Modeling the glucose regulatory system in extreme preterm infants

Background

Premature infants represent a significant proportion of the neonatal intensive care population. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the stress of their condition. Hypo- and hyperglycemia are frequently reported in very low birth weight infants, and more mature infants often experience low levels of glycemia. A model capturing the unique fundamental dynamics of the neonatal glucose regulatory system could be used to develop better blood glucose control methods.

Methods

A metabolic system model is adapted from adult critical care to the unique physiological case of the neonate. Integral-based fitting methods were used to identify time-varying insulin sensitivity and non-insulin mediated glucose uptake profiles. The clinically important predictive ability of the model was assessed by assuming insulin sensitivity was constant over prediction intervals of 1, 2 and 4 h forward and comparing model-simulated versus actual clinical glucose values for all recorded interventions. The clinical data included 1091 glucose measurements over 3567 total patient hours, along with all associated insulin and nutritional infusion data, for N = 25 total cases. Ethics approval was obtained from the Upper South A Regional Ethics Committee for this study.

Results

The identified model had a median absolute percentage error of 2.4% [IQR: 0.9-4.8%] between model-fitted and clinical glucose values. Median absolute prediction errors at 1-, 2- and 4-h intervals were 5.2% [IQR: 2.5-10.3%], 9.4% [IQR: 4.5-18.4%] and 13.6% [IQR: 6.3-27.6%] respectively.

Conclusions

The model accurately captures and predicts the fundamental dynamic behaviors of the neonatal metabolism well enough for effective clinical decision support in glycemic control. The adaptation from adult to a neonatal case is based on the data from the literature. Low prediction errors and very low fitting errors indicate that the fundamental dynamics of glucose metabolism in both premature neonates and critical care adults can be described by similar mathematical models.     

Layered tetrahedral meshing of thin-walled solids for plastic injection molding FEM

This paper describes a method for creating a well-shaped, layered tetrahedral mesh of a thin-walled solid by adapting the surface triangle sizes to the estimated wall thickness. The primary target application of the method is the finite element analysis of plastic injection molding, in which a layered mesh improves the accuracy of the solution. The edge lengths of the surface triangles must be proportional to the thickness of the domain to create well-shaped tetrahedrons; when the edge lengths are too short or too long, the shape of the tetrahedron tends to become thin or flat. The proposed method creates such a layered tetrahedral mesh in three steps: (1) create a preliminary tetrahedral mesh of the target geometric domain and estimate thickness distribution over the domain; (2) create a non-uniform surface triangular mesh with edge length adapted to the estimated thickness, then create a single-layer tetrahedral mesh using the surface triangular mesh; and (3) subdivide tetrahedrons of the single-layer mesh into multiple layers by applying a subdivision template. The effectiveness of the layered tetrahedral mesh is verified by running some experimental finite element analyses of plastic injection molding.     

Nurturing health-related online support groups: Exploring the experiences of patient moderators

The aim of this study was to examine the views of moderators across a diverse and geographically broad range of online support groups about their moderator experiences and to explore both the personal benefits as well as challenges involved. Thirty-three patient moderators completed an online questionnaire which included a series of open-ended questions. Thematic analysis identified three themes: emergence, empowerment, nurturing. Several moderators declared their own diagnosis and for some, being able to share personal insights motivated them to establish the group and in turn offered validation. They felt empowered by helping others and learned more about the condition through accessing the “communal brain”. Some felt the group aided patients’ access to health services and their ability to communicate with health professionals while others worried about them becoming over-dependent. Moderators described needing to nurture their group to ensure it offered a safe space for members. Clear rules of engagement, trust, organisation skills, compassion and kindness were considered essential. Patient moderated online support groups can be successfully developed and facilitated and can be empowering for both the group member and moderator alike.     

Self-efficacy and work-related performance: The integral role of individual differences.

The present study estimated the unique contribution of self-efficacy to work-related performance controlling for personality (the Big 5 traits), intelligence or general mental ability, and job or task experience. Results, based on a meta-analysis of the relevant literatures, revealed that overall, across all studies and moderator conditions, the contribution of self-efficacy relative to purportedly more distal variables is relatively small. Within moderator categories, there were several cases in which self-efficacy made unique contributions to work-related performance. For example, self-efficacy predicted performance in jobs or tasks of low complexity but not those of medium or high complexity, and self-efficacy predicted performance for task but not job performance. Overall, results suggest that the predictive validity of self-efficacy is attenuated in the presence of individual differences, though this attenuation does depend on the context. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Reliable representations for association rules

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.     

Punctuated simplification of man-made objects

We present a simplification algorithm for manifold polygonal meshes of plane-dominant models. Models of this type are likely to appear in man-made environments. While traditional simplification algorithms focus on generality and smooth meshes, the approach presented here considers a specific class of man-made models. By detecting and classifying edge loops on the mesh and providing a guided series of binary mesh partitions, our approach generates a series of simplified models, each of which better respects the semantics of these kinds of models than conventional approaches do. A guiding principle is to eliminate simplifications that do not make sense in constructed environments. This, coupled with the concept of “punctuated simplification”, leads to an approach that is both efficient and delivers high visual quality. Comparative results are given.     

基于谷歌眼镜传感器的曲线拟合计步算法

当前独立的计步器普遍要考虑行人佩戴的部位,如基于手机的计步方法可靠性受限于手机放置位置。根据谷歌眼镜的位置固定特性设计了一种新的计步算法,有效地解决了平地、斜坡、上下楼梯等场景下的计步问题。该算法综合运用谷歌眼镜采集的加速度传感器信号和旋转矢量传感器信号,对这些信号进行快速傅里叶变换滤波处理,用二次曲线拟合信号序列,根据曲线参数阈值进行计步判断。实验结果表明：该算法在平地、斜坡、上下楼梯等场景具有96%以上的准确率。     

A general theory of systems performance criteria

Based on general systems thinking of the past half century and the efficiency literature of the last 100 years, this research develops a theoretical model of systems performance criteria that provides a means to evaluate how well any living system, whether individual, organisational or societal, performs task-related activities in the pursuit of goals. The model is used as a foundation for organising the various concepts of performance that have evolved over the past century: efficiency, costs, productivity, effectiveness and cost-effectiveness. These performance concepts, derived from the theoretical model, are formalised into axioms that are used to deduce theorems and derivative laws in constructing a formal general theory of systems performance criteria.     

Strategy, networks and systems in the global translation services market

The globalisation of markets has led to an increased demand for language translation services that support and enable communication between economic partners. For example, technical documents, software systems, business documents and web sites all need to be translated into multiple languages for individual national markets, and the information that they contain changes periodically. This paper sets out a theoretical framework that describes the architectures of business processes within and between separate firms that are used to support the delivery and management of services. This is done by coordinating the fit between externally generated problem complexity, from customers, and the internally generated complexity of different potential network configuration solutions. The theoretical framework is an architecture of how complexity is generated and managed at the different structural levels and across the different processual stages of an industry. A case study of thebigword, a major international translation services company, illustrates how the framework is applied in practice. The case study analyses the implementation of an electronic market platform which enables the coordination of the different stakeholders involved in the translation services market. These stakeholders include translators, translation services companies and their clients in a global business network.     

Simulation of cardiovascular system diseases by including the autonomic nervous system into a minimal model

Diagnosing cardiovascular system (CVS) diseases from clinically measured data is difficult, due to the complexity of the hemodynamic and autonomic nervous system (ANS) interactions. Physiological models could describe these interactions to enable simulation of a variety of diseases, and could be combined with parameter estimation algorithms to help clinicians diagnose CVS dysfunctions. This paper presents modifications to an existing CVS model to include a minimal physiological model of ANS activation. A minimal model is used so as to minimise the number of parameters required to specify ANS activation, enabling the effects of each parameter on hemodynamics to be easily understood. The combined CVS and ANS model is verified by simulating a variety of CVS diseases, and comparing simulation results with common physiological understanding of ANS function and the characteristic hemodynamics seen in these diseases. The model of ANS activation is required to simulate hemodynamic effects such as increased cardiac output in septic shock, elevated pulmonary artery pressure in left ventricular infarction, and elevated filling pressures in pericardial tamponade. This is the first known example of a minimal CVS model that includes a generic model of ANS activation and is shown to simulate diseases from throughout the CVS.