Prediction of Dengue Incidence Using Search Query Surveillance

Improvements in surveillance, prediction of outbreaks and the monitoring of the epidemiology of dengue virus in countries with underdeveloped surveillance systems are of great importance to ministries of health and other public health decision makers who are often constrained by budget or man-power. Google Flu Trends has proven successful in providing an early warning system for outbreaks of influenza weeks before case data are reported. We believe that there is greater potential for this technique for dengue, as the incidence of this pathogen can vary by a factor of ten in some settings, making prediction all the more important in public health planning. In this paper, we demonstrate the utility of Google search terms in predicting dengue incidence in Singapore and Bangkok, Thailand using several regression techniques. Incidence data were provided by the Singapore Ministry of Health and the Thailand Bureau of Epidemiology. We find our models predict incident cases well (correlation greater than 0.8) and periods of high incidence equally well (AUC greater than 0.95). All data and analysis code used in our study are available free online and can be adapted to other settings

Genomic prediction in CIMMYT maize and wheat breeding programs

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.J Crossa, P Pérez, J Hickey, J Burgueño, L Ornella, J Cerón-Rojas, X Zhang, S Dreisigacker, R Babu, Y Li, D Bonnett and K Mathew

Environmental variables, habitat discontinuity and life history shaping the genetic structure of Pomatoschistus marmoratus

Coastal lagoons are semi-isolated ecosystems exposed to wide fluctuations of environmental conditions and showing habitat fragmentation. These features may play an important role in separating species into different populations, even at small spatial scales. In this study, we evaluate the concordance between mitochondrial (previous published data) and nuclear data analyzing the genetic variability of Pomatoschistus marmoratus in five localities, inside and outside the Mar Menor coastal lagoon (SE Spain) using eight microsatellites. High genetic diversity and similar levels of allele richness were observed across all loci and localities, although significant genic and genotypic differentiation was found between populations inside and outside the lagoon. In contrast to the FST values obtained from previous mitochondrial DNA analyses (control region), the microsatellite data exhibited significant differentiation among samples inside the Mar Menor and between lagoonal and marine samples. This pattern was corroborated using Cavalli-Sforza genetic distances. The habitat fragmentation inside the coastal lagoon and among lagoon and marine localities could be acting as a barrier to gene flow and contributing to the observed genetic structure. Our results from generalized additive models point a significant link between extreme lagoonal environmental conditions (mainly maximum salinity) and P. marmoratus genetic composition. Thereby, these environmental features could be also acting on genetic structure of coastal lagoon populations of P. marmoratus favoring their genetic divergence. The mating strategy of P. marmoratus could be also influencing our results obtained from mitochondrial and nuclear DNA. Therefore, a special consideration must be done in the selection of the DNA markers depending on the reproductive strategy of the species

Adipose Tissue Fatty Acid Patterns and Changes in Anthropometry: A Cohort Study

INTRODUCTION: Diets rich in n-3 long chain polyunsaturated fatty acids (LC-PUFA), but low in n-6 LC-PUFA and 18:1 trans-fatty acids (TFA), may lower the risk of overweight and obesity. These fatty acids have often been investigated individually. We explored associations between global patterns in adipose tissue fatty acids and changes in anthropometry. METHODS: 34 fatty acid species from adipose tissue biopsies were determined in a random sample of 1100 men and women from a Danish cohort study. We used sex-specific principal component analysis and multiple linear regression to investigate the associations of adipose tissue fatty acid patterns with changes in weight, waist circumference (WC), and WC controlled for changes in body mass index (WC(BMI)), adjusting for confounders. RESULTS: 7 principal components were extracted for each sex, explaining 77.6% and 78.3% of fatty acid variation in men and women, respectively. Fatty acid patterns with high levels of TFA tended to be positively associated with changes in weight and WC for both sexes. Patterns with high levels of n-6 LC-PUFA tended to be negatively associated with changes in weight and WC in men, and positively associated in women. Associations with patterns with high levels of n-3 LC-PUFA were dependent on the context of the rest of the fatty acid pattern. CONCLUSIONS: Adipose tissue fatty acid patterns with high levels of TFA may be linked to weight gain, but patterns with high n-3 LC-PUFA did not appear to be linked to weight loss. Associations depended on characteristics of the rest of the pattern

Gender, Obesity and Repeated Elevation of C-Reactive Protein: Data from the CARDIA Cohort

C-reactive Protein (CRP) measurements above 10 mg/L have been conventionally treated as acute inflammation and excluded from epidemiologic studies of chronic inflammation. However, recent evidence suggest that such CRP elevations can be seen even with chronic inflammation. The authors assessed 3,300 participants in The Coronary Artery Risk Development in Young Adults study, who had two or more CRP measurements between 1992/3 and 2005/6 to a) investigate characteristics associated with repeated CRP elevation above 10 mg/L; b) identify subgroups at high risk of repeated elevation; and c) investigate the effect of different CRP thresholds on the probability of an elevation being one-time rather than repeated. 225 participants (6.8%) had one-time and 103 (3.1%) had repeated CRP elevation above 10 mg/L. Repeated elevation was associated with obesity, female gender, low income, and sex hormone use. The probability of an elevation above 10 mg/L being one-time rather than repeated was lowest (51%) in women with body mass index above 31 kg/m2, compared to 82% in others. These findings suggest that CRP elevations above 10 mg/L in obese women are likely to be from chronic rather than acute inflammation, and that CRP thresholds above 10 mg/L may be warranted to distinguish acute from chronic inflammation in obese women

Massive mortality of invasive bivalves as a potential resource subsidy for the adjacent terrestrial food web

Large-scale mortality of invasive bivalves was observed in the River Danube basin in the autumn of 2011 due to a particularly low water discharge. The aim of this study was to quantify and compare the biomass of invasive and native bivalve die-offs amongst eight different sites and to assess the potential role of invasive bivalve die-offs as a resource subsidy for the adjacent terrestrial food web. Invasive bivalve die-offs dominated half of the study sites and their highest density and biomass were recorded at the warm water effluent. The density and biomass values recorded in this study are amongst the highest values recorded for aquatic ecosystems and show that a habitat affected by heated water can sustain an extremely high biomass of invasive bivalves. These mortalities highlight invasive bivalves as a major resource subsidy, possibly contributing remarkable amounts of nutrients and energy to the adjacent terrestrial ecosystem. Given the widespread occurrence of these invasive bivalves and the predicted increase in the frequency and intensity of extreme climatic events, the ecological impacts generated by their massive mortalities should be taken into account in other geographical areas as well.The authors are grateful to David Strayer for valuable comments on a previous version of the manuscript. Special thanks to the Danube-Ipoly National Park for the help in field work. Ronaldo Sousa was supported by the project "ECOIAS" funded by the Portuguese Foundation for the Science and the Technology and COMPETE funds (contract: PTDC/AAC-AMB/116685/2010)

Assessing the impact of a health intervention via user-generated Internet content

Assessing the effect of a health-oriented intervention by traditional epidemiological methods is commonly based only on population segments that use healthcare services. Here we introduce a complementary framework for evaluating the impact of a targeted intervention, such as a vaccination campaign against an infectious disease, through a statistical analysis of user-generated content submitted on web platforms. Using supervised learning, we derive a nonlinear regression model for estimating the prevalence of a health event in a population from Internet data. This model is applied to identify control location groups that correlate historically with the areas, where a specific intervention campaign has taken place. We then determine the impact of the intervention by inferring a projection of the disease rates that could have emerged in the absence of a campaign. Our case study focuses on the influenza vaccination program that was launched in England during the 2013/14 season, and our observations consist of millions of geo-located search queries to the Bing search engine and posts on Twitter. The impact estimates derived from the application of the proposed statistical framework support conventional assessments of the campaign

Development of a blood-based molecular biomarker test for identification of schizophrenia before disease onset

Recent research efforts have progressively shifted towards preventative psychiatry and prognostic identification of individuals before disease onset. We describe the development of a serum biomarker test for the identification of individuals at risk of developing schizophrenia based on multiplex immunoassay profiling analysis of 957 serum samples. First, we conducted a meta-analysis of five independent cohorts of 127 first-onset drug-naive schizophrenia patients and 204 controls. Using least absolute shrinkage and selection operator regression, we identified an optimal panel of 26 biomarkers that best discriminated patients and controls. Next, we successfully validated this biomarker panel using two independent validation cohorts of 93 patients and 88 controls, which yielded an area under the curve (AUC) of 0.97 (0.95-1.00) for schizophrenia detection. Finally, we tested its predictive performance for identifying patients before onset of psychosis using two cohorts of 445 pre-onset or at-risk individuals. The predictive performance achieved by the panel was excellent for identifying USA military personnel (AUC: 0.90 (0.86-0.95)) and help-seeking prodromal individuals (AUC: 0.82 (0.71-0.93)) who developed schizophrenia up to 2 years after baseline sampling. The performance increased further using the latter cohort following the incorporation of CAARMS (Comprehensive Assessment of At-Risk Mental State) positive subscale symptom scores into the model (AUC: 0.90 (0.82-0.98)). The current findings may represent the first successful step towards a test that could address the clinical need for early intervention in psychiatry. Further developments of a combined molecular/symptom-based test will aid clinicians in the identification of vulnerable patients early in the disease process, allowing more effective therapeutic intervention before overt disease onset