A novel approach is used to study the proliferating behaviour of primitive haematopoietic cell populations in response to different stimuli. A mathematical model based on the average proportion of apoptotic, dividing and quiescent cells in primitive haematopoietic cell populations is developed to describe the mitotic history of 5- (and 6-) carboxyfluorescein diacetate succinimidyl ester-labelled cells. The cell cycle distributions in different cytokine-supplemented cultures of primitive human and mouse bone marrow cells are determined and compared with those found in vivo. The results indicate that a combination of flt-3 ligand, Steel factor and interleukin-11 or hyper-interleukin-6 provide a level of mitogenic stimulation similar to that existing in vivo after a myeloablative radiation dose. The comparison of the cell cycle distribution obtained for different cultures of human bone marrow CD34(+)(45RA/71)(-) cells demonstrates that the addition of flt-3 ligand in these cultures decreases apoptosis significantly but does not reduce quiescence. In addition, in vivo and in vitro, it was found that more than 3 days of stimulation are required to recruit a maximum number of quiescent cells into active cell cycle. These kinetics of cell cycle activation are found to be similar to those identified for the haematopoietic stem cells compartment in the same cultures. This mathematical analysis provides a useful tool for the development of haematopoietic stem cell culture processes for clinical applications.

According to Jensen's inequality, the Bayesian information criterion (BIC) based on the Gaussian mixture model (GMM) is applied to speaker indexing. It can utilise the advantages of BIC and GMM. Experimental results have demonstrated that it is superior to both single-Gaussian-based BIC and GMM for speaker indexing.Introduction: Speaker indexing sequentially detects points where a speaker identity changes in a multispeaker audio stream, and categorises each speaker segment, without any prior knowledge about the speaker. In general, at the beginning, there is no sufficient speaker data to accurately estimate a speaker's model. So incremental speaker model updating has been popular in creating a speaker model in speaker indexing. That is to say, speaker model construction and speaker indexing are performed simultaneously [1]: for each utterance, the input speech is identified whether it belongs to one of the previous speakers. If so, the current speaker is regarded as one of the previous speakers and the corresponding speaker model can be updated. Otherwise, a new speaker model is created using the current speech.The conventional method of speaker indexing based on the Bayesian information criterion (BIC) is formulated in [2], which assumes a single Gaussian model for each speech segment and performs speaker clustering based on the BIC result. It utilises only a single Gaussian model (SGM) in BIC; and is called SGM-BIC. Because speaker information may not be fully represented with an SGM, SGM-BIC cannot cope with too short or too long speech segments effectively. To capture more speaker information, in this Letter, a Gaussian mixture model (GMM) is used to model every speech segment. According to Jensen's inequality, BIC based on GMM (GMM-BIC) is applied to speaker indexing.

This paper proposes a novel approach for appearance-based loop closure detection using incremental Bag of Words (BoW) with gradient orientation histograms. The presented approach involves dividing and clustering image blocks into local region features and representing them using gradient orientation histograms. To improve the efficiency of the loop closure detection process, the vocabulary Clustering Feature (CF) tree is generated and updated in real time using the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm, which is combined with an inverted index for the efficient selection of candidates and calculation of similarity. Moreover, temporally close and highly similar images are grouped to generate islands, which enhances the accuracy and efficiency of the loop closure detection process. The proposed approach is evaluated on publicly available datasets, and the results demonstrate high recall and precision.