A simplified approach to quasi-linear viscoelastic modeling

The fitting of quasi-linear viscoelastic (QLV) constitutive models to material data often involves somewhat cumbersome numerical convolution. A new approach to treating quasi-linearity in 1-D is described and applied to characterize the behavior of reconstituted collagen. This approach is based on a new principle for including nonlinearity and requires considerably less computation than other comparable models for both model calibration and response prediction, especially for smoothly applied stretching. Additionally, the approach allows relaxation to adapt with the strain history. The modeling approach is demonstrated through tests on pure reconstituted collagen. Sequences of "ramp-and-hold" stretching tests were applied to rectangular collagen specimens. The relaxation force data from the "hold" was used to calibrate a new "adaptive QLV model" and several models from literature, and the force data from the "ramp" was used to check the accuracy of model predictions. Additionally, the ability of the models to predict the force response on a reloading of the specimen was assessed. The "adaptive QLV model" based on this new approach predicts collagen behavior comparably to or better than existing models, with much less computation.     

A simplified approach for modeling diffusion into cells

Regulation of the intracellular concentration of substrates is essential for the maintenance of a stable cellular environment. Diffusion and reaction processes supply and consume substrates within cells and determine their steady-state concentrations. To realistically represent these processes by computer simulation they must be modeled in three dimensions. Yet three-dimensional models are inherently computing intensive. This study describes a method, which substantially simplifies the modeling of diffusion into a polyhedral body (a cube), that was used as a model representation of a cell. The method is applied to a case study of oxygen diffusion into nitrogen-fixing, rhizobia-infected cells in legume nodules. The method involved generating a one-dimensional representation of the three-dimensional problem to provide a "surface area profile" of three-dimensional diffusion. The one-dimensional models were significantly easier to program, several orders of magnitude faster to solve and in this study were validated by assessing their results against those of comparable three-dimensional models of diffusion into the same body. The results show the one-dimensional method to be a close approximation of a three-dimensional source-sink problem with systematic differences below 10% for fractional oxygenation of leghemoglobin, cell respiration and nitrogenase activity. Larger differences between models (up to 45%) in the predicted average and innermost O(2)concentrations had no effects on the physiological conclusions of the study, but were attributed to the poorer resolution of the three- than the one-dimensional model, and to an inherent simplification in the derivation of the one-dimensional surface area profiles. The one-dimensional modeling approach was found to be a simple, yet powerful tool for the study of diffusion and reaction in biological systems.     

A global approach to resistance monitoring

Transgenic crops producing insecticidal toxins from the bacterium Bacillus thuringiensis (Bt) have been grown in many parts of the world since 1996. In the United States, the Environmental Protection Agency (EPA) has required that industry submit insect resistance management (IRM) plans for each Bt corn and cotton product commercialized. A coalition of stakeholders including the EPA, USDA, academic scientists, industry, and grower organizations have cooperated in developing specific IRM strategies. Resistance monitoring (requiring submission of annual reports to the EPA), and a remedial action plan addressing any contingency if resistance should occur, are important elements of these strategies. At a global level, Monsanto conducts baseline susceptibility studies (prior to commercialization), followed by monitoring studies on target pest populations, for all of its commercialized Bt crop products. To date, Monsanto has conducted baseline/monitoring studies in Argentina, Australia, Brazil, Canada, China, Colombia, India, Mexico, the Philippines, South Africa, Spain, and the United States. Examples of pests on which resistance monitoring has been conducted include cotton bollworm, Helicoverpa zea, European corn borer, Ostrinia nubilalis, pink bollworm, Pectinophora gossypiella, Southwestern corn borer, Diatraea grandiosella, tobacco budworm, Heliothis virescens, and western corn rootworm, Diabrotica virgifera virgifera, in the United States, cotton bollworm, Helicoverpa armigera, in China, India and Australia, and H. virescens and H. zea in Mexico. No field-selected resistance to Bt crops has been documented.     

A fold-recognition approach to loop modeling

A novel approach is proposed for modeling loop regions in proteins. In this approach, a prerequisite sequence-structure alignment is examined for regions where the target sequence is not covered by the structural template. These regions, extended with a number of residues from adjacent stem regions, are submitted to fold recognition. The alignments produced by fold recognition are integrated into the initial alignment to create an alignment between the target sequence and several structures, where gaps in the main structural template are covered by local structural templates. This one-to-many (1:N) alignment is used to create a protein model by existing protein-modeling techniques. Several alternative approaches were evaluated using a set of ten proteins. One approach was selected and evaluated using another set of 31 proteins. The most promising result was for gap regions not located at the C-terminus or N-terminus of a protein, where the method produced an average RMSD 12% lower than the loop modeling provided with the program MODELLER. This improvement is shown to be statistically significant. Figure The method derived from the training set applied to CASP target T0191     

A GIS approach to identifying territorial resource competition

Computer simulations using a geographical information system were developed to investigate the relationships between the habitat composition of red grouse territories and the alignment of territory boundaries in relation to vegetation patterns. Field data were from a fifteen year study on Kerloch Moor, north-east Scotland, between 1963 and 1977.
In order to determine whether observed territories were arranged randomly with respect to a number of habitat characteristics, they were compared with many simulated sets of possible territory arrangements by translation of the territory boundaries to a number of different positions relative to the vegetation mosaic. A suite of summary statistics was calculated for the simulated configurations, against which the observed configurations were tested.
There was little evidence for territorial resource competition by red grouse. The positions of territories were not strongly influenced by their composition in terms of vegetation types or heather edge. Neither was there any obvious preference for territory boundaries to lie within particular vegetation types. However, there was clear evidence that physical features of the habitat (fences, walls, tracks, etc.) had a strong influence on the position of territory boundaries.
Temporal and spatial habitat heterogeneity led to a number of technical and analytical problems, and the implications of these are discussed.     

Tendency modeling: a new approach to obtain simplified kinetic models of metabolism applied to Saccharomyces cerevisiae

A novel approach to construct kinetic models of metabolic pathways, to be used in metabolic engineering, is presented: the tendency modeling approach. This approach greatly facilitates the construction of these models and can easily be applied to complex metabolic networks. The resulting models contain a minimal number of parameters; identification of their values is straightforward. Use of in vitro obtained information in the identification of the kinetic equations is minimized. The tendency modeling approach has been used to derive a dynamic model of primary metabolism for aerobic growth of Saccharomyces cerevisiae on glucose, in which compartmentation is included. Simulation results obtained with the derived model are satisfying for most of the carbon metabolites that have been measured. Compared to a more detailed model, the simulations of our model are less accurate, but taking into account the much smaller number of kinetic parameters (35 instead of 84), the tendency the modeling approach is considered promising.     

A simplified approach to derive Cleland model for enzymatic reactions

Metabolic modeling can suggest which is the key enzyme activity that needs to be controlled or its activity enhanced for the required production of a metabolite in a pathway. It also helps to find possible drug targets (enzymes to be inhibited). In metabolic modeling, knowing the kinetics of the enzymes involved in a pathway is mandatory. Most enzymatic reactions involve multi-substrates and follow an ordered sequential or ping–pong mechanism. The kinetic parameters involved in the model are obtained by fitting experimental data using a model based on the mechanism. The Cleland model has been used for some years. The grouping of parameters, such as dissociation constant and Michaelis–Menten constant, makes the strategy meaningful and hence the Cleland model is still in use. Although other alternate methods, e.g., the King-Altman method, are available, derivation by determinants can be used to derive a rate expression for the sequential or ping–pong mechanism, they are tedious. Hence, a meaningful modification is suggested in this communication for deriving the enzyme mechanism which is based on Thilakavathi et al. (Biotech Lett 28:1889–1894, 2006) to obtain the Cleland model in an easier way.     

A simplified approach to disulfide connectivity prediction from protein sequences

Background  

Prediction of disulfide bridges from protein sequences is useful for characterizing structural and functional properties of proteins. Several methods based on different machine learning algorithms have been applied to solve this problem and public domain prediction services exist. These methods are however still potentially subject to significant improvements both in terms of prediction accuracy and overall architectural complexity.     

A multilevel approach to cancer growth modeling

Cancer growth models may be divided into macroscopic models, which describe the tumor as a single entity, and microscopic ones, which consider the tumor as a complex system whose behavior emerges from the local dynamics of its basic components, the neoplastic cells. Mesoscopic models (e.g. as based on the Local Interaction Simulation Approach [Delsanto, P.P., Mignogna, R., Scalerandi, M., Schechter, R., 1998. In: Delsanto, P.P. Saenz, A.W. (Eds.), New Perspectives on Problems in Classical and Quantum Physics, vol. 2. Gordon & Breach, New Delhi, p. 5174]), which explicitly consider the behavior of cell clusters and their interactions, may be used instead of the microscopic ones, in order to study the properties of cancer biology that strongly depend on the interactions of small groups of cells at intermediate spatial and temporal scales. All these approaches have been developed independently, which limits their usefulness, since they all include relevant features and information that should be cross-correlated for a deeper understanding of the mechanisms involved. In this contribution we consider multicellular tumor spheroids as biological reference systems and propose an intermediate model to bridge the gap between a macroscopic formulation of tumor growth and a mesoscopic one. Thus we are able to establish, as an important result of our formalism, a direct correspondence between parameters characterizing processes occurring at different scales. In particular, we analyze their dependence on an important limiting factor to tumor growth, i.e. the extra-cellular matrix pressure. Since the macro and meso-models stem from totally different roots (energy conservation and clinical observations vs. cell groups dynamics), their consistency may be used to validate both approaches. It may also be interesting to note that the proposed formalism fits well into a recently proposed conjecture of growth laws universality.     

Geographic Information System (GIS) modeling approach to determine the fastest delivery routes

This study involves the adoption of the Geographic Information System (GIS) modeling approach to determine the quickest routes for fresh vegetable delivery. During transport, fresh vegetables mainly deteriorate on account of temperature and delivery time. Nonetheless, little attention has been directed to transportation issues in most areas within Kuala Lumpur. In addition, perishable food normally has a short shelf life, thus timely delivery significantly affects delivery costs. Therefore, selecting efficient routes would consequently reduce the total transportation costs. The regression model is applied in this study to determine the parameters that affect route selection with respect to the fastest delivery of fresh vegetables. For the purpose of this research, ArcGIS software with network analyst extension is adopted to solve the problem of complex networks. The final output of this research is a map of quickest routes with the best delivery times based on all variables. The variables tested from regression analysis are the most effective parameters to make the flow of road networks slower. The objective is to improve the delivery services by achieving the least drive time. The main findings of this research are that Land use such as residential area and population as variables are the effective parameters on drive time.     

Optimization of leaf morphology in relation to leaf water status: A theory

The leaf economic traits such as leaf area, maximum carbon assimilation rate, and venation are all correlated and related to water availability. Furthermore, leaves are often broad and large in humid areas and narrower in arid/semiarid and hot and cold areas. We use optimization theory to explain these patterns. We have created a constrained optimization leaf model linking leaf shape to vein structure that is integrated into coupled transpiration and carbon assimilation processes. The model maximizes net leaf carbon gain (NPP_leaf) over the loss of xylem water potential. Modeled relations between leaf traits are consistent with empirically observed patterns. As the results of the leaf shape–venation relation, our model further predicts that a broadleaf has overall higher NPP_leaf compared to a narrowleaf. In addition, a broadleaf has a lower stomatal resistance compared to a narrowleaf under the same level of constraint. With the same leaf area, a broadleaf will have, on average, larger conduits and lower total leaf xylem resistance and thus be more efficient in water transportation but less resistant to cavitation. By linking venation structure to leaf shape and using water potential as the constraint, our model provides a physical explanation for the general pattern of the covariance of leaf traits through the safety–efficiency trade‐off of leaf hydraulic design.     

A survival analysis approach to modeling human fecundity

Understanding conception probabilities is important not only for helping couples to achieve pregnancy but also in identifying acute or chronic reproductive toxicants that affect the highly timed and interrelated processes underlying hormonal profiles, ovulation, libido, and conception during menstrual cycles. Currently, 2 statistical approaches are available for estimating conception probabilities depending upon the research question and extent of data collection during the menstrual cycle: a survival approach when interested in modeling time-to-pregnancy (TTP) in relation to women or couples' purported exposure(s), or a hierarchical Bayesian approach when one is interested in modeling day-specific conception probabilities during the estimated fertile window. We propose a biologically valid discrete survival model that unifies the above 2 approaches while relaxing some assumptions that may not be consistent with human reproduction or behavior. This approach combines both the survival and the hierarchical models allowing investigators to obtain the distribution of TTP and day-specific probabilities during the fertile window in a single model. Our model allows for the consideration of covariate effects at both the cycle and the daily level while accounting for daily variation in conception. We conduct extensive simulations and utilize the New York State Angler Prospective Pregnancy Cohort Study to illustrate our approach. We also provide the code to implement the model in R software in the supplemental section of the supplementary material available at Biostatistics online.     

A climatic stability approach to prioritizing global conservation investments

Climate change is impacting species and ecosystems globally. Many existing templates to identify the most important areas to conserve terrestrial biodiversity at the global scale neglect the future impacts of climate change. Unstable climatic conditions are predicted to undermine conservation investments in the future. This paper presents an approach to developing a resource allocation algorithm for conservation investment that incorporates the ecological stability of ecoregions under climate change. We discover that allocating funds in this way changes the optimal schedule of global investments both spatially and temporally. This allocation reduces the biodiversity loss of terrestrial endemic species from protected areas due to climate change by 22% for the period of 2002-2052, when compared to allocations that do not consider climate change. To maximize the resilience of global biodiversity to climate change we recommend that funding be increased in ecoregions located in the tropics and/or mid-elevation habitats, where climatic conditions are predicted to remain relatively stable. Accounting for the ecological stability of ecoregions provides a realistic approach to incorporating climate change into global conservation planning, with potential to save more species from extinction in the long term.     

A simplified model to predict diurnal water temperature dynamics in a shallow tropical water pool

Water temperature is a critical regulator in the growth and development of malaria mosquito immatures, as they are poikilothermic. Measuring or estimating the diurnal temperature ranges to which these immatures are exposed is of the utmost importance, as these immatures will develop into adults that can transmit malaria. Recent attempts to predict the daily water temperature dynamics in mosquito breeding sites in Kenya have been successful. However, the developed model may be too complex, as the sophisticated equipment that was used for detailed meteorological observations is not widely distributed in Africa, making it difficult to predict the daily water temperature dynamics on a local scale. Therefore, we compared two energy budget models with earlier made observations of the daily water temperature dynamics in a small, shallow and clear water pool (diameter 0.96 m, depth 0.32 m) in Kenya. This paper describes (1) a complex 1-Dimensional model, and (2) a simplified second model, and (3) shows that both models mimic the water temperature dynamics in the water pool accurately. The latter model has the advantage that it only needs common weather data (air temperature, air humidity, wind speed and cloud cover) to estimate the diurnal temperature dynamics in breeding sites of African malaria mosquitoes.     

The Water Impact Index: a simplified single-indicator approach for water footprinting

Purpose

Along with climate change-related issues, improved water management is recognized as one of the major challenges to sustainability. However, there are still no commonly accepted methods for measuring sustainability of water uses, resulting in a recent proliferation of water footprint methodologies. The Water Impact Index presented in this paper aims to integrate the issues of volume, scarcity and quality into a single indicator to assess the reduction of available water for the environment induced by freshwater uses for human activities.

Methods

The Water Impact Index follows life cycle thinking principles. For each unit process, a volumetric water balance is performed; water flows crossing the boundaries between the techno-sphere and environment are multiplied by a water quality index and a water scarcity index. The methodology is illustrated on the current municipal wastewater management system of Milan (Italy). The Water Impact Index is combined with carbon footprint to introduce multi-impact thinking to decision makers. The Water Impact Index is further compared to results obtained using a set of three life cycle impact indicators related to water, from the ReCiPe life cycle impact assessment (LCIA) methodology.

Results and discussion

Onsite water use is the main contribution to the Water Impact Index for both wastewater management schemes. The release of better quality water is the main driver in favour of the scenario including a wastewater treatment plant, while the energy and chemicals consumed for the treatment increase the indirect water footprint and carbon footprint. Results obtained with the three midpoint indicators depict similar tendencies to the Water Impact Index.

Conclusions

This paper presents a simplified single-indicator approach for water footprinting, integrating volume, scarcity and quality issues, representing an initial step toward a better understanding and assessment of the environmental impacts of human activities on water resources. The wastewater treatment plant reduces the Water Impact Index of the wastewater management system. These results are consistent with the profile of the three midpoint indicators related to water from ReCiPe.     

A Bayesian approach to modeling associations between pulsatile hormones

Summary .  Many hormones are secreted in pulses. The pulsatile relationship between hormones regulates many biological processes. To understand endocrine system regulation, time series of hormone concentrations are collected. The goal is to characterize pulsatile patterns and associations between hormones. Currently each hormone on each subject is fitted univariately. This leads to estimates of the number of pulses and estimates of the amount of hormone secreted; however, when the signal-to-noise ratio is small, pulse detection and parameter estimation remains difficult with existing approaches. In this article, we present a bivariate deconvolution model of pulsatile hormone data focusing on incorporating pulsatile associations. Through simulation, we exhibit that using the underlying pulsatile association between two hormones improves the estimation of the number of pulses and the other parameters defining each hormone. We develop the one-to-one, driver–response case and show how birth–death Markov chain Monte Carlo can be used for estimation. We exhibit these features through a simulation study and apply the method to luteinizing and follicle stimulating hormones.