A Neural Network Model for Classifying Bubble-Based Instructor Evaluations, and an Accompanying Web Portal

We propose a neural network model for classifying bubbles (circles) used in instructor course evaluations. The model is trained on prior (labeled) objects consisting of bubbles and general text. The trained model is then used to determine the positions of bubble answer options on a given evaluation form. A Web portal accompanies the classification system and facilitates management of the network and analysis of the results. The departmental staff will upload an unevaluated form per course and the system will execute the neural network model on it; application logic will be responsible for ensuring data persistence of the bubble positions in addition to student long-form question answers. Once the departmental staff uploads an electronic copy of the filled out evaluations for a course into the portal, the application server will aggregate the results based on the output from the neural network. The instructor for the course is able to view the evaluation results once granted access by the departmental staff

Mapping olive varieties and within-field spatial variability using high resolution QuickBird imagery

[Abstract]: The growth of the Australian olive (Olea europaea L.) industry requires support from research to ensure its profitability and sustainability. To contribute to this goal, our project tested the ability of remote sensing imagery to map olive groves and their attributes. Specifically, this study aimed to: (a) discriminate olives varieties; and to (b) detect and interpret within-field spatial variability. Using high spatial resolution (2.8m) QuickBird multispectral imagery acquired over Yallamundi (southeast Queensland) on 24 December 2003, both visual interpretation and statistical (divergence) measures were employed to discriminate olive varieties. Similarly, the detection and interpretation of within-field spatial variability was conducted on enhanced false colour composite imagery, and confirmed by the use of statistical methods. Results showed that the two olive varieties (i.e. Kalamata and Frantoio) can be visually differentiated and mapped on the enhanced image based on texture. The spectral signature plots showed little difference in the mean spectral reflectance values, indicating that the two varieties have a very low spectral separability. In terms of within-field spatial variability, the presence or absence of Rhodes grass (Chloris gayana) was detected using visual interpretation, corroborated by the results of quantitative statistical measures. Spatial variability in soil properties, caused by the presence of a patch of sandy soil, was also detected visually. Finally, the “imprint” of former cover-type or land-use prior to olive plantation establishment in 1998 was identified. More work is being done to develop image classification techniques for mapping within-field spatial variability in olive varieties, biomass and condition using hyperspectral image data, as well as interpreting the cause of observed variability

ProteoClade: A taxonomic toolkit for multi-species and metaproteomic analysis

We present ProteoClade, a Python toolkit that performs taxa-specific peptide assignment, protein inference, and quantitation for multi-species proteomics experiments. ProteoClade scales to hundreds of millions of protein sequences, requires minimal computational resources, and is open source, multi-platform, and accessible to non-programmers. We demonstrate its utility for processing quantitative proteomic data derived from patient-derived xenografts and its speed and scalability enable a novel de novo proteomic workflow for complex microbiota samples

Predicting Tether Performance Under Different Space Weather Conditions: A Guide for Mission Planning and Design Decisions

Electrodynamic tethers have demonstrated to be effective for fuel-free de-orbiting or station-keeping for spacecraft in Low Earth Orbit. However, the effect of solar activity on the plasma environment around the tether is still an underestimated factor that can have a significant impact on the efficiency and viability of such systems. This study aims to enhance the understanding of tether system design by investigating the influence of solar conditions and space weather on critical parameters such as tether length and power requirements across different spacecraft sizes. The performance of tethers in space is significantly influenced by various environmental factors, including space weather phenomena such as Spread-F, geomagnetic storms, and ionospheric disturbances. The research assesses solar conditions encompassing Solar Maxima (F10.cm at 115), Solar Minima (F10.7cm at 69), and the 2015 solar storm (F10.cm at 250). Variations in solar activity caused changes in aerodynamic drag, impacting both tether design factors for its utility in de-orbiting and station-keeping. Elevated drag during periods of heightened solar activity needs increased thrust for station-keeping, resulting in bigger tether length and power consumption. Additionally, higher drag requires shorter tether lengths to achieve similar de-orbiting performance. These findings have important possibilities for mission planning and spacecraft design decisions, including the optimal tether length and power requirement

Restriction landmark genomic scanning (RLGS) spot identification by second generation virtual RLGS in multiple genomes with multiple enzyme combinations.

BackgroundRestriction landmark genomic scanning (RLGS) is one of the most successfully applied methods for the identification of aberrant CpG island hypermethylation in cancer, as well as the identification of tissue specific methylation of CpG islands. However, a limitation to the utility of this method has been the ability to assign specific genomic sequences to RLGS spots, a process commonly referred to as "RLGS spot cloning."ResultsWe report the development of a virtual RLGS method (vRLGS) that allows for RLGS spot identification in any sequenced genome and with any enzyme combination. We report significant improvements in predicting DNA fragment migration patterns by incorporating sequence information into the migration models, and demonstrate a median Euclidian distance between actual and predicted spot migration of 0.18 centimeters for the most complex human RLGS pattern. We report the confirmed identification of 795 human and 530 mouse RLGS spots for the most commonly used enzyme combinations. We also developed a method to filter the virtual spots to reduce the number of extra spots seen on a virtual profile for both the mouse and human genomes. We demonstrate use of this filter to simplify spot cloning and to assist in the identification of spots exhibiting tissue-specific methylation.ConclusionThe new vRLGS system reported here is highly robust for the identification of novel RLGS spots. The migration models developed are not specific to the genome being studied or the enzyme combination being used, making this tool broadly applicable. The identification of hundreds of mouse and human RLGS spot loci confirms the strong bias of RLGS studies to focus on CpG islands and provides a valuable resource to rapidly study their methylation