Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann)

This research optimized the adsorption performance of rice husk char (RHC4) for copper (Cu(II)) from an aqueous solution. Various physicochemical analyses such as Fourier transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FESEM), carbon, hydrogen, nitrogen, and sulfur (CHNS) analysis, Brunauer–Emmett–Teller (BET) surface area analysis, bulk density (g/mL), ash content (%), pH, and pHZPC were performed to determine the characteristics of RHC4. The effects of operating variables such as the influences of aqueous pH, contact time, Cu(II) concentration, and doses of RHC4 on adsorption were studied. The maximum adsorption was achieved at 120 min of contact time, pH 6, and at 8 g/L of RHC4 dose. The prediction of percentage Cu(II) adsorption was investigated via an artificial neural network (ANN). The Fletcher–Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R2 of 0.989). The pseudo-second-order kinetic model fitted well with the experimental data, thus indicating chemical adsorption. The intraparticle analysis showed that the adsorption process proceeded by boundary layer adsorption initially and by intraparticle diffusion at the later stage. The Langmuir and Freundlich isotherm models interpreted well the adsorption capacity and intensity. The thermodynamic parameters indicated that the adsorption of Cu(II) by RHC4 was spontaneous. The RHC4 adsorption capacity is comparable to other agricultural material-based adsorbents, making RHC4 competent for Cu(II) removal from wastewater

Managing globally distributed expertise with new competence management solutions: a big-science collaboration as a pilot case.

In today's global organisations and networks, a critical factor for effective innovation and project execution is appropriate competence and skills management. The challenges include selection of strategic competences, competence development, and leveraging the competences and skills to drive innovation and collaboration for shared goals. This paper presents a new industrial web-enabled competence management and networking solution and its implementation and piloting in a complex big-science environment of globally distributed competences

In situ transcriptomic and metabolomic study of the loss of photosynthesis in the leaves of mixotrophic plants exploiting fungi

International audienceMycoheterotrophic plants have lost photosynthesis and obtain carbon through mycorrhizal fungi colonizing their roots. They are likely to have evolved from mixotrophic ancestors, which rely on both photosynthesis and fungal carbon for their development. Whereas our understanding of the ecological and genomic changes associated with the evolutionary shift to mycoheterotrophy is deepening, little information is known about the specific metabolic and physiological features driving this evolution. We investigated this issue in naturally occurring achlorophyllous variants of temperate mixotrophic orchids. We carried out an integrated transcriptomic and metabolomic analysis of the response to achlorophylly in the leaves of three mixotrophic species sampled in natura. Achlorophyllous leaves showed major impairment of their photosynthetic and mineral nutrition functions, strong accumulation of free amino acids, overexpression of enzymes and transporters related to sugars, amino acids and fatty acid catabolism, as well as induction of some autophagy-related and biotic stress genes. Such changes were reminiscent of these reported for variegated leaves and appeared to be symptomatic of a carbon starvation response. Rather than decisive metabolic innovations, we suggest that the evolution towards mycoheterotrophy in orchids is more likely to be reliant on the versatility of plant metabolism and an ability to exploit fungal organic resources, especially amino acids, to replace missing photosynthates

Purification, Characterization and Partial Amino Acid Sequencing of Two New Aspartic Proteinases from Fresh Flowers of Cynara cardunculus L.

Two new aspartic proteinases have been isolated from stigmas of the cardoon Cynara cardunculus L. by a two-step purification procedure including extraction at low pH, gel filtration on Superdex 200, and ion-exchange chromatography on Mono Q. To follow the conventional nomenclature for aspartic proteinases, we have named these proteinases cardosin A and cardosin B. On SDS/PAGE, cardosin A migrated as two bands with apparent molecular masses of 31 000 Da and 15000 Da where as the chains of cardosin B migrated as bands of 34000 Da and 14000 Da. The partial amino acid sequences of the two cardosins revealed that they are similar but not identical, and that they differ horn the previously reported cardoon proteinases named cynarases, which were assumed to be derived from a common precursor. Although the cardosins show some degree of similarity to each other, we could detect no immunological cross-reactivity between them. Both cardosins were active at low pH and were inhibited by pepstatin, with Ki values of 3 nM for cardosin A and 1 nM for cardosin B, indicating that they belong to the class of aspartic proteinases. Significant differences between the two enzymes were also found for the Kcat/Km values for the hydrolysis of two chromophoric synthetic peptides. The active-site ionization constants, pKe1 and pKe2, for cardosin A are 2.5±0.2 and 5.3±20.2, whereas for cardosin R they are 3.73±10.09 and 6.7±50.1. The results herein described on the structural and kinetic properties of the cardosins indicate that they are the products of distinct genes which have probably arisen by gene duplication. A scheme for the proteolytic processing of the two enzymes is also proposed