The competitive ability of pea-barley intercrops against weeds and the interactions with crop productivity and soil N availability

Grain legumes, such as peas (Pisum sativum L.), are known to be weak competitors against weeds when grown as the sole crop. In this study, the weed-suppression effect of pea–barley (Hordeum vulgare L.)intercropping compared to the respective sole crops was examined in organic field experiments across Western Europe (i.e., Denmark, the United Kingdom, France, Germany and Italy). Spring pea (P) and barley(B) were sown either as the sole crop, at the recommended plant density (P100 and B100, respectively), or in replacement (P50B50) or additive (P100B50)intercropping designs for three seasons (2003–2005). The weed biomass was three times higher under the pea sole crops than under both the intercrops and barley sole crops at maturity. The inclusion of joint experiments in several countries and various growing conditions showed that intercrops maintain a highly asymmetric competition over weeds, regardless of the particular weed infestation (species and productivity), the crop biomass or the soil nitrogen availability. The intercropping weed suppression was highly resilient, whereas the weed suppression in pea sole crops was lower and more variable. The pea–barley intercrops exhibited high levels of weed suppression, even with a low percentage of barley in the total biomass. Despite a reduced leaf area in the case of a low soil N availability, the barley sole crops and intercrops displayed high weed suppression, probably because of their strong competitive capability to absorb soil N. Higher soil N availabilities entailed increased leaf areas and competitive ability for light, which contributed to the overall competitive ability against weeds for all of the treatments. The contribution of the weeds in the total dry matter and soil N acquisition was higher in the pea sole crop than in the other treatments, in spite of the higher leaf areas in the pea crops.

Effect of phonetic complexity on word reading and repetition in deep dyslexia

This investigation moves beyond the traditional studies of word reading to identify how the production complexity of words affects reading accuracy in an individual with deep dyslexia (JO). We examined JO’s ability to read words aloud while manipulating both the production complexity of the words and the semantic context. The classification of words as either phonetically simple or complex was based on the Index of Phonetic Complexity. The semantic context was varied using a semantic blocking paradigm (i.e., semantically blocked and unblocked conditions). In the semantically blocked condition words were grouped by semantic categories (e.g., table, sit, seat, couch,), whereas in the unblocked condition the same words were presented in a random order. JO’s performance on reading aloud was also compared to her performance on a repetition task using the same items. Results revealed a strong interaction between word complexity and semantic blocking for reading aloud but not for repetition. JO produced the greatest number of errors for phonetically complex words in semantically blocked condition. This interaction suggests that semantic processes are constrained by output production processes which are exaggerated when derived from visual rather than auditory targets. This complex relationship between orthographic, semantic, and phonetic processes highlights the need for word recognition models to explicitly account for production processes.

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics.

How to quantify the resolution of surface climate by circulation types: an example for Alpine precipitation

A novel approach is presented for the evaluation of circulation type classifications (CTCs) in terms of their capability to predict surface climate variations. The approach is analogous to that for probabilistic meteorological forecasts and is based on the Brier skill score. This score is shown to take a particularly simple form in the context of CTCs and to quantify the resolution of a climate variable by the classifications. The sampling uncertainty of the skill can be estimated by means of nonparametric bootstrap resampling. The evaluation approach is applied for a systematic intercomparison of 71 CTCs (objective and manual, from COST Action 733) with respect to their ability to resolve daily precipitation in the Alpine region. For essentially all CTCs, the Brier skill score is found to be higher for weak and moderate compared to intense precipitation, for winter compared to summer, and over the north and west of the Alps compared to the south and east. Moreover, CTCs with a higher number of types exhibit better skill than CTCs with few types. Among CTCs with comparable type number, the best automatic classifications are found to outperform the best manual classifications. It is not possible to single out one ‘best’ classification for Alpine precipitation, but there is a small group showing particularly high skill.

Purified phenolics from hydrothermal treatments of biomass: ability to protect sunflower bulk oil and model food emulsions from oxidation

The phenolic fractions released during hydrothermal treatment of selected feedstocks (corn cobs, eucalypt wood chips, almond shells, chestnut burs, and white grape pomace) were selectively recovered by extraction with ethyl acetate and washed with ethanol/water solutions. The crude extracts were purified by a relatively simple adsorption technique using a commercial polymeric, nonionic resin. Utilization of 96% ethanol as eluting agent resulted in 47.0-72.6% phenolic desorption, yielding refined products containing 49-60% w/w phenolics (corresponding to 30-58% enrichment with respect to the crude extracts). The refined extracts produced from grape pomace and from chestnut burs were suitable for protecting bulk oil and oil-in-water and water-in-oil emulsions. A synergistic action with bovine serum albumin in the emulsions was observed.

Early detection of ovarian cancer in samples pre-diagnosis using CA125 and MALDI-MS peaks

Aim: A nested case-control discovery study was undertaken 10 test whether information within the serum peptidome can improve on the utility of CA125 for early ovarian cancer detection. Materials and Methods: High-throughput matrix-assisted laser desorption ionisation mass spectrometry (MALDI-MS) was used to profile 295 serum samples from women pre-dating their ovarian cancer diagnosis and from 585 matched control samples. Classification rules incorporating CA125 and MS peak intensities were tested for discriminating ability. Results: Two peaks were found which in combination with CA125 discriminated cases from controls up to 15 and 11 months before diagnosis, respectively, and earlier than using CA125 alone. One peak was identified as connective tissue-activating peptide III (CTAPIII), whilst the other was putatively identified as platelet factor 4 (PF4). ELISA data supported the down-regulation of PF4 in early cancer cases. Conclusion: Serum peptide information with CA125 improves lead time for early detection of ovarian cancer. The candidate markers are platelet-derived chemokines, suggesting a link between platelet function and tumour development.

The timing and selection ability of UK property funds: an empirical investigation

This paper examines the selectivity and market timing performance of a sample of 21 UK property funds over the period Q3 1977 through to Q2 1987. The main finding of which that there is evidence of some superior selectivity performance on the part of UK property funds but that there are few funds who are able to successfully time the market.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.