The Gerbera cDNA Microarray: A Tool for Large-Scale Identification of Genes Involved in Flower Development

During the past ten years, large-scale transcript analysis using microarrays has become a powerful tool to identify and predict functions for new genes. It allows simultaneous monitoring of the expression of thousands of genes and has become a routinely used tool in laboratories worldwide. Microarray analysis will, together with other functional genomics tools, take us closer to understanding the functions of all genes in genomes of living organisms. Flower development is a genetically regulated process which has mostly been studied in the traditional model species Arabidopsis thaliana, Antirrhinum majus and Petunia hybrida. The molecular mechanisms behind flower development in them are partly applicable in other plant systems. However, not all biological phenomena can be approached with just a few model systems. In order to understand and apply the knowledge to ecologically and economically important plants, other species also need to be studied. Sequencing of 17 000 ESTs from nine different cDNA libraries of the ornamental plant Gerbera hybrida made it possible to construct a cDNA microarray with 9000 probes. The probes of the microarray represent all different ESTs in the database. From the gerbera ESTs 20% were unique to gerbera while 373 were specific to the Asteraceae family of flowering plants. Gerbera has composite inflorescences with three different types of flowers that vary from each other morphologically. The marginal ray flowers are large, often pigmented and female, while the central disc flowers are smaller and more radially symmetrical perfect flowers. Intermediate trans flowers are similar to ray flowers but smaller in size. This feature together with the molecular tools applied to gerbera, make gerbera a unique system in comparison to the common model plants with only a single kind of flowers in their inflorescence. In the first part of this thesis, conditions for gerbera microarray analysis were optimised including experimental design, sample preparation and hybridization, as well as data analysis and verification. Moreover, in the first study, the flower and flower organ-specific genes were identified. After the reliability and reproducibility of the method were confirmed, the microarrays were utilized to investigate transcriptional differences between ray and disc flowers. This study revealed novel information about the morphological development as well as the transcriptional regulation of early stages of development in various flower types of gerbera. The most interesting finding was differential expression of MADS-box genes, suggesting the existence of flower type-specific regulatory complexes in the specification of different types of flowers. The gerbera microarray was further used to profile changes in expression during petal development. Gerbera ray flower petals are large, which makes them an ideal model to study organogenesis. Six different stages were compared and specifically analysed. Expression profiles of genes related to cell structure and growth implied that during stage two, cells divide, a process which is marked by expression of histones, cyclins and tubulins. Stage 4 was found to be a transition stage between cell division and expansion and by stage 6 cells had stopped division and instead underwent expansion. Interestingly, at the last analysed stage, stage 9, when cells did not grow any more, the highest number of upregulated genes was detected. The gerbera microarray is a fully-functioning tool for large-scale studies of flower development and correlation with real-time RT-PCR results show that it is also highly sensitive and reliable. Gene expression data presented here will be a source for gene expression mining or marker gene discovery in the future studies that will be performed in the Gerbera Laboratory. The publicly available data will also serve the plant research community world-wide.

A novel MCMC algorithm for near-optimal detection in large-scale uplink mulituser MIMO systems

In this paper, we propose a low-complexity algorithm based on Markov chain Monte Carlo (MCMC) technique for signal detection on the uplink in large scale multiuser multiple input multiple output (MIMO) systems with tens to hundreds of antennas at the base station (BS) and similar number of uplink users. The algorithm employs a randomized sampling method (which makes a probabilistic choice between Gibbs sampling and random sampling in each iteration) for detection. The proposed algorithm alleviates the stalling problem encountered at high SNRs in conventional MCMC algorithm and achieves near-optimal performance in large systems with M-QAM. A novel ingredient in the algorithm that is responsible for achieving near-optimal performance at low complexities is the joint use of a randomized MCMC (R-MCMC) strategy coupled with a multiple restart strategy with an efficient restart criterion. Near-optimal detection performance is demonstrated for large number of BS antennas and users (e.g., 64, 128, 256 BS antennas/users).

Changes in Behavior, Movement, and Home Ranges of Largemouth Bass Following Large-scale Hydrilla Removal in Lake Seminole, Georgia

About 1,200 ha of hydrilla ( Hydrilla verticillata L.f. Royle) was eliminated in the Spring Creek embayment of Lake Seminole, Georgia, using a drip-delivery application of fluridone (1- methyl-3-phenyl-5-[3-(trifluoromethl) phenyl]-4(1H)-pyridinone) in 2000 and 2001. Two groups of 15 and 20 largemouth bass (Micropterus salmoides Lacepede) were implanted with 400-day radio tags in February 2000 and 2001 to determine changes in movement and behavior before and after hydrilla reduction.(PDF contains 8 pages.)

Reducing H2S production by O-2 feedback control during large-scale sewage sludge composting

Hydrogen sulfide (H2S) production patterns and the influence of oxygen (O-2) concentration were studied based on a well operated composting plant. A real-time, online multi-gas detection system was applied to monitor the concentrations of H2S and O-2 in the pile during composting. The results indicate that H2S was mainly produced during the early stage of composting, especially during the first 40 h. Lack of available O-2 was the main reason for H2S production. Maintaining the O-2 concentration higher than 14% in the pile could reduce H2S production. This study suggests that shortening the interval between aeration or aerating continuously to maintain a high O-2 concentration in the pile was an effective strategy for restraining H2S production in sewage sludge composting. (C) 2010 Elsevier Ltd. All rights reserved.

Large-scale isolation and purification of R-phycoerythrin from red alga Palmaria palmata using the expanded bed adsorption method

R-phycoerythrin, a light-harvesting protein in some marine algae, and can be widely used in medicine, was isolated and purified from a red alga, Palmaria palmata (Lannaeus) Kuntze, using the streamline column (expanded bed adsorption) combined with ion-exchange chromatography. Because the crude extract was applied to the column upwardly, the column would not be blocked by polysaccharides usually very abundant in the extract of marine alga, this kind of blockage could hardly lie overcome in ordinary chromatographic column. After applying the crude extract containing 0.5 mol/L (NH4)(2)SO4, (NH4)(2)SO4 solution of different concentrations (0.2 mol/L, 0.1 mol/L and 0.05 mol/L) was used to elute the column downwardly and the eluates were collected and desalted. The desalted eluates were then applied onto all ion-exchange chromatographic column loaded with Q-sepharose for further purification of the R-phycoerythrin. Through these two steps, the purity (OD565/OD280) of the R-phycoerythrin from P. palmata was up to 3.5, more than 3.2, the commonly accepted criterion for purity, and the yield of the purified R-phycoerythrin could reach 0.122 mg/g of frozen P. palmata, much higher than that of phycobiliproteins purified with the previous methods. The result indicated that the cost of R-phycoerythrin will drop down with the method reported in this article.

A study of the large-scale structure of the wind of WR 134

Diverses méthodes ont été utilisées pour étudier les étoiles Wolf-Rayet (WR) dans le but de comprendre les phénomènes physiques variés qui prennent place dans leur vent dense. Pour étudier la variabilité qui n'est pas strictement périodique et ayant des caractéristiques différentes d'une époque à l'autre, il faut observer pendant des périodes de temps suffisamment longues en adopter un échantillonnage temporel élevé pour être en mesure d'identifier les phénomènes physiques sous-jacents. À l'été 2013, des astronomes professionnels et amateurs du monde entier ont contribué à une campagne d'observation de 4 mois, principalement en spectroscopie, mais aussi en photométrie, polarimétrie et en interférométrie, pour observer les 3 premières étoiles Wolf-Rayet découvertes: WR 134 (WN6b), WR 135 (WC8) et WR 137 (WC7pd + O9). Chacune de ces étoiles est intéressante à sa manière, chacune présentant une variété différente de structures dans son vent. Les données spectroscopiques de cette campagne ont été réduites et analysées pour l'étoile présumée simple WR 134 pour mieux comprendre le comportement de sa variabilité périodique à long terme dans le cadre d'une étude des régions d'interactions en corotation (CIRs) qui se retrouvent dans son vent. Les résultats de cette étude sont présentés dans ce mémoire.

Large-scale length and time scales for use with stochastic convective parameterization

Many numerical models for weather prediction and climate studies are run at resolutions that are too coarse to resolve convection explicitly, but too fine to justify the local equilibrium assumed by conventional convective parameterizations. The Plant-Craig (PC) stochastic convective parameterization scheme, developed in this paper, solves this problem by removing the assumption that a given grid-scale situation must always produce the same sub-grid-scale convective response. Instead, for each timestep and gridpoint, one of the many possible convective responses consistent with the large-scale situation is randomly selected. The scheme requires as input the large-scale state as opposed to the instantaneous grid-scale state, but must nonetheless be able to account for genuine variations in the largescale situation. Here we investigate the behaviour of the PC scheme in three-dimensional simulations of radiative-convective equilibrium, demonstrating in particular that the necessary space-time averaging required to produce a good representation of the input large-scale state is not in conflict with the requirement to capture large-scale variations. The resulting equilibrium profiles agree well with those obtained from established deterministic schemes, and with corresponding cloud-resolving model simulations. Unlike the conventional schemes the statistics for mass flux and rainfall variability from the PC scheme also agree well with relevant theory and vary appropriately with spatial scale. The scheme is further shown to adapt automatically to changes in grid length and in forcing strength.

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.

Transition from suppressed to active convection modulated by a weak-temperature gradient derived large-scale circulation

Numerical simulations are performed to assess the influence of the large-scale circulation on the transition from suppressed to active convection. As a model tool, we used a coupled-column model. It consists of two cloud-resolving models which are fully coupled via a large-scale circulation which is derived from the requirement that the instantaneous domain-mean potential temperature profiles of the two columns remain close to each other. This is known as the weak-temperature gradient approach. The simulations of the transition are initialized from coupled-column simulations over non-uniform surface forcing and the transition is forced within the dry column by changing the local and/or remote surface forcings to uniform surface forcing across the columns. As the strength of the circulation is reduced to zero, moisture is recharged into the dry column and a transition to active convection occurs once the column is sufficiently moistened to sustain deep convection. Direct effects of changing surface forcing occur over the first few days only. Afterward, it is the evolution of the large-scale circulation which systematically modulates the transition. Its contributions are approximately equally divided between the heating and moistening effects. A transition time is defined to summarize the evolution from suppressed to active convection. It is the time when the rain rate within the dry column is halfway to the mean value obtained at equilibrium over uniform surface forcing. The transition time is around twice as long for a transition that is forced remotely compared to a transition that is forced locally. Simulations in which both local and remote surface forcings are changed produce intermediate transition times.

Intercomparison of methods of coupling between convection and large-scale circulation: 1. Comparison over uniform surface conditions

As part of an international intercomparison project, a set of single column models (SCMs) and cloud-resolving models (CRMs) are run under the weak temperature gradient (WTG) method and the damped gravity wave (DGW) method. For each model, the implementation of the WTG or DGW method involves a simulated column which is coupled to a reference state defined with profiles obtained from the same model in radiative-convective equilibrium. The simulated column has the same surface conditions as the reference state and is initialized with profiles from the reference state. We performed systematic comparison of the behavior of different models under a consistent implementation of the WTG method and the DGW method and systematic comparison of the WTG and DGW methods in models with different physics and numerics. CRMs and SCMs produce a variety of behaviors under both WTG and DGW methods. Some of the models reproduce the reference state while others sustain a large-scale circulation which results in either substantially lower or higher precipitation compared to the value of the reference state. CRMs show a fairly linear relationship between precipitation and circulation strength. SCMs display a wider range of behaviors than CRMs. Some SCMs under the WTG method produce zero precipitation. Within an individual SCM, a DGW simulation and a corresponding WTG simulation can produce different signed circulation. When initialized with a dry troposphere, DGW simulations always result in a precipitating equilibrium state. The greatest sensitivities to the initial moisture conditions occur for multiple stable equilibria in some WTG simulations, corresponding to either a dry equilibrium state when initialized as dry or a precipitating equilibrium state when initialized as moist. Multiple equilibria are seen in more WTG simulations for higher SST. In some models, the existence of multiple equilibria is sensitive to some parameters in the WTG calculations.

Intercomparison of methods of coupling between convection and large-scale circulation. 2: Comparison over non-uniform surface conditions

As part of an international intercomparison project, the weak temperature gradient (WTG) and damped gravity wave (DGW) methods are used to parameterize large-scale dynamics in a set of cloud-resolving models (CRMs) and single column models (SCMs). The WTG or DGW method is implemented using a configuration that couples a model to a reference state defined with profiles obtained from the same model in radiative-convective equilibrium. We investigated the sensitivity of each model to changes in SST, given a fixed reference state. We performed a systematic comparison of the WTG and DGW methods in different models, and a systematic comparison of the behavior of those models using the WTG method and the DGW method. The sensitivity to the SST depends on both the large-scale parameterization method and the choice of the cloud model. In general, SCMs display a wider range of behaviors than CRMs. All CRMs using either the WTG or DGW method show an increase of precipitation with SST, while SCMs show sensitivities which are not always monotonic. CRMs using either the WTG or DGW method show a similar relationship between mean precipitation rate and column-relative humidity, while SCMs exhibit a much wider range of behaviors. DGW simulations produce large-scale velocity profiles which are smoother and less top-heavy compared to those produced by the WTG simulations. These large-scale parameterization methods provide a useful tool to identify the impact of parameterization differences on model behavior in the presence of two-way feedback between convection and the large-scale circulation.

Marine biodiversity survey in the Northern Red Sea: a large-scale monitoring carried out in collaboration with volunteer divers

Concerns over global change and its effect on coral reef survivorship have highlighted the need for long-term datasets and proxy records, to interpret environmental trends and inform policymakers. Citizen science programs have showed to be a valid method for collecting data, reducing financial and time costs for institutions. This study is based on the elaboration of data collected by recreational divers and its main purpose is to evaluate changes in the state of coral reef biodiversity in the Red Sea over a long term period and validate the volunteer-based monitoring method. Volunteers recreational divers completed a questionnaire after each dive, recording the presence of 72 animal taxa and negative reef conditions. Comparisons were made between records from volunteers and independent records from a marine biologist who performed the same dive at the same time. A total of 500 volunteers were tested in 78 validation trials. Relative values of accuracy, reliability and similarity seem to be comparable to those performed by volunteer divers on precise transects in other projects, or in community-based terrestrial monitoring. 9301 recreational divers participated in the monitoring program, completing 23,059 survey questionnaires in a 5-year period. The volunteer-sightings-based index showed significant differences between the geographical areas. The area of Hurghada is distinguished by a medium-low biodiversity index, heavily damaged by a not controlled anthropic exploitation. Coral reefs along the Ras Mohammed National Park at Sharm el Sheikh, conversely showed high biodiversity index. The detected pattern seems to be correlated with the conservation measures adopted. In our experience and that of other research institutes, citizen science can integrate conventional methods and significantly reduce costs and time. Involving recreational divers we were able to build a large data set, covering a wide geographic area. The main limitation remains the difficulty of obtaining an homogeneous spatial sampling distribution.

Phenology for tropoclimatological surveys and large-scale mapping

Biotic and abiotic phenological observations can be collected from continental to local spatial scale. Plant phenological observations may only be recorded wherever there is vegetation. Fog, snow and ice are available as phenological para-meters wherever they appear. The singularity of phenological observations is the possibility of spatial intensification to a microclimatic scale where the equipment of meteorological measurements is too expensive for intensive campaigning. The omnipresence of region-specific phenological parameters allows monitoring for a spatially much more detailed assessment of climate change than with weather data. We demonstrate this concept with phenological observations with the use of a special network in the Canton of Berne, Switzerland, with up to 600 observations sites (more than 1 to 10 km² of the inhabited area). Classic cartography, gridding, the integration into a Geographic Information System GIS and large-scale analysis are the steps to a detailed knowledge of topoclimatic conditions of a mountainous area. Examples of urban phenology provide other types of spatially detailed applications. Large potential in phenological mapping in future analyses lies in combining traditionally observed species-specific phenology with remotely sensed and modelled phenology that provide strong spatial information. This is a long history from cartographic intuition to algorithm-based representations of phenology.