The adjuvants aerosil 200 and Gelita-Sol-P influence on the technological characteristics of spray-dried powders from Passiflora edulis var. flavicarpa

Passiflora edulis (passionflower) is a plant widely used in the Brazilian popular medicine and phytopharmaceutical industry for its sedative activity. This work refers to the development of spray-dried powders (SDPs) from the 40% ethanolic extractive solution of P. edulis aerial parts. The SDPs were prepared with a Büchi 190 Mini-spray dryer using as drying adjuvants Aerosil 200 alone (SDP1), an Aerosil 200: Gelita-Sol-P (1:1) mixture (SDP2) and an Aerosil 200:Gelita-Sol-P (1:3) mixture (SDP3). All the powders were obtained using 40 parts adjuvant and 60 parts extract dried residue. The comparison criteria applied were particle size distribution, hygroscopicity at 95%, 60%, and 35% relative humidity (RH), as well as the flavonoid process recovery. The particle size distributions were analyzed by way of (a) normal distribution parameters, (b) the RRSB grid and (c) considering diameter values in terms of an equivalent sphere. All the powders presented nonnormal distribution, and the RRSB analysis appeared to be, therefore, the analysis method of choice. The total flavonoid recovery was around 80%, and it was practically not affected by the SDP1, SDP2, and SDP3 compositions. At the 60% and 90% RH atmospheres, the SDP3 and SDP2 moisture uptakes were higher than that of the SDP1. All the formulations were, on the contrary, stable at 35% RH, showing a slight moisture loss tendency. The results showed in sum that the SDP prepared using Aerosil 200 as the drying adjuvant alone presented the best technological characteristics of all.     

Treatment of Ph1-positive chronic myelogenous leukemia in children: comparison between allogeneic bone marrow transplantation and conventional chemotherapy. Spanish Working Party for BMT in Children (GETMON)

BACKGROUND AND OBJECTIVE: To compare the estimated survival and disease-free survival between children with Ph1-positive chronic myeloid leukemia treated with allogeneic bone marrow transplantation or conventional chemotherapy. DESIGN AND METHODS: In this retrospective study we compared the results obtained in a group of 14 children who received allogeneic bone marrow transplantation (BMT) between 1983 and 1993, and another group of 27 children treated with busulfan, hydroxyurea or alpha-interferon during the same time period. Patients were transplanted at a median of 7 months from diagnosis and all except one were in their first chronic phase. Conditioning consisted in total body irradiation and cyclophosphamide in 12 cases, and busulfan was added in two. RESULTS: Of the 14 patients treated with BMT, two died of transplant-related complications and two relapsed 18 and 48 months after the BMT. Ten children remain alive and disease free at a median follow up of 60 months. The probability of DFS at 5 years is 70%. Of the 27 patients treated with chemotherapy, 22 have died at a median of 36 months from diagnosis. The probability of survival at 5 years is 5% versus 83% for the BMT group (p = 0.001). INTERPRETATION AND CONCLUSIONS: Allogeneic BMT is a safe and very effective treatment for Ph-positive CML in children. Patients who have an HLA-identical sibling donor must receive a transplant as soon as possible after being diagnosed.     

Convectively driven polymerase chain reaction thermal cycler

We have fabricated a low-cost disposable polymerase chain reaction thermal chamber that uses buoyancy forces to move the sample solution between the different temperatures necessary for amplification. Three-dimensional, unsteady finite element modeling and a simpler 1-D steady-state model are used together with digital particle image velocimetry data to characterize the flow within the device. Biological samples have been amplified using this novel thermal chamber. Time for amplification is less than 30 min. More importantly, an analysis of the energy consumption shows significant improvements over current technology.     

Mobile Learning

Works in progress from selected papers presented at the 2006 IADIS International Conference on Mobile Learning provide an overview of the diverse research being conducted in this field.     

Reduced-complexity delayed-decision algorithm for context-based image processing systems.

It is well known that the performance of context-based image processing systems can be improved by allowing the processor (e.g., an encoder or a denoiser) a delay of several samples before making a processing decision. Often, however, for such systems, traditional delayed-decision algorithms can become computationally prohibitive due to the growth in the size of the space of possible solutions. In this paper, we propose a reduced-complexity, one-pass, delayed-decision algorithm that systematically reduces the size of the search space, while also preserving its structure. In particular, we apply the proposed algorithm to two examples of adaptive context-based image processing systems, an image coding system that employs a context-based entropy coder, and a spatially adaptive image-denoising system. For these two types of widely used systems, we show that the proposed delayed-decision search algorithm outperforms instantaneous-decision algorithms with only a small increase in complexity. We also show that the performance of the proposed algorithm is better than that of other, higher complexity, delayed-decision algorithms.     

Green chemistry synthesis of nano‐cuprous oxide

Green chemistry and a central composite design, to evaluate the effect of reducing agent, temperature and pH of the reaction, were employed to produce controlled cuprous oxide (Cu₂ O) nanoparticles. Response surface method of the ultraviolet–visible spectroscopy is allowed to determine the most relevant factors for the size distribution of the nanoCu₂ O. X‐ray diffraction reflections correspond to a cubic structure, with sizes from 31.9 to 104.3 nm. High‐resolution transmission electron microscopy reveals that the different shapes depend strongly on the conditions of the green synthesis.Inspec keywords: nanostructured materials, copper compounds, nanofabrication, pH, response surface methodology, ultraviolet spectra, X‐ray diffraction, transmission electron microscopyOther keywords: green chemistry synthesis, nanocuprous oxide, reducing agent, reaction pH, response surface method, ultraviolet‐visible spectroscopy, size distribution, cubic structure, high‐resolution transmission electron microscopy, X‐ray diffraction reflection, central composite design, Cu₂ O     

A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows

The Capacitated Vehicle Routing Problem with Time Windows is an important combinatorial optimization problem consisting in the determination of the set of routes of minimum distance to deliver goods, using a fleet of identical vehicles with restricted capacity, so that vehicles must visit customers within a time frame. A large number of algorithms have been proposed to solve single-objective formulations of this problem, including meta-heuristic approaches, which provide high quality solutions in reasonable runtimes. Nevertheless, in recent years some authors have analyzed multi-objective variants that consider additional objectives to the distance travelled. This paper considers not only the minimum distance required to deliver goods, but also the workload imbalance in terms of the distances travelled by the used vehicles and their loads. Thus, MMOEASA, a Pareto-based hybrid algorithm that combines evolutionary computation and simulated annealing, is here proposed and analyzed for solving these multi-objective formulations of the VRPTW. The results obtained when solving a subset of Solomon’s benchmark problems show the good performance of this hybrid approach.     

AN ANALYSIS OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS FOR OPTIMIZATION PROBLEMS WITH TIME CONSTRAINTS

Many multiobjective optimization problems in the engineering field are required to be solved within more or less severe time restrictions. Because the optimization criteria, the parameters, and/or constraints might change with time, the optimization solutions must be recalculated when a change takes place. The time required by the optimization procedure to arrive at the new solutions should be bounded accordingly with the rate of change observed in these dynamic problems. This way, the faster the optimization algorithm is to obtain solutions, the wider is the set of dynamic problems to which that algorithm can be applied. Here, we analyze the performance of the nondominated sorting algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA2), and single front genetic algorithms (SFGA, and SFGA2) on two different multiobjective optimization problems, with two and three objectives, respectively. For these two studied problems, the single front genetic algorithms have obtained adequate quality in the solutions in very little time. Moreover, for the second and more complex problem approached, SFGA2 and NSGA-II obtain the best hypervolume in the found set of nondominated solutions, but SFGA2 employs much less time than NSGA-II. These results may suggest that single front genetic algorithms, especially SFGA2, could be appropiate to deal with optimization problems with high rates of change, and thus stronger time constraints.