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This paper investigates the skill of 90 day low flow forecasts using two conceptual hydrological models and two data-driven models based on Artificial Neural Networks (ANNs) for the Moselle River. One data-driven model, ANN-Indicator (ANN-I), requires historical inputs on precipitation (P ), potential evapotranspiration (PET), groundwater (G) and observed discharge (Q), whereas the other data-driven model, ANN-Ensemble (ANN-E), and the two conceptual models, HBV and GR4J, use forecasted meteorological inputs (P and PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low flow forecasts without any meteorological forecasts as input (ANN-I) and five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the other three models (GR4J, HBV and ANN-E). The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the four models are compared based on their skill of low flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict low flows using ensemble seasonal meteorological forcing. The largest range for 90 day low flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90 day ahead low flows in the very dry year 2003 without precipitation data, whereas ANN-I predicted the magnitude of the low flows better than the other three models. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Furthermore, the hit rate of ANN-E is higher than the two conceptual models for most lead times. However, ANN-I is not successful in distinguishing between low flow events and non-low flow events. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions. [ABSTRACT FROM AUTHOR]

Young drivers' high traffic violation involvement rate and significant contribution to traffic crashes compared to older drivers creates the need for detailed analyses of factors affecting young drivers' behaviors. This study is based on survey data collected from 2,057 18-29 year old young adults. Data were collected via face-to-face questionnaire surveys in four different cities in Turkey. The main objective of this study is to identify the relationship between socio-demographic characteristics, traffic rule violations, and traffic crashes among young drivers. Four main traffic rule violations are examined: red light violations, seat belt violations, speeding, and driving under the influence of alcohol, which are decisive in determining driving behavior and traffic crashes. The survey investigates the socio-demographic characteristics, traffic rule violation behavior and traffic crash histories of young adults. Four hypothetical scenarios were prepared for each traffic rule violation and data from the scenarios were modeled using the ordered probit model. Significant variables affecting each traffic rule violation are stated. Finally, significant variables that interact with crash involvements were investigated with binary logit models. According to the data analysis, 23.9% of drivers stated that they were involved in at least one traffic crash within the last three years. This crash rate increases to 38.3% for those who received at least one traffic citation/violation in last three years and peaks to 47.4% for those who were fined for seat belt violations in last three years., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Highly sensitive and selective biomarker detection is required for early detection of hepatocellular carcinoma (HCC). Disease progression has been shown to correlate with specific fucosylation of a validated HCC serum glycoprotein biomarker, alpha-fetoprotein (AFP) Carbohydrate binding proteins, such as lectins, can be used as diagnostic indicators for monitoring glycosylation changes during disease progression in hepatitis B virus (HBV) or hepatitis C virus (HCV) infected patients. We prepared surface-enhanced Raman spectroscopy (SERS) substrates, which provide controllable, well-organized nanoparticles on the surface, for the analysis of a fucose binding lectin AAL. The SERS based assay provides fast (<10 s), and reproducible (<5% variation) detection.

Fluctuations about the native conformation of proteins have proven to be suitably reproduced with a simple elastic network model, which has shown excellent agreement with a number of different properties for a wide variety of proteins. This scalar model simply investigates the magnitudes of motion of individual residues in the structure. To use the elastic model approach further for developing the details of protein mechanisms, it becomes essential to expand this model to include the added details of the directions of individual residue fluctuations. In this paper a new tool is presented for this purpose and applied to the retinol-binding protein, which indicates enhanced flexibility in the region of entry to the ligand binding site and for the portion of the protein binding to its carrier protein.

A number of recent studies called attention to the presence of kinetically important residues underlying the formation and stabilization of folding nuclei in proteins, and to the possible existence of a correlation between conserved residues and those participating in the folding nuclei. Here, we use the Gaussian network model (GNM), which recently proved useful in describing the dynamic characteristics of proteins for identifying the kinetically hot residues in folded structures. These are the residues involved in the highest frequency fluctuations near the native state coordinates. Their high frequency is a manifestation of the steepness of the energy landscape near their native state positions. The theory is applied to a series of proteins whose kinetically important residues have been extensively explored: chymotrypsin inhibitor 2, cytochrome c, and related C2 proteins. Most of the residues previously pointed out to underlie the folding process of these proteins, and to be critically important for the stabilization of the tertiary fold, are correctly identified, indicating a correlation between the kinetic hot spots and the early forming structural elements in proteins. Additionally, a strong correlation between kinetically hot residues and loci of conserved residues is observed. Finally, residues that may be important for the stability of the tertiary structure of CheY are proposed.