A general method for mapping tertiary interactions in membrane proteins using the visual pigment rhodopsin as a model is presented. In this approach, the protein is first assembled from two separately expressed gene fragments encoding nonoverlapping segments of the full-length polypeptide. Cys residues are then introduced into each of the two fragments such that juxtaposed residues are able to form disulfide cross-links in the protein either spontaneously or with the assistance of a Cu(2+)-(phenanthroline)3 oxidant. The cross-linked polypeptides are identified from a characteristic mobility shift on sodium dodecyl sulfate (SDS) gels as detected by Western blot analysis where the covalently bound heterodimer migrates with a mobility essentially identical to that of the native, full-length protein. Three different split rhodopsin mutants were prepared: one with a split in the loop connecting helices 3 and 4 (the 3/4 loop), one with a split in the 4/5 loop, and one with a split in the 5/6 loop. Each of these proteins when purified from transfected COS cells bound 11-cis-retinal, had a native absorption maximum at 500 nm, and activated transducin in a light-dependent manner. The cross-linking assay was tested with the rhodopsin mutant split in the 5/6 loop using the rho-1D4 antibody (which recognizes the carboxy terminal eight amino acids of rhodopsin) to detect the proteins on Western blots of SDS gels. Cys residues were substituted for Val-204 in the amino terminal fragment and Phe-276 in the carboxy terminal fragment of the rhodopsin mutant because Schwartz and co-workers [Elling et al.(ABSTRACT TRUNCATED AT 250 WORDS)

Dengue fever is one of the most widespread vector-borne diseases and has caused more than 50 million infections annually over the world. For the purposes of disease prevention and climate change health impact assessment, it is crucial to understand the weather-disease associations for dengue fever. This study investigated the nonlinear delayed impact of meteorological conditions on the spatiotemporal variations of dengue fever in southern Taiwan during 1998-2011. We present a novel integration of a distributed lag nonlinear model and Markov random fields to assess the nonlinear lagged effects of weather variables on temporal dynamics of dengue fever and to account for the geographical heterogeneity. This study identified the most significant meteorological measures to dengue fever variations, i.e., weekly minimum temperature, and the weekly maximum 24-hour rainfall, by obtaining the relative risk (RR) with respect to disease counts and a continuous 20-week lagged time. Results show that RR increased as minimum temperature increased, especially for the lagged period 5-18 weeks, and also suggest that the time to high disease risks can be decreased. Once the occurrence of maximum 24-hour rainfall is >50 mm, an associated increased RR lasted for up to 15 weeks. A temporary one-month decrease in the RR of dengue fever is noted following the extreme rain. In addition, the elevated incidence risk is identified in highly populated areas. Our results highlight the high nonlinearity of temporal lagged effects and magnitudes of temperature and rainfall on dengue fever epidemics. The results can be a practical reference for the early warning of dengue fever.

Dengue Fever (DF) has been identified by the World Health organization (WHO) as one of the most serious vector-borne infectious diseases in tropical and subtropical areas. During 2007, in particular, there were over 2,000 DF cases in Taiwan, which was the highest number of cases in the recorded history of Taiwan epidemics. Most DF studies have focused mainly on temporal DF patterns and its close association with climatic covariates, whereas they have understated spatial DF patterns (spatial dependence and clustering) and composite space-time effects. The present study proposes a spatio-temporal DF prediction approach based on stochastic Bayesian Maximum Entropy (BME) analysis. Core and site-specific knowledge bases are considered, including climate and health datasets under conditions of uncertainty, space-time dependence functions, and a Poisson regression model of climatic variables contributing to DF occurrences in southern Taiwan during 2007. The results show that the DF outbreaks in the study area are highly influenced by climatic conditions. Furthermore, the analysis can provide the required ''oneweek-ahead'' outbreak warnings based on spatio-temporal predictions of DF distributions. Therefore, the proposed approach can provide the Taiwan Disease Control Agency with a valuable tool to timely identify, control, and even efficiently prevent DF spreading across space-time.

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In previous studies, we developed a new method for detecting tertiary interactions in rhodopsin using split receptors and disulfide cross-linking. Cysteines are engineered into separate fragments of the split opsin, the disulfide bond can be formed between the juxtaposed residues by treatment with Cu(phen)3(2+), and then disulfide cross-links can be detected on the gel by an electrophoretic mobility shift. In this study, we utilized this method to examine the cross-linking reactions between native cysteines in the ground state and after photoexcitation of rhodopsin. In the dark, Cys140 on transmembrane segment (TM) 3 cross-links to Cys222 on TM5. After photobleaching, Cys140 cross-links to Cys316 and Cys222, and the rate of the cross-linking reaction between Cys140 and Cys222 significantly increases.