Effects of genotype and environment on free amino acid levels in wheat grain: implications for acrylamide formation during processing

Acrylamide forms from free asparagine and reducing sugars during cooking, with asparagine concentration being the key parameter determining the formation in foods produced from wheat flour. In this study free amino acid concentrations were measured in the grain of varieties Spark and Rialto and four doubled haploid lines from a Spark x Rialto mapping population. The parental and doubled haploid lines had differing levels of total free amino acids and free asparagine in the grain, with one line consistently being lower than either parent for both of these factors. Sulfur deprivation led to huge increases in the concentrations of free asparagine and glutamine, and canonical variate analysis showed clear separation of the grain samples as a result of treatment (environment, E) and genotype (G) and provided evidence of G x E interactions. Low grain sulfur and high free asparagine concentration were closely associated with increased risk of acrylamide formation. G, E, and G x E effects were also evident in grain from six varieties of wheat grown at field locations around the United Kingdom in 2006 and 2007. The data indicate that progress in reducing the risk of acrylamide formation in processed wheat products could be made immediately through the selection and cultivation of low grain asparagme varieties and that further genetically driven improvements should be achievable. However, genotypes that are selected should also be tested under a range of environmental conditions.

On-line processing of wh-questions in children with G-SLI and typically developing children

Background: The computational grammatical complexity ( CGC) hypothesis claims that children with G(rammatical)-specific language impairment ( SLI) have a domain-specific deficit in the computational system affecting syntactic dependencies involving 'movement'. One type of such syntactic dependencies is filler-gap dependencies. In contrast, the Generalized Slowing Hypothesis claims that SLI children have a domain-general deficit affecting processing speed and capacity. Aims: To test contrasting accounts of SLI we investigate processing of syntactic (filler-gap) dependencies in wh-questions. Methods & Procedures: Fourteen 10; 2 - 17; 2 G-SLI children, 14 age- matched and 17 vocabulary-matched controls were studied using the cross- modal picturepriming paradigm. Outcomes & Results: G-SLI children's processing speed was significantly slower than the age controls, but not younger vocabulary controls. The G- SLI children and vocabulary controls did not differ on memory span. However, the typically developing and G-SLI children showed a qualitatively different processing pattern. The age and vocabulary controls showed priming at the gap, indicating that they process wh-questions through syntactic filler-gap dependencies. In contrast, G-SLI children showed priming only at the verb. Conclusions: The findings indicate that G-SLI children fail to establish reliably a syntactic filler- gap dependency and instead interpret wh-questions via lexical thematic information. These data challenge the Generalized Slowing Hypothesis account, but support the CGC hypothesis, according to which G-SLI children have a particular deficit in the computational system affecting syntactic dependencies involving 'movement'. As effective remediation often depends on aetiological insight, the discovery of the nature of the syntactic deficit, along side a possible compensatory use of semantics to facilitate sentence processing, can be used to direct therapy. However, the therapeutic strategy to be used, and whether such similar strengths and weaknesses within the language system are found in other SLI subgroups are empirical issues that warrant further research.

Random number generation as an index of controlled processing

Random number generation (RNG) is a functionally complex process that is highly controlled and therefore dependent on Baddeley's central executive. This study addresses this issue by investigating whether key predictions from this framework are compatible with empirical data. In Experiment 1, the effect of increasing task demands by increasing the rate of the paced generation was comprehensively examined. As expected, faster rates affected performance negatively because central resources were increasingly depleted. Next, the effects of participants' exposure were manipulated in Experiment 2 by providing increasing amounts of practice on the task. There was no improvement over 10 practice trials, suggesting that the high level of strategic control required by the task was constant and not amenable to any automatization gain with repeated exposure. Together, the results demonstrate that RNG performance is a highly controlled and demanding process sensitive to additional demands on central resources (Experiment 1) and is unaffected by repeated performance or practice (Experiment 2). These features render the easily administered RNG task an ideal and robust index of executive function that is highly suitable for repeated clinical use.

From dichotic listening to the irrelevant sound effect: a behavioural and neuroimaging analysis of the processing of unattended speech

The assumption that ignoring irrelevant sound in a serial recall situation is identical to ignoring a non-target channel in dichotic listening is challenged. Dichotic listening is open to moderating effects of working memory capacity (Conway et al., 2001) whereas irrelevant sound effects (ISE) are not (Beaman, 2004). A right ear processing bias is apparent in dichotic listening, whereas the bias is to the left ear in the ISE (Hadlington et al., 2004). Positron emission tomography (PET) imaging data (Scott et al., 2004, submitted) show bilateral activation of the superior temporal gyrus (STG) in the presence of intelligible, but ignored, background speech and right hemisphere activation of the STG in the presence of unintelligible background speech. It is suggested that the right STG may be involved in the ISE and a particularly strong left ear effect might occur because of the contralateral connections in audition. It is further suggested that left STG activity is associated with dichotic listening effects and may be influenced by working memory span capacity. The relationship of this functional and neuroanatomical model to known neural correlates of working memory is considered.

OFDM joint data detection and phase noise cancellation based on minimum mean square prediction error

This paper proposes a new iterative algorithm for orthogonal frequency division multiplexing (OFDM) joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the relatively less studied problem of "overfitting" such that the iterative approach may converge to a trivial solution. Specifically, we apply a hard-decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the PHN, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical Simulations are also given to verify the proposed algorithm. (C) 2008 Elsevier B.V. All rights reserved.

OFDM joint data detection and phase noise cancellation for constant modulus modulations

This correspondence proposes a new algorithm for the OFDM joint data detection and phase noise (PHN) cancellation for constant modulus modulations. We highlight that it is important to address the overfitting problem since this is a major detrimental factor impairing the joint detection process. In order to attack the overfitting problem we propose an iterative approach based on minimum mean square prediction error (MMSPE) subject to the constraint that the estimated data symbols have constant power. The proposed constrained MMSPE algorithm (C-MMSPE) significantly improves the performance of existing approaches with little extra complexity being imposed. Simulation results are also given to verify the proposed algorithm.

A block coprocessor for user data rate improvements to GPRS coding scheme 4

The general packet radio service (GPRS) has been developed to allow packet data to be transported efficiently over an existing circuit-switched radio network, such as GSM. The main application of GPRS are in transporting Internet protocol (IP) datagrams from web servers (for telemetry or for mobile Internet browsers). Four GPRS baseband coding schemes are defined to offer a trade-off in requested data rates versus propagation channel conditions. However, data rates in the order of > 100 kbits/s are only achievable if the simplest coding scheme is used (CS-4) which offers little error detection and correction (EDC) (requiring excellent SNR) and the receiver hardware is capable of full duplex which is not currently available in the consumer market. A simple EDC scheme to improve the GPRS block error rate (BLER) performance is presented, particularly for CS-4, however gains in other coding schemes are seen. For every GPRS radio block that is corrected by the EDC scheme, the block does not need to be retransmitted releasing bandwidth in the channel and improving the user's application data rate. As GPRS requires intensive processing in the baseband, a viable field programmable gate array (FPGA) solution is presented in this paper.

Improving CS-4 user data rate in GPRS enabled devices by using a BLER co-processor

The General Packet Radio Service (GPRS) was developed to allow packet data to be transported efficiently over an existing circuit switched radio network. The main applications for GPRS are in transporting IP datagram’s from the user’s mobile Internet browser to and from the Internet, or in telemetry equipment. A simple Error Detection and Correction (EDC) scheme to improve the GPRS Block Error Rate (BLER) performance is presented, particularly for coding scheme 4 (CS-4), however gains in other coding schemes are seen. For every GPRS radio block that is corrected by the EDC scheme, the block does not need to be retransmitted releasing bandwidth in the channel, improving throughput and the user’s application data rate. As GPRS requires intensive processing in the baseband, a viable hardware solution for a GPRS BLER co-processor is discussed that has been currently implemented in a Field Programmable Gate Array (FPGA) and presented in this paper.

Adaptive modelling, estimation and fusion from data: a neurofuzzy approach

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

Algorithmic statistical analysis of electrophysiological data for the investigation of structure-activity relationship in single neurons

We are developing computational tools supporting the detailed analysis of the dependence of neural electrophysiological response on dendritic morphology. We approach this problem by combining simulations of faithful models of neurons (experimental real life morphological data with known models of channel kinetics) with algorithmic extraction of morphological and physiological parameters and statistical analysis. In this paper, we present the novel method for an automatic recognition of spike trains in voltage traces, which eliminates the need for human intervention. This enables classification of waveforms with consistent criteria across all the analyzed traces and so it amounts to reduction of the noise in the data. This method allows for an automatic extraction of relevant physiological parameters necessary for further statistical analysis. In order to illustrate the usefulness of this procedure to analyze voltage traces, we characterized the influence of the somatic current injection level on several electrophysiological parameters in a set of modeled neurons. This application suggests that such an algorithmic processing of physiological data extracts parameters in a suitable form for further investigation of structure-activity relationship in single neurons.

Blind equalisation of multilevel PAM data for nonminimum phase channels via second- and fourth-order cumulants

This paper introduces a new blind equalisation algorithm for the pulse amplitude modulation (PAM) data transmitted through nonminimum phase (NMP) channels. The algorithm itself is based on a noncausal AR model of communication channels and the second- and fourth-order cumulants of the received data series, where only the diagonal slices of cumulants are used. The AR parameters are adjusted at each sample by using a successive over-relaxation (SOR) scheme, a variety of the ordinary LMS scheme, but with a faster convergence rate and a greater robustness to the selection of the ‘step-size’ in iterations. Computer simulations are implemented for both linear time-invariant (LTI) and linear time-variant (LTV) NMP channels, and the results show that the algorithm proposed in this paper has a fast convergence rate and a potential capability to track the LTV NMP channels.

Pre-CME onset fuses: do the STEREO heliospheric imagers hold the clues to the CME onset process?

Understanding the onset of coronal mass ejections (CMEs) is surely one of the holy grails of solar physics today. Inspection of data from the Heliospheric Imagers (HI), which are part of the SECCHI instrument suite aboard the two NASA STEREO spacecraft, appears to have revealed pre-eruption signatures which may provide valuable evidence for identifying the CME onset mechanism. Specifically, an examination of the HI images has revealed narrow rays comprised of a series of outward-propagating plasma blobs apparently forming near the edge of the streamer belt prior to many CME eruptions. In this pilot study, we inspect a limited dataset to explore the significance of this phenomenon, which we have termed a pre-CME ‘fuse’. Although, the enhanced expulsion of blobs may be consistent with an increase in the release of outward-propagating blobs from the streamers themselves, it could also be interpreted as evidence for interchange reconnection in the period leading to a CME onset. Indeed, it is argued that the latter could even have implications for the end-of-life of CMEs. Thus, the presence of these pre-CME fuses provides evidence that the CME onset mechanism is either related to streamer reconnection processes or the reconnection between closed field lines in the streamer belt and adjacent, open field lines. We investigate the nature of these fuses, including their timing and location with respect to CME launch sites, as well as their speed and topology.

Word frequency and bigram frequency effects on linguistic processing and speech motor performance in individuals with aphasia and normal speakers

Models of normal word production are well specified about the effects of frequency of linguistic stimuli on lexical access, but are less clear regarding the same effects on later stages of word production, particularly word articulation. In aphasia, this lack of specificity of down-stream frequency effects is even more noticeable because there is relatively limited amount of data on the time course of frequency effects for this population. This study begins to fill this gap by comparing the effects of variation of word frequency (lexical, whole word) and bigram frequency (sub-lexical, within word) on word production abilities in ten normal speakers and eight mild–moderate individuals with aphasia. In an immediate repetition paradigm, participants repeated single monosyllabic words in which word frequency (high or low) was crossed with bigram frequency (high or low). Indices for mapping the time course for these effects included reaction time (RT) for linguistic processing and motor preparation, and word duration (WD) for speech motor performance (word articulation time). The results indicated that individuals with aphasia had significantly longer RT and WD compared to normal speakers. RT showed a significant main effect only for word frequency (i.e., high-frequency words had shorter RT). WD showed significant main effects of word and bigram frequency; however, contrary to our expectations, high-frequency items had longer WD. Further investigation of WD revealed that independent of the influence of word and bigram frequency, vowel type (tense or lax) had the expected effect on WD. Moreover, individuals with aphasia differed from control speakers in their ability to implement tense vowel duration, even though they could produce an appropriate distinction between tense and lax vowels. The results highlight the importance of using temporal measures to identify subtle deficits in linguistic and speech motor processing in aphasia, the crucial role of phonetic characteristics of stimuli set in studying speech production and the need for the language production models to account more explicitly for word articulation.

Possible pre-dissociation of diorganotin dihalide complexes: relationship between antitumour activity and structure

An examination of crystallographic data has indicated that the structure/activity relationship for diorganotin dihalide complexes is different from that of other metal dihalides, in that the SnN bond lengths appear to determine the antitumour activity.

Visible-near infrared spectroscopy sensor for predicting curd and whey composition during cheese processing

The potential of visible-near infrared spectra, obtained using a light backscatter sensor, in conjunction with chemometrics, to predict curd moisture and whey fat content in a cheese vat was examined. A three-factor (renneting temperature, calcium chloride, cutting time), central composite design was carried out in triplicate. Spectra (300–1,100 nm) of the product in the cheese vat were captured during syneresis using a prototype light backscatter sensor. Stirring followed upon cutting the gel, and samples of curd and whey were removed at 10 min intervals and analyzed for curd moisture and whey fat content. Spectral data were used to develop models for predicting curd moisture and whey fat contents using partial least squares regression. Subjecting the spectral data set to Jack-knifing improved the accuracy of the models. The whey fat models (R = 0.91, 0.95) and curd moisture model (R = 0.86, 0.89) provided good and approximate predictions, respectively. Visible-near infrared spectroscopy was found to have potential for the prediction of important syneresis indices in stirred cheese vats.