Fossilization and degradation of archaeal intact polar tetraether lipids in ODP Site 201-1229 sediments

Glycerol dibiphytanyl glycerol tetraether (GDGT) lipids are part of the cellular membranes of Thaumarchaeota, an archaeal phylum composed of aerobic ammonia oxidizers, and are used in the paleotemperature proxy TEX86. GDGTs in live cells possess polar head groups and are called intact polar lipids (IPL-GDGTs). Their transformation to core lipids (CL) by cleavage of the head group was assumed to proceed rapidly after cell death but it has been suggested that some of these IPL-GDGTs can, just like the CL-GDGTs, be preserved over geological timescales. Here, we examined IPL-GDGTs in deeply buried (0.2-186 mbsf, ~2.5 Myr) sediments from the Peru Margin. Direct measurements of the most abundant IPL-GDGT, IPL-crenarchaeol, specific for Thaumarchaeota, revealed depth profiles which differed per head group. Shallow sediments (<1 mbsf) contained IPL-crenarchaeol with both glycosidic- and phosphate headgroups, as also observed in thaumarchaeal enrichment cultures, marine suspended particulate matter and marine surface sediments. However, hexose, phosphohexose-crenarchaeol is not detected anymore below 6 mbsf (~7 kyr), suggesting a high lability. In contrast, IPL-crenarchaeol with glycosidic head groups is preserved over time scales of Myr. This agrees with previous analyses of deeply buried (>1 m) marine sediments, which only reported glycosidic and no phosphate-containing IPL-GDGTs. TEX86 values of CL-GDGTs did not markedly change with depth, and the TEX86 of IPL-derived GDGTs decreased only when the proportions of monohexose- to dihexose-GDGTs changed, likely due to the enhanced preservation of the monohexose GDGTs. Our results support the hypothesis that in situ GDGT production and differential IPL degradation in sediments is not substantially affecting TEX86 paleotemperature estimations based on CL GDGTs and indicate that likely only a small amount of IPL-GDGTs present in deeply buried sediments is part of cell membranes of active Archaea. The amount of archaeal biomass in the deep biosphere based on these IPLs may have been substantially overestimated.

Fractional abundances of isoprenoid and branched glycerol dialkyl glycerol tetraethers (GDGT) of cores SO201-2-12KL and SO201-2-114KL

It has been proposed that North Pacific sea surface temperature (SST) evolution was intimately linked to North Atlantic climate oscillations during the last glacial-interglacial transition. However, during the early deglaciation and the Last Glacial Maximum, the SST development in the subarctic northwest Pacific and the Bering Sea is poorly constrained as most existing deglacial SST records are based on alkenone paleothermometry, which is limited prior to 15 ka B.P. in the subarctic North Pacific realm. By applying the TEXL86 temperature proxy we obtain glacial-Holocene-SST records for the marginal northwest Pacific and the Western Bering Sea. Our TEXL86-based records and existing alkenone data suggest that during the past 15.5 ka, SSTs in the northwest Pacific and the Western Bering Sea closely followed millennial-scale climate fluctuations known from Greenland ice cores, indicating rapid atmospheric teleconnections with abrupt climate changes in the North Atlantic. Our SST reconstructions indicate that in the Western Bering Sea SSTs drop significantly during Heinrich Stadial 1 (HS1), similar to the known North Atlantic climate history. In contrast, progressively rising SST in the northwest Pacific is different to the North Atlantic climate development during HS1. Similarities between the northwest Pacific SST and climate records from the Gulf of Alaska point to a stronger influence of Alaskan Stream waters connecting the eastern and western basin of the North Pacific during this time. During the Holocene, dissimilar climate trends point to reduced influence of the Alaskan Stream in the northwest Pacific.

Intact and core tetraether lipids in water column profiles of suspended particulate matter and of surface sediment samples off Cape Blanc, NW Africa

In the reconstruction of sea surface temperature (SST) from sedimentary archives, secondary sources, lateral transport and selective preservation are considered to be mainly negligible in terms of influencing the primary signal. This is also true for the archaeal glycerol dialkyl glycerol tetraethers (GDGTs) that form the basis for the TEX86 SST proxy. Our samples represent four years variability on a transect off Cape Blanc (NW Africa). We studied the subsurface production, vertical and lateral transport of intact polar lipids and core GDGTs in the water column at high vertical resolution on the basis of suspended particulate matter (SPM) samples from the photic zone, the subsurface oxygen minimum zone (OMZ), nepheloid layers (NL) and the water column between these. Furthermore we compared the water column SPM GDGT composition with that in underlying surface sediments. This is the first study that reports TEX86 values from the precursor intact polar lipids (IPLs) associated with specific head groups (IPL -specific TEX86). We show a clear deviation from the sea surface GDGT composition in the OMZ between 300 and 600 m. Since neither lateral transport nor selective degradation provides a satisfactory explanation for the observed TEX-derived temperature profiles with a bias towards higher temperatures for both core- and IPL -specific TEX86 values, we suggest that subsurface in situ production of archaea with a distinct relationship between lipid biosynthesis and temperature is the responsible mechanism. However, in the NW-African upwelling system the GDGT contribution of the OMZ to the surface sediments does not seem to affect the sedimentary TEX86 as it shows no bias and still reflects the signal of the surface waters between 0 and 60 m.

Tab. I: Alkenon analyses on sample material of cores Krummland KR-1 und Dagebüll DA-1

Geochemical studies of organic "biomarker" compounds were applied to Eemian sediments cored at Dagebuell (DA-1) on the west coast, and at Krummland (KR-1) in the east of the Baltic Zone of Schleswig-Holstein, Germany. 10 samples from the early stage of the Eemian Transgression to the high Eem at Krummland, and 24 samples from the peak and late phases of the Eemian at Dagebuell provide new insights on the development of the Eemian Sea in the region. C37-C39-ethyl- and methyl-ketones in the Krummland sediments indicated unstable conditions at the onset of the marine trangression, and freshwater influence in keeping with their shallow nearshore environment. In the Dagebuell deposits, patterns typical of marine to brackish conditions were observed, comparable to those found today in the Skagerrak and Belt Sea areas. The sea-surface temperatures estimated from the alkenone unsaturation ratio UK37 at DA-1 corroborate the evidence from "standard" faunal and pollen assemblages, and lithological successions. Here, the temperature maximum attained in pollen assemblage zone PAZ Illc, indicates the early onset of very warm conditions, preceding the highest sea level of the penultimate interglacial by 8,000 years, based on previously published U/Th ages.

Concentrations and abundance ratios of long-chain alkenones and glycerol dialkyl glycerol tetraethers in sinking particles south of Java

In this study, we obtained concentrations and abundance ratios of long-chain alkenones and glycerol dialkyl glycerol tetraethers (GDGTs) in a one-year time-series of sinking particles collected with a sediment trap moored from December 2001 to November 2002 at 2200 m water depth south of Java in the eastern Indian Ocean. We investigate the seasonality of alkenone and GDGT fluxes as well as the potential habitat depth of the Thaumarchaeota producing the GDGTs entrained in sinking particles. The alkenone flux shows a pronounced seasonality and ranges from 1 µg m-**2 d**-1 to 35 µg m**-2 d**-1. The highest alkenone flux is observed in late September during the Southeast monsoon, coincident with high total organic carbon fluxes as well as high net primary productivity. Flux-weighted mean temperature for the high flux period using the alkenone-based sea-surface temperature (SST) index UK'37 is 26.7°C, which is similar to satellite-derived Southeast (SE) monsoon SST (26.4°C). The GDGT flux displays a weaker seasonality than that of the alkenones. It is elevated during the SE monsoon period compared to the Northwest (NW) monsoon and intermonsoon periods (approximately 2.5 times), which is probably related to seasonal variation of the abundance of Thaumarchaeota, or to enhanced export of GDGTs by aggregation with sinking phytoplankton detritus. Flux-weighted mean temperature inferred from the GDGT-based TEXH86 index is 26.2°C, which is 1.8 °C lower than mean annual (ma) SST but similar to SE monsoon SST. As the time series of TEXH86 temperature estimates, however, does not record a strong seasonal amplitude, we infer that TEXH86 reflects ma upper thermocline temperature at approximately 50 m water depth.

Potential Shifts in Canadian High Arctic Sedimentary Organic Matter Composition With Permafrost Active Layer Detachments

Increased temperature and precipitation in Arctic regions have led to deeper thawing and structural instability in permafrost soil. The resulting localized disturbances, referred to as active layer detachments (ALDs), may transport organic matter (OM) to more biogeochemically active zones. To examine this further, solid state cross polarization magic angle spinning 13C nuclear magnetic resonance (CPMAS NMR) and biomarker analysis were used to evaluate potential shifts in riverine sediment OM composition due to nearby ALDs within the Cape Bounty Arctic Watershed Observatory, Nunavut, Canada. In sedimentary OM near ALDs, NMR analysis revealed signals indicative of unaltered plant-derived material, likely derived from permafrost. Long chain acyclic aliphatic lipids, steroids, cutin, suberin and lignin occurred in the sediments, consistent with a dominance of plant-derived compounds, some of which may have originated from permafrost-derived OM released by ALDs. OM degradation proxies for sediments near ALDs revealed less alteration in acyclic aliphatic lipids, while constituents such as steroids, cutin, suberin and lignin were found at a relatively advanced stage of degradation. Phospholipid fatty acid analysis indicated that microbial activity was higher near ALDs than downstream but microbial substrate limitation was prevalent within disturbed regions. Our study suggests that, as these systems recover from disturbance, ALDs likely provide permafrost-derived OM to sedimentary environments. This source of OM, which is enriched in labile OM, may alter biogeochemical patterns and enhance microbial respiration within these ecosystems.

Exceptional activity of gallium(III) chloride and chlorogallate(III) ionic liquids for Baeyer–Villiger oxidation

Baeyer–Villiger oxidation of cyclic ketones, using H2O2 as the oxidising agent, was systematically studied using a range of metal chlorides in different solvents, and in neat chlorogallate(III) ionic liquids. The extremely high activity of GaCl3 in promoting oxidation with H2O2, irrespective of solvent, was reported for the first time. The activity of all other metal chlorides was strongly solvent-dependent. In particular, AlCl3 was very active in a protic solvent (ethanol), and tin chlorides, SnCl4 and SnCl2, were active in aprotic solvents (toluene and dioxane). In order to eliminate the need for volatile organic solvent, a Lewis acidic chlorogallate(III) ionic liquid was used in the place of GaCl3, which afforded typically 89–94% yields of lactones in 1–120 min, at ambient conditions. Raman and 71Ga NMR spectroscopic studies suggest that the active species, in both GaCl3 and chlorogallate(III) ionic liquid systems, are chlorohydroxygallate(III) anions, [GaCl3OH]−, which are the products of partial hydrolysis of GaCl3 and chlorogallate(III) anions; therefore, the presence of water is crucial.

Novel ketone diet enhances physical and cognitive performance

Ketone bodies are the most energy-efficient fuel and yield more ATP per mole of substrate than pyruvate and increase the free energy released from ATP hydrolysis. Elevation of circulating ketones via high-fat, low-carbohydrate diets has been used for the treatment of drug-refractory epilepsy and for neurodegenerative diseases, such as Parkinson's disease. Ketones may also be beneficial for muscle and brain in times of stress, such as endurance exercise. The challenge has been to raise circulating ketone levels by using a palatable diet without altering lipid levels. We found that blood ketone levels can be increased and cholesterol and triglycerides decreased by feeding rats a novel ketone ester diet: chow that is supplemented with (R)-3-hydroxybutyl (R)-3-hydroxybutyrate as 30% of calories. For 5 d, rats on the ketone diet ran 32% further on a treadmill than did control rats that ate an isocaloric diet that was supplemented with either corn starch or palm oil (P < 0.05). Ketone-fed rats completed an 8-arm radial maze test 38% faster than did those on the other diets, making more correct decisions before making a mistake (P < 0.05). Isolated, perfused hearts from rats that were fed the ketone diet had greater free energy available from ATP hydrolysis during increased work than did hearts from rats on the other diets as shown by using [(31)P]-NMR spectroscopy. The novel ketone diet, therefore, improved physical performance and cognitive function in rats, and its energy-sparing properties suggest that it may help to treat a range of human conditions with metabolic abnormalities.

Bayesian nonparametric latent variable models

L’un des problèmes importants en apprentissage automatique est de déterminer la complexité du modèle à apprendre. Une trop grande complexité mène au surapprentissage, ce qui correspond à trouver des structures qui n’existent pas réellement dans les données, tandis qu’une trop faible complexité mène au sous-apprentissage, c’est-à-dire que l’expressivité du modèle est insuffisante pour capturer l’ensemble des structures présentes dans les données. Pour certains modèles probabilistes, la complexité du modèle se traduit par l’introduction d’une ou plusieurs variables cachées dont le rôle est d’expliquer le processus génératif des données. Il existe diverses approches permettant d’identifier le nombre approprié de variables cachées d’un modèle. Cette thèse s’intéresse aux méthodes Bayésiennes nonparamétriques permettant de déterminer le nombre de variables cachées à utiliser ainsi que leur dimensionnalité. La popularisation des statistiques Bayésiennes nonparamétriques au sein de la communauté de l’apprentissage automatique est assez récente. Leur principal attrait vient du fait qu’elles offrent des modèles hautement flexibles et dont la complexité s’ajuste proportionnellement à la quantité de données disponibles. Au cours des dernières années, la recherche sur les méthodes d’apprentissage Bayésiennes nonparamétriques a porté sur trois aspects principaux : la construction de nouveaux modèles, le développement d’algorithmes d’inférence et les applications. Cette thèse présente nos contributions à ces trois sujets de recherches dans le contexte d’apprentissage de modèles à variables cachées. Dans un premier temps, nous introduisons le Pitman-Yor process mixture of Gaussians, un modèle permettant l’apprentissage de mélanges infinis de Gaussiennes. Nous présentons aussi un algorithme d’inférence permettant de découvrir les composantes cachées du modèle que nous évaluons sur deux applications concrètes de robotique. Nos résultats démontrent que l’approche proposée surpasse en performance et en flexibilité les approches classiques d’apprentissage. Dans un deuxième temps, nous proposons l’extended cascading Indian buffet process, un modèle servant de distribution de probabilité a priori sur l’espace des graphes dirigés acycliques. Dans le contexte de réseaux Bayésien, ce prior permet d’identifier à la fois la présence de variables cachées et la structure du réseau parmi celles-ci. Un algorithme d’inférence Monte Carlo par chaîne de Markov est utilisé pour l’évaluation sur des problèmes d’identification de structures et d’estimation de densités. Dans un dernier temps, nous proposons le Indian chefs process, un modèle plus général que l’extended cascading Indian buffet process servant à l’apprentissage de graphes et d’ordres. L’avantage du nouveau modèle est qu’il admet les connections entres les variables observables et qu’il prend en compte l’ordre des variables. Nous présentons un algorithme d’inférence Monte Carlo par chaîne de Markov avec saut réversible permettant l’apprentissage conjoint de graphes et d’ordres. L’évaluation est faite sur des problèmes d’estimations de densité et de test d’indépendance. Ce modèle est le premier modèle Bayésien nonparamétrique permettant d’apprendre des réseaux Bayésiens disposant d’une structure complètement arbitraire.

Highly efficient and reusable CNT supported iron(II) catalyst for microwave assisted alcohol oxidation

The highly efficient eco-friendly synthesis of ketones (yields over 99%) from secondary alcohols is achieved by combination of [FeCl2{eta(3)-HC(pz)(3)}] (pz = pyrazol-1-yl) supported on functionalized multi-walled carbon nanotubes and microwave irradiation, in a solvent-free medium. The carbon homoscorpionate iron(II) complex is the first one of this class to be used as catalyst for the oxidation of alcohols.

Mannich-Type reactions in the Gas-Phase: the addition of Enol Silanes to cyclic N-Acyliminium Ions

The intrinsic gas-phase reactivity of cyclic N-acyliminium ions in Mannich-type reactions with the parent enol silane, vinyloxytrimethylsilane, has been investigated by double- and triple-stage pentaquadrupole mass spectrometric experiments. Remarkably distinct reactivities are observed for cyclic N-acyliminium ions bearing either endocyclic or exocyclic carbonyl groups. NH-Acyliminium ions with endocyclic carbonyl groups locked in s-trans forms participate in a novel tandem N-acyliminium ion reaction:  the nascent adduct formed by simple addition is unstable and rearranges by intramolecular trimethylsilyl cation shift to the ring nitrogen, and an acetaldehyde enol molecule is eliminated. An NSi(CH3)3-acyliminium ion is formed, and this intermediate ion reacts with a second molecule of vinyloxytrimethylsilane by simple addition to form a stable acyclic adduct. N-Acyl and N,N-diacyliminium ions with endocyclic carbonyl groups, for which the s-cis conformation is favored, react distinctively by mono polar [4+ + 2] cycloaddition yielding stable, ressonance-stabilized cycloadducts. Product ions were isolated via mass-selection and structurally characterized by triple-stage mass spectrometric experiments. B3LYP/6-311G(d,p) calculations corroborate the proposed reaction mechanisms.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Reduction capability of soil humic substances from the Rio Negro basin, Brazil, towards Hg(II) studied by a multimethod approach and principal component analysis (PCA)

This paper characterizes humic substances (HS) extracted from soil samples collected in the Rio Negro basin in the state of Amazonas, Brazil, particularly investigating their reduction capabilities towards Hg(II) in order to elucidate potential mercury cycling/volatilization in this environment. For this reason, a multimethod approach was used, consisting of both instrumental methods (elemental analysis, EPR, solid-state NMR, FIA combined with cold-vapor AAS of Hg(0)) and statistical methods such as principal component analysis (PCA) and a central composite factorial planning method. The HS under study were divided into groups, complexing and reducing ones, owing to different distribution of their functionalities. The main functionalities (cor)related with reduction of Hg(II) were phenolic, carboxylic and amide groups, while the groups related with complexation of Hg(II) were ethers, hydroxyls, aldehydes and ketones. The HS extracted from floodable regions of the Rio Negro basin presented a greater capacity to retain (to complex, to adsorb physically and/or chemically) Hg(II), while nonfloodable regions showed a greater capacity to reduce Hg(II), indicating that HS extracted from different types of regions contribute in different ways to the biogeochemical mercury cycle in the basin of the mid-Rio Negro, AM, Brazil. (c) 2007 Published by Elsevier B.V.

Measurements of activity coefficients at infinite dilution of organic solutes and water on polar imidazolium-based ionic liquids

The activity coefficients at infinite dilution, gamma(infinity)(13), of 55 organic solutes and water in three ionic liquids with the common cation 1-butyl-3-methylimidazolium and the polar anions Cl--,Cl- [CH3SO3](-) and [(CH3)(2)PO4](-), were determined by (gas + liquid) chromatography at four temperatures in the range (358.15 to 388.15) K for alcohols and water, and T = (398.15 to 428.15) K for the other organic solutes including alkanes, cycloalkanes, alkenes, cycloalkenes, alkynes, ketones, ethers, cyclic ethers, aromatic hydrocarbons, esters, butyraldehyde, acetonitrile, pyridine, 1-nitropropane and thiophene. From the experimental gamma(infinity)(13) values, the partial molar excess Gibbs free energy, (G) over bar (E infinity)(m), enthalpy (H) over bar (E infinity)(m), and entropy (S) over bar (E infinity)(m), at infinite dilution, were estimated in order to provide more information about the interactions between the solutes and the ILs. Moreover, densities were measured and (gas + liquid) partition coefficients (KL) calculated. Selectivities at infinite dilution for some separation problems such as octane/benzene, cyclohexane/benzene and cyclohexane/thiophene were calculated using the measured gamma(infinity)(13), and compared with literature values for N-methyl-2-pyrrolidinone (NMP), sulfolane, and other ionic liquids with a common cation or anion of the ILs here studied. From the obtained infinite dilution selectivities and capacities, it can be concluded that the ILs studied may replace conventional entrainers applied for the separation processes of aliphatic/aromatic hydrocarbons.