Brain mass estimation by head circumference and body mass methods in neonatal glycaemic modelling and control

Introduction

Hyperglycaemia is a common complication of stress and prematurity in extremely low-birth-weight infants. Model-based insulin therapy protocols have the ability to safely improve glycaemic control for this group. Estimating non-insulin-mediated brain glucose uptake by the central nervous system in these models is typically done using population-based body weight models, which may not be ideal.

Method

A head circumference-based model that separately treats small-for-gestational-age (SGA) and appropriate-for-gestational-age (AGA) infants is compared to a body weight model in a retrospective analysis of 48 patients with a median birth weight of 750 g and median gestational age of 25 weeks. Estimated brain mass, model-based insulin sensitivity (S_I) profiles, and projected glycaemic control outcomes are investigated. SGA infants (5) are also analyzed as a separate cohort.

Results

Across the entire cohort, estimated brain mass deviated by a median 10% between models, with a per-patient median difference in S_I of 3.5%. For the SGA group, brain mass deviation was 42%, and per-patient S_I deviation 13.7%. In virtual trials, 87–93% of recommended insulin rates were equal or slightly reduced (Δ < 0.16 mU/h) under the head circumference method, while glycaemic control outcomes showed little change.

Conclusion

The results suggest that body weight methods are not as accurate as head circumference methods. Head circumference-based estimates may offer improved modelling accuracy and a small reduction in insulin administration, particularly for SGA infants.     

The annual cycle of Flehmen in black-tailed deer (Odocoileus hemionus columbianus)

Flehmen is a stereotyped response of ungulates and other mammals to urine. In black-tailed deer, Flehmen occurs typically in males (rarely in females), and its frequency shows an annual cycle. Flehmen in response to female urine was most frequent in November, and to male urine in January. The response minimum to both types of urine occurred in May. Fifty-five to 100% of initial responses to female urine resulted in Flehmen. The deer responded more often to female urine, and the difference between the responses to female and male urine increased from May to November. A male's own urine released Flehmen more often than did urine of other males. The response intensity varied with the spatial orientation of the male to the urinating female, and responses were usually limited to distances of 15 m or less.     

Electrophoretic deposition of carbon nanotubes

Electrophoretic deposition (EPD) has been gaining increasing interest as an economical and versatile processing technique for the production of novel coatings or films of carbon nanotubes (CNTs) on conductive substrates. The purpose of the paper is to present an up-to-date comprehensive overview of current research progress in the field of EPD of CNTs. The paper specifically reviews the preparation and characterisation of stable CNT suspensions, and the mechanism of the EPD process; it includes discussion of pure CNT coatings and CNT/nanoparticle composite films. A complete discussion of the EPD parameters is presented, including electrode materials, deposition time, electrode separation, deposition voltage and resultant electric field. The paper highlights potential applications of the resulting CNT and CNT/composite structures, in areas such as field emission devices, fuel cells, and supercapacitors.     

Hybrid clustering solution selection strategy

Cluster ensemble approaches make use of a set of clustering solutions which are derived from different data sources to gain a more comprehensive and significant clustering result over conventional single clustering approaches. Unfortunately, not all the clustering solutions in the ensemble contribute to the final result. In this paper, we focus on the clustering solution selection strategy in the cluster ensemble, and propose to view clustering solutions as features such that suitable feature selection techniques can be used to perform clustering solution selection. Furthermore, a hybrid clustering solution selection strategy (HCSS) is designed based on a proposed weighting function, which combines several feature selection techniques for the refinement of clustering solutions in the ensemble. Finally, a new measure is designed to evaluate the effectiveness of clustering solution selection strategies. The experimental results on both UCI machine learning datasets and cancer gene expression profiles demonstrate that HCSS works well on most of the datasets, obtains more desirable final results, and outperforms most of the state-of-the-art clustering solution selection strategies.     

Image clustering based on sparse patch alignment framework

Image clustering methods are efficient tools for applications such as content-based image retrieval and image annotation. Recently, graph based manifold learning methods have shown promising performance in extracting features for image clustering. Typical manifold learning methods adopt appropriate neighborhood size to construct the neighborhood graph, which captures local geometry of data distribution. Because the density of data points’ distribution may be different in different regions of the manifold, a fixed neighborhood size may be inappropriate in building the manifold. In this paper, we propose a novel algorithm, named sparse patch alignment framework, for the embedding of data lying in multiple manifolds. Specifically, we assume that for each data point there exists a small neighborhood in which only the points that come from the same manifold lie approximately in a low-dimensional affine subspace. Based on the patch alignment framework, we propose an optimization strategy for constructing local patches, which adopt sparse representation to select a few neighbors of each data point that span a low-dimensional affine subspace passing near that point. After that, the whole alignment strategy is utilized to build the manifold. Experiments are conducted on four real-world datasets, and the results demonstrate the effectiveness of the proposed method.     

Observability Analysis of Continuous‐Time LTI Systems with Limited Derivative Data

We consider continuous‐time LTI systems with either unknown‐input or with lack of information about the input and output derivatives. We compute the unknown‐input observability subspace and the observability subspace with unknown derivatives of input and output. We first formulate the unknown‐input observability subspace via projection matrices, then show that through having the unknown‐input observability subspace, one can easily evaluate the effect of known input and output signals but unknown derivatives on the observability subspace. Our method is demonstrated on the dynamics of a longitudinal aircraft in steady‐state flight.     

Processing, Microstructure, and Wear Behavior of Silicon Nitride Hot-Pressed with Alumina and Yttria

Commercial silicon nitride powder with A1₂O₃ and Y₂O₃ additives was hot-pressed to complete density. The resulting microstructure contained elongated grains with no trace of remaining α-Si₃N₄. The aspect ratio of the elongated grains increased with increasing soak time at a fixed hot-pressing temperature. X-ray diffraction analysis showed that the crystalline phase in the hot-pressed samples was β-sialon (Si_6−zAl_zO_zN_8−z) with z values that increased with soak time. The fracture strength and fracture toughness of the samples increased as the aspect ratio of the grains increased. The Vickers hardness decreased slightly as the soak time was increased, which was attributed to a grain size effect. Wear tests of silicon nitride against silicon nitride were conducted on a reciprocating pin-on-disk apparatus with paraffin oil as a lubricant. Correlation studies of wear with microstructure and mechanical properties were performed. The wear rate increased rapidly with increasing soak time in spite of the increased strength and toughness. This was attributed to increased third-body wear caused by pullout of pieces from the wear surface. The pullout mechanism was not conclusively identified. However, TEM examination showed clear evidence of dislocation motion under the wear scar. Grain boundary microstresses caused by the anisotropic thermal expansion and elastic properties of the elongated grains may have contributed to the observed pullout.     

Crystallization behavior of starch‐filled polypropylene

Starches of different granule sizes, including corn, rice, and amaranth starches, were used to prepare starch‐filled polypropylene (PP) and the effect of starch granule size on crystallization behavior PP was investigated. Differential scanning calorimetry and scanning electron microscopy were used to monitor the energy changes of the crystallization of the melt and to characterize the morphology of PP/starch composites, respectively. Little interaction was observed between starch and PP despite the difference in starch granule size. The crystallization temperature of PP decreased with the addition of starch and this decrease became more apparent with increasing starch granule size. During nonisothermal crystallization, the dependency of the relative degree of crystallinity on time was described by the Avrami equation. The addition of starch decreased the overall crystallization rate of PP, which was attributed to an increase in the activation energy of crystallization under nonisothermal conditions according to the Kissinger equation. An increase in starch granule size of starch would increase the crystallization activation energy of PP and consequently decrease its crystallization rate. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 484–492, 2004     

A visual language for modeling and executing traceability queries

Current software and systems engineering tools provide only basic trace features, and as a result users are often compelled to construct non-trivial traceability queries using generic query languages such as SQL. In this paper, we present an alternative approach which defines traceability strategies for a project using UML class diagrams and then constructs trace queries as constraints upon subsets of the model. The visual trace modeling language (VTML) allows users to model a broad range of trace queries while hiding underlying technical details and data structures. The viability and expressiveness of VTML for use in a real project are demonstrated through modeling a broadly representative set of queries for a web-based health-care system. It is then evaluated through an experiment with human users to assess the readability and writability of VTML queries in comparison to generic SQL queries. We found that users read and constructed traceability queries considerably faster using VTML than using SQL. Furthermore, visually constructed traceability queries were substantially more correct compared to the same queries constructed with SQL.     

MatSeek: An Ontology-Based Federated Search Interface for Materials Scientists

The MatSeek system is an ontology-based federated search interface to key materials science databases and analytical tools. By combining Semantic Web and Web 2.0 technologies, MatSeek provides materials scientists with a single Web interface that enables them to search across disparate databases containing crystal-structure data, ionic-conductivity data, and phase stability data; render 3D crystal-structure images; calculate bond lengths and angles; retrieve relevant scholarly references; and identify potential new materials with the structure and properties required to satisfy specific applications. The MatOnto ontology underlying MatSeek enables integration of data across disparate databases, and Web 2.0 technologies enable iterative searching across the databases. The results retrieved from searching the previous database are used as input to the query on the next database. By providing materials scientists with a single, integrated Web interface to the critical materials science databases and analytical tools, MatSeek represents a significant advance toward a full-fledged materials-informatics workbench.