A Perfusion Bioreactor for Longitudinal Monitoring of Bioengineered Liver Constructs

In the field of in vitro liver disease models, decellularised organ scaffolds maintain the original biomechanical and biological properties of the extracellular matrix and are established supports for in vitro cell culture. However, tissue engineering approaches based on whole organ decellularized scaffolds are hampered by the scarcity of appropriate bioreactors that provide controlled 3D culture conditions. Novel specific bioreactors are needed to support long-term culture of bioengineered constructs allowing non-invasive longitudinal monitoring. Here, we designed and validated a specific bioreactor for long-term 3D culture of whole liver constructs. Whole liver scaffolds were generated by perfusion decellularisation of rat livers. Scaffolds were seeded with Luc+HepG2 and primary human hepatocytes and cultured in static or dynamic conditions using the custom-made bioreactor. The bioreactor included a syringe pump, for continuous unidirectional flow, and a circuit built to allow non-invasive monitoring of culture parameters and media sampling. The bioreactor allowed non-invasive analysis of cell viability, distribution, and function of Luc+HepG2-bioengineered livers cultured for up to 11 days. Constructs cultured in dynamic conditions in the bioreactor showed significantly higher cell viability, measured with bioluminescence, distribution, and functionality (determined by albumin production and expression of CYP enzymes) in comparison to static culture conditions. Finally, our bioreactor supports primary human hepatocyte viability and function for up to 30 days, when seeded in the whole liver scaffolds. Overall, our novel bioreactor is capable of supporting cell survival and metabolism and is suitable for liver tissue engineering for the development of 3D liver disease models

The Teacher’s Role in Writing: A Study of Teacher Candidates’ Perceptions

A team of teacher education researchers conducted a qualitative study to explore how teacher candidates viewed the teacher’s role in teaching students to write. Participants (N = 107) enrolled in writing-focused methods courses across four universities completed a reflective quick write near the end of the course. Since writing is a complex and multidimensional activity, these responses were analyzed through the theoretical framework of social cognitive theory. When describing the role of the teacher, the data indicated candidates across all institutions primarily focused on the affective aspects of teaching writing, specifically supporting and developing students’ confidence in writing. Some mentioned the need for explicit instruction such as developing students’ writing skills and use of strategies. A smaller percentage included both the importance of affective and explicit instruction. Many candidates indicated the significance of the role that teachers play in students learning to write. Implications for writing pedagogy support within and beyond teacher preparation are discussed

Bayesian hierarchical clustering for studying cancer gene expression data with unknown statistics

Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm. Our Gaussian BHC (GBHC) algorithm represents data as a mixture of Gaussian distributions. It uses normal-gamma distribution as a conjugate prior on the mean and precision of each of the Gaussian components. We tested GBHC over 11 cancer and 3 synthetic datasets. The results on cancer datasets show that in sample clustering, GBHC on average produces a clustering partition that is more concordant with the ground truth than those obtained from other commonly used algorithms. Furthermore, GBHC frequently infers the number of clusters that is often close to the ground truth. In gene clustering, GBHC also produces a clustering partition that is more biologically plausible than several other state-of-the-art methods. This suggests GBHC as an alternative tool for studying gene expression data. The implementation of GBHC is available at https://sites. google.com/site/gaussianbhc

Structure and stability in TMC-1: Analysis of NH3molecular line andHerschelcontinuum data

Aims. We examined the velocity, density, and temperature structure of Taurus molecular cloud-1 (TMC-1), a filamentary cloud in a nearby quiescent star forming area, to understand its morphology and evolution. Methods. We observed high signal-to-noise (S/N), high velocity resolution NH3(1,1), and (2, 2) emission on an extended map. By fitting multiple hyperfine-split line profiles to the NH3(1, 1) spectra, we derived the velocity distribution of the line components and calculated gas parameters on several positions. Herschel SPIRE far-infrared continuum observations were reduced and used to calculate the physical parameters of the Planck Galactic Cold Clumps (PGCCs) in the region, including the two in TMC-1. The morphology of TMC-1 was investigated with several types of clustering methods in the parameter space consisting of position, velocity, and column density. Results. Our Herschel-based column density map shows a main ridge with two local maxima and a separated peak to the south-west. The H2 column densities and dust colour temperatures are in the range of 0.5−3.3 × 1022 cm−2 and 10.5−12 K, respectively. The NH3 column densities and H2 volume densities are in the range of 2.8−14.2 × 1014 cm−2 and 0.4−2.8 × 104 cm−3. Kinetic temperatures are typically very low with a minimum of 9 K at the maximum NH3 and H2 column density region. The kinetic temperature maximum was found at the protostar IRAS 04381+2540 with a value of 13.7 K. The kinetic temperatures vary similarly to the colour temperatures in spite of the fact that densities are lower than the critical density for coupling between the gas and dust phase. The k-means clustering method separated four sub-filaments in TMC-1 with masses of 32.5, 19.6, 28.9, and 45.9 Mo and low turbulent velocity dispersion in the range of 0.13−0.2 km s−1. Conclusions. The main ridge of TMC-1 is composed of four sub-filaments that are close to gravitational equilibrium. We label these TMC-1F1 through F4. The sub-filaments TMC-1F1, TMC-1F2, and TMC-1F4 are very elongated, dense, and cold. TMC-1F3 is a little less elongated and somewhat warmer, and probably heated by the Class I protostar, IRAS 04381+2540, which is embedded in it. TMC-1F3 is approximately 0.1 pc behind TMC1-F1. Because of its structure, TMC-1 is a good target to test filament evolution scenarios

Limitations of Gene Duplication Models: Evolution of Modules in Protein Interaction Networks

It has been generally acknowledged that the module structure of protein interaction networks plays a crucial role with respect to the functional understanding of these networks. In this paper, we study evolutionary aspects of the module structure of protein interaction networks, which forms a mesoscopic level of description with respect to the architectural principles of networks. The purpose of this paper is to investigate limitations of well known gene duplication models by showing that these models are lacking crucial structural features present in protein interaction networks on a mesoscopic scale. This observation reveals our incomplete understanding of the structural evolution of protein networks on the module level

IL-1β, IL-6, and RANTES as Biomarkers of Chikungunya Severity

Little is known about the immunopathogenesis of Chikungunya virus. Circulating levels of immune mediators and growth factors were analyzed from patients infected during the first Singaporean Chikungunya fever outbreak in early 2008 to establish biomarkers associated with infection and/or disease severity.Adult patients with laboratory-confirmed Chikungunya fever infection, who were referred to the Communicable Disease Centre/Tan Tock Seng Hospital during the period from January to February 2008, were included in this retrospective study. Plasma fractions were analyzed using a multiplex-microbead immunoassay. Among the patients, the most common clinical features were fever (100%), arthralgia (90%), rash (50%) and conjunctivitis (40%). Profiles of 30 cytokines, chemokines, and growth factors were able to discriminate the clinical forms of Chikungunya from healthy controls, with patients classified as non-severe and severe disease. Levels of 8 plasma cytokines and 4 growth factors were significantly elevated. Statistical analysis showed that an increase in IL-1beta, IL-6 and a decrease in RANTES were associated with disease severity.This is the first comprehensive report on the production of cytokines, chemokines, and growth factors during acute Chikungunya virus infection. Using these biomarkers, we were able to distinguish between mild disease and more severe forms of Chikungunya fever, thus enabling the identification of patients with poor prognosis and monitoring of the disease