Phononic drumhead surface state in the distorted kagome compound RhPb
Not Available
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Site Mixing for Engineering Magnetic Topological Insulators
The van der Waals compound, MnBi 2 Te 4 , is the first intrinsic magnetic topological insulator, providing a materials platform for exploring exotic quantum phenomena such as the axion insulator state and the quantum anomalous Hall effect. However, intrinsic structural imperfections lead to bulk conductivity, and the roles of magnetic defects are still unknown. With higher concentrations of the same types of magnetic defects, the isostructural compound MnSb 2 Te 4 is a better model system for a systematic investigation of the connections among magnetism, topology, and lattice defects. In this work, the impact of antisite defects on the magnetism and electronic structure is studied in MnSb 2 Te 4 . Mn-Sb site mixing leads to complex magnetic structures and tunes the interlayer magnetic coupling between antiferromagnetic and ferromagnetic. The detailed nonstoichiometry and site mixing of MnSb2Te4 crystals depend on the growth parameters, which can lead to ≈40% of Mn sites occupied by Sb and ≈15% of Sb sites by Mn in as-grown crystals. Single-crystal neutron diffraction and electron microscopy studies show nearly random distribution of the antisite defects. Band structure calculations suggest that the Mn-Sb site mixing favors a ferromagnetic interlayer coupling, consistent with experimental observation, but is detrimental to the band inversion required for a nontrivial topology. Overall, our results suggest a long-range magnetic order of Mn ions sitting on Bi sites in MnBi 2 Te 4 . The effects of site mixing should be considered in all layered heterostructures that consist of alternating magnetic and topological layers, including the entire family of MnTe(Bi 2 Te 3 )n, its Sb analogs, and their solid solution.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Novel Method for Overmolding of NdFeB Bonded Magnets Into a 3-D Printed Rotor
Not Available
Kemp, James W.↗
Forest Age Rivals Climate to Explain Reproductive Allocation Patterns in Forest Ecosystems Globally
ABSTRACT Forest allocation of net primary productivity (NPP) to reproduction (carbon required for flowers, fruits, and seeds) is poorly quantified globally, despite its critical role in forest regeneration and a well‐supported trade‐off with allocation to growth. Here, we present the first global synthesis of a biometric proxy for forest reproductive allocation (RA) across environmental and stand age gradients from a compiled dataset of 824 observations across 393 sites. We find that ecosystem‐scale RA increases ~60% from boreal to tropical forests. Climate shows important non‐linear relationships with RA, but is not the sole predictor. Forest age effects are comparable to climate in magnitude (MAT: ß = 0.24,p = 0.021; old growth forest: ß = 0.22,p < 0.001), while metrics of soil fertility show small but significant relationships with RA (soil pH: ß = 0.07,p = 0.001; soil N: ß = −0.07,p = 0.001). These results provide strong evidence that ecosystem‐scale RA is mediated by climate, forest age, and soil conditions, and is not a globally fixed fraction of positive NPP as assumed by most vegetation and ecosystem models. Our dataset and findings can be used by modellers to improve predictions of forest regeneration and carbon cycling.
Environmental Sciences & Ecology↗
Amoeba species colonizing the gills of rainbow trout ( Oncorhynchus mykiss ) in Swiss aquaculture
Nodular gill disease (NGD) is an infectious condition characterized by proliferative gill lesions leading to respiratory problems, oxygen deficiency and mortality in fish. Globally, NGD primarily impacts freshwater salmonids in intensive aquaculture systems. In recent years, numerous outbreaks of severe gill disease have affected more than half of the larger rainbow trout ( Oncorhynchus mykiss ) farms in Switzerland, mainly during spring and early summer. Mortality has reached up to 50% in cases where no treatment was administered. Freshwater amoeba are the presumed aetiologic agent of NGD. The gross gill score (GS) categorising severity of gill pathology is a valuable first-line diagnostic tool aiding fish farmers in identifying and quantifying amoebic gill disease (AGD) in farmed marine salmonids. In this study, the GS was adapted to the NGD outbreak in farmed trout in Switzerland. In addition to scoring disease severity, gill swabs from NGD-affected rainbow trout were sampled and amoeba were cultured from these swabs. Morphologic and molecular methods identified six amoeba strains: Cochliopodium sp., Naegleria sp., Vannella sp., Ripella sp., Saccamoeba sp. and Mycamoeba sp. However, the importance of the different amoeba species for the onset and progression of NGD still has to be evaluated. This paper presents the first description of NGD with associated amoeba infection in farmed rainbow trout in Switzerland.
59 BASIC BIOLOGICAL SCIENCES↗
Direct visualization of anionic electrons in an electride reveals inhomogeneities
Anionic electron within an electride was visualized with atomic resolution using differential phase-contrast imaging.
36 MATERIALS SCIENCE↗
Bacterial Community Assembly, Succession, and Metabolic Function during Outdoor Cultivation of Microchloropsis salina
Outdoor cultivation of microalgae has promising potential for renewable bioenergy, but there is a knowledge gap on the structure and function of the algal microbiome that coinhabits these ecosystems. Here, we describe the assembly mechanisms, taxonomic structure, and metabolic potential of bacteria associated with Microchloropsis salina cultivated outdoors. Open mesocosms were inoculated with algal cultures that were either free of bacteria or coincubated with one of two different strains of alga-associated bacteria and were sampled across five time points taken over multiple harvesting rounds of a 40-day experiment. Using quantitative analyses of metagenome-assembled genomes (MAGs), we tracked bacterial community compositional abundance and taxon-specific functional capacity involved in algal-bacterial interactions. One of the inoculated bacteria (Alteromonas sp.) persisted and dispersed across mesocosms, whereas the other inoculated strain (Phaeobacter gallaeciensis) disappeared by day 17 while a taxonomically similar but functionally distinct Phaeobacter strain became established. The inoculated strains were less abundant than 6 numerically dominant newly recruited taxa with functional capacities for mutualistic or saprophytic lifestyles, suggesting a generalist approach to persistence. This includes a highly abundant unclassified Rhodobacteraceae species that fluctuated between 25% and 77% of the total community. Overall, we did not find evidence for priority effects exerted by the distinct inoculum conditions; all mesocosms converged with similar microbial community compositions by the end of the experiment. Instead, we infer that the 15 total populations were retained due to host selection, as they showed high metabolic potential for algal-bacterial interactions such as recycling alga-produced carbon and nitrogen and production of vitamins and secondary metabolites associated with algal growth and senescence, including B vitamins, tropodithietic acid, and roseobacticides.
rhodobacteraceae↗
Models for synthetic data generation
The software includes a suite of probabilistic statistical/machine learning models that can generate discrete synthetic data. Each model is trained on a set of real (private) data and then it can be used to generate synthetic but statistically similar data. Once ready, the model can generate as many samples as we want. Finally, in addition to the actual models, the software includes code to process data, evaluate results (based on cross validation), and produce reports.
De Oliveira Sales, Ana Paula↗
Generation and evaluation of synthetic patient data
Background: Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges. Methods: In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed. Results: While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases. Conclusions: We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.
59 BASIC BIOLOGICAL SCIENCES↗
Experimental and theoretical insights into the effects of pH on catalysis of bond-cleavage by the lignin peroxidase isozyme H8 from Phanerochaete chrysosporium
Abstract Background Lignin peroxidases catalyze a variety of reactions, resulting in cleavage of both β-O-4′ ether bonds and C–C bonds in lignin, both of which are essential for depolymerizing lignin into fragments amendable to biological or chemical upgrading to valuable products. Studies of the specificity of lignin peroxidases to catalyze these various reactions and the role reaction conditions such as pH play have been limited by the lack of assays that allow quantification of specific bond-breaking events. The subsequent theoretical understanding of the underlying mechanisms by which pH modulates the activity of lignin peroxidases remains nascent. Here, we report on combined experimental and theoretical studies of the effect of pH on the enzyme-catalyzed cleavage of β-O-4′ ether bonds and of C–C bonds by a lignin peroxidase isozyme H8 from Phanerochaete chrysosporium and an acid stabilized variant of the same enzyme. Results Using a nanostructure initiator mass spectrometry assay that provides quantification of bond breaking in a phenolic model lignin dimer we found that catalysis of degradation of the dimer to products by an acid-stabilized variant of lignin peroxidase isozyme H8 increased from 38.4% at pH 5 to 92.5% at pH 2.6. At pH 2.6, the observed product distribution resulted from 65.5% β-O-4′ ether bond cleavage, 27.0% C α -C 1 carbon bond cleavage, and 3.6% C α -oxidation as by-product. Using ab initio molecular dynamic simulations and climbing-image Nudge Elastic Band based transition state searches, we suggest the effect of lower pH is via protonation of aliphatic hydroxyl groups under which extremely acidic conditions resulted in lower energetic barriers for bond-cleavages, particularly β-O-4′ bonds. Conclusion These coupled experimental results and theoretical explanations suggest pH is a key driving force for selective and efficient lignin peroxidase isozyme H8 catalyzed depolymerization of the phenolic lignin dimer and further suggest that engineering of lignin peroxidase isozyme H8 and other enzymes involved in lignin depolymerization should include targeting stability at low pH.
09 BIOMASS FUELS↗
Inelastic Neutron Scattering Data for FeSn
Inelastic neutron scattering data from FeSn collected with the SEQUOIA spectrometer at the Spallation Neutron Source located at Oak Ridge National Laboratory. A 4.43 g sample of FeSn was used for the measurements as described in doi: https://doi.org/10.1103/PhysRevB.105.L180403 and the associated supplemental information. The data were collected with an incident energy of 500 meV. The Fermi chopper frequency was 480 Hz, which provides a resolution of 38.2 meV (FWHM) for elastic scattering. Additional details are available in https://doi.org/10.1103/PhysRevB.105.L180403 and the associated supplemental information. The data may be viewed and analyzed with the software packages Mantid (http://dx.doi.org/10.5286/SOFTWARE/MANTID) and Dave (doi: 10.6028/jres.114.025). Sample angles measured: -20 deg to 20 deg and 160 deg to 200 deg where 0 deg is defined for ki//c* Sample Orientation vectors: ‘u': '-0.0032, 0.0201, -1.0' ‘v': '-1.0, 0.0278, -0.0059'
Inelastic Neutron Scattering↗
STM/S Grid LDOS Data and Analysis Code for Deciphering Majorana Zero Modes in Topological Superconductor
This dataset provides raw millikelvin scanning tunneling microscopy/spectroscopy (STM/S) grid spectroscopy data and Python analysis scripts supporting the manuscript “Deciphering Majorana Zero Modes in Topological Superconductor FeTe0.55Se0.45 with Machine-Learning-Assisted Spectral Deconvolution.” The dataset includes a raw grid spectroscopy file acquired on FeTe0.55Se0.45 at 40 mK under magnetic field, together with Python/Jupytext analysis scripts used for STM/S data processing, visualization, spectral deconvolution, Lorentzian peak fitting, feature extraction, machine-learning-assisted clustering, and figure generation. These files support the analysis of vortex-core local density of states and the identification of zero-bias-peak-related spectral components from complex in-gap states. The dataset is intended to provide a citable archival record of the data and analysis code associated with the published manuscript and to support transparency and reproducibility of the reported STM/S and machine-learning workflow.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Modeling the temporal network dynamics of neuronal cultures
Not Available
59 BASIC BIOLOGICAL SCIENCES↗
Machine learning mathematical models for incidence estimation during pandemics
Accurate estimates of the incidence of infectious diseases are key for the control of epidemics. However, healthcare systems are often unable to test the population exhaustively, especially when asymptomatic and paucisymptomatic cases are widespread; this leads to significant and systematic under-reporting of the real incidence. Here, we propose a machine learning approach to estimate the incidence of a pandemic in real-time, using reported cases and the overall test rate. In particular, we use Bayesian symbolic regression to automatically learn the closed-form mathematical models that most parsimoniously describe incidence. We develop and validate our models using COVID-19 incidence values for nine different countries, confirming their ability to accurately predict daily incidence. Remarkably, despite the differences in epidemic trajectories and dynamics across countries, we find that a single model for all countries offers a more parsimonious description and is more predictive of actual incidence compared to separate models for each country. Our results show the potential to accurately model incidence in real-time using closed-form mathematical models, providing a valuable tool for public health decision-makers.
Fajardo-Fontiveros, Oscar (ORCID:0000000207058972)↗
Improving five-year survival prediction via multitask learning across HPV-related cancers
Oncology is a highly siloed field of research in which sub-disciplinary specialization has limited the amount of information shared between researchers of distinct cancer types. This can be attributed to legitimate differences in the physiology and carcinogenesis of cancers affecting distinct anatomical sites. However, underlying processes that are shared across seemingly disparate cancers probably affect prognosis. The objective of the current study is to investigate whether multitask learning improves 5-year survival cancer patient survival prediction by leveraging information across anatomically distinct HPV related cancers. Furthermore, data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program database. The study cohort consisted of 29,768 primary cancer cases diagnosed in the United States between 2004 and 2015. Ten different cancer diagnoses were selected, all with a known association with HPV risk. In the analysis, the cancer diagnoses were categorized into three distinct topography groups of varying specificity. The most specific topography grouping consisted of 10 original cancer diagnoses differentiated by the first two digits of the ICD-O-3 topography code. The second topography grouping consisted of cancer diagnoses categorized into six distinct organ groups. Finally, the third topography grouping consisted of just two groups, head-neck cancers and ano-genital cancers. The tasks were to predict 5-year survival for patients within the different topography groups using 14 predictive features which were selected among descriptive variables available in the SEER database. The information from the predictive features was shared between tasks in three different ways, resulting in three distinct predictive models: 1) Information was not shared between patients assigned to different tasks (single task learning); 2) Information was shared between all patients, regardless of task (pooled model); 3) Only relevant information was shared between patients grouped to different tasks (multitask learning). Prediction performance was evaluated with Brier scores. All three models were evaluated against one another on each of the three distinct topography-defined tasks. The results showed that multitask classifiers achieved relative improvement for the majority of the scenarios studied compared to single task learning and pooled baseline methods. In this study, we have demonstrated that sharing information among anatomically distinct cancer types can lead to improved predictive survival models.
59 BASIC BIOLOGICAL SCIENCES↗
Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2. In this study, we used a combination of high throughput screening and machine learning (ML) models to identify HCAbs with potent efficacy against SARS-CoV-2 viral variants of interest (VOIs) and concern (VOCs). To start, we screened our highly diverse Nb phage display library against several pre-Omicron VOI and VOC receptor binding domains (RBDs) to identify panels of cross-reactive HCAbs. Using HCAb affinity for SARS-CoV-2 VOI and VOCs (pre-Omicron variants) and model features from other published data, we were able to develop a ML model that successfully identified HCAbs with efficacy against Omicron variants, independent of our experimental biopanning workflow. This biopanning informed ML approach reduced the experimental screening burden by 78% to 90% for the Omicron BA.5 and Omicron BA.1 variants, respectively. The combined approach can be applied to other emerging viruses with pandemic potential to rapidly identify effective therapeutic antibodies against emerging variants.
Antibodies↗
Reproductive and leaf litterfall fluxes in forest ecosystem sites globally (1950-2022)
Forest allocation of net primary productivity (NPP) to reproduction is poorly quantified globally, despite its critical role in forest regeneration and a well-supported trade-off with allocation to growth. Although field measurements of total NPP are rare, our work finds that a proxy for reproductive carbon allocation constructed from leaf (L) and reproductive (R) litterfall fluxes, R/(R+L), is strongly correlated with R/NPP, facilitating analysis across a wide range of sites where biometric estimates of NPP are not available (R² = 0.85; Hanbury-Brown et al., 2022, Ward et al., in prep). To investigate relationships between ecosystem-scale reproductive allocation (RA) and climate, soil fertility, and stand age gradients, we conducted a literature search and synthesized 824 observations of annual average leaf and reproductive litterfall fluxes across forest sites globally. The zip file includes 1) a folder Data/ containing the litterfall data ("GlobalForestRA_data.csv") and metadata ("GlobalForestRA_metadata.doc") files. The data file includes geographic coordinates, long-term mean annual temperature and precipitation (1970-2000, extracted from WorldClim2.1), leaf and reproductive litterfall fluxes, sampling interval and protocols, forest characteristics (dominant leaf morphology, information pertaining to forest age and successional stage, and disturbance history) and soil properties (% sand, %silt, %clay, total phosphorus (P), nitrogen (N), cation exchange capacity (CEC) and pH) extracted from SoilGrids250 and from on-site measurements, where available. The metadata file contains information about each variable reported in the data file, including data sources, processing methods, and all references. The Data folder contains two additional files used to create Figure 1; these are described in greater detail in the README.2) R scripts GloalForestRA_analysis.r and GlobalForestRA_SI.r and a folder /Functions used to produce results, figures, and tables in the manuscript Ward et al. (in press)3) a README file describing how the data and R scripts can be used to reproduce statistical results, figures, and tables found in the manuscript. Ward et al. (in press)This repository can also be found at: https://github.com/r-ward/Global_Analysis_ForestRA.Ward, R.E., Zhang-Zheng, H. Aernethy, K., Adu-Bredu, S., Arroyo, L., Bailey, A. et al. (in press). Forest age rivals climate to explain reproductive allocation patterns in forest ecosystems globally. Ecology Letters. Hanbury-Brown, A.R., Ward, R.E. & Kueppers, L.M. (2022). Forest regeneration within Earth system models: current process representations and ways forward. New Phytol., 235, 20–40.Ward et al. (2025), Forest age rivals climate to explain reproductive allocation patterns in forest ecosystems globally, in prep.
54 ENVIRONMENTAL SCIENCES↗
Multitask Recommender Systems for Cancer Drug Response
The problem we are currently trying to address is that there are many types of cancer drugs and many types of cancers and there is not always experimental data for a specific cancer type and cancer drug interaction. While there is a large possible set of feasible drug and cancer combinations, testing each pair is not realistic due to the high monetary cost of cell-based assays. Thus, this leaves researchers with a difficult choice of what drugs they should test on specific cancer types. This issue is known as the cold-start problem. Our focus is on developing recommender systems capable of addressing the cold-start problem as it relates to interaction between cancer types and cancer drugs. One of the most effective ways to address the cold-start problem is through large data analysis, however due to the cost prohibitive nature of cancer research the largest available data set size is the Genomics of Drug Sensitivity in Cancer with 494,973 genomic associations. To achieve optimal model performance on the cold-start problem, it is advantageous to employ multitask algorithms that are capable of transferring information between cancer datasets. The aim of this report is to draw from adaptations and state of the art developments in both algorithms for recommender systems and multitask learning to model the interaction between cancer cell lines and cancer drugs. Cancer cell lines are defined by the US National Cancer Institute as "cancer cells that keep dividing and growing over time, under certain conditions in a laboratory". This paper will focus on evaluating the performance of Neural Collaborative Filtering and Gaussian Processes, as well as their multitask adaptations, on cancer datasets from CCLE, NCI60, GDSC and CTRP. These methods will be evaluated on model performance in regression prediction but also in interpretability.
60 APPLIED LIFE SCIENCES↗