Genesis and expansion of metazoan transcription factor gene classes

We know little about the genomic events that led to the advent of a multicellular grade of organization in animals, one of the most dramatic transitions in evolution. Metazoan multicellularity is correlated with the evolution of embryogenesis, which presumably was underpinned by a gene regulatory network reliant on the differential activation of signaling pathways and transcription factors. Many transcription factor genes that play critical roles in bilaterian development largely appear to have evolved before the divergence of cnidarian and bilaterian lineages. In contrast, sponges seem to have a more limited suite of transcription factors, suggesting that the developmental regulatory gene repertoire changed markedly during early metazoan evolution. Using whole- genome information from the sponge Amphimedon queenslandica, a range of eumetazoans, and the choanoflagellate Monosiga brevicollis, we investigate the genesis and expansion of homeobox, Sox, T- box, and Fox transcription factor genes. Comparative analyses reveal that novel transcription factor domains ( such as Paired, POU, and T- box) arose very early in metazoan evolution, prior to the separation of extant metazoan phyla but after the divergence of choanoflagellate and metazoan lineages. Phylogenetic analyses indicate that transcription factor classes then gradually expanded at the base of Metazoa before the bilaterian radiation, with each class following a different evolutionary trajectory. Based on the limited number of transcription factors in the Amphimedon genome, we infer that the genome of the metazoan last common ancestor included fewer gene members in each class than are present in extant eumetazoans. Transcription factor orthologues present in sponge, cnidarian, and bilaterian genomes may represent part of the core metazoan regulatory network underlying the origin of animal development and multicellularity.

Molecular characterization of Iranian wheat stripe virus shows its taxonomic position as a distinct species in the genus Tenuivirus

The full lengths of three genome segments of Iranian wheat stripe virus (IWSV) were amplified by reverse transcription (RT) followed by polymerase chain reaction (PCR) using a primer complementary to tenuivirus conserved terminal sequences. The segments were sequenced and found to comprise 3469, 2337, and 1831 nt, respectively. The gene organization of these segments is similar to that of other known tenuiviruses, each displaying an ambisense coding strategy. IWSV segments, however, are different from those of other viruses with respect to the number of nucleotides and deduced amino acid sequence for each ORF. Depending on the segment, the first 16-22 nt at the 5' end and the first 16 nt at the 3' end are highly conserved among IWSV and rice hoja blanca virus (RHBV), rice stripe virus (RSV) and maize stripe virus ( MStV). In addition, the first 15-18 nt at the 5' end are complementary to the first 16-18 nt at the 3' end. Phylogenetic analyses showed close similarity and a common ancestor for IWSV, RHBV, and Echinochloa hoja blanca virus (EHBV). These findings confirm the position of IWSV as a distinct species in the genus Tenuivirus.

A system for studying epithelial-stromal interactions reveals distinct inductive abilities of stromal cells from benign prostatic hyperplasia and prostate cancer

The development of normal and abnormal glandular structures in the prostate is controlled at the endocrine and paracrine levels by reciprocal interactions between epithelium and stroma. To study these processes it is useful to have an efficient method of tissue acquisition for reproducible isolation of cells from defined histologies. Here we assessed the utility of a standardized system for acquisition and growth of prostatic cells from different regions of the prostate with different pathologies, and we compared the abilities of stromal cells from normal peripheral zone (PZ-S), benign prostatic hyperplasia (BPH-S), and cancer (CA-S) to induce the growth of a human prostatic epithelial cell line (BPH-1) in vivo. Using the tissue recombination method, we showed that grafting stromal cells (from any histology) alone, or BPH-1 epithelial cells alone produced no visible grafts. Recombining PZ-S with BPH-1 cells also produced no visible grafts (n = 15). Recombining BPH-S with BPH-1 cells generated small, well-organized and sharply demarcated grafts approximately 3-4 mm in diameter (n = 9), demonstrating a moderate inductive ability of BPH-S. Recombining CA-S with BPH-1 cells generated highly disorganized grafts that completely surrounded the host kidney and invaded into adjacent renal tissue, demonstrating induction of an aggressive phenotype. We conclude that acquisition of tissue from toluidine blue dye stained specimens is an efficient method to generate high quality epithelial and/or stromal cultures. Stromal cells derived by this method from areas of BPH and cancer induce epithelial cell growth in vivo which mimics the natural history of these diseases.

The distinct contributions of age of acquisition and word frequency in auditory word perception

We report two studies of the distinct effects that a word's age of acquisition (AoA) and frequency have on the mental lexicon. In the first study, a purely statistical analysis, we show that AoA and frequency are related in different ways to the phonological form and imageability of different words. In the second study, three groups of participants (34 seven-year-olds, 30 ten-year-olds, and 17 adults) took part in an auditory lexical decision task, with stimuli varying in AoA, frequency, length, neighbourhood density, and imageability. The principal result is that the influence of these different variables changes as a function of AoA: Neighbourhood density effects are apparent for early and late AoA words, but not for intermediate AoA, whereas imageability effects are apparent for intermediate AoA words but not for early or late AoA. These results are discussed from the perspective that AoA affects a word's representation, but frequency affects processing biases.

Identifying problematic classes in text classification

Real-world text classification tasks often suffer from poor class structure with many overlapping classes and blurred boundaries. Training data pooled from multiple sources tend to be inconsistent and contain erroneous labelling, leading to poor performance of standard text classifiers. The classification of health service products to specialized procurement classes is used to examine and quantify the extent of these problems. A novel method is presented to analyze the labelled data by selectively merging classes where there is not enough information for the classifier to distinguish them. Initial results show the method can identify the most problematic classes, which can be used either as a focus to improve the training data or to merge classes to increase confidence in the predicted results of the classifier.

Re-thinking commercial real estate market segmentation

Investments in direct real estate are inherently difficult to segment compared to other asset classes due to the complex and heterogeneous nature of the asset. The most common segmentation in real estate investment analysis relies on property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes including the equivalent yield and size of the property as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are able to better predict the resulting financial performance of the assets than the old dichotomous segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude from our analysis that each of the two approaches to segmenting the market has its strengths and weaknesses so that both might be applied gainfully in real estate investment analysis and fund management.

Multimodel estimate of the global terrestrial water balance: setup and first results

Six land surface models and five global hydrological models participate in a model intercomparison project (WaterMIP), which for the first time compares simulation results of these different classes of models in a consistent way. In this paper the simulation setup is described and aspects of the multi-model global terrestrial water balance are presented. All models were run at 0.5 degree spatial resolution for the global land areas for a 15-year period (1985-1999) using a newly-developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm year-1 (60,000 to 85,000 km3 year-1) and simulated runoff ranges from 290 to 457 mm year-1 (42,000 to 66,000 km3 year-1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically-based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between model are major sources of uncertainty. Climate change impact studies thus need to use not only multiple climate models, but also some other measure of uncertainty, (e.g. multiple impact models).

Advances in continuously profiling the thermodynamic state of the boundary layer: integration of measurements and methods

This paper describes advances in ground-based thermodynamic profiling of the lower troposphere through sensor synergy. The well-documented integrated profiling technique (IPT), which uses a microwave profiler, a cloud radar, and a ceilometer to simultaneously retrieve vertical profiles of temperature, humidity, and liquid water content (LWC) of nonprecipitating clouds, is further developed toward an enhanced performance in the boundary layer and lower troposphere. For a more accurate temperature profile, this is accomplished by including an elevation scanning measurement modus of the microwave profiler. Height-dependent RMS accuracies of temperature (humidity) ranging from 0.3 to 0.9 K (0.5–0.8 g m−3) in the boundary layer are derived from retrieval simulations and confirmed experimentally with measurements at distinct heights taken during the 2005 International Lindenberg Campaign for Assessment of Humidity and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH) of the German Weather Service. Temperature inversions, especially of the lower boundary layer, are captured in a very satisfactory way by using the elevation scanning mode. To improve the quality of liquid water content measurements in clouds the authors incorporate a sophisticated target classification scheme developed within the European cloud observing network CloudNet. It allows the detailed discrimination between different types of backscatterers detected by cloud radar and ceilometer. Finally, to allow IPT application also to drizzling cases, an LWC profiling method is integrated. This technique classifies the detected hydrometeors into three different size classes using certain thresholds determined by radar reflectivity and/or ceilometer extinction profiles. By inclusion into IPT, the retrieved profiles are made consistent with the measurements of the microwave profiler and an LWC a priori profile. Results of IPT application to 13 days of the LAUNCH campaign are analyzed, and the importance of integrated profiling for model evaluation is underlined.

Characterization of a highly biodiverse floodplain meadow using hyperspectral remote sensing within a plant functional trait framework

We assessed the potential for using optical functional types as effective markers to monitor changes in vegetation in floodplain meadows associated with changes in their local environment. Floodplain meadows are challenging ecosystems for monitoring and conservation because of their highly biodiverse nature. Our aim was to understand and explain spectral differences among key members of floodplain meadows and also characterize differences with respect to functional traits. The study was conducted on a typical floodplain meadow in UK (MG4-type, mesotrophic grassland type 4, according to British National Vegetation Classification). We compared two approaches to characterize floodplain communities using field spectroscopy. The first approach was sub-community based, in which we collected spectral signatures for species groupings indicating two distinct eco-hydrological conditions (dry and wet soil indicator species). The other approach was “species-specific”, in which we focused on the spectral reflectance of three key species found on the meadow. One herb species is a typical member of the MG4 floodplain meadow community, while the other two species, sedge and rush, represent wetland vegetation. We also monitored vegetation biophysical and functional properties as well as soil nutrients and ground water levels. We found that the vegetation classes representing meadow sub-communities could not be spectrally distinguished from each other, whereas the individual herb species was found to have a distinctly different spectral signature from the sedge and rush species. The spectral differences between these three species could be explained by their observed differences in plant biophysical parameters, as corroborated through radiative transfer model simulations. These parameters, such as leaf area index, leaf dry matter content, leaf water content, and specific leaf area, along with other functional parameters, such as maximum carboxylation capacity and leaf nitrogen content, also helped explain the species’ differences in functional dynamics. Groundwater level and soil nitrogen availability, which are important factors governing plant nutrient status, were also found to be significantly different for the herb/wetland species’ locations. The study concludes that spectrally distinguishable species, typical for a highly biodiverse site such as a floodplain meadow, could potentially be used as target species to monitor vegetation dynamics under changing environmental conditions.

Bistability through triadic closure

We propose and analyse a class of evolving network models suitable for describing a dynamic topological structure. Applications include telecommunication, on-line social behaviour and information processing in neuroscience. We model the evolving network as a discrete time Markov chain, and study a very general framework where, conditioned on the current state, edges appear or disappear independently at the next timestep. We show how to exploit symmetries in the microscopic, localized rules in order to obtain conjugate classes of random graphs that simplify analysis and calibration of a model. Further, we develop a mean field theory for describing network evolution. For a simple but realistic scenario incorporating the triadic closure effect that has been empirically observed by social scientists (friends of friends tend to become friends), the mean field theory predicts bistable dynamics, and computational results confirm this prediction. We also discuss the calibration issue for a set of real cell phone data, and find support for a stratified model, where individuals are assigned to one of two distinct groups having different within-group and across-group dynamics.

Asset allocation, cross-class correlation and the structure of property returns

Practical applications of portfolio optimisation tend to proceed on a “top down” basis where funds are allocated first at asset class level (between, say, bonds, cash, equities and real estate) and then, progressively, at sub-class level (within property to sectors, office, retail, industrial for example). While there are organisational benefits from such an approach, it can potentially lead to sub-optimal allocations when compared to a “global” or “side-by-side” optimisation. This will occur where there are correlations between sub-classes across the asset divide that are masked in aggregation – between, for instance, City offices and the performance of financial services stocks. This paper explores such sub-class linkages using UK monthly stock and property data. Exploratory analysis using clustering procedures and factor analysis suggests that property performance and equity performance are distinctive: there is little persuasive evidence of contemporaneous or lagged sub-class linkages. Formal tests of the equivalence of optimised portfolios using top-down and global approaches failed to demonstrate significant differences, whether or not allocations were constrained. While the results may be a function of measurement of market returns, it is those returns that are used to assess fund performance. Accordingly, the treatment of real estate as a distinct asset class with diversification potential seems justified.

Periplasmic adaptor protein AcrA has a distinct role in the antibiotic resistance and virulence of Salmonella enterica serovar Typhimurium

Objectives: AcrA can function as the periplasmic adaptor protein (PAP) in several RND tripartite efflux pumps, of which AcrAB-TolC is considered the most important. This system confers innate multiple antibiotic resistance. Disruption of acrB or tolC impairs the ability of Salmonella Typhimurium to colonize and persist in the host. The aim of this study was to investigate the role of AcrA alone in multidrug resistance and pathogenicity. Methods: The acrA gene was inactivated in Salmonella Typhimurium SL1344 by insertion of the aph gene and this mutant complemented with pWKS30acrA. The antimicrobial susceptibility of the mutant to six antibiotics as well as various dyes and detergents was determined. In addition, efflux activity was quantified. The ability of the mutant to adhere to, and invade, tissue culture cells in vitro was measured. Results: Following disruption of acrA, RT-PCR and western blotting confirmed that acrB/AcrB was still expressed when acrA was disrupted. The acrA mutant was hypersusceptible to antibiotics, dyes and detergents. In some cases, lower MICs were seen than for the acrB or tolC mutants. Efflux of the fluorescent dye Hoechst H33342 was less than in wild-type following disruption of acrA. acrA was also required for adherence to, and invasion of, tissue culture cells. Conclusions: Inactivation of acrA conferred a phenotype distinct to that of acrB::aph and tolC::aph. These data indicate a role for AcrA distinct to that of other protein partners in both efflux of substrates and virulence.

Aligning asset allocation and real estate investment: some lessons from the last cycle

Asset allocation is concerned with the development of multi--‐asset portfolio strategies that are likely to meet an investor’s objectives based on the interaction of expected returns, risk, correlation and implementation from a range of distinct asset classes or beta sources. Challenges associated with the discipline are often particularly significant in private markets. Specifically, composition differences between the ‘index’ or ‘benchmark’ universe and the investible universe mean that there can often be substantial and meaningful deviations between the investment characteristics implied in asset allocation decisions and those delivered by investment teams. For example, while allocation decisions are often based on relatively low--‐risk diversified real estate ‘equity’ exposure, implementation decisions frequently include exposure to higher risk forms of the asset class as well as investments in debt based instruments. These differences can have a meaningful impact on the contribution of the asset class to the overall portfolio and, therefore, lead to a potential misalignment between asset allocation decisions and implementation. Despite this, the key conclusion from this paper is not that real estate investors should become slaves to a narrowly defined mandate based on IPD / NCREIF or other forms of benchmark replication. The discussion suggests that such an approach would likely lead to the underutilization of real estate in multi--‐asset portfolio strategies. Instead, it is that to achieve asset allocation alignment, real estate exposure should be divided into multiple pools representing distinct forms of the asset class. In addition, the paper suggests that associated investment guidelines and processes should be collaborative and reflect the portfolio wide asset allocation objectives of each pool. Further, where appropriate they should specifically target potential for ‘additional’ beta or, more marginally, ‘alpha’.

Distribution of β-amylase I haplotypes among European cultivated barleys

The barley β-amylase I (Bmy1) locus encodes a starch breakdown enzyme whose kinetic properties and thermostability are critical during malt production. Studies of allelic variation at the Bmy1 locus have shown that the encoded enzyme can be commonly found in at least three distinct thermostability classes and demonstrated the nucleotide sequence variations responsible for such phenotypic differences. In order to explore the extent of sequence diversity at the Bmy1 locus in cultivated European barley, 464 varieties representing a cross-section of popular varieties grown in western Europe over the past 60 years, were genotyped for three single nucleotide polymorphisms chosen to tag the four common alleles found in the collection. One of these haplotypes, which has not been explicitly recognised in the literature as a distinct allele, was found in 95% of winter varieties in the sample. When release dates of the varieties were considered, the lowest thermostability allele (Bmy1-Sd2L) appeared to decrease in abundance over time, while the highest thermostability allele (Bmy1-Sd2H) was the rarest allele at 5.4% of the sample and was virtually confined to two-row spring varieties. Pedigree analysis was used to track transmission of particular alleles over time and highlighted issues of genetic stratification of the sample.