The fates of the labels from [14C] and [15N] trinitrotoluene were analyzed in bioreactors under aerobic conditions in soil treated by a fungal bioremediation process with Stropharia rugosoannulata and in control soil. Up to 17.5% of the 15N label had a different fate than the 14C label. Three N-mineralization processes were identified in detailed experiments with [15N]TNT. About 2% of the 15N l...

It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are sh...

Colon cancer classification has a significant guidance value in clinical diagnoses and medical prognoses. The classification of colon cancers with high accuracy is the premise of efficient treatment. Our task is to build a system for colon cancer detection and classification based on slide histopathological images. Some former researches focus on single label classification. Through analyzing l...

Osteoarthritis (OA) is characterized by a loss of extracellular matrix which is driven by catabolic cytokines. Proteomic analysis of the OA cartilage secretome enables the global study of secreted proteins. These are an important class of molecules with roles in numerous pathological mechanisms. Although cartilage studies have identified profiles of secreted proteins, quantitative proteomics te...

Fluorescein isothiocyanate (FITC)-labelled hyaluronic acid, when incubated with subconfluent cultures of mouse dermal fibroblasts, was incorporated into the extracellular matrix. Deposition of the fluorescent label reached the maximum about 48 h after its addition. Hyaluronic acid decasaccharide, but not octasaccharide, inhibited the incorporation of the fluorescent label, suggesting that at le...

We introduce relational variants of neural topographic maps including the selforganizing map and neural gas, which allow clustering and visualization of data given in terms of a pairwise similarity or dissimilarity matrix. It is assumed that this matrix originates from an euclidean distance or dot product, respectively, however, the underlying embedding of points is unknown. One can equivalentl...

Quality assurance in crowdsourced annotation often involves having a given example labeled multiple times by different workers, then aggregating these labels. Unfortunately, the worker-example label matrix is typically sparse and imbalanced for two reasons: 1) the average crowd worker judges few examples; and 2) few labels are typically collected per example to reduce cost. To address this miss...

We introduce relational variants of neural gas, a very efficient and powerful neural clustering algorithm, which allow a clustering and mining of data given in terms of a pairwise similarity or dissimilarity matrix. It is assumed that this matrix stems from Euclidean distance or dot product, respectively, however, the underlying embedding of points is unknown. One can equivalently formulate bat...

We introduce Probabilistic Matrix Addition (PMA) for modeling real-valued data matrices by simultaneously capturing covariance structure among rows and among columns. PMA additively combines two latent matrices drawn from two Gaussian Processes (GPs) respectively over rows and columns. The resulting joint distribution over the observed matrix does not factorize over entries, rows, or columns, a...

Many graph based semi-supervised dimensionality reduction algorithms utilize the projection matrix to linearly map the data matrix from the original feature space to a lower dimensional representation. But the dimensionality after reduction is inevitably restricted to the number of classes, and the learned non-orthogonal projection matrix usually fails to preserve distances well and balance the...