The Effect of Oryzalin on the Polyploidy of Lonicera maackii (Amur honeysuckle) as an Invasive Species Management Strategy

Overview

Lonicera maackii (Amur honeysuckle) is invasive to Missouri’s ecosystems. It has minimal natural predators, crowds out native wildlife, and provides little nutrition to organisms that eat its abundant berries. Current eradication methods are ineffective at reducing L. maackii’s large biomass. The purpose of this project is to complete the first step of an innovative management strategy. The strategy starts with inducing tetraploidy– a characteristic of having four sets of chromosomes– into sprouts. Once they reach adulthood, the tetraploid plants can be transplanted into ecosystems that are overtaken by diploid– genetically normal– L. maackii. When the tetraploid and diploid L. maackii cross-pollinate, their offspring will have an odd number of chromosomes and, therefore, be sterile. T o induce tetraploidy, L. maackii roots were soaked in low concentrations of oryzalin, which is a common herbicide at higher concentrations. Root tips were smashed and stained, allowing their chromosomes to be counted under a microscope. The test group soaked in 120 µM oryzalin consistently induced tetraploidy, with a significant p-value of 0.0058. With a successful first step, the management plan can be further studied to be implemented in Missouri’s ecosystems. Human-Smartphone Interaction using MediaPipe Hands and Active Machine Learning Christopher Wadley Lebanon High School, Lebanon, MO Due to the limitations of many human-computer interaction technologies, alternative methods of interaction are developing rapidly, such as voice recognition. Although voice recognition technology has been integrated into smartphones and other devices, it has yet to hold regular use by the majority because of its flaws. Other interaction alternatives, such as gesture recognition, also have advantages over physical hardware, but have yet to be largely implemented into consumer-use. Most gesture recognition systems require extra sensors, cameras, 59 or powerful computer processing not provided by a smartphone. Further, many are limited by the gestures themselves, being restricted to a small set of preset gestures or unable to handle motion, which many gesture- based interactions such as sign language require. This research proposes multiple methods for a smartphone interface system with the use of MediaPipe Hands, a hand segmentation and point extraction data pipeline, combined with custom-built artificial intelligence and machine learning algorithms, to provide a viable and customizable dynamic gesture recognition interface that has the capability to run efficiently on a smartphone, both of which performed at over 90% accuracy.

Competition history

• JSHS 2023 Category not listed

Resources

• National JSHS abstract book ↗

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