Comparison of flag leaf and ear photosynthesis with biomass and grain yield of durum wheat under various water conditions and genotypes

Photosynthetic activity of cereals has traditionally been studied using leaves, thus neglecting the role of other organs such as ears. Here, we studied the effects of water status and genotypes on the photosynthetic activity of the flag leaf blade and the ear of durum wheat. The various parameters related to the photosynthetic activity were analysed in relation to the total above-ground plant biomass and grain yield at maturity. Four local varieties plus two cultivars adapted to the semiarid areas of South Morocco were grown in pots in a greenhouse. Five different water treatments were maintained from the beginning of stem elongation to maturity, when shoot biomass and grain yield were recorded. The net photosynthesis (A), stomatal conductance (gs) and transpiration (T) of the ear and the flag leaf were measured at anthesis. In both organs these factors decreased significantly with water deficit, whereas the A/T and A/gs ratios increased. The genotype effect was also significant for all traits studied. Whole-organ photosynthesis was much higher in the ear than in the flag leaf in well-watered conditions. As water stress developed, photosynthesis decreased less in the ear than in the flag leaf. Whole-ear photosynthesis correlated better than flag leaf photosynthesis with biomass and yield. Nevertheless, the relationships of the whole flag leaf with biomass and yield improved as the water stress became more severe, suggesting a progressive shift of yield from sink to source limitation. For all water regimes the ratios A/gs and A/T of the ear also showed a higher (negative) correlation with both biomass and yield than those of the flag leaf. The results indicate that the ear has a greater photosynthetic role than the flag leaf in determining grain yield, not only in drought but also in the absence of stress.

Morphological and Magnetic Properties of Superparamagnetic Carbon-Coated Fe Nanoparticles Produced by Arc Discharge.

Spherical carbon coated iron particles of nanometric diameter in the 510 nm range have been produced by arc discharge at near-atmospheric pressure conditions (using 58·10 4 Pa of He). The particles exhibit a crystalline dense iron core with an average diameter 7.4 ± 2.0 nm surrounded by a sealed carbon shell, shown by transmission electron microscopy (TEM), selected-area diffrac- tion (SAED), energy-dispersive X-ray analysis (STEM-EDX) and electron energy loss spectroscopy (EELS). The SAED, EDX and EELS results indicate a lack of traces of core oxidized phases showing an efficient protection role of the carbon shell. The magnetic properties of the nanoparticles have been investigated in the 5300 K temperature range using a superconducting quantum interference device (SQUID). The results reveal a superparamagnetic behaviour with an average monodomain diameter of 7.6 nm of the nanoparticles. The zero field cooled and field cooled (ZFC-FC)magnetization curves show a blocking temperature (TB)at room temperature very suitable for biomedical applications (drug delivery, magnetic resonance imaging MRI, hyperthermia).

Automatic filtering and substantiation of drug safety signals

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions

Vanishing Clams on an iberian beach: local consequences and global implication of accelerating loss of shells to tourism

Multi-decadal increase in shell removal by tourists, a process that may accelerate degradation of natural habitats, was quantified via two series of monthly surveys, conducted thirty years apart (1978-1981 and 2008-2010) in one small embayment on the Mediterranean coast of Spain. Over the last three decades, the local tourist arrivals have increased almost three-fold (2.74), while the area has remained unaffected by urban encroachment and commercial fisheries. Concomitantly, abundance of mollusk shells along the shoreline decreased almost three-fold (2.62) and displayed a tight inverse correlation with tourist arrivals. A four-fold increase in tourist arrivals observed globally over the last 30 years has likely induced a comparable worldwide acceleration in shell removal from marine shorelines and exerted multiple negative (but currently unquantifiable) habitat changes that may include increased beach erosion, changes in carbon and calcium cycles, and decline in diversity and abundance of organisms dependent on shell availability.

Feeding of nauplii, copepodites and adults of Calanipeda aquaedulcis (Calanoida) in Mediterranean salt marshes

Feeding of the different developmental stages of Calanipeda aquaedulcis on natural particles (bacterio-, phyto- and microzooplankton) was measured in a Mediterranean salt marsh (Empordà wetlands, NE Iberian Peninsula). Bottle incubations were performed in the field both in autumn and spring. The results showed differences in the diet of the different developmental stages due to both prey type and size. In general, the size of the ingested prey increased with increasing size of the C. aquaedulcis stage. While C. aquaedulcis adults had high ingestion rates and selection coefficients for large prey (micro- and nanoplankton), nauplii preferentially consumed smaller prey items (picoplankton). Copepodites showed the widest prey size range, including pico-, nano- and microplankton. Nevertheless, the lower size limit for particle capture was similar for all stages, i.e. between 1.7 and 2.1 μm. Omnivory was observed in all stages of C. aquaedulcis. Heterotrophic prey (picoplankton, dinoflagellates and ciliates) were the most ingested items. The ability to partition the available food among the different developmental stages could represent an advantage in times of ood scarcity because it may reduce intraspecific competition. This may explain how C. aquaedulcis is able to predominate in the zooplankton community for several weeks during spring and summer ven in situations of low food availability

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

Non-Linear and Non-Conventional Speech Processing: Alternative Techniques

This special issue aims to cover some problems related to non-linear and nonconventional speech processing. The origin of this volume is in the ISCA Tutorial and Research Workshop on Non-Linear Speech Processing, NOLISP’09, held at the Universitat de Vic (Catalonia, Spain) on June 25–27, 2009. The series of NOLISP workshops started in 2003 has become a biannual event whose aim is to discuss alternative techniques for speech processing that, in a sense, do not fit into mainstream approaches. A selected choice of papers based on the presentations delivered at NOLISP’09 has given rise to this issue of Cognitive Computation.

EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts

EEG recordings are usually corrupted by spurious extra-cerebral artifacts, which should be rejected or cleaned up by the practitioner. Since manual screening of human EEGs is inherently error prone and might induce experimental bias, automatic artifact detection is an issue of importance. Automatic artifact detection is the best guarantee for objective and clean results. We present a new approach, based on the time–frequency shape of muscular artifacts, to achieve reliable and automatic scoring. The impact of muscular activity on the signal can be evaluated using this methodology by placing emphasis on the analysis of EEG activity. The method is used to discriminate evoked potentials from several types of recorded muscular artifacts—with a sensitivity of 98.8% and a specificity of 92.2%. Automatic cleaning ofEEGdata are then successfully realized using this method, combined with independent component analysis. The outcome of the automatic cleaning is then compared with the Slepian multitaper spectrum based technique introduced by Delorme et al (2007 Neuroimage 34 1443–9).

Place branding : a conceptual and theoretical framework

Within the context of rising competition between territories, identity has become the most important element of recognition, differentiation and commodification in the communicative process within which cities, regions and countries position themselves. Geographical spaces thus compete in terms of this identity, which is then subjected to fierce comparison and competition (Nogué, 1999; Anholt, 2007a). The territorial brand thus entails the reinvention of places through a process of brand construction (branding) based on the promotion of the individual and collective identities of geographical spaces; these identities, in turn, are imbued with the intangible factors associated with their respective territorial identities.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Post-Nonlinear Mixtures and Beyond

Although sources in general nonlinear mixturm arc not separable iising only statistical independence, a special and realistic case of nonlinear mixtnres, the post nonlinear (PNL) mixture is separable choosing a suited separating system. Then, a natural approach is based on the estimation of tho separating Bystem parameters by minimizing an indcpendence criterion, like estimated mwce mutual information. This class of methods requires higher (than 2) order statistics, and cannot separate Gaarsian sources. However, use of [weak) prior, like source temporal correlation or nonstationarity, leads to other source separation Jgw rithms, which are able to separate Gaussian sourra, and can even, for a few of them, works with second-order statistics. Recently, modeling time correlated s011rces by Markov models, we propose vcry efficient algorithms hmed on minimization of the conditional mutual information. Currently, using the prior of temporally correlated sources, we investigate the fesihility of inverting PNL mixtures with non-bijectiw non-liacarities, like quadratic functions. In this paper, we review the main ICA and BSS results for riunlinear mixtures, present PNL models and algorithms, and finish with advanced resutts using temporally correlated snu~sm

Initialisation of Nonlinearities for PNL and Wiener systems Inversion

This paper proposes a very fast method for blindly initial- izing a nonlinear mapping which transforms a sum of random variables. The method provides a surprisingly good approximation even when the basic assumption is not fully satis¯ed. The method can been used success- fully for initializing nonlinearity in post-nonlinear mixtures or in Wiener system inversion, for improving algorithm speed and convergence.

Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works.

Si3N4 single-crystal nanowires grown from silicon micro- and nanoparticles near the threshold of passive oxidation

A simple and most promising oxide-assisted catalyst-free method is used to prepare silicon nitride nanowires that give rise to high yield in a short time. After a brief analysis of the state of the art, we reveal the crucial role played by the oxygen partial pressure: when oxygen partial pressure is slightly below the threshold of passive oxidation, a high yield inhibiting the formation of any silica layer covering the nanowires occurs and thanks to the synthesis temperature one can control nanowire dimensions

Myofascial pain syndrome associated with trigger points: A literature review. (I): Epidemiology, clinical treatment and etiopathogeny

Over the last few decades, advances have been made in the understanding of myofascial pain syndrome epidemiology, clinical characteristics and aetiopathogenesis, but many unknowns remain. An integrated hypothesis has provided a greater understanding of the physiopathology of trigger points, which may allow the development of new diagnostic, and above all, therapeutic methods, as well as the establishment of prevention policies and protocols by the health profession. Nevertheless, randomized studies are needed to provide a better understanding and detection of the different factors involved in the origin of trigger points.