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At least 91 records · Page 5

Bio‐Imaging quorum sensing signal molecules in a soil‐mimic gel

Rhizosphere, the narrow region surrounding plant roots directly influenced by root exudates and the root associated microbiome, plays an important role in plant productivity and the rhizosphere is well known for stimulating microbial metabolic activities. How microbial communities interact to form stable, metabolically interconnected functional communities is an area of intense interest. The question remains on how microbially produced secreted molecules that function as intercellular communication signals shape the structure and function of microbial communities. Our goal is to detect and quantify signal molecules produced by microbes or plants in the root‐soil environment, spatially and temporarily. As a proof‐of‐concept, we are imaging diffusible extracellular microbial metabolites involved in a bacteria‐bacteria communication process called quorum‐sensing. Quorum‐sensing relies on the accumulation of high concentrations of signal molecules in the environment to control bacterial gene expression, influencing rhizosphere colonization and plant health. Local concentration of quorum sensing molecules are determined using aptamer based sensors. Aptamers that can specifically bind the desired signal molecules are selected through SELEX and immobilized on the surface of nano‐porous membranes. Binding of the signal molecules and aptamer covered surface results in changes in surface charge distribution and steric hinderance and thus modifying the transmembrane ionic transport. Changes in transmembrane impedance can be measured through electrochemical impedance spectroscopy methods to monitor local concentrations of signal molecules. Sensor responses were determined for different concentrations of signal molecules and results showed detection of C4‐HSL in a soil‐mimic solution with K D of 10 nM. We demonstrate that the quorum‐sensing signal molecule C4‐homoserine lactone and (C4‐HSL) can rapidly diffuse in a soil‐mimic gel, providing evidence for our use of a soil‐mimic for further aptasensor development and validation. The sensors were then inserted into soil mimic gel for monitoring C4‐HSL diffusion and the resulted impedance changes were used to determine the C4‐HSL concentration at different positions in the gel. Measurements of local C4‐HSL concentrations variation and numerical solution of diffusion equation were used to create 4D images of C4‐HSL molecule diffusion in the soil‐mimic gel.

Jiang, Nianyu↗

Wavefront shaping with a Hadamard basis for scattering soil imaging

Here, soil is a scattering medium that inhibits imaging of plant-microbial-mineral interactions that are essential to plant health and soil carbon sequestration. However, optical imaging in the complex medium of soil has been stymied by the seemingly intractable problems of scattering and contrast. Here, we develop a wavefront shaping method based on adaptive stochastic parallel gradient descent optimization with a Hadamard basis to focus light through soil mineral samples. Our approach allows a sparse representation of the wavefront with reduced dimensionality for the optimization. We further divide the used Hadamard basis set into subsets and optimize a certain subset at once. Simulation and experimental optimization results demonstrate our method has an approximately seven times higher convergence rate and overall better performance compared to that with optimizing all pixels at once. The proposed method can benefit other high-dimensional optimization problems in adaptive optics and wavefront shaping.

47 OTHER INSTRUMENTATION↗

Inferring microbial interactions with their environment from genomic and metagenomic data

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized , and so name our tool MetConSIN: Met abolically Con textualized S pecies I nteraction N etworks.

59 BASIC BIOLOGICAL SCIENCES↗

Five key aspects of metaproteomics as a tool to understand functional interactions in host-associated microbiomes

Host-associated microbial communities (microbiomes) play critical roles in human, animal, and plant health and development. However, interactions between the host, members of the microbiome, and invading pathogens are in most cases still poorly understood. Such interactions are multidimensional and can alter the taxonomic composition and/or the functional metabolic activities of the microbiome in response to disease or treatment conditions. For example, after 2 days of antibiotic treatment, the mouse gut microbiome is altered and more susceptible to invasion by the pathogen Clostridioides difficile. Studies of these multidimensional interactions have been fueled by the ability to use high-throughput sequencing of phylogenetic marker genes to profile microbial community composition and shotgun metagenomics to profile functional potential. However, many protein-coding genes predicted from metagenomes are not necessarily expressed under a given condition, and thus, it is difficult to assess the activities and functional interactions in microbial communities based on DNA sequencing data alone. The physiological and pathological processes expressed in these communities under specific conditions are better reflected by the abundances of transcripts or proteins. In this Pearl, we provide a brief introduction to metaproteomics, which is a tool for the large-scale analysis of proteins in microbiomes that allows researchers to address a diversity of questions related to functions and interactions in microbiomes. The term “metaproteomics” was first used in 2004 for “the large-scale characterization of the entire protein complement of environmental microbiota at a given point in time”, and since then, a large array of metaproteomics approaches have been developed. Our objective in this Pearl is to highlight what we feel are 5 essential elements to be considered for a metaproteomics research campaign and to introduce nonexpert readers to the topic without going into too much technical detail.

59 BASIC BIOLOGICAL SCIENCES↗

Multispectral and Thermal Imager Onboard Aerial Platforms Instrument Handbook

The MicaSense Altum imager is an off-the-shelf, synchronized multispectral and thermal camera, combined with a Global Positioning System (GPS) unit and downwelling light sensor (DLS). The Altum takes images of the land surface within its field of view (FOV) across five visible bands and one longwave infrared thermal band. The sensor records the radiance and converts it to digital numbers. Imagery is radiometrically corrected, taking into account the sensor calibration, lens distortions, vignette effects, sun angle, and atmospheric effects (scattering and absorption). The photogrammetry software Agisoft PhotoScan v 1.4 is used to align and stitch the images into a larger composite image using the technique of structure from motion image capture to construct a dense cloud and 3D model of the surface, which is used to produce a digital elevation model of the terrain surveyed and orthomosaic imagery. Land surface leaf area index, albedo, and surface skin temperature are provided for the user. The user can perform raster calculations on the imagery to produce various other vegetative indices, which are used to indicate plant health and land surface characteristics, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Water Index (NDWI), surface temperature, and Enhanced_vegetation_index (EVI). This code is provided with the readme file for each data set. The images have a resolution of 20-60 cm/pixel, which are subsampled to 100 cm/pixel.

47 OTHER INSTRUMENTATION↗

Siena College 2022 MRA Genome Announcement Narrative - 6 Isolates

Soil bacteria and endospore forming bacteria in particular play critical environmental roles in biomass degradation, plant health, and many have also been sources for commercially important secondary metabolites including antibiotics. Sequencing multiple isolates of known species also can identify new genes and gene clusters not found in other members of the same species, increasing our understanding of microbial diversity.

59 BASIC BIOLOGICAL SCIENCES↗

Five draft genome assemblies from Bacillaceae isolated from a degraded wetland environment

Abstract We isolated 5 Bacillaceae from a degraded wetland environment and sequenced their genomes using Illumina NextSeq. Here, we report draft genome sequences of Bacillus velezensus-SC119, Priestia megaterium-SC120, Bacillus zhangzhouensis-SC123, Bacillus pumilis-SC124, and Bacillus idriensis-SC127. The genomes range between 3,657,353 and 5,772,725 base pairs with %GC between 37.62% and 46.38%. Introduction Wetland environments play critical roles in the terrestrial carbon and water cycles and microbial communities are key players in healthy ecosystem function. Endospore forming bacteria in the Bacillaceae family are metabolically and genomically diverse soil heterotrophs that influence plant health, carbon and nitrogen cycling, and often produce diverse natural products that influence other bacterial and non-bacterial species in their environment (1). We collected two soil samples on January 19, 2023 from 42°43'12.7"N 73°45'01.4"W. One was highly hydrated and within a patch of invasive common reeds (Phragmites sp.) and the other was near the base of an Eastern cottonwood tree (Populus deltoides). Bacillus pumilis strain SC124 was isolated from the soil from near the cottonwood tree, while Bacillus velezensis strain SC119, Priestia megaterium strain SC120, Bacillus zhangzhouensis strain SC123, Bacillus idriensis strain SC127 and were isolated from the marshy soil.

isolate, wetlands, genome announcement↗

The Fight against Plant-Parasitic Nematodes: Current Status of Bacterial and Fungal Biocontrol Agents

Plant-parasitic nematodes (PPNs) are among the most notorious and underrated threats to food security and plant health worldwide, compromising crop yields and causing billions of dollars of losses annually. Chemical control strategies rely heavily on synthetic chemical nematicides to reduce PPN population densities, but their use is being progressively restricted due to environmental and human health concerns, so alternative control methods are urgently needed. Here, we review the potential of bacterial and fungal agents to suppress the most important PPNs, namely Aphelenchoides besseyi, Bursaphelenchus xylophilus, Ditylenchus dipsaci, Globodera spp., Heterodera spp., Meloidogyne spp., Nacobbus aberrans, Pratylenchus spp., Radopholus similis, Rotylenchulus reniformis, and Xiphinema index.

Pires, David (ORCID:0000000230383319)↗

Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models.

47 OTHER INSTRUMENTATION↗

Processed Surface Imagery & Raster Imagery taken onboard TigerShark (U3) UAS

Land-surface orthomosaic imagery derived from the images collected using the Micasense Altum Imager on board the UAS TigerShark (U3) on March 11th, 2021 at an altitude of 2000 ft. The Altum takes images of the land surface across five visible bands and one long wave infrared thermal band. The photogrametry software Agisoft PhotoScan v 1.4 is used to align and stitch the images into a larger composet image using the technique of structure from motion image capture to construct a dense cloud and 3-D model of the surface, which is used to produce a digital elevation model of the terraine surveyed and orthomosaic imagery. This dataset contains the raster calculated orthomosaics of various vegetative indicies, which are used to indicate plant health and land surface characteristics, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Water Index (NDWI), surface temperature, and Enhanced_vegetation_index (EVI). These orthomosaics were collected west of the Starkville, MS, airport, and include natural, agricultural, and industrial land coverage. The images have a resolution of 0.2 m/pix.

54 ENVIRONMENTAL SCIENCES↗

Implementation of sensor and control designs for bioregenerative systems

The EGM 4000/4001 Engineering Design class is an interdisciplinary design course that allows students to experience the design process. The projects involved the design of sensors and subsystems of a closed-loop life support system (CLLSS) with special emphasis on the Controlled Ecological Life Support System (CELSS) currently being developed at Kennedy Space Center (KSC) by NASA. To understand the work performed by the students, one must understand the purpose and concept of a CLLSS system. In the years to come, NASA will be constructing Moon bases and sending astronauts to other worlds on extended space missions. In order to support the crews, unreasonably large quantities of supplies would have to be sent from Earth. These supplies would be difficult to transport and require large holds. To remedy this problem, NASA plans to incorporate crops into the spacecraft. These crops would supply food for the crews, as well as provide beneficial psychological side effects. In addition, the plants would recycle the air and human waste and provide oxygen and water for the humans. The students in the design class were to work on supporting this project. In order to do this successfully, the course was separated into two phases. The first semester involved studying the various aspects of a CLLSS to determine sensing needs and develop ideas. The second semester involved first determining which of the ideas were most promising. Specific sensors were then designed and tested under laboratory conditions with promising results. Finally, recommendations for further development were proposed. Atmosphere and temperature control, nutrient delivery, plant health and propagation, and resource recycling are discussed.

Source record↗

Relating Land Cover Change to Runoff Distribution Using NASA Earth Observations in Riley County, Kansas

Riley County, Kansas, has observed increased levels of flooding, potentially due to changes in land use/land cover (LULC) and seasonal vegetation variation. This study contrasts two methods of generating runoff curve numbers (CN) from 2006-2020. (1) The traditional Soil Conservation Service CN calculation method uses a look-up table and tracked LULC to determine runoff changes. These tables allow for CN values specific to various land and crop cover types and account for various farming best practices but lack flexibility in calculations for various seasons or plant health. (2) A dynamic method employs normalized difference vegetation index (NDVI) compiled over the rainy season each year to calculate CN using seasonal vegetation. This method allows for a more precise analysis of runoff variability within and between rainy seasons because it can be updated with greater temporal detail and it captures higher spatial resolutions by using NDVI as a proxy for LULC. This study further uses inputs from the United States Geologic Survey (USGS) National Land Cover Database (NLCD), the United States Department of Agriculture (USDA) Cropland Data Layer, and Landsat imagery to create more precise LULC raster datasets including both urban cover and crop-specific land use and curve number maps of the area. Results can guide decision makers in the City of Manhattan, Riley County Department of Planning and Development, Riley County Conservation District, the Kansas Forest Service, and the Kansas Department of Health and Environment toward informed decisions on resiliency strategies to address future flooding.

Ella Griffith↗

Riley County Water Resources Project Summary - Comparing Runoff Curve Calculation Methods to Inform Local Resiliency Initiatives in Riley County, Kansas

Riley County, Kansas, has observed increased levels of flooding, potentially due to changes in land use/land cover (LULC) and seasonal vegetation variation. This study contrasts two methods of generating runoff curve numbers (CN) from 2006-2020. (1) The traditional Soil Conservation Service CN calculation method uses a look-up table and tracked LULC to determine runoff changes. These tables allow for land cover-specific CN and account for various farming techniques but lack flexibility in calculations for various seasons or plant health. (2) A dynamic method employs normalized difference vegetation index (NDVI) compiled over the rainy season each year to calculate CN using seasonal vegetation. This method allows for a more precise analysis of runoff variability within and between rainy seasons because it can be updated with greater temporal detail and captures higher spatial resolutions by using NDVI as a proxy for LULC. This study further uses inputs from the United States Geological Survey (USGS) National Land Cover Database (NLCD), the United States Department of Agriculture (USDA) Cropland Data Layer, and Landsat imagery to create more precise LULC raster datasets including both urban cover and crop-specific land use and curve number maps of the area. Results can guide decision makers in the City of Manhattan, Riley County Department of Planning and Development, Riley County Conservation District, the Kansas Forest Service, and the Kansas Department of Health and Environment toward informed decisions on resiliency strategies to address future flooding.

DEVELOP Project Summary↗

Colorado River Basin Water Resources: Pairing Phenology Citizen Science Observations with NASA Earth Observations to Monitor Riparian Vegetation

Native plant species in the Colorado River Basin are currently threatened by non-native species that are changing resource competition dynamics. Monitoring native species is essential for understanding changes in plant health throughout the year as well as how species’ distributions change inter-annually. However, current monitoring of species is limited to local efforts and cannot be efficiently conducted across more extensive regions of the basin. Project partners, including the USA National Phenology Network, United States Geological Survey, and Educating Children Outdoors, provided data and on-the-ground expertise to assist the project. NASA Earth observations, including Landsat 8 Operational Land Imager (OLI), Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI) from the European Space Agency, provided remote sensing data that covers the large spatial extent of the basin. This study combined both data sources over the study period of 2013 – 2019 and explored the relationship between on-the-ground phenology observations and remotely sensed phenology signals. The team found that pairing the datasets required spatial coordinates to be accurately logged and that it was necessary to have consistent observations throughout the year by citizen scientists. The team found promising relationships between phenology observations and the two vegetation indices, normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), when study locations were in an area with a high proportion of vegetation within a satellite pixel. The results and methods outlined in this study create new opportunities to leverage remote sensing alongside citizen science data for widespread monitoring of non-native species.

Tyler Pantle↗

The Effects of Plasma Application on Radish Seeds with Implications for Space Crop Production

In extended space missions, the astronaut diet will consist mostly of prepackaged foods. This could result in nutritional deficiencies due to the gradual breakdown of certain vitamins. To address this deficiency, fresh produce must be grown from seed during spaceflight. Stored seeds, however, can be vulnerable to microbial contamination which could jeopardize plant health and crop food safety. To alleviate this concern, the current practice is to sanitize seeds on the ground before spaceflight to the International Space Station (ISS). Methods of seed sanitization include alcohol soaking and chlorine gas fumigation, which have harmful effects on the environment and human health. Plasma application is a new sanitization approach that avoids these negative side effects while potentially elevating germination rate and improving growth rate; yet plasma application requires specific exposure time, power, and pressure to achieve these benefits. In the present study, Raphanus sativus ‘Cherry Belle’ radish seeds were exposed to either low pressure (Diener) plasma or atmospheric pressure plasma for varying increments of time (30s-1200s). Data collected includes immediate germination rate (viability) and microbial log reduction. Additionally, seeds were stored for later germination rate testing. Microbial assay controls have highlighted variability in initial microbial load between individual seeds, and further work is being performed to determine the standard microbial load of an untreated seed. In some samples, a negative log reduction was observed after plasma treatment encouraging further study to determine if the plasma is perforating the seed coat and releasing endophytic microbes. Treatment with Diener plasma shows promising microbial log reduction, but lower viability. In contrast, treatment with atmospheric pressure plasma offers high viability, but poor microbial log reduction. Further study will aim at determining whether plasma treatment is effective at not only sanitization, but sterilization. This research was funded by a NASA grant at the Kennedy Space Center.

Plasma↗

What are Needed Innovations for Space Crop Production That May Also Benefit Vertical Farming?

I am speaking about the space crop production gaps and challenges we face and how we need innovations in the area of plant health monitoring, food safety contamination monitoring, CEA IPM, understanding of and manipulation of the microbiome/ecosystem, sustainability needs, novel sensors needed, automation and robotics, and also new crops that could be developed and ways that our needs parallel those of the vertical farming industry. (Note: This presentation is in video mp4 format is only viewable by download)

Gioia Massa↗

The Effects of Plasma Application on Radish Seeds with Implications for Space Crop Production

In extended space missions, the astronaut diet will consist mostly of prepackaged foods. This could result in nutritional deficiencies due to the gradual breakdown of certain vitamins. To address this deficiency, fresh produce must be grown from seed during spaceflight. Stored seeds, however, can be vulnerable to microbial contamination which could jeopardize plant health and crop food safety. To alleviate this concern, the current practice is to sanitize seeds on the ground before spaceflight to the International Space Station (ISS). Methods of seed sanitization include alcohol soaking and chlorine gas fumigation, which have harmful effects on the environment and human health. Plasma application is a new sanitization approach that avoids these negative side effects while potentially elevating germination rate and improving growth rate; yet plasma application requires specific exposure time, power, and pressure to achieve these benefits. In the present study, Raphanus sativus ‘Cherry Belle’ radish seeds were exposed to either low pressure (Diener) plasma or atmospheric pressure plasma for varying increments of time (30s-1200s). Data collected includes immediate germination rate (viability) and microbial log reduction. Additionally, seeds were stored for later germination rate testing. Microbial assay controls have highlighted variability in initial microbial load between individual seeds, and further work is being performed to determine the standard microbial load of an untreated seed. In some samples, a negative log reduction was observed after plasma treatment encouraging further study to determine if the plasma is perforating the seed coat and releasing endophytic microbes. Treatment with Diener plasma shows promising microbial log reduction, but lower viability. In contrast, treatment with atmospheric pressure plasma offers high viability, but poor microbial log reduction. Further study will aim at determining whether plasma treatment is effective at not only sanitization, but sterilization. This research was funded by a NASA grant at the Kennedy Space Center.

Space Crop Production↗

Drought Monitoring with Drones: A Hundred Fields at a Time

Drought frequency and severity are likely to increase due to global warming. Droughts already have a substantial negative influence on agriculture and the economy and finding ways to reduce their effects could have a monumental impact. Although NASA already has satellites deployed to collect drought data, these satellites are more for global drought indexing than local. To alleviate droughts on a local level, this paper proposes the use of drones to map soil moisture, plant health, and other drought indicators. The proposed drone design is a fixed-wing UAV equipped with a hyperspectral camera, a LiDAR sensor, and an array of weather sensors. These tools will permit it to reliably capture the necessary data to enhance suggestions on improving drought management practices. The collected data from the drones will be deployable in many ways, including for agricultural and non-agricultural applications. The hyperspectral camera has applications in monitoring the health of crops within a field to direct relief measures to the crops most in need. Thermal and LiDAR imaging can be deployed for locating leaks, predicting shortages of water bodies, and determining a field’s water needs. To implement a drought-monitoring drone, the recommended steps include building a prototype drone design that is equipped with the outlined instruments. The prototype drone could then be deployed to collect training data to guide a neural network that would provide interpretations and predictions from the data for users. After the prototype design is iterated upon, it will be ready for deployment and inform water management methods to serve the world in our battle against drought.

climate change↗