Collaboration of the weather and climate communities to advance subseasonal-to-seasonal prediction

The World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP) have identified collaborations and scientific priorities to accelerate advances in analysis and prediction at subseasonal-to-seasonal time scales, which include i) advancing knowledge of mesoscale–planetary-scale interactions and their prediction; ii) developing high-resolution global–regional climate simulations, with advanced representation of physical processes, to improve the predictive skill of subseasonal and seasonal variability of high-impact events, such as seasonal droughts and floods, blocking, and tropical and extratropical cyclones; iii) contributing to the improvement of data assimilation methods for monitoring and predicting used in coupled ocean–atmosphere–land and Earth system models; and iv) developing and transferring diagnostic and prognostic information tailored to socioeconomic decision making. The document puts forward specific underpinning research, linkage, and requirements necessary to achieve the goals of the proposed collaboration.

Data assimilation with correlated observation errors: experiments with a 1-D shallow water model

Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information. As operational centres move towards higher-resolution forecasting, there is a requirement to retain data providing detail on appropriate scales. Thus an alternative approach to dealing with observation error correlations is needed. In this article, we consider several approaches to approximating observation error correlation matrices: diagonal approximations, eigendecomposition approximations and Markov matrices. These approximations are applied in incremental variational assimilation experiments with a 1-D shallow water model using synthetic observations. Our experiments quantify analysis accuracy in comparison with a reference or ‘truth’ trajectory, as well as with analyses using the ‘true’ observation error covariance matrix. We show that it is often better to include an approximate correlation structure in the observation error covariance matrix than to incorrectly assume error independence. Furthermore, by choosing a suitable matrix approximation, it is feasible and computationally cheap to include error correlation structure in a variational data assimilation algorithm.

Advanced graph mining methods for protein analysis

As one of the primary substances in a living organism, protein defines the character of each cell by interacting with the cellular environment to promote the cell’s growth and function [1]. Previous studies on proteomics indicate that the functions of different proteins could be assigned based upon protein structures [2,3]. The knowledge on protein structures gives us an overview of protein fold space and is helpful for the understanding of the evolutionary principles behind structure. By observing the architectures and topologies of the protein families, biological processes can be investigated more directly with much higher resolution and finer detail. For this reason, the analysis of protein, its structure and the interaction with the other materials is emerging as an important problem in bioinformatics. However, the determination of protein structures is experimentally expensive and time consuming, this makes scientists largely dependent on sequence rather than more general structure to infer the function of the protein at the present time. For this reason, data mining technology is introduced into this area to provide more efficient data processing and knowledge discovery approaches.

Unlike many data mining applications which lack available data, the protein structure determination problem and its interaction study, on the contrary, could utilize a vast amount of biologically relevant information on protein and its interaction, such as the protein data bank (PDB) [4], the structural classification of proteins (SCOP) databases [5], CATH databases [6], UniProt [7], and others. The difficulty of predicting protein structures, specially its 3D structures, and the interactions between proteins as shown in Figure 6.1, lies in the computational complexity of the data. Although a large number of approaches have been developed to determine the protein structures such as ab initio modelling [8], homology modelling [9] and threading [10], more efficient and reliable methods are still greatly needed.

In this chapter, we will introduce a state-of-the-art data mining technique, graph mining, which is good at defining and discovering interesting structural patterns in graphical data sets, and take advantage of its expressive power to study protein structures, including protein structure prediction and comparison, and protein-protein interaction (PPI). The current graph pattern mining methods will be described, and typical algorithms will be presented, together with their applications in the protein structure analysis.

The rest of the chapter is organized as follows: Section 6.2 will give a brief introduction of the fundamental knowledge of protein, the publicly accessible protein data resources and the current research status of protein analysis; in Section 6.3, we will pay attention to one of the state-of-the-art data mining methods, graph mining; then Section 6.4 surveys several existing work for protein structure analysis using advanced graph mining methods in the recent decade; finally, in Section 6.5, a conclusion with potential further work will be summarized.

A dietary guideline adherence score is positively associated with dietary biomarkers but not lipid profile in healthy children

BACKGROUND: Whether dietary indexes are associated with biomarkers of children's dietary intake is unclear. OBJECTIVE: The study aim was to examine the relations between diet quality and selected plasma biomarkers of dietary intake and serum lipid profile. METHODS: The study sample consisted of 130 children aged 4-13 y (mean ± SD: 8.6 ± 2.9 y) derived by using baseline data from an intervention study. The Dietary Guideline Index for Children and Adolescents (DGI-CA) comprises the following 11 components with age-specific criteria: 5 core food groups, whole-grain bread, reduced-fat dairy foods, discretionary foods (nutrient poor; high in saturated fat, salt, and added sugar), healthy fats/oils, water, and diet variety (possible score of 100). A higher score reflects greater compliance with dietary guidelines. Venous blood was collected for measurements of serum lipids, fatty acid composition, plasma carotenoids, lutein, lycopene, and α-tocopherol. Linear regression was used to examine the relation between DGI-CA score (independent variable) and concentrations of biomarkers by using the log-transformed variable (outcome), controlling for confounders. RESULTS: DGI-CA score was positively associated (P < 0.05) with plasma concentrations of lutein (standardized β = 0.17), α-carotene (standardized β = 0.28), β-carotene (standardized β = 0.26), and n-3 (ω-3) fatty acids (standardized β = 0.51) and inversely associated with plasma concentrations of lycopene (standardized β = -0.23) and stearic acid (18:0) (standardized β = -0.22). No association was observed between diet quality and α-tocopherol, n-6 fatty acids, or serum lipid profile (all P > 0.05). CONCLUSION: Diet quality, conceptualized as adherence to national dietary guidelines, is cross-sectionally associated with plasma biomarkers of dietary exposure but not serum lipid profile. This trial was registered with the Australia New Zealand Clinical Trial Registry (www.anztr.org.au) as ACTRN12609000453280.

Foot-and-mouth disease in red deer - experimental infection and test methods performance

Summary: The aim of this study was to evaluate a number of foot-and-mouth disease (FMD) test methods for use in red deer. Ten animals were intranasally inoculated with the FMD virus (FMDV) O UKG 11/2001, monitored for clinical signs, and samples taken regularly (blood, serum, oral swabs, nasal swabs, probang samples and lesion swabs, if present) over a 4-week period. Only one animal, deer 1103, developed clinical signs (lesions under the tongue and at the coronary band of the right hind hoof). It tested positive by 3D and IRES real-time reverse transcription polymerase chain reaction (rRT-PCR) in various swabs, lesion materials and serum. In a non-structural protein (NSP) in-house ELISA (NSP-ELISA-IH), one commercial ELISA (NSP-ELISA-PR) and a commercial antibody NSP pen side test, only deer 1103 showed positive results from day post-inoculation (dpi) 14 onwards. Two other NSP-ELISAs detected anti-NSP serum antibodies with lower sensitivity. It also showed rising antibody levels in the virus neutralization test (VNT), the in-house SPO-ELISA-IH and the commercial SPO-ELISA-PR at dpi 9, and in another two commercial SPO-ELISAs at dpi 12 (SPO-ELISA-IV) and dpi 19 (SPO-ELISA-IZ), respectively. Six of the red deer that had been rRT-PCR and antibody negative were re-inoculated intramuscularly with the same O-serotype FMDV at dpi 14. None of these animals became rRT-PCR or NSP-ELISA positive, but all six animals became positive in the VNT, the in-house SPO-ELISA-IH and the commercial SPO-ELISA-PR. Two other commercial SPO-ELISAs were less sensitive or failed to detect animals as positive. The rRT-PCRs and the four most sensitive commercial ELISAs that had been used for the experimentally inoculated deer were further evaluated for diagnostic specificity (DSP) using 950 serum samples and 200 nasal swabs from non-infected animals. DSPs were 100% for the rRT-PCRs and between 99.8 and 100% for the ELISAs.

Near-infrared Raman spectroscopy to detect anti-Toxoplasma gondii antibody in blood sera of domestic cats: quantitative analysis based on partial least-squares multivariate statistics

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Smoking status and tumor necrosis factor-alpha mediated systemic inflammation in COPD patients

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An optimised electrochemical biosensor for the label-free detection of C-reactive protein in blood

C-reactive protein (CRP) is an acute phase protein whose levels are increased in many disorders. There exists, in particular, a great deal of interest in the correlation between blood serum levels and the severity of risk for cardiovascular disease. A sensitive, label-free, non-amplified and reusable electrochemical impedimetric biosensor for the detection of CRP in blood serum was developed herein based on controlled and coverage optimised antibody immobilization on standard polycrystalline gold electrodes. Charge transfer resistance changes were highly target specific, linear with log. CRP. concentration across a 0.5-50. nM range and associated with a limit of detection of 176. pM. Significantly, the detection limits are better than those of current CRP clinical methods and the assays are potentially cheap, relatively automated, reusable, multiplexed and highly portable. The generated interfaces were capable not only of comfortably quantifying CRP across a clinically relevant range of concentrations but also of doing this in whole blood serum with interfaces that were, subsequently, reusable. The importance of optimising receptor layer resistance in maximising assay sensitivity is also detailed. © 2012.

Dietary, anthropometric, and biochemical determinants of uric acid in free-living adults

Background: High plasma uric acid (UA) is a prerequisite for gout and is also associated with the metabolic syndrome and its components and consequently risk factors for cardiovascular diseases. Hence, the management of UA serum concentrations would be essential for the treatment and/or prevention of human diseases and, to that end, it is necessary to know what the main factors that control the uricemia increase. The aim of this study was to evaluate the main factors associated with higher uricemia values analyzing diet, body composition and biochemical markers. Methods. 415 both gender individuals aged 21 to 82 years who participated in a lifestyle modification project were studied. Anthropometric evaluation consisted of weight and height measurements with later BMI estimation. Waist circumference was also measured. The muscle mass (Muscle Mass Index - MMI) and fat percentage were measured by bioimpedance. Dietary intake was estimated by 24-hour recalls with later quantification of the servings on the Brazilian food pyramid and the Healthy Eating Index. Uric acid, glucose, triglycerides (TG), total cholesterol, urea, creatinine, gamma-GT, albumin and calcium and HDL-c were quantified in serum by the dry-chemistry method. LDL-c was estimated by the Friedewald equation and ultrasensitive C-reactive protein (CRP) by the immunochemiluminiscence method. Statistical analysis was performed by the SAS software package, version 9.1. Linear regression (odds ratio) was performed with a 95% confidence interval (CI) in order to observe the odds ratio for presenting UA above the last quartile (♂UA > 6.5 mg/dL and ♀ UA > 5 mg/dL). The level of significance adopted was lower than 5%. Results: Individuals with BMI ≥ 25 kg/m§ssup§2§esup§ OR = 2.28(1.13-4.6) and lower MMI OR = 13.4 (5.21-34.56) showed greater chances of high UA levels even after all adjustments (gender, age, CRP, gamma-gt, LDL, creatinine, urea, albumin, HDL-c, TG, arterial hypertension and glucose). As regards biochemical markers, higher triglycerides OR = 2.76 (1.55-4.90), US-CRP OR = 2.77 (1.07-7.21) and urea OR = 2.53 (1.19-5.41) were associated with greater chances of high UA (adjusted for gender, age, BMI, waist circumference, MMI, glomerular filtration rate, and MS). No association was found between diet and UA. Conclusions: The main factors associated with UA increase were altered BMI (overweight and obesity), muscle hypotrophy (MMI), higher levels of urea, triglycerides, and CRP. No dietary components were found among uricemia predictors. © 2013 de Oliveira et al.; licensee BioMed Central Ltd.

Experimental infection of commercial layers with wild or attenuated Salmonella Gallinarum mutant strains: Anatomic pathology, total blood cell count and serum protein levels

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Daily intake of pasteurized orange juice decreases serum cholesterol, fasting glucose and diastolic blood pressure in adults

Previous studies have shown that fresh squeezed orange juice or juice from reconstituted concentrate can help prevent the development of atherosclerosis. Pasteurized orange juice presently represents the major orange juice available in the market, and because of this, it becomes necessary to determine the healthy benefits associated with this product. In this study we investigated the effect of regular consumption of pasteurized orange juice on the nutritional status, biochemical profile, and arterial blood pressure in healthy men and women. Men and women volunteered to consume pasteurized orange juice (500 mL·d–1 and 750 mL·d–1, respectively), for 8 weeks. Anthropometric, biochemical, hemodynamic, and dietary assessments were evaluated at baseline and at the end of the experimental period. Total cholesterol and LDL-C significantly decreased in both men and women after the consumption of orange juice, and an increase in HDL-C level was detected exclusively in women. Fasting glucose, diastolic blood pressure, and triglyceride levels dropped in men after the consumption of orange juice. Anthropometric variables did not change with orange juice consumption, only waist circumference decreased significantly in women. Consumption of orange juice increased the energy and carbohydrate intake for women; however, vitamin C and folate increased after the orange juice period for both men and women. Regular consumption of pasteurized orange juice by men (750 mL·d–1) and women (500 mL·d–1) reduced the risk of developing atherosclerosis, and increased the nutritional quality of their diets.

Kernel Methods for Tree Structured Data

Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of objects of a domain, the kernel function. Designing fast and good kernel functions is a challenging problem. In the case of tree structured data two issues become relevant: kernel for trees should not be sparse and should be fast to compute. The sparsity problem arises when, given a dataset and a kernel function, most structures of the dataset are completely dissimilar to one another. In those cases the classifier has too few information for making correct predictions on unseen data. In fact, it tends to produce a discriminating function behaving as the nearest neighbour rule. Sparsity is likely to arise for some standard tree kernel functions, such as the subtree and subset tree kernel, when they are applied to datasets with node labels belonging to a large domain. A second drawback of using tree kernels is the time complexity required both in learning and classification phases. Such a complexity can sometimes prevents the kernel application in scenarios involving large amount of data. This thesis proposes three contributions for resolving the above issues of kernel for trees. A first contribution aims at creating kernel functions which adapt to the statistical properties of the dataset, thus reducing its sparsity with respect to traditional tree kernel functions. Specifically, we propose to encode the input trees by an algorithm able to project the data onto a lower dimensional space with the property that similar structures are mapped similarly. By building kernel functions on the lower dimensional representation, we are able to perform inexact matchings between different inputs in the original space. A second contribution is the proposal of a novel kernel function based on the convolution kernel framework. Convolution kernel measures the similarity of two objects in terms of the similarities of their subparts. Most convolution kernels are based on counting the number of shared substructures, partially discarding information about their position in the original structure. The kernel function we propose is, instead, especially focused on this aspect. A third contribution is devoted at reducing the computational burden related to the calculation of a kernel function between a tree and a forest of trees, which is a typical operation in the classification phase and, for some algorithms, also in the learning phase. We propose a general methodology applicable to convolution kernels. Moreover, we show an instantiation of our technique when kernels such as the subtree and subset tree kernels are employed. In those cases, Direct Acyclic Graphs can be used to compactly represent shared substructures in different trees, thus reducing the computational burden and storage requirements.

Analytical challenges in the fields of Nanobioscience and Nanobiotecnology: integrated methods for separation and characterization

Nano(bio)science and nano(bio)technology play a growing and tremendous interest both on academic and industrial aspects. They are undergoing rapid developments on many fronts such as genomics, proteomics, system biology, and medical applications. However, the lack of characterization tools for nano(bio)systems is currently considered as a major limiting factor to the final establishment of nano(bio)technologies. Flow Field-Flow Fractionation (FlFFF) is a separation technique that is definitely emerging in the bioanalytical field, and the number of applications on nano(bio)analytes such as high molar-mass proteins and protein complexes, sub-cellular units, viruses, and functionalized nanoparticles is constantly increasing. This can be ascribed to the intrinsic advantages of FlFFF for the separation of nano(bio)analytes. FlFFF is ideally suited to separate particles over a broad size range (1 nm-1 μm) according to their hydrodynamic radius (rh). The fractionation is carried out in an empty channel by a flow stream of a mobile phase of any composition. For these reasons, fractionation is developed without surface interaction of the analyte with packing or gel media, and there is no stationary phase able to induce mechanical or shear stress on nanosized analytes, which are for these reasons kept in their native state. Characterization of nano(bio)analytes is made possible after fractionation by interfacing the FlFFF system with detection techniques for morphological, optical or mass characterization. For instance, FlFFF coupling with multi-angle light scattering (MALS) detection allows for absolute molecular weight and size determination, and mass spectrometry has made FlFFF enter the field of proteomics. Potentialities of FlFFF couplings with multi-detection systems are discussed in the first section of this dissertation. The second and the third sections are dedicated to new methods that have been developed for the analysis and characterization of different samples of interest in the fields of diagnostics, pharmaceutics, and nanomedicine. The second section focuses on biological samples such as protein complexes and protein aggregates. In particular it focuses on FlFFF methods developed to give new insights into: a) chemical composition and morphological features of blood serum lipoprotein classes, b) time-dependent aggregation pattern of the amyloid protein Aβ1-42, and c) aggregation state of antibody therapeutics in their formulation buffers. The third section is dedicated to the analysis and characterization of structured nanoparticles designed for nanomedicine applications. The discussed results indicate that FlFFF with on-line MALS and fluorescence detection (FD) may become the unparallel methodology for the analysis and characterization of new, structured, fluorescent nanomaterials.

Automatisierte Flüssigchromatographie mit Säulenschaltung und Ultraviolettspektroskopie oder Massenspektrometrie für therapeutisches Drug Monitoring von Antipsychotika und Antidepressiva

Therapeutisches Drug Monitoring (TDM) wird zur individuellen Dosiseinstellung genutzt, um die Effizienz der Medikamentenwirkung zu steigern und das Auftreten von Nebenwirkungen zu senken. Für das TDM von Antipsychotika und Antidepressiva besteht allerdings das Problem, dass es mehr als 50 Medikamente gibt. Ein TDM-Labor muss dementsprechend über 50 verschiedene Wirkstoffe und zusätzlich aktive Metaboliten messen. Mit der Flüssigchromatographie (LC oder HPLC) ist die Analyse vieler unterschiedlicher Medikamente möglich. LC mit Säulenschaltung erlaubt eine Automatisierung. Dabei wird Blutserum oder -plasma mit oder ohne vorherige Proteinfällung auf eine Vorsäule aufgetragen. Nach Auswaschen von störenden Matrixbestandteilen werden die Medikamente auf einer nachgeschalteten analytischen Säule getrennt und über Ultraviolettspektroskopie (UV) oder Massenspektrometrie (MS) detektiert. Ziel dieser Arbeit war es, LC-Methoden zu entwickeln, die die Messung möglichst vieler Antipsychotika und Antidepressiva erlaubt und die für die TDM-Routine geeignet ist. Eine mit C8-modifiziertem Kieselgel gefüllte Säule (20 µm 10x4.0 mm I.D.) erwies sich in Vorexperimenten als optimal geeignet bezüglich Extraktionsverhalten, Regenerierbarkeit und Stabilität. Mit einer ersten HPLC-UV-Methode mit Säulenschaltung konnten 20 verschiedene Psychopharmaka einschließlich ihrer Metabolite, also insgesamt 30 verschiedene Substanzen quantitativ erfasst werden. Die Analysenzeit betrug 30 Minuten. Die Vorsäule erlaubte 150 Injektionen, die analytische Säule konnte mit mehr als 300 Plasmainjektionen belastet werden. Abhängig vom Analyten, musste allerdings das Injektionsvolumen, die Flussrate oder die Detektionswellenlänge verändert werden. Die Methode war daher für eine Routineanwendung nur eingeschränkt geeignet. Mit einer zweiten HPLC-UV-Methode konnten 43 verschiedene Antipsychotika und Antidepressiva inklusive Metaboliten nachgewiesen werden. Nach Vorreinigung über C8-Material (10 µm, 10x4 mm I.D.) erfolgte die Trennung auf Hypersil ODS (5 µm Partikelgröße) in der analytischen Säule (250x4.6 mm I.D.) mit 37.5% Acetonitril im analytischen Eluenten. Die optimale Flussrate war 1.5 ml/min und die Detektionswellenlänge 254 nm. In einer Einzelprobe, konnten mit dieser Methode 7 bis 8 unterschiedliche Substanzen gemessen werden. Für die Antipsychotika Clozapin, Olanzapin, Perazin, Quetiapin und Ziprasidon wurde die Methode validiert. Der Variationskoeffizient (VK%) für die Impräzision lag zwischen 0.2 und 6.1%. Im erforderlichen Messbereich war die Methode linear (Korrelationskoeffizienten, R2 zwischen 0.9765 und 0.9816). Die absolute und analytische Wiederfindung lagen zwischen 98 und 118 %. Die für das TDM erforderlichen unteren Nachweisgrenzen wurden erreicht. Für Olanzapin betrug sie 5 ng/ml. Die Methode wurde an Patienten für das TDM getestet. Sie erwies sich für das TDM als sehr gut geeignet. Nach retrospektiver Auswertung von Patientendaten konnte erstmalig ein möglicher therapeutischer Bereich für Quetiapin (40-170 ng/ml) und Ziprasidon (40-130 ng/ml) formuliert werden. Mit einem Massenspektrometer als Detektor war die Messung von acht Neuroleptika und ihren Metaboliten möglich. 12 Substanzen konnten in einem Lauf bestimmt werden: Amisulprid, Clozapin, N-Desmethylclozapin, Clozapin-N-oxid, Haloperidol, Risperidon, 9-Hydroxyrisperidon, Olanzapin, Perazin, N-Desmethylperazin, Quetiapin und Ziprasidon. Nach Vorreinigung mit C8-Material (20 µm 10x4.0 mm I.D.) erfolgte die Trennung auf Synergi MAX-RP C12 (4 µm 150 x 4.6 mm). Die Validierung der HPLC-MS-Methode belegten einen linearen Zusammenhang zwischen Konzentration und Detektorsignal (R2= 0,9974 bis 0.9999). Die Impräzision lag zwischen 0.84 bis 9.78%. Die für das TDM erforderlichen unteren Nachweisgrenzen wurden erreicht. Es gab keine Hinweise auf das Auftreten von Ion Suppression durch Matrixbestandteile. Die absolute und analytische Wiederfindung lag zwischen 89 und 107 %. Es zeigte sich, dass die HPLC-MS-Methode ohne Modifikation erweitert werden kann und anscheinend mehr als 30 verschiedene Psychopharmaka erfasst werden können. Mit den entwickelten flüssigchromatographischen Methoden stehen neue Verfahren für das TDM von Antipsychotika und Antidepressiva zur Verfügung, die es erlauben, mit einer Methode verschiedene Psychopharmaka und ihre aktiven Metabolite zu messen. Damit kann die Behandlung psychiatrischer Patienten insbesondere mit Antipsychotika verbessert werden.

Mobile phone technologies and advanced data analysis towards the enhancement of diabetes self-management

Advances in the area of mobile and wireless communication for healthcare (m-Health) along with the improvements in information science allow the design and development of new patient-centric models for the provision of personalised healthcare services, increase of patient independence and improvement of patient's self-control and self-management capabilities. This paper comprises a brief overview of the m-Health applications towards the self-management of individuals with diabetes mellitus and the enhancement of their quality of life. Furthermore, the design and development of a mobile phone application for Type 1 Diabetes Mellitus (T1DM) self-management is presented. The technical evaluation of the application, which permits the management of blood glucose measurements, blood pressure measurements, insulin dosage, food/drink intake and physical activity, has shown that the use of the mobile phone technologies along with data analysis methods might improve the self-management of T1DM.