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- Creators: School of Molecular Sciences
- Member of: ASU Regents' Professors Open Access Works

Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.





Background: Staphylococcus aureus and S. epidermidis biofilms differ in structure, growth and regulation, and thus the high-throughput method of evaluating biofilm susceptibility that has been published for S. epidermidis cannot be applied to S. aureus without first evaluating the assay's reproducibility and reliability with S. aureus biofilms.
Methods: Staphylococcus aureus biofilms were treated with eleven approved antibiotics, lysostaphin, or Conflikt®, exposed to the oxidation reduction indicator Alamar blue, and reduction relative to untreated controls was determined visually and spectrophotometrically. The minimum biofilm inhibitory concentration (MBIC) was defined as ≤ 50% Alamar blue reduction and a purple/blue well 60 min after the addition of Alamar blue. Because all of the approved antibiotics had MBICs >128 μg/ml (most >2048 μg/ml), lysostaphin and Conflikt®, with relatively low MBICs, were used to correlate Alamar blue reduction with 2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide (XTT) reduction and viable counts (CFU/ml) for S. aureus ATCC 29213 and three clinical isolates. Alamar blue's stability and lack of toxicity allowed CFU/ml to be determined from the same wells as Alamar blue absorbances.
Results: Overall, Alamar blue reduction had excellent correlation with XTT reduction and with CFU/ml. For ATCC 29213 and two clinical isolates treated with lysostaphin or Conflikt®, Alamar blue reduction had excellent correlation with XTT reduction (r = 0.93-0.99) and with CFU/ml (r = 0.92-0.98). For one of the clinical isolates, the results were moderately correlated for Conflikt® (r = 0.76, Alamar blue vs. XTT; r = 0.81, Alamar blue vs. CFU/ml) and had excellent correlation for lysostaphin (r = 0.95, Alamar blue vs. XTT; r = 0.97, Alamar blue vs. CFU/ml).
Conclusion: A reliable, reproducible method for evaluating biofilm susceptibility was successfully applied to S. aureus biofilms. The described method provides researchers with a simple, nontoxic, relatively inexpensive, high throughput measure of viability after drug treatment. A standardized biofilm Alamar blue assay should greatly increase the rate of discovery of S. aureus biofilm specific agents.



X-ray free-electron lasers provide novel opportunities to conduct single particle analysis on nanoscale particles. Coherent diffractive imaging experiments were performed at the Linac Coherent Light Source (LCLS), SLAC National Laboratory, exposing single inorganic core-shell nanoparticles to femtosecond hard-X-ray pulses. Each facetted nanoparticle consisted of a crystalline gold core and a differently shaped palladium shell. Scattered intensities were observed up to about 7 nm resolution. Analysis of the scattering patterns revealed the size distribution of the samples, which is consistent with that obtained from direct real-space imaging by electron microscopy. Scattering patterns resulting from single particles were selected and compiled into a dataset which can be valuable for algorithm developments in single particle scattering research.

Single particle diffractive imaging data from Rice Dwarf Virus (RDV) were recorded using the Coherent X-ray Imaging (CXI) instrument at the Linac Coherent Light Source (LCLS). RDV was chosen as it is a well-characterized model system, useful for proof-of-principle experiments, system optimization and algorithm development. RDV, an icosahedral virus of about 70 nm in diameter, was aerosolized and injected into the approximately 0.1 μm diameter focused hard X-ray beam at the CXI instrument of LCLS. Diffraction patterns from RDV with signal to 5.9 Ångström were recorded. The diffraction data are available through the Coherent X-ray Imaging Data Bank (CXIDB) as a resource for algorithm development, the contents of which are described here.