The Columbia root-knot nematode (Meloidogyne chitwoodi) is a destructive soil borne pest that can cause serious economic damage to potato tubers within infected, unfumigated fields. There are very few known sources of genetic resistance to root-knot nematodes and no released potato cultivars exhibit this trait. Literature indicates that nematode resistance introgressed from Solanum bulbocastanum is dominantly inherited across many genetic backgrounds. We generated a 32 family half-diallel progeny test population utilizing 3 root-knot nematode resistant clones (female) and 11 clones (male) with russet skin type (1,600 clones, between 25 – 60 clones per family). In 2023, 1,200 progeny were evaluated relative to 6 control varieties planted at high replication at the Washington State University Experiment Station in Othello, WA. A scale, RGB-D imaging conveyor, and index scoring system was used to record: total yield, the number of tubers per plant, tuber size distribution, aspect ratio, skin color, starch content, and defect severity from all samples within this population. The distribution of phenotypic values observed from the progeny suggest that choice of parent is a highly significant factor that influences almost all traits evaluated. The phenotyping strategy utilized by our team is inexpensive, expandable, and flexible enough to be adopted by many different types of vegetable breeding programs. Data from this experiment is being used to develop potato tuber defect classification models and assess the utility of adopting genomic selection within our potato breeding program.
Data science is an interdisciplinary subject that uses scientific methodologies, data mining techniques, machine-learning algorithms, and large amounts of data to extract information and insights. The article presents an overview of the current state and future possibilities of Data Science in a variety of sectors, explores the benefits, outlines the frameworks and methodologies employed, explains the current obstacles, and offers feasible solutions.
Light levels change throughout the day, are affected by climate and weather, and are filtered by the local environment. Switching between low and high levels of light over varying periods of time experienced by an organism in its environment shapes the tempo and mode of its light detection system. Plants must respond to dynamic environmental conditions and thus switch between efficient photosynthesis and photoprotection. Receptors on the plasma membrane perceive extracellular signals, such as photosynthetically-fixed sugars, are coupled to cytoplasmic G proteins to transduce information to cytoplasmic proteins and to amplify that signal to bring about changes like photosynthetic efficiency in both short (e.g. enzymatic reactions) and long (e.g. plant development) time scales. While G proteins have been shown to be important in regulating various aspects of stomata and photosynthesis, their role has yet to be fully understood. A regulator of G signaling (RGS) has been shown to sense sugars fixed in photosynthesis. Thus, we hypothesize that RGS mediates responses to dynamic light. The sequenced genomes within the grass family are the only genomes throughout Plantae known to lack RGS. By contrast, Setaria retains the RGS gene. Thus, the RGS gene from Setaria was expressed in rice to better understand the function of RGS. In this study, multiple transgenic events were grown to investigate their phenotypic response. We identified lines with altered stomatal patterning and rates of stomatal closure in response to changing light levels that will be used in future experiments.
Quantifying 3D phenotypic traits for plant shoots and roots is essential to monitor and evaluate plant growth and development. Multi-view stereo (MVS) is a low-cost and widely used photogrammetry method to build 3D point clouds in many agricultural applications. However, it is challenging to adopt MVS directly to obtain complete 3D structures of fine roots for plants such as soybeans. To address this problem, we propose a data processing pipeline incorporating super-resolution (SR) and 3D Gaussian Splatting (GS) to enhance the resolution of 3D root reconstruction, aiming to recover a highly detailed 3D root structure. To this end, first, multi-view images of a soybean root are collected using an RGB camera; second, SR is used to optimize the resolution of the images; third, the processed images are fed to the algorithm structure from motion to obtain a point cloud; and then, 3D GS is applied to enhance the implicit 3D surface reconstruction; finally, perceptual similarity and peak signal-to-noise ratio (PSNR) are used to evaluate the output quality. The method is expected to obtain a high-fidelity 3D reconstruction of plant roots for soybeans and other crops, assisting in the extraction of comprehensive phenotypic traits to accelerate the selection of new varieties for plant breeding.
Hydrological observation networks are inadequate and declining, hindering advancements in hydrology, particularly in hydrological budgeting, where interception loss remains unmeasured, and a subjective loss figure is assumed. Measuring net rainfall reaching the ground surface is crucial for precise water balance estimates. This involves measuring throughfall & stemflow fractions of precipitation on the canopy. Current methods use collector channels and static storage systems requiring periodic visits for measurements, which becomes extremely difficult and unsafe in inaccessible forests, leading to data losses. For time-resolved data, an array of standard rain gauges is placed under the canopy, or water is collected by troughs for throughfall (and collars for stemflow measurement), draining into a central pipe and directed into a tipping bucket flow gauge. Commercial flow gauges are expensive for use in throughfall & stemflow measurement, since, multiple instruments are required in each study location to capture the variability of the canopy. To address this, we designed and fabricated an open-source, low-cost Tipping Bucket Flow Gauge to automatically monitor flow rates of throughfall & stemflow. It is designed to have a larger adjustable tipping resolution (10 ml – 200 ml). The open-source Arduino-based data logger automatically collects time-resolved data and is powered using solar energy, ensuring remote functionality even in harsh environments. A modular electronics approach was followed for designing the datalogger, facilitating rapid prototyping, easy repair, and upgrades, enabling someone with even an introductory knowledge in Arduino to implement the design. Almost 75% of the instrument is 3D printed and can be fabricated using any standard desktop FDM 3D printer and assembled by hand. The instrument showed 86% accuracy in preliminary testing at a calibrated tipping resolution of 120ml. The cost of prototyping was 100$ - 120$, thus proving cost-effective for accurate hydrological budgeting as compared to the cost of the nearest available commercial solution (~1800$). Through our research and product design, we intend to reduce the barrier of entry and simplify the steep learning curve faced in developing hydrological instrumentation.
Advances in quantitative genetics and high-throughput pipelines have allowed for rapid identification of genomic markers associated with changes in phenotype. However, linking those markers to causal genes is still difficult, as many genes may be linked to one marker. We aimed to improve candidate gene selection by creating a new method that identifies conserved genes underlying GWAS loci in multiple species. So far, we have tested this method in two different experiments: 1) using GWAS from Arabidopsis, soybean, rice, maize, and sorghum measuring 19 elemental uptake (ionomic) traits and 2) GWAS from Arabidopsis, soybean, rice, and maize measuring seed weight traits. We identified 14,336 candidates in the ionomics GWAS comparison. The most likely candidates belonged to ortholog groups linked to GWAS loci in all five species for their given trait according to calculations using random permutations of the data. For the seed weight GWAS comparison, we identified 192 candidates, and again, the most likely candidates belonged to ortholog groups linked to GWAS loci in all species in the comparison. Focusing on these most likely candidate genes from Arabidopsis, we obtained T-DNA lines with mutant alleles for each candidate gene and performed a high-throughput phenotyping screen utilizing ICP-MS for ionomics and the image analysis software PlantCV for seed weight. Preliminary results show 59 ionomic candidates and 9 seed weight candidates have one line with confirmed phenotypes. We plan to further verify these preliminary confirmations by obtaining and screening additional T-DNA lines with different alleles for each candidate gene.
In recent years, transformer-based models like BERT and ChatGPT/GPT-3/4 have shown remarkable performance in various natural language understanding tasks. However, it's crucial to note that while these models exhibit impressive surface-level language understanding, they may not truly understand the intent and meaning beyond the superficial sentences. This paper is a survey of studies of the popular Large Language Models (LLMs) from various research and industry papers and review the abilities in term of comprehending language understanding like what human have, revealing key challenges and limitations associated with popular LLMs including BERTology and GPT alike models.
aInstitute of Environmental Management, Faculty of Earth Science, University of Miskolc, 3515 Miskolc- Egyetemváros, Hungary; [email protected]; [email protected]; [email protected]. bGeology Department, Faculty of Science, Beni-Suef University, Beni-Suef, 65211, Egypt; [email protected] * Corresponding author: Mohamed Hamdy Eid a,b*; [email protected] ORCID: 0000-0002-3383-1826Final Paper Number: H31U-1778 Presentation Type: Poster Session Number and Title: H31U: Frontiers in Water Quality I Poster Session Date and Time: Wednesday, December 13th; 8:30 AM - 12:50 PM PST Location: MC, Poster Hall A-C – SouthAbstract The current study evaluates the different factors threatening the sustainability of Siwa Oasis including soil salinization, water quality deterioration, water logging, depletion of non-rechargeable water resources and providing water management plan. GIS and remote sensing supported with machine learning were used for change detection in the land cover from 1990 to 2020. The hydrodynamic condition in the deep Nubian sandstone aquifer (NSSA) was investigated using pressure-depth pro le. The groundwater salinity was monitored from 1998 to 2022. Geochemical model using PHREEQC was conducted to detect the types of minerals that have the ability to precipitate in the soil from irrigation water and decrease its permeability. The change detection in the land cover showed rapid increase in the surface area of the salt lakes from 22.6 km2 in 1990 to 60.6 km2 in 2020. The soil salinization increased in the central Siwa Oasis due to evaporation of water logged in the soil. Monitoring the water salinity from 1998 to 2022 showed rapid deterioration in groundwater quality of the Tertiary carbonate aquifer (TCA). The pressure-depth pro le showed that the water in NSSA is over hydrostatic pressure in the eastern and western part of the study area and the central part is under hydrostatic pressure indicating pressure decrease. Chadha diagram and piper diagram showed that the water type changed upward from Ca-Mg-HCO3 in the rst stage in NSSA to Na-Cl type in the last stage in TCA and surface water. The saturation index revealed that the majority of water samples were supersaturated with respect to calcite, dolomite, talc, Ca-montmorillonite, chlorite, gibbsite, illite, K-mica, hematite, chrysotile and kaolinite, while the samples were undersaturated with halite, anhydrite, gypsum, and CO2. The irrigation water quality indices showed that NSSA is suitable for irrigation purposes while TCA is not suitable for irrigation regarding magnesium hazards (MH) and potential salinity (PS). The water quality regarding sodium adsorption ratio (SAR) and sodium percent (Na%) range from good to poor and good according to residual sodium carbonate (RSC). Application of subsurface drip irrigation, and mixing water of TCA and NSSA could be the best management of the water resources in Siwa Oasis.
Number: 1424249 Marginalized vulnerable coastal communities living along the urban coasts are continuously under the dual threat of natural hazards and the adverse impact of infrastructure development, which results in the increase of cumulative risk for these communities. Further, the irreversible impacts of climate change have also exacerbated the risks associated with aging infrastructure and vulnerable coastal communities. Therefore, strengthening the climate resilience of such communities stands as a duly acknowledged priority for developing nations. One of the possible solutions to strengthen climate resilience is through the development and implementation of sustainable hybrid infrastructure alternatives. In this work, we characterized the data-driven Coastal Infrastructure Resilience Index (CIRI) to assess the performance of existing coastal infrastructure along the coast of Mumbai City in India. This study thoroughly utilized the potential of high-resolution remote-sensing imagery and socio-economic parameters from SEDAC data to derive CIRI. The robustness of the CIRI is improved with integrated value function and expert knowledge. As both grey infrastructure, such as seawalls, levees, and bulkheads and green infrastructure, such as salt marshes, mangroves, beaches, dunes, oysters and coral reefs have limited resilience in a multi-hazard environment, we identified the major hotspots of concerns through CIRI to propose the plausible hybrid (green-grey) infrastructure alternatives (green-grey) using Adaptive Gradient Framework for Mumbai’s coastal context. Adapting Hybrid infrastructure alternatives empowers coastal communities with heightened climate benefits and co-benefits. The major findings of this study contribute as a science-policy instrument to localize the Sustainable Development Goals 11(11.5, 11. b), 13, and 14.2 of the United Nations.Keyword- Integrated Coastal Management, Adaptation, risk-informed, Urban coastal areas, decision-analysis
GRACE(-FO) monthly products are produced routinely using measurements of the orbital motion and the K-Band measurements of the distance between the two satellites. From these solutions, we gained great insight into the water cycle, ice mass balance, and ocean currents for the past twenty years. But these monthly solutions are not the full picture. More information about sub-monthly processes can be extracted from the Level-1B data. Currently, the data used to extract the GRACE-FO orbits contains the K-band ranging (KBR) measurements but not the data from the Laser Ranging Interferometer (LRI). It has been shown several times that using orbits and LRI data, one can calculate residual Line-of-sight gravity signals. These studies mainly focused on the Amazon River region to study changes in the water storage which happen faster than over one month.We use this approach to look at the changes in the speed of the llulissat glacier in Greenland. This is one of the biggest and fastest glaciers in Greenland and significant changes in its speed can happen over as little as one week during exceptional summer melt periods, as demonstrated a.o. by SAR interferometry. We demonstrate how LRI can help to give further constraints on when these changes in speed happen, within the restrictions of the limited spatial resolution.
The Gulf of Mexico (GoM) is a passive rift margin that is shrouded in thick sedimentary layers, making it challenging to trace its Mesozoic evolution. Traditionally, plate tectonic models have required an assumption of rigid plates, limiting our ability to understand the evolution of passive margins given the wealth of geological and geophysical evidence indicating significant crustal deformation during rifting processes. However, recent advances have been made in our ability to incorporate deformation into plate tectonic models.Here, we present a novel approach to reconstruct the evolution of the GoM by using an optimized and focused deformable plate model. Our new model uses a time-evolving focused deformation along the rift, where the strain rate evolves over time from being more uniform initially to increasing exponentially seaward to the point of continental rupture and ocean crust formation.By incorporating time-evolving deformation into our plate reconstruction, we can additionally derive crustal thickness and thermal and tectonic subsidence through time, which allows us to better explore the depositional history of the presalt deposition and crustal architecture evolution of the GoM basin. Our deformation model is optimized to minimize the root mean squared error (RMSE) between predicted present-day crustal thickness and the GEMMA crustal thickness model, resulting in an RMSE of 5.6 km compared to GEMMA, with <2 km absolute error in the northwest and northeast GoM. The resulting tectonic subsidence of ~1.5 km before the Yucatán block drifted in Late Triassic providing routes for the deposition of red beds through the paleo-drainage systems of the northern GoM as successor basin infilling. We find rapid subsidence occurred in the central GoM during the Early Jurassic shifting red bed deposition to a location that presently lies beneath the salt formation, potentially reconciling ~40 Myrs of missing sedimentary strata. Extension rate and stretching factor calculations reveal a transition from a magma-rich to a hyperextended margin, with possible mantle exhumation.Through our study, we highlight the significance of adopting optimized deformable plate reconstruction models, which enables quantitative interpretations of the tectonic history and geological evolution in rift basins globally.
The Cameroon Volcanic Line (CVL) and other tectonic features in Cameroon remain enigmatic, prompting ongoing debates about their detailed structure, composition, and geodynamic evolution. To shed light on these complexities, we leverage the ambient noise tomography (ANT) method to invert shear wave velocity (Vs) and image subsurface structures, providing crucial insights into both subsurface geology and deep crustal processes. Specifically, we employed two different methods: Markov chain Monte Carlo (MCMC) and Evolutionary Algorithm (EA) inversions to robustly constrain the Vs velocity structure, Vp/Vs ratio, and density beneath the CVL and its surrounding area.Our results reveal a prominent high-velocity structure at depths of 25 to 35 km, which precisely aligns with the CVL. Within this region, Vs velocities reach up to 4.0 km/s, accompanied by a Vp/Vs ratio ranging between 1.85 and 1.88 and density varying from 2.9 to 3.1 g/cm3. These characteristics suggest the presence of cooled mafic material that has intruded the crust. Our 2D depth cross-sections along the CVL indicate that these cooled mafic intrusions are ubiquitous along the entire volcanic line. However, they are spatially separated from the upper crust's volcano-plutonic structure by a thin intermediate structure exhibiting a Vp/Vs ratio of 1.68 to 1.71 and an average Vs velocity of 3.8 km/s, indicative of felsic to intermediate crust, which may be linked to the Pan-African Orogeny.The high Vp/Vs ratio and Vs velocity structures are found closer to the surface in the recently active volcanic provinces, accompanied by a thinner low Vp/Vs structure. We posit that this thinned low Vp/Vs structure may have facilitated the ascent of mafic material, contributing to recent volcanic activity in the region. Conversely, beneath the Oubanguides belt and Congo craton, these low Vp/Vs structures appear thicker, with mafic intrusions present at depth > 35 km. This observation suggests a dynamic process involving the pushing and exhumation of lower crustal material by the mafic material.Our crustal imaging results hold significant implications for our understanding of the region's geodynamic evolution, suggesting an interaction with deeper structures, may be responsible for the crustal intrusions and volcanism observed along the CVL.
Observational investigations of Earth's bow shock have highlighted distinct variations in turbulence characteristics when comparing fluctuations in the shock transition with those in the upstream and downstream plasma regions. To gain a more focused understanding in each of these areas, we have examined a range of local 2D and 3D hybrid simulations, using kinetic ions and fluid electrons. Each simulation has been chosen to cover a range of shock geometries, from quasi-parallel to quasi-perpendicular and high to low Mach number. In-situ observations, such as those from the Magnetospheric Multiscale (MMS) mission, are often unable to fully disentangle spatial and temporal effects. This is particularly evident in the shock transition and the magnetosheath, where, for example, whistler waves may have speeds comparable to the bulk flow and thus locally violate Taylor’s hypothesis for kinetic-scale fluctuations. Simulations overcome these limitations, enabling us to model the evolution of turbulence in the shock transition and further downstream. We characterize the turbulent fluctuations using the following three methods: Firstly, we examine the magnetic spectral indices spanning the inertial range and extending into the ion range as they change across the shock. Secondly, we investigate intermittency by means of the scale-dependent kurtosis. Lastly, we quantify the correlation lengths as measured across the shock, offering insights into the physical dimensions of fluctuations at scales smaller than the shock width. We will discuss the application of these measures to simulations in understanding the kinetic-scale behaviour of turbulence at Earth's bow shock.
Speculations extend the opportunity space of possible future climates by increasing the potential to provide plausible estimated qualities and quantities to further scientific research and aid engineering solutions. This novel work outlines the first steps to achieving an Anthropocene reversal that completes in Zoomers’ lifetimes — by 2100. The novel experimental high-scale carbon removal pathway, which was studied in MAGICC 6.8, required CDR to counterbalance all accumulated anthropogenic emissions since 1750 to return to preindustrial temperature (0.07ºC over the 1720-1800 and 0.14ºC over the 1850-1900) means by 2100 and complete GHG phaseouts by 2077, excluding Ammonia. The experimental pathway set extreme front loading of emissions reductions to reach net zero, and avoid tipping points, then achieve scaled removal to reach 300 ppm of CO2 concentration by roughly mid-century. This work’s findings recommend exploring carbon removal of cumulative anthropogenic emissions totaling 600 GtC to 775 GtC on a recent model ensemble with 1.55 to 1.7 times preindustrial CO2 concentration driven by forcings from emissions and calibrate to reproduce present-day temperatures to provide more detailed projections of temperature, holding below 1.5ºC, regional temperatures, below ground CO2 mineralization, sea-level rise, ENSO, AMOC, and jet-stream turnover, evolve. Continued fossil-fuel use is unable to yield complete emissions phaseouts or deep removals necessary to match a preindustrial climate. The findings support the utmost urgency to attain a maximally scaled sustainable zero-carbon intensity green growth development. And reinforce the increased global commitment to achieve net zero sooner and to avoid setting off more climate tipping points. The possibility of reaching a preindustrial climate should help inform the debate of maximally scaled sustainable green growth development for the fastest path to net zero, phase out of anthropogenic emissions sources, and scaled carbon removals with zero-carbon intensity to develop a more equal future world.
Sea ice ridges are an important morphological feature that stabilize shorefast ice across the Northern Alaskan coastline. This stability is important to local communities and ecosystems that rely on this habitat for food security and safety. Investigating the development of shorefast ice around Utqiagvik, AK, we describe an approach to identify grounded ridges throughout the winter season. To do this, we utilize high resolution altimetry data from NASA’s ICESat-2 satellite which provides unprecedented along-track detail that allows, for the first time, the detection of individual pressure ridges. We apply the University of Maryland Ridge Detection Algorithm (Duncan and Farrell, 2022) using ICESat-2 elevation data to identify and calculate ridge sail heights along each satellite track. From these heights, we estimate the depth of the ridge using sail/keel height ratios described in the literature. The calculated ridge depths are compared with high-resolution bathymetric data (NCEI Digital Elevation Model Mosaic) to classify potentially grounded ridges. This methodology for identifying and quantifying grounded ridges in shorefast ice will improve our understanding of coastal ice processes in a changing environment.
The present study tries to assess and mitigate the geohazard-prone area in Barora Coalfield using geophysical methods. Barora being, a significant subsidiary of the Jharia coalfield, plays a crucial role in India's growing energy demand. The study area is affected by coal fire in the subsurface as illustrated by its thermal images. To evaluate subsurface conditions, magnetic data was taken initially. The magnetic data was first then reduced-to-pole and further centroid method was applied to determine the curie depth. The average curie depth was found to be approximately 10.74±0.9 meters, indicating active coal fires beneath this depth. Furthermore, using the multichannel analysis of surface wave (MASW) method, a significant change in velocity gradient was observed at a depth of around 11 meters, indicating variations in the velocity layers. To gain a comprehensive understanding of the subsurface, the ambient noise tomography method was employed. This revealed the presence of a coal seam at an approximate depth of 30 meters and also indicated the existence of abandoned underground galleries below 40 meters depth. Appending the dispersion curve from both seismic methods, we determined that the active coal seam at approximately 30 meters depth extended around 15 meters and was significantly affected by coal fires. The outcomes established the presence of underground galleries, oriented in the NE-SW direction, leading toward the SouthEastern railways which might be prone to subsidence.