It sometimes happens (for instance in case control studies) that a classifier is trained on a data set that does not reflect the true a priori probabilities of the target classes on real-world data. This may have a negative effect on the classification accuracy obtained on the real-world data set, especially when the classifier's decisions are based on the a posteriori probabilities of class membership. Indeed, in this case, the trained classifier provides estimates of the a posteriori probabilities that are not valid for this real-world data set (they rely on the a priori probabilities of the training set). Applying the classifier as is (without correcting its outputs with respect to these new conditions) on this new data set may thus be suboptimal. In this note, we present a simple iterative procedure for adjusting the outputs of the trained classifier with respect to these new a priori probabilities without having to refit the model, even when these probabilities are not known in advance. As a by-product, estimates of the new a priori probabilities are also obtained. This iterative algorithm is a straightforward instance of the expectation-maximization (EM) algorithm and is shown to maximize the likelihood of the new data. Thereafter, we discuss a statistical test that can be applied to decide if the a priori class probabilities have changed from the training set to the real-world data. The procedure is illustrated on different classification problems involving a multilayer neural network, and comparisons with a standard procedure for a priori probability estimation are provided. Our original method, based on the EM algorithm, is shown to be superior to the standard one for a priori probability estimation. Experimental results also indicate that the classifier with adjusted outputs always performs better than the original one in terms of classification accuracy, when the a priori probability conditions differ from the training set to the real-world data. The gain in classification accuracy can be significant.

Aristolochic acid contamination in herbal remedies leads to interstitial fibrosis, tubular atrophy, and renal failure in humans. To study the cellular mechanisms contributing to the pathophysiology of this renal disease, we studied Wistar rats treated with aristolochic acid and measured tubular and interstitial cell proliferation, epithelial/mesenchymal cell marker expression, tubular membrane integrity, myofibroblast accumulation, oxidative stress, mitochondrial damage, tubular apoptosis, and fibrosis. Oxidative stress, a loss of cadherin concomitant with vimentin expression, basement membrane denudation with active caspase-3 expression, and mitochondrial injury within tubular cells were evident within 5 days of administration of the toxin. During the chronic phase, interstitial mesenchymal cells accumulated in areas of collagen deposits. Impaired regeneration and apoptosis of proximal tubular cells resulted in tubule atrophy with a near absence of dedifferentiated cell transmembrane migration. We suggest that resident fibroblast activation plays a critical role in the process of renal fibrosis during aristolochic acid toxicity.

Abstract-In this paper, we propose a combination of mean-shift-based tracking processes to establish migrating cell trajectories through in vitro phase-contrast video microscopy. After a recapitulation on how the mean-shift algorithm permits efficient object tracking we describe the proposed extension and apply it to the in vitro cell tracking problem. In this application, the cells are unmarked (i.e., no fluorescent probe is used) and are observed under classical phase-contrast microscopy. By introducing an adaptive combination of several kernels, we address several problems such as variations in size and shape of the tracked objects (e.g., those occurring in the case of cell membrane extensions), the presence of incomplete (or noncontrasted) object boundaries, partially overlapping objects and object splitting (in the case of cell divisions or mitoses). Comparing the tracking results automatically obtained to those generated manually by a human expert, we tested the stability of the different algorithm parameters and their effects on the tracking results. We also show how the method is resistant to a decrease in image resolution and accidental defocusing (which may occur during long experiments, e.g., dozens of hours). Finally, we applied our methodology on cancer cell tracking and showed that cytochalasin-D significantly inhibits cell motility.

The epithelial polymeric immunoglobulin receptor/transmembrane secretory component (pIgR/SC) transports into secretions polymeric immunoglobulin A (pIgA), which is considered the first line of defense of the respiratory tract. The present study, done with quantitative immunohistochemistry, evaluated epithelial expression of secretory component (SC) and Clara cell protein (CC16) and neutrophil infiltration into the airways of eight patients with severe chronic obstructive pulmonary disease (COPD) who were undergoing lung transplantation, as compared with these processes in six nonsmoking patients with pulmonary hypertension who were used as controls and in lung specimens from five smokers without chronic bronchitis. Staining for SC was significantly decreased in the COPD patients as compared with the controls, both in large (mean optical density [MOD]: 23.4 [range: 21.1 to 27.8] versus 42.2 [range: 28.2 to 49.3], p = 0.003) and in small airways (MOD: 30.8 [range: 20.3 to 39.4] versus 41.5 [range: 39.2 to 46.2], p = 0.003). SC expression in small airways correlated strongly with functional parameters such as FEV1 (Kendall's tau (K) = 0.76, p = 0.008), FVC (K = 0.64, p = 0.03), and midexpiratory flow at 50% of VC (MEF50) (K = 0.74, p = 0.01). The reduced expression of SC in large airways correlated with neutrophil infiltration in submucosal glands (K = -0.47, p = 0.03). Expression of CC16 in the bronchial epithelium of COPD patients was also significantly decreased as compared with that of controls, especially in small airways (MOD: 28.3 [range: 26.8 to 32.4] versus 45.8 [range: 40.7 to 56.0], p = 0.002), but no correlation was observed with lung function tests. In conclusion, this study shows that reduced expression of SC in airway epithelium is associated with airflow obstruction and neutrophil infiltration in severe COPD.