These results suggest that statin-mediated reduction in cholesterol levels within phagosomal membranes counteract M. tuberculosis-induced inhibition of phagosomal maturation and promote host-induced autophagy, thereby augmenting host protection against tuberculosis.

Screening for tuberculosis (TB) in low-and middle-income countries is centered on the microscope. We present methods for the automated identification of Mycobacterium tuberculosis in images of Ziehl-Neelsen (ZN) stained sputum smears obtained using a bright-field microscope. We segment candidate bacillus objects using a combination of two-class pixel classifiers. The algorithm produces results that agree well with manual segmentations, as judged by the Hausdorff distance and the modified Williams index. The extraction of geometric-transformation-invariant features and optimization of the feature set by feature subset selection and Fisher transformation follow. Finally, different two-class object classifiers are compared. The sensitivity and specificity of all tested classifiers is above 95% for the identification of bacillus objects represented by Fisher-transformed features. Our results may be used to reduce technician involvement in screening for TB, and would be particularly useful in laboratories in countries with a high burden of TB, where, typically, ZN rather than auramine staining of sputum smears is the method of choice.

Summary Screening for tuberculosis in high‐prevalence countries relies on sputum smear microscopy. We present a method for the automated identification of Mycobacterium tuberculosis in images of Ziehl‐Neelsen‐stained sputum smears obtained using a bright‐field microscope. We use two stages of classification. The first comprises a one‐class pixel classifier for object segmentation. Geometric transformation invariant features are extracted for implementation of the second stage, namely one‐class object classification. Different classifiers are compared; the sensitivity of all tested classifiers is above 90% for the identification of a single bacillus object using all extracted features. The mixture of Gaussians classifier performed well in both stages of classification. This method may be used as a step in the automation of tuberculosis screening, in order to reduce technician involvement in the process.

upregulated, suggesting IL-4Rα-independent alternative activation. In summary, L. major parasites may use Ly6C + CD11b + inflammatory DCs derived from monocytes recruited to infection as "Trojan horses" to migrate to secondary lymphoid organs and peripheral sites, and DC IL-4Rα expression is important for controlling infection.

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Biometric fingerprint scanners scan the external skin's features onto a 2-D image. The performance of the automatic fingerprint identification system suffers first and foremost if the finger skin is wet, worn out or a fake fingerprint is used. We present an automatic segmentation of the papillary layer method, from images acquired using contact-less 3-D swept source optical coherence tomography (OCT). The papillary contour represents the internal fingerprint, which does not suffer from the external finger problems. It is embedded between the upper epidermis and papillary layers. Speckle noise is first reduced using non-linear filters from the slices composing the 3-D image. Subsequently, the stratum corneum is used to extract the epidermis. The epidermis, with its depth known, is used as the target class of the ensuing novelty detection. The outliers resulting from novelty detection represent the papillary layer. The contour of the papillary layer is segmented as the boundary between target and rejection classes. Using a mixture of Gaussian's novelty detection routine on images pre-processed with a regularized anisotropic diffusion filter, the papillary contours-internal fingerprints-are consistent with those segmented manually, with the modified Williams index above 0.9400.

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