Photodissociation of bromobenzene at 266 nm

The photodissociation of C6H5Br at 266 nm has been investigated on the universal crossed molecular beam machine, and time-of-flight spectra as well as the angular distribution of Br atom have been measured. Photofragment translational energy distribution P(E-t) reveals that about 47% of the available energy is partitioned into translational energy. The anisotropy parameter beta at this wavelength is -0.7+/-0.2. From P(E-t) and beta, we deduce that C6H5Br photodissociation is a fast process and the transition dipole moment is almost perpendicular to the C-Br bond. Ab initio calculations have been performed, and the calculated results show that the geometry of the first excited state of bromobenzene has changed apparently compared with that of the ground state. Two kinds of possible fast dissociation mechanism have also been proposed. (C) 1999 American Institute of Physics. [S0021-9606(99)01206-4].

Feature Matching for Object Localization in the Presence of Uncertainty

We consider the problem of matching model and sensory data features in the presence of geometric uncertainty, for the purpose of object localization and identification. The problem is to construct sets of model feature and sensory data feature pairs that are geometrically consistent given that there is uncertainty in the geometry of the sensory data features. If there is no geometric uncertainty, polynomial-time algorithms are possible for feature matching, yet these approaches can fail when there is uncertainty in the geometry of data features. Existing matching and recognition techniques which account for the geometric uncertainty in features either cannot guarantee finding a correct solution, or can construct geometrically consistent sets of feature pairs yet have worst case exponential complexity in terms of the number of features. The major new contribution of this work is to demonstrate a polynomial-time algorithm for constructing sets of geometrically consistent feature pairs given uncertainty in the geometry of the data features. We show that under a certain model of geometric uncertainty the feature matching problem in the presence of uncertainty is of polynomial complexity. This has important theoretical implications by demonstrating an upper bound on the complexity of the matching problem, an by offering insight into the nature of the matching problem itself. These insights prove useful in the solution to the matching problem in higher dimensional cases as well, such as matching three-dimensional models to either two or three-dimensional sensory data. The approach is based on an analysis of the space of feasible transformation parameters. This paper outlines the mathematical basis for the method, and describes the implementation of an algorithm for the procedure. Experiments demonstrating the method are reported.

Learning Object-Independent Modes of Variation with Feature Flow Fields

We present a unifying framework in which "object-independent" modes of variation are learned from continuous-time data such as video sequences. These modes of variation can be used as "generators" to produce a manifold of images of a new object from a single example of that object. We develop the framework in the context of a well-known example: analyzing the modes of spatial deformations of a scene under camera movement. Our method learns a close approximation to the standard affine deformations that are expected from the geometry of the situation, and does so in a completely unsupervised (i.e. ignorant of the geometry of the situation) fashion. We stress that it is learning a "parameterization", not just the parameter values, of the data. We then demonstrate how we have used the same framework to derive a novel data-driven model of joint color change in images due to common lighting variations. The model is superior to previous models of color change in describing non-linear color changes due to lighting.

Development of an in vitro multicellular tumor spheroid model using microencapsulation and its application in anticancer drug screening and testing

In this study, an in vitro multicellular tumor spheroid model was developed using microencapsulation, and the feasibility of using the microencapsulated. multicellular tumor spheroid (MMTS) to test the effect of chemotherapeutic drugs was investigated. Human MCF-7 breast cancer cells were encapsulated in alginate-poly-L-lysine-alginate (APA) microcapsules, and a single multicellular spheroid 150 mu m in diameter was formed in the microcapsule after 5 days of cultivation. The cell morphology, proliferation, and viability of the MMTS were characterized using phase contrast microscopy, BrdU-Iabeling, MTT stain, calcein AM/ED-2 stain, and H&E stain. It demonstrated that the MMTS was viable and that the proliferating cells were mainly localized to the periphery of the cell spheroid and the apoptotic cells were in the core. The MCF-7 MMTS was treated with mitomycin C (MC) at a concentration of 0.1, 1, or 10 times that of peak plasma concentration (ppc) for up to 72 h. The cytotoxicity was demonstrated. clearly by the reduction in cell spheroid size and the decrease in cell viability. The MMTS was further used to screen the anticancer effect of chemotherapeutic drugs, treated with MC, adriamycin (ADM) and 5-fluorouracil (5-FU) at concentrations of 0.1, 1, and 10 ppc for 24, 48, and 72 h. MCF-7 monolayer culture was used as control. Similar to monolayer culture, the cell viability of MMTS was reduced after treatment with anticancer drugs. However, the inhibition rate of cell viability in MMTS was much lower than that in monolayer culture. The MMTS was more resistant to anticancer drugs than monolayer culture. The inhibition rates of cell viability were 68.1%, 45.1%, and 46.8% in MMTS and 95.1%, 86.8%, and 91.6% in monolayer culture treated with MC, ADM, and 5-FU at 10 ppc for 72 h, respectively. MC showed the strongest cytotoxicity in both MMTS and monolayer, followed by 5-FU and ADM. It demonstrated that the MMTS has the potential to be a rapid and valid in vitro model to screen chemotherapeutic drugs with a feature to mimic in vivo three-dimensional (3-D) cell growth pattern.

Effects of melatonin on ovarian follicles

Objective: To evaluate the histomorphometry and expression of Ki-67 and c-kit in ovarian follicles of pinealectomized or melatonin-treated pinealectomized rats.Study design: Forty adult rats were randomly divided into four groups of 10 animals: Group I - control; Group II - sham-pinealectomized; Group III - pinealectomized (Px), and Group IV - Px treated with melatonin (10 mu g/night, per animal). After two months' treatment, on the night of proestrous, the animals were placed in metabolic cages for night urine collection and subsequent measurement of 6-sulfatoxymelatonin (6-SMT). the rats were anesthetized, blood samples were taken for estrogen and progesterone determinations, and they were then euthanized. the ovaries were dissected out for further histological and immunohistochemical analyses. Data were first submitted to analysis of variance (ANOVA) complemented with the Tukey-Kramer test for multiple comparisons (P < 0.05).Results: the urinary levels of 6-SMT and serum progesterone were lower in the Px group (GIII). Exogenous melatonin treatment restored both blood melatonin and 6-SMT urinary levels. the histomorphometric data in Group III revealed a significant increase of degenerating antral and nonantral follicles with regard to the other groups. in addition no corpora lutea were observed in this group. No significant differences were noticed regarding the number of corpora lutea among the other groups (I, II and IV), but the number of cells and the thickness of the theca interna of Px animals (Group III) were higher than in the other groups. Conversely, the density of progesterone receptors (fmol/g) in the ovaries of Group III was significantly lower than in the other groups.Conclusion: Our data indicate that melatonin exerts a role on the maintenance of a proper follicular function, and is thus important for ovulation and progesterone production. (C) 2012 Elsevier Ireland Ltd. All rights reserved.

Towards Fuzzy Compositional Modelling

X. Fu, Q. Shen and R. Zhao. 'Towards fuzzy compositional modelling,' In Proceedings of the 16th International Conference on Fuzzy Systems, 2007, pp. 1233-1238. Sponsorship: EPSRC

Fuzzy rules from ant-inspired computation

M. Galea and Q. Shen. Fuzzy rules from ant-inspired computation. Proceedings of the 13th International Conference on Fuzzy Systems, pages 1691-1696, 2004.

Fuzzy interpolation with generalized representative values

Z. Huang and Q. Shen. Fuzzy interpolation with generalized representative values. Proceedings of the 2004 UK Workshop on Computational Intelligence, pages 161-171.

Encouraging Complementary Fuzzy Rules within Iterative Rule Learning

M. Galea, Q. Shen and V. Singh. Encouraging Complementary Fuzzy Rules within Iterative Rule Learning. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 15-22.

Modelling random fuzzy renewal reward processes

Shen, Q., Zhao, R., Tang, W. (2008). Modelling random fuzzy renewal reward processes. IEEE Transactions on Fuzzy Systems, 16 (5),1379-1385

Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes

Similarly to protein folding, the association of two proteins is driven by a free energy funnel, determined by favorable interactions in some neighborhood of the native state. We describe a docking method based on stochastic global minimization of funnel-shaped energy functions in the space of rigid body motions (SE(3)) while accounting for flexibility of the interface side chains. The method, called semi-definite programming-based underestimation (SDU), employs a general quadratic function to underestimate a set of local energy minima and uses the resulting underestimator to bias further sampling. While SDU effectively minimizes functions with funnel-shaped basins, its application to docking in the rotational and translational space SE(3) is not straightforward due to the geometry of that space. We introduce a strategy that uses separate independent variables for side-chain optimization, center-to-center distance of the two proteins, and five angular descriptors of the relative orientations of the molecules. The removal of the center-to-center distance turns out to vastly improve the efficiency of the search, because the five-dimensional space now exhibits a well-behaved energy surface suitable for underestimation. This algorithm explores the free energy surface spanned by encounter complexes that correspond to local free energy minima and shows similarity to the model of macromolecular association that proceeds through a series of collisions. Results for standard protein docking benchmarks establish that in this space the free energy landscape is a funnel in a reasonably broad neighborhood of the native state and that the SDU strategy can generate docking predictions with less than 5 � ligand interface Ca root-mean-square deviation while achieving an approximately 20-fold efficiency gain compared to Monte Carlo methods.

Intersubject Regularity in the Intrinsic Shape of Human V1

Previous studies have reported considerable intersubject variability in the three-dimensional geometry of the human primary visual cortex (V1). Here we demonstrate that much of this variability is due to extrinsic geometric features of the cortical folds, and that the intrinsic shape of V1 is similar across individuals. V1 was imaged in ten ex vivo human hemispheres using high-resolution (200 μm) structural magnetic resonance imaging at high field strength (7 T). Manual tracings of the stria of Gennari were used to construct a surface representation, which was computationally flattened into the plane with minimal metric distortion. The instrinsic shape of V1 was determined from the boundary of the planar representation of the stria. An ellipse provided a simple parametric shape model that was a good approximation to the boundary of flattened V1. The aspect ration of the best-fitting ellipse was found to be consistent across subject, with a mean of 1.85 and standard deviation of 0.12. Optimal rigid alignment of size-normalized V1 produced greater overlap than that achieved by previous studies using different registration methods. A shape analysis of published macaque data indicated that the intrinsic shape of macaque V1 is also stereotyped, and similar to the human V1 shape. Previoud measurements of the functional boundary of V1 in human and macaque are in close agreement with these results.

Speaker Normalization Methods for Vowel Cognition: Comparative Analysis Using Neural Network and Nearest Neighbor Classifiers

Intrinsic and extrinsic speaker normalization methods are systematically compared using a neural network (fuzzy ARTMAP) and L1 and L2 K-Nearest Neighbor (K-NN) categorizers trained and tested on disjoint sets of speakers of the Peterson-Barney vowel database. Intrinsic methods include one nonscaled, four psychophysical scales (bark, bark with endcorrection, mel, ERB), and three log scales, each tested on four combinations of F0 , F1, F2, F3. Extrinsic methods include four speaker adaptation schemes, each combined with the 32 intrinsic methods: centroid subtraction across all frequencies (CS), centroid subtraction for each frequency (CSi), linear scale (LS), and linear transformation (LT). ARTMAP and KNN show similar trends, with K-NN performing better, but requiring about ten times as much memory. The optimal intrinsic normalization method is bark scale, or bark with endcorrection, using the differences between all frequencies (Diff All). The order of performance for the extrinsic methods is LT, CSi, LS, and CS, with fuzzy ARTMAP performing best using bark scale with Diff All; and K-NN choosing psychophysical measures for all except CSi.

A Neural Network Architecture for Autonomous Learning, Recognition, and Prediction in a Nonstationary World

In a constantly changing world, humans are adapted to alternate routinely between attending to familiar objects and testing hypotheses about novel ones. We can rapidly learn to recognize and narne novel objects without unselectively disrupting our memories of familiar ones. We can notice fine details that differentiate nearly identical objects and generalize across broad classes of dissimilar objects. This chapter describes a class of self-organizing neural network architectures--called ARTMAP-- that are capable of fast, yet stable, on-line recognition learning, hypothesis testing, and naming in response to an arbitrary stream of input patterns (Carpenter, Grossberg, Markuzon, Reynolds, and Rosen, 1992; Carpenter, Grossberg, and Reynolds, 1991). The intrinsic stability of ARTMAP allows the system to learn incrementally for an unlimited period of time. System stability properties can be traced to the structure of its learned memories, which encode clusters of attended features into its recognition categories, rather than slow averages of category inputs. The level of detail in the learned attentional focus is determined moment-by-moment, depending on predictive success: an error due to over-generalization automatically focuses attention on additional input details enough of which are learned in a new recognition category so that the predictive error will not be repeated. An ARTMAP system creates an evolving map between a variable number of learned categories that compress one feature space (e.g., visual features) to learned categories of another feature space (e.g., auditory features). Input vectors can be either binary or analog. Computational properties of the networks enable them to perform significantly better in benchmark studies than alternative machine learning, genetic algorithm, or neural network models. Some of the critical problems that challenge and constrain any such autonomous learning system will next be illustrated. Design principles that work together to solve these problems are then outlined. These principles are realized in the ARTMAP architecture, which is specified as an algorithm. Finally, ARTMAP dynamics are illustrated by means of a series of benchmark simulations.

The role of the dopaminergic neurotrophin growth/differentiation factor-5 in the developing rat ventral midbrain

Growth differentiation factor-5 (GDF-5) is a member of the transforming growth factor-β superfamily, a family of proteins that play diverse roles in many aspects of cell growth, proliferation and differentiation. GDF-5 has also been shown to be a trophic factor for embryonic midbrain dopaminergic neurons in vitro (Krieglstein et al. 1995) and after transplantation to adult rats in vivo (Sullivan et al. 1998). GDF-5 has also been shown to have neuroprotective and neurorestorative effects on adult dopaminergic neurons in the substantia nigra in animal models of Parkinson’s disease (Sullivan et al. 1997, 1999; Hurley et al. 2004). This experimental evidence has lead to GDF-5 being proposed as a neurotrophic factor with potential for use in the treatment of Parkinson’s disease. However, it is not know if GDF-5 is expressed in the brain and whether it plays a role in dopaminergic neuron development. The experiments presented here aim to address these questions. To that end this thesis is divided into five separate studies each addressing a particular question associated with GDF-5 and its expression patterns and roles during the development of the rat midbrain. Expression of the GDF-5 in the developing rat ventral mesencephalon (VM) was found to begin at E12 and peak on E14, the day that dopaminergic neurons undergo terminal differentiation. In the adult rat, GDF-5 was found to be restricted to heart and brain, being expressed in many areas of the brain, including striatum and midbrain. This indicated a role for GDF-5 in the development and maintenance of dopaminergic neurons. The appropriate receptors for GDF-5 (BMPR-II and BMPR-Ib) were found to be expressed at high levels in the rat VM at E14 and BMPR-II expression was demonstrated on dopaminergic neurons in the E13 mouse VM. GDF-5 resulted in a three-fold increase in the numbers of dopaminergic neurons in cultures of E14 rat VM, without affecting the numbers of neurones or total cells. GDF-5 was found to increase the proportion of neurons that were dopaminergic. The numbers of Nurr1-positive cells were not affected by GDF-5 treatment, but GDF-5 did increase the numbers of Nurr1- positive cells that expressed tyrosine hydroxylase (TH). Taken together this data indicated that GDF-5 increases the conversion of Nurr1-positive, TH-negative cells to Nurr1-positive, TH-positive cells. In GDF-5 treated cultures, total neurite length, neurite arborisation and somal area of dopaminergic were all significantly increased compared to control cultures. Thus this study showed that GDF-5 increased the numbers and morphological differentiation of VM dopaminergic neurones in vitro. In order to examine if GDF-5 could induce a dopaminergic phenotype in neural progenitor cells, neurosphere cultures prepared from embryonic rat VM were established. The effect of the gestational age of the donor VM on the proportion of cell types generated from neurospheres from E12, E13 and E14 VM was examined. Dopaminergic neurons could only be generated from neurospheres which were prepared from E12 VM. Thus in subsequent studies the effect of GDF-5 on dopaminergic induction was examined in progentior cell cultures prepared from the E12 rat VM. In primary cultures of E12 rat VM, GDF-5 increased the numbers of TH-positive cells without affecting the proliferation or survival of these cells. In cultures of expanded neural progenitor cells from the E12 rat VM, GDF-5 increased the expression of Nurr1 and TH, an action that was dependent on signalling through the BMPR-Ib receptor. Taken together, these experiments provide evidence that GDF-5 is expressed in the developing rat VM, is involved in both the induction of a dopaminergic phenotype in cells of the VM and in the subsequent morphological development of these dopaminergic neurons