Floral organogenesis and development of two taxa of the Solanaceae - Anisodus tanguticus and Atropa belladonna

Floral organogenesis and development of two Solanaceae species, Anisodus tanguticus and Atropa belladonna, were studied by using scanning electron microscopy (SEM) as part of a project on systematics and evolution in the tribe Hyoscyameae. These two species share the following common characters of floral organ initiation and development: (1) initiation of the floral organs in the two species follows Hofmeister's rule; (2) the mode of corolla tube development belongs to the "late sympetaly" type, namely, petals are initiated separately and later become joined by fusion of their basal meristem, then rise together and form a corolla tube; (3) primordia of the floral appendages are initiated in a pentamerous pattern and acropetal order: sepals are initiated first, followed by the petals and stamens, and finally the carpels. The whorl of five stamen primordia forms almost simultaneously and originates opposite the sepal primordia, but initiation of the sepal primordia shows different modes in the two species. The sepal primordia of Anisodus tanguticus have simultaneously whorled initiation, while those of Atropa belladonna have helical initiation. The systematic significance of the present results in the genera Anisodus and Atropa is discussed in this paper.

微动机器人的致动机理研究综述

MEMS是当前研究的一个热点,微机器人对于发展MEMS具有重要意义,也是MEMS的一项不可缺少的内容。微动技术是机器人学理论的一个重要分支,也是发展微机器人及相关微技术的基础。目前,各种新型微驱动器层出不穷,极大地推动了微机器人技术的发展。对于微动原理进行分析,从本质上弄清微动产生的机理,不仅可以丰富机器人学理论,还有可能使微动技术产生质的飞跃。从这一角度出发,对各种微动原理加以详细分析和比较,以期得出有意义的结论。

学习不良少年的自我意识情绪研究

Self-conscious emotions (guilt, shame, embarrassment, pride, etc) are social emotions, and involve complex appraisals of how one’s behavior has been evaluated by the self and other people according to some value standards. Self-conscious emotions play an important role in human life by arousing and regulating human action tendencies, feeling and thoughts, which can promote people to work hard in achievement and task fields, maintain good interpersonal relationship according with social morality and expectation. The present study aimed to examine complex self-conscious emotional understanding capabilities in junior middle school students with and without learning disabilities, how the self-conscious emotions generate, and relationship between self-conscious emotions and self-representation in academic and interpersonal fields. Situational experimental methods were used in this research, and the results would give further supports for learning disabilities intervention. The main results of present research are as follows. 1. The study included 4 parts and 6 experiments. The aim of study 1 was to explore whether juveniles with learning disabilities understood complex self-conscious emotions differently from juveniles without learning disabilities. We surveyed the self-conscious emotions understanding of 37 learning disabilities and 45 non-learning disabilities with the emotional situation stories. The results indicated that the self-conscious emotional recognition in others for learning disabilities was lower than that of non-learning disabilities in different emotional recognition tasks. Moreover, children with learning disabilities were more inclined to recognize emotions in themselves as elemental emotions, however, children without learning disabilities were more inclined to recognize emotions in themselves as self-conscious emotions. 2. The aim of study 2 was to explore the generative mechanism of self-conscious emotions in academic and interpersonal fields with the method of situational experiments, namely to examine whether the self-discrepancy could cause self-conscious emotions for learning disabilities. 84 learning disabilities (in experiment 1) and 80 learning disabilities (in experiment 2) participated in the research, and the results were as follows. (1) Self discrepancy caused participants’ self-conscious emotions effectively in academic and interpersonal fields. One’s own and parents’ perspercive on the actual-ideal self-discrepancy both produced dejection-related emotions (shame、embarrassment) and agitation-related emotions (guilt). （2）In academic fields, children with learning disabilities caused higher level negative self-conscious emotions (embarrassment, shame, and guilt) and lower level positive self-conscious emotion (pride). However, there were no differences of self-conscious emotions for children with and without learning disabilities in non-academic fields. 3. The aim of study 3 was to explore what influence had self-conscious emotions on self-representation for learning disabilities with the method of situational experiments. 57 learning disabilities (in experiment 1) and 67 learning disabilities (in experiment 2) participated in the research, and the results were as follows. （1）The negative self-conscious for learning disabilities could influence their positive or negative academic and positive interpersonal self-representation stability, the ways in which self-evaluation of ability mediate these effects. However, there was no significant effect for the negative self-conscious and self-evaluation of ability predicting negative interpersonal self-representation stability. （2）The stability level of positive academic and interpersonal self-representation for learning disabilities was lower than that of non-learning disabilities. There was no significant difference of the negative interpersonal self-representation stability for children with and without learning disabilities in the positive self-conscious valence condition. However, the stability level of negative interpersonal self-representation for learning disabilities was lower than that of non-learning disabilities in the negative self-conscious valence condition. 4. The aim of study 4 was to explore the intervention effects for self-conscious emotions training course on emotional comprehension cability. 65 learning disabilities (34 in experimental group, and 31 in control group) participated in the research. The results showed that self-conscious emotions course boosted the self-conscious emotions apprehensive level for children with learning disabilities.

Permutation Tests for Classification

We introduce and explore an approach to estimating statistical significance of classification accuracy, which is particularly useful in scientific applications of machine learning where high dimensionality of the data and the small number of training examples render most standard convergence bounds too loose to yield a meaningful guarantee of the generalization ability of the classifier. Instead, we estimate statistical significance of the observed classification accuracy, or the likelihood of observing such accuracy by chance due to spurious correlations of the high-dimensional data patterns with the class labels in the given training set. We adopt permutation testing, a non-parametric technique previously developed in classical statistics for hypothesis testing in the generative setting (i.e., comparing two probability distributions). We demonstrate the method on real examples from neuroimaging studies and DNA microarray analysis and suggest a theoretical analysis of the procedure that relates the asymptotic behavior of the test to the existing convergence bounds.

A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration

We formulate and interpret several multi-modal registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the "auto-information function", as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the "auto-information" as well as verify them empirically on multi-modal imagery. Among the useful aspects of the "auto-information function" is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.

Intervenção dos Médicos Dentistas na cessação tabágica

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Medicina Dentária

Farmacocinética do paracetamol

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Farmacêuticas

Análise dos efeitos de fármacos no meio ambiente aquático

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Farmacêuticas

O prazer associado ao consumo de alimentos e factores influenciadores

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Farmacêuticas

Lupus Eritematoso Sistémico

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Farmacêuticas

Generating Representative ISP Technologies From First-Principles

Understanding and modeling the factors that underlie the growth and evolution of network topologies are basic questions that impact capacity planning, forecasting, and protocol research. Early topology generation work focused on generating network-wide connectivity maps, either at the AS-level or the router-level, typically with an eye towards reproducing abstract properties of observed topologies. But recently, advocates of an alternative "first-principles" approach question the feasibility of realizing representative topologies with simple generative models that do not explicitly incorporate real-world constraints, such as the relative costs of router configurations, into the model. Our work synthesizes these two lines by designing a topology generation mechanism that incorporates first-principles constraints. Our goal is more modest than that of constructing an Internet-wide topology: we aim to generate representative topologies for single ISPs. However, our methods also go well beyond previous work, as we annotate these topologies with representative capacity and latency information. Taking only demand for network services over a given region as input, we propose a natural cost model for building and interconnecting PoPs and formulate the resulting optimization problem faced by an ISP. We devise hill-climbing heuristics for this problem and demonstrate that the solutions we obtain are quantitatively similar to those in measured router-level ISP topologies, with respect to both topological properties and fault-tolerance.

An Information Theoretic Framework for Field Monitoring Using Autonomously Mobile Sensors

We consider a mobile sensor network monitoring a spatio-temporal field. Given limited cache sizes at the sensor nodes, the goal is to develop a distributed cache management algorithm to efficiently answer queries with a known probability distribution over the spatial dimension. First, we propose a novel distributed information theoretic approach in which the nodes locally update their caches based on full knowledge of the space-time distribution of the monitored phenomenon. At each time instant, local decisions are made at the mobile nodes concerning which samples to keep and whether or not a new sample should be acquired at the current location. These decisions account for minimizing an entropic utility function that captures the average amount of uncertainty in queries given the probability distribution of query locations. Second, we propose a different correlation-based technique, which only requires knowledge of the second-order statistics, thus relaxing the stringent constraint of having a priori knowledge of the query distribution, while significantly reducing the computational overhead. It is shown that the proposed approaches considerably improve the average field estimation error by maintaining efficient cache content. It is further shown that the correlation-based technique is robust to model mismatch in case of imperfect knowledge of the underlying generative correlation structure.

Adaptive Neural Networks for Control of Movement Trajectories Invariant under Speed and Force Rescaling

This article describes two neural network modules that form part of an emerging theory of how adaptive control of goal-directed sensory-motor skills is achieved by humans and other animals. The Vector-Integration-To-Endpoint (VITE) model suggests how synchronous multi-joint trajectories are generated and performed at variable speeds. The Factorization-of-LEngth-and-TEnsion (FLETE) model suggests how outflow movement commands from a VITE model may be performed at variable force levels without a loss of positional accuracy. The invariance of positional control under speed and force rescaling sheds new light upon a familiar strategy of motor skill development: Skill learning begins with performance at low speed and low limb compliance and proceeds to higher speeds and compliances. The VITE model helps to explain many neural and behavioral data about trajectory formation, including data about neural coding within the posterior parietal cortex, motor cortex, and globus pallidus, and behavioral properties such as Woodworth's Law, Fitts Law, peak acceleration as a function of movement amplitude and duration, isotonic arm movement properties before and after arm-deafferentation, central error correction properties of isometric contractions, motor priming without overt action, velocity amplification during target switching, velocity profile invariance across different movement distances, changes in velocity profile asymmetry across different movement durations, staggered onset times for controlling linear trajectories with synchronous offset times, changes in the ratio of maximum to average velocity during discrete versus serial movements, and shared properties of arm and speech articulator movements. The FLETE model provides new insights into how spina-muscular circuits process variable forces without a loss of positional control. These results explicate the size principle of motor neuron recruitment, descending co-contractive compliance signals, Renshaw cells, Ia interneurons, fast automatic reactive control by ascending feedback from muscle spindles, slow adaptive predictive control via cerebellar learning using muscle spindle error signals to train adaptive movement gains, fractured somatotopy in the opponent organization of cerebellar learning, adaptive compensation for variable moment-arms, and force feedback from Golgi tendon organs. More generally, the models provide a computational rationale for the use of nonspecific control signals in volitional control, or "acts of will", and of efference copies and opponent processing in both reactive and adaptive motor control tasks.

The role of glucocorticoid-induced tumour necrosis factor receptor in developing mouse sympathetic neurons

Hereditary sensory autonomic neuropathy IV (HSAN IV) is an autosomal recessive disorder characterised by inability to feel pain and anhidrosis and is a consequence of defective NGF/TrkA signalling and growth of sensory and sympathetic neurons. Glucocortiocoid-induced tumour necrosis factors receptor (GITR), a transmembrane protein, activated by its specific ligand, GITRL, is well known for its role in the regulation of innate and acquired immune system responses. Recently, GITR was found to be required for NGF-dependant and extracellular signal-related kinase 1/2 (ERK1/2)-induced neurite growth and target innervation in the developing sympathetic nervous system (SNS). Given this novel role of GITR, it is possible that strategies targeting GITR have potential therapeutic benefit in promoting neurite growth in autonomic neuropathies such as HSAN IV. Using P1 mouse SCG neurons as a model, in addition to various SCG cell treatments, knock down models and transfection methods, we investigated whether GITR increases the sensitivity of sympathetic neurons to NGF; the region of GITR required for the enhancement of NGF-promoted growth, the signalling pathways downstream of GITR and how extensively GITR is involved in regulating peripheral innervation of the SNS. Results indicate that the region responsible for the growth promoting effects of GITR lies in its juxtamembrane intracellular region (here termed the growth promoting domain (GPD)) of GITR. The GPD of GITR activates ERK1/2 and inhibits nuclear factor kappa B (NF-κB) in an inverse fashion to provide an optimal cellular growth environment for P1 SCG neurons. While deleting the GPD of GITR had no effect on TrkA expression, constitutive phosphorylation of specific sites in the GPD reduced TrkA expression indicating a possible role for GITR in increasing the sensitivity of SCG neurons to NGF by the regulation of these sites, TrkA expression and subsequent NGF/TrkA binding. GITR appears to be heterogeneously required for NGF-promoted target innervation of SCG neurons in some organs, implying additional factors are involved in extensive NGF-target innervation of the SNS. In conclusion, this study answers basic biological questions regarding the molecular mechanism behind the role of GITR in the development of the SNS, and provides a basis for future research if GITR modulation is to be developed as a strategy for promoting axonal growth.

Stress-induced visceral pain in rodents: neurochemical, hormonal, immune and epigenetic mechanisms

Visceral pain is a debilitating disorder which affects up to 25% of the population at any one time. It is a global term used to describe pain originating from the internal organs, which is distinct from somatic pain. Currently the treatment strategies are unsatisfactory, with development of novel therapeutics hindered by a lack of detailed knowledge of the underlying mechanisms. The work presented in this thesis aimed to redress this issue and look in more detail at the molecular mechanisms of visceral pain in preclinical models. Stress has long been implicated in the pathophysiology of visceral pain in both preclinical and clinical studies. Here a mouse model of early-life stress-induced visceral hypersensitivity was validated. Moreover, mouse strain differences were also apparent in visceral sensitivity suggesting a possible genetic component to the underlying pathophysiology. Furthermore, gender and sex hormones were also implicated in stress sensitivity and visceral pain. Using the rat model of maternal separation, some of the epigenetic mechanisms underpinning visceral hypersensitivity, specifically the contribution of histone acetylation were unravelled. Glutamate has been well established in somatic pain processing, however, its contribution to visceral pain has not been extensively characterised. It was found that glutamate uptake is impaired in viscerally hypersensitive animals, an effect which could be reversed by treatment with riluzole, a glutamate uptake activator. Moreover, negative modulation of the metabotropic glutamate (mGlu) receptor 7 was sufficient to reverse visceral hypersensitivity in a stress sensitive rat strain, the Wistar Kyoto rat. Furthermore, toll-like receptor 4 (TLR4) was implicated in chronic stress-induced visceral hypersensitivity. Taken together, these findings have furthered our knowledge of the pathophysiology of visceral pain. In addition, we have identified glutamate transporters, mGlu7 receptor, histone acetylation and TLR4 as novel targets, amenable to pharmacological manipulation for the specific treatment of visceral pain.