Pathogen-Induced Defense Signaling and Signal Crosstalk in Arabidopsis

Erwinia carotovora subsp. carotovora is a bacterial phytopathogen that causes soft rot in various agronomically important crop plants. A genetically specified resistance to E. carotovora has not been defined, and plant resistance to this pathogen is established through nonspecific activation of basal defense responses. This, together with the broad host range, makes this pathogen a good model for studying the activation of plant defenses. Production and secretion of plant cell wall-degrading enzymes (PCWDE) are central to the virulence of E. carotovora. It also possesses the type III secretion system (TTSS) utilized by many Gram-negative bacteria to secrete virulence- promoting effector proteins to plant cells. This study elucidated the role of E. carotovora HrpN (HrpNEcc), an effector protein secreted through TTSS, and the contribution of this protein in the virulence of E. carotovora. Treatment of plants with HrpNEcc was demonstrated to induce a hypersensitive response (HR) as well as resistance to E. carotovora. Resistance induced by HrpNEcc required both salicylic acid (SA)- and jasmonate/ethylene (JA/ET)-dependent defense signaling in Arabidopsis. Simultaneous treatment of Arabidopsis with HrpNEcc and PCWDE polygalacturonase PehA elicited accelerated and enhanced induction of defense genes but also increased production of superoxide and lesion formation. This demonstrates mutual amplification of defense signaling by these two virulence factors of E. carotovora. Identification of genes that are rapidly induced in response to a pathogen can provide novel information about the early events occurring in the plant defense response. CHLOROPHYLLASE 1 (AtCLH1) and EARLY RESPONSIVE TO DEHYDRATION 15 (ERD15) are both rapidly triggered by E. carotovora in Arabidopsis. Characterization of AtCLH1 encoding chlorophyll-degrading enzyme chlorophyllase indicated that it might have a role in chlorophyll degradation during plant tissue damage. Silencing of this gene resulted in increased accumulation of reactive oxygen species (ROS) in response to pathogen infection in a light-dependent manner. This led to enhanced SA-dependent defenses and resistance to E. carotovora. Moreover, crosstalk between different defense signaling pathways was observed; JA-dependent defenses and resistance to fungal pathogen Alternaria brassicicola were impaired, indicating antagonism between SA- and JA-dependent signaling. Characterization of ERD15 suggested that it is a novel, negative regulator of abscisic acid (ABA) signaling in Arabidopsis. Overexpression of ERD15 resulted in insensitivity to ABA and reduced tolerance of the plants to dehydration stress. However, simultaneously, the resistance of the plants to E. carotovora was enhanced. Silencing of ERD15 improved freezing and drought tolerance of transgenic plants. This, together with the reducing effect of ABA on seed germination, indicated hypersensitivity to this phytohormone. ERD15 was hypothesized to act as a capacitor that controls the appropriate activation of ABA responses in Arabidopsis.

Novel Molecular Mechanisms of Arabidopsis Disease Resistance

Plants are capable of recognizing phytopathogens through the perception of pathogen-derived molecules or plant cell-wall degradation products due to the activities of pathogen-secreted enzymes. Such elicitor recognition events trigger an array of inducible defense responses involving signal transduction networks and massive transcriptional re-programming. The outcome of a pathogen infection relies on the balance between different signaling pathways, which are integrated by regulatory proteins. This thesis characterized two key regulatory components: a damage control enzyme, chlorophyllase 1 (AtCHL1), and a transcription factor, WRKY70. Their roles in defense signaling were then investigated. The Erwinia-derived elicitors rapidly activated the expression of AtCLH1 and WRKY70 through different signaling pathways. The expression of the AtCHL1 gene was up-regulated by jasmonic acid (JA) but down-regulated by salicylic acid (SA), whereas WRKY70 was activated by SA and repressed by JA. In order to elucidate the functions of AtCLH1 and WRKY70 in plant defense, stable transgenic lines were produced where these genes were overexpressed or silenced. Additionally, independent knockout lines were also characterized. Bacterial and fungal pathogens were then used to assess the contribution of these genes to the Arabidopsis disease resistance. The transcriptional modulation of AtCLH1 by either the constitutive over-expression or RNAi silencing caused alterations in the chlorophyll-to-chlorophyllide ratio, supporting the claim that chlorophyllase 1 has a role in the chlorophyll degradation pathway. Silencing of this gene led to light-dependent over-accumulation of the reactive oxygen species (ROS) in response to infection by Erwinia carotovora subsp. carotovora SCC1. This was followed by an enhanced induction of SA-dependent defense genes and an increased resistance to this pathogen. Interestingly, little effect on the pathogen-induced SA accumulation at the early infection was observed, suggesting that action of ROS might potentiate SA signaling. In contrast, the pathogen-induced JA production was significantly reduced in the RNAi silenced plants. Moreover, JA signaling and resistance to Alternaria brassicicola were impaired. These observations provide support for the argument that the ROS generated in chloroplasts might have a negative impact on JA signaling. The over-expression of WRKY70 resulted in an enhanced resistance to E. carotovora subsp. carotovora SCC1, Pseudomonas syringae pv. tomato DC3000 and Erysiphe cichoracearum UCSC1, whilst an antisense suppression or an insertional inactivation of WRKY70 led to a compromised resistance to E. carotovora subsp. carotovora SCC1 and to E. cichoracearum UCSC1 but not to P. syringae pv. tomato DC3000. Gene expression analysis revealed that WRKY70 activated many known defense-related genes associated with the SAR response but suppressed a subset of the JA-responsive genes. In particular, I was able to show that both the basal and the induced expression of AtCLH1 was enhanced by the antisense silencing or the insertional inactivation of WRKY70, whereas a reduction in AtCLH1 expression was observed in the WRKY70 over-expressors following an MeJA application or an A. brassicicola infection. Moreover, the SA-induced suppression of AtCLH1 was relieved in wrky70 mutants. These results indicate that WRKY70 down-regulates AtCLH1. An epistasis analysis suggested that WRKY70 functions downstream of the NPR1 in an SA-dependent signaling pathway. When challenged with A. brassicicola, WRKY70 over-expressing plants exhibited a compromised disease resistance while wrky70 mutants had the opposite effect. These results confirmed the WRKY70-mediated inhibitory effects on JA signaling. Furthermore, the WRKY70-controlled suppression of A. brassicicola resistance was mainly through an NPR1-dependent mechanism. Taking all the data together, I suggest that the pathogen-responsive transcription factor WRKY70 is a common component in both SA- and JA-dependent pathways and plays a crucial role in the SA-mediated suppression of JA signaling.

Towards the Use of Radar Winds in Numerical Weather Prediction

Numerical weather prediction (NWP) models provide the basis for weather forecasting by simulating the evolution of the atmospheric state. A good forecast requires that the initial state of the atmosphere is known accurately, and that the NWP model is a realistic representation of the atmosphere. Data assimilation methods are used to produce initial conditions for NWP models. The NWP model background field, typically a short-range forecast, is updated with observations in a statistically optimal way. The objective in this thesis has been to develope methods in order to allow data assimilation of Doppler radar radial wind observations. The work has been carried out in the High Resolution Limited Area Model (HIRLAM) 3-dimensional variational data assimilation framework. Observation modelling is a key element in exploiting indirect observations of the model variables. In the radar radial wind observation modelling, the vertical model wind profile is interpolated to the observation location, and the projection of the model wind vector on the radar pulse path is calculated. The vertical broadening of the radar pulse volume, and the bending of the radar pulse path due to atmospheric conditions are taken into account. Radar radial wind observations are modelled within observation errors which consist of instrumental, modelling, and representativeness errors. Systematic and random modelling errors can be minimized by accurate observation modelling. The impact of the random part of the instrumental and representativeness errors can be decreased by calculating spatial averages from the raw observations. Model experiments indicate that the spatial averaging clearly improves the fit of the radial wind observations to the model in terms of observation minus model background (OmB) standard deviation. Monitoring the quality of the observations is an important aspect, especially when a new observation type is introduced into a data assimilation system. Calculating the bias for radial wind observations in a conventional way can result in zero even in case there are systematic differences in the wind speed and/or direction. A bias estimation method designed for this observation type is introduced in the thesis. Doppler radar radial wind observation modelling, together with the bias estimation method, enables the exploitation of the radial wind observations also for NWP model validation. The one-month model experiments performed with the HIRLAM model versions differing only in a surface stress parameterization detail indicate that the use of radar wind observations in NWP model validation is very beneficial.

Communicative and Anticipatory Decision-making Supported by Bayesian Networks

The purpose of this research is to draw up a clear construction of an anticipatory communicative decision-making process and a successful implementation of a Bayesian application that can be used as an anticipatory communicative decision-making support system. This study is a decision-oriented and constructive research project, and it includes examples of simulated situations. As a basis for further methodological discussion about different approaches to management research, in this research, a decision-oriented approach is used, which is based on mathematics and logic, and it is intended to develop problem solving methods. The approach is theoretical and characteristic of normative management science research. Also, the approach of this study is constructive. An essential part of the constructive approach is to tie the problem to its solution with theoretical knowledge. Firstly, the basic definitions and behaviours of an anticipatory management and managerial communication are provided. These descriptions include discussions of the research environment and formed management processes. These issues define and explain the background to further research. Secondly, it is processed to managerial communication and anticipatory decision-making based on preparation, problem solution, and solution search, which are also related to risk management analysis. After that, a solution to the decision-making support application is formed, using four different Bayesian methods, as follows: the Bayesian network, the influence diagram, the qualitative probabilistic network, and the time critical dynamic network. The purpose of the discussion is not to discuss different theories but to explain the theories which are being implemented. Finally, an application of Bayesian networks to the research problem is presented. The usefulness of the prepared model in examining a problem and the represented results of research is shown. The theoretical contribution includes definitions and a model of anticipatory decision-making. The main theoretical contribution of this study has been to develop a process for anticipatory decision-making that includes management with communication, problem-solving, and the improvement of knowledge. The practical contribution includes a Bayesian Decision Support Model, which is based on Bayesian influenced diagrams. The main contributions of this research are two developed processes, one for anticipatory decision-making, and the other to produce a model of a Bayesian network for anticipatory decision-making. In summary, this research contributes to decision-making support by being one of the few publicly available academic descriptions of the anticipatory decision support system, by representing a Bayesian model that is grounded on firm theoretical discussion, by publishing algorithms suitable for decision-making support, and by defining the idea of anticipatory decision-making for a parallel version. Finally, according to the results of research, an analysis of anticipatory management for planned decision-making is presented, which is based on observation of environment, analysis of weak signals, and alternatives to creative problem solving and communication.

Social Networks and Everyday Practices in Russia

My doctoral dissertation in sociology and Russian studies, Social Networks and Everyday Practices in Russia, employs a "micro" or "grassroots" perspective on the transition. The study is a collection of articles detailing social networks in five different contexts. The first article examines Russian birthdays from a network perspective. The second takes a look at health care to see whether networks have become obsolete in a sector that is still overwhelmingly public, but increasingly being monetarised. The third article investigates neighbourhood relations. The fourth details relationships at work, particularly from the vantage point of internal migration. The fifth explores housing and the role of networks and money both in the Soviet and post-Soviet era. The study is based on qualitative social network and interview data gathered among three groups, teachers, doctors and factory workers, in St. Petersburg during 1993-2000. Methodologically it builds on a qualitative social network approach. The study adds a critical element to the discussion on networks in post-socialism. A considerable consensus exists that social networks were vital in state socialist societies and were used to bypass various difficulties caused by endemic shortages and bureaucratic rigidities, but a more debated issue has been their role in post-socialism. Some scholars have argued that the importance of networks has been dramatically reduced in the new market economy, whereas others have stressed their continuing importance. If a common denominator in both has been a focus on networks in relation to the past, a more overlooked aspect has been the question of inequality. To what extent is access to networks unequally distributed? What are the limits and consequences of networks, for those who have access, those outside networks or society at large? My study provides some evidence about inequalities. It shows that some groups are privileged over others, for instance, middle-class people in informal access to health care. Moreover, analysing the formation of networks sheds additional light on inequalities, as it highlights the importance of migration as a mechanism of inequality, for example. The five articles focus on how networks are actually used in everyday life. The article on health care, for instance, shows that personal connections are still important and popular in post-Soviet Russia, despite the growing importance of money and the emergence of "fee for service" medicine. Fifteen of twenty teachers were involved in informal medical exchange during a two-week study period, so that they used their networks to bypass the formal market mechanisms or official procedures. Medicines were obtained through personal connections because some were unavailable at local pharmacies or because these connections could provide medicines for a cheaper price or even for free. The article on neighbours shows that "mutual help" was the central feature of neighbouring, so that the exchange of goods, services and information covered almost half the contacts with neighbours reported. Neighbours did not provide merely small-scale help but were often exchange partners because they possessed important professional qualities, had access to workplace resources, or knew somebody useful. The article on the Russian work collective details workplace-related relationships in a tractor factory and shows that interaction with and assistance from one's co-workers remains important. The most interesting finding was that co-workers were even more important to those who had migrated to the city than to those who were born there, which is explained by the specifics of Soviet migration. As a result, the workplace heavily influenced or absorbed contexts for the worker migrants to establish relationships whereas many meeting-places commonly available in Western countries were largely absent or at least did not function as trusted public meeting places to initiate relationships. More results are to be found from my dissertation: Anna-Maria Salmi: Social Networks and Everyday Practices in Russia, Kikimora Publications, 2006, see www.kikimora-publications.com.

Search for the Higgs Boson Using Neural Networks in Events with Missing Energy and b-Quark Jets in pp̅ Collisions at √s=1.96 TeV

We report on a search for the standard model Higgs boson produced in association with a $W$ or $Z$ boson in $p\bar{p}$ collisions at $\sqrt{s} = 1.96$ TeV recorded by the CDF II experiment at the Tevatron in a data sample corresponding to an integrated luminosity of 2.1 fb$^{-1}$. We consider events which have no identified charged leptons, an imbalance in transverse momentum, and two or three jets where at least one jet is consistent with originating from the decay of a $b$ hadron. We find good agreement between data and predictions. We place 95% confidence level upper limits on the production cross section for several Higgs boson masses ranging from 110$\gevm$ to 150$\gevm$. For a mass of 115$\gevm$ the observed (expected) limit is 6.9 (5.6) times the standard model prediction.