SEU ground and flight data in static random access memories

This paper presents the vulnerabilities of single event effects (SEEs) simulated by heavy ions on ground and observed oil SJ-5 research satellite in space for static random access memories (SRAMs). A single event upset (SEU) prediction code has been used to estimate the proton-induced upset rates based oil the ground test curve of SEU cross-section versus heavy ion linear energy transfer (LET). The result agrees with that of the flight data.

Insulin-like growth factor-binding protein-3 plays an important role in regulating pharyngeal skeleton and inner ear formation and differentiation

Insulin-like growth factor-binding protein (IGFBP)-3 is the major insulin-like growth factor (IGF) carrier protein in the bloodstream. IGFBP-3 prolongs the half-life of circulating IGFs and prevents their potential hypo-glycemic effect. IGFBP-3 is also expressed in many peripheral tissues in fetal and adult stages. In vitro, IGFBP-3 can inhibit or potentiate IGF actions and even possesses IGF-independent activities, suggesting that local IGFBP-3 may also have paracrine/autocrine function(s). The in vivo function of IGFBP-3, however, is unclear. In this study, we elucidate the developmental role of IGFBP-3 using the zebrafish model. IGFBP-3 mRNA expression is first detected in the migrating cranial neural crest cells and subsequently in pharyngeal arches in zebrafish embryos. IGFBP-3 mRNA is also persistently expressed in the developing inner ears. To determine the role of IGFBP-3 in these tissues, we ablated the IGFBP-3 gene product using morpholino-modified antisense oligonucleotides (MOs). The IGFBP-3 knocked down embryos had delayed pharyngeal skeleton morphogenesis and greatly reduced pharyngeal cartilage differentiation. Knockdown of IGFBP-3 also significantly decreased inner ear size and disrupted hair cell differentiation and semicircular canal formation. Furthermore, reintroduction of a MO-resistant form of IGFBP-3 "rescued" the MO-induced defects. These findings suggest that IGFBP-3 plays an important role in regulating pharyngeal cartilage and inner car development and growth in zebrafish.

Three tetraspanins from Chinese shrimp, Fenneropenaeus chinensis, may play important roles in WSSV infection

Three members of the tetraspanin/TM4SF superfamily were cloned from Chinese shrimp, Fenneropenaeus chinensis. The deduced amino acid sequences of the three proteins have typical motifs of the tetraspanin/TM4SF superfamily. Phylogenetic analysis of the proteins, together with the known tetraspanins of invertebrates and vertebrates, revealed that they belong to different tetraspanin subfamilies: CD9, CD63 and tetraspanin-3. The three cloned genes of CD9, CD63 and tetraspanin-3 showed apparently different tissue distributions. The CD9 gene (FcCD9) was specifically expressed in the hepatopancreas. While for the CD63 gene (FcCD63), the highest expression was detected in nerves, epidermis and heart, with low expression in haemocytes, ovary, gill, hepatopancreas and stomach and no expression in intestine, muscle and lymphoid organ. Compared with FcCD9 and FcCD63, the tetraspanin-3 gene (FcTetraspanin-3) was more broadly expressed and its highest expression was detected in the intestine. Its expression in nerves was lower than in the intestine, but was higher than in other tissues. Expression in haemocytes, ovary and muscle was much lower than in other tissues. The expression profiles of FcCD9, FcCD63 and FcTetraspanin-3 in different tissues, including haemocytes, lymphoid organ and hepatopancreas, were compared by real-time PCR when shrimp were challenged by live white spot syndrome virus (WSSV) and heat-inactivated WSSV. All three tetraspanins were markedly up-regulated in the live WSSV-challenged shrimp tissues. The data suggested that the three cloned members of TM4SF superfamily in Chinese shrimp may play a key role in the route of WSSV infection.

Seed Germination of Swertia mussotii,an Important Application in Tibetan Folk Medicine

In this study,the effects of temperature,the growth regulator GA3 and storage conditions on seed germination of the biennial Swertia mussotii Franch were compared in seeds from a natural high altitude alpine site and after one-cycle of artificial cultivation at a low altitude.The untreated seeds from high altitude displayed higher germination than those from low altitude.Both GA3 and storage conditions enhanced germination from all sources.After treatment,the final germination of seeds from different sources shows no distinct difference.These results suggest that:(i) all seeds of this species,irrespective of their sources,have similar kinds of dormancy;(ii) the dormancy of this species can not been broken through one-cycle adaptation in ex situ cultivation and thus might be genetically controlled;(iii) both GA3 and 4℃ cold storage are effective for dormancy breaking in ex situ cultivation of this important medicinal species.

Variation of active constituents of an important Tibet folk medicine Swertia mussotii Franch. (Gentianaceae) between artificially cultivated and naturally distributed

Concentrations of seven phytochemical constituents (swertiamarin, mangiferin, swertisin, oleanolic acid, 1,5,8-trihydroxy-3methoxyxanthone, 1,8-dihydroxy-3,7-dimethoxyxanthone and 1,8-dihydroxy-3,5-dimethoxyxanthone) of "ZangYinChen" (Swertia mussotii, a herb used in Tibetan folk medicine) were determined and compared in plants collected from naturally distributed high-altitude populations and counterparts that had been artificially cultivated at low altitudes. Levels of mangiferin, the most abundant active compound in this herb, were significantly lower in cultivated samples and showed a negative correlation with altitude. The other constituents were neither positively nor negatively correlated with cultivation at low altitude. Concentrations of all of the constituents varied substantially with growth stage and were highest at the bud stage in the cultivars, but there were no distinct differences between flowering and fruiting stages in this respect. (c) 2005 Elsevier Ireland Ltd. All rights reserved.

Distributed Parallel Computing in Mermera: Mixing Noncoherent Shared Memories

Programmers of parallel processes that communicate through shared globally distributed data structures (DDS) face a difficult choice. Either they must explicitly program DDS management, by partitioning or replicating it over multiple distributed memory modules, or be content with a high latency coherent (sequentially consistent) memory abstraction that hides the DDS' distribution. We present Mermera, a new formalism and system that enable a smooth spectrum of noncoherent shared memory behaviors to coexist between the above two extremes. Our approach allows us to define known noncoherent memories in a new simple way, to identify new memory behaviors, and to characterize generic mixed-behavior computations. The latter are useful for programming using multiple behaviors that complement each others' advantages. On the practical side, we show that the large class of programs that use asynchronous iterative methods (AIM) can run correctly on slow memory, one of the weakest, and hence most efficient and fault-tolerant, noncoherence conditions. An example AIM program to solve linear equations, is developed to illustrate: (1) the need for concurrently mixing memory behaviors, and, (2) the performance gains attainable via noncoherence. Other program classes tolerate weak memory consistency by synchronizing in such a way as to yield executions indistinguishable from coherent ones. AIM computations on noncoherent memory yield noncoherent, yet correct, computations. We report performance data that exemplifies the potential benefits of noncoherence, in terms of raw memory performance, as well as application speed.

Variable Rate Working Memories for Phonetic Categorization and Invariant Speech Perception

Speech can be understood at widely varying production rates. A working memory is described for short-term storage of temporal lists of input items. The working memory is a cooperative-competitive neural network that automatically adjusts its integration rate, or gain, to generate a short-term memory code for a list that is independent of item presentation rate. Such an invariant working memory model is used to simulate data of Repp (1980) concerning the changes of phonetic category boundaries as a function of their presentation rate. Thus the variability of categorical boundaries can be traced to the temporal in variance of the working memory code.

From Synapse to Self: Spikes, Synchrony, and Attentive Learning by Laminar Thalamocortical Circuits

How do our brains transform the "blooming buzzing confusion" of daily experience into a coherent sense of self that can learn and selectively attend to important information? How do local signals at multiple processing stages, none of which has a global view of brain dynamics or behavioral outcomes, trigger learning at multiple synaptic sites when appropriate, and prevent learning when inappropriate, to achieve useful behavioral goals in a continually changing world? How does the brain allow synaptic plasticity at a remarkably rapid rate, as anyone who has gone to an exciting movie is readily aware, yet also protect useful memories from catastrophic forgetting? A neural model provides a unified answer by explaining and quantitatively simulating data about single cell biophysics and neurophysiology, laminar neuroanatomy, aggregate cell recordings (current-source densities, local field potentials), large-scale oscillations (beta, gamma), and spike-timing dependent plasticity, and functionally linking them all to cognitive information processing requirements.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

Working Memories for Storage and Recall of Arbitrary Temporal Sequences

A working memory model is described that is capable of storing and recalling arbitrary temporal sequences of events, including repeated items. These memories encode the invariant temporal order of sequential events that may be presented at widely differing speeds, durations, and interstimulus intervals. This temporal order code is designed to enable all possible groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system.

Rapid separation of biologically important low molecular weight compounds by microchip electrophoresis and ultra-fast performance liquid chromatography

The research work in this thesis reports rapid separation of biologically important low molecular weight compounds by microchip electrophoresis and ultrahigh liquid chromatography. Chapter 1 introduces the theory and principles behind capillary electrophoresis separation. An overview of the history, different modes and detection techniques coupled to CE is provided. The advantages of microchip electrophoresis are highlighted. Some aspects of metal complex analysis by capillary electrophoresis are described. Finally, the theory and different modes of the liquid chromatography technology are presented. Chapter 2 outlines the development of a method for the capillary electrophoresis of (R, S) Naproxen. Variable parameters of the separation were optimized (i.e. buffer concentration and pH, concentration of chiral selector additives, applied voltage and injection condition).The method was validated in terms of linearity, precision, and LOD. The optimized method was then transferred to a microchip electrophoresis system. Two different types of injection i.e. gated and pinched, were investigated. This microchip method represents the fastest reported chiral separation of Naproxen to date. Chapter 3 reports ultra-fast separation of aromatic amino acid by capillary electrophoresis using the short-end technique. Variable parameters of the separation were optimized and validated. The optimized method was then transferred to a microchip electrophoresis system where the separation time was further reduced. Chapter 4 outlines the use of microchip electrophoresis as an efficient tool for analysis of aluminium complexes. A 2.5 cm channel with linear imaging UV detection was used to separate and detect aluminium-dopamine complex and free dopamine. For the first time, a baseline, separation of aluminium dopamine was achieved on a 15 seconds timescale. Chapter 5 investigates a rapid, ultra-sensitive and highly efficient method for quantification of histamine in human psoriatic plaques using microdialysis and ultrahigh performance liquid chromatography with fluorescence detection. The method utilized a sub-two-micron packed C18 stationary phase. A fluorescent reagent, 4-(1-pyrene) butyric acid N-hydroxysuccinimide ester was conjugated to the primary and secondary amino moieties of histamine. The dipyrene-labeled histamine in human urine was also investigated by ultrahigh pressure liquid chromatography using a C18 column with 1.8 μm particle diameter. These methods represent one of the fastest reported separations to date of histamine using fluorescence detection.