Pattern Formation Controlled by External Forcing in a Spatial Harvesting Predator-Prey Model

Predator–prey models in ecology serve a variety of purposes, which range from illustrating a scientific concept to representing a complex natural phenomenon. Due to the complexity and variability of the environment, the dynamic behavior obtained from existing predator–prey models often deviates from reality. Many factors remain to be considered, such as external forcing, harvesting and so on. In this chapter, we study a spatial version of the Ivlev-type predator-prey model that includes reaction-diffusion, external periodic forcing, and constant harvesting rate on prey. Using this model, we study how external periodic forcing affects the stability of predator-prey coexistence equilibrium. The results of spatial pattern analysis of the Ivlev-type predator-prey model with zero-flux boundary conditions, based on the Euler method and via numerical simulations in MATLAB, show that the model generates rich dynamics. Our results reveal that modeling by reaction-diffusion equations with external periodic forcing and nonzero constant prey harvesting could be used to make general predictions regarding predator-prey equilibrium,which may be used to guide management practice, and to provide a basis for the development of statistical tools and testable hypotheses.

A New Mechanical Algorithm for Calculating the Amplitude Equation of the Reaction-Diffusion Systems

Amplitude equation may be used to study pattern formatio. In this chapter, we establish a new mechanical algorithm AE_Hopf for calculating the amplitude equation near Hopf bifurcation based on the method of normal form approach in Maple. The normal form approach needs a large number of variables and intricate calculations. As a result, deriving the amplitude equation from diffusion-reaction is a difficult task. Making use of our mechanical algorithm, we derived the amplitude equations from several biology and physics models. The results indicate that the algorithm is easy to apply and effective. This algorithm may be useful for learning the dynamics of pattern formation of reaction-diffusion systems in future studies.

In Silico Prediction of Blood Brain Barrier Permeability

The ability of penetration of the blood-brain barrier is an important property for the development of Central Nervous System drugs, which is commonly expressed by logBB (logBB = log(Cbrain/Cblood). In this work, a support vector machine was used to build quantitative models of blood brain barrier permeability. Molecular descriptors for 182 compounds were calculated by ADRIANA.Code and 12 descriptors were selected using the automatic variable selection function in Weka. Based on two common physicochemical descriptors (xlogP and Topological Polar Surface Area (TPSA)) and 10 2D property autocorrelation descriptors on atom pair properties, an SVM regression model was built. The built model was validated by an external test set. The reliable predictions of the test set demonstrate that this model performs well and can be used for estimation of logBB values for drug and drug-like molecules.

Improving PSI-BLAST’s Fold Recognition Performance through Combining Consensus Sequences and Support Vector Machine

Profile-profile alignment may be the most sensitive and useful computational resource for identifying remote homologies and recognizing protein folds. However, profile-profile alignment is usually much more complex and slower than sequence-sequence or profile-sequence alignment. The profile or PSSM (position-specific scoring matrix) can be used to represent the mutational variability at each sequence position of a protein by using a vector of amino acid substitution frequencies and it is a much richer encoding of a protein sequence. Consensus sequence, which can be considered as a simplified profile, was used to improve sequence alignment accuracy in the early time. Recently, several studies were carried out to improve PSI-BLAST’s fold recognition performance by using consensus sequence information. There are several ways to compute a consensus sequence. Based on these considerations, we propose a method that combines the information of different types of consensus sequences with the assistance of support vector machine learning in this chapter. Benchmark results suggest that our method can further improve PSI-BLAST’s fold recognition performance.

Root Water Uptake Model of Populus Euphratica in Desert Riparian Forest in Extreme Arid Region

Plant root water uptake is a key way to transfer soil water to the atmosphere. It is an important part of the research on water transforming patterns in the SPAC (Soil-Plant-Air Continuum). So understanding the water absorption patterns of plant root system is a base to recognize the SPAC. Recently there are many studies on the water absorption patterns of plant root system. However, the researched plants are mostly crops and the main researched areas are regions with adequate precipitation. There are only a few studies on the water absorption of natural plants in extreme arid desert regions. This paper studied the root water absorption patterns of Populus euphratica and established the corresponding mathematical model based on the data of root density and soil water dynamics in root zone in desert riparian forest in extreme arid region. The finite difference method was used to discretize the soil water movement equation with evaporation boundary conditions. Numerical simulation analysis of soil water movement in root zone of Populus euphratica showed that the simulated values were consistent with the measurement values with 92-98% precision. This work provides a theoretical basis for the study of water movement in the SPAC.

Structure of Hydrogenase in Biohydrogen Production Anaerobic Bacteria

Hydrogenase plays an important role in the process of biohydrogen production. Hydrogenases have very unique active sites and are classified into three groups according to the metal composition of the active sites: the [Ni-Fe] hydrogenase, [Fe-Fe] hydrogenase, and [Fe-only] hydrogenase. In this paper, the crystal structures and active sites of three kinds of hydrogenases are examined and compared. These enzymes have an unusual structural feature in common. Their similar active site indicates that the catalytic mechanism of hydrogen activation is probably similar. The understanding of the catalytic mechanisms for the three kinds of hydrogenases may help achieve the industrialization process of hydrogen energy production. Moreover, the future research direction about the hydrogenases from auto-aggregative bacteria and the chemical mimic of hydrogenases structure is discussed.

A Two-Layer Learning Architecture for Multi-Class Protein Folds Classification

Classification of protein folds plays a very important role in the protein structure discovery process, especially when traditional sequence alignment methods fail to yield convincing structural homologies. In this chapter, we have developed a two-layer learning architecture, named TLLA, for multi-class protein folds classification. In the first layer, OET-KNN (Optimized Evidence-Theoretic K Nearest Neighbors) is used as the component classifier to find the most probable K-folds of the query protein. In the second layer, we use support vector machine (SVM) to build the multi-class classifier just on the K-folds, generated in the first layer, rather than on all the 27 folds. For multi-feature combination, ensemble strategy based on voting is selected to give the final classification result. The standard percentage accuracy of our method at ~63% is achieved on the independent testing dataset, where most of the proteins have <25% sequence identity with those in the training dataset. The experimental evaluation based on a widely used benchmark dataset has shown that our approach outperforms the competing methods, implying our approach might become a useful vehicle in the literature.

Molecular Dynamics Simulation of Interlayer Structure and Hydration Properties of Glycine Intercalated Layered Double Hydroxides

Interlayer structure, hydrogen-bond, hydration and swelling properties of glycine intercalated layered double hydroxides (LDHs-Gly) were investigated with molecular dynamics (MD) methods. The results show that the interlayer spacing dc increases as hydration level increases. The computed hydration energies reach the most negative values at low water contents and change rapidly over the range 1 = NW = 6, and slowly and gradually approach the potential energy for bulk SPC water at NW > 6. But there are no local minima in the energy over the entire hydration range. This result suggests that LDHs-Gly tend to absorb water continuously in water-rich environments and enhance swelling to delaminate the hydroxide layers. The interlayers of LDHs-Gly exhibit complex hydrogen-bond network. With water content increasing, the glycine molecules progressively change their orientation from parallel to the layers to nearly perpendicular. Water molecules firstly form hydrogen-bond with M-OH layers at low water contents. While the hydroxide layers gradually get to saturation state at Nw > 3. And then water molecules continuously fill the interlayer to expand interlayer spacing.

Digital Auscultation System of Traditional Chinese Medicine and Its Signals Acquisition

Digital auscultation of Traditional Chinese Medicine (TCM) is a relatively new technology which has been developed for several years. This system makes diagnoses by analyzing sound signals of patients using signal processing and pattern recognition. The paper discusses TCM auscultation in both traditional and current digital auscultation methods. First, this article discusses demerits of traditional TCM auscultation methods. It is through these demerits that a conclusion is drawn that digital auscultation of TCM is indispensable. Then this article makes an introduction to voice analysis methods from linear and nonlinear analysis aspects to pattern recognition methods in common use. Finally this article establishes a new TCM digital auscultation system based on wavelet analysis and Back-propagation neural network (BPNN).

Theoretical Study on the Antioxidant Activity of Alizarin, Purpurin, and Pseudopurpurin

Hydroxyanthraquinone derivatives are a large group of natural polyphenolic compounds found widely in plants. The cytotoxic activities of hydroxyanthraquinone derivatives have been demonstrated using cancer cell lines. The pharmacological effect can be explained by their antioxidant activity and their inhibition of certain enzymes. There are two main kinds of mechanism, H-atom transfer and one-electron transfer, by which antioxidants can play their role. The structural and electronic properties of hydroxyanthraquinone derivatives, alizarin, purpurin, pseudopurpurin, and their radicals were investigated using density functional theory. It turned out that these three molecules appear to be good candidates for high antioxidant activity species, particularly for pseudopurpurin. Taking this system as an example, we present an efficient method for the investigation of antioxidant activity for such kind of hydroxyanthraquinone derivatives from theoretical point of view. With the current work, we hope to highlight the antioxidant activity of hydroxyanthraquinone derivatives and stimulate the interest for further studies and exploitation in pharmaceutical industry.