Josiah Johnston

WND-CHARM: Multi-purpose image classification using compound image transforms

We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art task-specific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are computed on the raw image, transforms of the image, and transforms of transforms of the image. The feature values are then used to classify test images into a set of pre-defined image classes. This classifier was tested on several different problems including biological image classification and face recognition. Although we cannot make a claim of universality, our experimental results show that this classifier performs as well or better than classifiers developed specifically for these image classification tasks. Our classifiers high performance on a variety of classification problems is attributed to (i) a large set of features extracted from images; and (ii) an effective feature selection and weighting algorithm sensitive to specific image classification problems. The algorithms are available for free download from openmicroscopy.org.

Wndchrm – an open source utility for biological image analysis

BackgroundBiological imaging is an emerging field, covering a wide range of applications in biological and clinical research. However, while machinery for automated experimenting and data acquisition has been developing rapidly in the past years, automated image analysis often introduces a bottleneck in high content screening.MethodsWndchrm is an open source utility for biological image analysis. The software works by first extracting image content descriptors from the raw image, image transforms, and compound image transforms. Then, the most informative features are selected, and the feature vector of each image is used for classification and similarity measurement.ResultsWndchrm has been tested using several publicly available biological datasets, and provided results which are favorably comparable to the performance of task-specific algorithms developed for these datasets. The simple user interface allows researchers who are not knowledgeable in computer vision methods and have no background in computer programming to apply image analysis to their data.ConclusionWe suggest that wndchrm can be effectively used for a wide range of biological image analysis tasks. Using wndchrm can allow scientists to perform automated biological image analysis while avoiding the costly challenge of implementing computer vision and pattern recognition algorithms.

Sarcopenia in the Caenorhabditis elegans pharynx correlates with muscle contraction rate over lifespan

In muscles, sarcopenia, the loss of muscle mass, is the major cause of aging-related functional decline and frailty. Several factors are correlated with sarcopenia during aging, including contraction-related cellular injury, oxidative stress, endocrine changes and reduced regenerative potential. However the involvement of these factors has not been experimentally investigated. Here, we report that contraction-related injury may significantly promote the progression of sarcopenia in the pharynx of the nematode, Caenorhabditis elegans, a model of aging in non-regenerative tissues. Both functional and structural declines in the pharynx during aging were significantly delayed in mutants with reduced muscle contraction rates. We also examined the role of bacteria in pharynx muscle decline during aging, as previous studies reported that antimicrobial treatments could extend C. elegans lifespan. Although microbial infection may have enhanced functional decline in the pharynx during aging, it was not the sole cause of decreased pumping rates in old animals. This study identifies contraction-related injury as a factor affecting the initiation and progression of sarcopenia during aging. Further, characterization of the specific types of damage induced by muscle contraction will be helpful for understanding the underlying causes of sarcopenia.

Deep carbon reductions in California require electrification and integration across economic sectors

Meeting a greenhouse gas (GHG) reduction target of 80% below 1990 levels in the year 2050 requires detailed long-term planning due to complexity, inertia, and path dependency in the energy system. A detailed investigation of supply and demand alternatives is conducted to assess requirements for future California energy systems that can meet the 2050 GHG target. Two components are developed here that build novel analytic capacity and extend previous studies: (1) detailed bottom-up projections of energy demand across the building, industry and transportation sectors; and (2) a high-resolution variable renewable resource capacity planning model (SWITCH) that minimizes the cost of electricity while meeting GHG policy goals in the 2050 timeframe. Multiple pathways exist to a low-GHG future, all involving increased efficiency, electrification, and a dramatic shift from fossil fuels to low-GHG energy. The electricity system is found to have a diverse, cost-effective set of options that meet aggressive GHG reduction targets. This conclusion holds even with increased demand from transportation and heating, but the optimal levels of wind and solar deployment depend on the temporal characteristics of the resulting load profile. Long-term policy support is found to be a key missing element for the successful attainment of the 2050 GHG target in California.

SunShot Solar Power Reduces Costs and Uncertainty in Future Low-Carbon Electricity Systems

The United States Department of Energys SunShot Initiative has set cost-reduction targets of

SWITCH-China: A Systems Approach to Decarbonizing China’s Power System

1/watt for central-station solar technologies. We use SWITCH, a high-resolution electricity system planning model, to study the implications of achieving these targets for technology deployment and electricity costs in western North America, focusing on scenarios limiting carbon emissions to 80% below 1990 levels by 2050. We find that achieving the SunShot target for solar photovoltaics would allow this technology to provide more than a third of electric power in the region, displacing natural gas in the medium term and reducing the need for nuclear and carbon capture and sequestration (CCS) technologies, which face technological and cost uncertainties, by 2050. We demonstrate that a diverse portfolio of technological options can help integrate high levels of solar generation successfully and cost-effectively. The deployment of GW-scale storage plays a central role in facilitating solar deployment and the availability of flexible loads could increase the solar penetration level further. In the scenarios investigated, achieving the SunShot target can substantially mitigate the cost of implementing a carbon cap, decreasing power costs by up to 14% and saving up to

Pattern recognition approaches to compute image similarities: application to age related morphological change

20 billion (

Evidence and future scenarios of a low-carbon energy transition in Central America: A case study in Nicaragua

2010) annually by 2050 relative to scenarios with Reference solar costs.

High-resolution modeling of the western North American power system demonstrates low-cost and low-carbon futures

We present an integrated model, SWITCH-China, of the Chinese power sector with which to analyze the economic and technological implications of a medium to long-term decarbonization scenario while accounting for very-short-term renewable variability. On the basis of the model and assumptions used, we find that the announced 2030 carbon peak can be achieved with a carbon price of ∼

Addendum: Biomass enables the transition to a carbon-negative power system across western North America

40/tCO2. Current trends in renewable energy price reductions alone are insufficient to replace coal; however, an 80% carbon emission reduction by 2050 is achievable in the Intergovernmental Panel on Climate Change Target Scenario with an optimal electricity mix in 2050 including nuclear (14%), wind (23%), solar (27%), hydro (6%), gas (1%), coal (3%), and carbon capture and sequestration coal energy (26%). The co-benefits of carbon-price strategy would offset 22% to 42% of the increased electricity costs if the true cost of coal and the social cost of carbon are incorporated. In such a scenario, aggressive attention to research and both technological and financial innovation mechanisms are crucial to enabling the transition at a reasonable cost, along with strong carbon policies.