. A phenotype-phenotype network (PPN) represents phenotypes as nodes and the relationships between them as edges, which allows for the visualization of complex relationships between phenotypes, making it easier to identify clusters and helps us intuitively grasp how different phenotypes are related. In this study, we propose a new method to construct a PPN using the sparse Gaussian Graphical Model (sGGM) based on GWAS summary statistics. This approach isolates the direct relationship between phenotypes, making it easier to identify clusters of phenotypes conditional on other phenotypes that reflect more meaningful biological or functional connections. We then applied a community detection method to partition phenotypes into disjoint modules based on the partial correlation matrix of phenotypes. For each module, various multiple phenotype association tests can be employed to test the association between a SNP and phenotypes in that module. We conducted a comprehensive simulation study to compare the performance of several multiple phenotype association tests using the network modules obtained from sGGM, the correlation matrix, as well as using all phenotypes without modular segmentation. The simulation results demonstrated that most of the multiple phenotype association tests based on network modules from sGGM not only effectively control the Type I error rate but also exhibit higher power compared to network modules derived from the correlation matrix and the association tests on all phenotypes without modular segmentation. We applied this method to the GWAS summary statistics of 92 phenotypes derived from Chapter IX (Diseases of the circulatory system) of ICD-10 codes in the UK Biobank. The results showed that applying multiple phenotype association tests using network modules from the sGGM detected more significant SNPs than using the network modules from the correlation matrix
Breeding Habitat Prediction and Nest-site Characteristics of the Fairy Pitta (Pitta nympha) in Geoje-si, South Korea: Insights from a species distribution model
No full textThe fairy pitta (Pitta nympha), listed as Vulnerable by the IUCN, breeds in forested valleys across East Asia, yet its nesting habitat in South Korea remains poorly characterized. This study aimed to identify key environmental drivers of nest-site selection and predict suitable breeding habitats in Geoje-si, a core breeding region in southern Korea. Field surveys conducted from 2019 to 2023 confirmed 47 nests, primarily located on rocky substrates or in the forks of large-diameter trees (≥60 cm DBH). Using presence-only data, we developed a MaxEnt species distribution model incorporating ten ecologically relevant variables. Model optimization identified the LQH feature class with a regularization multiplier of 4 as the best-performing configuration, yielding strong predictive accuracy (AUC = 0.881 ± 0.026). The most influential predictors were the Topographic Wetness Index (TWI), dominant tree species, and slope aspect, suggesting a preference for moist, broadleaf-dominated forests on southwest-facing slopes. Habitat suitability was concentrated in low-disturbance, structurally complex valleys, yet only 2.25 km² (23.8 %) of the predicted high-suitability area overlapped with legally protected zones. These findings underscore a spatial mismatch between ecological value and conservation coverage, highlighting the need to expand protection to climatically buffered breeding habitats outside current protected areas
Thermodynamic Basis of Sugar-Dependent Polymer Stabilization: Informing Biologic Formulation Design
No full textThe stabilization of macromolecules is fundamental to developing biological formulations, such as vaccines and protein therapeutics. In this study, we employ coarse-grained polymer models to investigate the impact of four sugars: α-glucose, β-fructose, trehalose, and sucrose on macromolecule stability. Free energy decomposition and preferential interaction analysis indicate that polymer-sugar interactions favor folding at low concentrations while driving unfolding at higher concentrations. In contrast, the polymer–solvent soft interaction entropy consistently favors unfolding across all sugar concentrations under study. At low sugar concentrations, polymer–solvent interactions predominantly govern stabilization, whereas at higher concentrations, entropic penalties dictate polymer stability. Local mixing entropy demonstrates that binary sugar mixtures introduce entropic contributions that preferentially stabilize the folded state. These findings contribute to a more nuanced understanding of sugar-based excipient stabilization mechanisms, offering guidance for the rational design of stable biological formulations
Energy Spectrum of Ultrahigh-Energy Cosmic Rays across Declinations -90° to +44.8° as Measured at the Pierre Auger Observatory
No full textThe energy spectrum of cosmic rays above 2.5 EeV has been measured across the declination range -90°≤δ≤+44.8° using ∼310 000 events accrued at the Pierre Auger Observatory from an exposure of (104 900±3 100) km2 sr yr. No significant variations of energy spectra with declination are observed, after allowing or not for nonuniformities across the sky arising from the well-established dipolar anisotropies in the arrival directions of ultrahigh-energy cosmic rays. The instep feature in the spectrum at ≃10 EeV reported previously is now established at a significance above 5σ. Within the statistics, the energy spectra are indistinguishable across declinations so disfavoring an origin for the instep from a few distinctive sources
Electronic structure prediction of medium and high entropy alloys across composition space
No full textWe propose machine learning (ML) models to predict the electron density — the fundamental unknown of a material’s ground state — across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of descriptors and sampled compositions required for accurate prediction grows rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to the strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries while keeping descriptor-vector size nearly constant as alloy complexity increases. Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space