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

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 ∼$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.

SunShot solar power reduces costs and uncertainty in future low-carbon electricity systems.

The United States Department of Energy's SunShot Initiative has set cost-reduction targets of $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 $20 billion ($2010) annually by 2050 relative to scenarios with Reference solar costs.

Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT.

OBJECTIVE: To evaluate whether an imaging classifier for radiology practice can improve lung nodule classification and follow-up. METHODS: A machine learning classifier was developed and trained using imaging data from the National Lung Screening Trial (NSLT) to produce a malignancy risk score (malignancy Similarity Index [mSI]) for individual lung nodules. In addition to NLST cohorts, external cohorts were developed from a tertiary referral lung cancer screening program data set and an external nonscreening data set of all nodules detected on CT. Performance of the mSI combined with Lung-RADS was compared with Lung-RADS alone and the Mayo and Brock risk calculators. RESULTS: We analyzed 963 subjects and 1,331 nodules across these cohorts. The mSI was comparable in accuracy (area under the curve = 0.89) to existing clinical risk models (area under the curve = 0.86-0.88) and independently predictive in the NLST cohort of 704 nodules. When compared with Lung-RADS, the mSI significantly increased sensitivity across all cohorts (25%-117%), with significant increases in specificity in the screening cohorts (17%-33%). When used in conjunction with Lung-RADS, use of mSI would result in earlier diagnoses and reduced follow-up across cohorts, including the potential for early diagnosis in 42% of malignant NLST nodules from prior-year CT scans. CONCLUSION: A computer-assisted diagnosis software improved risk classification from chest CTs of screening and incidentally detected lung nodules compared with Lung-RADS. mSI added predictive value independent of existing radiological and clinical variables. These results suggest the generalizability and potential clinical impact of a tool that is straightforward to implement in practice.

CENP-V is required for centromere organization, chromosome alignment and cytokinesis.

The mechanism of mitotic chromosome condensation is poorly understood, but even less is known about the mechanism of formation of the primary constriction, or centromere. A proteomic analysis of mitotic chromosome scaffolds led to the identification of CENP-V, a novel kinetochore protein related to a bacterial enzyme that detoxifies formaldehyde, a by-product of histone demethylation in eukaryotic cells. Overexpression of CENP-V leads to hypercondensation of pericentromeric heterochromatin, a phenotype that is abolished by mutations in the putative catalytic site. CENP-V depletion in HeLa cells leads to abnormal expansion of the primary constriction of mitotic chromosomes, mislocalization and destabilization of the chromosomal passenger complex (CPC) and alterations in the distribution of H3K9me3 in interphase nucleoplasm. CENP-V-depleted cells suffer defects in chromosome alignment in metaphase, lagging chromosomes in anaphase, failure of cytokinesis and rapid cell death. CENP-V provides a novel link between centromeric chromatin, the primary constriction and the CPC.

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 classifier's 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.

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.

Wndchrm - an open source utility for biological image analysis.

BACKGROUND: Biological 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. METHODS: Wndchrm 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. RESULTS: Wndchrm 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. CONCLUSION: We 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.

Quantitative image analysis reveals distinct structural transitions during aging in Caenorhabditis elegans tissues.

Aging is associated with functional and structural declines in many body systems, even in the absence of underlying disease. In particular, skeletal muscles experience severe declines during aging, a phenomenon termed sarcopenia. Despite the high incidence and severity of sarcopenia, little is known about contributing factors and development. Many studies focus on functional aspects of aging-related tissue decline, while structural details remain understudied. Traditional approaches for quantifying structural changes have assessed individual markers at discrete intervals. Such approaches are inadequate for the complex changes associated with aging. An alternative is to consider changes in overall morphology rather than in specific markers. We have used this approach to quantitatively track tissue architecture during adulthood and aging in the C. elegans pharynx, the neuromuscular feeding organ. Using pattern recognition to analyze aged-grouped pharynx images, we identified discrete step-wise transitions between distinct morphologies. The morphology state transitions were maintained in mutants with pharynx neurotransmission defects, although the pace of the transitions was altered. Longitudinal measurements of pharynx function identified a predictive relationship between mid-life pharynx morphology and function at later ages. These studies demonstrate for the first time that adult tissues undergo distinct structural transitions reflecting postdevelopmental events. The processes that underlie these architectural changes may contribute to increased disease risk during aging, and may be targets for factors that alter the aging rate. This work further demonstrates that pattern analysis of an image series offers a novel and generally accessible approach for quantifying morphological changes and identifying structural biomarkers.

The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging.

The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. OME is designed to support high-content cell-based screening as well as traditional image analysis applications. The OME Data Model, expressed in Extensible Markup Language (XML) and realized in a traditional database, is both extensible and self-describing, allowing it to meet emerging imaging and analysis needs.