A new principle of specificity in anaerobic infections—Mathematical model of mitosis-stimulated bacterial growth. The application of the tumor-tetanus phenomenon

The tumor-tetanus phenomenon describes the selective growth of anaerobic bacteria in the tumorous tissue. The present paper offers a qualitative and quantitative explanation by means of a mathematical model based on stochastical considerations. The main outcome of our studies is the theoretical demonstration that the growth of anaerobic bacteria in the living organism during the early phase of infection—contrary to the generally accepted opinion—is stimulated by the dividing host cells. This could be due to the transiently strongly enhanced oxygen consumption of these cells. It is shown that, by means of a mathematical model, the parameters characterizing the kinetics of the host cells can be deduced from the cumulative tetanus lethality curves.     

RNA Folding Argues Against a Hot-Start Origin of Life

Opinion is strongly divided on whether life arose on earth under hot or cold conditions, the hot-start and cold-start scenarios, respectively. The origin of life close to deep thermal vents appears as the majority opinion among biologists, but there is considerable biochemical evidence that high temperatures are incompatible with an RNA world. To be functional, RNA has to fold into a three-dimensional structure. We report both theoretical and experimental results on RNA folding and show that (as expected) hot conditions strongly reduce RNA folding. The theoretical results come from energy-minimization calculations of the average extent of folding of RNA, mainly from 0–90°C, for both random sequences and tRNA sequences. The experimental results are from circular-dichroism measurements of tRNA over a similar range of temperatures. The quantitative agreement between calculations and experiment is remarkable, even to the shape of the curves indicating the cooperative nature of RNA folding and unfolding. These results provide additional evidence for a lower temperature stage being necessary in the origin of life. Received: 1 March 2000 / Accepted: 14 June 2000     

Algorithm for Automatic Forced Spirometry Quality Assessment: Technological Developments

We hypothesized that the implementation of automatic real-time assessment of quality of forced spirometry (FS) may significantly enhance the potential for extensive deployment of a FS program in the community. Recent studies have demonstrated that the application of quality criteria defined by the ATS/ERS (American Thoracic Society/European Respiratory Society) in commercially available equipment with automatic quality assessment can be markedly improved. To this end, an algorithm for assessing quality of FS automatically was reported. The current research describes the mathematical developments of the algorithm. An innovative analysis of the shape of the spirometric curve, adding 23 new metrics to the traditional 4 recommended by ATS/ERS, was done. The algorithm was created through a two-step iterative process including: (1) an initial version using the standard FS curves recommended by the ATS; and, (2) a refined version using curves from patients. In each of these steps the results were assessed against one expert''s opinion. Finally, an independent set of FS curves from 291 patients was used for validation purposes. The novel mathematical approach to characterize the FS curves led to appropriate FS classification with high specificity (95%) and sensitivity (96%). The results constitute the basis for a successful transfer of FS testing to non-specialized professionals in the community.     

A comprehensive description and evolutionary analysis of 9 Loliginidae mitochondrial genomes

Forty South American aquatic plant species were selected and categorised in four a priori status classes (alien naturalised, alien invasive, native and absent) according to expert opinion, for 16 South American regions (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Falklands Islands, French Guiana, Galapagos, Guyana, Paraguay, Peru, South Georgia and South Sandwich Islands, Suriname, Uruguay and Venezuela). The 40 aquatic plant species were assessed using the US Aquatic Weed Risk Assessment (USAqWRA) scheme for each of the 16 South American regions, for a total number of 644 assessments and for South America (153 assessments). The method was benchmarked against expert opinion (invasive, non-invasive). We ranked 17 of them as naturalised, and 15 as invasive species in at least one South American region. The USAqWRA distinguished between non-invaders and invaders with an overall accuracy of 84.9% in South America and 54.1% in the 16 regions, with areas under the curves equal to 0.893 and 0.853, at a threshold score of 51.5 and 43.5, respectively. The study highlights that the USAqWRA could represent a suitable screening protocol to prioritise aquatic species that have the potential to cause negative impacts, prevent attempts of introduction and to manage risky aquatic plants in South America.     

l-Tryptophan. A non-allosteric organspecific uncompetitive inhibitor of human placental alkaline phosphatase

l-Tryptophan, but not d-tryptophan, inhibits human placental and intestinal alkaline phosphatases, but not those of liver and bone. The nature of this stereospecific organ-specific inhibition has been elucidated. Thus, from a study of the effect of substrate concentration on inhibition in which double-reciprocal plots of 1/v versus 1/s at various inhibitor concentrations were made, this inhibition is judged to be ;uncompetitive'. That the inhibition is non-allosteric is an opinion based on (1) hyperbolic curves obtained from plotting the percentage inhibition against inhibitor concentration; (2) the independence of the inhibition to heat denaturation and urea treatment; (3) the relatively low value of entropy change; and (4) a value close to unity for n, the number of l-tryptophan molecules that combine with one molecule of enzyme. Finally, a homosteric mechanism is further postulated for the inhibition by l-tryptophan based on the increase of optimum temperature for maximum velocity and the decrease of this inhibition with increasing temperature. The mechanism of this inhibition is discussed.     

Influence of Opinion Dynamics on the Evolution of Games

Under certain circumstances such as lack of information or bounded rationality, human players can take decisions on which strategy to choose in a game on the basis of simple opinions. These opinions can be modified after each round by observing own or others payoff results but can be also modified after interchanging impressions with other players. In this way, the update of the strategies can become a question that goes beyond simple evolutionary rules based on fitness and become a social issue. In this work, we explore this scenario by coupling a game with an opinion dynamics model. The opinion is represented by a continuous variable that corresponds to the certainty of the agents respect to which strategy is best. The opinions transform into actions by making the selection of an strategy a stochastic event with a probability regulated by the opinion. A certain regard for the previous round payoff is included but the main update rules of the opinion are given by a model inspired in social interchanges. We find that the fixed points of the dynamics of the coupled model are different from those of the evolutionary game or the opinion models alone. Furthermore, new features emerge such as the independence of the fraction of cooperators with respect to the topology of the social interaction network or the presence of a small fraction of extremist players.     

Examining the Gap between Science and Public Opinion about Genetically Modified Food and Global Warming

There is great uncertainty due to challenges of escalating population growth and climate change. Public perception that diverges from the scientific community may decrease the effectiveness of scientific inquiry and innovation as tools to solve these challenges. The objective of this study was to identify the factors associated with the divergence of public opinion from scientific consensus regarding the safety of genetically modified (GM) foods and human involvement in global warming (GW). Results indicate that the effects of knowledge on public opinion are complex and non-uniform across types of knowledge (i.e., perceived and actual) or issues. Political affiliation affects agreement with science; Democrats were more likely to agree that GM food is safe and human actions cause GW. Respondents who had relatively higher cognitive function or held illusionary correlations about GM food or GW were more likely to have an opinion that differed from the scientific community.     

Getting biodiversity intactness indices right: ensuring that 'biodiversity' reflects 'diversity'

The 2010 biodiversity target of a ‘significant reduction in the current rate of biodiversity loss’ presents challenges for effective measurement of changes in global/regional biodiversity. A simple ‘biodiversity intactness index’ (BII) is attractive in using available data and expert opinion, but is seen to be only weakly linked to ‘biodiversity’ in its usual sense of ‘variation’. An example illustrates how an improved BII score could result even when there are large species losses. A family of alternative biodiversity ‘representativeness indices’ better reflect variation. They use the same readily available information, plus simple species–area relationships (SAR) and genetic diversity curves. A new genetic‐diversity abundance‐fraction curve, like SAR, is linear in log–log space. The new representativeness indices incorporate, through range‐abundance curves, the abundance fraction estimates normally used for BII. Land use or climate change impacts therefore can reflect partial rather than total biodiversity losses at localities. Estimates of biodiversity gains/losses using these indices enable a novel regional planning‐based approach for addressing the 2010 target.     

Opinion Formation and the Collective Dynamics of Risk Perception

The formation of collective opinion is a complex phenomenon that results from the combined effects of mass media exposure and social influence between individuals. The present work introduces a model of opinion formation specifically designed to address risk judgments, such as attitudes towards climate change, terrorist threats, or children vaccination. The model assumes that people collect risk information from the media environment and exchange them locally with other individuals. Even though individuals are initially exposed to the same sample of information, the model predicts the emergence of opinion polarization and clustering. In particular, numerical simulations highlight two crucial factors that determine the collective outcome: the propensity of individuals to search for independent information, and the strength of social influence. This work provides a quantitative framework to anticipate and manage how the public responds to a given risk, and could help understanding the systemic amplification of fears and worries, or the underestimation of real dangers.     

Opinion Dynamics with Confirmation Bias

Background

Confirmation bias is the tendency to acquire or evaluate new information in a way that is consistent with one''s preexisting beliefs. It is omnipresent in psychology, economics, and even scientific practices. Prior theoretical research of this phenomenon has mainly focused on its economic implications possibly missing its potential connections with broader notions of cognitive science.

Methodology/Principal Findings

We formulate a (non-Bayesian) model for revising subjective probabilistic opinion of a confirmationally-biased agent in the light of a persuasive opinion. The revision rule ensures that the agent does not react to persuasion that is either far from his current opinion or coincides with it. We demonstrate that the model accounts for the basic phenomenology of the social judgment theory, and allows to study various phenomena such as cognitive dissonance and boomerang effect. The model also displays the order of presentation effect–when consecutively exposed to two opinions, the preference is given to the last opinion (recency) or the first opinion (primacy) –and relates recency to confirmation bias. Finally, we study the model in the case of repeated persuasion and analyze its convergence properties.

Conclusions

The standard Bayesian approach to probabilistic opinion revision is inadequate for describing the observed phenomenology of persuasion process. The simple non-Bayesian model proposed here does agree with this phenomenology and is capable of reproducing a spectrum of effects observed in psychology: primacy-recency phenomenon, boomerang effect and cognitive dissonance. We point out several limitations of the model that should motivate its future development.     

Public Opinion, Political Communication and the Internet

In contemporary society public opinion is generally mediated by the mass media, which has come to encompass the Habermasian 'public sphere'. This arena is now characterised by the conflict between market and democratic principles, by competing interests of politicians and the media. The presentation of information for debate becomes distorted. The opinion of the 'public' is no longer created through deliberation, but is constructed through systems of communication, in conflict with political actors, who seek to retain control of the dissemination of information. The expansion of the internet as a new method of communication provides a potential challenge to the primacy of the traditional media and political parties as formers of public opinion.     

Hrdlicka revisited (external auditory canal exostoses)

To determine possible matter of opinion as to the existence of exostoses in the outer ear canals of Indian skulls from Dakota as an explanation for the marked difference in incidence of these tumors found by Hrdlicka and by us in previous studies, we have examined skeletal specimens reportedly seen by Hrdlicka, independently, and compared his and our results. The Smithsonian-Mobridge skulls both in Hrdlicka's and our opinion show definitely more exostoses than are found in other South Dakota Indian skeletal material we have examined. Our results here suggest a 5–6% difference attributable to opinion of the examiner. Reasons for the dissimilarity in frequency of exostoses between the Smithsonian-Mobridge skulls and other Dakota Indian skeletal collections are advanced.     

Measures, perceptions and scaling patterns of aggregated species distributions

Non-random (aggregated) species distributions arise from habitat heterogeneity and nonlinear biotic processes. A comprehensive understanding of the concept of aggregation, as well as its measurement, is pivotal to our understanding of species distributions and macroecological patterns. Here, using an individual-based model, we analyzed opinions on the concept of aggregation from the public and experts (trained ecologists), in addition to those calculated from a variety of aggregation indices. Three forms of scaling patterns (logarithmic, power-law and lognormal) and four groups of scaling trajectories emerged. The experts showed no significant difference from the public, although with a much lower deviation. The public opinion was partially influenced by the abundance of individuals in the spatial map, which was not found in the experts. With the increase of resolution (decrease of grain), aggregation indices showed a general trend from significantly different to significantly similar to the expert opinion. The over-dispersion index (i.e. the clumping parameter k in the negative binomial distribution) performed, at certain scales, as the closest index to the expert opinion. Examining performance of aggregation measures from different groups of scaling patterns was proposed as a practical way of analyzing spatial structures. The categorization of the scaling patterns of aggregation measures, as well as their over- and in-sensitivity towards spatial structures, thus not only provides a potential solution to the modifiable areal unit problem, but also unveils the interrelationship among the concept, measures and perceptions of aggregated species distributions.     

Opinion Formation by Social Influence: From Experiments to Modeling

Predicting different forms of collective behavior in human populations, as the outcome of individual attitudes and their mutual influence, is a question of major interest in social sciences. In particular, processes of opinion formation have been theoretically modeled on the basis of a formal similarity with the dynamics of certain physical systems, giving rise to an extensive collection of mathematical models amenable to numerical simulation or even to exact solution. Empirical ground for these models is however largely missing, which confine them to the level of mere metaphors of the real phenomena they aim at explaining. In this paper we present results of an experiment which quantifies the change in the opinions given by a subject on a set of specific matters under the influence of others. The setup is a variant of a recently proposed experiment, where the subject’s confidence on his or her opinion was evaluated as well. In our realization, which records the quantitative answers of 85 subjects to 20 questions before and after an influence event, the focus is put on characterizing the change in answers and confidence induced by such influence. Similarities and differences with the previous version of the experiment are highlighted. We find that confidence changes are to a large extent independent of any other recorded quantity, while opinion changes are strongly modulated by the original confidence. On the other hand, opinion changes are not influenced by the initial difference with the reference opinion. The typical time scales on which opinion varies are moreover substantially longer than those of confidence change. Experimental results are then used to estimate parameters for a dynamical agent-based model of opinion formation in a large population. In the context of the model, we study the convergence to full consensus and the effect of opinion leaders on the collective distribution of opinions.     

Opinion Formation Models on a Gradient

Statistical physicists have become interested in models of collective social behavior such as opinion formation, where individuals change their inherently preferred opinion if their friends disagree. Real preferences often depend on regional cultural differences, which we model here as a spatial gradient g in the initial opinion. The gradient does not only add reality to the model. It can also reveal that opinion clusters in two dimensions are typically in the standard (i.e., independent) percolation universality class, thus settling a recent controversy about a non-consensus model. However, using analytical and numerical tools, we also present a model where the width of the transition between opinions scales , not as in independent percolation, and the cluster size distribution is consistent with first-order percolation.     

全媒体时代医院舆情管理危机的应对措施

随着科学技术的快速发展,各类新媒体的出现让部分医疗事件有了更高的关注度。近年来医疗纠纷日益严重,在全媒体时代的背景下,医院舆情危机频繁发生,对医院声誉和医患关系产生较大的不良影响。如何正确的引导网络舆情、降低医院舆情危机带来的不利影响成为急需解决的问题。本研究就全媒体时代医院舆情管理危机展开探讨,从医院舆情管理现状、医院舆情管理所存在的问题及相关的应对措施这三个方面入手,旨在为医院管理者在全媒体背景下控制医院舆情、化解医院突发事件、改善医患关系提供参考。     

Cross-Language Opinion Lexicon Extraction Using Mutual-Reinforcement Label Propagation

There is a growing interest in automatically building opinion lexicon from sources such as product reviews. Most of these methods depend on abundant external resources such as WordNet, which limits the applicability of these methods. Unsupervised or semi-supervised learning provides an optional solution to multilingual opinion lexicon extraction. However, the datasets are imbalanced in different languages. For some languages, the high-quality corpora are scarce or hard to obtain, which limits the research progress. To solve the above problems, we explore a mutual-reinforcement label propagation framework. First, for each language, a label propagation algorithm is applied to a word relation graph, and then a bilingual dictionary is used as a bridge to transfer information between two languages. A key advantage of this model is its ability to make two languages learn from each other and boost each other. The experimental results show that the proposed approach outperforms baseline significantly.     

Beyond Rational Decision-Making: Modelling the Influence of Cognitive Biases on the Dynamics of Vaccination Coverage

Background

Theoretical studies predict that it is not possible to eradicate a disease under voluntary vaccination because of the emergence of non-vaccinating “free-riders” when vaccination coverage increases. A central tenet of this approach is that human behaviour follows an economic model of rational choice. Yet, empirical studies reveal that vaccination decisions do not necessarily maximize individual self-interest. Here we investigate the dynamics of vaccination coverage using an approach that dispenses with payoff maximization and assumes that risk perception results from the interaction between epidemiology and cognitive biases.

Methods

We consider a behaviour-incidence model in which individuals perceive actual epidemiological risks as a function of their opinion of vaccination. As a result of confirmation bias, sceptical individuals (negative opinion) overestimate infection cost while pro-vaccines individuals (positive opinion) overestimate vaccination cost. We considered a feedback between individuals and their environment as individuals could change their opinion, and thus the way they perceive risks, as a function of both the epidemiology and the most common opinion in the population.

Results

For all parameter values investigated, the infection is never eradicated under voluntary vaccination. For moderately contagious diseases, oscillations in vaccination coverage emerge because individuals process epidemiological information differently depending on their opinion. Conformism does not generate oscillations but slows down the cultural response to epidemiological change.

Conclusion

Failure to eradicate vaccine preventable disease emerges from the model because of cognitive biases that maintain heterogeneity in how people perceive risks. Thus, assumptions of economic rationality and payoff maximization are not mandatory for predicting commonly observed dynamics of vaccination coverage. This model shows that alternative notions of rationality, such as that of ecological rationality whereby individuals use simple cognitive heuristics, offer promising new avenues for modelling vaccination behaviour.     

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.     

Social Influence and the Collective Dynamics of Opinion Formation

Social influence is the process by which individuals adapt their opinion, revise their beliefs, or change their behavior as a result of social interactions with other people. In our strongly interconnected society, social influence plays a prominent role in many self-organized phenomena such as herding in cultural markets, the spread of ideas and innovations, and the amplification of fears during epidemics. Yet, the mechanisms of opinion formation remain poorly understood, and existing physics-based models lack systematic empirical validation. Here, we report two controlled experiments showing how participants answering factual questions revise their initial judgments after being exposed to the opinion and confidence level of others. Based on the observation of 59 experimental subjects exposed to peer-opinion for 15 different items, we draw an influence map that describes the strength of peer influence during interactions. A simple process model derived from our observations demonstrates how opinions in a group of interacting people can converge or split over repeated interactions. In particular, we identify two major attractors of opinion: (i) the expert effect, induced by the presence of a highly confident individual in the group, and (ii) the majority effect, caused by the presence of a critical mass of laypeople sharing similar opinions. Additional simulations reveal the existence of a tipping point at which one attractor will dominate over the other, driving collective opinion in a given direction. These findings have implications for understanding the mechanisms of public opinion formation and managing conflicting situations in which self-confident and better informed minorities challenge the views of a large uninformed majority.