Switching and diffusion models for gene regulation networks

We analyze a hierarchy of three regimes for modeling gene regulation. The most complete model is a continuous time, discrete state space, Markov jump process. An intermediate 'switch plus diffusion' model takes the form of a stochastic differential equation driven by an independent continuous time Markov switch. In the third 'switch plus ODE' model the switch remains but the diffusion is removed. The latter two models allow for multi-scale simulation where, for the sake of computational efficiency, system components are treated differently according to their abundance. The 'switch plus ODE' regime was proposed by Paszek (Modeling stochasticity in gene regulation: characterization in the terms of the underlying distribution function, Bulletin of Mathematical Biology, 2007), who analyzed the steady state behavior, showing that the mean was preserved but the variance only approximated that of the full model. Here, we show that the tools of stochastic calculus can be used to analyze first and second moments for all time. A technical issue to be addressed is that the state space for the discrete-valued switch is infinite. We show that the new 'switch plus diffusion' regime preserves the biologically relevant measures of mean and variance, whereas the 'switch plus ODE' model uniformly underestimates the variance in the protein level. We also show that, for biologically relevant parameters, the transient behaviour can differ significantly from the steady state, justifying our time-dependent analysis. Extra computational results are also given for a protein dimerization model that is beyond the scope of the current analysis

Service Time Scheduling Method for Wireless Networks Powered by Renewable Energy

Tohoku University加藤

The influence of cultural values on brand loyalty

It is well documented that culture can influence consumer attitudes and behavior. While there have been numerous studies on how culture influences the four Ps of the marketing mix, few researchers have examined its effect on customer loyalty. More specifically, how consumers who identify more with certain cultural traits are likely to be more brand loyal. Using Hofstede&rsquo;s cultural dimensions, this study empirically examines cultural effects on consumer-reported &ldquo;proneness&rdquo; to brand loyalty and finds that those who scored highly in individualism and uncertainty avoidance have greater affinity for exhibiting loyalty to a brand.<br /

How well does the index of receptivity to tobacco industry promotion discriminate between smoking and never smoking adolescents.

Tobacco advertising is often named as the culprit that causes children to start smoking (Lancaster &amp; Lancaster, 2003). This belief can partly be attributed to the Index of Receptivity to Tobacco Industry Promotion (IRTIP) developed by Evans, Farkas, Gilpin, Berry, &amp; Pierce (1995). IRTIP was later modified and used by Pierce, Choi, Gilpin, Farkas, &amp; Berry (1998) in a longitudinal study that claimed to have found a causal link between advertising and adolescent cigarette trial. The model is advertised by the American National Cancer Institute (2004) as being able to measure the likelihood of an adolescent starting smoking. Because of Pierce&rsquo;s causality claim and this endorsement, IRTIP has been widely adopted by tobacco-control researchers. Consequently, the results from IRTIP based surveys have played a central role in influencing tobacco control policy. Based on the logic that a model used to predict the chances of a non-smoker becoming a smoker should be able to distinguish between these two groups, discriminant analysis with dummy coded variables was used to validate IRTIP. The results show that while IRTIP classifies never-smokers well, it grossly misclassifies smokers. This leads to questions about the validity of the model and of studies using IRTIP.<br /

On the galaxy-halo connection in the EAGLE simulation

Empirical models of galaxy formation require assumptions about the correlations between galaxy and halo properties. These may be calibrated against observations or inferred from physical models such as hydrodynamical simulations. In this Letter, we use the EAGLE simulation to investigate the correlation of galaxy size with halo properties. We motivate this analysis by noting that the common assumption of angular momentum partition between baryons and dark matter in rotationally supported galaxies overpredicts both the spread in the stellar mass–size relation and the anticorrelation of size and velocity residuals, indicating a problem with the galaxy–halo connection it implies. We find the EAGLE galaxy population to perform significantly better on both statistics, and trace this success to the weakness of the correlations of galaxy size with halo mass, concentration and spin at fixed stellar mass. Using these correlations in empirical models will enable fine-grained aspects of galaxy scalings to be matched

Bunchy top disease of bananas

Short publication describing symptoms and control of banana bunchy top disease

On Adversarial Examples and Stealth Attacks in Artificial Intelligence Systems

In this work we present a formal theoretical framework for assessing and analyzing two classes of malevolent action towards generic Artificial Intelligence (AI) systems. Our results apply to general multi-class classifiers that map from an input space into a decision space, including artificial neural networks used in deep learning applications. Two classes of attacks are considered. The first class involves adversarial examples and concerns the introduction of small perturbations of the input data that cause misclassification. The second class, introduced here for the first time and named stealth attacks, involves small perturbations to the AI system itself. Here the perturbed system produces whatever output is desired by the attacker on a specific small data set, perhaps even a single input, but performs as normal on a validation set (which is unknown to the attacker). We show that in both cases, i.e., in the case of an attack based on adversarial examples and in the case of a stealth attack, the dimensionality of the AI's decision-making space is a major contributor to the AI's susceptibility. For attacks based on adversarial examples, a second crucial parameter is the absence of local concentrations in the data probability distribution, a property known as Smeared Absolute Continuity. According to our findings, robustness to adversarial examples requires either (a) the data distributions in the AI's feature space to have concentrated probability density functions or (b) the dimensionality of the AI's decision variables to be sufficiently small. We also show how to construct stealth attacks on high-dimensional AI systems that are hard to spot unless the validation set is made exponentially large

Assessing the current and future potential geographic distribution of the American dog tick, Dermacentor variabilis (Say) (Acari: Ixodidae) in North America

The American dog tick, Dermacentor variabilis, is a veterinary- and medically- significant tick species that is known to transmit several diseases to animal and human hosts. The spatial distribution of this species in North America is not well understood, however; and knowledge of likely changes to its future geographic distribution owing to ongoing climate change is needed for proper public health planning and messaging. Two recent studies have evaluated these topics for D. variabilis; however, less-rigorous modeling approaches in those studies may have led to erroneous predictions. We evaluated the present and future distribution of this species using a correlative maximum entropy approach, using publicly available occurrence information. Future potential distributions were predicted under two representative concentration pathway (RCP) scenarios; RCP 4.5 for low-emissions and RCP 8.5 for high-emissions. Our results indicated a broader current distribution of this species in all directions relative to its currently known extent, and dramatic potential for westward and northward expansion of suitable areas under both climate change scenarios. Implications for disease ecology and public health are discussed.PHS grant number AI070908National Institutes of Allergy and Infectious DiseaseIIA-1920946National Science Foundatio

Genetic study of congenital bile-duct dilatation identifies de novo and inherited variants in functionally related genes

Background: Congenital dilatation of the bile-duct (CDD) is a rare, mostly sporadic, disorder that results in bile retention with severe associated complications. CDD affects mainly Asians. To our knowledge, no genetic study has ever been conducted. Methods: We aim to identify genetic risk factors by a “trio-based” exome-sequencing approach, whereby 31 CDD probands and their unaffected parents were exome-sequenced. Seven-hundred controls from the local population were used to detect gene-sets significantly enriched with rare variants in CDD patients. Results: Twenty-one predicted damaging de novo variants (DNVs; 4 protein truncating and 17 missense) were identified in several evolutionarily constrained genes (p &#60; 0.01). Six genes carrying DNVs were associated with human developmental disorders involving epithelial, connective or bone morphologies (PXDN, RTEL1, ANKRD11, MAP2K1, CYLD, ACAN) and four linked with cholangio- and hepatocellular carcinomas (PIK3CA, TLN1 CYLD, MAP2K1). Importantly, CDD patients have an excess of DNVs in cancer-related genes (p &#60; 0.025). Thirteen genes were recurrently mutated at different sites, forming compound heterozygotes or functionally related complexes within patients. Conclusions: Our data supports a strong genetic basis for CDD and show that CDD is not only genetically heterogeneous but also non-monogenic, requiring mutations in more than one genes for the disease to develop. The data is consistent with the rarity and sporadic presentation of CDD

The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning

In this work, we assess the theoretical limitations of determining guaranteed stability and accuracy of neural networks in classification tasks. We consider classical distribution-agnostic framework and algorithms minimising empirical risks and potentially subjected to some weights regularisation. We show that there is a large family of tasks for which computing and verifying ideal stable and accurate neural networks in the above settings is extremely challenging, if at all possible, even when such ideal solutions exist within the given class of neural architectures