Quantitative competitive PCR for the detection of genetically modified soybean and maize

 The surveillance of food labelling concerning genetically modified organisms (GMOs) requires DNA-based analytical techniques. Present assay systems allow the detection of GMO in food; however, they do not permit their quantitation. In this study, we report the development of quantitative competitive polymerase chain reaction (QC-PCR) systems for the detection and quantitation of the Roundup Ready soybean (RRS) and the Maximizer maize (MM) in food samples. Three DNA fragments that differ from the GMO-specific sequences by an insertion were constructed and used as internal standards in the PCR. These standards were calibrated by co-amplifying with mixtures containing RRS DNA and MM DNA, respectively. The calibrated QC-PCR systems were applied to nine commercial food samples containing RRS DNA and to three certified RRS flour mixtures in order to elucidate whether these food samples contain more or less than 1% RRS DNA. Finally, the GMO contents of four samples that were found to contain more than 1% RRS were determined by QC-PCR using various amounts of standard DNA. Received: 13 January 1998 / Revised version: 4 March 1998     

Towards biomimetic models of the reduced [FeFe]-hydrogenase that preserve the key structural features of the enzyme active site; a DFT investigation

[FeFe]-hydrogenases are the most efficient biological catalysts available for the H₂ evolution reaction. Their active site – the H-cluster – features a diiron subsite which has the peculiar characteristic of bearing cyanide groups hydrogen-bonded to the apoprotein as well as carbonyl ligands. Notably, one of the CO ligands is disposed in bridging position between the metal centers. This allows one of the Fe ions to retain a square pyramidal coordination – which determines the assumption of the so-called “rotated structure” – with a vacant coordination site in trans to the μ-CO group, ready to bind protons when the active site is in the Fe^IFe^I state. Many Fe^IFe^I biomimetic models have been synthesized and characterized so far, but most of them fail to reproduce the orientation of the diatomic ligands that is observed in the enzyme active site.     

Multiclass classification with bandit feedback using adaptive regularization

We present a new multiclass algorithm in the bandit framework, where after making a prediction, the learning algorithm receives only partial feedback, i.e., a single bit indicating whether the predicted label is correct or not, rather than the true label. Our algorithm is based on the second-order Perceptron, and uses upper-confidence bounds to trade-off exploration and exploitation, instead of random sampling as performed by most current algorithms. We analyze this algorithm in a partial adversarial setting, where instances are chosen adversarially, while the labels are chosen according to a linear probabilistic model which is also chosen adversarially. We show a regret of $\mathcal{O}(\sqrt{T}\log T)$ , which improves over the current best bounds of $\mathcal{O}(T^{2/3})$ in the fully adversarial setting. We evaluate our algorithm on nine real-world text classification problems and on four vowel recognition tasks, often obtaining state-of-the-art results, even compared with non-bandit online algorithms, especially when label noise is introduced.     

A model approach to assess the long-term trends of indirect photochemistry in lake water. The case of Lake Maggiore (NW Italy)

A model-based approach is here developed and applied to predict the long-term trends of indirect photochemical processes in the surface layer (5 m water depth) of Lake Maggiore, NW Italy. For this lake, time series of the main parameters of photochemical importance that cover almost two decades are available. As a way to assess the relevant photochemical reactions, the modelled steady-state concentrations of important photogenerated transients (^•OH, ³CDOM* and CO₃^-•) were taken into account. A multivariate analysis approach was adopted to have an overview of the system, to emphasise relationships among chemical, photochemical and seasonal variables, and to highlight annual and long-term trends. Over the considered time period, because of the decrease of the dissolved organic carbon (DOC) content of water and of the increase of alkalinity, a significant increase is predicted for the steady-state concentrations of the radicals ^•OH and CO₃^−•. Therefore, the photochemical degradation processes that involve the two radical species would be enhanced. Another issue of potential photochemical importance is related to the winter maxima of nitrate (a photochemical ^•OH source) and the summer maxima of DOC (^•OH sink and ³CDOM* source) in the lake water under consideration. From the combination of sunlight irradiance and chemical composition data, one predicts that the processes involving ^•OH and CO₃^−• would be most important in spring, while the reactions involving ³CDOM* would be most important in summer.     

Use of Ponkan mandarin peels as biosorbent for toxic metals uptake from aqueous solutions

Waste Ponkan mandarin (Citrus reticulata) peel was used as biosorbent to extract Ni(II), Co(II) and Cu(II) from aqueous solutions at room temperature. To achieve the best adsorption conditions the influence of pH and contact time were investigated. The isotherms of adsorption were fitted to the Langmuir equation. Based on the capacity of adsorption of the natural biosorbent to interact with the metallic ions, the following results were obtained 1.92, 1.37 and 1.31 mmol g(-1) for Ni(II), Co(II) and Cu(II), respectively, reflecting a maximum adsorption order of Ni(II)>Co(II)>Cu(II). The quick adsorption process reached the equilibrium before 5, 10 and 15 min for Ni(II), Co(II) and Cu(II), respectively, with maximum adsorptions at pH 4.8. In order to evaluate the Ponkan mandarin peel a biosorbent in dynamic system, a glass column was fulfilled with 1.00 g of this natural adsorbent, and it was fed with 5.00 x 10(-4)mol l(-1) of Ni(II) or Co(II) or Cu(II) at pH 4.8 and 3.5 ml min(-1). The lower breakpoints (BP(1)) were attained at concentrations of effluent of the column attained the maximum limit allowed of these elements in waters (>0.1 mg l(-1)) which were: 110, 100 and 130 bed volumes (V(effluent)/V(adsorbent)), for Ni(II), Co(II) and Cu(II), respectively. The higher breakpoints (BP(2)) were attained when the complete saturation of the natural adsorbent occurred, and the values obtained were: 740, 540 and 520 bed volumes for Ni(II), Co(II) and Cu(II), respectively.     

Cyclic Nigerosyl-Nigerose as Oxygen Nanocarrier to Protect Cellular Models from Hypoxia/Reoxygenation Injury: Implications from an In Vitro Model

Heart failure (HF) prevalence is increasing among the aging population, and the mortality rate remains unacceptably high despite improvements in therapy. Myocardial ischemia (MI) and, consequently, ischemia/reperfusion injury (IRI), are frequently the basis of HF development. Therefore, cardioprotective strategies to limit IRI are mandatory. Nanocarriers have been proposed as alternative therapy for cardiovascular disease. Controlled reoxygenation may be a promising strategy. Novel nanocarriers, such as cyclic nigerosyl-nigerose (CNN), can be innovative tools for oxygen delivery in a controlled manner. In this study we analyzed new CNN-based formulations as oxygen nanocarriers (O₂-CNN), and compared them with nitrogen CNN (N₂-CNN). These different CNN-based formulations were tested using two cellular models, namely, cardiomyoblasts (H9c2), and endothelial (HMEC) cell lines, at different concentrations. The effects on the growth curve during normoxia (21% O₂, 5% CO₂ and 74% N₂) and their protective effects during hypoxia (1% O₂, 5% CO₂ and 94% N₂) and reoxygenation (21% O₂, 5% CO₂ and 74% N₂) were studied. Neither O₂-CNN nor N₂-CNN has any effect on the growth curve during normoxia. However, O₂-CNN applied before hypoxia induces a 15–30% reduction in cell mortality after hypoxia/re-oxygenation when compared to N₂-CNN. O₂-CNN showed a marked efficacy in controlled oxygenation, which suggests an interesting potential for the future medical application of soluble nanocarrier systems for MI treatment.     

SEL: A unified algorithm for salient entity linking

The entity linking task consists in automatically identifying and linking the entities mentioned in a text to their uniform resource identifiers in a given knowledge base. This task is very challenging due to its natural language ambiguity. However, not all the entities mentioned in the document have the same utility in understanding the topics being discussed. Thus, the related problem of identifying the most relevant entities present in the document, also known as salient entities (SE), is attracting increasing interest. In this paper, we propose salient entity linking, a novel supervised 2‐step algorithm comprehensively addressing both entity linking and saliency detection. The first step is aimed at identifying a set of candidate entities that are likely to be mentioned in the document. The second step, besides detecting linked entities, also scores them according to their saliency. Experiments conducted on 2 different data sets show that the proposed algorithm outperforms state‐of‐the‐art competitors and is able to detect SE with high accuracy. Furthermore, we used salient entity linking for extractive text summarization. We found that entity saliency can be incorporated into text summarizers to extract salient sentences from text. The resulting summarizers outperform well‐known summarization systems, proving the importance of using the SE information.     

A quantitative evalution of the photocatalytic performance of TiO2 slurries

This paper reports the results of a comparison between two TiO₂ photocatalysts that differ for particle size and absorption/scattering optical properties. The catalyst with larger particles and lower surface area performed better in the degradation of phenol than the specimen with smaller particles and larger surface area. Following carefully designed experiments, it is possible to assess the relative role of light absorption/scattering properties and catalyst-related efficiency by means of a basic kinetic model for the rate of photocatalytic reactions. Explicit relationships are derived in the framework of the steady-state approximation for the quantum yield as a function of one a-dimensional number collecting surface kinetic constants for charge carrier reactions at the interface, absorbed light and surface substrate concentrations. The dimensionality change to volume-defined quantities allows derivation of the explicit dependence of the quantum yield on substrate concentration and partition constants, catalyst concentration, and the rate of volumetric light absorption. Following this approach, the rate expression for slurry systems, valid in the absence of back reactions, is directly derived. Some further simplification of the rate equation for the case of low quantum yield regime leads to analytical relationships able to account for the dependence of the rate on catalyst concentration and absorbed light in the case of stirred and unstirred conditions. The reported properly designed experiments allow the estimation of catalyst-specific micro-kinetic constants.     

Laguerre-Gauss and Bessel-Gauss beams in uniaxial crystals

A simple correspondence between the paraxial propagation formulas along the optical axis of a uniaxial crystal and inside an isotropic medium is found in the case of beams with linearly polarized circularly symmetric boundary distributions. The electric fields of the ordinary and the extraordinary beams are related to the corresponding expressions in a medium with refractive index n(o) and n(e)2/n(o), where n(o) and n(e) are the ordinary and the extraordinary refractive indexes, respectively. Closed-form expressions for Laguerre-Gauss and Bessel-Gauss beams propagating through an anisotropic crystal are given.