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An approach is disclosed that tracks memory transactions by a node. The approach establishes a transaction processing state corresponding to common virtual addresses accessed by a processing threads. Transactions are executed by the threads. A selected transaction is allowed to complete. In response to detecting a conflict in the transaction processing state, completion of a non-selected transaction is inhibited.
Broad immune responses are needed to mitigate viral evolution and escape. To induce antibodies against conserved receptor-binding domain (RBD) regions of SARS-like betacoronavirus (sarbecovirus) spike proteins that recognize SARS-CoV-2 variants of concern and zoonotic sarbecoviruses, we developed mosaic-8b RBD nanoparticles presenting eight sarbecovirus RBDs arranged randomly on a 60-mer nanoparticle. Mosaic-8b immunizations protected animals from challenges from viruses whose RBDs were matched or mismatched to those on nanoparticles. Here, we describe neutralizing mAbs isolated from mosaic-8b-immunized rabbits, some on par with Pemgarda, the only currently FDA-approved therapeutic mAb. Deep mutational scanning, in vitro selection of spike resistance mutations, and single-particle cryo-electron microscopy structures of spike–antibody complexes demonstrated targeting of conserved RBD epitopes. Rabbit mAbs included critical D-gene segment RBD-recognizing features in common with human anti-RBD mAbs, despite rabbit genomes lacking an equivalent human D-gene segment, thus demonstrating that the immune systems of humans and other mammals can utilize different antibody gene segments to arrive at similar modes of antigen recognition. These results suggest that animal models can be used to elicit anti-RBD mAbs with similar properties to those raised in humans, which can then be humanized for therapeutic use, and that mosaic RBD nanoparticle immunization coupled with multiplexed screening represents an efficient way to generate and select broadly cross-reactive therapeutic pan-sarbecovirus and pan-SARS-CoV-2 variant mAbs.
The primary objective of the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission is to return to Eartha pristine sample of carbonaceous material from the primitive asteroid (101955) Bennu. To support compositional mapping of Bennu as part of sample site selection and characterization, we tested 95 spectral indices on visible to near infrared laboratory reflectance data from minerals and carbonaceous meteorites. Our aim was to determine which indices reliably identify spectral features of interest. Most spectral indices had high positive detection rates when applied to spectra of pure, single-component materials. The meteorite spectra have fewer and weaker absorption features and, as a result, fewer detections with the spectral indices. Indices targeting absorptions at 0.7 and 2.7–3lm, which are attributable to hydrated minerals, were most successful for the meteorites. Based on these results, we identified a set of 17 indices that are most likely to be useful at Bennu. These indices detect olivines, pyroxenes, carbonates, water/OH-bearing minerals, serpentines, ferric minerals, and organics. Particle size and albedo are known to affect band depth but had a negligible impact on interpretive success with spectral indices. Preliminary analysis of the disk-integrated Bennu spectrum with these indices is consistent with expectations given the observed absorption near 3lm. Our study prioritizes spectral indices to be used for OSIRIS-REx spectral analysis and mapping and informs the reliability of all index-derived data products, including a science value map for sample site selection.
The primary objective of the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission is to return to Earth a pristine sample of carbonaceous material from the primitive asteroid (101955) Bennu. To support compositional mapping of Bennu as part of sample site selection and characterization, we tested 95 spectral indices on visible to near infrared laboratory reflectance data from minerals and carbonaceous meteorites. Our aim was to determine which indices reliably identify spectral features of interest. Most spectral indices had high positive detection rates when applied to spectra of pure, single-component materials. The meteorite spectra have fewer and weaker absorption features and, as a result, fewer detections with the spectral indices. Indices targeting absorptions at 0.7 and 2.7–3 µm, which are attributable to hydrated minerals, were most successful for the meteorites. Based on these results, we identified a set of 17 indices that are most likely to be useful at Bennu. These indices detect olivines, pyroxenes, carbonates, water/OH-bearing minerals, serpentines, ferric minerals, and organics. Particle size and albedo are known to affect band depth but had a negligible impact on interpretive success with spectral indices. Preliminary analysis of the disk-integrated Bennu spectrum with these indices is consistent with expectations given the observed absorption near 3 lm. Our study prioritizes spectral indices to be used for OSIRIS-REx spectral analysis and mapping and informs the reliability of all index-derived data products, including a science value map for sample site selection.
The Nakhla meteorite possesses discrete, well defined, structurally coherent morphologies of carbonaceous phases present within iddingsite alteration zones. Based upon both isotopic measurements and analysis of organic phases the presence of pre-terrestrial organics is now recognized. Within the microcrystalline layers of Nakhla s iddingsite, discrete clusters of salt crystals are present. These salts are predominantly halite (NaCl) with minor MgCl2 crystals. Some CaSO4, likely gypsum, appears to be partially intergrown with some of the halite. EDX mapping shows discrete C-rich features are interspersed among these crystals. A hollow semi-spherical bowl structure (~ 3 m ) has been identified and analyzed after using a focused ion beam (FIB) to cut a transverse TEM thin section of the feature and the underlying iddingsite. TEM/EDX analysis reveals that the feature is primarily carbonaceous containing C with lesser amounts of Si, S, Ca, Cl, F, Na, and minor Mn and Fe; additionally a small peak consistent with N, which has been previously seen in Nakhla carbonaceous matter, is also present. Selected area electron diffraction (SAED) shows that this C-rich material is amorphous (lacking any long-range crystallographic order) and is not graphite or carbonate. Micro-Raman spectra acquired from the same surface from which the FIB section was extracted demonstrate a typical kerogen-like D and G band structure with a weak absorption peak at 1350 and a stronger peak at 1600/cm. The C-rich feature is intimately associated with both the surrounding halite and underlying iddingsite matrix. Both iddingsite and salts are interpreted as having formed as evaporate assemblages from progressive evaporation of water bodies on Mars. This assemblage, sans the carbonaceous moieties, closely resembles iddingsite alteration features previously described which were interpreted as indigenous Martian assemblages. These distinctive macromolecular carbonaceous structures in Nakhla may represent one of the sources of the high molecular weight organic material previously identified in Nakhla. While we do not speculate on the origin of these unique carbonaceous structures, we note that the significance of such observations is that it may allow us to construct a C-cycle for Mars based on the C chemistry of the Martian meteorites with obvious implications for astrobiology and the prebiotic evolution of Mars. In any case, our observations strongly suggest that organic C exists as micrometersize, discrete structures on Mars.
Abstract Predicting the structure of multi‐protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether it is possible to learn characteristics of accurate models directly from atomic coordinates of protein complexes, with no prior assumptions. Here we introduce a machine learning method that learns directly from the 3D positions of all atoms to identify accurate models of protein complexes, without using any precomputed physics‐inspired or statistical terms. Our neural network architecture combines multiple ingredients that together enable end‐to‐end learning from molecular structures containing tens of thousands of atoms: a point‐based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. When used in combination with previously developed scoring functions, our network substantially improves the identification of accurate structural models among a large set of possible models. Our network can also be used to predict the accuracy of a given structural model in absolute terms. The architecture we present is readily applicable to other tasks involving learning on 3D structures of large atomic systems.
Fatigue short-cracks in Mg alloys display complex growth behavior due to high plastic anisotropy and crack path dependence on local microstructural features. In this study, the three-dimensional crystallography of short-crack paths in Mg alloy WE43 was characterized by mapping near-field high-energy X-ray diffraction microscopy (HEDM) reconstructed grain maps to high-resolution X-ray CT reconstructions of the fracture surfaces in the crack initiation and short-crack growth regions of six ultrasonic fatigue specimens. Crack–grain–boundary intersections were analyzed at 81 locations across the six crack paths. The basal intragranular, non-basal intragranular, or intergranular character of short-crack growth following each boundary intersection was correlated to crystallographic and geometric parameters of the trailing and leading grains, three-dimensional grain boundary plane, and advancing crack front. The results indicate that crack paths are dependent on the combined crystallographic and geometric character of the local microstructure, and crack path prediction can be improved by use of dimensionality reduction on subsets of high-linear-correlation microstructural parameters.
From neutron user principal investigator: We kindly request the public release of three neutron imaging datasets through ONCat. All datasets were collected from two forensic specimens, B12W and M8N, sectioned from zinc-filled steel-wire sockets recovered from the collapsed Arecibo Telescope. The dataset titled “Neutron radiographs of B12W and M8N socket sections of the Arecibo telescope” contains normalized two-dimensional (2D) neutron radiographs of the specimens, showing the geometry and spatial distribution of the steel wires embedded within the zinc matrix, as well as internal features such as voids and cracks. The dataset titled “Neutron computed tomography of B12W and M8N socket sections of the Arecibo telescope” contains normalized 2D neutron projection images acquired over a range of specimen rotation angles for one selected region of each specimen. These projection images were used to reconstruct three-dimensional (3D) tomographic volumes that reveal the embedded-wire geometry and internal defects. The dataset titled “Bragg edge imaging (BEI) of B12W and M8N socket sections of the Arecibo telescope” contains six time-of-flight (TOF) neutron imaging datasets, three from each specimen, acquired at regions of interest selected based on the radiographs. The spatially resolved 2D TOF images show the zinc matrix and embedded steel wires, and the wavelength-dependent neutron transmission data were used to characterize crystallographic texture within the zinc. All components and their condition are in the public domain as they are the property of the National Science Foundation (NSF). The neutron imaging data, part geometries, and detailed forensic information have been widely published in the Arecibo Telescope Collapse Forensic Report by Thornton Tomasetti Engineers and others (NASA report and NASEM report).
On the one hand, locating the right dataset, then figuring out how to use it, is a daunting task that is familiar to almost any scientist or graduate student in the fields of Earth system science. On the other hand, the ability to explore authentic Earth system science data, through inquiry-based education, is an important goal in US national education standards. Fortunately, in the digital age, tools are emerging that can make such data exploration commonplace at all educational levels. This paper describes the conception and development of one project that aims to bridge this gap: Mentoring and inquiry using NASA Data on Atmospheric and Earth science for Teachers and Amateurs (MY NASA DATA; mynasadata.larc.nasa.gov). With funding from NASA's Science Mission Directorate, this project was launched in early 2004 with the aim of developing microsets and identifying other enablers for making data accessible. A key feature of the project is a Live Access Server, the first educational implementation of this open source software, developed by NOAA, that makes it possible to explore multiple data formats through a single interface. This powerful tool is made more useful to the primary target audiences (K-12 and amateur scientists) through careful selection of the data offered, user-friendly explanations of the tool itself, and age-appropriate explanations of the parameters. However experience already shows that graduate students and even practicing scientists can also make use of this resource. The website also hosts teacher-contributed lesson plans, and seeks to feature reports of research projects that use the data.
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We present a learning-based, demand-supply-coupled optimization model for the charging station location problem (CSLP), aiming to integrate the concept of electric vehicle (EV) charging demand management into the planning of charging infrastructures. In stage one, a gradient boosting-based learning model is developed to predict the charging demand of a charging station based on 15 defined features. Next, in stage two, a demand–supply-coupled CSLP model is developed to optimize the total charging usage rates of both existing and newly selected charging stations. We design a gradient-based stochastic spatial search algorithm to solve the proposed model. A case study with 6-year charging event data from Kansas City Missouri is performed. Results show that the proposed method can generate satisfactory charging demand predictions, and can increase charging usage rates by 14%, outperforming two benchmark approaches. Furthermore, the results of this research are poised to guide agencies in identifying optimal locations for new charging stations.
Here, the development of many optical quantum technologies depends on the availability of solid-state single quantum emitters with near-perfect optical coherence. However, a standing issue that limits systematic improvement is the significant sample heterogeneity and lack of mechanistic understanding of microscopic energy flow at the single emitter level and ultrafast timescales. Here we develop solution-phase single-particle pump-probe spectroscopy with photon correlation detection that captures sample-averaged dynamics in single molecules and/or defect states with unprecedented clarity at femtosecond resolution. We apply this technique to single quantum emitters in two-dimensional hexagonal boron nitride, which suffers from significant heterogeneity and low quantum efficiency. From millisecond to nanosecond timescales, the translation diffusion, metastable-state-related bunching shoulders, rotational dynamics, and antibunching features are disentangled by their distinct photon-correlation timescales, which collectively quantify the normalized two-photon emission quantum yield. Leveraging its femtosecond resolution, spectral selectivity and ultralow noise (two orders of magnitude improvement over solid-state methods), we visualize electron-phonon coupling in the time domain at the single defect level, and discover the acceleration of polaronic formation driven by multi-electron excitation. Corroborated with results from a theoretical polaron model, we show how this translates to sample-averaged photon fidelity characterization of cascaded emission efficiency and optical decoherence time. Our work provides a framework for ultrafast spectroscopy in single emitters, molecules, or defects prone to photoluminescence intermittency and heterogeneity, opening new avenues of extreme-scale characterization and synthetic improvements for quantum information applications.
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ABSTRACT The Nancy Grace Roman Space Telescope (RST), with its field of view and high sensitivity will make surveys of cosmological large-scale structure possible at high redshifts. We investigate the possibility of detecting baryon acoustic oscillations (BAO) at redshifts z > 7.6 for use as a standard ruler. We use data from the hydrodynamic simulation bluetides in conjunction with the gigaparsec-scale Outer Rim simulation and a model for patchy reionization to create mock RST High Latitude Survey grism data for Lyman α emission line selected galaxies at redshifts z = 7.4 to z = 10, covering 2280 deg2. We measure the monopoles of galaxies in the mock catalogues and fit the BAO features. We find that for a line flux of $L = 7\times 10^{-17} \ {\rm erg\, s^{-1}\, cm}^{-2}$, the 5σ detection limit for the current design, the BAO feature is partially detectable (measured in three out of four survey quadrants analysed independently). The resulting root mean square error on the angular diameter distance to z = 7.7 is 7.9 ${{\ \rm per\ cent}}$. If we improve the detection sensitivity by a factor of two (i.e. $L = 3.5\times 10^{-17} \ {\rm erg\, s^{-1}\, cm}^{-2}$), the distance error reduces to $1.4{{\ \rm per\ cent}}$. We caution that many more factors are yet to be modelled, including dust obscuration, the damping wing due to the intergalactic medium, and low redshift interlopers. If these issues do not strongly affect the results, or different observational techniques (such as use of multiple lines) can mitigate them, RST, or similar instruments may be able to constrain the angular diameter distance to the high redshift universe.
The polyatomic molecules YbOH and YbOCH 3 have been recognized as being of potential value for spectroscopic experiments that explore charge-parity and time reversal symmetry violation effects. These measurements require very high precision, which, in turn, will necessitate that the molecules be manipulated at ultra-cold temperatures. Both YbOH and YbOCH 3 have electronic transitions that appear suitable for laser cooling ($\tilde{A}$ 2 Π 1/2 -$\tilde{X}$ 2 Σ + and $\tilde{A}$2E 1/2 -$\tilde{X}$ 2 A 1 , respectively) but the currently available spectroscopic data is not sufficient to determine the extent to which population leaks may compromise the optical cooling processes. A further complication is that the quantum states of interest for these measurements will need to be selectively populated. The $\tilde{A}$-$\tilde{X}$ band systems of both YbOH and YbOCH 3 show evidence of vibronic perturbations, such that there are unassigned vibronic features at energies that are just above the origin bands. Here, in the present study we have recorded spectra for the $\tilde{A}$ 2 Π 1/2 -$\tilde{X}$ 2 Σ + transition of jet-cooled YbOD to facilitate the vibronic assignments. In addition, spectra for the $\tilde{B}$ 2 Σ + - $\tilde{X}$ 2 Σ + transition of YbOH were recorded, establishing the origin band at 20473.8 cm -1 . Previously, the reaction of Yb with CH 3 OH has been used to generate gas-phase YbOCH 3 . As this reaction also yields YbOH, there have been complications in spectroscopic studies of YbOCH 3 due to overlap of the $\tilde{A} -\tilde{X}$ band systems. To identify specific regions of overlap, resonantly enhanced two-photon ionization spectra were recorded using mass-resolved detection of the YbOH + and YbOCH 3 + ions. These data confirmed the overlap of vibronic bands near 17640 and 17680 cm -1 . Two-photon ionization spectroscopy also provided accurate ionization energies of IE(YbOH)=45788(10) and IE(YbOCH 3 )=45283(10) cm -1 . The IE for YbOH is relevant to problems encountered in previous attempts to determine the bond dissociation energy of YbOH + .
Here, we present a machine learning (ML) guided approach to predict saturation magnetization (𝑀 S ) and coercivity (𝐻 C ) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveal that increasing Si and B content reduces 𝑀 S from 1.81 T (DFT ≈ 2.04 T) to ≈1.54 T (DFT ≈ 1.56T) in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2.09 T), Fe-5Si-5B (2.01 T), and Fe-10Si-10B (1.54 T) alloy compositions further supports our findings. These trends are consistent with density functional theory predictions, which link increased electronic disorder and band broadening to lower 𝑀 S values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveal that 𝑀 S is governed by a nonlinear interplay between Fe content and early transition metal ratios, while 𝐻 C is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudoquaternary compositional space, which shows comparable magnetic properties to NANOMET (Fe 84.8 Si 0.5 B 9.4 Cu 0.8 P 3.5 C 1 ), FINEMET (Fe 73.5 Si 13.5 B 9 Cu 1 Nb 3 ), NANOPERM (Fe 88 Zr 7 B 4 Cu 1 ), and HITPERM (Fe 44 Co 44 Zr 7 B 4 Cu 1 . Our findings demonstrate the potential of the ML framework for accelerated search of high-performance soft magnetic materials.
Cyber-Physical Systems (CPSs) are becoming increasingly complex and interconnected as they attempt to meet the demands of evolving society. As a result, monitoring and maintaining them becomes a more complex and demanding task for control system operators and cyber defenders. While the literature on visualization techniques in the context of cybersecurity is extensive, the same cannot be said for studies on visualization for the security of cyber-physical systems. This paper aims to fill that gap by: 1) defining the main features of a visualizations workflow for security visualizations in cyber-physical systems. The workflow includes the acquisition of cyber and physical data, processing of data, selection, and configuration of both visualization tools and end-user interactions. 2) Providing an overview of cyber-physical security visualization systems, with a focus on smart grids as a case study. Finally, we use the perspectives gained from this analysis to provide insights and directions for future research and design of cyber-physical visualization techniques.
Abstract Background Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors. Methods We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers. Results We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations inTP53,PLXNB3, Zinc finger genes, and fewer mutations inSDC2,CDH1,PIK3CA,SVIL, andPTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA–protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, includingKIAA1522,DCK,FOXO3,MYO9B,ARID1A,EPRS,ZC3HAV1, andRBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors. Conclusions This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.