How should pathogen transmission be modelled?

Host-pathogen models are essential for designing strategies for managing disease threats to humans, wild animals and domestic animals. The behaviour of these models is greatly affected by the way in which transmission between infected and susceptible hosts is modelled. Since host-pathogen models were first developed at the beginning of the 20th century, the 'mass action' assumption has almost always been used for transmission. Recently, however, it has been suggested that mass action has often been modelled wrongly. Alternative models of transmission are beginning to appear, as are empirical tests of transmission dynamics.     

How should dysplastic human hips be evaluated?

Dysplasia of the human hip is characterised by insufficient anterolateral covering of femoral head by the acetabulum. In our study, we evaluated dysplastic human hip joints using biomechanical parameters (the peak contact stress in the weight-bearing area of the hip - pmax) and X-ray image parameters (the centre-edge angle - thetaCE, the transverse acetabular inclination angle - thetaUS, the acetabular index of the weight-bearing zone - thetaAC, the ACM angle - thetaACM, and the hip value - HV). The purpose of this study is to make use of X-ray and biomechanical parameters to evaluate hips diagnosed with "hip dysplasia", and to establish whether or not there is a correlation between the two. Our results show a statistically significant correlation between pmax and thetaCE, thetaUS and thetaAC. The correlation between pmax and thetaACM and HV is not statistically significant.     

How should we be selecting our graduate students?

We use many quantitative undergraduate metrics to help select our graduate students, but which of these usefully discriminate successful from underperforming students and which should be ignored? Almost everyone has his or her own pet theory of the most predictive criteria, but I hoped to address this question in a more unbiased manner. I conducted a retrospective analysis of the highest- and lowest-ranked graduate students over the past 20 years in the Tetrad program at the University of California at San Francisco to identify undergraduate metrics that significantly differed between these groups. Only the number of years of research experience and subject graduate record exams (GREs) were strong discriminators between the highest- and lowest-ranked students, whereas many other commonly used admissions metrics (analytical, verbal, and quantitative GREs, grade point average, and ranking of undergraduate institution) showed no correlation with graduate performance. These are not necessarily the same criteria that matter at other graduate programs, but I would urge faculty elsewhere to conduct similar analyses to improve the admissions process and to minimize the use of useless metrics in selecting our students.     

How should xanthine oxidase-generated superoxide yields be measured?

The rate of formation of superoxide measured by its reduction of tetranitromethane (TNM) and by its reduction of ferric cytochrome c (Fe(III) cc) are in excellent agreement when the superoxide is generated from a simple chemical precursor. In contrast, the rate of formation of superoxide generated in the reaction of xanthine oxidase with acetaldehyde is much higher (up to a factor of 6) when measured with TNM and compared with Fe(III) cc. It is shown that Fe(III) cc measures superoxide that has diffused from the enzyme, and that TNM probably scavenges all the dioxygen that is reduced by one electron by the enzyme. The TNM traps enzyme-bound superoxide in competition with the second-electron transfer and proton transfer, which normally yield hydrogen peroxide. The proton transfer is probably rate determining, k(p) 相似文献   

Detecting climate change induced range shifts: Where and how should we be looking?

Abstract: Global climate warming is expected to cause systematic shifts in the distribution of species and consequently increase extinction risk. Conservation managers must be able to detect, measure and accurately predict range shifts in order to mitigate impacts on biodiversity. However, important responses to climate change may go unnoticed or be dismissed if we fail to collect sufficient baseline data and apply the most sensitive analytical tests. Here we use randomizations of a contemporary data set on rainforest birds of north‐eastern Australia to quantify the sensitivity of three measures for assessing range shifts along altitudinal gradients. We find that smaller range shifts are detectable by analysing change in the mean altitude of presence records rather than upper or lower range boundaries. For a moderate survey effort of 96 surveys, measurements of change in the mean altitude of 34 species have the capacity to provide strong inference for a mean altitudinal range shift as small as 40 m across the species assemblage. We also show that range shifts measured at range boundaries can be potentially misleading when differences in sampling effort between contemporary and historical data sets are not taken into account.     

How well do reduced models capture the dynamics in models of interacting neurons?

This paper introduces a class of stochastic models of interacting neurons with emergent dynamics similar to those seen in local cortical populations. Rigorous results on existence and uniqueness of nonequilibrium steady states are proved. These network models are then compared to very simple reduced models driven by the same mean excitatory and inhibitory currents. Discrepancies in firing rates between network and reduced models are investigated and explained by correlations in spiking, or partial synchronization, working in concert with “nonlinearities” in the time evolution of membrane potentials. The use of simple random walks and their first passage times to simulate fluctuations in neuronal membrane potentials and interspike times is also considered.

     

How should we define goodness?--reputation dynamics in indirect reciprocity

Theory of indirect reciprocity is important in explaining cooperation between humans. Since a partner of a social interaction often changes, an individual should assess his partner by using social information such as reputation and make decisions whether to help him or not. To those who have 'good' social reputation does a player give aid as reciprocation, whereas he has to refuse to help those who have 'bad' reputation. Otherwise benefits of altruism is easily exploited by them. Little has been known, however, about the definition of 'goodness' in reputation. What kind of actions are and should be regarded as good and what kind of actions bad? And what sort of goodness enables sustaining exchange of altruism? We herein challenge this question with an evolutionary perspective. We generalize social reputation as 'Honor-score' (H-score) and examine the conditions under which individuals in a group stably maintain cooperative relationships based on indirect reciprocity. We examine the condition for evolutionarily stable strategies (ESSs) over 4096 possible cases exhaustively. Mathematical analysis reveals that only eight cases called 'leading eight' are crucial to the evolution of indirect reciprocity. Each in the leading eight shares two common characteristics: (i) cooperation with good persons is regarded as good while defection against them is regarded as bad, and (ii) defection against bad persons should be regarded as a good behavior because it works as sanction. Our results give one solution to the definition of goodness from an evolutionary viewpoint. In addition, we believe that the formalism of reputation dynamics gives general insights into the way social information is generated, handled, and transmitted in animal societies.     

From complex spatial dynamics to simple Markov chain models: do predators and prey leave footprints?

In this paper we present a concept for using presence–absence data to recover information on the population dynamics of predator–prey systems. We use a highly complex and spatially explicit simulation model of a predator–prey mite system to generate simple presence–absence data: the number of patches with both prey and predators, with prey only, with predators only, and with neither species, along with the number of patches that change from one state to another in each time step. The average number of patches in the four states, as well as the average transition probabilities from one state to another, are then depicted in a state transition diagram, constituting the footprints of the underlying population dynamics. We investigate to what extent changes in the population processes modeled in the complex simulation (i.e. the predator's functional response and the dispersal rates of both species) are reflected by different footprints
The transition probabilities can be used to forecast the expected fate of a system given its current state. However, the transition probabilities in the modeled system depend on the number of patches in each state. We develop a model for the dependence of transition probabilities on state variables, and combine this information in a Markov chain transition matrix model. Finally, we use this extended model to predict the long-term dynamics of the system and to reveal its asymptotic steady state properties.     

How good are comparative models in the understanding of protein dynamics?

The 3D structure of a protein is essential to understand protein dynamics. If experimentally determined structure is unavailable, comparative models could be used to infer dynamics. However, the effectiveness of comparative models, compared to experimental structures, in inferring dynamics is not clear. To address this, we compared dynamics features of ~800 comparative models with their crystal structures using normal mode analysis. Average similarity in magnitude, direction, and correlation of residue motions is >0.8 (where value 1 is identical) indicating that the dynamics of models and crystal structures are highly similar. Accuracy of 3D structure and dynamics is significantly higher for models built on multiple and/or high sequence identity templates (>40%). Three-dimensional (3D) structure and residue fluctuations of models are closer to that of crystal structures than to templates (TM score 0.9 vs 0.7 and square inner product 0.92 vs 0.88). Furthermore, long-range molecular dynamics simulations on comparative models of RNase 1 and Angiogenin showed significant differences in the conformational sampling of conserved active-site residues that characterize differences in their activity levels. Similar analyses on two EGFR kinase variant models highlight the effect of mutations on the functional state-specific αC helix motions and these results corroborate with the previous experimental observations. Thus, our study adds confidence to the use of comparative models in understanding protein dynamics.     

Genome scan methods against more complex models: when and how much should we trust them?

The recent availability of next‐generation sequencing (NGS) has made possible the use of dense genetic markers to identify regions of the genome that may be under the influence of selection. Several statistical methods have been developed recently for this purpose. Here, we present the results of an individual‐based simulation study investigating the power and error rate of popular or recent genome scan methods: linear regression, Bayescan, BayEnv and LFMM. Contrary to previous studies, we focus on complex, hierarchical population structure and on polygenic selection. Additionally, we use a false discovery rate (FDR)‐based framework, which provides an unified testing framework across frequentist and Bayesian methods. Finally, we investigate the influence of population allele frequencies versus individual genotype data specification for LFMM and the linear regression. The relative ranking between the methods is impacted by the consideration of polygenic selection, compared to a monogenic scenario. For strongly hierarchical scenarios with confounding effects between demography and environmental variables, the power of the methods can be very low. Except for one scenario, Bayescan exhibited moderate power and error rate. BayEnv performance was good under nonhierarchical scenarios, while LFMM provided the best compromise between power and error rate across scenarios. We found that it is possible to greatly reduce error rates by considering the results of all three methods when identifying outlier loci.