Identifying adaptive genetic divergence among populations from genome scans

The identification of signatures of natural selection in genomic surveys has become an area of intense research, stimulated by the increasing ease with which genetic markers can be typed. Loci identified as subject to selection may be functionally important, and hence (weak) candidates for involvement in disease causation. They can also be useful in determining the adaptive differentiation of populations, and exploring hypotheses about speciation. Adaptive differentiation has traditionally been identified from differences in allele frequencies among different populations, summarised by an estimate of F-ST. Low outliers relative to an appropriate neutral population-genetics model indicate loci subject to balancing selection, whereas high outliers suggest adaptive (directional) selection. However, the problem of identifying statistically significant departures from neutrality is complicated by confounding effects on the distribution of F-ST estimates, and current methods have not yet been tested in large-scale simulation experiments. Here, we simulate data from a structured population at many unlinked, diallelic loci that are predominantly neutral but with some loci subject to adaptive or balancing selection. We develop a hierarchical-Bayesian method, implemented via Markov chain Monte Carlo (MCMC), and assess its performance in distinguishing the loci simulated under selection from the neutral loci. We also compare this performance with that of a frequentist method, based on moment-based estimates of F-ST. We find that both methods can identify loci subject to adaptive selection when the selection coefficient is at least five times the migration rate. Neither method could reliably distinguish loci under balancing selection in our simulations, even when the selection coefficient is twenty times the migration rate.

Syntheses of two new 1D and 3D networks of Cu(II) and Co(II) using malonate and urotropine as bridging ligands: Crystal structures and magnetic studies

Two new metal-organic based polymeric complexes, [Cu-4(O2CCH2CO2)(4)(L)].7H(2)O (1) and [CO2(O2CCH2CO2)(2)(L)].2H(2)O (2) [L = hexamethylenetetramine (urotropine)], have been synthesized and characterized by X-ray crystal structure determination and magnetic studies. Complex 1 is a 1D coordination polymer comprising a carboxylato, bridged Cu-4 moiety linked by a tetradentate bridging urotropine. Complex 2 is a 3D coordination polymer made of pseudo-two-dimensional layers of Co(II) ions linked by malonate anions in syn-anticonformation which are bridged by bidentate urotropine in trans fashion, Complex 1 crystallizes in the orthothombic system, space group Pmmn, with a = 14,80(2) Angstrom, b = 14.54(2) Angstrom, c = 7.325(10) Angstrom, beta = 90degrees, and Z = 4. Complex 2 crystallizes in the orthorhombic system, space group Imm2, a = 7.584(11) Angstrom, b = 15.80(2) Angstrom, c = 6.939(13) Angstrom, beta = 90.10degrees(1), and Z = 4. Variable temperature (300-2 K) magnetic behavior reveals the existence of ferro- and antiferromagnetic interactions in 1 and only antiferromagnetic interactions in 2. The best fitted parameters for complex 1 are J = 13.5 cm(-1), J = -18.1 cm(-1), and g = 2.14 considering only intra-Cu-4 interactions through carboxylate and urotropine pathways. In case of complex 2, the fit of the magnetic data considering intralayer interaction through carboxylate pathway as well as interlayer interaction via urotropine pathway gave no satisfactory result at this moment using any model known due to considerable orbital contribution of Co(II) ions to the magnetic moment and its complicated structure. Assuming isolated Co(II) ions (without any coupling, J = 0) the shape of the chi(M)T curve fits well with experimental data except at very low temperatures.

EnvironalPlanning: an analytical network process model for environmentally conscious construction planning

Although the construction pollution index has been put forward and proved to be an efficient approach to reducing or mitigating pollution level during the construction planning stage, the problem of how to select the best construction plan based on distinguishing the degree of its potential adverse environmental impacts is still a research task. This paper first reviews environmental issues and their characteristics in construction, which are critical factors in evaluating potential adverse impacts of a construction plan. These environmental characteristics are then used to structure two decision models for environmental-conscious construction planning by using an analytic network process (ANP), including a complicated model and a simplified model. The two ANP models are combined and called the EnvironalPlanning system, which is applied to evaluate potential adverse environmental impacts of alternative construction plans.

Qualitative modelling of sustainable energy scenarios: An application and extension of the Bon Qualitative Input-Output model

Climate change is one of the major challenges facing economic systems at the start of the 21st century. Reducing greenhouse gas emissions will require both restructuring the energy supply system (production) and addressing the efficiency and sufficiency of the social uses of energy (consumption). The energy production system is a complicated supply network of interlinked sectors with 'knock-on' effects throughout the economy. End use energy consumption is governed by complex sets of interdependent cultural, social, psychological and economic variables driven by shifts in consumer preference and technological development trajectories. To date, few models have been developed for exploring alternative joint energy production-consumption systems. The aim of this work is to propose one such model. This is achieved in a methodologically coherent manner through integration of qualitative input-output models of production, with Bayesian belief network models of consumption, at point of final demand. The resulting integrated framework can be applied either (relatively) quickly and qualitatively to explore alternative energy scenarios, or as a fully developed quantitative model to derive or assess specific energy policy options. The qualitative applications are explored here.

Language, education and ethnicity: whose rights will prevail in an age of globalisation?

Education and ethnicity cannot be discussed without taking language into account. This paper will argue that any discussion of ethnic minorities cannot ignore the question of language, nor can any discussion of human rights ignore the question of language rights. Unfortunately, in today's globalised world, governments and minorities are faced with conflicting pressures: on the one hand, for the development and use of education in a global/international language; on the other for the use and development of mother tongue, local or indigenous languages in education. Language complexity and ethnic plurality were largely brought about as a result of the creation of nation-states, which were spread around the world as a result of European colonialism. European languages and formal education systems were used as a means of political and economic control. The legacy that was left by the colonial powers has complicated ethnic relations and has frequently led to conflict. While there is now greater recognition of the importance of language both for economic and educational development, as well as for human rights, the forces of globalisation are leading towards uniformity in the languages used, in culture and even in education. They are working against the development of language rights for smaller groups. We are witnessing a sharp decline in the number of languages spoken. Only those languages which are numerically, economically and politically strong are likely to survive. As a result many linguistic and ethnic groups are in danger of being further marginalised. This paper will illustrate this thesis both historically and from several contemporary societies, showing how certain policies have exacerbated ethnic conflict while others are seeking to promote harmony and reconciliation. Why this should be so will be explored. (c) 2006 Elsevier Ltd. All rights reserved.

The use of colloidal gas aphrons as novel downstream processing for the recovery of astaxanthin from cells of Phaffia rhodozyma

BACKGROUND: There is an increasing interest in obtaining natural products with bioactive properties, using fermentation technology. However, the downstream processing consisting of multiple steps can be complicated, leading to increase in the final cost of the product. Therefore there is a need for integrated, cost-effective and scalable separation processes. RESULTS: The present study investigates the use of colloidal gas aphrons (CGA), which are surfactant-stabilized microbubbles, as a novel method for downstream processing. More particularly, their application for the recovery of astaxanthin from the cells of Phaffia rhodozyma is explored. Research carried out with standard solutions of astaxanthin and CGA generated from the cationic surfactant hexadecyl. trimethyl ammonium bromide (CTAB) showed that up to 90% recovery can be achieved under optimum conditions, i.e., pH 11 with NaOH 0.2 mol L-1. In the case of the cells' suspension from the fermentation broth, three different approaches were investigated: (a) the conventional integrated approach where CGA were applied directly; (b) CGA were applied to the clarified suspension of cells; and finally (c) the in situ approach, where CGA are generated within the clarified suspension of cells. Interestingly, in the case of the whole suspension (approach a) highest recoveries (78%) were achieved under the same conditions found to be optimal for the standard solutions. In addition, up to 97% recovery of total carotenoids could be achieved from the clarified suspension after pretreatment with NaOH. This pretreatment led to maximum cell disruption as well as optimum conditioning for subsequent CGA separation. CONCLUSIONS: These results demonstrate the potential of CGA for the recovery of bioactive components from complex feedstock. (c) 2008 Society of Chemical Industry.

Intensive case management for severe psychotic illness: is there a general benefit for patients with complex needs? A secondary analysis of the UK700 trial data

The UK700 trial failed to demonstrate an overall benefit of intensive case management (ICM) in patients with severe psychotic illness. This does not discount a benefit for particular subgroups, and evidence of a benefit of ICM for patients of borderline intelligence has been presented. The aim of this study is to investigate whether this effect is part of a general benefit for patients with severe psychosis complicated by additional needs. In the UK700 trial patients with severe psychosis were randomly allocated to ICM or standard case management. For each patient group with complex needs the effect of ICM is compared with that in the rest of the study cohort. Outcome measures are days spent in psychiatric hospital and the admission and discharge rates. ICM may be of benefit to patients with severe psychosis complicated by borderline intelligence or depression, but may cause patients using illicit drugs to spend more time in hospital. There was no convincing evidence of an effect of ICM in a further seven patient groups. ICM is not of general benefit to patients with severe psychosis complicated by additional needs. The benefit of ICM for patients with borderline intelligence is an isolated effect which should be interpreted cautiously until further data are available.

Computational complexity of weighted splitting schemes on parallel computers

In models of complicated physical-chemical processes operator splitting is very often applied in order to achieve sufficient accuracy as well as efficiency of the numerical solution. The recently rediscovered weighted splitting schemes have the great advantage of being parallelizable on operator level, which allows us to reduce the computational time if parallel computers are used. In this paper, the computational times needed for the weighted splitting methods are studied in comparison with the sequential (S) splitting and the Marchuk-Strang (MSt) splitting and are illustrated by numerical experiments performed by use of simplified versions of the Danish Eulerian model (DEM).

Wiener-System subspace identification for mobile wireless mm-wave networks

We discuss the feasibility of wireless terahertz communications links deployed in a metropolitan area and model the large-scale fading of such channels. The model takes into account reception through direct line of sight, ground and wall reflection, as well as diffraction around a corner. The movement of the receiver is modeled by an autonomous dynamic linear system in state space, whereas the geometric relations involved in the attenuation and multipath propagation of the electric field are described by a static nonlinear mapping. A subspace algorithm in conjunction with polynomial regression is used to identify a single-output Wiener model from time-domain measurements of the field intensity when the receiver motion is simulated using a constant angular speed and an exponentially decaying radius. The identification procedure is validated by using the model to perform q-step ahead predictions. The sensitivity of the algorithm to small-scale fading, detector noise, and atmospheric changes are discussed. The performance of the algorithm is tested in the diffraction zone assuming a range of emitter frequencies (2, 38, 60, 100, 140, and 400 GHz). Extensions of the simulation results to situations where a more complicated trajectory describes the motion of the receiver are also implemented, providing information on the performance of the algorithm under a worst case scenario. Finally, a sensitivity analysis to model parameters for the identified Wiener system is proposed.

Design of a minimum variance multiple input-multiple output neuro self-tuning proportional-integral-derivative controller for non-linear dynamic systems

In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.

Buffering of recovery from acidification by organic acids

In the United Kingdom, as in other regions of Europe and North America, recent decreases in surface water sulphate concentrations, due to reduced sulphur emissions, have coincided with marked increases in dissolved organic carbon (DOC) concentrations. Since many of the compounds comprising DOC are acidic, the resulting increases in organic acidity may have the potential to offset the benefits of a decrease in mineral (sulphate) acidity. To test this, we used a triprotic model of organic acid dissociation to estimate the proportional organic acid buffering of reduced mineral acidity as measured in the 22 lakes and streams monitored by the UK Acid Waters Monitoring Network. For an average non-marine sulphate decrease of 30 μeq l− 1 over 15 years from 1988–2003, we estimate that around 28% was counterbalanced by rising strong organic acids, 20% by rising alkalinity (partly attributable to an increase in weak organic acids), 11% by falling inorganic aluminium and 41% by falling non-marine base cations. The situation is complicated by a concurrent decrease in marine ion concentrations, and the impact this may have had on both DOC and acidity, but results clearly demonstrate that organic acid increases have substantially limited the amount of recovery from acidification (in terms of rising alkalinity and falling aluminium) that have resulted from reducing sulphur emissions. The consistency and magnitude of sulphate and organic acid changes are consistent with a causal link between the two, possibly due to the effects of changing acidity, ionic strength and aluminium concentrations on organic matter solubility. If this is the case, then organic acids can be considered effective but partial buffers to acidity change in organic soils, and this mechanism needs to be considered in assessing and modelling recovery from acidification, and in defining realistic reference conditions. However, large spatial variations in the relative magnitude of organic acid and sulphate changes, notably for low-deposition sites in northwestern areas where organic acid increases apparently exceed non-marine sulphate decreases, suggest that additional factors, such as changes in sea-salt deposition and climatic factors, may be required to explain the full magnitude of DOC increases in UK surface waters.

Speed sensorless vector control of induction machines

A novel rotor velocity estimation scheme applicable to vector controlled induction motors has been described. The proposed method will evaluate rotor velocity, ωr, on-line, does not require any extra transducers or injection of any signals, nor does it employ complicated algorithms such as MRAS or Kalman filters. Furthermore, the new scheme will operate at all velocities including zero with very little error. The procedure employs motor model equations, however all differential and integral terms have been eliminated giving a very fast, low-cost, effective and practical alternative to the current available methods. Simulation results verify the operation of the scheme under ideal and PWM conditions.

Integrating features for man-made target tracking from FLIR image sequence using particle filter

A new man-made target tracking algorithm integrating features from (Forward Looking InfraRed) image sequence is presented based on particle filter. Firstly, a multiscale fractal feature is used to enhance targets in FLIR images. Secondly, the gray space feature is defined by Bhattacharyya distance between intensity histograms of the reference target and a sample target from MFF (Multi-scale Fractal Feature) image. Thirdly, the motion feature is obtained by differencing between two MFF images. Fourthly, a fusion coefficient can be automatically obtained by online feature selection method for features integrating based on fuzzy logic. Finally, a particle filtering framework is developed to fulfill the target tracking. Experimental results have shown that the proposed algorithm can accurately track weak or small man-made target in FLIR images with complicated background. The algorithm is effective, robust and satisfied to real time tracking.

Acceleration of radio network optimisation algorithms

The increasing demand for cheaper-faster-better services anytime and anywhere has made radio network optimisation much more complex than ever before. In order to dynamically optimise the serving network, Dynamic Network Optimisation (DNO), is proposed as the ultimate solution and future trend. The realization of DNO, however, has been hindered by a significant bottleneck of the optimisation speed as the network complexity grows. This paper presents a multi-threaded parallel solution to accelerate complicated proprietary network optimisation algorithms, under a rigid condition of numerical consistency. ariesoACP product from Arieso Ltd serves as the platform for parallelisation. This parallel solution has been benchmarked and results exhibit a high scalability and a run-time reduction by 11% to 42% based on the technology, subscriber density and blocking rate of a given network in comparison with the original version. Further, it is highly essential that the parallel version produces equivalent optimisation quality in terms of identical optimisation outputs.

A three-shot improvement scheme for hard-decision based linear decorrelating detector in DS/CDMA

This paper proposes a three-shot improvement scheme for the hard-decision based method (HDM), an implementation solution for linear decorrelating detector (LDD) in asynchronous DS/CDMA systems. By taking advantage of the preceding (already reconstructed) bit and the matched filter output for the following two bits, the coupling between temporally adjacent bits (TABs), which always exists for asynchronous systems, is greatly suppressed and the performance of the original HDM is substantially improved. This new scheme requires no signaling overhead yet offers nearly the same performance as those more complicated methods. Also, it can easily accommodate the change in the number of active users in the channel, as no symbol/bit grouping is involved. Finally, the influence of synchronisation errors is investigated.