KARYOTYPE AND BANDING-PATTERNS OF FEA TREE RAT (CHIROMYSCUS-CHIROPUS)

Fea's tree rat (Chiromyscus chiropus) is a very rare species which there are only a few specimens in the world. The chromosomes of two male specimens, collected from Xishuanbanna, Yunnan, are analysed by several banding technique (G-, C-bands, as well as Ag-staining). The diploid chromosome number is 22, and autosomes comprise 5 pairs of metacentrics, 2 pairs of subacrocentrics, and 3 pairs of acrocentrics. The X chromosome is a acrocentric, and Y is a micro-chromosome, almost a point, which could be a marker chromosome of the species and the genus. The centromeric C-bands are very faint, and C-bands of Nos. 1, 2, 9 and Y chromosome are negative. Only one pair Ag-NORs was found on No. 10 in the silver-stained karyotype. The relationship between morphologic and chromosomal features was discussed, and C-banded karyotype evolutionary trend has also been discussed. Moreover, the conventional karyotype of Niviventer confucianus was described.

Unsupervised intra-lingual and cross-lingual speaker adaptation for HMM-based speech synthesis using two-pass decision tree construction

Hidden Markov model (HMM)-based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to estimate the transcription of the adaptation data. This paper first presents an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for such supplementary acoustic models. This is achieved by defining a mapping between HMM-based synthesis models and ASR-style models, via a two-pass decision tree construction process. Second, it is shown that this mapping also enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data. Third, this paper demonstrates how this technique lends itself to the task of unsupervised cross-lingual adaptation of HMM-based speech synthesis models, and explains the advantages of such an approach. Finally, listener evaluations reveal that the proposed unsupervised adaptation methods deliver performance approaching that of supervised adaptation.

DT-MRI data visualisation using the dual tree Complex Wavelet transform

In this paper a novel visualisation method for diffusion tensor MRI datasets is introduced. This is based on the use of Complex Wavelets in order to produce "stripy" textures which depict the anisotropic component of the diffusion tensors. Grey-scale pixel intensity is used to show the isotropic component. This paper also discusses enhancements of the technique for 3D visualisation. © 2004 IEEE.

Deliberation in the motor system: reflex gains track evolving evidence leading to a decision.

Both decision making and sensorimotor control require real-time processing of noisy information streams. Historically these processes were thought to operate sequentially: cognitive processing leads to a decision, and the outcome is passed to the motor system to be converted into action. Recently, it has been suggested that the decision process may provide a continuous flow of information to the motor system, allowing it to prepare in a graded fashion for the probable outcome. Such continuous flow is supported by electrophysiology in nonhuman primates. Here we provide direct evidence for the continuous flow of an evolving decision variable to the motor system in humans. Subjects viewed a dynamic random dot display and were asked to indicate their decision about direction by moving a handle to one of two targets. We probed the state of the motor system by perturbing the arm at random times during decision formation. Reflex gains were modulated by the strength and duration of motion, reflecting the accumulated evidence in support of the evolving decision. The magnitude and variance of these gains tracked a decision variable that explained the subject's decision accuracy. The findings support a continuous process linking the evolving computations associated with decision making and sensorimotor control.

Effects of gamma-aminobutyric acid, progesterone and ionophore A23187 on acrosome reaction of tree shrew sperm in vitro: examination of acrosome reaction with an improved fluorescence microscopy

A number of acrosome reaction (AR) initiators have been found to be effective in inducing AR of human, laboratory and domestic animal sperm. Using an improved simple fluorescence microscopy, effects of gamma-aminobutyric acid (GABA), progesterone and ionophore A23187 on sperm AR of tree shrew, a useful animal model in biomedical research, have been investigated. Spontaneous AR in 4.92-7.53% of viable sperm was observed. Complete AR in 10.31-18.25% of viable tree shrew sperm was obviously induced by 5 mu M and 10 mu M calcium ionophore A23187, 1 mM GABA, and 5 mu M progesterone, and there were no significant differences between their abilities to initiate complete AR. No significant differences of AR percentages between 1- and 2-h treatments with A23187, progesterone and/or GABA were observed. These results suggested that the responses of tree shrew sperm to these AR initiators are similar to that of human and other mammalian sperm. (C) 1997 Elsevier Science B.V.

Ovulation in the tree shrew (Tupaia belangeri) induced by gonadotrophins

The induction of ovulation by exogenous gonadotrophins is an important approach for recovering oocytes used for studies on the reproductive biology of some mammals. In the present study, pregnant mare serum gonadotrophin (PMSG) and human chorionic gonadot

Making a shallow network deep: Conversion of a boosting classifier into a decision tree by boolean optimisation

This paper presents a novel way to speed up the evaluation time of a boosting classifier. We make a shallow (flat) network deep (hierarchical) by growing a tree from decision regions of a given boosting classifier. The tree provides many short paths for speeding up while preserving the reasonably smooth decision regions of the boosting classifier for good generalisation. For converting a boosting classifier into a decision tree, we formulate a Boolean optimization problem, which has been previously studied for circuit design but limited to a small number of binary variables. In this work, a novel optimisation method is proposed for, firstly, several tens of variables i.e. weak-learners of a boosting classifier, and then any larger number of weak-learners by using a two-stage cascade. Experiments on the synthetic and face image data sets show that the obtained tree achieves a significant speed up both over a standard boosting classifier and the Fast-exit-a previously described method for speeding-up boosting classification, at the same accuracy. The proposed method as a general meta-algorithm is also useful for a boosting cascade, where it speeds up individual stage classifiers by different gains. The proposed method is further demonstrated for fast-moving object tracking and segmentation problems. © 2011 Springer Science+Business Media, LLC.

Multiple classifier boosting and tree-structured classifiers

Visual recognition problems often involve classification of myriads of pixels, across scales, to locate objects of interest in an image or to segment images according to object classes. The requirement for high speed and accuracy makes the problems very challenging and has motivated studies on efficient classification algorithms. A novel multi-classifier boosting algorithm is proposed to tackle the multimodal problems by simultaneously clustering samples and boosting classifiers in Section 2. The method is extended into an online version for object tracking in Section 3. Section 4 presents a tree-structured classifier, called Super tree, to further speed up the classification time of a standard boosting classifier. The proposed methods are demonstrated for object detection, tracking and segmentation tasks. © 2013 Springer-Verlag Berlin Heidelberg.