In vivo and in silico anti-inflammatory mechanism of action of the semisynthetic (−)-cubebin derivatives (−)-hinokinin and (−)-O-benzylcubebin

(?)-Cubebin (CUB), isolated from seeds of Piper cubeba, was used as starting material to obtain the derivatives (?)-hinokinin (HK) and (?)-O-benzyl cubebin (OBZ). Using paw edema as the experimental model and different chemical mediators (prostaglandin and dextran), it was observed that both derivatives were active in comparison with both negative (5% Tween® 80 in saline) and positive (indomethacin) controls. The highest reduction in the prostaglandin-induced edema was achieved by OBZ (66.0%), while HK caused a 59.2% reduction. Nonetheless, the dextran-induced paw edema was not significantly reduced by either of the derivatives (HK or OBZ), which inhibited edema formation by 18.3% and 3.5%, respectively, in contrast with the positive control, cyproheptadine, which reduced the edema by 56.0%. The docking analysis showed that OBZ presented the most stable ligand-receptor (COX-2 – cyclooxygenase-2) interaction in comparison with CUB and HK.     

Biochemical methane potential and biodegradability of complex organic substrates

The biomethane potential and biodegradability of an array of substrates with highly heterogeneous characteristics, including mono- and co-digestion samples with dairy manure, was determined using the biochemical methane potential (BMP) assay. In addition, the ability of two theoretical methods to estimate the biomethane potential of substrates and the influence of biodegradability was evaluated. The results of about 175 individual BMP assays indicate that substrates rich in lipids and easily-degradable carbohydrates yield the highest methane potential, while more recalcitrant substrates with a high lignocellulosic fraction have the lowest. Co-digestion of dairy manure with easily-degradable substrates increases the specific methane yields when compared to manure-only digestion. Additionally, biomethane potential of some co-digestion mixtures suggested synergistic activity. Evaluated theoretical methods consistently over-estimated experimentally-obtained methane yields when substrate biodegradability was not accounted. Upon correcting the results of theoretical methods with observed biodegradability data, an agreement greater than 90% was achieved.     

Segmentation and Tracking of Adherens Junctions in 3D for the Analysis of Epithelial Tissue Morphogenesis

Epithelial morphogenesis generates the shape of tissues, organs and embryos and is fundamental for their proper function. It is a dynamic process that occurs at multiple spatial scales from macromolecular dynamics, to cell deformations, mitosis and apoptosis, to coordinated cell rearrangements that lead to global changes of tissue shape. Using time lapse imaging, it is possible to observe these events at a system level. However, to investigate morphogenetic events it is necessary to develop computational tools to extract quantitative information from the time lapse data. Toward this goal, we developed an image-based computational pipeline to preprocess, segment and track epithelial cells in 4D confocal microscopy data. The computational pipeline we developed, for the first time, detects the adherens junctions of epithelial cells in 3D, without the need to first detect cell nuclei. We accentuate and detect cell outlines in a series of steps, symbolically describe the cells and their connectivity, and employ this information to track the cells. We validated the performance of the pipeline for its ability to detect vertices and cell-cell contacts, track cells, and identify mitosis and apoptosis in surface epithelia of Drosophila imaginal discs. We demonstrate the utility of the pipeline to extract key quantitative features of cell behavior with which to elucidate the dynamics and biomechanical control of epithelial tissue morphogenesis. We have made our methods and data available as an open-source multiplatform software tool called TTT (http://github.com/morganrcu/TTT)     

Evaluation of dynamic behavior forecasting parameters in the process of transition rule induction of unidimensional cellular automata

The simulation of the dynamics of a cellular systems based on cellular automata (CA) can be computationally expensive. This is particularly true when such simulation is part of a procedure of rule induction to find suitable transition rules for the CA. Several efforts have been described in the literature to make this problem more treatable. This work presents a study about the efficiency of dynamic behavior forecasting parameters (DBFPs) used for the induction of transition rules of CA for a specific problem: the classification by the majority rule. A total of 8 DBFPs were analyzed for the 31 best-performing rules found in the literature. Some of these DBFPs were highly correlated each other, meaning they yield the same information. Also, most rules presented values of the DBFPs very close each other. An evolutionary algorithm, based on gene expression programming, was developed for finding transition rules according a given preestablished behavior. The simulation of the dynamic behavior of the CA is not used to evaluate candidate transition rules. Instead, the average values for the DBFPs were used as reference. Experiments were done using the DBFPs separately and together. In both cases, the best induced transition rules were not acceptable solutions for the desired behavior of the CA. We conclude that, although the DBFPs represent interesting aspects of the dynamic behavior of CAs, the transition rule induction process still requires the simulation of the dynamics and cannot rely only on the DBFPs.     

Exergy-based accounting for land as a natural resource in life cycle assessment

Purpose

In life cycle assessment (LCA), literature suggests accounting for land as a resource either by what it delivers (e.g., biomass content) or the time and space needed to produce biomass (land occupation), in order to avoid double-counting. This paper proposes and implements a new framework to calculate exergy-based spatial explicit characterization factors (CF) for land as a resource, which deals with both biomass and area occupied on the global scale.

Methods

We created a schematic overview of the Earth, dividing it into two systems (human-made and natural), making it possible to account for what is actually extracted from nature, i.e., the biomass content was set as the elementary flow to be accounted at natural systems and the land occupation (through the potential natural net primary production) was set as the elementary flow at human-made systems. Through exergy, we were able to create CF for land resources for these two different systems. The relevancy of the new CF was tested for a number of biobased products.

Results and discussion

Site-generic CF were created for land as a resource for natural systems providing goods to humans, and site-generic and site-dependent CF (at grid, region, country, and continent level) were created for land as a resource within human-made systems. This framework differed from other methods in the sense of accounting for both land occupation and biomass content but without double-counting. It is set operationally for LCA and able to account for land resources with more completeness, allowing spatial differentiation. When site-dependent CF were considered for land resources, the overall resource consumption of certain products increased up to 77 % in comparison with site-generic CF-based data.

Conclusions

This paper clearly distinguished the origin of the resource (natural or human-made systems), allowing consistent accounting for land as a resource. Site-dependent CF for human-made systems allowed spatial differentiation, which was not considered in other resource accounting life cycle impact assessment methods.