2型糖尿病患者认知功能特点及相关影响因素研究

Objective: Type 2 diabetes patients’ performances of action memory , semantic memory and working memory and the related factors were explored. Methods: 60 Type 2 diabetes patients were compared with 60 age and gender and level of education matched non-diabetes controls. Mood were tested by SAS and SDS, MMSE was used to test the basic cognitive function, Trail Making Test A and B, Verbal fluency test, Go-No/Go test, and Stroop color-word test were used to investigate the executive function of Type 2 diabetes patients and normal controls (NC). Patients’ GLU, TG, TCH, HbA1c, insulin and Cp were tested and correlated with their action memory and working memory. Results: There was no difference between NC group and Type 2 diabetes patients in MMSE scores. There is depression and anxiety mood in Type 2 diabetes patients. Type 2 diabetes patients get lower score in action memory test. Comparing to NC group, Type 2 diabetes patients performed significantly worse in Trail Making Test A and B and verbal fluency test. In Stroop Test, NC group showed significant Stroop Effect and Repeated Distraction Promotion Effect and Negative Priming Effect. However, In Type 2 diabetes group, only the Stroop Effect appeared, but no Repeated Distraction Promotion Effect and Negative Priming Effect. There is no difference between Type 2 diabetes and NC in Stroop Effect. In Go-No/Go test, both of two groups showed significant Stroop Effect, however, there was no difference between them. And also there is no difference on error rate of all levels between them. The course of disease, GL, HbA1c, TG, TCH, INS and Cp affected action memory and working memory. Conclusion: Type 2 diabetes patients’ action memory, semantic memory and working memory were partially impaired. Controlling the levels of GLU, TG and TCH can delay these kinds of impairment.

心算年老化的认知心理机制及功能磁共振成像研究

Mechanisms underlying cognitive psychology and cerebral physiological of mental arithmetic with increasing are were studied by using behavioral methods and functional magnetic resonance imaging (fMRI). I. Studies on mechanism underlying cognitive psychology of mental arithmetic with increasing age These studies were accomplished in 172 normal subjects ranging from 20 to 79 years of age with above 12 years of education (Mean = 1.51, SD = 1.5). Five mental arithmetic tasks, "1000-1", "1000-3", "1000-7", "1000-13", "1000-17", were designed with a serial calculation in which subjects sequentially subtracted the same prime number (1, 3, 7, 13, 17) from another number 1000. The variables studied were mental arithmetic, age, working memory, and sensory-motor speed, and four studies were conducted: (1) Aging process of mental arithmetic with different difficulties, (2) mechanism of aging of mental arithmetic processing. (3) effects of working memory and sensory-motor speed on aging process of mental arithmetic, (4) model of cognitive aging of mental arithmetic, with statistical methods such as MANOVA, hierarchical multiple regression, stepwise regression analysis, structural equation modelling (SEM). The results were indicated as following: Study 1: There was an obvious interaction between age and mental arithmetic, in which reaction time (RT) increased with advancing age and more difficult mental arithmetic, and mental arithmetic efficiency (the ratio of accuracy to RT) deceased with advancing age and more difficult mental arithmetic; Mental arithmetic efficiency with different difficulties decreased in power function: Study 2: There were two mediators (latent variables) in aging process of mental arithmetic, and age had an effect on mental arithmetic with different difficulties through the two mediators; Study 3: There were obvious interactions between age and working memory, working memory and mental arithmetic; Working memory and sensory-motor speed had effects on aging process of mental arithmetic, in which the effect of working memory on aging process of mental arithmetic was about 30-50%, and the effect of sensory-motor speed on aging process of mental arithmetic was above 35%. Study 4: Age, working memory, and sensory-motor speed had effects on two latent variables (factor 1 and factor 2), then had effects on mental arithmetic with different difficulties through factor 1 which was relative to memory component, and factor 2 which relative to speed component and had an effect on factor 1 significantly. II. Functional magnetic resonance imaging study on metal arithmetic with increasing age This study was accomplished in 14 normal right-handed subjects ranging from 20 to 29 (7 subjects) and 60 to 69 (7 subjects) years of age by using functional magnetic resonance imaging apparatus, a superconductive Signa Horizon 1.5T MRI system. Two mental arithmetic tasks, "1000-3" and "1000-17", were designed with a serial calculation in which subjects sequentially subtracted the same prime number (3 or 17) from another number 1000 silently, and controlling task, "1000-0", in which subjects continually rehearsed number 1000 silently, was regarded as baseline, based on current "baseline-task" OFF-ON subtraction pattern. Original data collected by fMRI apparatus, were analyzed off-line in SUN SPARC working station by using current STIMULATE software. The analytical steps were composed of within-subject analysis, in which brain activated images about mental arithmetic with two difficulties were obtained by using t-test, and between-subject analysis, in which features of brain activation about mental arithmetic with two difficulties, the relationship between left and right hemisphere during mental arithmetic, and age differences of brain activation in young and elderly adults were examined by using non-parameter Wilcoxon test. The results were as following:

印象形成中同化和对比效应研究

Impression formation is an important aspect of person perception and has important interpersonal consequences. There are assimilation and contrast effects in impression formation and is still considerable debate regarding the best way to account for them. This present research used trait-implying sentences as priming materials, trait inferences sever as self-generated primes, examined the effect of different trait knowledge in assimilation and contrast effects. Experiment 1 determined the priming and target stimuli of this research by pretest. In experiment 2, participants read trait-implying sentences and resulted in trait inference as self-generated primes, examined the influence of trait activation on impression formation. The results indicated that participants instructed to memorize trait-implying sentences showed assimilation effect, whereas participants instructed to form impression from trait-implying sentences showed contrast effect. Difference to previous studies that emphasized the impact of awareness of the prime in impression formation, this research paid attention to the impact of different trait knowledge that resulted from trait inference. Experiment 3 studied the influence of actor salience on impression formation. The results indicated that when trait-implying sentences that described actors with names and were accompanied with photos of the actors, participants showed contrast under both memorization and impression instructions. Experiment 4 studied the influence of attribution context on assimilation and contrasts. The results showed that contrast ensued when trait-implying sentences were accompanied with the information that suggested a person attribution, whereas assimilation ensued when that information suggested a situation attribution, independent of processing goals. Experiment 5 made a direct test of the effect of different trait knowledge in impression formation. The results discovered that when abstract trait concepts were activated they act as a general interpretation frame in encoding stage, whereas when specific actor-trait links were activated, the activated information is likely to be used as a comparative standard in judgment stage. All studied indicated that there are two types of activated trait knowledge in trait inference: abstract trait concepts versus specific actor-trait links. When trait inference activated abstract trait concepts, the activated information serves as interpretation frame and lead to assimilation effect during impression formation, when trait inference activated specific actor-trait links, the activated information is more likely to be used as a comparative standards and resulted in contrast effects. These findings have important implications for understanding the mechanism of impression formation and practical values for interpersonal communication.

大脑序列记忆信息的提取

The purpose of this study was to examine the cognitive and neural mechanism underlying the serial position effects using cognitive experiments and ERPs(the event related potentials), for 11 item lists in very short-term and the continuous-distractor paradigm with Chinese character. The results demonstrated that when the length of list was 11 Chinese character, and the presentation time, the item interval and the retention interval was 400ms, the primacy effect and recency effect belong to the associative memory and absolute memory respectively. The retrieval of the item at the primacy part depended mainly on the context cues, but the retrieval of the item at the recency part depended mainly on the memory trace. The same results was concluded in the continuous-distractor paradigm (the presentation time was 1sec, the item interval is 12sec, and the retention interval was 30sec). Cognitive results revealed the robust serial position effects in the continuous-distractor paradigm. The different retrieval process between items at the primacy part and items at the recency part of the serial position curve was found. The behavioral responses data of ERP illustrated that the responses for the prime and recent items differed neither in accuracy nor reaction time, the retrieval time for the items at the primacy part was longer than that for the items at the recency part. And the accuracy of retrieval for the primacy part item was lower than that for the recency part items. That meant the retrieval of primacy part items needed more cognitive processes. The recent items, compared with the prime items, evoked ERPs that were more positive, this enhanced positivity occurred in a positive component peaking around 360ms. And for the same retrieval direction (forward or backward), the significant positive component difference between the retrieval for prime items and the retrieval for recent items was found. But there was no significant difference between the forward and backward retrieval at both the primacy and recency part of the serial position curve. These revealed the two kind of retrieval (forward and backward) at the same part of the serial position curve belonged to the same property. These findings fit more closely with the notion of the distinct between the associative memory and the absolute memory.

Blair's Africa: The Politics of Securitization and Fear

Abrahamsen, R. (2005). Blair's Africa: The Politics of Securitization and Fear. Alternatives: Global, Local, Political. 30(1), pp.55-80 RAE2008

Kennedy, Johnson and NATO: Britain, America and the Dynamics of Alliance, 1962-68

Priest, Andrew, Kennedy, Johnson and NATO: Britain, America and the Dynamics of Alliance, 1962-68 (New York: Routledge, 2006), wpp.xiv+222 RAE2008

Total quality leadership : Employing organisational learning as a conduit

Oxtoby, B.; Morgan, R.; McGuinness, T.; and Jones, M. (2001). Total quality leadership: Employing organisational learning as a conduit. International Journal of Management. 18(2), pp.245-251 RAE2008

Recovering a lost inheritance : The marital economy and its absence from the Prehistory of Economics in Britain

Roberts, Michael. 'Recovering a lost inheritance: the marital economy and its absence from the Prehistory of Economics in Britain', in: 'The Marital Economy in Scandinavia and Britain 1400-1900', (Eds) Argen, Maria., Erickson, Amy Louise., Farnham: Ashgate, 2005, pp.239-256 RAE2008

Information sets and partial permutation decoding for codes from finite geometries

Mavron, Vassili; McDonough, T.P.; Key, J.D., (2006) 'Information sets and partial permutation decoding for codes from finite geometries', Finite Fields and their applications 12(2) pp.232-247 RAE2008

On Learning Counting Functions With Queries

We investigate the problem of learning disjunctions of counting functions, which are general cases of parity and modulo functions, with equivalence and membership queries. We prove that, for any prime number p, the class of disjunctions of integer-weighted counting functions with modulus p over the domain Znq (or Zn) for any given integer q ≥ 2 is polynomial time learnable using at most n + 1 equivalence queries, where the hypotheses issued by the learner are disjunctions of at most n counting functions with weights from Zp. The result is obtained through learning linear systems over an arbitrary field. In general a counting function may have a composite modulus. We prove that, for any given integer q ≥ 2, over the domain Zn2, the class of read-once disjunctions of Boolean-weighted counting functions with modulus q is polynomial time learnable with only one equivalence query, and the class of disjunctions of log log n Boolean-weighted counting functions with modulus q is polynomial time learnable. Finally, we present an algorithm for learning graph-based counting functions.

Online and Offline Character Recognition Using Alignment to Prototypes

Nearest neighbor classifiers are simple to implement, yet they can model complex non-parametric distributions, and provide state-of-the-art recognition accuracy in OCR databases. At the same time, they may be too slow for practical character recognition, especially when they rely on similarity measures that require computationally expensive pairwise alignments between characters. This paper proposes an efficient method for computing an approximate similarity score between two characters based on their exact alignment to a small number of prototypes. The proposed method is applied to both online and offline character recognition, where similarity is based on widely used and computationally expensive alignment methods, i.e., Dynamic Time Warping and the Hungarian method respectively. In both cases significant recognition speedup is obtained at the expense of only a minor increase in recognition error.

Large-Scale Neural Systems for Vision and Cognition

— Consideration of how people respond to the question What is this? has suggested new problem frontiers for pattern recognition and information fusion, as well as neural systems that embody the cognitive transformation of declarative information into relational knowledge. In contrast to traditional classification methods, which aim to find the single correct label for each exemplar (This is a car), the new approach discovers rules that embody coherent relationships among labels which would otherwise appear contradictory to a learning system (This is a car, that is a vehicle, over there is a sedan). This talk will describe how an individual who experiences exemplars in real time, with each exemplar trained on at most one category label, can autonomously discover a hierarchy of cognitive rules, thereby converting local information into global knowledge. Computational examples are based on the observation that sensors working at different times, locations, and spatial scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels, which are reconciled by implicit underlying relationships that the network’s learning process discovers. The ARTMAP information fusion system can, moreover, integrate multiple separate knowledge hierarchies, by fusing independent domains into a unified structure. In the process, the system discovers cross-domain rules, inferring multilevel relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, the ARTMAP information fusion network features distributed code representations which exploit the model’s intrinsic capacity for one-to-many learning (This is a car and a vehicle and a sedan) as well as many-to-one learning (Each of those vehicles is a car). Fusion system software, testbed datasets, and articles are available from http://cns.bu.edu/techlab.

Biased ART: A Neural Architecture that Shifts Attention Toward Previously Disregarded Features Following an Incorrect Prediction

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Twodimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/.

Self-Supervised ARTMAP

Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semisupervised learning). In each case input patterns have a fixed number of features throughout training and testing. Human and machine learning contexts present additional opportunities for expanding incomplete knowledge from formal training, via self-directed learning that incorporates features not previously experienced. This article defines a new self-supervised learning paradigm to address these richer learning contexts, introducing a neural network called self-supervised ARTMAP. Self-supervised learning integrates knowledge from a teacher (labeled patterns with some features), knowledge from the environment (unlabeled patterns with more features), and knowledge from internal model activation (self-labeled patterns). Self-supervised ARTMAP learns about novel features from unlabeled patterns without destroying partial knowledge previously acquired from labeled patterns. A category selection function bases system predictions on known features, and distributed network activation scales unlabeled learning to prediction confidence. Slow distributed learning on unlabeled patterns focuses on novel features and confident predictions, defining classification boundaries that were ambiguous in the labeled patterns. Self-supervised ARTMAP improves test accuracy on illustrative lowdimensional problems and on high-dimensional benchmarks. Model code and benchmark data are available from: http://techlab.bu.edu/SSART/.

Adaptive Resonance Theory

SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378)