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DOE OSTI2021

Pre-Cambrian roots of novel Antarctic cryptoendolithic bacterial lineages

Cryptoendolithic communities are microbial ecosystems dwelling inside porous rocks that are able to persist at the edge of the biological potential for life in the ice-free areas of the Antarctic desert. These regions include the McMurdo Dry Valleys, often accounted as the closest terrestrial counterpart of the Martian environment and thought to be devoid of life until the discovery of these cryptic life-forms. Despite their interest as a model for the early colonization by living organisms of terrestrial ecosystems and for adaptation to extreme conditions of stress, little is known about the evolution, diversity, and genetic makeup of bacterial species that reside in these environments. Using the Illumina Novaseq platform, we generated the first metagenomes from rocks collected in Continental Antarctica over a distance of about 350 km along an altitudinal transect from 834 up to 3100 m above sea level (a.s.l.). A total of 497 draft bacterial genome sequences were assembled and clustered into 269 candidate species that lack a representative genome in public databases. Actinobacteria represent the most abundant phylum, followed by Chloroflexi and Proteobacteria. The “Candidatus Jiangella antarctica” has been recorded across all samples, suggesting a high adaptation and specialization of this species to the harshest Antarctic desert environment. The majority of these new species belong to monophyletic bacterial clades that diverged from related taxa in a range from 1.2 billion to 410 Ma and are functionally distinct from known related taxa. Our findings significantly increase the repertoire of genomic data for several taxa and, to date, represent the first example of bacterial genomes recovered from endolithic communities. Their ancient origin seems to not be related to the geological history of the continent, rather they may represent evolutionary remnants of pristine clades that evolved across the Tonian glaciation. These unique genomic resources will underpin future studies on the structure, evolution, and function of these ecosystems at the edge of life.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Data for Influence of Particle Size on NIR Spectroscopic Characterization of Sorghum Biomass for the Biofuel Industry

NIR spectroscopy is a rapid and accurate green technology for high-throughput biomass characterization, including sorghum ( Sorghum bicolor ), a promising energy crop for the biofuel industry. This study assessed the influence of particle size on NIR spectroscopic analysis (wavelength range: 867–2535 nm) of sorghum biomass composition. Grown under field conditions, a total of 113 types of genetically diverse sorghum accessions were dried, ground, and sieved (<250, 250–600, 600–850, and > 850 µm particle size) for developing partial least square regression (PLSR) prediction models for moisture, ash, extractive, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin (ASL + AIL). Overall, smaller particle sizes provided better model performance, while no single particle size provided the best performance for all the selected components. With only 9 selected bands and 4 latent variables (LVs), the best PLSR model was obtained for moisture with particle size of 600–850 µm with the square root of the coefficient of determination (R) of 0.85, the ratio of prediction to deviation (RPD) of 2.2, and the root mean square error (RMSE) of 0.46 % in external validation. Similar model performances were also obtained for ash, extractive, glucan, and xylan. This study showed that size reduction could effectively improve NIR spectroscopic analysis for lipid-producing sorghum biomass for the biofuel industry.

Biomass Analytics↗

NASA NTRS1993

WNN 92; Proceedings of the 3rd Workshop on Neural Networks: Academic/Industrial/NASA/Defense, Auburn Univ., AL, Feb. 10-12, 1992 and South Shore Harbour, TX, Nov. 4-6, 1992

The present conference discusses such neural networks (NN) related topics as their current development status, NN architectures, NN learning rules, NN optimization methods, NN temporal models, NN control methods, NN pattern recognition systems and applications, biological and biomedical applications of NNs, VLSI design techniques for NNs, NN systems simulation, fuzzy logic, and genetic algorithms. Attention is given to missileborne integrated NNs, adaptive-mixture NNs, implementable learning rules, an NN simulator for travelling salesman problem solutions, similarity-based forecasting, NN control of hypersonic aircraft takeoff, NN control of the Space Shuttle Arm, an adaptive NN robot manipulator controller, a synthetic approach to digital filtering, NNs for speech analysis, adaptive spline networks, an anticipatory fuzzy logic controller, and encoding operations for fuzzy associative memories.

Padgett, Mary L.↗

NASA NTRS2002

Galacturonomannan and Golgi-derived membrane linked to growth and shaping of biogenic calcite

The coccolithophores are valuable models for the design and synthesis of composite materials, because the cellular machinery controlling the nucleation, growth, and patterning of their calcitic scales (coccoliths) can be examined genetically. The coccoliths are formed within the Golgi complex and are the major CaCO(3) component in limestone sediments-particularly those of the Cretaceous period. In this study, we describe mutants lacking a sulfated galacturonomannan and show that this polysaccharide in conjunction with the Golgi-derived membrane is directly linked to the growth and shaping of coccolith calcite but not to the initial orientated nucleation of the mineral phase.

NASA Discipline Cell Biology↗

NASA NTRS2017

Behavioral, Brain Imaging and Genomic Measures to Predict Functional Outcomes Post-Bed Rest and Space Flight

Astronauts experience sensorimotor disturbances during their initial exposure to microgravity and during the re-adaptation phase following a return to an Earth-gravitational environment. These alterations may disrupt crewmembers' ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from spaceflight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts are affected will improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual spaceflight, which crewmembers are likely to experience greater challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures. Our approach includes: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features, using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; and 3) assessment of genetic polymorphisms in the catechol-O-methyl transferase, dopamine receptor D2, and brain-derived neurotrophic factor genes and genetic polymorphisms of alpha2-adrenergic receptors that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate that these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration spaceflight and exposure to an analog bed rest environment. We will be conducting a retrospective study, leveraging data already collected from relevant ongoing or completed bed rest and spaceflight studies. This data will be combined with predictor metrics that will be collected prospectively (as described for behavioral, brain imaging and genomic measures) from these returning subjects to build models for predicting post spaceflight and bed rest adaptive capability. In this presentation we will discuss the optimized set of tests for predictive metrics to be used for evaluating post mission adaptive capability as manifested in their outcome measures. Comparisons of model performance will allow us to better design and implement sensorimotor adaptability training countermeasures against decrements in post-mission adaptive capability that are customized for each crewmember's sensory biases, adaptive ability, brain structure, brain function, and genetic predispositions. The ability to customize adaptability training will allow more efficient use of crew time during training and will optimize training prescriptions for astronauts to mitigate the deleterious effects of spaceflight.

Mulavara, A. P.↗

DOE OSTI2023

Aberrant light sensing and motility in the green alga Chlamydomonas priscuii from the ice-covered Antarctic Lake Bonney

The Antarctic green alga Chlamydomonas priscuii is an obligate psychrophile and an emerging model for photosynthetic adaptation to extreme conditions. Endemic to the ice-covered Lake Bonney, this alga thrives at highly unusual light conditions characterized by very low light irradiance (<15 μmol m -2 s -1 ), a narrow wavelength spectrum enriched in blue light, and an extreme photoperiod. Genome sequencing of C. priscuii exposed an unusually large genome, with hundreds of highly similar gene duplicates and expanded gene families, some of which could be aiding its survival in extreme conditions. In contrast to the described expansion in the genetic repertoire in C. priscuii, here we suggest that the gene family encoding for photoreceptors is reduced when compared to related green algae. This alga also possesses a very small eyespot and exhibits an aberrant phototactic response, compared to the model Chlamydomonas reinhardtii. We also investigated the genome and behavior of the closely related psychrophilic alga Chlamydomonas sp. ICE-MDV, that is found throughout the photic zone of Lake Bonney and is naturally exposed to higher light levels. Our analyses revealed a photoreceptor gene family and a robust phototactic response similar to those in the model Chlamydomonas reinhardtii. These results suggest that the aberrant phototactic response in C. priscuii is a result of life under extreme shading rather than a common feature of all psychrophilic algae. We discuss the implications of these results on the evolution and survival of shade adapted polar algae.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Magmatic, hydrothermal and ore element transfer processes of the southeastern Archean Superior Province implied from electrical resistivity structure

Although magmatic- and metamorphic- derived fluids are widely recognized mineralizing agents, the role of crustal architecture in defining source and sink zones within the middle to lower crust and upper mantle of ancient orogens remains enigmatic. The globally largest and best-preserved Archean greenstone belts lie in the Superior Province, Canada. They provide an ideal location to investigate the influence of igneous construction and subsequent syn-deformational plutonism and metamorphism on the localization of metal-rich melts and fluids throughout the crustal column. Integration of three-dimensional magnetotelluric modelling and seismic reflection sections across the Abitibi subprovince reveals details of a 'whole-of-crust' magmatic and hydrothermal system. East-west low resistivity structures broadly underlie the surface traces of the major deformation zones that are host to significant gold endowment (>200 Moz), while mid-crustal cross trends suggest mineralized fluids flowed along 'pipes' within fault planes. Most low resistivity structures are inferred to represent domains containing interconnected zones of graphite and/or sulfide. Furthermore, these delineate relict mantle source/transit domains and crustal pathways enriched by the flow of magmas or metamorphic fluids genetically related to a late-stage pulse of ore-bearing magmatism, possibly as a result of slab break-off or delamination. Using the combined MT and seismic data, we develop a 3-D crustal-scale model which highlights how evolving orogenic architecture-controlled mass transfer and metallogenic processes developed.

58 GEOSCIENCES↗

NASA NTRS2016

Behavioral, Brain Imaging and Genomic Measures to Predict Functional Outcomes Post - Bed Rest and Spaceflight

Astronauts experience sensorimotor disturbances during their initial exposure to microgravity and during the re-adaptation phase following a return to an Earth-gravitational environment. These alterations may disrupt crewmembers' ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from spaceflight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts would be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual spaceflight, which crewmembers are likely to experience the greatest challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures. Our approach includes: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features, using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; and 3) assessment of genotypic markers of genetic polymorphisms in the catechol-O-methyl transferase, dopamine receptor D2, and brain-derived neurotrophic factor genes and genetic polymorphisms of alpha2-adrenergic receptors that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate that these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration spaceflight and exposure to an analog bed rest environment. We will be conducting a retrospective study, leveraging data already collected from relevant ongoing or completed bed rest and spaceflight studies. These data will be combined with predictor metrics that will be collected prospectively (as described for behavioral, brain imaging and genomic measures) from these returning subjects to build models for predicting post-mission (bed rest - non-astronauts or space flight - astronauts) adaptive capability as manifested in their outcome measures. To date we have completed a study on 15 normal subjects with all of the above measures. In this presentation we will discuss the optimized set of tests for predictive metrics to be used for evaluating post mission adaptive capability as manifested in their outcome measures. Comparisons of model performance will allow us to better design and implement sensorimotor adaptability training countermeasures against decrements in post-mission adaptive capability that are customized for each crewmember's sensory biases, adaptive capacity, brain structure and functional capacities, and genetic predispositions. The ability to customize adaptability training will allow more efficient use of crew time during training and will optimize training prescriptions for astronauts to ensure expected outcomes.

Mulavara, A. P.↗

DOE OSTI2025

Recent developments of oleaginous yeasts toward sustainable biomanufacturing

Oleaginous yeast are remarkably versatile organisms, distinguished by their natural capacities to accumulate high levels of neutral lipids and broad substrate range. With recent growing interests in engineering non-model organisms as superior biomanufacturing platforms, oleaginous yeasts have emerged as promising chassis for oleochemicals, terpenoids, organic acids, and other valuable products. Advancement in systems biology along with genetic tool development have significantly expanded our understanding of the metabolism in these species and enabled engineering efforts to produce biofuels and bioproducts from diverse feedstocks. This review examines the latest technical advances in oleaginous yeast research toward sustainable biomanufacturing. We cover recent developments in systems biology-enabled metabolism understanding, genetic tools, feedstock utilization, and strain engineering approaches for the production of various valuable chemicals.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

NCAP: Noncanonical Amino Acid Parameterization Software for CHARMM Potentials

Noncanonical Amino Acids (NCAAs) provide numerous avenues for introduction of novel functionality to peptides and proteins. NCAAs can be incorporated through solid phase synthesis or genetic code expansion in conjugation with heterologous expression of the encoded protein modification. Due to the difficulty of synthesis, wide chemical space and lack of empirically resolved structures modeling the effects of NCAA mutation is critical for rational protein design. To evaluate the structural and functional perturbations NCAAs introduce we utilize molecular potentials that describe the forces in protein structure. Most potentials such as CHARMM are designed to model canonical residues but can be parameterized in include novel NCAAs. Here, in this work, we introduce NCAP a software package to generate CHARMM compatible parameters from quantum chemical calculation. Unlike currently available tools NCAP is designed to recognize NCAA structure and automatically bridge the gap between DFT calculations and potential parameters. For our software we discuss workflow, validation against canonical parameter sets and comparison to published NCAA-protein structures.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Moderate high temperature is beneficial or detrimental depending on carbon availability in the green alga Chlamydomonas reinhardtii

High temperatures impair plant growth and reduce agricultural yields, but the underlying mechanisms remain elusive. The unicellular green alga Chlamydomonas reinhardtii is an excellent model to study heat responses in photosynthetic cells due to its fast growth rate, many similarities in cellular processes to land plants, simple and sequenced genome, and ample genetic and genomics resources. Chlamydomonas grows in light by photosynthesis and with externally supplied acetate as an organic carbon source. Understanding how organic carbon sources affect heat responses is important for the algal industry but remains understudied. We cultivated wild-type Chlamydomonas under highly controlled conditions in photobioreactors at 25 °C (control), 35 °C (moderate high temperature), or 40 °C (acute high temperature) with or without constant acetate supply for 1 or 4 day. Treatment at 35 °C increased algal growth with constant acetate supply but reduced algal growth without sufficient acetate. The overlooked and dynamic effects of 35 °C could be explained by induced acetate uptake and metabolism. Heat treatment at 40 °C for more than 2 day was lethal to algal cultures with or without constant acetate supply. Furthermore, our findings provide insights to understand algal heat responses and help improve thermotolerance in photosynthetic cells.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Performance Portability of Molecular Docking Miniapp On Leadership Computing Platforms

Rapidly changing computer architectures, such as those found at high-performance computing (HPC) facilities, present the need for mini-applications (miniapps) that capture essential algorithms used in large applications to test program performance and portability, aiding transitions to new systems. The COVID-19 pandemic has fueled a flurry of activity in computational drug discovery, including the use of supercomputers and GPU acceleration for massive virtual screens for therapeutics. Recent work targeting COVID-19 at the Oak Ridge Leadership Computing Facility (OLCF) used the GPU-accelerated program AutoDock-GPU to screen billions of compounds on the Summit supercomputer. In this paper we present the development of a new miniapp, miniAutoDock-GPU, that can be used to evaluate the performance and portability of GPU-accelerated protein-ligand docking programs on different computer architectures. These tests are especially relevant as facilities transition from petascale systems and prepare for upcoming exascale systems that will use a variety of GPU vendors. The key calculations, namely, the Lamarckian genetic algorithm combined with a local search using a Solis-Wets based random optimization algorithm, are implemented. We developed versions of the miniapp using several different programming models for GPU acceleration, including a version using the CUDA runtime API for NVIDIA GPUs, and the Kokkos middle-ware API which is facilitated by C++ template libraries. A third version, currently in progress, uses the HIP programming model. These efforts will help facilitate the transition to exascale systems for this important emerging HPC application, as well as its use on a wide range of heterogeneous platforms.

Thavappiragasam, Mathialakan↗

DOE OSTI2025

From the bench to the reactor: engineered filamentous fungi for biochemical and biomaterial production

Filamentous fungi can convert a wide variety of naturally occurring chemical compounds, including organic biomass and waste streams, into a range of products. They have long been used for industrial organic acid production and food preparation. In this review, we will discuss production of products such as organic acids, lipids, small molecules, enzymes, materials, and foods, and highlight advances in metabolic and protein engineering, including CRISPR-Cas9-mediated strain improvements. We discuss to what extent these products are already being made on a commercial scale, as well as what is still required to make certain promising concepts industrially and commercially relevant. Despite significant progress, the systematic application of synthetic biology to filamentous fungi remains in its infancy, with many opportunities for discovery and innovation as new strains and genetic tools are developed. The integration of fungal biotechnology into circular and bio-based economies promises to address critical challenges in waste management, resource sustainability, and the development of new materials for terrestrial and extraterrestrial applications, but requires further developments in genetic engineering and process design.

09 BIOMASS FUELS↗

NASA NTRS2004

Optimum Actuator Selection with a Genetic Algorithm for Aircraft Control

The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. For example, the desired actuators produce a pure roll moment without at the same time causing much pitch or yaw. For a typical wing, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements and mission constraints. A genetic algorithm has been developed for finding the best placement for four actuators to produce an uncoupled pitch moment. The genetic algorithm has been extended to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control. A simplified, untapered, unswept wing is the model for each application.

Rogers, James L.↗

NASA NTRS1994

(abstract) Modeling Protein Families and Human Genes: Hidden Markov Models and a Little Beyond

We will first give a brief overview of Hidden Markov Models (HMMs) and their use in Computational Molecular Biology. In particular, we will describe a detailed application of HMMs to the G-Protein-Coupled-Receptor Superfamily. We will also describe a number of analytical results on HMMs that can be used in discrimination tests and database mining. We will then discuss the limitations of HMMs and some new directions of research. We will conclude with some recent results on the application of HMMs to human gene modeling and parsing.

Hidden Markov Models HMMs proteins computational m↗

NASA NTRS2024

Metabolic Stress in Space: ROS-Induced Mutations in Mice Hint at A New Path to Cancer

Long-duration spaceflight beyond Earth's magnetosphere poses serious health risks, including muscle atrophy, bone loss, liver and kidney damage, and the Spaceflight-Associated Neuro-ocular Syndrome (SANS). RNA-seq of mice aboard the International Space Station (ISS) for 37 days revealed extraordinary hypermutation in tissue-specific genes, with guanine base conversion predominating, potentially contributing to spaceflight-associated health risks. Our results suggest that the genome-wide accelerated mutation that we measured, seemingly independent of radiation dose, was induced by oxidative damage from higher atmospheric carbon dioxide (CO 2 ) levels and increased reactive oxygen species (ROS) on the ISS. This accelerated mutation, faster via RNA transcription than replication and more numerous than by radiation alone, unveils novel hotspots in the mammalian proteome. Notably, these hotspots correlate with commonly mutated genes across various human cancers, highlighting the ISS as a crucial platform for studying accelerated mutation, genome instability, and the induction of disease-causing mutations in model organisms. Our results suggest that metabolic processes can contribute to somatic mutation, and thus may play a role in the development of cancer. A metabolic link to genetic instability potentially has far-reaching implications for various diseases, with implications for human health on Earth and in space.

mice↗

NASA NTRS2004

Chondrites and the Protoplanetary Disk, Part 2

Contents include the following: On the Dynamical Evolution of a Nebula and Its Effect on Dust Coagulation and the Formation of Centimeter-sized Particles. The Mineralogy and Grain Properties of the Disk Surfaces in Three Herbig Ae/Be Stars. Astrophysical Observations of Disk Evolution Around Solar Mass Stars. The Systematic Petrology of Chondrites: A Consistent Approach to Assist Classification and Interpretation. Understanding Our Origins: Formation of Sun-like Stars in H II Region Environments. Chondrule Crystallization Experiments. Formation of SiO2-rich Chondrules by Fractional Condensation. Refractory Forsterites from Murchison (CM2) and Yamato 81020 (CO3.0) Chondrites: Cathodoluminescence, Chemical Compositions and Oxygen Isotopes. Apparent I-Xe Cooling Rates of Chondrules Compared with Silicates from the Colomera Iron Meteorite. Chondrule Formation in Planetesimal Bow Shocks: Physical Processes in the Near Vicinity of the Planetesimal. Genetic Relationships Between Chondrules, Rims and Matrix. Chondrite Fractionation was Cosmochemical; Chondrule Fractionation was Geochemical. Chondrule Formation and Accretion of Chondrite Parent Bodies: Environmental Constraints. Amoeboid Olivine Aggregates from the Semarkona LL3.0 Chondrite. The Evolution of Solids in Proto-Planetary Disks. New Nickel Vapor Pressure Measurements: Possible Implications for Nebular Condensates. Chemical, Mineralogical and Isotopic Properties of Chondrules: Clues to Their Origin. Maximal Size of Chondrules in Shock-Wave Heating Model: Stripping of Liquid Surface in Hypersonic Rarefied Gas Flow. The Nature and Origin of Interplanetary Dust: High Temperature Components. Refractory Relic Components in Chondrules from Ordinary Chondrites. Constraints on the Origin of Chondrules and CAIs from Short-lived and Long-lived Radionuclides. The Genetic Relationship Between Refractory Inclusions and Chondrules. Contemporaneous Chondrule Formation Between Ordinary and Carbonaceous Chondrites. Chondrules and Isolated Grains in the Fountain Hills Bencubbinite. Implications of Chondrule Formation in a Gas of Solar Composition. Implications of Meteoritic Cl-36 Abundance for the Origin of Short-lived Radionuclides in the Early Solar System. Size Sorting and the Chondrule Size Spectrum. Comparative Study of Refractory Inclusions from Different Groups of Chondrites. In Situ Investigation of Mg Isotope Distributions in an Allende CAI by Combined LA-ICPMS and SIMS Analyses Photochemical Speciation of Oxygen Isotopes in the Solar Nebula.

Source record↗

DOE OSTI2026

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been thoroughly investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impact on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and data quality, dominate detection performance. Increases in spectral noise and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol %. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying an ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Fungi↗