Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method

In order to optimally design materials, it is crucial to understand the structure-property relations in the material by analyzing the effect of microstructure parameters on the macroscopic properties. In computational homogenization, the microstructure is thus explicitly modeled inside the macrostructure, leading to a coupled two-scale formulation. Unfortunately, the high computational costs of such multiscale simulations often render the solution of design, optimization, or inverse problems infeasible. To address this issue, we propose in this work a non-intrusive reduced basis method to construct inexpensive surrogates for parametrized microscale problems; the method is specifically well-suited for multiscale simulations since the coupled simulation is decoupled into two independent problems: (1) solving the microscopic problem for different (loading or material) parameters and learning a surrogate model from the data; and (2) solving the macroscopic problem with the learned material model. The proposed method has three key features. First, the microscopic stress field can be fully recovered. Second, the method is able to accurately predict the stress field for a wide range of material parameters; furthermore, the derivatives of the effective stress with respect to the material parameters are available and can be readily utilized in solving optimization problems. Finally, it is more data efficient, i.e. requiring less training data, as compared to directly performing a regression on the effective stress. For the microstructures in the two test problems considered, the mean approximation error of the effective stress is as low as 0.1% despite using a relatively small training dataset. Embedded into the macroscopic problem, the reduced order model leads to an online speed up of approximately three orders of magnitude while maintaining a high accuracy as compared to the FE$^2$ solver

A reduced order model for geometrically parameterized two-scale simulations of elasto-plastic microstructures under large deformations

In recent years, there has been a growing interest in understanding complex microstructures and their effect on macroscopic properties. In general, it is difficult to derive an effective constitutive law for such microstructures with reasonable accuracy and meaningful parameters. One numerical approach to bridge the scales is computational homogenization, in which a microscopic problem is solved at every macroscopic point, essentially replacing the effective constitutive model. Such approaches are, however, computationally expensive and typically infeasible in multi-query contexts such as optimization and material design. To render these analyses tractable, surrogate models that can accurately approximate and accelerate the microscopic problem over a large design space of shapes, material and loading parameters are required. In this work, we develop a reduced order model based on Proper Orthogonal Decomposition (POD), Empirical Cubature Method (ECM) and a geometrical transformation method with the following key features: (i) large shape variations of the microstructure are captured, (ii) only relatively small amounts of training data are necessary, and (iii) highly non-linear history-dependent behaviors are treated. The proposed framework is tested and examined in two numerical examples, involving two scales and large geometrical variations. In both cases, high speed-ups and accuracies are achieved while observing good extrapolation behavior.</p

A reduced order model for geometrically parameterized two-scale simulations of elasto-plastic microstructures under large deformations

In recent years, there has been a growing interest in understanding complex microstructures and their effect on macroscopic properties. In general, it is difficult to derive an effective constitutive law for such microstructures with reasonable accuracy and meaningful parameters. One numerical approach to bridge the scales is computational homogenization, in which a microscopic problem is solved at every macroscopic point, essentially replacing the effective constitutive model. Such approaches are, however, computationally expensive and typically infeasible in multi-query contexts such as optimization and material design. To render these analyses tractable, surrogate models that can accurately approximate and accelerate the microscopic problem over a large design space of shapes, material and loading parameters are required. In previous works, such models were constructed in a data-driven manner using methods such as Neural Networks (NN) or Gaussian Process Regression (GPR). However, these approaches currently suffer from issues, such as need for large amounts of training data, lack of physics, and considerable extrapolation errors. In this work, we develop a reduced order model based on Proper Orthogonal Decomposition (POD), Empirical Cubature Method (ECM) and a geometrical transformation method with the following key features: (i) large shape variations of the microstructure are captured, (ii) only relatively small amounts of training data are necessary, and (iii) highly non-linear history-dependent behaviors are treated. The proposed framework is tested and examined in two numerical examples, involving two scales and large geometrical variations. In both cases, high speed-ups and accuracies are achieved while observing good extrapolation behavior

Transmission of Schistosoma japonicum in Marshland and Hilly Regions of China: Parasite Population Genetic and Sibship Structure

The transmission dynamics of Schistosoma japonicum remain poorly understood, as over forty species of mammals are suspected of serving as reservoir hosts. However, knowledge of the population genetic structure and of the full-sibship structuring of parasites at two larval stages will be useful in defining and tracking the transmission pattern between intermediate and definitive hosts. S. japonicum larvae were therefore collected in three marshland and three hilly villages in Anhui Province of China across three time points: April and September-October 2006, and April 2007, and then genotyped with six microsatellite markers. Results from the population genetic and sibling relationship analyses of the parasites across two larval stages demonstrated that, within the marshland, parasites from cattle showed higher genetic diversity than from other species; whereas within the hilly region, parasites from dogs and humans displayed higher genetic diversity than those from rodents. Both the extent of gene flow and the estimated proportion of full-sib relationships of parasites between two larval stages indicated that the cercariae identified within intermediate hosts in the marshlands mostly came from cattle, whereas in the hilly areas, they were varied between villages, coming primarily from rodents, dogs or humans. Such results suggest a different transmission process within the hilly region from within the marshlands. Moreover, this is the first time that the sibling relationship analysis was applied to the transmission dynamics for S. japonicum

Population genomic data reveal genes related to important traits of quail

Background: Japanese quail (Coturnix japonica), a recently domesticated poultry species, is important not only as an agricultural product, but also as a model bird species for genetic research. However, most of the biological questions concerning genomics, phylogenetics, and genetics of some important economic traits have not been answered. It is thus necessary to complete a high-quality genome sequence as well as a series of comparative genomics, evolution, and functional studies. Results: Here, we present a quail genome assembly spanning 1.04 Gb with 86.63% of sequences anchored to 30 chromosomes (28 autosomes and 2 sex chromosomes Z/W). Our genomic data have resolved the long-term debate of phylogeny among Perdicinae (Japanese quail), Meleagridinae (turkey), and Phasianinae (chicken). Comparative genomics and functional genomic data found that four candidate genes involved in early maturation had experienced positive selection, and one of them encodes follicle stimulating hormone beta (FSHβ), which is correlated with different FSHβ levels in quail and chicken. We re-sequenced 31 quails (10 wild, 11 egg-type, and 10 meat-type) and identified 18 and 26 candidate selective sweep regions in the egg-type and meat-type lines, respectively. That only one of them is shared between egg-type and meat-type lines suggests that they were subject to an independent selection. We also detected a haplotype on chromosome Z, which was closely linked with maroon/yellow plumage in quail using population resequencing and a genome-wide association study. This haplotype block will be useful for quail breeding programs. Conclusions: This study provided a high-quality quail reference genome, identified quail-specific genes, and resolved quail phylogeny. We have identified genes related to quail early maturation and a marker for plumage color, which is significant for quail breeding. These results will facilitate biological discovery in quails and help us elucidate the evolutionary processes within the Phasianidae family

Aberrant Cell Cycle Regulation in Cervical Carcinoma

Carcinoma of the uterine cervix is one of the most common malignancies among women worldwide. Human papillomaviruses (HPV) have been identified as the major etiological factor in cervical carcinogenesis. However, the time lag between HPV infection and the diagnosis of cancer indicates that multiple steps, as well as multiple factors, may be necessary for the development of cervical cancer. The development and progression of cervical carcinoma have been shown to be dependent on various genetic and epigenetic events, especially alterations in the cell cycle checkpoint machinery. In mammalian cells, control of the cell cycle is regulated by the activity of cyclin-dependent kinases (CDKs) and their essential activating coenzymes, the cyclins. Generally, CDKs, cyclins, and CDK inhibitors function within several pathways, including the p16INK4A-cyclin D1-CDK4/6-pRb-E2F, p21WAF1-p27KIP1-cyclinE-CDK2, and p14ARF-MDM2-p53 pathways. The results from several studies showed aberrant regulation of several cell cycle proteins, such as cyclin D, cyclin E, p16INK4A, p21WAF1, and p27KIP1, as characteristic features of HPV-infected and HPV E6/E7 oncogene-expressing cervical carcinomas and their precursors. These data suggested further that interactions of viral proteins with host cellular proteins, particularly cell cycle proteins, are involved in the activation or repression of cell cycle progression in cervical carcinogenesis

Recent Progress in Electrospun Nanofibres: Reinforcement Effect and Mechanical Performance

Composite materials are becoming increasingly important as structural materials for aeronautical and space engineering, naval, automotive, and civil engineering, sporting goods, and other consumer products. Fiber-based reinforcement represents one of the most effective manufacturing strategies for enhancing the mechanical strength and other properties of composite materials. Electrospinning has gained widespread interest in the last two decades because of its ability to fabricate continuous ultrafine nanofibers with unique characteristics. The impact of electrospinning on fiber synthesis and processing, characterization, and applications in drug delivery, nanofiltration, tissue scaffolding, and electronics has been extensively studied in the past. In this article, the authors have focused on a comprehensive review of the mechanical performance and properties of electrospun nanofibers as potential reinforcements as well as their advanced nanocomposites

High-performance liquid chromatography–tandem mass spectrometry in the identification and determination of phase I and phase II drug metabolites

Applications of tandem mass spectrometry (MS/MS) techniques coupled with high-performance liquid chromatography (HPLC) in the identification and determination of phase I and phase II drug metabolites are reviewed with an emphasis on recent papers published predominantly within the last 6 years (2002–2007) reporting the employment of atmospheric pressure ionization techniques as the most promising approach for a sensitive detection, positive identification and quantitation of metabolites in complex biological matrices. This review is devoted to in vitro and in vivo drug biotransformation in humans and animals. The first step preceding an HPLC-MS bioanalysis consists in the choice of suitable sample preparation procedures (biomatrix sampling, homogenization, internal standard addition, deproteination, centrifugation, extraction). The subsequent step is the right optimization of chromatographic conditions providing the required separation selectivity, analysis time and also good compatibility with the MS detection. This is usually not accessible without the employment of the parent drug and synthesized or isolated chemical standards of expected phase I and sometimes also phase II metabolites. The incorporation of additional detectors (photodiode-array UV, fluorescence, polarimetric and others) between the HPLC and MS instruments can result in valuable analytical information supplementing MS results. The relation among the structural changes caused by metabolic reactions and corresponding shifts in the retention behavior in reversed-phase systems is discussed as supporting information for identification of the metabolite. The first and basic step in the interpretation of mass spectra is always the molecular weight (MW) determination based on the presence of protonated molecules [M+H]+ and sometimes adducts with ammonium or alkali-metal ions, observed in the positive-ion full-scan mass spectra. The MW determination can be confirmed by the [M-H]- ion for metabolites providing a signal in negative-ion mass spectra. MS/MS is a worthy tool for further structural characterization because of the occurrence of characteristic fragment ions, either MSn analysis for studying the fragmentation patterns using trap-based analyzers or high mass accuracy measurements for elemental composition determination using time of flight based or Fourier transform mass analyzers. The correlation between typical functional groups found in phase I and phase II drug metabolites and corresponding neutral losses is generalized and illustrated for selected examples. The choice of a suitable ionization technique and polarity mode in relation to the metabolite structure is discussed as well

Why Are Outcomes Different for Registry Patients Enrolled Prospectively and Retrospectively? Insights from the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF).

Background: Retrospective and prospective observational studies are designed to reflect real-world evidence on clinical practice, but can yield conflicting results. The GARFIELD-AF Registry includes both methods of enrolment and allows analysis of differences in patient characteristics and outcomes that may result. Methods and Results: Patients with atrial fibrillation (AF) and ≥1 risk factor for stroke at diagnosis of AF were recruited either retrospectively (n = 5069) or prospectively (n = 5501) from 19 countries and then followed prospectively. The retrospectively enrolled cohort comprised patients with established AF (for a least 6, and up to 24 months before enrolment), who were identified retrospectively (and baseline and partial follow-up data were collected from the emedical records) and then followed prospectively between 0-18 months (such that the total time of follow-up was 24 months; data collection Dec-2009 and Oct-2010). In the prospectively enrolled cohort, patients with newly diagnosed AF (≤6 weeks after diagnosis) were recruited between Mar-2010 and Oct-2011 and were followed for 24 months after enrolment. Differences between the cohorts were observed in clinical characteristics, including type of AF, stroke prevention strategies, and event rates. More patients in the retrospectively identified cohort received vitamin K antagonists (62.1% vs. 53.2%) and fewer received non-vitamin K oral anticoagulants (1.8% vs . 4.2%). All-cause mortality rates per 100 person-years during the prospective follow-up (starting the first study visit up to 1 year) were significantly lower in the retrospective than prospectively identified cohort (3.04 [95% CI 2.51 to 3.67] vs . 4.05 [95% CI 3.53 to 4.63]; p = 0.016). Conclusions: Interpretations of data from registries that aim to evaluate the characteristics and outcomes of patients with AF must take account of differences in registry design and the impact of recall bias and survivorship bias that is incurred with retrospective enrolment. Clinical Trial Registration: - URL: http://www.clinicaltrials.gov . Unique identifier for GARFIELD-AF (NCT01090362)