Characterization of Blue Agave Bagasse (BAB) as Raw Material for Bioethanol Production Processes by Gravimetric,Thermal, Chromatographic,X-ray Diffraction,Microscopy, and Laser Light Scattering Techniques

A detailed characterization of the main types of blue agave bagasse (BAB) obtained from the four largest tequila factories in the State of Jalisco (Mexico) is presented here. After milling/sieving the agave bagasses, two particle size fractions were identified, one rich in fibers and the other consisting of dust/fine particles. Both fractions were analyzed to determine the content of cellulose, hemicellulose, lignin, organic-soluble compounds, absorbed remaining sugars, minerals, and organic matter. After detailed analyses of both fractions by wet, thermal (thermo-gravimetric analysis (TGA)/differential thermo-gravimetric analysis (DTA)), and other methods (high-performance liquid chromatography (HPLC), microscopy, particle size by laser diffraction light scattering, and crystallinity by X-ray diffraction), a moderate-to-intensive method was devised for further processing the fibrous fraction, which had a high crystalline cellulose content, as well as for its subsequent enzymatic saccharification under well-defined moderate conditions. Alternative processing options were also devised for the dust/fine particle fraction, which has a moderate crystalline cellulose that is rich in adsorbed sugars and that has a high mineral matter content.     

Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions

This paper presents a case study which uses simulation to analyze patient flows in a hospital emergency department in Hong Kong. We first analyze the impact of the enhancements made to the system after the relocation of the Emergency Department. After that, we developed a simulation model (using ARENA) to capture all the key relevant processes of the department. When developing the simulation model, we faced the challenge that the data kept by the Emergency Department were incomplete so that the service-time distributions were not directly obtainable. We propose a simulation–optimization approach (integrating simulation with meta-heuristics) to obtain a good set of estimate of input parameters of our simulation model. Using the simulation model, we evaluated the impact of possible changes to the system by running different scenarios. This provides a tool for the operations manager in the Emergency Department to “foresee” the impact on the daily operations when making possible changes (such as, adjusting staffing levels or shift times), and consequently make much better decisions.     

Targeting secret handshakes of biological processes for novel drug development

In multicellular organisms, several biological processes control the rise and fall of life. Different cell types communicate and co-operate in response to different stimulus through cell to cell signaling and regulate biologic processes in the cell/organism. Signaling in multicellular organism has to be made very secretly so that only the target cell responds to the signal. Of all the biomolecules, nature chose mainly proteins for secret delivery of information both inside and outside the cell. During cell signaling, proteins physically interact and shake hands for transfer of secret information by a phenomenon called as protein–protein interactions (PPIs). In both, extra and intracellular signaling processes PPIs play a crucial role. PPIs involved in cellular signaling are the primary cause for cell proliferation, differentiation, movement, metabolism, death and various other biological processes not mentioned here. These secret handshakes are very specific for specific functions. Any alterations/malfunctions in particular PPIs results in diseased condition. An overview of signaling pathways and importance of PPIs in cellular function and possibilities of targeting PPIs for novel drug development are discussed in this review.     

Numerical simulation of fibrous biomaterials with randomly distributed fiber network structure

This paper presents a computational framework to simulate the mechanical behavior of fibrous biomaterials with randomly distributed fiber networks. A random walk algorithm is implemented to generate the synthetic fiber network in 2D used in simulations. The embedded fiber approach is then adopted to model the fibers as embedded truss elements in the ground matrix, which is essentially equivalent to the affine fiber kinematics. The fiber–matrix interaction is partially considered in the sense that the two material components deform together, but no relative movement is considered. A variational approach is carried out to derive the element residual and stiffness matrices for finite element method (FEM), in which material and geometric nonlinearities are both included. Using a data structure proposed to record the network geometric information, the fiber network is directly incorporated into the FEM simulation without significantly increasing the computational cost. A mesh sensitivity analysis is conducted to show the influence of mesh size on various simulation results. The proposed method can be easily combined with Monte Carlo (MC) simulations to include the influence of the stochastic nature of the network and capture the material behavior in an average sense. The computational framework proposed in this work goes midway between homogenizing the fiber network into the surrounding matrix and accounting for the fully coupled fiber–matrix interaction at the segment length scale, and can be used to study the connection between the microscopic structure and the macro-mechanical behavior of fibrous biomaterials with a reasonable computational cost.     

Cell surface receptors for CCN proteins

The CCN family (CYR61; CTGF; NOV; CCN1–6; WISP1–3) of matricellular proteins in mammals is comprised of six homologous members that play important roles in development, inflammation, tissue repair, and a broad range of pathological processes including fibrosis and cancer. Despite considerable effort to search for a high affinity CCN-specific receptor akin to growth factor receptors, no such receptor has been found. Rather, CCNs bind several groups of multi-ligand receptors as characteristic of other matricellular proteins. The most extensively documented among CCN-binding receptors are integrins, including α_vβ₃, α_vβ₅, α₅β₁, α₆β₁, α_IIbβ₃, α_Mβ₂, and α_Dβ₂, which mediate diverse CCN functions in various cell types. CCNs also bind cell surface heparan sulfate proteoglycans (HSPGs), low density liproprotein receptor-related proteins (LRPs), and the cation-independent mannose-6-phosphate (M6P) receptor, which are endocytic receptors that may also serve as co-receptors in cooperation with other cell surface receptors. CCNs have also been reported to bind FGFR-2, Notch, RANK, and TrkA, potentially altering the affinities of these receptors for their ligands. The ability of CCNs to bind a multitude of receptors in various cell types may account for the remarkable versatility of their functions, and underscore the diverse signaling pathways that mediate their activities.     

A multifunctional green infrastructure design to protect and improve native biodiversity in Rio de Janeiro

The city of Rio de Janeiro now covers what was formerly coastal Atlantic rainforest. Native biodiversity has been lost from most of the metropolitan area due to changes in land use, introduction of exotic species, and influence of foreign designs in public and private parks and gardens. This paper presents a multifunctional green infrastructure planning proposal to protect and regenerate native biodiversity in the last remaining natural ecosystem fragments in a watershed within the city’s limits. The proposal is based on social–ecological assessments and analyses. The watershed contains an extensive mangrove remnant surrounded by a protected massif partially covered by coastal Atlantic rainforest and is one of the very last productive landscapes where residents experience a strong sense of place. The strategy is to reconcile urban development with environmental achievements to conserve existing ecosystem patches, which will be connected by way of riparian corridors and other open spaces, such as streets, private yards and public areas, by using native species and ecological design. The green infrastructure framework is planned at the watershed scale. On a neighborhood and local scale, the plan is to promote public participation, develop new ecological aesthetics, encourage the planting of native species, and establish an innovative circulation system within a low-impact design framework to achieve various abiotic, biotic, and cultural goals. In addition to the economic benefits, accessible food production, rural tourism and ecotourism have the potential to unite people and nature within the city.     

Permutation Testing for Treatment–Covariate Interactions and Subgroup Identification

We consider the problem of using permutation-based methods to test for treatment–covariate interactions from randomized clinical trial data. Testing for interactions is common in the field of personalized medicine, as subgroups with enhanced treatment effects arise when treatment-by-covariate interactions exist. Asymptotic tests can often be performed for simple models, but in many cases, more complex methods are used to identify subgroups, and non-standard test statistics proposed, and asymptotic results may be difficult to obtain. In such cases, it is natural to consider permutation-based tests, which shuffle selected parts of the data in order to remove one or more associations of interest; however, in the case of interactions, it is generally not possible to remove only the associations of interest by simple permutations of the data. We propose a number of alternative permutation-based methods, designed to remove only the associations of interest, but preserving other associations. These methods estimate the interaction term in a model, then create data that “looks like” the original data except that the interaction term has been permuted. The proposed methods are shown to outperform traditional permutation methods in a simulation study. In addition, the proposed methods are illustrated using data from a randomized clinical trial of patients with hypertension.