Speech motor control in fluent and dysfluent speech production of an individual with apraxia of speech and Broca’s aphasia

Apraxia of speech (AOS) is typically described as a motor-speech disorder with clinically well-defined symptoms, but without a clear understanding of the underlying problems in motor control. A number of studies have compared the speech of subjects with AOS to the fluent speech of controls, but only a few have included speech movement data and if so, this was primarily restricted to the study of single articulators. If AOS reflects a basic neuromotor dysfunction, this should somehow be evident in the production of both dysfluent and perceptually fluent speech. The current study compared motor control strategies for the production of perceptually fluent speech between a young woman with apraxia of speech (AOS) and Broca’s aphasia and a group of age-matched control speakers using concepts and tools from articulation-based theories. In addition, to examine the potential role of specific movement variables on gestural coordination, a second part of this study involved a comparison of fluent and dysfluent speech samples from the speaker with AOS. Movement data from the lips, jaw and tongue were acquired using the AG-100 EMMA system during the reiterated production of multisyllabic nonwords. The findings indicated that although in general kinematic parameters of fluent speech were similar in the subject with AOS and Broca’s aphasia to those of the age-matched controls, speech task-related differences were observed in upper lip movements and lip coordination. The comparison between fluent and dysfluent speech characteristics suggested that fluent speech was achieved through the use of specific motor control strategies, highlighting the potential association between the stability of coordinative patterns and movement range, as described in Coordination Dynamics theory.

Farm business survey 2008/09: horticulture Production in England

The report gives an overview of the horticultural industry in the United Kingdom, including a snapshot of the different sectors of production, together with other information of interest about the business of horticulture. The data includes the economic performance of horticultural businesses in Ebgland during 2008/09.

Farm business survey 2009/10: horticulture production in England

Thereport gives an overview of the horticultural industry in the United Kingdom, including a snapshot of the different sectors of production, together with other information of interest about the business of horticulture. The data includes the economic perforamnce of horticulture businesses in England during 2009/10

Farm business survey 2008/09: poultry production in England

The report provides a commentary on the UK poultry industry focusing on its structure, development and contribution to the UK agricultural economy. It also includes data on the economic performance of poultry businesses in England during 2008/09.

Production and dissolution rates of earthworm-secreted calcium carbonate

Earthworms secrete granules of calcium carbonate. These are potentially important in soil biogeochemical cycles and are routinely recorded in archaeological studies of Quaternary soils. Production rates of calcium carbonate granules by the earthworm Lumbricus terrestris L. were determined over 27 days in a range of soils with differing chemical properties (pH, organic matter content, water holding capacity, bulk composition, cation exchange capacity and exchangeable cations). Production rate varied between soils, lay in the range 0–0.043 mmolCaCO3 (0–4.3 mg) earthworm−1 d−1 with an average rate of 8 × 10−3 mmolCaCO3 (0.8 mg) earthworm−1 d−1 and was significantly correlated (r = 0.68, P ≤ 0.01) with soil pH. In a second experiment lasting 315 days earthworms repeatedly (over periods of 39–57 days) produced comparable masses of granules. Converting individual earthworm granule production rates into fluxes expressed on per hectare of land per year basis depends heavily on estimates of earthworm numbers. Using values of 10–20 L. terrestris m−2 suggests a rate of 18– 3139 molCaCO3 ha−1 yr−1. Data obtained from flow-through dissolution experiments suggest that at near neutral pH, granule geometric surface areanormalised dissolution rates are similar to those for other biogenic and inorganic calcium carbonate. Fits of the data to the dissolution relationship r = k(1 − ˝)n where r = dissolution rate, k = a rate constant, ˝ = relative saturation and n = the reaction order gave values of k = 1.72 × 10−10 mol cm−2 s−1 and n = 1.8 for the geometric surface area-normalised rates and k = 3.51 × 10−13 mol cm−2 s−1 and n = 1.8 for the BET surface area-normalised rates. In 196 day leaching column experiments trends in granule dissolution rate referenced to soil chemistry corresponded to predictions made by the SLIM model for dissolution of limestone in soil. If soil solution approaches saturation with respect to calcium carbonate granule dissolution will slow or even stop and granules be preserved indefinitely. Granules have the potential to be a small but significant component of the biogeochemical cycling of C and Ca in soil.

The ERA-Interim reanalysis: configuration and performance of the data assimilation system

ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF

Do Integrated Arable Farming Systems provide a more sustainable form of agricultural production: the UK case

Integrated Arable Farming Systems (IAFS) projects utilise a range of novel and different farming techniques, often associated with optimising or reducing the use of inputs. Here, data is presented from the LINK-IFS project which suggests that, although input levels are being reduced, the overall profitability of the system can be maintained. The effect of thus reduction in inputs, however, in terms of impact on key environmental indicators is unclear.

Effect of dairy-based protein sources and temperature on growth, acidification and exopolysaccharide production of Bifidobacterium strains in skim milk

The aim of the present study was to find out the best growing conditions for exopolysaccharide (EPS) producing bifidobacteria, which improve their functionality in yoghurt-like products. Two Bifidobacterium strains were used in this study, Bifidobacterium longum subsp. infantis CCUG 52486 and Bifidobacterium infantis NCIMB 702205. In the first part of the study the effect of casein hydrolysate, lactalbumin hydrolysate, whey protein concentrate and whey protein isolate, added at 1.5% w/v in skim milk, was evaluated in terms of cell growth and EPS production; skim milk supplemented with yeast extract served as the control. Among the various nitrogen sources, casein hydrolysate (CH) showed the highest cell growth and EPS production for both strains after 18 h incubation and therefore it was selected for subsequent work. Based on fermentation experiments using different levels of CH (from 0.5 to 2.5% w/v) it was deduced that 1.5% (w/v) CH resulted in the highest EPS production, yielding 102 and 285 mg L− 1 for B. infantis NCIMB 702205 and B. longum subsp. infantis CCUG 52486, respectively. The influence of temperature on growth and EPS production of both strains was further evaluated at 25, 30, 37 and 42 °C for up to 48 h in milk supplemented with 1.5% (w/v) CH. The temperature had a significant effect on growth, acidification and EPS production. The maximum growth and EPS production were recorded at 37 °C for both strains, whereas no EPS production was observed at 25 °C. Lower EPS production for both strains were observed at 42 °C, which is the common temperature used in yoghurt manufacturing compared to that at 37 °C. The results showed that the culture conditions have a clear effect on the growth, acidification and EPS production, and more specifically, that skim milk supplemented with 1.5% (w/v) CH could be used as a substrate for the growth of EPS-producing bifidobacteria, at 37 °C for 24 h, resulting in the production of a low fat yoghurt-like product with improved functionality.

Production of angiotensin converting enzyme inhibitory peptides from β-lactoglobulin and casein derived peptides: an integrative approach

Angiotensin I-converting enzyme (ACE) inhibition is one of the mechanisms by which reduction in blood pressure is exerted. Whey proteins are a rich source of ACE inhibitory peptides and have shown a blood pressure reduction effect i.e. antihypertensive activity. The aim of this work was to develop a simplified process using a combination of adsorption and microfiltration steps for the production of hydrolysates from whey with high ACE inhibitory activity and potency; the latter was measured as the IC50, which is the peptide concentration required to reduce ACE activity by half. This process integrates the selective separation of β-lactoglobulin and casein derived peptides (CDP) from rennet whey and their hydrolysis, which results in partially pure, less complex hydrolysates with high bioactive potency. Hydrolysis was carried out with protease N ‘Amano’ in a thermostatically controlled membrane reactor operated in a batch mode. By applying the integrative approach it was possible to produce from the same feedstock two different hydrolysates that exhibited high ACE inhibition. One hydrolysate was mainly composed of casein-derived peptides with IC50= 285 μg/mL. In this hydrolysate we identified the well known potent ACE-I and anti-hypertensive tri-peptide Ile-Pro-Pro (IPP) and another novel octa-peptide Gln-Asp-Lys-Thr-Glu-Ile-Pro-Thr (QDKTEIPT). The second hydrolysate was mainly composed of β-lactoglobulin derived peptides with IC50=128 µg/mL. This hydrolysate contained a tetra-peptide (Ile-Ile-Ala-Glu) IIAE as one of the two major peptides. A further advantage to this process is that enzyme activity was substantially increased as enzyme product inhibition was reduced.

An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest

Canopy leaf area index (LAI), defined as the single-sided leaf area per unit ground area, is a quantitative measure of canopy foliar area. LAI is a controlling biophysical property of vegetation function, and quantifying LAI is thus vital for understanding energy, carbon and water fluxes between the land surface and the atmosphere. LAI is routinely available from Earth Observation (EO) instruments such as MODIS. However EO-derived estimates of LAI require validation before they are utilised by the ecosystem modelling community. Previous validation work on the MODIS collection 4 (c4) product suggested considerable error especially in forested biomes, and as a result significant modification of the MODIS LAI algorithm has been made for the most recent collection 5 (c5). As a result of these changes the current MODIS LAI product has not been widely validated. We present a validation of the MODIS c5 LAI product over a 121 km2 area of mixed coniferous forest in Oregon, USA, based on detailed ground measurements which we have upscaled using high resolution EO data. Our analysis suggests that c5 shows a much more realistic temporal LAI dynamic over c4 values for the site we examined. We find improved spatial consistency between the MODIS c5 LAI product and upscaled in situ measurements. However results also suggest that the c5 LAI product underestimates the upper range of upscaled in situ LAI measurements.

The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data

We describe a model-data fusion (MDF) inter-comparison project (REFLEX), which compared various algorithms for estimating carbon (C) model parameters consistent with both measured carbon fluxes and states and a simple C model. Participants were provided with the model and with both synthetic net ecosystem exchange (NEE) of CO2 and leaf area index (LAI) data, generated from the model with added noise, and observed NEE and LAI data from two eddy covariance sites. Participants endeavoured to estimate model parameters and states consistent with the model for all cases over the two years for which data were provided, and generate predictions for one additional year without observations. Nine participants contributed results using Metropolis algorithms, Kalman filters and a genetic algorithm. For the synthetic data case, parameter estimates compared well with the true values. The results of the analyses indicated that parameters linked directly to gross primary production (GPP) and ecosystem respiration, such as those related to foliage allocation and turnover, or temperature sensitivity of heterotrophic respiration, were best constrained and characterised. Poorly estimated parameters were those related to the allocation to and turnover of fine root/wood pools. Estimates of confidence intervals varied among algorithms, but several algorithms successfully located the true values of annual fluxes from synthetic experiments within relatively narrow 90% confidence intervals, achieving >80% success rate and mean NEE confidence intervals <110 gC m−2 year−1 for the synthetic case. Annual C flux estimates generated by participants generally agreed with gap-filling approaches using half-hourly data. The estimation of ecosystem respiration and GPP through MDF agreed well with outputs from partitioning studies using half-hourly data. Confidence limits on annual NEE increased by an average of 88% in the prediction year compared to the previous year, when data were available. Confidence intervals on annual NEE increased by 30% when observed data were used instead of synthetic data, reflecting and quantifying the addition of model error. Finally, our analyses indicated that incorporating additional constraints, using data on C pools (wood, soil and fine roots) would help to reduce uncertainties for model parameters poorly served by eddy covariance data.

A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems

We present an approach for dealing with coarse-resolution Earth observations (EO) in terrestrial ecosystem data assimilation schemes. The use of coarse-scale observations in ecological data assimilation schemes is complicated by spatial heterogeneity and nonlinear processes in natural ecosystems. If these complications are not appropriately dealt with, then the data assimilation will produce biased results. The “disaggregation” approach that we describe in this paper combines frequent coarse-resolution observations with temporally sparse fine-resolution measurements. We demonstrate the approach using a demonstration data set based on measurements of an Arctic ecosystem. In this example, normalized difference vegetation index observations are assimilated into a “zero-order” model of leaf area index and carbon uptake. The disaggregation approach conserves key ecosystem characteristics regardless of the observation resolution and estimates the carbon uptake to within 1% of the demonstration data set “truth.” Assimilating the same data in the normal manner, but without the disaggregation approach, results in carbon uptake being underestimated by 58% at an observation resolution of 250 m. The disaggregation method allows the combination of multiresolution EO and improves in spatial resolution if observations are located on a grid that shifts from one observation time to the next. Additionally, the approach is not tied to a particular data assimilation scheme, model, or EO product and can cope with complex observation distributions, as it makes no implicit assumptions of normality.

The socioeconomics of food crop production and climate change vulnerability: a global scale quantitative analysis of how grain crops are sensitive to drought

Many studies warn that climate change may undermine global food security. Much work on this topic focuses on modelling crop-weather interactions but these models do not generally account for the ways in which socio-economic factors influence how harvests are affected by weather. To address this gap, this paper uses a quantitative harvest vulnerability index based on annual soil moisture and grain production data as the dependent variables in a Linear Mixed Effects model with national scale socio-economic data as independent variables for the period 1990-2005. Results show that rice, wheat and maize production in middle income countries were especially vulnerable to droughts. By contrast, harvests in countries with higher investments in agriculture (e.g higher amounts of fertilizer use) were less vulnerable to drought. In terms of differences between the world's major grain crops, factors that made rice and wheat crops vulnerable to drought were quite consistent, whilst those of maize crops varied considerably depending on the type of region. This is likely due to the fact that maize is produced under very different conditions worldwide. One recommendation for reducing drought vulnerability risks is coordinated development and adaptation policies, including institutional support that enables farmers to take proactive action.

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

International office investment in global cities: the production of financial space and systemic risk

The paper explores the relationships between UK commercial real estate and regional economic development as a foundation for the analysis of the role of real estate investment in local economic development. Linkages between economic growth, development, real estate performance and investment allocations are documented. Long-run regional property performance is not the product of long-run economic growth, and weakly related to indicators of long-run supply and demand. Changes in regional portfolio weights seem driven by neither market performance nor underlying fundamentals. In the short run, regional investment shifts show no clear leads or lags with market performance.