Pattern-based sensing of nucleotides in aqueous solution with a multicomponent indicator displacement assay

A multicomponent indicator displacement assay ( MIDA) based on an organometallic receptor and three dyes can be used for the identification and quantification of nucleotides in aqueous solution at neutral pH.

Sense of coherence and job characteristics in predicting burnout in a South African sample

Orientation: Research that considers the effects of individual characteristics and job characteristics jointly in burnout is necessary, especially when one considers the possibility of curvilinear relationships between job characteristics and burnout. Research purpose: This study examines the contribution of sense of coherence (SOC) and job characteristics to predicting burnout by considering direct and moderating effects. Motivation for this study: Understanding the relationships of individual and job characteristics with burnout is necessary for preventing burnout. It also informs the design of interventions. Research design, approach and method: The participants were 632 working adults (57% female) in South Africa. The measures included the Job Content Questionnaire, the Sense of Coherence Questionnaire and the Maslach Burnout Inventory. The authors analysed the data using hierarchical multiple regression with the enter method. Main findings: Job characteristics and SOC show the expected direct effects on burnout. SOC has a direct negative effect on burnout. Job demands and supervisor social support show nonlinear relationships with burnout. SOC moderates the effect of demands on burnout and has a protective function so that the demands-burnout relationship differs for those with high and low SOC. Practical/managerial implications: The types of effects, the shape of the stressor-strain relationship and the different contributions of individual and job characteristics have implications for designing interventions. Contribution/value add: SOC functions differently when combined with demands, control and support. These different effects suggest that it is not merely the presence or absence of a job characteristic that is important for well-being outcomes but how people respond to its presence or absence.

Predicting the physicochemical profile of diastereoisomeric histidine-containing dipeptides by property space analysis.

OBJECTIVES: This study aimed at measuring the lipophilicity and ionization constants of diastereoisomeric dipeptides, interpreting them in terms of conformational behavior, and developing statistical models to predict them. METHODS: A series of 20 dipeptides of general structure NH(2) -L-X-(L or D)-His-OMe was designed and synthetized. Their experimental ionization constants (pK(1) , pK(2) and pK(3) ) and lipophilicity parameters (log P(N) and log D(7.4) ) were measured by potentiometry. Molecular modeling in three media (vacuum, water, and chloroform) was used to explore and sample their conformational space, and for each stored conformer to calculate their radius of gyration, virtual log P (preferably written as log P(MLP) , meaning obtained by the molecular lipophilicity potential (MLP) method) and polar surface area (PSA). Means and ranges were calculated for these properties, as was their sensitivity (i.e., the ratio between property range and number of rotatable bonds). RESULTS: Marked differences between diastereoisomers were seen in their experimental ionization constants and lipophilicity parameters. These differences are explained by molecular flexibility, configuration-dependent differences in intramolecular interactions, and accessibility of functional groups. Multiple linear equations correlated experimental lipophilicity parameters and ionization constants with PSA range and other calculated parameters. CONCLUSION: This study documents the differences in lipophilicity and ionization constants between diastereoisomeric dipeptides. Such configuration-dependent differences are shown to depend markedly on differences in conformational behavior and to be amenable to multiple linear regression. Chirality 24:566-576, 2012. © 2012 Wiley Periodicals, Inc.

An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data

1. Few examples of habitat-modelling studies of rare and endangered species exist in the literature, although from a conservation perspective predicting their distribution would prove particularly useful. Paucity of data and lack of valid absences are the probable reasons for this shortcoming. Analytic solutions to accommodate the lack of absence include the ecological niche factor analysis (ENFA) and the use of generalized linear models (GLM) with simulated pseudo-absences. 2. In this study we tested a new approach to generating pseudo-absences, based on a preliminary ENFA habitat suitability (HS) map, for the endangered species Eryngium alpinum. This method of generating pseudo-absences was compared with two others: (i) use of a GLM with pseudo-absences generated totally at random, and (ii) use of an ENFA only. 3. The influence of two different spatial resolutions (i.e. grain) was also assessed for tackling the dilemma of quality (grain) vs. quantity (number of occurrences). Each combination of the three above-mentioned methods with the two grains generated a distinct HS map. 4. Four evaluation measures were used for comparing these HS maps: total deviance explained, best kappa, Gini coefficient and minimal predicted area (MPA). The last is a new evaluation criterion proposed in this study. 5. Results showed that (i) GLM models using ENFA-weighted pseudo-absence provide better results, except for the MPA value, and that (ii) quality (spatial resolution and locational accuracy) of the data appears to be more important than quantity (number of occurrences). Furthermore, the proposed MPA value is suggested as a useful measure of model evaluation when used to complement classical statistical measures. 6. Synthesis and applications. We suggest that the use of ENFA-weighted pseudo-absence is a possible way to enhance the quality of GLM-based potential distribution maps and that data quality (i.e. spatial resolution) prevails over quantity (i.e. number of data). Increased accuracy of potential distribution maps could help to define better suitable areas for species protection and reintroduction.

Predicting spinor condensate dynamics from simple principles

We study the spin dynamics of quasi-one-dimensional F=1 condensates both at zero and finite temperatures for arbitrary initial spin configurations. The rich dynamical evolution exhibited by these nonlinear systems is explained by surprisingly simple principles: minimization of energy at zero temperature and maximization of entropy at high temperature. Our analytical results for the homogeneous case are corroborated by numerical simulations for confined condensates in a wide variety of initial conditions. These predictions compare qualitatively well with recent experimental observations and can, therefore, serve as a guidance for ongoing experiments.

Nondeclarative learning in children with Specific Language Impairment: Predicting regularities in the visuomotor, phonological, and cognitive domains.

Ullman (2004) suggested that Specific Language Impairment (SLI) results from a general procedural learning deficit. In order to test this hypothesis, we investigated children with SLI via procedural learning tasks exploring the verbal, motor, and cognitive domains. Results showed that compared with a Control Group, the children with SLI (a) were unable to learn a phonotactic learning task, (b) were able but less efficiently to learn a motor learning task and (c) succeeded in a cognitive learning task. Regarding the motor learning task (Serial Reaction Time Task), reaction times were longer and learning slower than in controls. The learning effect was not significant in children with an associated Developmental Coordination Disorder (DCD), and future studies should consider comorbid motor impairment in order to clarify whether impairments are related to the motor rather than the language disorder. Our results indicate that a phonotactic learning but not a cognitive procedural deficit underlies SLI, thus challenging Ullmans' general procedural deficit hypothesis, like a few other recent studies.

Comparison of microsatellite instability and chromosomal instability in predicting survival of patients with T3N0 colorectal cancer.

BACKGROUND: At least 2 apparently independent mechanisms, microsatellite instability (MSI) and chromosomal instability, are implicated in colorectal tumorigenesis. Their respective roles in predicting clinical outcomes of patients with T3N0 colorectal cancer remain unknown. METHODS: Eighty-eight patients with a sporadic T3N0 colon or rectal adenocarcinoma were followed up for a median of 67 months. For chromosomal instability analysis, Ki-ras mutations were determined by single-strand polymerase chain reaction, and p53 protein staining was studied by immunohistochemistry. For MSI analysis, DNA was amplified by polymerase chain reaction at 7 microsatellite targets (BAT25, BAT26, D17S250, D2S123, D5S346, transforming growth factor receptor II, and BAX). RESULTS: Overall 5-year survival rate was 72%. p53 protein nuclear staining was detected in 39 patients (44%), and MSI was detected in 21 patients (24%). MSI correlated with proximal location (P <.001) and mucinous content (P <.001). In a multivariate analysis, p53 protein expression carried a significant risk of death (relative risk = 4.0, 95% CI = 1.6 to 10.1, P =.004). By comparison, MSI was not a statistically significant prognostic factor for survival in this group (relative risk = 2.2, 95% CI = 0.6 to 7.3, P =.21). CONCLUSIONS: p53 protein overexpression provides better prognostic discrimination than MSI in predicting survival of patients with T3N0 colorectal cancer. Although MSI is associated with specific clinicopathologic parameters, it did not predict overall survival in this group. Assessment of p53 protein expression by immunocytochemistry provides a simple means to identify a subset of T3N0 patients with a 4-times increased risk for death.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

What matters for predicting spatial distributions of trees: techniques, data, or species' characteristics?

Data characteristics and species traits are expected to influence the accuracy with which species' distributions can be modeled and predicted. We compare 10 modeling techniques in terms of predictive power and sensitivity to location error, change in map resolution, and sample size, and assess whether some species traits can explain variation in model performance. We focused on 30 native tree species in Switzerland and used presence-only data to model current distribution, which we evaluated against independent presence-absence data. While there are important differences between the predictive performance of modeling methods, the variance in model performance is greater among species than among techniques. Within the range of data perturbations in this study, some extrinsic parameters of data affect model performance more than others: location error and sample size reduced performance of many techniques, whereas grain had little effect on most techniques. No technique can rescue species that are difficult to predict. The predictive power of species-distribution models can partly be predicted from a series of species characteristics and traits based on growth rate, elevational distribution range, and maximum elevation. Slow-growing species or species with narrow and specialized niches tend to be better modeled. The Swiss presence-only tree data produce models that are reliable enough to be useful in planning and management applications.

Preoperative upper gastrointestinal testing can help predicting long-term outcome after gastric banding for morbid obesity.

BACKGROUND: Gastric banding (GB) is one of the most popular bariatric procedures for morbid obesity. Apart from causing weight loss by alimentary restriction, it can interfere with functions of the esophagus and upper stomach. The aim of this study was to evaluate if the results of extensive preoperative upper GI testing were correlated with long-term outcome and complications after GB. METHODS: Using a prospectively maintained computerized database including all the patients undergoing bariatric operations in both our hospitals, we performed a retrospective analysis of the patients who underwent complete upper gastrointestinal (GI) testing (endoscopy, pH monitoring, and manometry) before GB. RESULTS: One hundred thirty-four patients underwent complete testing before GB. Abnormal pH monitoring (increased total reflux time, increased diurnal reflux time, increased number of reflux episodes) predicted the development of complications and especially pouch dilatation and food intolerance. The mean De Meester score was higher among patients who developed complications than in the remaining ones (25.4 vs 17.7, P=0.03). High lower esophageal sphincter pressure also predicted progressive long-term food intolerance. Endoscopic findings were not predictive of the long-term outcome. CONCLUSIONS: There is some association between the function of the upper digestive tract and long-term complications after gastric banding. Abnormal pH monitoring predicts overall long-term complications, especially food intolerance with or without reflux, and pouch dilatation, and a high lower esophageal sphincter pressure predicts long-term food intolerance. Extended upper gastrointestinal testing with endoscopy, 24-h pH monitoring, and esophageal manometry is probably worthwhile in selecting patients for gastric banding.

Predicting the occurrence of alate aphids in Brassicaceae

The objective of this work was to predict the occurrence of alates of Brevicoryne brassicae, Lipaphis erysimi, and Myzus persicae (Hemiptera, Aphididae) in Brassicaceae. The alate aphids were collected in yellow water traps from July 1997 to August 2005. Aphid population peaks were predicted using a degree‑day model. The meteorological factors, temperature, air relative humidity, rainfall, and sunshine hours, were used to provide precision indexes to evaluate the best predictor for the date of the first capture of alate aphids by the traps. The degree‑day model indicated that the peak population of the evaluated aphid species can be predicted using one of the following biofix dates: January 1st, June 1st, and the date of the first capture of the alate aphid species by the yellow water traps. The best predictor of B. brassicae occurrence is the number of days with minimum temperature >15°C, and of L. erysimi and M. persicae, the number of days with rainfall occurrence.

Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.

The INTERMED questionnaire for predicting return to work after a multidisciplinary rehabilitation program for chronic low back pain.

OBJECTIVES: To evaluate the performance of the INTERMED questionnaire score, alone or combined with other criteria, in predicting return to work after a multidisciplinary rehabilitation program in patients with non-specific chronic low back pain. METHODS: The INTERMED questionnaire is a biopsychosocial assessment and clinical classification tool that separates heterogeneous populations into subgroups according to case complexity. We studied 88 patients with chronic low back pain who followed an intensive multidisciplinary rehabilitation program on an outpatient basis. Before the program, we recorded the INTERMED score, radiological abnormalities, subjective pain severity, and sick leave duration. Associations between these variables and return to full-time work within 3 months after the end of the program were evaluated using one-sided Fisher tests and univariate logistic regression followed by multivariate logistic regression. RESULTS: The univariate analysis showed a significant association between the INTERMED score and return to work (P<0.001; odds ratio, 0.90; 95% confidence interval, 0.86-0.96). In the multivariate analysis, prediction was best when the INTERMED score and sick leave duration were used in combination (P=0.03; odds ratio, 0.48; 95% confidence interval, 0.25-0.93). CONCLUSION: The INTERMED questionnaire is useful for evaluating patients with chronic low back pain. It could be used to improve the selection of patients for intensive multidisciplinary programs, thereby improving the quality of care, while reducing healthcare costs.

Predicting rapeseed oil content with near-infrared spectroscopy

The objective of this work was to establish a calibration equation and to estimate the efficiency of near-infrared reflectance (NIR) spectroscopy for evaluating rapeseed oil content in Southern Brazil. Spectral data from 124 half-sib families were correlated with oil contents determined by the chemical method. The accuracy of the equation was verified by coefficient of determination (R²) of 0.92, error of calibration (SEC) of 0.78, and error of performance (SEP) of 1.22. The oil content of ten genotypes, which were not included in the calibration with NIR, was similar to the one obtained by the standard chemical method. NIR spectroscopy is adequate to differentiate oil content of rapeseed genotypes.