Evolution of Adaptation and Mate Choice: Parental Relatedness Affects Expression of Phenotypic Variance in a Natural Population

Mating between relatives generally results in reduced offspring viability or quality, suggesting that selection should favor behaviors that minimize inbreeding. However, in natural populations where searching is costly or variation among potential mates is limited, inbreeding is often common and may have important consequences for both offspring fitness and phenotypic variation. In particular, offspring morphological variation often increases with greater parental relatedness, yet the source of this variation, and thus its evolutionary significance, are poorly understood. One proposed explanation is that inbreeding influences a developing organism’s sensitivity to its environment and therefore the increased phenotypic variation observed in inbred progeny is due to greater inputs from environmental and maternal sources. Alternatively, changes in phenotypic variation with inbreeding may be due to additive genetic effects alone when heterozygotes are phenotypically intermediate to homozygotes, or effects of inbreeding depression on condition, which can itself affect sensitivity to environmental variation. Here we examine the effect of parental relatedness (as inferred from neutral genetic markers) on heritable and nonheritable components of developmental variation in a wild bird population in which mate choice is often constrained, thereby leading to inbreeding. We found greater morphological variation and distinct contributions of variance components in offspring from highly related parents: inbred offspring tended to have greater environmental and lesser additive genetic variance compared to outbred progeny. The magnitude of this difference was greatest in late-maturing traits, implicating the accumulation of environmental variation as the underlying mechanism. Further, parental relatedness influenced the effect of an important maternal trait (egg size) on offspring development. These results support the hypothesis that inbreeding leads to greater sensitivity of development to environmental variation and maternal effects, suggesting that the evolutionary response to selection will depend strongly on mate choice patterns and population structure.     

Components of Attention in Grapheme-Color Synesthesia: A Modeling Approach

Grapheme-color synesthesia is a condition where the perception of graphemes consistently and automatically evokes an experience of non-physical color. Many have studied how synesthesia affects the processing of achromatic graphemes, but less is known about the synesthetic processing of physically colored graphemes. Here, we investigated how the visual processing of colored letters is affected by the congruence or incongruence of synesthetic grapheme-color associations. We briefly presented graphemes (10–150 ms) to 9 grapheme-color synesthetes and to 9 control observers. Their task was to report as many letters (targets) as possible, while ignoring digit (distractors). Graphemes were either congruently or incongruently colored with the synesthetes’ reported grapheme-color association. A mathematical model, based on Bundesen’s (1990) Theory of Visual Attention (TVA), was fitted to each observer’s data, allowing us to estimate discrete components of visual attention. The models suggested that the synesthetes processed congruent letters faster than incongruent ones, and that they were able to retain more congruent letters in visual short-term memory, while the control group’s model parameters were not significantly affected by congruence. The increase in processing speed, when synesthetes process congruent letters, suggests that synesthesia affects the processing of letters at a perceptual level. To account for the benefit in processing speed, we propose that synesthetic associations become integrated into the categories of graphemes, and that letter colors are considered as evidence for making certain perceptual categorizations in the visual system. We also propose that enhanced visual short-term memory capacity for congruently colored graphemes can be explained by the synesthetes’ expertise regarding their specific grapheme-color associations.     

A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Analysis and an Application to Orientation Tuning

We explore a computationally efficient method of simulating realistic networks of neurons introduced by Knight, Manin, and Sirovich (1996) in which integrate-and-fire neurons are grouped into large populations of similar neurons. For each population, we form a probability density that represents the distribution of neurons over all possible states. The populations are coupled via stochastic synapses in which the conductance of a neuron is modulated according to the firing rates of its presynaptic populations. The evolution equation for each of these probability densities is a partial differential-integral equation, which we solve numerically. Results obtained for several example networks are tested against conventional computations for groups of individual neurons.We apply this approach to modeling orientation tuning in the visual cortex. Our population density model is based on the recurrent feedback model of a hypercolumn in cat visual cortex of Somers et al. (1995). We simulate the response to oriented flashed bars. As in the Somers model, a weak orientation bias provided by feed-forward lateral geniculate input is transformed by intracortical circuitry into sharper orientation tuning that is independent of stimulus contrast.The population density approach appears to be a viable method for simulating large neural networks. Its computational efficiency overcomes some of the restrictions imposed by computation time in individual neuron simulations, allowing one to build more complex networks and to explore parameter space more easily. The method produces smooth rate functions with one pass of the stimulus and does not require signal averaging. At the same time, this model captures the dynamics of single-neuron activity that are missed in simple firing-rate models.     

Specific Midgut Region Controlling the Symbiont Population in an Insect-Microbe Gut Symbiotic Association

Many insects possess symbiotic bacteria that affect the biology of the host. The level of the symbiont population in the host is a pivotal factor that modulates the biological outcome of the symbiotic association. Hence, the symbiont population should be maintained at a proper level by the host''s control mechanisms. Several mechanisms for controlling intracellular symbionts of insects have been reported, while mechanisms for controlling extracellular gut symbionts of insects are poorly understood. The bean bug Riptortus pedestris harbors a betaproteobacterial extracellular symbiont of the genus Burkholderia in the midgut symbiotic organ designated the M4 region. We found that the M4B region, which is directly connected to the M4 region, also harbors Burkholderia symbiont cells, but the symbionts therein are mostly dead. A series of experiments demonstrated that the M4B region exhibits antimicrobial activity, and the antimicrobial activity is specifically potent against the Burkholderia symbiont but not the cultured Burkholderia and other bacteria. The antimicrobial activity of the M4B region was detected in symbiotic host insects, reaching its highest point at the fifth instar, but not in aposymbiotic host insects, which suggests the possibility of symbiont-mediated induction of the antimicrobial activity. This antimicrobial activity was not associated with upregulation of antimicrobial peptides of the host. Based on these results, we propose that the M4B region is a specialized gut region of R. pedestris that plays a critical role in controlling the population of the Burkholderia gut symbiont. The molecular basis of the antimicrobial activity is of great interest and deserves future study.     

Multiple Human Taste Receptor Sites: A Molecular Modeling Approach

Numerous experimental data on the human peripheral taste systemsuggest the existence of multiple low-affinity and low-specificityreceptor sites which are responsible for the detection and thecomplete discrimination of a very large number of organic molecules.According to this hypothesis, a given molecule interacts withnumerous taste receptors and vice versa. Statistical analysisof taste intensities estimated by 58 human subjects for variousmolecules enables the calculation of taste intermolecular distances.For the present modeling study, we hypothesized that a shorttaste distance (i.e. taste similarity) between two distinctmolecules indicates that they bind with similar distributionsof affinities to the taste receptors, and hence display similarbinding motifs. In order to find common molecular binding motifsamong 14 selected organic tastants, hydrogen-bonding and hydrophobicinteraction properties were mapped onto their molecular surfaces.The 14 surfaces were then cut in 240 fragments, most of whichwere made up of 2–4 potentially interacting zones. A correspondenceindex was defined to measure the analogy between two optimallysuperimposed fragments. The 75 most representative fragmentswere all matched pairwise. Twelve distinct clusters of fragmentswere isolated from the 2775 calculated comparisons. These 12fragment types were used to calculate structural similaritydistances. We then performed a combinatorial analysis to identifywhich fragment combination best reconciled structural and tastedistances. We finally identified an optimal subset of sevenfragment types out of the 12, which significantly and best accountedfor the 91 pairwise taste distances between all 14 modeled tastants.These seven validated fragment types are therefore presentedas good candidates to be recognized by the same number of distincttaste receptor sites. Potential applications of these identifiedbinding motifs to tastant design are suggested. Chem. Senses21: 425–445, 1996.     

Environmentally Conscious Product Design: A Collaborative Internet-based Modeling Approach

This paper proposes a computer-based method for providing product designers with real-time environmental impact assessment. In this concurrent modeling approach, environmental experts build life-cycle models, define their interfaces, and publish them as distributed objects on the Internet. Traditional designers integrating these objects into their design models have access to the impact assessment methods provided by the environmental expert. In this paradigm, the focus shifts from providing techniques that let non-expert designers perform life-cycle impact assessments to tools that facilitate timely communication and information transfer between designers and appropriate environmental experts. Establishing real-time communication between the product design models and the environmental life-cycle models is the primary focus of this paper. Methods for establishing and maintaining the interaction between life-cycle and product design models are described. A beverage container design example illustrates how this collaborative approach can use environmental and traditional design goals to determine effective tradeoffs between design alternatives.     

A MAP3k1 SNP Predicts Survival of Gastric Cancer in a Chinese Population

Objectives

Genome-wide association studies (GWAS) have demonstrated that the single nucleotide polymorphism (SNP) MAP3K1 rs889312 is a genetic susceptibility marker significantly associated with a risk of hormone-related tumors such as breast cancer. Considering steroid hormone-mediated signaling pathways have an important role in the progression of gastric cancer, we hypothesized that MAP3K1 rs889312 may be associated with survival outcomes in gastric cancer. The purpose of this study was to test this hypothesis.

Methods

We genotyped MAP3K1 rs889312 using TaqMan in 884 gastric cancer patients who received subtotal or total gastrectomy. Kaplan-Meier survival analysis and Cox proportional hazard regression were used to analyze the association between MAP3K1 rs889312 genotypes and survival outcomes of gastric cancer.

Results

Our findings reveal that the rs889312 heterozygous AC genotype was significantly associated with an increased rate of mortality among patients with diffuse-type gastric cancer (log-rank P = 0.028 for AC versus AA/CC, hazard ratio [HR] = 1.32, 95% confidence interval [CI] = 1.03–1.69), compared to those carrying the homozygous variant genotypes (AA/CC). Additionally, univariate and multivariate Cox regression analysis demonstrate that rs889312 polymorphism was an independent risk factor for poor survival in these patients.

Conclusions

In conclusion, we demonstrate that MAP3K1 rs889312 is closely correlated with outcome among diffuse-type gastric cancer. This raises the possibility for rs889312 polymorphisms to be used as an independent indicator for predicting the prognosis of diffuse-type gastric cancer within the Chinese population.     

Crop Yields and Nematode Population Densities in Triticale-Cotton and Triticale-Soybean Rotations

Triticale cv. Beagle 82, cotton cv. McNair 235, and soybean cv. Twiggs were arranged in three cropping sequences to determine the effects of fenamiphos and cropping sequence on nematode population densities and crop yields under conservation tillage for 4 years. The cropping sequences were triticale (T)-cotton (C)-T-C, T-soybean (S)-T-S, and T-C-T-S. Numbers of Meloidogyne incognita second-stage juveniles declined on trificale but increased on cotton and soybean each year. Root-gall indices of cotton and soybean ranged from 1.00 to 1.08 (1 to 5 scale: 1 = 0%, 2 = 1% to 25%, 3 = 26% to 50%, 4 = 51% to 75%, and 5 = 76% to 100% of roots galled) each year and were not affected by fenamiphos treatment or cropping sequence. Numbers of Pratylenchus brachyurus were maintained on trificale and generally increased more on soybean than on cotton. Population densities of Helicotylenchus dihystera were near or below detection levels in all plots during the first year and increased thereafter in untreated plots in the T-C-T-C and T-S-T-S sequences. Generally, yields of triticale in all cropping sequences declined over the years. Yields of cotton and soybean were not affected by fenamiphos at 6.7 kg a.i./ha. Cotton and soybean were grown successfully with little or no suppression in yields caused by nematodes in conservation tillage following triticale harvested for grain.     

海南省尖峰岭保护区海南特有两栖类分布和种群密度调查

2005年8月、10月和2006年3月、7月,4次对海南省尖峰岭自然保护区的两栖类进行了专项调查,共发现海南特有两栖类8种,样带法计算种群数量最大的是细刺蛙（681只／km^2）,依次为脆皮蛙（376只／km^2）、小湍蛙（298只／km^2）、海南溪树蛙（207只/km^2）、海南湍蛙（90只／km^2）、海南疣螈（31只／km^2）、鳞皮厚蹼蟾（12只／km^2）、眼斑小树蛙（4只／km^2）。建议保护区对这些特有种开展长期的种群数量监测和分布范围调查。     

Landscape-Level Phenology of a Threatened Butterfly: A GIS-Based Modeling Approach

Phenology of organismal development varies between growing seasons according to the weather and also varies within growing seasons across topoclimatic gradients. Combining these factors is necessary to predict landscape-level patterns of phenology and their consequences for population dynamics. We developed a model on a Geographic Information System (GIS) that predicts phenology of adult emergence of the threatened Bay checkerspot butterfly across complex terrain under variable weather. Physiological time was modeled by accumulated slope-specific direct insolation. Insolation sums through growing seasons were calculated for each cell of a digital terrain model (skipping over cloudy days) until a threshold for adult emergence was reached. Emergence times of adult butterflies for a given year were then mapped out across a 100-ha area. To generate predicted emergence curves for the population in a given year, these maps ofemergence times were then modified by incorporating microdistributions of postdiapause larvae. Different larval microdistributions changed both the magnitude and shape of emergence curves under the same yearly weather and could change mean population-wide emergence dates by 11 days. Reproductive success in this butterfly is strongly dependent on the timing of adult emergence, and these models provide insights into the effects of weather, topography, and population history on population dynamics. Because adult emergence phenology is often a key component of reproductive success for insects, understanding the components of phenological variation in space and time in complex terrain may provide insights into population dynamics for management of pests and conservation of rare species. Received 2 December 1997; accepted 24 March 1998.     

Pediatric Health-Related Quality of Life: A Structural Equation Modeling Approach

Objectives

One of the most referenced theoretical frameworks to measure Health Related Quality of Life (HRQoL) is the Wilson and Cleary framework. With some adaptions this framework has been validated in the adult population, but has not been tested in pediatric populations. Our goal was to empirically investigate it in children.

Methods

The contributory factors to Health Related Quality of Life that we included were symptom status (presence of chronic disease or hospitalizations), functional status (developmental status), developmental aspects of the individual (social-emotional) behavior, and characteristics of the social environment (socioeconomic status and area of education). Structural equation modeling was used to assess the measurement structure of the model in 214 German children (3–5 years old) participating in a follow-up study that investigates pediatric health outcomes.

Results

Model fit was χ2 = 5.5; df = 6; p = 0.48; SRMR  = 0.01. The variance explained of Health Related Quality of Life was 15%. Health Related Quality of Life was affected by the area education (i.e. where kindergartens were located) and development status. Developmental status was affected by the area of education, socioeconomic status and individual behavior. Symptoms did not affect the model.

Conclusions

The goodness of fit and the overall variance explained were good. However, the results between children'' and adults'' tests differed and denote a conceptual gap between adult and children measures. Indeed, there is a lot of variety in pediatric Health Related Quality of Life measures, which represents a lack of a common definition of pediatric Health Related Quality of Life. We recommend that researchers invest time in the development of pediatric Health Related Quality of Life theory and theory based evaluations.     

Modeling Brain Energy Metabolism and Function: A Multiparametric Monitoring Approach

Mathematical modeling of brain function is an important tool needed for a better understanding of experimental results and clinical situations. In the present study, we are constructing and testing a mathematical model capable of simulating changes in brain energy metabolism that develop in real time under various pathophysiological conditions. The model incorporates the following parameters: cerebral blood flow, partial oxygen pressure, mitochondrial NADH redox state, and extracellular potassium. Accordingly, all the model variables are only time dependent (`point-model' approach). Numerical runs demonstrate the ability of the model to mimic pathological conditions, such as complete and partial ischemia, cortical spreading depression under normoxic and partial ischemic conditions. They also show that, when properly tuned, a model of this type permits the monitoring of only one or two crucial variables and the computation of the remaining variables in real time during clinical or experimental procedures.     

Modelling Population Dynamics in Realistic Landscapes with Linear Elements: A Mechanistic-Statistical Reaction-Diffusion Approach

We propose and develop a general approach based on reaction-diffusion equations for modelling a species dynamics in a realistic two-dimensional (2D) landscape crossed by linear one-dimensional (1D) corridors, such as roads, hedgerows or rivers. Our approach is based on a hybrid “2D/1D model”, i.e, a system of 2D and 1D reaction-diffusion equations with homogeneous coefficients, in which each equation describes the population dynamics in a given 2D or 1D element of the landscape. Using the example of the range expansion of the tiger mosquito Aedes albopictus in France and its main highways as 1D corridors, we show that the model can be fitted to realistic observation data. We develop a mechanistic-statistical approach, based on the coupling between a model of population dynamics and a probabilistic model of the observation process. This allows us to bridge the gap between the data (3 levels of infestation, at the scale of a French department) and the output of the model (population densities at each point of the landscape), and to estimate the model parameter values using a maximum-likelihood approach. Using classical model comparison criteria, we obtain a better fit and a better predictive power with the 2D/1D model than with a standard homogeneous reaction-diffusion model. This shows the potential importance of taking into account the effect of the corridors (highways in the present case) on species dynamics. With regard to the particular case of A. albopictus, the conclusion that highways played an important role in species range expansion in mainland France is consistent with recent findings from the literature.     

Phenotypic Plasticity and Population Differentiation in an Ongoing Species Invasion

The ability to succeed in diverse conditions is a key factor allowing introduced species to successfully invade and spread across new areas. Two non-exclusive factors have been suggested to promote this ability: adaptive phenotypic plasticity of individuals, and the evolution of locally adapted populations in the new range. We investigated these individual and population-level factors in Polygonum cespitosum, an Asian annual that has recently become invasive in northeastern North America. We characterized individual fitness, life-history, and functional plasticity in response to two contrasting glasshouse habitat treatments (full sun/dry soil and understory shade/moist soil) in 165 genotypes sampled from nine geographically separate populations representing the range of light and soil moisture conditions the species inhabits in this region. Polygonum cespitosum genotypes from these introduced-range populations expressed broadly similar plasticity patterns. In response to full sun, dry conditions, genotypes from all populations increased photosynthetic rate, water use efficiency, and allocation to root tissues, dramatically increasing reproductive fitness compared to phenotypes expressed in simulated understory shade. Although there were subtle among-population differences in mean trait values as well as in the slope of plastic responses, these population differences did not reflect local adaptation to environmental conditions measured at the population sites of origin. Instead, certain populations expressed higher fitness in both glasshouse habitat treatments. We also compared the introduced-range populations to a single population from the native Asian range, and found that the native population had delayed phenology, limited functional plasticity, and lower fitness in both experimental environments compared with the introduced-range populations. Our results indicate that the future spread of P. cespitosum in its introduced range will likely be fueled by populations consisting of individuals able to express high fitness across diverse light and moisture conditions, rather than by the evolution of locally specialized populations.     

Estimation of Immune Cell Densities in Immune Cell Conglomerates: An Approach for High-Throughput Quantification

Background

Determining the correct number of positive immune cells in immunohistological sections of colorectal cancer and other tumor entities is emerging as an important clinical predictor and therapy selector for an individual patient. This task is usually obstructed by cell conglomerates of various sizes. We here show that at least in colorectal cancer the inclusion of immune cell conglomerates is indispensable for estimating reliable patient cell counts. Integrating virtual microscopy and image processing principally allows the high-throughput evaluation of complete tissue slides.

Methodology/Principal findings

For such large-scale systems we demonstrate a robust quantitative image processing algorithm for the reproducible quantification of cell conglomerates on CD3 positive T cells in colorectal cancer. While isolated cells (28 to 80 µm²) are counted directly, the number of cells contained in a conglomerate is estimated by dividing the area of the conglomerate in thin tissues sections (≤6 µm) by the median area covered by an isolated T cell which we determined as 58 µm². We applied our algorithm to large numbers of CD3 positive T cell conglomerates and compared the results to cell counts obtained manually by two independent observers. While especially for high cell counts, the manual counting showed a deviation of up to 400 cells/mm² (41% variation), algorithm-determined T cell numbers generally lay in between the manually observed cell numbers but with perfect reproducibility.

Conclusion

In summary, we recommend our approach as an objective and robust strategy for quantifying immune cell densities in immunohistological sections which can be directly implemented into automated full slide image processing systems.     

RFMix: A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference

Local-ancestry inference is an important step in the genetic analysis of fully sequenced human genomes. Current methods can only detect continental-level ancestry (i.e., European versus African versus Asian) accurately even when using millions of markers. Here, we present RFMix, a powerful discriminative modeling approach that is faster (∼30×) and more accurate than existing methods. We accomplish this by using a conditional random field parameterized by random forests trained on reference panels. RFMix is capable of learning from the admixed samples themselves to boost performance and autocorrect phasing errors. RFMix shows high sensitivity and specificity in simulated Hispanics/Latinos and African Americans and admixed Europeans, Africans, and Asians. Finally, we demonstrate that African Americans in HapMap contain modest (but nonzero) levels of Native American ancestry (∼0.4%).     

Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification

Generalized linear models (GLMs) represent a popular choice for the probabilistic characterization of neural spike responses. While GLMs are attractive for their computational tractability, they also impose strong assumptions and thus only allow for a limited range of stimulus-response relationships to be discovered. Alternative approaches exist that make only very weak assumptions but scale poorly to high-dimensional stimulus spaces. Here we seek an approach which can gracefully interpolate between the two extremes. We extend two frequently used special cases of the GLM—a linear and a quadratic model—by assuming that the spike-triggered and non-spike-triggered distributions can be adequately represented using Gaussian mixtures. Because we derive the model from a generative perspective, its components are easy to interpret as they correspond to, for example, the spike-triggered distribution and the interspike interval distribution. The model is able to capture complex dependencies on high-dimensional stimuli with far fewer parameters than other approaches such as histogram-based methods. The added flexibility comes at the cost of a non-concave log-likelihood. We show that in practice this does not have to be an issue and the mixture-based model is able to outperform generalized linear and quadratic models.     

Mathematical Modeling of Learning from an Inconsistent Source: A Nonlinear Approach

Continuing the discussion of how children can modify and regularize linguistic inputs from adults, we present a new interpretation of existing algorithms to model and investigate the process of a learner learning from an inconsistent source. On the basis of this approach is a (possibly nonlinear) function (the update function) that relates the current state of the learner with an increment that it receives upon processing the source’s input, in a sequence of updates. The model can be considered a nonlinear generalization of the classic Bush–Mosteller algorithm. Our model allows us to analyze and present a theoretical explanation of a frequency boosting property, whereby the learner surpasses the fluency of the source by increasing the frequency of the most common input. We derive analytical expressions for the frequency of the learner, and also identify a class of update functions that exhibit frequency boosting. Applications to the Feature-Label-Order effect in learning are presented.     

t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks

Protein-protein interaction (PPI) networks provide insights into understanding of biological processes, function and the underlying complex evolutionary mechanisms of the cell. Modeling PPI network is an important and fundamental problem in system biology, where it is still of major concern to find a better fitting model that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called t-logistic semantic embedding (t-LSE) to model PPI networks. t-LSE tries to adaptively learn a metric embedding under the simple geometric assumption of PPI networks, and a non-convex cost function was adopted to deal with the noise in PPI networks. The experimental results show the superiority of the fit of t-LSE over other network models to PPI data. Furthermore, the robust loss function adopted here leads to big improvements for dealing with the noise in PPI network. The proposed model could thus facilitate further graph-based studies of PPIs and may help infer the hidden underlying biological knowledge. The Matlab code implementing the proposed method is freely available from the web site: http://home.ustc.edu.cn/~yzh33108/PPIModel.htm.