Platelets are known to contribute to vascular pathologies, however, their role in inflammatory disorders of the central nervous system (CNS), such as multiple sclerosis (MS) and its mouse model, experimental autoimmune encephalomyelitis (EAE), is thus far poorly defined. Although there is emerging evidence that platelets might accumulate in the CNS parenchyma along with an increased activation ...

We used fluorescence imaging of individual exocytic events together with electron microscopy to study the regulation of dense core granule-to-plasma membrane fusion and granule-to-granule fusion events that occur during secretion from rat pituitary lactotrophs. Stimulating secretion with elevated extracellular potassium, with the calcium ionophore ionomycin, or with thyrotropin releasing hormon...

An important aspect of machine learning (ML) involves controlling the process for ML method in question to maximize its performance. Hyperparameter tuning (HPT) selecting suitable parameters that control process. Given HPT can be conceptualized as a black box optimization problem subject stochasticity, simulation (SO) methods appear well suited this purpose. Therefore, we conceptualize discrete...

W propose an adaptive hyperbox algorithm (AHA), which is an instance of a locally convergent, random search algorithm for solving discrete optimization via simulation problems. Compared to the COMPASS algorithm, AHA is more efficient in high-dimensional problems. By analyzing models of the behavior of COMPASS and AHA, we show why COMPASS slows down significantly as dimension increases, whereas ...

We propose a new methodology to classify temporal data with imprecise hidden Markov models. For each sequence we learn a different model by coupling the EM algorithm with the imprecise Dirichlet model. As a model descriptor, we consider the expected value of the observable variable in the limit of stationarity of the Markov chain. In the imprecise case, only the bounds of this descriptor can be...

This paper gives some known theoretical results about fuzzy rule-based classifiers and offers a few new ones. The ability of Takagi-Sugeno-Kang (TSK) fuzzy classifiers to match exactly and to approximate classification boundaries is discussed. The lemma by Klawonn and Klement about the exact match of a classification boundary in R (2) is extended from monotonous to arbitrary functions. Equivale...

A generalized Neuro-Fuzzy based Content Based Image Retrieval (CBIR) system is proposed. The system is trained for colour, texture and shape features using General Fuzzy Min-Max Neural Network (GFMNN). Flexibility and robustness is achieved by accepting any number and types of different input features as well with the concept of class labels assigned for each hyperbox. The existing architecture...