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.     

Fast Lighting Independent Background Subtraction

This paper describes a simple method of fast background subtraction based upon disparity verification that is invariant to arbitrarily rapid run-time changes in illumination. Using two or more cameras, the method requires the off-line construction of disparity fields mapping the primary background images. At runtime, segmentation is performed by checking background image to each of the additional auxiliary color intensity values at corresponding pixels. If more than two cameras are available, more robust segmentation can be achieved and, in particular, the occlusion shadows can be generally eliminated as well. Because the method only assumes fixed background geometry, the technique allows for illumination variation at runtime. Since no disparity search is performed, the algorithm is easily implemented in real-time on conventional hardware.     

Cerebral: visualizing multiple experimental conditions on a graph with biological context

Systems biologists use interaction graphs to model the behavior of biological systems at the molecular level. In an iterative process, such biologists obser ve the reactions of living cells under various experimental conditions, view the results in the context of the interaction graph, and then propose changes to the graph model. These graphs ser ve as a form of dynamic knowledge representation of the biological system being studied and evolve as new insight is gained from the experimental data. While numerous graph layout and drawing packages are available, these tools did not fully meet the needs of our immunologist collaborators. In this paper, we describe the data information display needs of these immunologists and translate them into design decisions. These decisions led us to create Cerebral, a system that uses a biologically guided graph layout and incor porates experimental data directly into the graph display. Small multiple views of different experimental conditions and a data-driven parallel coordinates view enable correlations between experimental conditions to be analyzed at the same time that the data is viewed in the graph context. This combination of coordinated views allows the biologist to view the data from many different perspectives simultaneously. To illustrate the typical analysis tasks performed, we analyze two datasets using Cerebral. Based on feedback from our collaboratorsweconcludethat Cerebral is a valuable tool for analyzing experimental data in the context of an interaction graph model.      

Optofluidic waveguides: II. Fabrication and structures

We review fabrication methods and common structures for optofluidic waveguides, defined as structures capable of optical confinement and transmission through fluid filled cores. Cited structures include those based on total internal reflection, metallic coatings, and interference based confinement. Configurations include optical fibers and waveguides fabricated on flat substrates (integrated waveguides). Some examples of optofluidic waveguides that are included in this review are Photonic Crystal Fibers (PCFs) and two-dimensional photonic crystal arrays, Bragg fibers and waveguides, and Anti Resonant Reflecting Optical Waveguides (ARROWs). An emphasis is placed on integrated ARROWs fabricated using a thin-film deposition process, which illustrates how optofluidic waveguides can be combined with other microfluidic elements in the creation of lab-on-a-chip devices.     

Shorthand Universal Cycles for Permutations

The set of permutations of ??n??={1,??,n} in one-line notation is ??(n). The shorthand encoding of a ₁?a _n ????(n) is a ₁?a _n?1. A shorthand universal cycle for permutations (SP-cycle) is a circular string of length n! whose substrings of length n?1 are the shorthand encodings of ??(n). When an SP-cycle is decoded, the order of ??(n) is a Gray code in which successive permutations differ by the prefix-rotation ?? _i =(1 2 ? i) for i??{n?1,n}. Thus, SP-cycles can be represented by n! bits. We investigate SP-cycles with maximum and minimum ??weight?? (number of ?? _n?1s in the Gray code). An SP-cycle n a n b?n z is ??periodic?? if its ??sub-permutations?? a,b,??,z equal ??(n?1). We prove that periodic min-weight SP-cycles correspond to spanning trees of the (n?1)-permutohedron. We provide two constructions: B(n) and C(n). In B(n) the spanning trees use ??half-hunts?? from bell-ringing, and in C(n) the sub-permutations use cool-lex order by Williams (SODA, 987?C996, 2009). Algorithmic results are: (1)?memoryless decoding of B(n) and C(n), (2)?O((n?1)!)-time generation of B(n) and C(n) using sub-permutations, (3)?loopless generation of B(n)??s binary representation n bits at a time, and (4)?O(n+??(n))-time ranking of B(n)??s permutations where ??(n) is the cost of computing a permutation??s inversion vector. Results (1)?C(4) improve on those for the previous SP-cycle construction D(n) by Ruskey and Williams (ACM Trans. Algorithms 6(3):Art.?45, 2010), which we characterize here using ??recycling??.     

Psychological and Physiological Adaptations to Sperm Competition in Humans.

Postcopulatory competition between males, in the form of sperm competition, is a widespread phenomenon in many animal species. The extent to which sperm competition has been an important selective pressure during human evolution remains controversial, however. The authors review critically the evidence that human males and females have psychological, behavioral, and physiological adaptations that evolved in response to selection pressures associated with sperm competition. The authors consider, using evidence from contemporary societies, whether sperm competition is likely to have been a significant adaptive problem for ancestral humans and examine the evidence suggesting that human males have physiological and psychological mechanisms that allow for "prudent" sperm allocation in response to variations in the risk of sperm competition. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Compression pre-cracking to generate near threshold fatigue-crack-growth rates in two aluminum alloys

A test program was conducted to generate threshold and near-threshold data on two aluminum alloys: 2324-T39 and 7075-T7351, using compression pre-cracking constant-amplitude (CPCA) threshold testing procedures. Compact tension specimens were tested under stress ratios (R) of 0.1, 0.4, 0.7 or 0.8. Results were compared with either existing load-reduction data from the literature or with data that were generated using the load-reduction procedures. On the 2324 alloy, very little difference was observed between the load-reduction and CPCA test methods, although the CPCA test method consistently produced slightly lower thresholds or slightly faster crack-growth rates. But on the 2324 alloy, most of the load-reduction tests used extremely low initial ΔK values to grow the cracks under load reduction, which may explain the reason for the agreement. These low ΔK values were achieved by using compression pre-cracking to initiate a crack at the crack-starter notch. In contrast, the 7075 alloy showed very large differences between the CPCA and load-reduction results in the near-threshold and threshold regimes for R=0.1 and 0.4. Results under high R conditions (R=0.7) agreed very well between the two threshold test methods. An expression was also developed to generate fatigue-crack-growth-rate data using the CPCA threshold test method that would be unaffected by compressive yielding at the crack-starter notch and produce ‘steady-state’ constant-amplitude data in the near threshold regime.     

A Trustworthy Proof Checker

Proof-carrying code (PCC) and other applications in computer security require machine-checkable proofs of properties of machine-language programs. The main advantage of the PCC approach is that the amount of code that must be explicitly trusted is very small: it consists of the logic in which predicates and proofs are expressed, the safety predicate, and the proof checker. We have built a minimal proof checker, and we explain its design principles and the representation issues of the logic, safety predicate, and safety proofs. We show that the trusted computing base (TCB) in such a system can indeed be very small. In our current system the TCB is less than 2,700 lines of code (an order of magnitude smaller even than other PCC systems), which adds to our confidence of its correctness.     

Robust Algorithms for Object Localization

Model-based localization, the task of estimating an object's pose from sensed and corresponding model features, is a fundamental task in machine vision. Exact constant time localization algorithms have been developed for the case where the sensed features and the model features are the same type. Still, it is not uncommon for the sensed features and the model features to be of different types, i.e., sensed data points may correspond to model faces or edges. Previous localization approaches have handled different model and sensed features of different types via sampling and synthesizing virtual features to reduce the problem of matching features of dissimilar types to the problem of matching features of similar types. Unfortunately, these approaches may be suboptimal because they introduce artificial errors. Other localization approaches have reformulated object localization as a nonlinear least squares problem where the error is between the sensed data and model features in image coordinates (the Euclidean image error metric). Unfortunately, all of the previous approaches which minimized the Euclidean image error metric relied on gradient descent methods to find the global minima, and gradient descent methods may suffer from problems of local minima. In this paper, we describe an exact, efficient solution to the nonlinear least squares minimization problem based upon resultants, linear algebra, and numerical techniques. On a SPARC 20, our localization algorithm runs in a few microseconds for rectilinear polygonal models, a few milliseconds for generic polygonal models, and one second for generalized polygonal models (models composed of linear edges and circular arcs).