Soybean disease control guide - 1982

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Evaluation of magnetic resonance imaging relaxation time in wrist cartilage with scapholunate ligament injury

Objective: The overall goal of this research is to identify completely non-invasive in vivo markers of cartilage degeneration following wrist injury in order to facilitate assessment and treatment of wrist injuries and prevention of osteoarthritis as a result of injury. In this study, the transverse relaxation time, T2, from magnetic resonance imaging (MRI) of the wrist cartilage of subjects exhibiting unilateral scapholunate dissociation was analyzed to evaluate changes in the biochemical status of the cartilage in the wrist following injury. Methods: Data collection consisted of MRI scans of the wrist using 2 separate 3T scanners. Fourteen subjects were analyzed, each subject completed scans to evaluate T2 relaxation times on both their injured and contralateral (normal) wrist. Scans were conducted with a maximum of 0.390625 mm/pixel in-plane pixel size and 1 mm slice thickness. A series of four time echo scans ranging from 15-80 ms were collected. T2 relaxation time for each subject was calculated by registering these echo time scans and fitting the corresponding intensity values to an exponential decay curve. Results: The T2 results from all subjects indicated no statistically significant changes with presence of injury. The use of two separate MRI scanners of the same strength of magnet coil did not cause a significant change in measurement values. Conclusions: Our data suggests that either T2 relaxation time does not change with the presence of scapholunate injury in the wrist or that the change was insufficient to be detected in this study. The results from this study may function as a baseline for future studies examining the potential positive effect surgical repair has on T2 relaxation times

Where Did They Come From, Where Did They Go: Grazing Fireballs

For centuries extremely long grazing fireball displays have fascinated observers and inspired people to ponder about their origins. The Desert Fireball Network is the largest single fireball network in the world, covering about one third of Australian skies. This expansive size has enabled us to capture a majority of the atmospheric trajectory of a spectacular grazing event that lasted over 90 s, penetrated as deep as ∼58.5 km, and traveled over 1300 km through the atmosphere before exiting back into interplanetary space. Based on our triangulation and dynamic analyses of the event, we have estimated the initial mass to be at least 60 kg, which would correspond to a 30 cm object given a chondritic density (3500 kg m-3). However, this initial mass estimate is likely a lower bound, considering the minimal deceleration observed in the luminous phase. The most intriguing quality of this close encounter is that the meteoroid originated from an Apollo-type orbit and was inserted into a Jupiter-family comet (JFC) orbit due to the net energy gained during the close encounter with Earth. Based on numerical simulations, the meteoroid will likely spend ∼200 kyr on a JFC orbit and have numerous encounters with Jupiter, the first of which will occur in 2025 January-March. Eventually the meteoroid will likely be ejected from the solar system or be flung into a trans-Neptunian orbit

Determining Fireball Fates Using the α-β Criterion

As fireball networks grow, the number of events observed becomes unfeasible to manage by manual efforts. Reducing and analyzing big data requires automated data pipelines. Triangulation of a fireball trajectory can swiftly provide information on positions and, with timing information, velocities. However, extending this pipeline to determine the terminal mass estimate of a meteoroid is a complex next step. Established methods typically require assumptions to be made of the physical meteoroid characteristics (such as shape and bulk density). To determine which meteoroids may have survived entry there are empirical criteria that use a fireball's final height and velocity - low and slow final parameters are likely the best candidates. We review the more elegant approach of the dimensionless coefficient method. Two parameters, α (ballistic coefficient) and β (mass loss), can be calculated for any event with some degree of deceleration, given only velocity and height information. α and β can be used to analytically describe a trajectory with the advantage that they are not mere fitting coefficients; they also represent the physical meteoroid properties. This approach can be applied to any fireball network as an initial identification of key events and determine on which to concentrate resources for more in-depth analyses. We used a set of 278 events observed by the Desert Fireball Network to show how visualization in an α-β diagram can quickly identify which fireballs are likely meteorite candidates

Fireball streak detection with minimal CPU processing requirements for the Desert Fireball Network data processing pipeline

The detection of fireballs streaks in astronomical imagery can be carried out by a variety of methods. The Desert Fireball Network uses a network of cameras to track and triangulate incoming fireballs to recover meteorites with orbits and to build a fireball orbital dataset. Fireball detection is done on-board camera, but due to the design constraints imposed by remote deployment, the cameras are limited in processing power and time. We describe the processing software used for fireball detection under these constrained circumstances. Two different approaches were compared: (1) A single-layer neural network with 10 hidden units that were trained using manually selected fireballs and (2) a more traditional computational approach based on cascading steps of increasing complexity, whereby computationally simple filters are used to discard uninteresting portions of the images, allowing for more computationally expensive analysis of the remainder. Both approaches allowed a full night's worth of data (over a thousand 36-megapixel images) to be processed each day using a low-power single-board computer. We distinguish between large (likely meteorite-dropping) fireballs and smaller fainter ones (typical 'shooting stars'). Traditional processing and neural network algorithms both performed well on large fireballs within an approximately 30 000-image dataset, with a true positive detection rate of 96% and 100%, respectively, but the neural network was significantly more successful at smaller fireballs, with rates of 67% and 82%, respectively. However, this improved success came at a cost of significantly more false positives for the neural network results, and additionally the neural network does not produce precise fireball coordinates within an image (as it classifies). Simple consideration of the network geometry indicates that overall detection rate for triangulated large fireballs is calculated to be better than 99.7% and 99.9%, by ensuring that there are multiple double-station opportunities to detect any one fireball. As such, both algorithms are considered sufficient for meteor-dropping fireball event detection, with some consideration of the acceptable number of false positives compared to sensitivity

The Dingle Dell meteorite: A Halloween treat from the Main Belt

We describe the fall of the Dingle Dell (L/LL 5) meteorite near Morawa in Western Australia on October 31, 2016. The fireball was observed by six observatories of the Desert Fireball Network (DFN), a continental-scale facility optimized to recover meteorites and calculate their pre-entry orbits. The 30 cm meteoroid entered at 15.44 km s-1, followed a moderately steep trajectory of 51° to the horizon from 81 km down to 19 km altitude, where the luminous flight ended at a speed of 3.2 km s-1. Deceleration data indicated one large fragment had made it to the ground. The four person search team recovered a 1.15 kg meteorite within 130 m of the predicted fall line, after 8 h of searching, 6 days after the fall. Dingle Dell is the fourth meteorite recovered by the DFN in Australia, but the first before any rain had contaminated the sample. By numerical integration over 1 Ma, we show that Dingle Dell was most likely ejected from the Main Belt by the 3:1 mean motion resonance with Jupiter, with only a marginal chance that it came from the ?6 resonance. This makes the connection of Dingle Dell to the Flora family (currently thought to be the origin of LL chondrites) unlikely