Spatially-explicit modelling of grassland classes - an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterized by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the IDW approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were "cool temperate-arid temperate zonal semi-desert", "cool temperate-humid forest steppe and deciduous broad-leaved forest", "temperate-extra-arid temperate zonal desert", and "frigid per-humid rain tundra and alpine meadow". The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies' decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities which will help to prevent overgrazing and land degradation.

Health monitoring of short-and medium-spen bridges using an improved modal strain energy method

Increasing the importance and use of infrastructures such as bridges, demands more effective structural health monitoring (SHM) systems. SHM has well addressed the damage detection issues through several methods such as modal strain energy (MSE). Many of the available MSE methods either have been validated for limited type of structures such as beams or their performance is not satisfactory. Therefore, it requires a further improvement and validation of them for different types of structures. In this study, an MSE method was mathematically improved to precisely quantify the structural damage at an early stage of formation. Initially, the MSE equation was accurately formulated considering the damaged stiffness and then it was used for derivation of a more accurate sensitivity matrix. Verification of the improved method was done through two plane structures: a steel truss bridge and a concrete frame bridge models that demonstrate the framework of a short- and medium-span of bridge samples. Two damage scenarios including single- and multiple-damage were considered to occur in each structure. Then, for each structure, both intact and damaged, modal analysis was performed using STRAND7. Effects of up to 5 per cent noise were also comprised. The simulated mode shapes and natural frequencies derived were then imported to a MATLAB code. The results indicate that the improved method converges fast and performs well in agreement with numerical assumptions with few computational cycles. In presence of some noise level, it performs quite well too. The findings of this study can be numerically extended to 2D infrastructures particularly short- and medium-span bridges to detect the damage and quantify it more accurately. The method is capable of providing a proper SHM that facilitates timely maintenance of bridges to minimise the possible loss of lives and properties.

Feeding beliefs and practices of Chinese immigrant mothers : validation of a modified version of the child feeding questionnaire

The Child Feeding Questionnaire (CFQ) developed by Birch and colleagues (2001) is a widely used tool for measuring parental feeding beliefs, attitudes and practices. However, the appropriateness of the CFQ for use with Chinese populations is unknown. This study tested the construct validity of a novel Chinese version of the CFQ using confirmatory factor analysis (CFA). Participants included a convenience sample of 254 Chinese-Australian mothers of children aged 1-4 years. Prior to testing, the questionnaire was translated into Chinese using a translation-back-translation method, one item was re-worded to be culturally appropriate, a new item was added (monitoring), and five items that were not age-appropriate for the sample were removed. Based on previous literature, both a 7-factor and an 8-factor model were assessed via CFA. Results showed that the 8-factor model, which separated restriction and use of food rewards, improved the conceptual clarity of the constructs and provided a good fit to the data. Internal consistency of all eight factors was acceptable (Cronbach’s α: .60−.93). This modified 8-factor CFQ appears to be a linguistically and culturally appropriate instrument for assessing feeding beliefs and practices in Chinese-Australian mothers of young children.

Validation of a parent-proxy quality of life questionnaire for paediatric chronic cough (PC-QOL)

Background Quality of life (QOL) measures are an important patient-relevant outcome measure for clinical studies. Currently there is no fully validated cough-specific QOL measure for paediatrics. The objective of this study was to validate a cough-specific QOL questionnaire for paediatric use. Method 43 children (28 males, 15 females; median age 29 months, IQR 20–41 months) newly referred for chronic cough participated. One parent of each child completed the 27-item Parent Cough-Specific QOL questionnaire (PC-QOL), and the generic child (Pediatric QOL Inventory 4.0 (PedsQL)) and parent QOL questionnaires (SF-12) and two cough-related measures (visual analogue score and verbal category descriptive score) on two occasions separated by 2–3 weeks. Cough counts were also objectively measured on both occasions. Results Internal consistency for both the domains and total PC-QOL at both test times was excellent (Cronbach alpha range 0.70–0.97). Evidence for repeatability and criterion validity was established, with significant correlations over time and significant relationships with the cough measures. The PC-QOL was sensitive to change across the test times and these changes were significantly related to changes in cough measures (PC-QOL with: verbal category descriptive score, rs=−0.37, p=0.016; visual analogue score, rs=−0.47, p=0.003). Significant correlations of the difference scores for the social domain of the PC-QOL and the domain and total scores of the PedsQL were also noted (rs=0.46, p=0.034). Conclusion The PC-QOL is a reliable and valid outcome measure that assesses QOL related to childhood cough at a given time point and measures changes in cough-specific QOL over time.

Calibration and validation of wearable monitors

Background Wearable monitors are increasingly being used to objectively monitor physical activity in research studies within the field of exercise science. Calibration and validation of these devices are vital to obtaining accurate data. This article is aimed primarily at the physical activity measurement specialist, although the end user who is conducting studies with these devices also may benefit from knowing about this topic. Best Practices Initially, wearable physical activity monitors should undergo unit calibration to ensure interinstrument reliability. The next step is to simultaneously collect both raw signal data (e.g., acceleration) from the wearable monitors and rates of energy expenditure, so that algorithms can be developed to convert the direct signals into energy expenditure. This process should use multiple wearable monitors and a large and diverse subject group and should include a wide range of physical activities commonly performed in daily life (from sedentary to vigorous). Future Directions New methods of calibration now use "pattern recognition" approaches to train the algorithms on various activities, and they provide estimates of energy expenditure that are much better than those previously available with the single-regression approach. Once a method of predicting energy expenditure has been established, the next step is to examine its predictive accuracy by cross-validating it in other populations. In this article, we attempt to summarize the best practices for calibration and validation of wearable physical activity monitors. Finally, we conclude with some ideas for future research ideas that will move the field of physical activity measurement forward.

A finite volume scheme with preconditioned Lanczos method for two-dimensional space-fractional reaction–diffusion equations

Fractional differential equations have been increasingly used as a powerful tool to model the non-locality and spatial heterogeneity inherent in many real-world problems. However, a constant challenge faced by researchers in this area is the high computational expense of obtaining numerical solutions of these fractional models, owing to the non-local nature of fractional derivatives. In this paper, we introduce a finite volume scheme with preconditioned Lanczos method as an attractive and high-efficiency approach for solving two-dimensional space-fractional reaction–diffusion equations. The computational heart of this approach is the efficient computation of a matrix-function-vector product f(A)bf(A)b, where A A is the matrix representation of the Laplacian obtained from the finite volume method and is non-symmetric. A key aspect of our proposed approach is that the popular Lanczos method for symmetric matrices is applied to this non-symmetric problem, after a suitable transformation. Furthermore, the convergence of the Lanczos method is greatly improved by incorporating a preconditioner. Our approach is show-cased by solving the fractional Fisher equation including a validation of the solution and an analysis of the behaviour of the model.

Ultra sensitive label free surface enhanced Raman spectroscopy method for the detection of biomolecules

We present a proof of concept for a novel nanosensor for the detection of ultra-trace amounts of bio-active molecules in complex matrices. The nanosensor is comprised of gold nanoparticles with an ultra-thin silica shell and antibody surface attachment, which allows for the immobilization and direct detection of bio-active molecules by surface enhanced Raman spectroscopy (SERS) without requiring a Raman label. The ultra-thin passive layer (~1.3 nm thickness) prevents competing molecules from binding non-selectively to the gold surface without compromising the signal enhancement. The antibodies attached on the surface of the nanoparticles selectively bind to the target molecule with high affinity. The interaction between the nanosensor and the target analyte result in conformational rearrangements of the antibody binding sites, leading to significant changes in the surface enhanced Raman spectra of the nanoparticles when compared to the spectra of the un-reacted nanoparticles. Nanosensors of this design targeting the bio-active compounds erythropoietin and caffeine were able to detect ultra-trace amounts the analyte to the lower quantification limits of 3.5×10−13 M and 1×10−9 M, respectively.

Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

This article presents the field applications and validations for the controlled Monte Carlo data generation scheme. This scheme was previously derived to assist the Mahalanobis squared distance–based damage identification method to cope with data-shortage problems which often cause inadequate data multinormality and unreliable identification outcome. To do so, real-vibration datasets from two actual civil engineering structures with such data (and identification) problems are selected as the test objects which are then shown to be in need of enhancement to consolidate their conditions. By utilizing the robust probability measures of the data condition indices in controlled Monte Carlo data generation and statistical sensitivity analysis of the Mahalanobis squared distance computational system, well-conditioned synthetic data generated by an optimal controlled Monte Carlo data generation configurations can be unbiasedly evaluated against those generated by other set-ups and against the original data. The analysis results reconfirm that controlled Monte Carlo data generation is able to overcome the shortage of observations, improve the data multinormality and enhance the reliability of the Mahalanobis squared distance–based damage identification method particularly with respect to false-positive errors. The results also highlight the dynamic structure of controlled Monte Carlo data generation that makes this scheme well adaptive to any type of input data with any (original) distributional condition.

Determination of the ethylene oxide adduct S-(2-hydroxyethyl)cysteine by a fluorometric HPLC method in albumin and globin from human blood

A new method has been developed for the quantification of 2-hydroxyethylated cysteine resulting as adduct in blood proteins after human exposure to ethylene oxide, by reversed-phase HPLC with fluorometric detection. The specific adduct is analysed in albumin and in globin. After isolation of albumin and globin from blood, acid hydrolysis of the protein and precolumn derivatisation of the digest with 9-fluorenylmethoxycarbonylchloride, the levels of derivatised S-hydroxyethylcysteine are analysed by RP-HPLC and fluorescence detection, with a detection limit of 8 nmol/g protein. Background levels of S-hydroxyethylcysteine were quantified in both albumin and globin, under special consideration of the glutathione transferase GSTT1 and GSTM1 polymorphisms. GSTT1 polymorphism had a marked influence on the physiological background alkylation of cysteine. While S-hydroxyethylcysteine levels in "non-conjugators" were between 15 and 50 nmol/g albumin, "low conjugators" displayed levels between 8 and 21 nmol/g albumin, and "high conjugators" did not show levels above the detection limit. The human GSTM1 polymorphism had no apparent effect on background levels of blood protein 2-hydroxyethylation.

A rapid and cost-effective method of producing recombinant proBNP and NT-proBNP variants in Escherichia coli for immunoassay of heart failure

The measurements of plasma natriuretic peptides (NT-proBNP, proBNP and BNP) are used to diagnose heart failure but these are expensive to produce. We describe a rapid, cheap and facile production of proteins for immunoassays of heart failure. DNA encoding N-terminally His-tagged NT-proBNP and proBNP were cloned into the pJexpress404 vector. ProBNP and NT-proBNP peptides were expressed in Escherichia coli, purified and refolded in vitro. The analytical performance of these peptides were comparable with commercial analytes (NT-proBNP EC50 for the recombinant is 2.6 ng/ml and for the commercial material is 5.3 ng/ml) and the EC50 for recombinant and commercial proBNP, are 3.6 and 5.7 ng/ml respectively). Total yield of purified refolded NT-proBNP peptide was 1.75 mg/l and proBNP was 0.088 mg/l. This approach may also be useful in expressing other protein analytes for immunoassay applications. To develop a cost effective protein expression method in E. coli to obtain high yields of NT-proBNP (1.75 mg/l) and proBNP (0.088 mg/l) peptides for immunoassay use.

Validation of an immunoassay to measure plasminogen-activator inhibitor-1 concentrations in human saliva

Introduction We have previously shown that the concentrations of D-dimer are significantly elevated in saliva compared with plasma. Saliva offers several advantages compared with blood analysis. We hypothesised that human saliva contains plasminogen activator inhibitor-1 (PAI-1) and that the concentrations are not affected by the time of saliva collection. The aim was to adopt and validate an immunoassay to quantify PAI-1 concentrations in saliva and to determine whether saliva collection time has an influence in the measurement. Materials and methods Two saliva samples (morning and afternoon) from the same day were collected from healthy subjects (N = 40) who have had no underlying heart conditions. A customized AlphaLISA® immunoassay (PerkinElmer®, MA, USA) was adopted and used to quantify PAI-1 concentrations. We validated the analytical performance of the customized immunoassay by calculating recovery of known amount of analyte spiked in saliva. Results: The recovery (95.03%), intra- (8.59%) and inter-assay (7.52%) variations were within the acceptable ranges. The median salivary PAI-1 concentrations were 394 pg/mL (interquartile ranges (IQR) 243.4-833.1 pg/mL) in the morning and 376 (129.1-615.4) pg/mL in the afternoon and the plasma concentration was 59,000 (24,000-110,000) pg/mL. Salivary PAI-1 did not correlate with plasma (P = 0.812). Conclusions The adopted immunoassay produced acceptable assay sensitivity and specificity. The data demonstrated that saliva contains PAI-1 and that its concentration is not affected by the time of saliva collection. There is no correlation between salivary and plasma PAI-1 concentrations. Further studies are required to demonstrate the utility of salivary PAI-1 in CVD risk factor studies.

An acute cough-specific quality of life questionnaire for children: Development and validation

Background Patient-relevant outcome measures are essential for high-quality clinical research, and quality-of-life (QoL) tools are the current standard. Currently, there is no validated children's acute cough-specific QoL questionnaire. Objective The objective of this study was to develop and validate the Parent-proxy Children's Acute Cough-specific QoL Questionnaire (PAC-QoL). Methods Using focus groups, a 48-item PAC-QoL questionnaire was developed and later reduced to 16 items by using the clinical impact method. Parents of children with a current acute cough (<2 weeks) at enrollment completed 2 validated cough score measures, the preliminary 48-item PAC-QoL, and 3 other questionnaires (the State Trait Anxiety Inventory [STAI], the Short-Form 8-item 24-hour recall Health Survey [SF-8], and the Depression, Anxiety, and Stress 21-item Scale [DASS21]). All measures were repeated on days 3 and 14. Results The median age of the 155 children enrolled was 2.3 years (interquartile range, 1.3-4.6). Median cough duration at enrollment was 3 days (interquartile range, 2-5). The reduced 16-item scale had high internal consistency (Cronbach α = 0.95). Evidence for repeatability and criterion validity was shown by significant correlations between the domains and total PAC-QoL scores and the SF-8 (r = −0.36 and −0.51), STAI (r = −0.27 and −0.39), and DASS21 (r = −0.32 and −0.41) scales on days 0 and 3, respectively. The final PAC-QoL questionnaire was sensitive to change over time, with changes significantly relating to changes in cough score measures (P < .001). Conclusion The 16-item PAC-QoL is a reliable and valid outcome measure that assesses QoL related to childhood acute cough at a given time point and reflects changes in acute cough-specific QoL over time.