Dispersal Dynamics in a Wind-Driven Benthic System

Bedload and water column traps were used with simultaneous wind and water velocity measurements to study postlarval macrofaunal dispersal dynamics in Manukau Harbour, New Zealand. A 12-fold range in mean wind condition resulted in large differences in water flow (12-fold), sediment flux (285-fold), and trap collection of total number of individuals (95-fold), number of the dominant infaunal organism (84-fold for the bivalve Macomona liliana), and number of species (4-fold). There were very strong, positive relationships among wind condition, water velocity, sediment flux, and postlarval dispersal, especially in the bedload. Local density in the ambient sediment was not a good predictor of dispersal. Results indicate that postlarval dispersal may influence benthic abundance pat- terns over a range of spatial scales

Online polymer crystallization experiment

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (leaves 119-120).An architecture for online remote operation of a polymer crystallization experiment was refined, beta tested in actual use conditions, and extended based on feedback from those tests. In addition, an application for graphically simulating macroscopic crystal spherulite growth was developed for use as an educational tool. Finally, the experiment was used in the design process for modifying the generic iLab framework to incorporate interactive functionality. Specifically, a reservation model and design changes to the experiment storage and service broker were proposed based on the Polymerlab, and the experiment was used as a testbed for initial implementation of some of the proposed systems.by Derik A. Pridmore.M.Eng

Zirconium−nitrogen intermolecular frustrated Lewis pairs

A series of intermolecular transition metal frustrated Lewis pairs (FLPs) based on zirconocene alkoxide complexes ([Cp2Zr­(OMes)]+ 1 or ([Cp*2Zr­(OMes)]+ 2) with nitrogen Lewis bases (NEt3, NEtiPr2, pyridine, 2-methylpyridine, 2,6-lutidine) are reported. The interaction between Zr and N depends on the specific derivatives used, in general more sterically encumbered pairs leading to a more frustrated interaction; however, DOSY NMR spectroscopy reveals these interactions to be dynamic in nature. The pairs undergo typical FLP-type reactivity with D2, CO2, THF, and PhCCD. The catalytic dehydrocoupling of Me2NH·BH3 is also reported. Comparisons can be made with previous work employing phosphines as Lewis bases suggesting that hard–hard or hard–soft acid–base considerations are of little importance compared to the more prominent roles of steric bulk and basicity

Low-cost automated vectors and modular environmental sensors for plant phenotyping

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of “affordable phenotyping” solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols

Recovering Wind-induced Plant motion in Dense Field Environments via Deep Learning and Multiple Object Tracking

Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in most agricultural systems, yet the complexity of the field environment means that it remained understudied. Despite the ready availability of image sequences showing plant motion, the cultivation of crop plants in dense field stands makes it difficult to detect features and characterize their general movement traits. Here, we present a robust method for characterizing motion in field-grown wheat plants (Triticum aestivum) from time-ordered sequences of red, green and blue (RGB) images. A series of crops and augmentations was applied to a dataset of 290 collected and annotated images of ear tips to increase variation and resolution when training a convolutional neural network. This approach enables wheat ears to be detected in the field without the need for camera calibration or a fixed imaging position. Videos of wheat plants moving in the wind were also collected and split into their component frames. Ear tips were detected using the trained network, then tracked between frames using a probabilistic tracking algorithm to approximate movement. These data can be used to characterize key movement traits, such as periodicity, and obtain more detailed static plant properties to assess plant structure and function in the field. Automated data extraction may be possible for informing lodging models, breeding programmes and linking movement properties to canopy light distributions and dynamic light fluctuation

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

© The Author(s) 2019. Published by Oxford University Press. BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. RESULTS: We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. CONCLUSIONS: We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever

A patch-based approach to 3D plant shoot phenotyping

The emerging discipline of plant phenomics aims to measure key plant characteristics, or traits, though as yet the set of plant traits that should be measured by automated systems is not well defined. Methods capable of recovering generic representations of the 3D structure of plant shoots from images would provide a key technology underpinning quantification of a wide range of current and future physiological and morphological traits. We present a fully automatic approach to image-based 3D plant reconstruction which represents plants as series of small planar sections that together model the complex architecture of leaf surfaces. The initial boundary of each leaf patch is refined using a level set method, optimising the model based on image information, curvature constraints and the position of neighbouring surfaces. The reconstruction process makes few assumptions about the nature of the plant material being reconstructed. As such it is applicable to a wide variety of plant species and topologies, and can be extended to canopy-scale imaging. We demonstrate the effectiveness of our approach on real images of wheat and rice plants, an artificial plant with challenging architecture, as well as a novel virtual dataset that allows us to compute distance measures of reconstruction accuracy. We also illustrate the method’s potential to support the identification of individual leaves, and so the phenotyping of plant shoots, using a spectral clustering approach

The Synergistic Action of Melittin and Phospholipase A2 with Lipid Membranes: Development of Linear Dichroism for Membrane-Insertion Kinetics

Here we present data on the kinetics of insertion of melittin, a peptide from bee venom, into lipid membranes of different composition. Another component of bee venom is the enzyme phospholipase A2 (PLA₂). We have examined the interaction of melittin and PLA₂ with liposomes both separately and combined and demonstrate that they work synergistically to disrupt the membranes. A dramatic difference in the action of melittin and PLA₂ is observed when the composition of the membrane is altered. Temperature also has a large effect on the kinetics of insertion and membrane disruption. We use a combination of techniques to measure liposome size (dynamic light scattering), peptide secondary structure (circular dichroism spectroscopy), peptide orientation relative to the membrane (linear dichroism spectroscopy) and enzymatic digestion of the lipids (mass spectrometry)