Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Advancing biomaterials of human origin for tissue engineering

Extracellular matrix hydrogels from decellularized tissues: Structure and function.

In tissue engineering and regenerative medicine, a biomaterial provides mechanical support and biochemical signals to encourage cell attachment and modulate cell behaviour. Nature’s template for a biomaterial is the extracellular matrix (ECM). The ECM contains intrinsic biochemical and mechanical cues that regulate cell phenotype and function in development, in homeostasis and in response to injury. The use of ECM-based materials in biomedical research has advanced from coating cell culture plates with purified ECM components to the design of ECM-mimicking biomaterials and the engineering of decellularized tissues aimed at recapitulating the dynamics, composition and structure of the ECM. In this Review, we highlight important matrix properties and functions in the context of tissue engineering and regenerative medicine, consider techniques such as proteomics for the investigation of matrix structure and composition and discuss different engineering strategies for the design of matrix-mimicking biomaterials. Tissue, whole organ and cell culture decellularization approaches are examined for their potential to preserve the tissue-specific biochemical composition and ultrastructure of the ECM and for the development of biomaterials that promote the formation of functional tissues in clinical applications. Finally, we investigate challenges and opportunities of ECM biomaterials for the design of organotypic models to study disease progression, for the ex vivo creation of engineered tissue and for the clinical translation of functional tissue reconstruction strategies in vivo. The extracellular matrix is nature’s template for an ideal biomaterial to guide tissue homeostasis and repair. In this Review, matrix-mimicking biomaterials and decellularized matrices are discussed for their potential to reconstruct and repair tissues in vitro and in vivo.

Extracellular matrix-based materials for regenerative medicine

Non-innocent ligands in bioinorganic chemistry—An overview

Background: We have previously reported altered gene expression in adults with asthma compared to healthy controls from the U-BIOPRED study (Bigler, 2016). Altered transcripts may define dysregulated biological pathways and identify novel therapeutic targets. We hypothesised that similar dysregulation would be seen in children with asthma. Aim: To compare blood transcriptomic profiles of children and adults with asthma. Methods: Affymetrix blood transcriptomic profiles of severe asthmatic adult non-smokers, n=152, were compared to mild moderate asthmatics, n=50 (Shaw, 2015; Fleming, 2015). Profiles of school-aged children with severe asthma, n=75, were compared to mild moderate asthmatics, n=37, and in the preschool age group severe wheeze, n=62, was compared to mild moderate wheeze, n=42. Differentially expressed genes (DEG) were identified as probe sets with maximum median group intensity >log2 5, with a significant (raw P≤0.01) change ≥ 20%. Overlapping genes were determined and pathway analysis performed. Results: We found 1887 DEG comparing severe and mild moderate asthmatic adults. Only 28 DEG were found between the severe wheeze and mild pre-school age children, with a larger signature (569 DEG) in the school aged children. 480 genes were specific to school-aged children and 1801 specific to adults, with 89 DEG in common between the adults and school-aged children. Conclusions: Preschool age children were poorly defined in terms of blood transcriptomics by the clinical definitions used. While the school-aged children showed some DEG overlap with the adults, they were distinct in many DEG and pathways indicating that childhood and adult asthma may be mechanistically different.

Late Breaking Abstract - Comparison of the blood transcriptomic profiles of adults and children from the U-BIOPRED asthma study

Introduction: There is ongoing controversy to whether the clinical or pathophysiologic significance of a high blood eosinophil (EOS) count in asthma is the same as it is in COPD. In this study, we used a regression transcriptome analysis of bronchial brushings (BB) vs blood EOS level from a COPD cohort (Emphysema vs airway disease (EvA) and from Asthma U-BIOPRED. Aims: To determine if airway gene expression is similar in eosinophilic patients in COPD and Asthma. Methods: A regression analysis determined the relationship between BB gene expression and EOS count in blood. In EvA (n=185) RNA seq was used to characterise RNA expression, in U-BIOPRED (n=95) arrays were used. Differentially expressed genes (DEG9s) showing a significant regression coefficient (RC) after FDR correction p Results: In EvA there were 12 DEG’s with a positive RC, whereas in U-BIOPRED 1197 DEG’s showed significant RC,266 positive and 931 negative. The top 5 positive RC DEG’s for asthma and COPD and negative RC DEG’s for asthma are shown in figure 1. A cystatin (CST1) was significant in both. CLCA1 in the COPD set is a T2 associated gene. Conclusions: The transcriptome analysis showed little overlap between DEG’s of COPD and asthma and few DEG’s in COPD. This data indicates despite shared ‘treatable traits’ between asthma and COPD, the mechanisms underlying these clinical entities may be different.

Relationship between blood eosinophil count and bronchial epithelial gene expression in COPD versus Asthma

Discrepancies or defects in active ingredients, excipients and coatings that form solid oral dosage forms can both impact product quality and provide hallmarks of off‐brand or counterfeit products. There is therefore a need for rapid and continuous analytical techniques that can assess and classify product differences of intact samples at or near the production line, or in analytical labs, ideally without resorting to product dissolution. Here we test the ability of two rapid ambient chemical characterisation methods to discriminate between solid dosage forms: Desorption electrospray ionisation mass spectrometry (DESI MS) and transmission Raman spectroscopy. These two techniques are highly complementary, offering greater sensitivity to the analysis of the surface and the tablet bulk, respectively. The data sets generated were then used to test a variety of classification algorithms including linear discriminate analysis (LDA), tree‐based methods, a simple neural network, and support vector machines (SVM). The highest performing algorithms for DESI MS were the SVM, with an additional performance boost when used with a polynomial kernel. For transmission Raman data, an LDA model was found to be the most effective.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cem.3412

Adam J. Taylor

Papers

Late Breaking Abstract - Comparison of the blood transcriptomic profiles of adults and children from the U-BIOPRED asthma study

Relationship between blood eosinophil count and bronchial epithelial gene expression in COPD versus Asthma

Classification of tablet formulations by desorption electrospray ionisation mass spectrometry and transmission Raman spectroscopy

Bone Tissue Engineering: Using surfaces to direct function