Boundary Attention Mapping (BAM): Fine-grained saliency maps for segmentation of Burn Injuries

Burn injuries can result from mechanisms such as thermal, chemical, and electrical insults. A prompt and accurate assessment of burns is essential for deciding definitive clinical treatments. Currently, the primary approach for burn assessments, via visual and tactile observations, is approximately 60%-80% accurate. The gold standard is biopsy and a close second would be non-invasive methods like Laser Doppler Imaging (LDI) assessments, which have up to 97% accuracy in predicting burn severity and the required healing time. In this paper, we introduce a machine learning pipeline for assessing burn severities and segmenting the regions of skin that are affected by burn. Segmenting 2D colour images of burns allows for the injured versus non-injured skin to be delineated, clearly marking the extent and boundaries of the localized burn/region-of-interest, even during remote monitoring of a burn patient. We trained a convolutional neural network (CNN) to classify four severities of burns. We built a saliency mapping method, Boundary Attention Mapping (BAM), that utilises this trained CNN for the purpose of accurately localizing and segmenting the burn regions from skin burn images. We demonstrated the effectiveness of our proposed pipeline through extensive experiments and evaluations using two datasets; 1) A larger skin burn image dataset consisting of 1684 skin burn images of four burn severities, 2) An LDI dataset that consists of a total of 184 skin burn images with their associated LDI scans. The CNN trained using the first dataset achieved an average F1-Score of 78% and micro/macro- average ROC of 85% in classifying the four burn severities. Moreover, a comparison between the BAM results and LDI results for measuring injury boundary showed that the segmentations generated by our method achieved 91.60% accuracy, 78.17% sensitivity, and 93.37% specificity

Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa L.) grown at four international field sites

Peer reviewedPublisher PD

Ultraviolet through Infrared Spectral Energy Distributions from 1000 SDSS Galaxies: Dust Attenuation

The meaningful comparison of models of galaxy evolution to observations is critically dependent on the accurate treatment of dust attenuation. To investigate dust absorption and emission in galaxies we have assembled a sample of ~1000 galaxies with ultraviolet (UV) through infrared (IR) photometry from GALEX, SDSS, and Spitzer and optical spectroscopy from SDSS. The ratio of IR to UV emission (IRX) is used to constrain the dust attenuation in galaxies. We use the 4000A break as a robust and useful, although coarse, indicator of star formation history (SFH). We examine the relationship between IRX and the UV spectral slope (a common attenuation indicator at high-redshift) and find little dependence of the scatter on 4000A break strength. We construct average UV through far-IR spectral energy distributions (SEDs) for different ranges of IRX, 4000A break strength, and stellar mass (M_*) to show the variation of the entire SED with these parameters. When binned simultaneously by IRX, 4000A break strength, and M_* these SEDs allow us to determine a low resolution average attenuation curve for different ranges of M_*. The attenuation curves thus derived are consistent with a lambda^{-0.7} attenuation law, and we find no significant variations with M_*. Finally, we show the relationship between IRX and the global stellar mass surface density and gas-phase-metallicity. Among star forming galaxies we find a strong correlation between IRX and stellar mass surface density, even at constant metallicity, a result that is closely linked to the well-known correlation between IRX and star-formation rate.Comment: 12 pages, 8 figures, 2 tables, appearing in the Dec 2007 GALEX special issue of ApJ Supp (29 papers

Gate-Controlled Ionization and Screening of Cobalt Adatoms on a Graphene Surface

We describe scanning tunneling spectroscopy (STS) measurements performed on individual cobalt (Co) atoms deposited onto backgated graphene devices. We find that Co adatoms on graphene can be ionized by either the application of a global backgate voltage or by the application of a local electric field from a scanning tunneling microscope (STM) tip. Large screening clouds are observed to form around Co adatoms ionized in this way, and we observe that some intrinsic graphene defects display a similar behavior. Our results provide new insight into charged impurity scattering in graphene, as well as the possibility of using graphene devices as chemical sensors.Comment: 19 pages, 4 figure

Polycation-π Interactions Are a Driving Force for Molecular Recognition by an Intrinsically Disordered Oncoprotein Family

Molecular recognition by intrinsically disordered proteins (IDPs) commonly involves specific localized contacts and target-induced disorder to order transitions. However, some IDPs remain disordered in the bound state, a phenomenon coined "fuzziness", often characterized by IDP polyvalency, sequence-insensitivity and a dynamic ensemble of disordered bound-state conformations. Besides the above general features, specific biophysical models for fuzzy interactions are mostly lacking. The transcriptional activation domain of the Ewing's Sarcoma oncoprotein family (EAD) is an IDP that exhibits many features of fuzziness, with multiple EAD aromatic side chains driving molecular recognition. Considering the prevalent role of cation-π interactions at various protein-protein interfaces, we hypothesized that EAD-target binding involves polycation- π contacts between a disordered EAD and basic residues on the target. Herein we evaluated the polycation-π hypothesis via functional and theoretical interrogation of EAD variants. The experimental effects of a range of EAD sequence variations, including aromatic number, aromatic density and charge perturbations, all support the cation-π model. Moreover, the activity trends observed are well captured by a coarse-grained EAD chain model and a corresponding analytical model based on interaction between EAD aromatics and surface cations of a generic globular target. EAD-target binding, in the context of pathological Ewing's Sarcoma oncoproteins, is thus seen to be driven by a balance between EAD conformational entropy and favorable EAD-target cation-π contacts. Such a highly versatile mode of molecular recognition offers a general conceptual framework for promiscuous target recognition by polyvalent IDPs. © 2013 Song et al