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At least 307 records · Page 17

An atypical BRCT–BRCT interaction with the XRCC1 scaffold protein compacts human DNA Ligase IIIα within a flexible DNA repair complex

The XRCC1–DNA ligase IIIα complex (XL) is critical for DNA single-strand break repair, a key target for PARP inhibitors in cancer cells deficient in homologous recombination. Here, we combined biophysical approaches to gain insights into the shape and conformational flexibility of the XL as well as XRCC1 and DNA ligase IIIα (LigIIIα) alone. Structurally-guided mutational analyses based on the crystal structure of the human BRCT–BRCT heterodimer identified the network of salt bridges that together with the N-terminal extension of the XRCC1 C-terminal BRCT domain constitute the XL molecular interface. Coupling size exclusion chromatography with small angle X-ray scattering and multiangle light scattering (SEC-SAXS–MALS), we determined that the XL is more compact than either XRCC1 or LigIIIα, both of which form transient homodimers and are highly disordered. The reduced disorder and flexibility allowed us to build models of XL particles visualized by negative stain electron microscopy that predict close spatial organization between the LigIIIα catalytic core and both BRCT domains of XRCC1. Together our results identify an atypical BRCT–BRCT interaction as the stable nucleating core of the XL that links the flexible nick sensing and catalytic domains of LigIIIα to other protein partners of the flexible XRCC1 scaffold.

59 BASIC BIOLOGICAL SCIENCES↗

A small core in Vesta inferred from Dawn’s observations

Vesta’s large-scale interior structure had previously been constrained primarily using the gravity and shape data from the Dawn mission. However, these data alone still allow a wide range of possibilities for the differentiation state of the body. The moment of inertia is arguably the most diagnostic parameter related to the radial density distribution of a planetary body, making it crucial for assessing the body’s state of internal differentiation. Determining the moment of inertia requires additional measurements of the amplitudes of small rotational motions, such as precession and nutation. Here we report an updated estimate of the moment of inertia of Vesta inferred from Dawn’s Doppler tracking via the Deep Space Network and onboard imaging data. The recovered value for Vesta’s normalized polar moment of inertia is $\overline{C}$/MR 2 = 0.4208 ± 0.0047 (where M is the mass of Vesta and R is the reference radius), which is only 6.6% lower than the homogeneous value of 0.4505. This value, combined with the gravity field and global shape, suggests that Vesta’s interior has limited density stratification beneath its howardite–eucrite–diogenite-dominated crust. We propose two possible origin scenarios that are consistent with the observed constraints. In the first scenario, Vesta’s interior did not undergo full differentiation due to late accretion. In the second scenario, Vesta originated as an impact remnant of a larger differentiated body re-accreted with non-chondritic bulk composition produced from a catastrophic impact. Vesta did not experience complete differentiation in either scenario, suggesting that its current state reflects a complex interplay between its accretion timing, thermal evolution, redistribution of 26 Al bearing melt and/or impact processes.

CNEOS 2014-01-08 bolide↗

Water transport through monolayer fullerene membrane

Water transport through nanoporous materials is important in water treatment, desalination, and nanofiltration. Two-dimensional (2D) membranes such as porous graphene have been explored for high-permeance water transport. However, water transport through a new class of 2D membranes based on two-dimensional covalently linked fullerene monolayers has not been fully explored. Here we use classical molecular dynamics simulations to investigate both vapor and liquid water transport through a monolayer fullerene membrane. We find that a quasi-tetragonal phase fullerene membrane possesses the right pore size and geometry that allows fast water vapor transport (∼ 50 g m −2 day −1 Pa −1 ) and water liquid transport (∼ 2.0 g m −2 day −1 Pa −1 ). Furthermore, simulation of sea water transport through the fullerene membrane shows 100 % salt rejection. The much faster vapor transport rate is attributed to the funnel-shaped pore and the optimal size that allows free rotation of water molecules permeating through, while the slower liquid transport is due to the need to desolvate a water molecule to break its hydrogen-bond network across the hydrophobic pore. This work shows the great potential of using monolayer fullerene membranes as 2D membranes for fast and selective water transport.

Desalination↗

Electric Power Grid and Natural Gas Network Operations and Coordination

In this report, we present findings from three studies related to the coordination of natural gas and electricity system operations. We first propose and demonstrate a modeling platform for examining the interdependence of natural gas and electricity networks based on a direct current unit-commitment and economic dispatch model for the power system and a transient hydraulic gas model for the gas system. We use this platform to analyze the value of day-ahead coordination of power and natural gas network operations and to show the importance of considering gas system constraints when analyzing power systems operation with high penetration of gas generators and variable renewable energy sources. In the second study, we utilize our modeling platform to consider the U.S. Federal Energy Regulatory Commission (FERC) Order 809, issued in 2015 to improve day-ahead and intraday coordination of power and gas systems. Finally, in the third study we expand our modeling platform to focus on market-based coordination of electricity and natural gas system operations for a real system, namely a subset of the power and gas networks in the Front Range region of Colorado. We use real system data to evaluate the benefits of coordination operations under different conditions, including different levels of renewable penetration and the use of time-variant, shaped flow nominations. Our results indicate that coordination at various timescales can contribute to a reduction in curtailed gas in high-stress periods (such as those with large ramps in gas offtakes) and a reduction in energy consumption of gas compressor stations. We find that intraday coordination can reduce total power system production costs and natural gas deliverability constraints, yielding cost and reliability benefits. We observe these benefits for the test system as well as in the Colorado case study, where we find that coordination and shaped flows may provide additional value for systems with high penetration of variable renewable energy. Together, these three studies demonstrate a pathway for integrated gas and electricity grid modeling and for studying the benefits of coordinated operations of these increasingly interdependent energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Unifying simulation and inference with normalizing flows

There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators. We show that these two tasks can be unified by using maximum likelihood estimation (MLE) from conditional generative models for energy regression. Unlike direct regression techniques, the MLE approach is prior independent and non-Gaussian resolutions can be determined from the shape of the likelihood near the maximum. Using an ATLAS-like calorimeter simulation, we demonstrate this concept in the context of calorimeter energy calibration. Published by the American Physical Society 2025

Hadronic calorimiters↗

Global transcriptome profile of the developmental principles of in vitro iPSC-to-motor neuron differentiation

Abstract Background Human induced pluripotent stem cells (iPSC) have opened new avenues for regenerative medicine. Consequently, iPSC-derived motor neurons have emerged as potentially viable therapies for spinal cord injuries and neurodegenerative disorders including Amyotrophic Lateral Sclerosis. However, direct clinical application of iPSC bears in itself the risk of tumorigenesis and other unforeseeable genetic or epigenetic abnormalities. Results Employing RNA-seq technology, we identified and characterized gene regulatory networks triggered by in vitro chemical reprogramming of iPSC into cells with the molecular features of motor neurons (MNs) whose function in vivo is to innervate effector organs. We present meta-transcriptome signatures of 5 cell types: iPSCs, neural stem cells, motor neuron progenitors, early motor neurons, and mature motor neurons. In strict response to the chemical stimuli, along the MN differentiation axis we observed temporal downregulation of tumor growth factor-β signaling pathway and consistent activation of sonic hedgehog, Wnt/β-catenin, and Notch signaling. Together with gene networks defining neuronal differentiation (neurogenin 2, microtubule-associated protein 2, Pax6, and neuropilin-1), we observed steady accumulation of motor neuron-specific regulatory genes, including Islet-1 and homeobox protein HB9. Interestingly, transcriptome profiling of the differentiation process showed that Ca 2+ signaling through cAMP and LPC was downregulated during the conversion of the iPSC to neural stem cells and key regulatory gene activity of the pathway remained inhibited until later stages of motor neuron formation. Pathways shaping the neuronal development and function were well-represented in the early motor neuron cells including, neuroactive ligand-receptor interactions, axon guidance, and the cholinergic synapse formation. A notable hallmark of our in vitro motor neuron maturation in monoculture was the activation of genes encoding G-coupled muscarinic acetylcholine receptors and downregulation of the ionotropic nicotinic acetylcholine receptors expression. We observed the formation of functional neuronal networks as spontaneous oscillations in the extracellular action potentials recorded on multi-electrode array chip after 20 days of differentiation. Conclusions Detailed transcriptome profile of each developmental step from iPSC to motor neuron driven by chemical induction provides the guidelines to novel therapeutic approaches in the re-construction efforts of muscle innervation.

59 BASIC BIOLOGICAL SCIENCES↗

Magnetic charge and geometry confluence for ultra-low forward voltage diode in artificial honeycomb lattice

We report spin diode is important prerequisite to practical manifestation of spin electronics. Yet, a functioning magnetic diode at room temperature is still illusive. Here, we reveal diode-type phenomena due to magnetic charge mediated conduction in artificial honeycomb geometry, made of concave shape single domain permalloy element. We find that honeycomb lattice defies symmetry by populating vertices with low and high multiplicity magnetic charges, causing asymmetric magnetization, in applied current of opposite polarity. High multiplicity units create highly resistive network, thereby inhibiting magnetic charge dynamics propelled electrical conduction. However, practical realization of this effect requires modest demagnetization factor in constituting element. Concave structure fulfills the condition. Subsequently, magnetic diode behavior emerges across broad thermal range of T = 40 K–300 K. The finding is a departure from the prevailing notion of spin-charge interaction as the sole guiding principle behind spintronics. Consequently, a new vista, mediated by magnetic charge interaction, is envisaged for spintronic research.

36 MATERIALS SCIENCE↗

Structure and Sulfur: Tuning the Viscoelastic and Surface Properties of Natural Keratin Fibers

Natural keratin fibers, such as wool, possess a complex hierarchical structure that governs their mechanical properties and surface energy. However, the extent to which these characteristics are influenced by combined contributions of structural variations (e.g., fiber diameter, intermediate filament (IF) packing) and chemical composition (e.g., disulfide bond density) remains poorly understood. In this study, we investigate wool fibers from five sheep breeds (Merino, Polwarth, Cheviot, Eider, and Devon) to elucidate how these factors influence viscoelasticity and surface interactions. Using a multimodal approach integrating interfacial and bulk characterization methods, including inverse gas chromatography (IGC), atomic force microscopy-infrared spectroscopy (AFM-IR), X-ray photoelectron spectroscopy (XPS), uniaxial tensile testing, and synchrotron small-angle X-ray scattering (SAXS), we show that the nanometer-thick 18-methyleicosanoic acid (18-MEA) layer is consistently present across all wool types and plays a key role in governing hydrophobicity and surface heterogeneity. A controlled isothermal treatment at 200 °C, designed to cleave disulfide bonds, results in a nearly 40% reduction in specific surface area across all fiber types, accompanied by a significant decrease in tensile strength and 80% reduction in elongation at break for Merino and Devon wool, but limited influence on the mechanical properties of Eider fibers. Furthermore, rate-dependent tensile testing within the elastic regime reveals distinct viscoelastic responses among the fiber types, suggesting that the sulfur-rich protein matrix surrounding IFs and its structure contribute actively to stress partitioning. Altogether, when combined with conclusions from SAXS measurements of IF spacing, our work offers compelling insights into the role of the keratin-associated protein (KAP) matrix in shaping wool fiber mechanics. Differences in mechanical behavior among wool types, despite similar IF spacing or sulfur content, highlight the importance of matrix composition and cross-linking density, suggesting that the molecular architecture of the KAP network may be a dominant factor in determining fiber performance.

X-ray scattering↗

Supramolecular Assembly of U(IV) Clusters and Superatoms with Unconventional Countercations

Superatoms are nanometer-sized molecules or particles that form ordered lattices, mimicking their atomic counterparts. Hierarchical assembly of superatoms gives rise to emergent properties in lattices of quantum dots, p-block clusters, and fullerenes. In this report, we introduce a family of uranium-oxysulfate cluster anions whose hierarchical assembly in water is controlled by two parameters: acidity and the lanthanide or transition-metal countercation. In acid, larger Ln III (Ln = La–Ho) link hexamer (U 6 ) oxoclusters into body-centered cubic frameworks, while smaller Ln III (Ln = Er–Lu and Y) promote linking of 14 U 6 clusters into hollow superclusters (U 84 superatoms). U 84 assembles into superlattices including cubic-closest packed, body-centered cubic, and interpenetrating networks, bridged by interstitial countercations and U 6 clusters. Divalent transition metals (TM = Mn II and Zn II ) charge-balance and promote the fusion of 10 U 6 and 10 U monomers into a wheel-shaped cluster (U 70 ). Dissolution of U 70 in organic media shows (by small-angle X-ray scattering) that differing supramolecular assemblies are accessed, controlled by TM II -linking of U 70 clusters. Magnetic measurements of these assemblies reveal Curie–Weiss behavior at high temperatures, without pairing of the 5f 2 -electrons down to 2 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Anisotropic magnetism and Kondo-lattice behavior in the frustrated antiferromagnet Ce 3 ⁢MgBi 5

Here, we report the synthesis and physical characterization of single-crystalline Ce 3 ⁢MgBi 5 , a previously unexplored member of the Ce 3 ⁢𝑀⁢𝑃⁢𝑛 5 family. This compound crystallizes in the hexagonal 𝑃⁢6 3 /𝑚⁢𝑐⁢𝑚 structure, featuring an anisotropic Ce sublattice composed of zigzag chains along the 𝑐 axis and a distorted kagome-like network in the basal plane. Magnetization measurements reveal antiferromagnetic order below 𝑇 𝑁 ≈ 4.2K with strong magnetic anisotropy and multiple field-induced metamagnetic transitions for fields applied perpendicular to [001], leading to a dome-shaped 𝐻–𝑇 phase diagram. Electrical transport exhibits characteristic signatures of a Ce-based Kondo lattice, including broad resistivity maxima and pronounced field-dependent anomalies in the magnetoresistance and Hall response that track the magnetic phase boundaries. Specific-heat measurements confirm the magnetic transition and show that the full R ⁢ln⁡ 2 entropy expected for a Ce 3+ Kramers doublet is recovered by 20 K, indicating an extended temperature range of magnetic fluctuations consistent with Kondo correlations. Our results establish Ce 3 ⁢MgBi 5 as a platform within the Ce 3 ⁢𝑀⁢𝑃⁢𝑛 5 family for exploring the interplay of geometric frustration, magnetic anisotropy, and Kondo-lattice physics under applied magnetic fields.

Kondo effect↗

The International Soil Moisture Network: serving Earth system science for over a decade

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

54 ENVIRONMENTAL SCIENCES↗

Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue

Abstract Fatigue initiation in additively manufactured samples/parts often occurs at processed-induced defects such as lack-of-fusion (LoF), keyhole, or other morphological/microstructural defects that have unique characteristics and measurable qualities. Attempts at identifying and minimizing such defects have utilized optimized processing conditions along with in situ and ex situ characterization that includes metallography and/or X-ray computed tomography (XCT). This paper highlights the benefits of using fracture surface analyses to detect and quantify defects that may not be detected by metallography/XCT due to sectioning and resolution limits. In addition to using manual quantification of fatigue initiating LoF and keyhole defects on fracture surfaces, image-based machine learning using convolutional neural networks such as U-Net were also used to automate the process. Statistical analyses were used to identify the extreme cases of defects that initiated and accelerated fatigue and to model the distribution of defect size and shape characteristics to distinguish the type of defect. Initial results show agreement between trained machine learning models and ground truth data in defect segmentation, and the distributions of defect characteristics are distinguishable to particular process-induced defect types.

Materials Science↗

Combining direct ink writing with reactive melt infiltration to create architectured thermoelectric legs

Here, we present a new additive-reactive synthesis method where inks – cast into molds or 3D-additively extruded into architectured shapes – are reacted into intermetallic thermoelectric compounds. The new method, as demonstrated for equiatomic TiNiSn, combines: (i) extrusion printing (or casting) of inks containing Ni and Ti powders, (ii) debinding and reactive sintering to form a porous NiTi network, (iii) network infiltration with liquid Sn and subsequent reaction to synthesize the TiNiSn phase. Thin plates, created through this method, show high phase purity and low residual porosity. A thermoelectric figure of merit zT = 0.47 ± 0.05 is achieved at 800 K, within the broad range of values (zT= 0.3–1.0) reported for TiNiSn created via traditional powder hot-pressing methods. Layered TiNiSn microlattices – after 3D ink-extrusion printing and infiltration – exhibit high relative densities and minimal undesirable secondary phase content, with a Seebeck coefficient on par with the ink-cast TiNiSn plates, demonstrating that thermoelectric legs with far-ranging architectural freedom can be created additively with this novel reactive manufacturing method. Numerous other thermoelectric (and other) compounds, currently limited to basic geometries due to brittleness, are amenable to this new method based on a sequence of reactive sintering/infiltration of cast or ink-extruded precursors.

36 MATERIALS SCIENCE↗

Modelling populations of kilonovae

Abstract The 2017 detection of a kilonova coincident with gravitational-wave emission has identified neutron star mergers as the major source of the heaviest elements and dramatically constrained alternative theories of gravity. Observing a population of such sources has the potential to transform cosmology, nuclear physics, and astrophysics. However, with only one confident multi-messenger detection currently available, modelling the diversity of signals expected from such a population requires improved theoretical understanding. In particular, models that are quick to evaluate and are calibrated with more detailed multi-physics simulations are needed to design observational strategies for kilonovae detection and to obtain rapid-response interpretations of new observations. We use grey-opacity models to construct populations of kilonovae, spanning ejecta parameters predicted by numerical simulations. Our modelling focuses on wavelengths relevant for upcoming optical surveys, such as the Rubin Observatory Legacy Survey of Space and Time (LSST). In these simulations, we implement heating rates that are based on nuclear reaction network calculations. We create a Gaussian-process emulator for kilonova grey opacities, calibrated with detailed radiative transfer simulations. Using recent fits to numerical relativity simulations, we predict how the ejecta parameters from binary neutron star (BNS) mergers shape the population of kilonovae, accounting for the viewing-angle dependence. Our simulated population of BNS mergers produce peak i-band absolute magnitudes of −20 ≤ Mi ≤ −11. A comparison with detailed radiative transfer calculations indicates that further improvements are needed to accurately reproduce spectral shapes over the full light curve evolution.

79 ASTRONOMY AND ASTROPHYSICS↗

Mechanism and dynamics of fatty acid photodecarboxylase

Photoenzymes are rare biocatalysts driven by absorption of a photon at each catalytic cycle; they inspire development of artificial photoenzymes with valuable activities. Fatty acid photodecarboxylase (FAP) is a natural photoenzyme that has potential applications in the bio-based production of hydrocarbons, yet its mechanism is far from fully understood. RATIONALE To elucidate the mechanism of FAP, we studied the wild-type (WT) enzyme from Chlorella variabilis (CvFAP) and variants with altered active-site residues using a wealth of techniques, including static and time-resolved crystallography and spectroscopy, as well as biochemical and computational approaches. RESULTS A 1.8-Å-resolution CvFAP x-ray crystal structure revealed a dense hydrogen-bonding network positioning the fatty acid carboxyl group in the vicinity of the flavin adenine dinucleotide (FAD) cofactor. Structures solved from free electron laser and low-dose synchrotron x-ray crystal data further highlighted an unusual bent shape of the oxidized flavin chromophore, and showed that the bending angle (14°) did not change upon photon absorption (step 1) or throughout the photocycle. Calculations showed that bending substantially affected the energy levels of the flavin. Structural and spectroscopic analysis of WT and mutant proteins targeting two conserved active-site residues, R451 and C432, demonstrated that both residues were crucial for proper positioning of the substrate and water molecules and for oxidation of the fatty acid carboxylate by 1 FAD* (~300 ps in WT FAP) to form FAD ∙– (step 2). Time-resolved infrared spectroscopy demonstrated that decarboxylation occured quasi-instantaneously upon this forward electron transfer, consistent with barrierless bond cleavage predicted by quantum chemistry calculations and with snapshots obtained by time-resolved crystallography. Transient absorption spectroscopy in H 2 O and D 2 O buffers indicated that back electron transfer from FAD ∙– was coupled to and limited by transfer of an exchangeable proton or hydrogen atom (step 3). Unexpectedly, concomitant with FAD ∙– reoxidation (to a red-shifted form FAD RS ) in 100 ns, most of the CO 2 product was converted, most likely into bicarbonate (as inferred from FTIR spectra of the cryotrapped FAD RS intermediate). Calculations indicated that this catalytic transformation involved an active-site water molecule. Cryo-Fourier transform infrared spectroscopy studies suggested that bicarbonate formation (step 4) was preceded by deprotonation of an arginine residue (step 3). At room temperature, the remaining CO 2 left the protein in 1.5 μs (step 4'). The observation of residual electron density close to C432 in electron density maps derived from time-resolved and cryocrystallography data suggests that this residue may play a role in stabilizing CO 2 and/or bicarbonate. Three routes for alkane formation were identified by quantum chemistry calculations; the one shown in the figure is favored by the ensemble of experimental data. CONCLUSION Finally, we provide a detailed and comprehensive characterization of light-driven hydrocarbon formation by FAP, which uses a remarkably complex mechanism including unique catalytic steps. We anticipate that our results will help to expand the green chemistry toolkit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Geophysical survey associated with NEON AOP survey, East River, CO 2018

The package contains data layers developed and used in Falco et al. 2024: “EcoImaging: Advanced Sensing to Investigate Plant and Abiotic Hierarchical Spatial Patterns in Mountainous Watersheds". The package is part of the DOE Watershed Function Science Focus Area (SFA) project and includes geophysical measurements collected at the East River, Colorado, in conjunction with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey conducted in June 2018. This dataset provide soil geophysical information and were used to investigate soil-plant relationships. The dataset consists of: - NEON_2018_EMI_survey.zip: the electromagnetic induction (EMI) survey as shape-file; - NEON_plot_TDR.csv: plot‑level data from Time‑Domain Reflectometry (TDR) measurements, providing: * volumetric water content (VWC) in percent (%); * soil temperature in degrees Celsius (°C); - file level metadata (flmd.csv) - data dictionary (dd.csv) file This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗

Utilizing a Dynamic Segmentation Convolutional Neural Network for Microstructure Analysis of Additively Manufactured Superalloy 718

Additive manufacturing (AM) is revolutionizing almost all industries through the production of intricate geometries previously prohibited by cost or machinability. Ni-based superalloys form a primary alloy class for high temperature applications in the petrochemical, aerospace, and nuclear industries because of their intrinsic resistance to creep and the ability to heat treat the superalloy for high strength. Despite these attractive properties, the extreme work hardening of Ni-based superalloys makes traditional manufacturing of complex shapes difficult and thus, these alloys are an attractive target for AM. Superalloy 718 was chosen as an example superalloy because of the wide variety of precipitates that can form within its composition space from the repetitive heating and cooling cycles of the AM process. The precipitates and other microstructure features, such as grain boundaries and dislocations, will dictate the mechanical properties and thus, there is an extensive challenge to characterize the size, number density, composition, and volume fraction of each microstructural feature from AM fabrication using analytical electron microscopy. This work focused on the application of a pixel-wise classification machine learning (ML) model called a dynamic segmentation convolutional neural network (DSCNN) to identify the microstructural features of an as-fabricated additively manufactured superalloy 718.

Taller, Stephen↗

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F 1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F 1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. In conclusion, these results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

3D neural networks↗