Germband extension during Drosophila development is primarily driven by cell intercalation, which involves three key components: planar cell polarity, anisotropic myosin contractile forces on cellular junctions, and cellular deformation and movement. Prior experimental work probed each of these factors in depth, but the connection between them remains unclear. This paper presents an integrated chemomechanical model that combines the three factors into a coherent mathematical framework for studying cell intercalation in the germband tissue. The model produces the planar cell polarization of key proteins, including Rho-kinase, Bazooka and myosin, the development of anisotropic contractile forces, and subsequent cell deformation and rearrangement. Cell intercalation occurs through T1 transitions among four neighboring cells and rosettes involving six cells. Such six-cell rosettes entail stronger myosin-based contractile forces, and on average produce a moderately larger amount of germband extension than the T1 transitions. The resolution of T1 and rosettes is driven by contractile forces on junctions anterior and posterior to the assembly as well as the pulling force of the medial myosin in the anterior and posterior cells. The global stretching due to posterior midgut invagination also plays a minor role. These model predictions are in reasonable agreement with experimental observations.

also need to occur in order to ensure that the engineered tissues faithfully recapitulate the in vivo tissue microenvironment and exhibit appropriate mechanical properties. [15][16][17][18][19][20][21][22][23] The community also needs to minimize the gap between current understanding of tissue remodeling and cellular behavior in vivo [24][25][26][27][28] and how this knowledge is applied to improve the performance and reliability of TE products. Technologies also need to be developed to effectively preserve manufactured tissues and achieve consistent manufacturing outcomes. [29][30][31][32][33][34][35][36][37] Moreover, the inherent complexity of TE products has significantly muddied the regulatory process for these products, and companies and regulators often do not know which regulatory pathways would be applicable. For Tissue Engineering

This work describes the conceptual development of a new process for palm oil refining using supercritical fluid extraction (SFE) technology. The first step was to model the phase equilibrium behavior of a crude palm oil (CPO)-supercritical CO 2 mixture. Next, a new flowsheet structure was synthesized to recover highpurity palm oil and its minor components. The new SFE process was finally simulated using Aspen Plus commercial process simulator version 10.2.1 based on the Redlich-Kwong-Aspen (RKA) thermodynamic model. The results obtained were in good agreement with the pilot plant data reported in the literature. It is envisioned that the development of a new, intensified palm oil refining process that is based on SFE technology can make the refining process drastically simpler and overcome the limitations of the existing technology for palm oil refining.

Chemical–biological systems, such as bioreactors, contain stochastic and non-linear interactions which are difficult to characterize. The highly complex interactions between microbial species and communities may not be sufficiently captured using first-principles, stationary, or low-dimensional models. This paper compares and contrasts multiple data analysis strategies, which include three predictive models (random forests, support vector machines, and neural networks), three clustering models (hierarchical, Gaussian mixtures, and Dirichlet mixtures), and two feature selection approaches (mean decrease in accuracy and its conditional variant). These methods not only predict the bioreactor outcome with sufficient accuracy, but the important features correlated with said outcome are also identified. The novelty of this work lies in the extensive exploration and critique of a wide arsenal of methods instead of single methods, as observed in many papers of similar nature. The results show that random forest models predict the test set outcomes with the highest accuracy. The identified contributory features include process features which agree with domain knowledge, as well as several different biomarker operational taxonomic units (OTUs). The results reinforce the notion that both chemical and biological features significantly affect bioreactor performance. However, they also indicate that the quality of the biological features can be improved by considering non-clustering methods, which may better represent the true behaviour within the OTU communities.

Co-processing biogenic feedstocks allows oil refiners to use their infrastructure while reducing the carbon intensity of the fuels they produce. Although policies such as British Columbia and California’s low carbon...