Computing the Stochastic Complexity of Simple Probabilistic Graphical Models

Minimum Description Length (MDL) is an information-theoretic principle that can be used for model selection and other statistical inference tasks. There are various ways to use the principle in practice. One theoretically valid way is to use the normalized maximum likelihood (NML) criterion. Due to computational difficulties, this approach has not been used very often. This thesis presents efficient floating-point algorithms that make it possible to compute the NML for multinomial, Naive Bayes and Bayesian forest models. None of the presented algorithms rely on asymptotic analysis and with the first two model classes we also discuss how to compute exact rational number solutions.Koneoppimisessa ollaan kiinnostuneita löytämään automaattisesti malleja, jotka sopivat yhteen mahdollisimman hyvin havaintojen kanssa. Nämä havainnot esitetään usein mittaustuloksina taulukkomuodossa. Tällaisen taulukon toivotaan sisältävän kaikki tarkasteltavan ilmiön kannalta oleelliset ominaisuudet. Ilmiötä on kuitenkin vaikea hahmottaa vain tarkastelemalla taulukkoa, mistä johtuen taulukon sisältämästä tiedosta rakennetaan usein malli. Koneoppimisessa annetaan tietokoneen etsiä tällainen malli automaattisesti ennalta määritellystä valtavan suuresta mallijoukosta. Hyvä malli on sellainen, joka ei pyri kuvaamaan esitettyä äärellistä aineistoa mahdollisimman tarkasti, vaan pystyy yleistämään ja kuvaamaan siten myös tulevaisuudessa kerättävät havainnot. Koneoppimismenetelmät sisältävät useita erilaisia mittareita mallien hyvyyden määrittämiseksi. Hyvä mittari pystyy löytämään hyvän, ilmiötä kuvaavan mallin myös pienen havaintoaineiston perusteella. Nämä mittarit, joita kutsutaan mallinvalintakriteereiksi, ovat yleisiä mallijoukosta riippumattomia periaatteita, joskin ne joudutaan käytännössä usein sovittamaan tiettyyn mallijoukkoon soveltuviksi. Tällainen sovittaminen saattaa olla monesti hankalaa ja sovitettua menetelmää käytettäessä saatetaan tarvita paljon laskentatehoa. Yksi mallinvalintamenetelmistä on informaatioteoriaan pohjautuva, erityisesti lyhimmän kuvauspituuden periaatteeseen ja stokastisen kompleksisuuden käsitteeseen pohjautuva normalisoidun suurimman uskottavuuden kriteeri. Tämä menetelmä on teoreettisesti hyvin perusteltu ja osoittautunut myös useissa testeissä hyvin toimivaksi. Kuitenkin monien tilastomallityyppien hyvyyden arvioiminen tällä menetelmällä on laskennallisesti erittäin työlästä, joten monissa sovelluksissa kyseisen menetelmän käyttö on ollut pitkälti mahdotonta. Tässä väitöskirjassa esitetään tehokkaita normalisoidun suurimman uskottavuuden laskentamenetelmiä kolmelle yksinkertaiselle graafisiin malleihin kuuluvalle mallityypille. Lisäksi työssä selkiytetään kokonaiskuvaa aikaisempien laskentamenetelmien suhteen ja osoitetaan yhteyksiä muihin tutkimusongelmiin

Spatial disease dynamics of free-living pathogens under pathogen predation

The epidemiological dynamics of potentially free-living pathogens are often studied with respect to a specific pathogen species (e.g., cholera) and most studies concentrate only on host-pathogen interactions. Here we show that metacommunity-level interactions can alter conventional spatial disease dynamics. We introduce a pathogen eating consumer species and investigate a deterministic epidemiological model of two habitat patches, where both patches can be occupied by hosts, pathogens, and consumers of free-living pathogens. An isolated habitat patch shows periodic disease outbreaks in the host population, arising from cyclic consumer-pathogen dynamics. On the other hand, consumer dispersal between the patches generate asymmetric disease prevalence, such that the host population in one patch stays disease-free, while disease outbreaks occur in the other patch. Such asymmetry can also arise with host dispersal, where infected hosts carry pathogens to the other patch. This indirect movement of pathogens causes also a counter-intuitive effect: decreasing morbidity in a focal patch under increasing pathogen immigration. Our results underline that community-level interactions influence disease dynamics and consistent spatial asymmetry can arise also in spatially homogeneous systems.Peer reviewe

Understanding cellular growth strategies via optimal control

Evolutionary prediction and control are increasingly interesting research topics that are expanding to new areas of application. Unravelling and anticipating successful adaptations to different selection pressures becomes crucial when steering rapidly evolving cancer or microbial populations towards a chosen target. Here we introduce and apply a rich theoretical framework of optimal control to understand adaptive use of traits, which in turn allows eco-evolutionarily informed population control. Using adaptive metabolism and microbial experimental evolution as a case study, we show how demographic stochasticity alone can lead to lag time evolution, which appears as an emergent property in our model. We further show that the cycle length used in serial transfer experiments has practical importance as it may cause unintentional selection for specific growth strategies and lag times. Finally, we show how frequency-dependent selection can be incorporated to the state-dependent optimal control framework allowing the modelling of complex eco-evolutionary dynamics. Our study demonstrates the utility of optimal control theory in elucidating organismal adaptations and the intrinsic decision making of cellular communities with high adaptive potential.Peer reviewe

Strong selective environments determine evolutionary outcome in time-dependent fitness seascapes

The impact of fitness landscape features on evolutionary outcomes has attracted considerable interest in recent decades. However, evolution often occurs under time-dependent selection in so-called fitness seascapes where the landscape is under flux. Fitness seascapes are an inherent feature of natural environments, where the landscape changes owing both to the intrinsic fitness consequences of previous adaptations and extrinsic changes in selected traits caused by new environments. The complexity of such seascapes may curb the predictability of evolution. However, empirical efforts to test this question using a comprehensive set of regimes are lacking. Here, we employed an in vitro microbial model system to investigate differences in evolutionary outcomes between time-invariant and time-dependent environments, including all possible temporal permutations, with three subinhibitory antimicrobials and a viral parasite (phage) as selective agents. Expectedly, time-invariant environments caused stronger directional selection for resistances compared to time-dependent environments. Intriguingly, however, multidrug resistance outcomes in both cases were largely driven by two strong selective agents (rifampicin and phage) out of four agents in total. These agents either caused cross-resistance or obscured the phenotypic effect of other resistance mutations, modulating the evolutionary outcome overall in time-invariant environments and as a function of exposure epoch in time-dependent environments. This suggests that identifying strong selective agents and their pleiotropic effects is critical for predicting evolution in fitness seascapes, with ramifications for evolutionarily informed strategies to mitigate drug resistance evolution.Peer reviewe

Evolution of Camouflage Drives Rapid Ecological Change in an Insect Community

SummaryBackgroundEvolutionary change in individual species has been hypothesized to have far-reaching consequences for entire ecological communities [1–3], and such coupling of ecological and evolutionary dynamics (“eco-evolutionary dynamics”) has been demonstrated for a variety systems [4–7]. However, the general importance of evolutionary dynamics for ecological dynamics remains unclear. Here, we investigate how spatial patterns of local adaptation in the stick insect Timema cristinae, driven by the interaction between multiple evolutionary processes, structure metapopulations, communities, and multitrophic interactions.ResultsObservations of a wild T. cristinae metapopulation show that locally imperfect camouflage reduces population size and that the effect of such maladaptation is comparable to the effects of more traditional ecological factors, including habitat patch size and host-plant species identity. Field manipulations of local adaptation and bird predation support the hypothesis that maladaptation reduces population size through an increase in bird predation. Furthermore, these field experiments show that maladaptation in T. cristinae and consequent increase in bird predation reduce the pooled abundance and species richness of the co-occurring arthropod community, and ultimately cascade to decrease herbivory on host plants. An eco-evolutionary model of the observational data demonstrates that the demographic cost of maladaptation decreases habitat patch occupancy by T. cristinae but enhances metapopulation-level adaptation.ConclusionsThe results demonstrate a pervasive effect of ongoing evolution in a spatial context on population and community dynamics. The eco-evolutionary model makes testable predictions about the influence of the spatial configuration of the patch network on metapopulation size and the spatial scale of adaptation

Drug-induced resistance evolution necessitates less aggressive treatment

Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies. Author summary Evolution of drug resistance to anticancer and antimicrobial therapies is widespread among cancer and pathogen cell populations. Classical theory posits strictly that genetic and phenotypic variation is generated in evolving populations independently of the selection pressure. However, recent experimental findings among antimicrobial agents, traditional cytotoxic chemotherapies and targeted cancer therapies suggest that treatment not only imposes selection but can also affect the rate of adaptation by increasing mutability. Here we analyse a model with drug-induced increase in mutation rate and explore its consequences for treatment optimisation. We argue that the true biological cost of treatment is not limited to the harmful side-effects, but instead realises even more profoundly by fundamentally changing the underlying eco-evolutionary dynamics within the microenvironment. Using the concept of evolutionary rescue, we formulate the treatment as an optimal control problem and solve the optimal elimination strategy, which minimises the probability of evolutionary rescue. We show that aggressive elimination strategies, which aim at eradication as fast as possible and which represent the current standard of care, can be detrimental even with modest drug-induced increases (fold changePeer reviewe

The relationship between electrophysiological and hemodynamic measures of neural activity varies across picture naming tasks: A multimodal magnetoencephalography-functional magnetic resonance imaging study

Different neuroimaging methods can yield different views of taskdependent neural engagement. Studies examining the relationship between electromagnetic and hemodynamic measures have revealed correlated patterns across brain regions but the role of the applied stimulation or experimental tasks in these correlation patterns is still poorly understood. Here, we evaluated the across-tasks variability of MEG-fMRI relationship using data recorded during three distinct naming tasks (naming objects and actions from action images, and objects from object images), from the same set of participants. Our results demonstrate that the MEG-fMRI correlation pattern varies according to the performed task, and that this variability shows distinct spectral profiles across brain regions. Notably, analysis of the MEG data alone did not reveal modulations across the examined tasks in the timefrequency windows emerging from the MEG-fMRI correlation analysis. Our results suggest that the electromagnetic-hemodynamic correlation could serve as a more sensitive proxy for task-dependent neural engagement in cognitive tasks than isolated within-modality measures.Peer reviewe

Observational evidence that maladaptive gene flow reduces patch occupancy in a wild insect metapopulation: BRIEF COMMUNICATION

Theory predicts that dispersal throughout metapopulations has a variety of consequences for the abundance and distribution of species. Immigration is predicted to increase abundance and habitat patch occupancy, but gene flow can have both positive and negative demographic consequences. Here, we address the eco-evolutionary effects of dispersal in a wild metapopulation of the stick insect Timema cristinae, which exhibits variable degrees of local adaptation throughout a heterogeneous habitat patch network of two host-plant species. To disentangle the ecological and evolutionary contributions of dispersal to habitat patch occupancy and abundance, we contrasted the effects of connectivity to populations inhabiting conspecific host plants and those inhabiting the alternate host plant. Both types of connectivity should increase patch occupancy and abundance through increased immigration and sharing of beneficial alleles through gene flow. However, connectivity to populations inhabiting the alternate host-plant species may uniquely cause maladaptive gene flow that counters the positive demographic effects of immigration. Supporting these predictions, we find the relationship between patch occupancy and alternate-host connectivity to be significantly smaller in slope than the relationship between patch occupancy and conspecific-host connectivity. Our findings illustrate the ecological and evolutionary roles of dispersal in driving the distribution and abundance of species

Finnish late adolescents' physical activity during COVID-19 spring 2020 lockdown

Background Physical activity (PA) is recognised as one of the leading and effective strategies to prevent non-communicable diseases that boosts the immune system to fight against diseases. Closures of schools, sport clubs and facilities because of COVID-19 reduced the opportunities to participate in PA. We aimed to examine physical activity levels of late adolescents, the contexts to be physical active and its changes during the spring 2020 lockdown. Methods A national representative sample of late adolescents in general upper secondary school (n = 2408, females = 64%, mean age = 17.2y, SD = 0.63) completed self-report online surveys on PA behaviours between March and June 2020. Multinominal logistic regression analyses were performed to identify correlates with PA, and decision tree analyses to ascertain the perceived changes on PA during lockdown based on sport club aspirations and levels of PA. Results Among the late adolescents, the distribution of PA frequency was 23% (0-2 days/week), 35% (3-4 days/week), 30% (5-6 days/week) and 12% (7 days/week), and differences between males and females were not statistically significant. Participation in both indoor and outdoor PA were 50 times more likely to report daily PA (OR = 54.28, CI = 15.16-194.37) than non-participation. A quarter of late adolescents were not part of a sports club, yet their PA levels increased. Although sports club members generally perceived they did less PA during lockdown, over a third of sport club members with competitive aspirations reported daily PA. Conclusions Overall, most late adolescents reported their PA levels decreased during lockdown. Findings from this study continue to demonstrate factors associated with PA in the context of the COVID-19 lockdown