Determination of chemical species of iodine in some seaweeds .1.

The chemical species of iodine in seven marine algae Codium fragile, Ulva pertusa, Monostroma nitidum, Gracilaria confervoides, Sargassum Kjellmanianum, Dictyopteris divaricata and Laminaria japonica were studied using neutron activation analysis combined with chemical separation. The contents of total iodine, water-soluble iodine, soluble organic iodine, I- and IO3- were determined. The results indicate that the chemical species and contents of iodine in various algae are remarkably different. The highest iodine content of 734 mg/kg (wet basis) was found in Laminaria japonica, with 99.2% of the total iodine being water soluble. The iodine contents of the other six algae are lower and soluble iodine makes up 16-41% of the total. In the aqueous leachate, iodine is mainly I-, which amounts to 61-93% of total water-soluble iodine; the percentages of organic iodine making up 5.5-37.4%, while the contents of IO3- are the lowest, 1.4-4.5%. This result suggests that the mechanism of iodine enrichment is different for various algae and that its bioavailability varies as well. (C) 1997 Elsevier Science B.V.

Residues of enrofloxacin, furazolidone and their metabolites in Nile tilapia (Oreochromis niloticus)

The residues of enrofloxacin and its metabolite in Nile tilapia (Oreochromis niloticus) were studied after oral dose of 50 mg/kg for 7 days. To find the differences between Nile tilapia and Chinese shrimp (Penaeus chinensis), the residues of enrofloxacin in P chinensis were also studied under the same conditions. The results showed that enrofloxacin metabolized into ciprofloxacin in both Nile tilapia and P chinensis, the maximal concentration of enrofloxacin in muscle, liver and plasma of Nile tilapia were 3.61 mu g/g, 5.96 mu g/g, 1.25 mu g/ml respectively, and ciprofloxacin in muscle was 0.22 mu g/g. The maximal concentration of enrofloxacin and ciprofloxacin in P chinensis were 1.68 mu g/g and 0.07 mu g/g respectively. The predicted withdrawal time for Nile tilapia was 22 days, and P. chinensis was 12 days under our experiment conditions. The residues of fitrazolidone [3-(5-nitrofurfurylidenamino)-2-oxazolidinone] and its main metabolite 3-amina-2-oxazolidinone (AOZ) in Nile tilapia were first determined by HPLC/MS. Results showed that after oral dose of 30 mg/kg for 7 days, the maximum concentration of farazolidone in Nile tilapia was 413 mu g/kg after 6 h, whereas AOZ residue reached its maximum (31 mu g/kg) right after stopping treatment. In contrast to the high metabolic rate of furazolidone, AOZ was very difficult to eliminate in vivo, thus the withdrawal time of furazolidone in Nile tilapia was 22 days at least. (c) 2005 Elsevier B.V. All rights reserved.

Selective and specific detection of sulfate-reducing bacteria using potentiometric stripping analysis

A fast, sensitive and reliable potentiometric stripping analysis (PSA) is described for the selective detection of the marine pathogenic sulfate-reducing bacterium (SRB). Desulforibrio caledoiensis. The chemical and electrochemical parameters that exert influence on the deposition and stripping of lead ion, such as deposition potential, deposition time and pH value were carefully studied. The concentration of SRB was determined in acetate buffer solution (pH 5.2) under the optimized condition (deposition potential of -1.3 V. deposition time of 250 s, ionic strength of 0.2 mol L-1 and oxidant mercury (II) concentration of 40 mg L-1). A linear relationship between the stripping response and the logarithm of the bacterial concentration was observed in the range of 2.3 x 10 to 2.3 x 10(7) cfu mL(-1). In addition, the potentiometric stripping technique gave a distinct response to the SRB, but had no obvious response to Escherichia coli. The measurement system has a potential for further applications and provides a facile and sample method for detection of pathogenic bacteria. (C) 2010 Elsevier B.V. All rights reserved.

A Computational Model for the Acquisition and Use of Phonological Knowledge

Does knowledge of language consist of symbolic rules? How do children learn and use their linguistic knowledge? To elucidate these questions, we present a computational model that acquires phonological knowledge from a corpus of common English nouns and verbs. In our model the phonological knowledge is encapsulated as boolean constraints operating on classical linguistic representations of speech sounds in term of distinctive features. The learning algorithm compiles a corpus of words into increasingly sophisticated constraints. The algorithm is incremental, greedy, and fast. It yields one-shot learning of phonological constraints from a few examples. Our system exhibits behavior similar to that of young children learning phonological knowledge. As a bonus the constraints can be interpreted as classical linguistic rules. The computational model can be implemented by a surprisingly simple hardware mechanism. Our mechanism also sheds light on a fundamental AI question: How are signals related to symbols?

Specialized Mappings Architecture with Applications to Vision-based Estimation of Articulated Body Pose

A fundamental task of vision systems is to infer the state of the world given some form of visual observations. From a computational perspective, this often involves facing an ill-posed problem; e.g., information is lost via projection of the 3D world into a 2D image. Solution of an ill-posed problem requires additional information, usually provided as a model of the underlying process. It is important that the model be both computationally feasible as well as theoretically well-founded. In this thesis, a probabilistic, nonlinear supervised computational learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human body or human hands, given images obtained via one or more uncalibrated cameras. The SMA consists of several specialized forward mapping functions that are estimated automatically from training data, and a possibly known feedback function. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). A probabilistic model for the architecture is first formalized. Solutions to key algorithmic problems are then derived: simultaneous learning of the specialized domains along with the mapping functions, as well as performing inference given inputs and a feedback function. The SMA employs a variant of the Expectation-Maximization algorithm and approximate inference. The approach allows the use of alternative conditional independence assumptions for learning and inference, which are derived from a forward model and a feedback model. Experimental validation of the proposed approach is conducted in the task of estimating articulated body pose from image silhouettes. Accuracy and stability of the SMA framework is tested using artificial data sets, as well as synthetic and real video sequences of human bodies and hands.

AIRSTREAM: A Neural Network Model of Auditory Scene Analysis and Source Segregation

Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an "auditory scene analysis" enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the AIRSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a give acoustic source may be coherently grouped together into distinct streams based on pitch and spatial cues. The model also clarifies how multiple streams may be distinguishes and seperated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representaion of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to harmonics of the sound's pitch. The filter activates a pitch category which, in turn, activate a top-down expectation that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the "old-plus-new-heuristic" of Bregman. Multiple simultaneously occuring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize durin learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cures. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their interection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis the ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.

SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequence to Navigate Towards a Rewarded Goal

How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.

Learning Temporal Contexts and Priming-Preparation Modes for Pattern Recognition

The system presented here is based on neurophysiological and electrophysiological data. It computes three types of increasingly integrated temporal and probability contexts, in a bottom-up mode. To each of these contexts corresponds an increasingly specific top-down priming effect on lower processing stages, mostly pattern recognition and discrimination. Contextual learning of time intervals, events' temporal order or sequential dependencies and events' prior probability results from the delivery of large stimuli sequences. This learning gives rise to emergent properties which closely match the experimental data.

Unsupervised Neural Network for the Control of a Mobile Robot

This article introduces an unsupervised neural architecture for the control of a mobile robot. The system allows incremental learning of the plant during robot operation, with robust performance despite unexpected changes of robot parameters such as wheel radius and inter-wheel distance. The model combines Vector associative Map (VAM) learning and associate learning, enabling the robot to reach targets at arbitrary distances without knowledge of the robot kinematics and without trajectory recording, but relating wheel velocities with robot movements.

Texture Segregation By Visual Cortex: Perceptual Grouping, Attention, and Learning

A neural model is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model brings together five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits a large set of human psychophysical data on orientation-based textures. Object boundar output of the model is compared to computer vision algorithms using a set of human segmented photographic images. The model classifies textures and suppresses noise using a multiple scale oriented filterbank and a distributed Adaptive Resonance Theory (dART) classifier. The matched signal between the bottom-up texture inputs and top-down learned texture categories is utilized by oriented competitive and cooperative grouping processes to generate texture boundaries that control surface filling-in and spatial attention. Topdown modulatory attentional feedback from boundary and surface representations to early filtering stages results in enhanced texture boundaries and more efficient learning of texture within attended surface regions. Surface-based attention also provides a self-supervising training signal for learning new textures. Importance of the surface-based attentional feedback in texture learning and classification is tested using a set of textured images from the Brodatz micro-texture album. Benchmark studies vary from 95.1% to 98.6% with attention, and from 90.6% to 93.2% without attention.

A Neural Network Realization of Fuzzy ART

A neural network realization of the fuzzy Adaptive Resonance Theory (ART) algorithm is described. Fuzzy ART is capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns, thus enabling the network to learn both analog and binary input patterns. In the neural network realization of fuzzy ART, signal transduction obeys a path capacity rule. Category choice is determined by a combination of bottom-up signals and learned category biases. Top-down signals impose upper bounds on feature node activations.

Comparing impacts of new cropping systems on biodiversity in traditional rural China

This study selected six geographically-similar villages with traditional and alternative cultivation methods (two groups of three, one traditional and two alternatives) in two counties of Henan Province, China—a representative area of the Huang-huai-hai Plain representing traditional rural China. Soil heavy metal concentrations, floral and faunal biodiversity, and socio-economic data were recorded. Heavy metal concentrations of surface soils from three sites in each village were analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS, chromium, nickel, copper, cadmium, and lead) and Atomic Absorption Spectrophotometer (AAS, zinc). The floral biodiversity of four land-use types was recorded following the Braun-Blanquet coverage-abundance method using 0.5×0.5m quadrats. The faunal biodiversity of two representative farmland plots was recorded using 0.3×0.3m quadrats at four 0.1m layers. The socio-economic data were recorded through face-to-face interviews of one hundred randomly selected households at each village. Results demonstrate different cultivation methods lead to different impact on above variables. Traditional cultivation led to lower heavy metal concentrations; both alternative managements were associated with massive agrochemical input causing heavy metal pollution in farmlands. Floral distribution was significantly affected by village factors. Diverse cultivation supported high floral biodiversity through multi-scale heterogeneous landscapes containing niches and habitats. Faunal distribution was also significantly affected by village factor nested within soil depth. Different faunal groups responded differently, with Acari being taxonomically diverse and Collembola high in densities. Increase in manual labour and crop number in villages using alternative cultivation may positively affect biodiversity. The results point to the conservation potential of diverse cultivation methods in traditional rural China and other regions under social and political reforms, where traditional agriculture is changing to unified, large-scale mechanized agriculture. This study serves as a baseline for conservation in small-holding agricultural areas of China, and points to the necessity of further studies at larger and longer scales.

A comprehensive description of the competencies required for the performance of an ultrasound-guided axillary brachial plexus blockade

We addressed four research questions, each relating to the training and assessment of the competencies associated with the performance of ultrasound-guided axillary brachial plexus blockade (USgABPB). These were: (i) What are the most important determinants of learning of USgABPB? (ii) What is USgABPB? What are the errors most likely to occur when trainees learn to perform this procedure? (iii) How should end-user input be applied to the development of a novel USgABPB simulator? (iv) Does structured simulation based training influence novice learning of the procedure positively? We demonstrated that the most important determinants of learning USgABPB are: (a) Access to a formal structured training programme. (b) Frequent exposure to clinical learning opportunity in an appropriate setting (c) A clinical learning opporunity requires an appropriate patient, trainee and teacher being present at the same time, in an appropriate environment. We carried out a comprehensive description of the procedure. We performed a formal task analysis of USgABPB, identifying (i) 256 specific tasks associated with the safe and effective performance of the procedure, and (ii) the 20 most critical errors likely to occur in this setting. We described a methodology for this and collected data based on detailed, sequential evaluation of prototypes by trainees in anaesthesia. We carried out a pilot randomised control trial assessing the effectiveness of a USgABPB simulator during its development. Our data did not enable us to draw a reliable conclusion to this question; the trail did provide important new learning (as a pilot) to inform future investigation of this question. We believe that the ultimate goal of designing effective simulation-based training and assessment of ultrasound-guided regional anaesthesia is closer to realisation as a result of this work. It remains to be proven if this approach will have a positive impact on procedural performance, and more importantly improve patient outcomes.

Novel structured emulsions for delivering of food flavours

Flavour release from food is determined by the binding of flavours to other food ingredients and the partition of flavour molecules among different phases. Food emulsions are used as delivery systems for food flavours, and tailored structuring in emulsions provides novel means to better control flavour release. The current study investigated four structured oil-in-water emulsions with structuring in the oil phase, oil-water interface, and water phase. Oil phase structuring was achieved by the formation of monoglyceride (MG) liquid crystals in the oil droplets (MG structured emulsions). Structured interface was created by the adsorption of a whey protein isolate (WPI)-pectin double layer at the interface (multilayer emulsion). Water phase structured emulsions referred to emulsion filled protein gels (EFP gels), where emulsion droplets were embedded in WPI gel network, and emulsions with maltodextrins (MDs) of different dextrose-equivalent (DE) values. Flavour compounds with different physicochemical properties were added into the emulsions, and flavour release (release rate, headspace concentration and air-emulsion partition coefficient) was described by GC headspace analysis. Emulsion structures, including crystalline structure, particle size, emulsion stability, rheology, texture, and microstructures, were characterized using differential scanning calorimetry and X-ray diffraction, light scattering, multisample analytical centrifuge, rheometry, texture analysis, and confocal laser scanning microscopy, respectively. In MG structured emulsions, MG self-assembled into liquid crystalline structures and stable β-form crystals were formed after 3 days of storage at 25 °C. The inclusion of MG crystals allowed tween 20 stabilized emulsions to present viscoelastic properties, and it made WPI stabilized emulsions more sensitive to the change of pH and NaCl concentrations. Flavour compounds in MG structured emulsions had lower initial headspace concentration and air-emulsion partition coefficients than those in unstructured emulsions. Flavour release can be modulated by changing MG content, oil content and oil type. WPI-pectin multilayer emulsions were stable at pH 5.0, 4.0, and 3.0, but they presented extensive creaming when subjected to salt solutions with NaCl ≥ 150 mM and mixed with artificial salivas. Increase of pH from 5.0 to 7.0 resulted in higher headspace concentration but unchanged release rate, and increase of NaCl concentration led to increased headspace concentration and release rate. The study also showed that salivas could trigger higher release of hydrophobic flavours and lower release of hydrophilic flavours. In EFP gels, increases in protein content and oil content contributed to gels with higher storage modulus and force at breaking. Flavour compounds had significantly reduced release rates and air-emulsion partition coefficients in the gels than the corresponding ungelled emulsions, and the reduction was in line with the increase of protein content. Gels with stronger gel network but lower oil content were prepared, and lower or unaffected release rates of the flavours were observed. In emulsions containing maltodextrins, water was frozen at a much lower temperature, and emulsion stability was greatly improved when subjected to freeze-thawing. Among different MDs, MD DE 6 offered the emulsion the highest stability. Flavours had lower air-emulsion partition coefficients in the emulsions with MDs than those in the emulsion without MD. Moreover, the involvement of MDs in the emulsions allowed most flavours had similar release profiles before and after freeze-thaw treatment. The present study provided information about different structured emulsions as delivery systems for flavour compounds, and on how food structure can be designed to modulate flavour release, which could be helpful in the development of functional foods with improved flavour profile.

Training in and assessment of the procedural skills required to perform peripheral nerve blockade

The training and ongoing education of medical practitioners has undergone major changes in an incremental fashion over the past 15 years. These changes have been driven by patient safety, educational, economic and legislative/regulatory factors. In the near future, training in procedural skills will undergo a paradigm shift to proficiency based progression with associated requirements for competence-based programmes, valid, reliable assessment tools and simulation technology. Before training begins, the learning outcomes require clear definition; any form of assessment applied should include measurement of these outcomes. Currently training in a procedural skill often takes place on an ad hoc basis. The number of attempts necessary to attain a defined degree of proficiency varies from procedure to procedure. Convincing evidence exists that simulation training helps trainees to acquire skills more efficiently rather than relying on opportunities in their clinical practice. Simulation provides a safe, stress free environment for trainees for skill acquisition, generalization and transfer via deliberate practice. The work described in this thesis contributes to a greater understanding of how medical procedures can be performed more safely and effectively through education. The effect of feedback, provided to novices in a standardized setting on a bench model, based on knowledge of performance was associated with an increase in the speed of skill acquisition and a decrease in error rate during initial learning. The timing of feedback was also associated with effective learning of skill. A marked attrition of skills (independent of the type of feedback provided) was demonstrable 24 hrs after they have first been learned. Using the principles of feedback as described above, when studying the effect of an intense training program on novices of varied years of experience in anaesthesia (i.e. the present training programmes / courses of an intense training day for one or more procedures). There was a marked attrition of skill at 24 hours with a significant correlation with increasing years of experience; there also appeared to be an inverse relationship between years of experience in anaesthesia and performance. The greater the number of years of practice experience, the longer it required a learner to acquire a new skill. The findings of the studies described in this thesis may have important implications for the trainers, trainees and training bodies in the design and implementation of training courses and the formats of delivery of changing curricula. Both curricula and training modalities will need to take account of characteristics of individual learners and the dynamic nature of procedural healthcare.