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.

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.

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.

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.