On-line concentration of proteins in pressurized capillary electrochromatography coupled with electrospray ionization-mass spectrometry

Pressurized capillary electrochromatography (pCEC) and electrospray ionization-mass spectrometry (ESI-MS) have been hyphenated for protein analysis. Taken cytochrome c, lysozyme, and insulin as samples, the limits of detection (LODs) for absolute concentrations are 10(-11) mol (signal-to-noise ratio S/N = 3) with relative standard deviations (RSDs) of retention time and peak area, respectively, of less than 1.7% and 4.8%. In order to improve the detection sensitivity, on-line concentration by field-enhanced sample-stacking effect and chromatographic zone-sharpening effect has been developed, and parameters affecting separation and detection, such as pH and electrolyte concentration in the mobile phase, separation voltage, as well as enrichment voltage and time, have been studied systematically. Under the optimized conditions, the LODs of the three proteins could be decreased up to 100-fold. In addition, the feasibility of such techniques has been further demonstrated by the analysis of modified insulins at a concentration of 20 mu g/mL.

On-line concentration of peptides and proteins with the hyphenation of polymer monolithic immobilized metal affinity chromatography and capillary electrophoresis

An iminodiacetic acid (IDA)-type adsorbent is prepared at the one end of a capillary by covalently bonding IDA to the monolithic rods of macroporous poly(glycidyl methacrylate-co-ethylene dimethacrylate). Cu(II) is later introduced to the support via the interaction with IDA. By this means, polymer monolithic immobilized metal affinity chromatography (IMAC) materials are prepared. With such a column, IMAC for on-line concentration and capillary electrophoresis (CE) for the subsequent analysis are hyphenated for the analysis of peptides and proteins. The reproducibility of such a column has been proved good with relative standard deviations (RSDs) of dead time of less than 5% for injection-to-injection and 12% for column-to-column (n = 3). Through application on the analysis of standard peptides and real protein samples, such a technique has shown promising in proteome study.

AINFO versão 5.0: manual on-line.

Introdução; Conhecendo o AINFO; Gerenciador de Dados e outros procedimentos especiais; Administrando o AINFO; Atualizando as bases de dados; Sobre as bases de dados; Imprimindo relatórios; Sobre o SIR - Recuperação de Informação; Descrição dos campos; Descrição dos relatórios; Anexos.

AINFO - versão 5.0: manual on-line.

O AINFO é um sistema para automação de bibliotecas e recuperação de informação, desenvolvimento em padrão Windows, com arquitetura cliente/servidor baseada no sistema gerenciador de banco de dados relacional Firebird. Pemite o gerenciamento de informação técnico-científica, integrando bases de dados documentais, cadastrais e processos bibliográficos através do armazenamento, atualização, indexação e recuperação de informação de forma simples e rápida, utilizando não apenas recursos de um istema gerenciador de banco de dados, como controle de concorrência e manutenção de integridade das bases de dados, mas também oferecendo facilidades de recuperação de informação textual não disponíveis nesses sistemas.

Cursos semipresenciales y telepresenciales, con soporte on-line, de adaptación al grado de ingeniería de edificación para arquitectos técnicos

Sin índice de impacto (2010)

A Two-Tiered On-Line Server-Side Bandwidth Reservation Framework for the Real-Time Delivery of Multiple Video Streams

The advent of virtualization and cloud computing technologies necessitates the development of effective mechanisms for the estimation and reservation of resources needed by content providers to deliver large numbers of video-on-demand (VOD) streams through the cloud. Unfortunately, capacity planning for the QoS-constrained delivery of a large number of VOD streams is inherently difficult as VBR encoding schemes exhibit significant bandwidth variability. In this paper, we present a novel resource management scheme to make such allocation decisions using a mixture of per-stream reservations and an aggregate reservation, shared across all streams to accommodate peak demands. The shared reservation provides capacity slack that enables statistical multiplexing of peak rates, while assuring analytically bounded frame-drop probabilities, which can be adjusted by trading off buffer space (and consequently delay) and bandwidth. Our two-tiered bandwidth allocation scheme enables the delivery of any set of streams with less bandwidth (or equivalently with higher link utilization) than state-of-the-art deterministic smoothing approaches. The algorithm underlying our proposed frame-work uses three per-stream parameters and is linear in the number of servers, making it particularly well suited for use in an on-line setting. We present results from extensive trace-driven simulations, which confirm the efficiency of our scheme especially for small buffer sizes and delay bounds, and which underscore the significant realizable bandwidth savings, typically yielding losses that are an order of magnitude or more below our analytically derived bounds.

Scientific writing: a randomized controlled trial comparing standard and on-line instruction.

BACKGROUND: Writing plays a central role in the communication of scientific ideas and is therefore a key aspect in researcher education, ultimately determining the success and long-term sustainability of their careers. Despite the growing popularity of e-learning, we are not aware of any existing study comparing on-line vs. traditional classroom-based methods for teaching scientific writing. METHODS: Forty eight participants from a medical, nursing and physiotherapy background from US and Brazil were randomly assigned to two groups (n = 24 per group): An on-line writing workshop group (on-line group), in which participants used virtual communication, google docs and standard writing templates, and a standard writing guidance training (standard group) where participants received standard instruction without the aid of virtual communication and writing templates. Two outcomes, manuscript quality was assessed using the scores obtained in Six subgroup analysis scale as the primary outcome measure, and satisfaction scores with Likert scale were evaluated. To control for observer variability, inter-observer reliability was assessed using Fleiss's kappa. A post-hoc analysis comparing rates of communication between mentors and participants was performed. Nonparametric tests were used to assess intervention efficacy. RESULTS: Excellent inter-observer reliability among three reviewers was found, with an Intraclass Correlation Coefficient (ICC) agreement = 0.931882 and ICC consistency = 0.932485. On-line group had better overall manuscript quality (p = 0.0017, SSQSavg score 75.3 +/- 14.21, ranging from 37 to 94) compared to the standard group (47.27 +/- 14.64, ranging from 20 to 72). Participant satisfaction was higher in the on-line group (4.3 +/- 0.73) compared to the standard group (3.09 +/- 1.11) (p = 0.001). The standard group also had fewer communication events compared to the on-line group (0.91 +/- 0.81 vs. 2.05 +/- 1.23; p = 0.0219). CONCLUSION: Our protocol for on-line scientific writing instruction is better than standard face-to-face instruction in terms of writing quality and student satisfaction. Future studies should evaluate the protocol efficacy in larger longitudinal cohorts involving participants from different languages.

On-line algorithms for single machine scheduling with family setup times

The paper considers an on-line single machine scheduling problem where the goal is to minimize the makespan. The jobs are partitioned into families and a setup is performed every time the machine starts processing a batch of jobs of the same family. The scheduler is aware of the number of families and knows the setup time of each family, although information about a job only becomes available when that job is released. We give a lower bound on the competitive ratio of any on-line algorithm. Moreover, for the case of two families, we provide an algorithm with a competitive ratio that achieves this lower bound. As the number of families increases, the lower bound approaches 2, and we give a simple algorithm with a competitive ratio of 2.