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Chemical cartography with LAMOST and Gaia reveal azimuthal and spiral structure in the Galactic disc
Chemical Cartography, or mapping, of our Galaxy has the potential to fully transform our view of its structure and formation. In this work, we use chemical cartography to explore the metallicity distribution of OBAF-type disc stars from the LAMOST survey and a complementary sample of disc giant stars from Gaia DR3. We use these samples to constrain the radial and vertical metallicity gradients across the Galactic disc. We also explore whether there are detectable azimuthal variations in the metallicity distribution on top of the radial gradient. For the OBAF-type star sample from LAMOST, we find a radial metallicity gradient of Δ[Fe/H]/ΔR ~–0.078 ± 0.001 dex kpc –1 in the plane of the disc and a vertical metallicity gradient of Δ[Fe/H]/ΔZ ~–0.15 ± 0.01 dex kpc –1 in the solar neighbourhood. The radial gradient becomes shallower with increasing vertical height, while the vertical gradient becomes shallower with increasing Galactocentric radius, consistent with other studies. We also find detectable spatially dependent azimuthal variations on top of the radial metallicity gradient at the level of ~0.10 dex. Interestingly, the azimuthal variations appear be close to the Galactic spiral arms in one data set (Gaia DR3) but not the other (LAMOST). Furthermore, these results suggest that there is azimuthal structure in the Galactic metallicity distribution and that in some cases it is co-located with spiral arms.
Two-dimensional phase cartography for high-harmonic spectroscopy
Extremely nonlinear spectroscopy based on high-order-harmonic generation has become a powerful investigation method for attosecond dynamics in gas and solid targets. In particular, the phase of harmonic emission was shown to carry profound insight into atomic and molecular structure and dynamics. However, current techniques offer phase measurements only along specific directions, thus providing partial characterization. Here we report on a new approach combining optical and quantum interferometers measuring along two dimensions the intensity and phase of harmonic emission from aligned molecules in the exact same experimental conditions. This two-dimensional cartography technique measures the phase with no arbitrary offset and no uncertainty on its sign. Measurements along different dimensions can be combined in two ways: either a single mapping or a redundant mapping allowing high-precision phase recovery using a Shack–Hartmann-like algorithm. We demonstrate both methods in a nitrogen test case, which allows disentangling structural and dynamical effects. Two-dimensional phase cartography paves the way to high-resolution high-harmonic spectroscopy for applications such as quantum orbital tomography and attosecond charge migration in molecules.
National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions
A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts’ opinion on the development and application of multi-omic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular and functional aberrations within the lesion and within individual cells. Additionally, single-cell proteomic data will be essential for the establishment of new tools that with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included: • The best way/s to analyze single-cells from fresh and preserved tissue • Detection and analysis of secreted molecules and from single cells, especially from a tissue slice • Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment • Multi-omic integration of data to support and inform proteomic measurements • Subcellular organelles – identifying abnormal structure, function, distribution and location within individual premalignant and malignant cells • How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post translational modifications (PTM) • The depth of coverage measured concurrently using single-cell techniques • Quantitation - absolute or semiquantitative? • Single methodology or multiplexed combinations? • Application of analytical methods for identification of biologically significant subsets • Data visualization of N-dimensional datasets • How to construct intercellular signaling networks in individual cells within premalignant tumor microenvironments (TME) • Associations between intrinsic cellular processes and extrinsic stimuli • How to predict cellular responses to stress inducing stimuli • Identification of new markers for prediction of progression from precursor, benign and localized lesions to invasive cancer, based on spatial and temporal changes within individual cells • Identification of new targets for immunoprevention or immunotherapy – identification of neoantigens and surfactome of individual cells within a lesion
Electron Cartography in Clusters
Abstract Deconvoluting the atom‐specific electron density within polynuclear systems remains a challenge. A multiple‐wavelength anomalous diffraction study on four clusters that share the same [Co 6 Se 8 ] core was performed. Two cluster types were designed, one having a symmetric ligand sphere and the other having an asymmetric ligand sphere. It was found that in the neutral, asymmetric, CO‐bound cluster, the Co−CO site is more highly oxidized than the other five Co atoms; when an electron is removed, the hole is distributed among the Se atoms. In the neutral, symmetric cluster, the Co atoms divide by electron population into two sets of three, each set being meridional; upon removal of an electron, the hole is distributed among all the Co atoms. This ligand‐dependent tuning of the electron/hole distribution relates directly to the performance of clusters in biological and synthetic systems.
Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints
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Cartography of opportunistic pathogens and antibiotic resistance genes in a tertiary hospital environment
Although disinfection is key to infection control, the colonization patterns and resistomes of hospital-environment microbes remain underexplored. We report the first extensive genomic characterization of microbiomes, pathogens and antibiotic resistance cassettes in a tertiary-care hospital, from repeated sampling (up to 1.5 years apart) of 179 sites associated with 45 beds. Deep shotgun metagenomics unveiled distinct ecological niches of microbes and antibiotic resistance genes characterized by biofilm-forming and human-microbiome-influenced environments with corresponding patterns of spatiotemporal divergence. Quasi-metagenomics with nanopore sequencing provided thousands of high-contiguity genomes, phage and plasmid sequences (>60% novel), enabling characterization of resistome and mobilome diversity and dynamic architectures in hospital environments. Phylogenetics identified multidrug-resistant strains as being widely distributed and stably colonizing across sites. Comparisons with clinical isolates indicated that such microbes can persist in hospitals for extended periods (>8 years), to opportunistically infect patients. These findings highlight the importance of characterizing antibiotic resistance reservoirs in hospitals and establish the feasibility of systematic surveys to target resources for preventing infections.
Core Cartography
This is 200x microscope image of a cross section of a magnetic core.
Chemical Cartography. I. A Carbonicity Map of the Galactic Halo
We present the first map of carbonicity, [C/Fe], for the halo system of the Milky Way, based on a sample of over 100,000 main-sequence turnoff stars with available spectroscopy from the Sloan Digital Sky Survey. This map, which explores distances up to 15 kpc from the Sun, reveals clear evidence for the dual nature of the Galactic halo, based on the spatial distribution of stellar carbonicity. The metallicity distribution functions of stars in the inner- and outer-halo regions of the carbonicity map reproduce those previously argued to arise from contributions of the inner- and outer-halo populations, with peaks at [Fe/H] = −1.5 and −2.2, respectively. From consideration of the absolute carbon abundances for our sample, A (C), we also confirm that the carbon-enhanced metal-poor (CEMP) stars in the outer-halo region exhibit a higher frequency of CEMP-no stars (those with no overabundances of heavy neutron-capture elements) than of CEMP- s stars (those with strong overabundances of elements associated with the s -process), whereas the stars in the inner-halo region exhibit a higher frequency of CEMP- s stars. We argue that the contrast in the behavior of the CEMP-no and CEMP- s fractions in these regions arises from differences in the mass distributions of the mini-halos from which the stars of the inner- and outer-halo populations formed, which gives rise in turn to the observed dichotomy of the Galactic halo.
Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances
We apply a novel statistical analysis to measurements of 16 elemental abundances in 34,410 Milky Way disk stars from the final data release (DR17) of APOGEE-2. Building on recent work, we fit median abundance ratio trends [X/Mg] versus [Mg/H] with a 2-process model, which decomposes abundance patterns into a "prompt" component tracing core-collapse supernovae and a "delayed" component tracing Type Ia supernovae. For each sample star, we fit the amplitudes of these two components, then compute the residuals Δ[X/H] from this two-parameter fit. The rms residuals range from ~0.01–0.03 dex for the most precisely measured APOGEE abundances to ~0.1 dex for Na, V, and Ce. The correlations of residuals reveal a complex underlying structure, including a correlated element group comprised of Ca, Na, Al, K, Cr, and Ce and a separate group comprised of Ni, V, Mn, and Co. Selecting stars poorly fit by the 2-process model reveals a rich variety of physical outliers and sometimes subtle measurement errors. Residual abundances allow for the comparison of populations controlled for differences in metallicity and [α/Fe]. Relative to the main disk (R = 3–13 kpc), we find nearly identical abundance patterns in the outer disk (R = 15–17 kpc), 0.05–0.2 dex depressions of multiple elements in LMC and Gaia Sausage/Enceladus stars, and wild deviations (0.4–1 dex) of multiple elements in ω Cen. The residual abundance analysis opens new opportunities for discovering chemically distinctive stars and stellar populations, for empirically constraining nucleosynthetic yields, and for testing chemical evolution models that include stochasticity in the production and redistribution of elements.
Interface Modalities
Because digital maps are dynamic in their output and responsive to user input, a central yet relatively recent concern in modern cartography and visualization is the design of the user interface that connects the user to the map (and thus to the information contained within the map). Digital maps are primarily driven by point-and-clicks of a mouse and primarily display the results of the interaction visually on a screen. However, there exist several other modalities that can be employed to interact with a digital map. While visual output and mouse input will likely endure, novel approaches for haptic and audio feedback, gesture and speech input, and other modalities have been developed, particularly in response to the demands of diverse uses, and needs of diverse users of maps. These comprise the multiple interface modalities associated with cartography and geographic visualization.
Novel real-time 3D radiological mapping solution for ALARA maximization, D and D assessments and radiological management
Good management of dismantling and decontamination (D and D) operations and activities is requiring safety, time saving and perfect radiological knowledge of the contaminated environment as well as optimization for personnel dose and minimization of waste volume. In the same time, Fukushima accident has imposed a stretch to the nuclear measurement operational approach requiring in such emergency situation: fast deployment and intervention, quick analysis and fast scenario definition. AREVA, as return of experience from his activities carried out at Fukushima and D and D sites has developed a novel multi-sensor solution as part of his D and D research, approach and method, a system with real-time 3D photo-realistic spatial radiation distribution cartography of contaminated premises. The system may be hand-held or mounted on a mobile device (robot, drone, e.g). In this paper, we will present our current development based on a SLAM technology (Simultaneous Localization And Mapping) and integrated sensors and detectors allowing simultaneous topographic and radiological (dose rate and/or spectroscopy) data acquisitions. This enabling technology permits 3D gamma activity cartography in real-time. (authors)
Evolution and Antigenic Advancement of N2 Neuraminidase of Swine Influenza A Viruses Circulating in the United States following Two Separate Introductions from Human Seasonal Viruses
Two separate introductions of human seasonal N2 neuraminidase genes were sustained in U.S. swine since 1998 (N2-98) and 2002 (N2-02). Herein, we characterized the antigenic evolution of the N2 of swine influenza A virus (IAV) across 2 decades following each introduction. The N2-98 and N2-02 expanded in genetic diversity, with two statistically supported monophyletic clades within each lineage. To assess antigenic drift in swine N2 following the human-to-swine spillover events, we generated a panel of swine N2 antisera against representative N2 and quantified the antigenic distance between wild-type viruses using enzyme-linked lectin assay and antigenic cartography. The antigenic distance between swine and human N2 was smallest between human N2 circulating at the time of each introduction and the archetypal swine N2. However, sustained circulation and evolution in swine of the two N2 lineages resulted in significant antigenic drift, and the N2-98 and N2-02 swine N2 lineages were antigenically distinct. Although intralineage antigenic diversity was observed, the magnitude of antigenic drift did not consistently correlate with the observed genetic differences. These data represent the first quantification of the antigenic diversity of neuraminidase of IAV in swine and demonstrated significant antigenic drift from contemporary human seasonal strains as well as antigenic variation among N2 detected in swine. These data suggest that antigenic mismatch may occur between circulating swine IAV and vaccine strains. Consequently, consideration of the diversity of N2 in swine IAV for vaccine selection may likely result in more effective control and aid public health initiatives for pandemic preparedness.
Wind and solar resource data sets
The range of resource data sets spans from static cartography showing the mean annual wind speed or solar irradiance across a region to high temporal and high spatial resolution products that provide detailed information at a potential wind or solar energy facility. These data sets are used to support continental-scale, national, or regional renewable energy development; facilitate prospecting by developers; and enable grid integration studies. This review first provides an introduction to the wind and solar resource data sets, then provides an overview of the common methods used for their creation and validation. A brief history of wind and solar resource data sets is then presented, followed by areas for future research.
Towards the next generation of Geospatial Artificial Intelligence
Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote sensing, urban computing, Earth system science, cartography, and geospatial semantics. Finally, we highlight several unique future research directions of GeoAI which are classified into two groups: GeoAI method development challenges and GeoAI Ethics challenges. Topics include heterogeneity-aware GeoAI, knowledge-guided GeoAI, spatial representation learning, geo-foundation models, fairness-aware GeoAI, privacy-aware GeoAI, as well as interpretable and explainable GeoAI. We hope our review of GeoAI’s past, present, and future is comprehensive and can enlighten the next generation of GeoAI research.
A NEA review on innovative structural materials solutions, including advanced manufacturing processes for nuclear applications based on technology readiness assessment
The Nuclear Energy Agency (NEA) Expert Group on Innovative Structural Materials (EGISM) was established in 2008 under the guidance of the Nuclear Science Committee (NSC). Its objectives are to conduct joint and comparative studies to support the development, selection and characterisation of innovative structural materials that can be implemented in advanced nuclear fuel cycles, under long service lifetime and extreme conditions, such as high temperature, high dose/dose rate and corrosive chemical environments. In this context of growing interest and initiatives, the EGISM initiated at the beginning of 2018 an activity among its members to: Identify, in a non-exhaustive way, the currently existing programs on innovative materials and fabrication processes in NEA member countries and China; Establish a first cartography of the activities that are underway on these topics and identify common subjects and thematic; Propose a Technology Readiness Level scale to estimate the maturity of both innovative materials and fabrication processes; Carry out a reflection on what the enablers are to quickly climb this TRL scale, as well as the obstacles, in order to identify solutions to overcome them. This paper first gives definitions shared between the EGISM members on what are considered as advanced structural materials solutions. Next, some international initiatives for the accelerated development of high performance materials are presented both in non-nuclear and nuclear fields. Then, the methodology adopted for technology readiness assessment is explained. A non-exhaustive synthesis of the projects identified among the EGISM members on innovative structural materials and advanced manufacturing solutions such as additive manufacturing is presented. The TRL level of these projects is evaluated. A focus is also made on some of these projects to illustrate and explain the TRLs chosen as well as highlighting enablers or obstacles identified to climbing up the TRL scale.
Space charge distributions in insulating polymers: A new non-contacting way of measurement
A new technique for the determination of space charge profiles in insulating polymers is proposed. Based on the evolution of an existing thermal wave technique called Focused Laser Intensity Modulation Method ((F)LIMM), it allows non-contact measurements on thin films exhibiting an internal charge to be studied. An electrostatic model taking into account the new sample-cell geometry proposed was first developed. It has been shown, in particular, that it was theoretically possible to calculate the internal charge from experimental measurements while allowing an evaluation of the air layer appearing between the sample and the electrode when non-contact measurements are performed. These predictions were confirmed by an experimental implementation for two thin polymer samples (25 μm-polyvinylidenefluoride and 50 μm-polytetrafluoroethylene (PTFE)) used as tests. In these cases, minimum air-layer thickness was determined with an accuracy of 3% and 20%, respectively, depending on the signal-to-noise ratio during the experimental procedure. In order to illustrate the reachable possibilities of this technique, 2D and 3D cartographies of a negative space charge implanted by electron beam within the PTFE test sample were depicted: like in conventional (F)LIMM, a multidimensional representation of a selectively implanted charge remains possible at a few microns depth, but using a non-contacting way of measurement.
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis
Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.