Intrahepatic cholestasis of pregnancy : genetic background, epidemiology and hepatobiliary consequences

Intrahepatic cholestasis of pregnancy (ICP) is the most common cholestatic liver disease during pregnancy. The reported incidence varies from 0.4 to 15% of full-term pregnancies. The etiology is heterogeneous but familial clustering is known to occur. Here we have studied the genetic background, epidemiology, and long-term hepatobiliary consequences of ICP. In a register-based nation-wide study (n=1 080 310) the incidence of ICP was 0.94% during 1987-2004. A slightly higher incidence, 1.3%, was found in a hospital-based series (n=5304) among women attending the University Hospital of Helsinki in 1992-1993. Of these 16% (11/69) were familial and showed a higher (92%) recurrence rate than the sporadic (40%) cases. In the register-based epidemiological study, advanced maternal age and, to a lesser degree, parity were identified as new risk factors for ICP. The risk was 3-fold higher in women >39 years of age compared to women <30 years. Multiple pregnancy also associated with an elevated risk. In a genetic study we found no association of ICP with the genes regulating bile salt transport (ABCB4, ABCB11 and ATP8B1). The livers of postmenopausal women with a history of ICP tolerated well the short-term exposure to oral and transdermal estradiol, although the doses used were higher than those in routine clinical use. The response of serum levels of sex hormone-binding globulin (SHBG) to oral estradiol was slightly reduced in the ICP group. Transdermal estradiol had no effect on C-reactive protein (CRP) or SHBG. A number of liver and biliary diseases were found to be associated with ICP. Women with a history of ICP showed elevated risks for non-alcoholic liver cirrhosis (8.2 CI 1.9-36), cholelithiasis and cholecystitis (3.7 CI 3.2-4.2), hepatitis C (3.5 CI 1.6-7.6) and non-alcoholic pancreatitis (3.2 CI 1.7-5.7). In conclusion, ICP complicates around 1% of all full-term pregnancies in Finland and its incidence has remained unchanged since 1987. It is familial in 16% of cases with a higher recurrence rate. Although the cause remains unknown, several risk factors, namely advanced maternal age, parity and multiple pregnancies, can be identified. Both oral and transdermal regimens of postmenopausal hormone therapy (HT) are safe for women with a history of ICP when liver function is considered. Some ICP patients are at risk of other liver and biliary diseases and, contrary to what has been thought, a follow-up is warranted.

Uplink Power Control and Base Station Association in Multichannel Cellular Networks

A combined base station association and power control problem is studied for the uplink of multichannel multicell cellular networks, in which each channel is used by exactly one cell (i.e., base station). A distributed association and power update algorithm is proposed and shown to converge to a Nash equilibrium of a noncooperative game. We consider network models with discrete mobiles (yielding an atomic congestion game), as well as a continuum of mobiles (yielding a population game). We find that the equilibria need not be Pareto efficient, nor need they be system optimal. To address the lack of system optimality, we propose pricing mechanisms. It is shown that these mechanisms can be implemented in a distributed fashion.

Real-coded genetic algorithm for machining condition optimization

In this paper, we consider the machining condition optimization models presented in earlier studies. Finding the optimal combination of machining conditions within the constraints is a difficult task. Hence, in earlier studies standard optimization methods are used. The non-linear nature of the objective function, and the constraints that need to be satisfied makes it difficult to use the standard optimization methods for the solution. In this paper, we present a real coded genetic algorithm (RCGA), to find the optimal combination of machining conditions. We present various issues related to real coded genetic algorithm such as solution representation, crossover operators, and repair algorithm in detail. We also present the results obtained for these models using real coded genetic algorithm and discuss the advantages of using real coded genetic algorithm for these problems. From the results obtained, we conclude that real coded genetic algorithm is reliable and accurate for solving the machining condition optimization models.

Role of 16S ribosomal RNA methylations in translation initiation in Escherichia coli

Translation initiation from the ribosomal P-site is the specialty of the initiator tRNAs (tRNA(fMet)). Presence of the three consecutive G-C base pairs (G29-C41, G30-C40 and G31-C39) in their anticodon stems, a highly conserved feature of the initiator tRNAs across the three kingdoms of life, has been implicated in their preferential binding to the P-site. How this feature is exploited by ribosomes has remained unclear. Using a genetic screen, we have isolated an Escherichia coli strain, carrying a G122D mutation in folD, which allows initiation with the tRNA(fMet) containing mutations in one, two or all the three G-C base pairs. The strain shows a severe deficiency of methionine and S-adenosylmethionine, and lacks nucleoside methylations in rRNA. Targeted mutations in the methyltransferase genes have revealed a connection between the rRNA modifications and the fundamental process of the initiator tRNA selection by the ribosome.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

Ageing and learning in Australia: Arguing an evidence base for informed and equitable policy

Given Australia’s population ageing and predicted impacts related to health, productivity, equity and enhancing quality of life outcomes for senior Australians, lifelong learning has been identified as a pathway for addressing the risks associated with an ageing population. To date Australian governments have paid little attention to addressing these needs and thus, there is an urgent need for policy development for lifelong learning as a national priority. The purpose of this article is to explore the current lifelong learning context in Australia and to propose a set of factors that are most likely to impact learning in later years.

A Dynamic Migration Model for Self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

A real coded genetic algorithm for data partitioning and scheduling in networks with arbitrary processor release time

The problem of scheduling divisible loads in distributed computing systems, in presence of processor release time is considered. The objective is to find the optimal sequence of load distribution and the optimal load fractions assigned to each processor in the system such that the processing time of the entire processing load is a minimum. This is a difficult combinatorial optimization problem and hence genetic algorithms approach is presented for its solution.

The syntheses, characterizations, X-ray crystal structures and properties of Cu(I) complexes of a bis-bidentate schiff base ligand

Bis-bidentate Schiff base ligand L and its two mononuclear complexes [CuL(CH3CN)(2)]ClO4 (1)and [CuL(PPh3)(2)]ClO4 (2)have been prepared and thoroughly characterized by elemental analyses, IR, UV-Vis, NMR spectroscopy and X-ray diffraction analysis. In both the complexes the metal ion auxiliaries adopt tetrahedral coordination environment. Their reactivity, electrochemical and photophysical behavior have been studied. Complex 1 shows reversible Cu-II/I couple with potential 0.74 V versus Ag/AgCl in CH2Cl2. At room temperature L is weakly fluorescent in CH2Cl2, however in Cu(I)complexes 1 and 2 the emission in quenched. (C) 2009 Elsevier B. V. All rights reserved.

Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm

Many optimal control problems are characterized by their multiple performance measures that are often noncommensurable and competing with each other. The presence of multiple objectives in a problem usually give rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Evolutionary algorithms have been recognized to be well suited for multi-objective optimization because of their capability to evolve a set of nondominated solutions distributed along the Pareto front. This has led to the development of many evolutionary multi-objective optimization algorithms among which Nondominated Sorting Genetic Algorithm (NSGA and its enhanced version NSGA-II) has been found effective in solving a wide variety of problems. Recently, we reported a genetic algorithm based technique for solving dynamic single-objective optimization problems, with single as well as multiple control variables, that appear in fed-batch bioreactor applications. The purpose of this study is to extend this methodology for solution of multi-objective optimal control problems under the framework of NSGA-II. The applicability of the technique is illustrated by solving two optimal control problems, taken from literature, which have usually been solved by several methods as single-objective dynamic optimization problems. (C) 2004 Elsevier Ltd. All rights reserved.