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NASA NTRS2000

Pollination and embryo development in Brassica rapa L. in microgravity

Plant reproduction under spaceflight conditions has been problematic in the past. In order to determine what aspect of reproductive development is affected by microgravity, we studied pollination and embryo development in Brassica rapa L. during 16 d in microgravity on the space shuttle (STS-87). Brassica is self-incompatible and requires mechanical transfer of pollen. Short-duration access to microgravity during parabolic flights on the KC-135A aircraft was used initially to confirm that equal numbers of pollen grains could be collected and transferred in the absence of gravity. Brassica was grown in the Plant Growth Facility flight hardware as follows. Three chambers each contained six plants that were 13 d old at launch. As these plants flowered, thin colored tape was used to indicate the date of hand pollination, resulting in silique populations aged 8-15 d postpollination at the end of the 16-d mission. The remaining three chambers contained dry seeds that germinated on orbit to produce 14-d-old plants just beginning to flower at the time of landing. Pollen produced by these plants had comparable viability (93%) with that produced in the 2-d-delayed ground control. Matched-age siliques yielded embryos of equivalent developmental stage in the spaceflight and ground control treatments. Carbohydrate and protein storage reserves in the embryos, assessed by cytochemical localization, were also comparable. In the spaceflight material, growth and development by embryos rescued from siliques 15 d after pollination lagged behind the ground controls by 12 d; however, in the subsequent generation, no differences between the two treatments were found. The results demonstrate that while no stage of reproductive development in Brassica is absolutely dependent upon gravity, lower embryo quality may result following development in microgravity.

short duration↗

DOE OSTI2023

Machine learning-based inversion for acoustic impedance with large synthetic training data: Workflow and data characterization

Where wells are sparse or training data are difficult to label with high-quality wireline-derived impedance logs, machine learning (ML)-based inversion of acoustic impedance typically depends on small training data sets, leading to biased prediction. We have advanced a novel workflow that applies large synthetic seismic training data to reduce facies-related bias. Using a geologically realistic model as the truth model, we randomly select sparse seed wells to perform sequential Gaussian simulation (SGS) for impedance models of the same geometry and simulate facies variability. We implement random forest regression on 30 features extracted from the synthetic volume. We observe that more seed wells tend to reduce facies-induced bias by sampling more types of facies, resulting in a better prediction. We then focus on the responses of SGS models to facies changes, the number of seed wells necessary for a useful synthetic model, and how much a synthetic model can help ML-based inversion. Here, we observe that the SGS synthetic training model outperforms well-direct training in general. For modeled clastic shore-zone systems in Miocene Gulf of Mexico, two or more seed wells are necessary for a significant reduction of root-mean-square error and outliners, and improvement of facies imaging. In a field-data test, we apply a similar workflow to quantitatively predict acoustic impedance, which is then converted to a sand-volume map at a high-frequency sequence (10–100 m), revealing detailed facies and sandstone patterns. Such results are valuable in many geologic and engineering applications, such as hydrocarbon and CO 2 reservoir prospecting, reserve estimation, simulation, etc.

3D seismic↗

NASA NTRS2003

Seed-to-seed growth of Arabidopsis thaliana on the International Space Station

The assembly of the International Space Station (ISS) as a permanent experimental outpost has provided the opportunity for quality plant research in space. To take advantage of this orbital laboratory, engineers and scientists at the Wisconsin Center for Space Automation and Robotics (WCSAR), University of Wisconsin-Madison, developed a plant growth facility capable of supporting plant growth in the microgravity environment. Utilizing this Advanced Astroculture (ADVASC) plant growth facility, an experiment was conducted with the objective to grow Arabidopsis thaliana plants from seed-to-seed on the ISS. Dry Arabidopsis seeds were anchored in the root tray of the ADVASC growth chamber. These seeds were successfully germinated from May 10 until the end of June 2001. Arabidopsis plants grew and completed a full life cycle in microgravity. This experiment demonstrated that ADVASC is capable of providing environment conditions suitable for plant growth and development in microgravity. The normal progression through the life cycle, as well as the postflight morphometric analyses, demonstrate that Arabidopsis thaliana does not require the presence of gravity for growth and development. c2003 COSPAR. Published by Elsevier Ltd. All rights reserved.

ISS Project↗

DOE OSTI2022

Purple acid phosphatase2 stimulates a futile cycle of lipid synthesis and degradation, and mitigates the negative growth effects of triacylglycerol accumulation in vegetative tissues

Storage lipids (mostly triacylglycerols, TAGs) serve as an important energy and carbon reserve in plants and hyperaccumulation of TAG in vegetative tissues can cause negative effects on plant growth. Purple acid phosphatase2 (PAP2) was shown previously to affect carbon metabolism and boost plant growth. However, the effects of PAP2 on lipid metabolism remain unknown. Here, we demonstrated PAP2 as a factor that can stimulate a futile cycle of fatty acid (FA) synthesis and degradation and mitigate negative growth effects associated with TAG accumulation in vegetative tissues. Constitutive expression of PAP2 in Arabidopsis thaliana enhanced both lipid synthesis and degradation in leaves and led to substantial increase in seed oil yield. Suppressing lipid degradation in a PAP2-overexpressing line by disrupting sugar-dependent1 (SDP1), a predominant TAG lipase, significantly elevated vegetative TAG content and improved plant growth. Diverting FAs from membrane lipids to TAGs in PAP2-overexpressing plants by constitutively expressing phospholipid:diacylglycerol acyltransferase1 (PDAT1) increased TAG contents to up to 6% of dry weight in vegetative tissues without compromising biomass yield. Overall, these results highlight the potential of combining PAP2 with TAG-promoting factors to enhance carbon assimilation, FA synthesis and allocation to TAGs for optimized plant growth and storage lipid accumulation in vegetative tissues.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Data for Purple Acid Phosphatase2 Stimulates a Futile Cycle of Lipid Synthesis and Degradation, and Mitigates the Negative Growth Effects of Triacylglycerol Accumulation in Vegetative Tissues

Storage lipids (mostly triacylglycerols, TAGs) serve as an important energy and carbon reserve in plants, and hyperaccumulation of TAG in vegetative tissues can have negative effects on plant growth. Purple acid phosphatase2 (PAP2) was previously shown to affect carbon metabolism and boost plant growth. However, the effects of PAP2 on lipid metabolism remain unknown. Here, we demonstrated that PAP2 can stimulate a futile cycle of fatty acid (FA) synthesis and degradation, and mitigate negative growth effects associated with high accumulation of TAG in vegetative tissues. Constitutive expression of PAP2 in Arabidopsis thaliana enhanced both lipid synthesis and degradation in leaves and led to a substantial increase in seed oil yield. Suppressing lipid degradation in a PAP2-overexpressing line by disrupting sugar-dependent1 (SDP1), a predominant TAG lipase, significantly elevated vegetative TAG content and improved plant growth. Diverting FAs from membrane lipids to TAGs in PAP2-overexpressing plants by constitutively expressing phospholipid:diacylglycerol acyltransferase1 (PDAT1) greatly increased TAG content in vegetative tissues without compromising biomass yield. These results highlight the potential of combining PAP2 with TAG-promoting factors to enhance carbon assimilation, FA synthesis and allocation to TAGs for optimized plant growth and storage lipid accumulation in vegetative tissues.

Biomass Analytics↗

DOE OSTI2020

Genarris 2.0: A Random Structure Generator for Molecular Crystals

Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. Genarris finds the experimental structures of the two polymorphs of benzene and the three polymorphs of glycine. Program summary Program Title: Genarris 2.0 Program Files doi: http://dx.doi.org/10.17632/grx6mz4pjn.1 Licensing provisions: BSD-3 Clause Programming language: Python, C External routines/libraries: Spglib, ASE, pymatgen, SciPy, mpi4py, scikit-learn, PyTorch, FHI-aims. Nature of problem: Molecular crystal structure prediction. Solution method: Genarris 2.0 generates molecular crystal structures over the 230 space groups, on general and special Wyckoff positions, using physical constraints. Down-sampling of the generated structures may be performed subsequently, based on molecular crystal packing descriptors and an unsupervised machine learning algorithm. Lastly, ab initio structure relaxation may be performed for the final pool. Depending on the user-defined workflow implemented, Genarris may be used to generate diverse molecular crystal datasets to seed evolutionary algorithms or to train machine learning algorithms or as a standalone crystal structure prediction method. Restrictions: For crystal structure generation, the molecule of interest must be semi-rigid with no bond rotational degrees of freedom. Unusual features: Genarris 2.0 is a highly distributed program, making use of MPI for Python parallelization. The user has the ability to design and implement workflows by executing a user-defined list of procedures. Genarris 2.0 offers new features including a machine learning model for estimating the molecular volume in the solid state from the single molecule structure, structure generation in special Wyckoff positions of space groups, hierarchical structure checks including rigorous treatment of non-orthogonal structures, and clustering and down-selection workflows combining first principles simulations with machine learning. (C) 2020 Elsevier B.V. All rights reserved.

Crystal structure prediction↗

NASA NTRS2014

Chirality of Meteoritic Free and IOM-Derived Monocarboxylic Acids and Implications for Prebiotic Organic Synthesis

The origin of homochirality and its role in the development of life on Earth are among the most intriguing questions in science. It has been suggested that carbonaceous chondrites seeded primitive Earth with the initial organic compounds necessary for the origin of life. One of the strongest pieces of evidence supporting this theory is that certain amino acids in carbonaceous chondrites display a significant L-enantiomeric excess (ee), similar to those use by terrestrial life. Analyses of ee in meteoritic molecules other than amino acids would shed more light on the origins of homochirality. In this study we investigated the stereochemistry of two groups of compounds: (1) free monocarboxylic acids (MCAs) from CM2 meteorites LON 94101 and Murchison; and (2) the aliphatic side chains present in the insoluble organic matter (IOM) and extracted in the form of monocarboxylic acids (MCAs) from EET 87770 (CR2) and Orgueil (CI1). Contrary to the well-known ee observed for amino acids in meteorites, we found that meteoritic branched free and IOM-derived MCAs with 5-8 carbon atoms are essentially racemic. The racemic nature of these compounds is used to discuss the possible influence of ultraviolet circularly polarized light (UVCPL) and aqueous alterations on the parent body on chirality observed in in carbonaceous chondrites. 2014 Elsevier Ltd. All rights reserved.

monocarboxylic acids↗