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

Comparative analyses of proteins from Haemophilus influenzae biofilm and planktonic populations using metabolic labeling and mass spectrometry

BACKGROUND: Non-typeable H. influenzae (NTHi) is a nasopharyngeal commensal that can become an opportunistic pathogen causing infections such as otitis media, pneumonia, and bronchitis. NTHi is known to form biofilms. Resistance of bacterial biofilms to clearance by host defense mechanisms and antibiotic treatments is well-established. In the current study, we used stable isotope labeling by amino acids in cell culture (SILAC) to compare the proteomic profiles of NTHi biofilm and planktonic organisms. Duplicate continuous-flow growth chambers containing defined media with either “light” (L) isoleucine or “heavy” (H) (13)C(6)-labeled isoleucine were used to grow planktonic (L) and biofilm (H) samples, respectively. Bacteria were removed from the chambers, mixed based on weight, and protein extracts were generated. Liquid chromatography-mass spectrometry (LC-MS) was performed on the tryptic peptides and 814 unique proteins were identified with 99% confidence. RESULTS: Comparisons of the NTHi biofilm to planktonic samples demonstrated that 127 proteins showed differential expression with p-values ≤0.05. Pathway analysis demonstrated that proteins involved in energy metabolism, protein synthesis, and purine, pyrimidine, nucleoside, and nucleotide processes showed a general trend of downregulation in the biofilm compared to planktonic organisms. Conversely, proteins involved in transcription, DNA metabolism, and fatty acid and phospholipid metabolism showed a general trend of upregulation under biofilm conditions. Selected reaction monitoring (SRM)-MS was used to validate a subset of these proteins; among these were aerobic respiration control protein ArcA, NAD nucleotidase and heme-binding protein A. CONCLUSIONS: The present proteomic study indicates that the NTHi biofilm exists in a semi-dormant state with decreased energy metabolism and protein synthesis yet is still capable of managing oxidative stress and in acquiring necessary cofactors important for biofilm survival. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12866-014-0329-9) contains supplementary material, which is available to authorized users

A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-up

PURPOSE: The selection of patients for further therapy after meningioma surgery remains a challenge. Progress has been made in this setting in selecting patients that are more likely to have an aggressive disease course by using molecular tests such as gene panel sequencing and DNA methylation profiling. The aim of this study was to create a preselection tool warranting further molecular work-up. METHODS: All patients undergoing surgery for resection or biopsy of a cranial meningioma from January 2013 until December 2018 at the University Hospital Zurich with available tumor histology were included. Various prospectively collected clinical, radiological, histological and immunohistochemical variables were analyzed and used to train a logistic regression model to predict tumor recurrence or progression. Regression coefficients were used to generate a scoring system grading every patient into low, intermediate, and high-risk group for tumor progression or recurrence. RESULTS: Out of a total of 13 variables preselected for this study, previous meningioma surgery, Simpson grade, progesterone receptor staining as well as presence of necrosis and patternless growth on histopathological analysis of 378 patients were included into the final model. Discrimination showed an AUC of 0.81 (95% CI 0.73 - 0.88), the model was well-calibrated. Recurrence-free survival was significantly decreased in patients in intermediate and high-risk score groups (p-value < 0.001). CONCLUSION: The proposed prediction model showed good discrimination and calibration. This prediction model is based on easily obtainable information and can be used as an adjunct for patient selection for further molecular work-up in a tertiary hospital setting