In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have dengue. Using just four input variables [age, body temperature, white blood cells counts (WBCs) and platelets], not only the state-of-the-art deep neural network (DNN) prediction models but also the conventional decision tree (DT) and logistic regression (LR) models delivered performances with receiver operating characteristic (ROC) curves areas under curves (AUCs) of the ranging from 83.75% to 85.87% [for DT, DNN and LR: 84.60% ± 0.03%, 85.87% ± 0.54%, 83.75% ± 0.17%, respectively]. Subgroup analyses found all the models were very sensitive particularly in the pre-epidemic period. Pre-peak sensitivities (<35 weeks) were 92.6%, 92.9%, and 93.1% in DT, DNN, and LR respectively. Adjusted odds ratios examined with LR for low WBCs [≤ 3.2 (x103/μL)], fever (≥38°C), low platelet counts [< 100 (x103/μL)], and elderly (≥ 65 years) were 5.17 [95% confidence interval (CI): 3.96–6.76], 3.17 [95%CI: 2.74–3.66], 3.10 [95%CI: 2.44–3.94], and 1.77 [95%CI: 1.50–2.10], respectively. Our prediction models can readily be used in resource-poor countries where viral/serologic tests are inconvenient and can also be applied for real-time syndromic surveillance to monitor trends of dengue cases and even be integrated with mosquito/environment surveillance for early warning and immediate prevention/control measures. In other words, a local community hospital/clinic with an instrument of complete blood counts (including platelets) can provide a sentinel screening during outbreaks. In conclusion, the machine learning approach can facilitate medical and public health efforts to minimize the health threat of dengue epidemics. However, laboratory confirmation remains the primary goal of surveillance and outbreak investigation.

No abstract

The N-phenyl-substituted hexaaza[1(6)]paracyclophane (3, hexamer) has been synthesized successfully in two steps and the noncoplanar conformation was calculated by gaussian program. The electrochemical properties exhibited lots of interesting results and each overlapping oxidative wave contained two-electron transfer.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractWe propose a novel approach to history matching finitedifference models that combines the advantage of the streamline models with the versatility of finite-difference simulation. Current streamline models are limited in their ability to incorporate complex physical processes and crossstreamline mechanisms in a computationally efficient manner. A unique feature of streamline models is their ability to efficiently compute the sensitivity of the production data with respect to reservoir parameters using a single flow simulation. These sensitivities define the relationship between changes in production response because of small changes in reservoir parameters and thus, form the basis for many history matching algorithms. In our approach, we utilize the streamline-derived sensitivities to facilitate history matching during finitedifference simulation. First, the velocity field from the finitedifference model is used to compute streamline trajectories, time of flight and parameter sensitivities. The sensitivities are then utilized in an inversion algorithm to update the reservoir model during finite-difference simulation.The use of finite-difference model allows us to account for detailed process physics and compressibility effects. Although the streamline-derived sensitivities are only approximate, they do not seem to noticeably impact the quality of the match or efficiency of the approach. For history matching, we use 'a generalized travel-time inversion' that is shown to be extremely robust because of its quasi-linear properties and converges in only a few iterations. The approach is very fast and avoids much of the subjective judgments and timeconsuming trial-and-errors associated with manual history matching. We demonstrate the power and utility of our approach using a synthetic example and two field examples. The first one is from a CO 2 pilot area in the Goldsmith San Andreas Unit, a dolomite formation in west Texas with over 20 years of waterflood production history. The second example is from a giant middle-eastern reservoir and involves history matching a multimillion cell geologic model with 16 injectors and 70 producers. The final model preserved all of the prior geologic constraints while matching 30 years of production history.