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.

     

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.     

A soft computing approach for privacy requirements engineering: The PriS framework

Soft computing continuously gains interest in many fields of academic and industrial domain; among the most notable characteristics for using soft computing methodological tools is the ability to handle with vague and imprecise data in decision making processes. Similar conditions are often encountered in requirements engineering. In this paper, we introduce the PriS approach, a security and privacy requirements engineering framework which aims at incorporating privacy requirements early in the system development process. Specifically, PriS provides a set of concepts for modelling privacy requirements in the organisation domain and a systematic way-of-working for translating these requirements into system models. The conceptual model of PriS uses a goal hierarchy structure. Every privacy requirement is either applied or not on every goal. To this end every privacy requirement is a variable that can take two values [0,1] on every goal meaning that the requirements constraints the goal (value 1) or not (value 0). Following this way of working PriS ends up suggesting a number of implementation techniques based on the privacy requirements constraining the respective goals. Taking into account that the mapping of privacy variables to a crisp set consisting of two values [0,1] is constraining, we extend also the PriS framework so as to be able to address the degree of participation of every privacy requirement towards achieving the generic goal of privacy. Therefore, we propose a fuzzification of privacy variables that maps the expression of the degree of participation of each privacy variable to the [0,1] interval. We also present a mathematical framework that allows the concurrent management of combined independent preferences towards the necessity of a privacy measure; among the advantages of the presented extended framework is the scalability of the approach in such a way that the results are not limited by the number of independent opinions or by the number of factors considered while reasoning for a specific selection of privacy measures.     

On l∞ and l2 robustness of spatially invariant systems

We consider spatiotemporal systems and study their l_∞ and l₂ robustness properties in the presence of spatiotemporal perturbations. In particular, we consider spatially invariant nominal models and provide necessary and sufficient conditions for system robustness for the cases when the underlying perturbations are linear spatiotemporal varying, and nonlinear spatiotemporal invariant, unstructured or structured. It turns out that these conditions are analogous to the scaled small gain condition (which is equivalent to a spectral radius condition and a linear matrix inequality for the l_∞ and l₂ cases, respectively) derived for standard linear time‐invariant models subject to time‐varying linear and time‐invariant nonlinear perturbations. Copyright © 2009 John Wiley & Sons, Ltd.     

Providing advanced remote medical treatment services through pervasive environments

Advances on sensor technology, wireless environments and data mining introduce new possibilities in the healthcare sector, realizing the anytime-anywhere access to medical information. Towards this direction, integration of packet-switched networks and sensor devices can be effective in deploying assistive environments, such as home monitoring for elderly or patients. In this paper we describe a policy-based architecture that utilizes wireless sensor devices, advanced network topologies and software agents to enable remote monitoring of patients and elderly people; through the aforementioned technologies we achieve continuous monitoring of a patient’s condition and we can proceed when necessary with proper actions. We also present a software framework and network architecture that realizes the provision of remote medical services, in compliance with the imposed security and privacy requirements. A proof of concept prototype is also deployed, along with an evaluation of the overall architecture’s performance.     

A hybrid approach for movie recommendation

Collaborative and content-based filtering are the major methods in recommender systems that predict new items that users would find interesting. Each method has advantages and shortcomings of its own and is best applied in specific situations. Hybrid approaches use elements of both methods to improve performance and overcome shortcomings. In this paper, we propose a hybrid approach based on content-based and collaborative filtering, implemented in MoRe, a movie recommendation system. We also provide empirical comparison of the hybrid approach to the base methods of collaborative and content-based filtering and draw useful conclusions upon their performance.