What causes individual tree death in tropical forests remains a major gap in our understanding of the biology of tropical trees and leads to significant uncertainty in predicting global carbon cycle dynamics. We measured individual characteristics (diameter at breast height, wood density, growth rate, crown illumination and crown form) and environmental conditions (soil fertility and habitat suitability) for 26 425 trees ≥ 10 cm diameter at breast height belonging to 416 species in a 52-ha plot in Lambir Hills National Park, Malaysia. We used structural equation models to investigate the relationships among the different factors and tree mortality. Crown form (a proxy for mechanical damage and other stresses) and prior growth were the two most important factors related to mortality. The effect of all variables on mortality (except habitat suitability) was substantially greater than expected by chance. Tree death is the result of interactions between factors, including direct and indirect effects. Crown form/damage and prior growth mediated most of the effect of tree size, wood density, fertility and habitat suitability on mortality. Large-scale assessment of crown form or status may result in improved prediction of individual tree death at the landscape scale.
Thermal performance curves (TPCs), which quantify how an ectotherm's body temperature (Tb ) affects its performance or fitness, are often used in an attempt to predict organismal responses to climate change. Here, we examine the key - but often biologically unreasonable - assumptions underlying this approach; for example, that physiology and thermal regimes are invariant over ontogeny, space and time, and also that TPCs are independent of previously experienced Tb. We show how a critical consideration of these assumptions can lead to biologically useful hypotheses and experimental designs. For example, rather than assuming that TPCs are fixed during ontogeny, one can measure TPCs for each major life stage and incorporate these into stage-specific ecological models to reveal the life stage most likely to be vulnerable to climate change. Our overall goal is to explicitly examine the assumptions underlying the integration of TPCs with Tb , to develop a framework within which empiricists can place their work within these limitations, and to facilitate the application of thermal physiology to understanding the biological implications of climate change.
Determining statistical patterns irrespective of interacting agents (i.e. macroecology) is useful to explore the mechanisms driving population fluctuations and extinctions in natural food webs. Here, we tested four predictions of a neutral model on the distribution of community fluctuations (CF) and the distributions of persistence times (APT). Novel predictions for the food web were generated by combining (1) body size-density scaling, (2) Taylor's law and (3) low efficiency of trophic transference. Predictions were evaluated on an exceptional data set of plankton with 15 years of weekly samples encompassing c. 250 planktonic species from three trophic levels, sampled in the western English Channel. Highly symmetric non-Gaussian distributions of CF support zero-sum dynamics. Variability in CF decreased while a change from an exponential to a power law distribution of APT from basal to upper trophic positions was detected. Results suggest a predictable but profound effect of trophic position on fluctuations and extinction in natural communities.
The extensive amount of available information on global warming suggests that this issue has become prevalent worldwide. Majority of countries have issued laws and policies in response to this concern by requiring their industrial sectors to reduce greenhouse gas emissions, such as CO2. Thus, introducing new and more effective treatment methods, such as biological techniques, is crucial to control the emission of greenhouse gases. Many studies have demonstrated CO2 fixation using photo-bioreactors and raceway ponds, but a comprehensive review is yet to be published on biological CO2 fixation. A comprehensive review of CO2 fixation through biological process is presented in this paper as biological processes are ideal to control both organic and inorganic pollutants. This process can also cover the classification of methods, functional mechanisms, designs, and their operational parameters, which are crucial for efficient CO2 fixation. This review also suggests the bio-trickling filter process as an appropriate approach in CO2 fixation to assist in creating a pollution-free environment. Finally, this paper introduces optimum designs, growth rate models, and CO2 fixation of microalgae, functions, and operations in biological CO2 fixation.
Most current models of human population structure view migration solely as a deterministic force reducing the variance in gene frequencies among the local colonies of a subdivided population. By an empirical example and through simulation experiments, it is shown that migration structured along kinship lines (by analogy to the lineal or 'kinship' effect) does not always reduce the variances of gene frequencies arising through intergenerational random genetic drift. Thus populations experiencing high rates of migration may not be genetically homogenous.
Mechanism diagrams exhibit visually the organized parts and operations of a biological mechanism. A mechanism diagram can facilitate mechanistic research by providing a mechanistic explanation of the phenomenon of interest. Much research has been focusing on the mechanistic explanation and the explanatory mechanistic models. As a specific type of scientific diagram, a simple mechanism diagram can be explanatory by drawing on the rich explanatory resources of non-depicted background knowledge. The relationship between the visually depicted and the background knowledge is underexplored. It is unclear how the non-depicted background knowledge of a mechanism diagram contributes to providing a better-informed explanation of the phenomenon of interest in biological sciences. With the aim to explore this relationship, I articulate that a mechanism diagram provides a mechanistic explanation by a process called abstraction-by-aggregation. Through visual cues, the unified relevant background knowledge provides an epistemic access to a better-informed explanation.
In this paper, the Duckworth-Lewis-Stern (DLS) and Duckworth-Lewis-McHale-Asif (DLMA) methods of revising targets for a team batting in second innings in an interrupted Limited Overs International Cricket (LOI), are examined for fairness. The work discusses four significant points: flexibility, intuition, simplicity, and goodness-of-fit of the two mentioned methods. The research findings have shown that the DLMA method is better in every aspect than the DLS method. Further, the data of 1764 ODI matches played during 2004-2021 to investigate the compatibility of the DLMA for high run-scoring One-Day International matches. The results show that DLMA is compatible to the situation of the well-above run-scoring situation.
This study aimed to determine the effect of added noise, filtering and time series length on the largest Lyapunov exponent (LyE) value calculated for time series obtained from a passive dynamic walker. The simplest passive dynamic walker model comprising of two massless legs connected by a frictionless hinge joint at the hip was adopted to generate walking time series. The generated time series was used to construct a state space with the embedding dimension of 3 and time delay of 100 samples. The LyE was calculated as the exponential rate of divergence of neighboring trajectories of the state space using Rosenstein's algorithm. To determine the effect of noise on LyE values, seven levels of Gaussian white noise (SNR=55-25dB with 5dB steps) were added to the time series. In addition, the filtering was performed using a range of cutoff frequencies from 3Hz to 19Hz with 2Hz steps. The LyE was calculated for both noise-free and noisy time series with different lengths of 6, 50, 100 and 150 strides. Results demonstrated a high percent error in the presence of noise for LyE. Therefore, these observations suggest that Rosenstein's algorithm might not perform well in the presence of added experimental noise. Furthermore, findings indicated that at least 50 walking strides are required to calculate LyE to account for the effect of noise. Finally, observations support that a conservative filtering of the time series with a high cutoff frequency might be more appropriate prior to calculating LyE.
The majority of the eye models developed in the late 90s and early 00s considers only heat conduction inside the eye. This assumption is not entirely correct, since the anterior and posterior chambers are filled aqueous humor (AH) that is constantly in motion due to thermally-induced buoyancy. In this paper, a three-dimensional model of the human eye is developed to investigate the effects AH hydrodynamics have on the human eye temperature under exposure to external heat sources. If the effects of AH flow are negligible, then future models can be developed without taking them into account, thus simplifying the modeling process. Two types of external thermal loads are considered; volumetric and surface irradiation. Results showed that heat convection due to AH flow contributes to nearly 95% of the total heat flow inside the anterior chamber. Moreover, the circulation inside the anterior chamber can cause an upward shift of the location of hotspot. This can have significant consequences to our understanding of heat-induced cataractogenesis.
When Darwin visited the Galapagos archipelago, he observed that, in spite of the islands' physical similarity, members of species that had dispersed to them recently were beginning to diverge from each other. He postulated that these divergences must have resulted primarily from interactions with sets of other species that had also diverged across these otherwise similar islands. By extrapolation, if Darwin is correct, such complex interactions must be driving species divergences across all ecosystems. However, many current general ecological theories that predict observed distributions of species in ecosystems do not take the details of between-species interactions into account. Here we quantify, in sixteen forest diversity plots (FDPs) worldwide, highly significant negative density-dependent (NDD) components of both conspecific and heterospecific between-tree interactions that affect the trees' distributions, growth, recruitment, and mortality. These interactions decline smoothly in significance with increasing physical distance between trees. They also tend to decline in significance with increasing phylogenetic distance between the trees, but each FDP exhibits its own unique pattern of exceptions to this overall decline. Unique patterns of between-species interactions in ecosystems, of the general type that Darwin postulated, are likely to have contributed to the exceptions. We test the power of our null-model method by using a deliberately modified data set, and show that the method easily identifies the modifications. We examine how some of the exceptions, at the Wind River (USA) FDP, reveal new details of a known allelopathic effect of one of the Wind River gymnosperm species. Finally, we explore how similar analyses can be used to investigate details of many types of interactions in these complex ecosystems, and can provide clues to the evolution of these interactions.
Understanding social organization is fundamental for the analysis of animal societies. In this study, animal single-file movement data-serialized order movements generated by simple bottom-up rules of collective movements-are informative and effective observations for the reconstruction of animal social structures using agent-based models. For simulation, artificial 2-dimensional spatial distributions were prepared with the simple assumption of clustered structures of a group. Animals in the group are either independent or dependent agents. Independent agents distribute spatially independently each one another, while dependent agents distribute depending on the distribution of independent agents. Artificial agent spatial distributions aim to represent clustered structures of agent locations-a coupling of "core" or "keystone" subjects and "subordinate" or "follower" subjects. Collective movements were simulated following two simple rules, 1) initiators of the movement are randomly chosen, and 2) the next moving agent is always the nearest neighbor of the last moving agents, generating "single-file movement" data. Finally, social networks were visualized, and clustered structures reconstructed using a recent major social network analysis (SNA) algorithm, the Louvain algorithm, for rapid unfolding of communities in large networks. Simulations revealed possible reconstruction of clustered social structures using relatively minor observations of single-file movement, suggesting possible application of single-file movement observations for SNA use in field investigations of wild animals.
Carbon nanotubes (CNTs) are potentially ideal tips for atomic force microscopy (AFM) due to the robust mechanical properties, nanoscale diameter and also their ability to be functionalized by chemical and biological components at the tip ends. This contribution develops the idea of using CNTs as an AFM tip in computational analysis of the biological cells. The proposed software was ABAQUS 6.13 CAE/CEL provided by Dassault Systems, which is a powerful finite element (FE) tool to perform the numerical analysis and visualize the interactions between proposed tip and membrane of the cell. Finite element analysis employed for each section and displacement of the nodes located in the contact area was monitored by using an output database (ODB). Mooney-Rivlin hyperelastic model of the cell allows the simulation to obtain a new method for estimating the stiffness and spring constant of the cell. Stress and strain curve indicates the yield stress point which defines as a vertical stress and plan stress. Spring constant of the cell and the local stiffness was measured as well as the applied force of CNT-AFM tip on the contact area of the cell. This reliable integration of CNT-AFM tip process provides a new class of high performance nanoprobes for single biological cell analysis.
Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.
Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.
The use of wireless communication using inductive links to transfer data and power to implantable microsystems to stimulate and monitor nerves and muscles is increasing. This paper deals with the development of the theoretical analysis and optimization of an inductive link based on coupling and on spiral circular coil geometry. The coil dimensions offer 22 mm of mutual distance in air. However, at 6 mm of distance, the coils offer a power transmission efficiency of 80% in the optimum case and 73% in the worst case via low input impedance, whereas, transmission efficiency is 45% and 32%, respectively, via high input impedance. The simulations were performed in air and with two types of simulated human biological tissues such as dry and wet-skin using a depth of 6 mm. The performance results expound that the combined magnitude of the electric field components surrounding the external coil is approximately 98% of that in air, and for an internal coil, it is approximately 50%, respectively. It can be seen that the gain surrounding coils is almost constant and confirms the omnidirectional pattern associated with such loop antennas which reduces the effect of non-alignment between the two coils. The results also show that the specific absorption rate (SAR) and power loss within the tissue are lower than that of the standard level. Thus, the tissue will not be damaged anymore.
This paper presents a study on gene knockout strategies to identify candidate genes to be knocked out for improving the production of succinic acid in Escherichia coli. Succinic acid is widely used as a precursor for many chemicals, for example production of antibiotics, therapeutic proteins and food. However, the chemical syntheses of succinic acid using the traditional methods usually result in the production that is far below their theoretical maximums. In silico gene knockout strategies are commonly implemented to delete the gene in E. coli to overcome this problem. In this paper, a hybrid of Ant Colony Optimization (ACO) and Minimization of Metabolic Adjustment (MoMA) is proposed to identify gene knockout strategies to improve the production of succinic acid in E. coli. As a result, the hybrid algorithm generated a list of knockout genes, succinic acid production rate and growth rate for E. coli after gene knockout. The results of the hybrid algorithm were compared with the previous methods, OptKnock and MOMAKnock. It was found that the hybrid algorithm performed better than OptKnock and MOMAKnock in terms of the production rate. The information from the results produced from the hybrid algorithm can be used in wet laboratory experiments to increase the production of succinic acid in E. coli.
We describe the work on infusion of emotion into a limited-task autonomous spoken conversational agent situated in the domestic environment, using a need-inspired task-independent emotion model (NEMO). In order to demonstrate the generation of affect through the use of the model, we describe the work of integrating it with a natural-language mixed-initiative HiFi-control spoken conversational agent (SCA). NEMO and the host system communicate externally, removing the need for the Dialog Manager to be modified, as is done in most existing dialog systems, in order to be adaptive. The first part of the paper concerns the integration between NEMO and the host agent. The second part summarizes the work on automatic affect prediction, namely, frustration and contentment, from dialog features, a non-conventional source, in the attempt of moving towards a more user-centric approach. The final part reports the evaluation results obtained from a user study, in which both versions of the agent (non-adaptive and emotionally-adaptive) were compared. The results provide substantial evidences with respect to the benefits of adding emotion in a spoken conversational agent, especially in mitigating users' frustrations and, ultimately, improving their satisfaction.
Efforts to model the human upper respiratory system have undergone many phases. Geometrical proximity to the realistic shape has been the subject of many research projects. In this study, three different geometries of the trachea and main bronchus were modelled, which were reconstructed from computed tomography (CT) scan images. The geometrical variations were named realistic, simplified and oversimplified. Realistic refers to the lifelike image taken from digital imaging and communications in medicine format CT scan images, simplified refers to the reconstructed image based on natural images without realistic details pertaining to the rough surfaces, and oversimplified describes the straight wall geometry of the airway. The characteristics of steady state flows with different flow rates were investigated, simulating three varied physical activities and passing through each model. The results agree with previous studies where simplified models are sufficient for providing comparable results for airflow in human airways. This work further suggests that, under most exercise conditions, the idealised oversimplified model is not favourable for simulating either airflow regimes or airflow with particle depositions. However, in terms of immediate analysis for the prediction of abnormalities of various dimensions of human airways, the oversimplified techniques may be used.
This study aimed to develop a three-dimensional finite element model of a functionally graded femoral prosthesis. The model consisted of a femoral prosthesis created from functionally graded materials (FGMs), cement, and femur. The hip prosthesis was composed of FGMs made of titanium alloy, chrome-cobalt, and hydroxyapatite at volume fraction gradient exponents of 0, 1, and 5, respectively. The stress was measured on the femoral prosthesis, cement, and femur. Stress on the neck of the femoral prosthesis was not sensitive to the properties of the constituent material. However, stress on the stem and cement decreased proportionally as the volume fraction gradient exponent of the FGM increased. Meanwhile, stress became uniform on the cement mantle layer. In addition, stress on the femur in the proximal part increased and a high surface area of the femoral part was involved in absorbing the stress. As such, the stress-shielding area decreased. The results obtained in this study are significant in the design and longevity of new prosthetic devices because FGMs offer the potential to achieve stress distribution that more closely resembles that of the natural bone in the femur.