Partial costs of global climate change adaptation for the supply of raw industrial and municipal water: a methodology and application

Despite growing recognition of the importance of climate change adaptation, few global estimates of the costs involved are available for the water supply sector. We present a methodology for estimating partial global and regional adaptation costs for raw industrial and domestic water supply, for a limited number of adaptation strategies, and apply the method using results of two climate models. In this paper, adaptation costs are defined as those for providing enough raw water to meet future industrial and municipal water demand, based on country-level demand projections to 2050. We first estimate costs for a baseline scenario excluding climate change, and then additional climate change adaptation costs. Increased demand is assumed to be met through a combination of increased reservoir yield and alternative backstop measures. Under such controversial measures, we project global adaptation costs of $12 bn p.a., with 83-90$73 bn p.a.), which supports the notion of mainstreaming climate change adaptation into broader policy aims. The method provides a tool for estimating broad costs at the global and regional scale; such information is of key importance in international negotiations. © 2010 IOP Publishing Ltd

Application of Consensus Scoring and Principal Component Analysis for Virtual Screening against β-Secretase (BACE-1)

BACKGROUND: In order to identify novel chemical classes of β-secretase (BACE-1) inhibitors, an alternative scoring protocol, Principal Component Analysis (PCA), was proposed to summarize most of the information from the original scoring functions and re-rank the results from the virtual screening against BACE-1. METHOD: Given a training set (50 BACE-1 inhibitors and 9950 inactive diverse compounds), three rank-based virtual screening methods, individual scoring, conventional consensus scoring and PCA, were judged by the hit number in the top 1% of the ranked list. The docking poses were generated by Surflex, five scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, and PMF_Score) were used for pose extraction. For each pose group, twelve scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, PMF_Score, LigScore1, LigScore2, PLP1, PLP2, jain, Ludi_1, and Ludi_2) were used for the pose rank. For a test set, 113,228 chemical compounds (Sigma-Aldrich® corporate chemical directory) were docked by Surflex, then ranked by the same three ranking methods motioned above to select the potential active compounds for experimental test. RESULTS: For the training set, the PCA approach yielded consistently superior rankings compared to conventional consensus scoring and single scoring. For the test set, the top 20 compounds according to conventional consensus scoring were experimentally tested, no inhibitor was found. Then, we relied on PCA scoring protocol to test another different top 20 compounds and two low micromolar inhibitors (S450588 and 276065) were emerged through the BACE-1 fluorescence resonance energy transfer (FRET) assay. CONCLUSION: The PCA method extends the conventional consensus scoring in a quantitative statistical manner and would appear to have considerable potential for chemical screening applications

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules

Benzene, toluene, ethyl benzene and xylenes in ambient air and Pinus sylvestris L. needles: a comparative study between Belgium, Hungary and Latvia.

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Classification of gilthead sea bream (Sparus aurata) from 1H-NMR lipid profiling combined with Principal Component and Linear Discriminant Analysis

The combination of 1H NMR fingerprinting of lipids from gilthead sea bream (Sparus aurata) with nonsupervised and supervised multivariate analysis was applied to differentiate wild and farmed fish and to classify farmed specimen according to their areas of production belonging to the Mediterranean basin. Principal component analysis (PCA) applied on processed 1H NMR profiles made a clear distinction between wild and farmed samples. Linear discriminant analysis (LDA) allowed classification of samples according to the geographic origin, as well as for the wild and farmed status using both PCA scores and NMR data as variables. Variable selection for LDA was achieved with forward selection (stepwise) with a predefined 5% error level. The methods allowed the classification of 100% of the samples according to their wild and farmed status and 85\u201397% to geographic origin. Probabilistic neural network (PNN) analyses provided complementary means for the successful discrimination among classes investigated

Metabolomics applied to exhaled breath condensate in childhood asthma

RATIONALE: Metabolomic analysis provides biochemical profiles of low-molecular-weight endogenous metabolites in biological fluids. OBJECTIVES: The aim of this study was to assess the feasibility of nuclear magnetic resonance (NMR)-based metabolomic analysis applied to exhaled breath condensate ("breathomics"). Information coming from NMR spectra was analyzed with a view to establish the NMR variables that best discriminate between children with asthma and healthy children. METHODS: Twenty-five children with asthma (17 with persistent asthma treated with inhaled corticosteroids, 8 with intermittent asthma inhaled corticosteroid naive; age, 7-15 yr) and 11 healthy age-matched control subjects were enrolled. Every child performed exhaled nitric oxide measurement, spirometry, and condensate collection. Condensate samples were analyzed by means of NMR spectroscopy. Linear and partial least squares discriminant analyses were applied to data obtained from the NMR spectra. MEASUREMENTS AND MAIN RESULTS: The combination of exhaled nitric oxide and FEV(1) discriminates children with asthma and healthy children with a success rate of approximately 81%, whereas selected signals from NMR spectra offer a slightly better discrimination (approximately 86%). The selected NMR variables derive from the region of 3.2 to 3.4 ppm, indicative of oxidized compounds, and from the region of 1.7 to 2.2 ppm, indicative of acetylated compounds. CONCLUSIONS: Metabolomics can be applied to exhaled breath condensate, leading to the characterization of airway biochemical fingerprints. The presence of acetylated compounds suggests new metabolic pathways that may have a role in asthma pathophysiology