A Survey on Fused Filament Fabrication to Produce Functionally Gradient Materials.

Fused filament fabrication (FFF) is a key extrusion-based additive manufacturing (AM) process for fabricating components from polymers and their composites. Functionally gradient materials (FGMs) exhibit spatially varying properties by modulating chemical compositions, microstructures, and design attributes, offering enhanced performance over homogeneous materials and conventional composites. These materials are pivotal in aerospace, automotive, and medical applications, where the optimization of weight, cost, and functional properties is critical.

Development of Al-MgSi Alloy Hybrid Surface Composites by Friction Stir Processing: Mechanical, Wear, and Microstructure Evaluation.

Surface composites are viable choices for various applications in the aerospace and automotive industries. Friction Stir Processing (FSP) is a promising method for fabricating surface composites. Aluminum Hybrid Surface Composites (AHSC) are fabricated using the FSP to strengthen a hybrid mixture prepared with equal parts of Boron carbide (BC), Silicon Carbide (SiC), and Calcium Carbonate (CaCO) particles.

A Comprehensive Review of Self-Healing Polymer, Metal, and Ceramic Matrix Composites and Their Modeling Aspects for Aerospace Applications.

Composites can be divided into three groups based on their matrix materials, namely polymer, metal and ceramic. Composite materials fail due to micro cracks. Repairing is complex and almost impossible if cracks appear on the surface and interior, which minimizes reliability and material life.

How accurate can genetic predictions be?

Background: Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution.

Results: Here, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk.

Context-specific ontology integration: a bayesian approach.

We introduce a principled computational framework and methodology for automated discovery of context-specific functional links between ontologies. Our model leverages over disparate free-text literature resources to score the model of dependency linking two terms under a context against their model of independence. We identify linked terms as those having a significant bayes factor (p < 0.

Transcriptional network predicts viral set point during acute HIV-1 infection.

Background: HIV-1-infected individuals with higher viral set points progress to AIDS more rapidly than those with lower set points. Predicting viral set point early following infection can contribute to our understanding of early control of HIV-1 replication, to predicting long-term clinical outcomes, and to the choice of optimal therapeutic regimens.

Methods: In a longitudinal study of 10 untreated HIV-1-infected patients, we used gene expression profiling of peripheral blood mononuclear cells to identify transcriptional networks for viral set point prediction.

Is the reduction of dimensionality to a small number of features always necessary in constructing predictive models for analysis of complex diseases or behaviours?

Gene expression and genome wide association data have provided researchers the opportunity to study many complex traits and diseases. When designing prognostic and predictive models capable of phenotypic classification in this area, significant reduction of dimensionality through stringent filtering and/or feature selection is often deemed imperative. Here, this work challenges this presumption through both theoretical and empirical analysis.

Glycosaminoglycans and glucose prevent apoptosis in 4-methylumbelliferone-treated human aortic smooth muscle cells.

Smooth muscle cells (SMCs) have a pivotal role in cardiovascular diseases and are responsible for hyaluronan (HA) deposition in thickening vessel walls. HA regulates SMC proliferation, migration, and inflammation, which accelerates neointima formation. We used the HA synthesis inhibitor 4-methylumbelliferone (4-MU) to reduce HA production in human aortic SMCs and found a significant increase of apoptotic cells.

Inferring cell cycle feedback regulation from gene expression data.

Feedback control is an important regulatory process in biological systems, which confers robustness against external and internal disturbances. Genes involved in feedback structures are therefore likely to have a major role in regulating cellular processes. Here we rely on a dynamic Bayesian network approach to identify feedback loops in cell cycle regulation.

Mapping transcription mechanisms from multimodal genomic data.

Background: Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data.

Results: We develop a method to identify eQTLs.

Association of linear growth impairment in pediatric Crohn's disease and a known height locus: a pilot study.

The etiology of growth impairment in Crohn's disease (CD) has been inadequately explained by nutritional, hormonal, and/or disease-related factors, suggesting that genetics may be an additional contributor. The aim of this cross-sectional study was to investigate genetic variants associated with linear growth in pediatric-onset CD. We genotyped 951 subjects (317 CD patient-parent trios) for 64 polymorphisms within 14 CD-susceptibility and 23 stature-associated loci.

A transcriptional network signature characterizes lung cancer subtypes.

Background: Transcriptional networks play a central role in cancer development. The authors described a systems biology approach to cancer classification based on the reverse engineering of the transcriptional network surrounding the 2 most common types of lung cancer: adenocarcinoma (AC) and squamous cell carcinoma (SCC).

Methods: A transcriptional network classifier was inferred from the molecular profiles of 111 human lung carcinomas.

Genome-wide association for smoking cessation success in a trial of precessation nicotine replacement.

Abilities to successfully quit smoking display substantial evidence for heritability in classic and molecular genetic studies. Genome-wide association (GWA) studies have demonstrated single-nucleotide polymorphisms (SNPs) and haplotypes that distinguish successful quitters from individuals who were unable to quit smoking in clinical trial participants and in community samples. Many of the subjects in these clinical trial samples were aided by nicotine replacement therapy (NRT).

A systems biology approach to modeling vibrio cholerae gene expression under virulence-inducing conditions.

Vibrio cholerae is a Gram-negative bacillus that is the causative agent of cholera. Pathogenesis in vivo occurs through a series of spatiotemporally controlled events under the control of a gene cascade termed the ToxR regulon. Major genes in the ToxR regulon include the master regulators toxRS and tcpPH, the downstream regulator toxT, and virulence factors, the ctxAB and tcpA operons.

Genomic characterization of Wilms' tumor suppressor 1 targets in nephron progenitor cells during kidney development.

The Wilms' tumor suppressor 1 (WT1) gene encodes a DNA- and RNA-binding protein that plays an essential role in nephron progenitor differentiation during renal development. To identify WT1 target genes that might regulate nephron progenitor differentiation in vivo, we performed chromatin immunoprecipitation (ChIP) coupled to mouse promoter microarray (ChIP-chip) using chromatin prepared from embryonic mouse kidney tissue. We identified 1663 genes bound by WT1, 86% of which contain a previously identified, conserved, high-affinity WT1 binding site.

High-throughput discovery and characterization of fetal protein trafficking in the blood of pregnant women.

Although the measurement of fetal proteins in maternal serum is part of standard prenatal screening for aneuploidy and neural tube defects, attempts to better understand the extent of feto-maternal protein trafficking and its clinical and biological significance have been hindered by the presence of abundant maternal proteins. The objective of this study was to circumvent maternal protein interference by using a computational predictive approach for the development of a noninvasive, comprehensive, protein network analysis of the developing fetus in maternal whole blood. From a set of 157 previously identified fetal gene transcripts, 46 were classified into known protein networks, and 222 downstream proteins were predicted.

Ontology engineering.

Gene Ontology and similar biomedical ontologies are critical tools of today genetic research. These ontologies are crafted through a painstaking process of manual editing, and their organization relies on the intuition of human curators. Here we describe a method that uses information theory to automatically organize the structure of GO and optimize the distribution of the information within it.

Integrative predictive model of coronary artery calcification in atherosclerosis.

Background: Many different genetic and clinical factors have been identified as causes or contributors to atherosclerosis. We present a model of preclinical atherosclerosis based on genetic and clinical data that predicts the presence of coronary artery calcification in healthy Americans of European descent 45 to 84 years of age in the Multi-Ethnic Study of Atherosclerosis (MESA).

Methods And Results: We assessed 712 individuals for the presence or absence of coronary artery calcification and assessed their genotypes for 2882 single-nucleotide polymorphisms.

The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms.

The field of synthetic biology holds an inspiring vision for the future; it integrates computational analysis, biological data and the systems engineering paradigm in the design of new biological machines and systems. These biological machines are built from basic biomolecular components analogous to electrical devices, and the information flow among these components requires the augmentation of biological insight with the power of a formal approach to information management. Here we review the informatics challenges in synthetic biology along three dimensions: in silico, in vitro and in vivo.

Dynamic gene expression analysis links melanocyte growth arrest with nevogenesis.

Like all primary cells in vitro, normal human melanocytes exhibit a physiologic decay in proliferative potential as it transitions to a growth-arrested state. The underlying transcriptional program(s) that regulate this phenotypic change is largely unknown. To identify molecular determinants of this process, we performed a Bayesian-based dynamic gene expression analysis on primary melanocytes undergoing proliferative arrest.

Transcriptional network classifiers.

Background: Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biological processes are outcomes of regulatory gene interactions, this paper develops a system biology approach to infer function-dependent transcriptional networks modulating phenotypic traits, which serve as a classifier to identify tissue states.

Predicting response to short-acting bronchodilator medication using Bayesian networks.

Aims: Bronchodilator response tests measure the effect of beta(2)-agonists, the most commonly used short-acting reliever drugs for asthma. We sought to relate candidate gene SNP data with bronchodilator response and measure the predictive accuracy of a model constructed with genetic variants.

Materials & Methods: Bayesian networks, multivariate models that are able to account for simultaneous associations and interactions among variables, were used to create a predictive model of bronchodilator response using candidate gene SNP data from 308 Childhood Asthma Management Program Caucasian subjects.

Links between regimen-related toxicities in patients being treated for colorectal cancer.

Purpose Of Review: To summarize the available evidence on cooccurring gastrointestinal toxicities and their potential link with other symptoms in cancer patients. The information obtained from colorectal cancer patient cohorts will be used as an example.

Recent Findings: In recent years, it has become clear that gastrointestinal toxicities do not occur in isolation in cancer patients.

Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records.

Objective: Identify clinical factors that modulate the risk of progression to COPD among asthma patients using data extracted from electronic medical records.

Design: Demographic information and comorbidities from adult asthma patients who were observed for at least 5 years with initial observation dates between 1988 and 1998, were extracted from electronic medical records of the Partners Healthcare System using tools of the National Center for Biomedical Computing "Informatics for Integrating Biology to the Bedside" (i2b2).

Measurements: A predictive model of COPD was constructed from a set of 9,349 patients (843 cases, 8,506 controls) using Bayesian networks.