A comparison of emulsifiers for the formation of oil-in-water emulsions: stability of the emulsions within 9 h after production and MR signal properties

Objective

To provide a basis for the selection of suitable emulsifiers in oil-in-water emulsions used as tissue analogs for MRI experiments. Three different emulsifiers were investigated with regard to their ability to stabilize tissue-like oil-in-water emulsions. Furthermore, MR signal properties of the emulsifiers themselves and influences on relaxation times and ADC values of the aqueous phase were investigated.

Materials and methods

Polysorbate 60, sodium dodecyl sulfate (SDS) and soy lecithin were used as emulsifiers. MR characteristics of emulsifiers were assessed in aqueous solutions and their function as a stabilizer was examined in oil-in-water emulsions of varying fat content (10, 20, 30, 40, 50%). Stability and homogeneity of the oil-in-water emulsions were evaluated with a delay of 3 h and 9 h after preparation using T₁ mapping and visual control. Signal properties of the emulsifiers were investigated by ¹H-MRS in aqueous emulsifier solutions. Relaxometry and diffusion weighted MRI (DWI) were performed to investigate the effect of various emulsifier concentrations on relaxation times (T₁ and T₂) and ADC values of aqueous solutions.

Results

Emulsions stabilized by polysorbate 60 or soy lecithin were stable and homogeneous across all tested fat fractions. In contrast, emulsions with SDS showed a significantly lower stability and homogeneity. Recorded T₁ maps revealed marked creaming of oil droplets in almost all of the emulsions with SDS. The spectral analysis showed several additional signals for polysorbate and SDS. However, lecithin remained invisible in ¹H-MRS. Relaxometry and DWI revealed different influences of the emulsifiers on water: Polysorbate and SDS showed only minor effects on relaxation times and ADC values of aqueous solutions, whereas lecithin showed a strong decrease in both relaxation times (r_1,lecithin = 0.11 wt.%⁻¹ s⁻¹, r_2,lecithin = 0.57 wt.%⁻¹ s⁻¹) and ADC value (Δ(ADC)_lecithin =  − 0.18 × 10^–3 mm²/s⋅wt.%) with increasing concentration.

Conclusion

Lecithin is suggested as the preferred emulsifier of oil-in-water emulsions in MRI as it shows a high stabilizing ability and remains invisible in MRI experiments. In addition, lecithin is suitable as an alternative means of adjusting relaxation times and ADC values of water.

     

Sub-optimal solutions to track detection problem using graph theoretic concepts

This paper demonstrates how the problem of tracking targets, which appear as either straight or curved lines in two-dimensional display images (or data images) can be formulated in terms of a directed weighted graph model and how dynamic programming techniques can be efficiently applied to reach an optimal or sub-optimal solution. In general, track detection algorithms providing optimal solutions have good detective ability, but most of them suffer from the inability to detect discontinuous lines or to resolve efficiently pairs of crossing lines. A sub-optimal solution is provided that efficiently overcomes these weaknesses. We focus on modeling the track detection problem in terms of a graph, formulating fast sequential/parallel sub-optimal track detection algorithms and testing them on simulated data in order to show their detective ability. Moreover, we specify the conditions under which sub-optimal algorithms can perform at least as well as their corresponding optimal algorithms. This is significant for the track detection problem where fast, accurate and real-time detection is considered a necessity.     

A quantitative analysis of the European Automakers’ voluntary commitment to reduce CO2 emissions from new passenger cars based on independent experimental data

The reduction of CO₂ emissions and fuel consumption from road transportation constitutes an important pillar of the European Union strategy for implementing the Kyoto Protocol. The commitment to reduce passenger car average CO₂ emissions at 140 g/km in 2008 signed by European car manufacturers and the European Commission is up to now the most important initiative towards limiting CO₂ emissions from road transportation and particularly from passenger cars. Until today, annual reports show the manufacturers’ efforts in limiting CO₂ emissions is within the intermediate target set by the commitment and these results are incorporated in emissions estimations and scientific studies. This paper analyses the origin of the progress achieved so far in CO₂ emissions and attempts an assessment of the commitment using independent experimental emission data. Additionally, the applicability of the commitment-monitoring data into policy and decision-making tools is being examined. The results indicate that a significant part of the reductions achieved so far is due to a market shift towards diesel vehicle sales and that no reduction factors should be applied yet in CO₂ emissions estimation models.     

Design of radiation-emission measurements of an air-traffic surveillance radar

The current work presents the procedure of designing and carrying out measurements of radiation emission from an air-traffic surveillance radar. The system requirements are first given, and then the equipment used is described. The steps to determine the operating characteristics of the radar are specified in detail. The necessary methods for manual power measurements and calculations are next explained. Since the final measurement system was fully automated, its main features (topology, protocol of operation) are also given. Finally, some measurement results taken during the initial phase of system operation are presented and discussed.     

PBURC: a patterns-based,unsupervised requirements clustering framework for distributed agile software development

Agile software development methodologies are increasingly adopted by organizations because they focus on the client’s needs, thus safeguarding business value for the final product. At the same time, as the economy and society move toward globalization, more organizations shift to distributed development of software projects. From this perspective, while adopting agile techniques seems beneficial, there are still a number of challenges that need to be addressed; among these notable is the effective cooperation between the stakeholders and the geographically distributed development team. In addition, data collection and validation for requirements engineering demands efficient processing techniques in order to handle the volume of data as well as to manage different inconsistencies, when the data are collected using online tools. In this paper, we present “PBURC,” a patterns-based, unsupervised requirements clustering framework, which makes use of machine-learning methods for requirements validation, being able to overcome data inconsistencies and effectively determine appropriate requirements clusters for optimal definition of software development sprints.     

Formal verification of collaboration patterns in healthcare

We propose a computer-based framework for the formal verification of collaboration patterns in healthcare teams. In this, the patterns are constructed diagrammatically as compositions of keystones that are viewed as abstract processes. The approach provides mechanisms for ensuring that safety properties are enforced and exceptional events are handled systematically. Additionally, a fully verified, executable model is obtained as an end product, enabling a simulation of its associated collaboration scenarios.     

A k-anonymity privacy-preserving approach in wireless medical monitoring environments

With the proliferation of wireless sensor networks and mobile technologies in general, it is possible to provide improved medical services and also to reduce costs as well as to manage the shortage of specialized personnel. Monitoring a person’s health condition using sensors provides a lot of benefits but also exposes personal sensitive information to a number of privacy threats. By recording user-related data, it is often feasible for a malicious or negligent data provider to expose these data to an unauthorized user. One solution is to protect the patient’s privacy by making difficult a linkage between specific measurements with a patient’s identity. In this paper we present a privacy-preserving architecture which builds upon the concept of k-anonymity; we present a clustering-based anonymity scheme for effective network management and data aggregation, which also protects user’s privacy by making an entity indistinguishable from other k similar entities. The presented algorithm is resource aware, as it minimizes energy consumption with respect to other more costly, cryptography-based approaches. The system is evaluated from an energy-consuming and network performance perspective, under different simulation scenarios.     

CUSUM Control Charts for the Monitoring of Zero‐inflated Binomial Processes

Zero‐inflated probability models are used to model count data that have an excessive number of zeros. These models are mostly useful in modeling high‐yield or health‐related processes. The zero‐inflated binomial distribution is an extension of the ordinary binomial distribution that takes into account the excess of zeros. In this paper, one‐sided cumulative sum (CUSUM)‐type control charts are proposed for monitoring increases or decreases in the parameter p of a zero‐inflated binomial process. The results of an extensive numerical study concerning the statistical design of the proposed schemes as well as their practical implementation are provided. Copyright © 2015 John Wiley & Sons, Ltd.     

On the Modelling and Monitoring of General Inflated Poisson Processes

In this work, we propose and study general inflated probability distributions that can be used for modelling and monitoring unusual count data. The considered models extend the well‐known zero‐inflated Poisson distribution because they allow the excess of values, other than zero. Four simple upper‐sided control schemes are considered for the monitoring of count data based on the proposed general inflated Poisson distributions, and their performance is evaluated under various out‐of‐control situations. The usefulness of the considered models and techniques is illustrated via two real‐data examples, while practical guidelines are provided as well. Copyright © 2015 John Wiley & Sons, Ltd.