Assessing the Severity of Color Vision Loss with Implications for Aviation and other Occupational Environments

Introduction: The Ishihara Test (IT) is arguably the most sensitive and commonly used color vision test within aviation and other occupational environments, but when no errors are allowed; 20% of normal trichromats fail the test. The number of allowed errors varies in different occupations and sometimes within the same environment (such as aviation) in order to reflect the difficulties of the color-related tasks. The implicit assumption is that the plates can be ranked in order of difficulty. The principal aim of this study was to investigate whether appropriate "weights" can be attached to each IT plate to reflect the likelihood of producing a correct response. A second aim was to justify the use of color thresholds for quantifying the loss of red-green (RG) and yellow-blue (YB) chromatic sensitivity. Methods: We investigated 742 subjects (236 normals, 340 deutans, and 166 protans) using the first 25 plates of the 38-plate IT and measured RG chromatic sensitivity using the Color Assessment and Diagnosis (CAD) test. The IT error scores provided platespecifi c "weights" which were used to calculate a Severity Index (SI) of color vision loss for each subject. Results: Error scores, SI values, and CAD thresholds were measured and compared in each of the three subject groups. Conclusions: Color thresholds can provide a good measure of the severity of both RG and YB color vision loss. Neither the number of IT plates failed nor the SI value computed in this way can be used to determine reliably the severity of color vision loss

Prevalence, genetic diversity and antiretroviral drugs resistance-associated mutations among untreated HIV-1-infected pregnant women in Gabon, central Africa

BACKGROUND: In Africa, the wide genetic diversity of HIV has resulted in emergence of new strains, rapid spread of this virus in sub-Saharan populations and therefore spread of the HIV epidemic throughout the continent. METHODS: To determine the prevalence of antibodies to HIV among a high-risk population in Gabon, 1098 and 2916 samples were collected from pregnant women in 2005 and 2008, respectively. HIV genotypes were evaluated in 107 HIV-1-positive samples to determine the circulating subtypes of strains and their resistance to antiretroviral drugs (ARVs). RESULTS: The seroprevalences were 6.3% in 2005 and 6.0% in 2008. The main subtype was recombinant CRF02_AG (46.7%), followed by the subtypes A (19.6%), G (10.3%), F (4.7%), H (1.9%) and D (0.9%) and the complex recombinants CRF06_cpx (1.9%) and CRF11_cpx (1.9%); 12.1% of subtypes could not be characterized. Analysis of ARVs resistance to the protease and reverse transcriptase coding regions showed mutations associated with extensive subtype polymorphism. In the present study, the HIV strains showed reduced susceptibility to ARVs (2.8%), particularly to protease inhibitors (1.9%) and nucleoside reverse transcriptase inhibitors (0.9%). CONCLUSIONS: The evolving genetic diversity of HIV calls for continuous monitoring of its molecular epidemiology in Gabon and in other central African countries

PENGARUH PENAMBAHAN OKLUSAL REST TERHADAP DUKUNGAN GIGITIRUAN FLEKSIBEL KELAS III KENNEDY

xiii, 74 hl

Protein Fold Recognition using Markov Logic Networks

Protein fold recognition is the problem of determining whether a given protein sequence folds into a previously observed structure. An uncertainty complication is that it is not always true that the structure has been previously observed. Markov logic networks (MLNs) are a powerful representation that combines first-order logic and probability by attaching weights to first-order formulas and using these as templates for features of Markov networks. In this chapter, we describe a simple temporal extension of MLNs that is able to deal with sequences of logical atoms. We also propose iterated robust tabu search (IRoTS) for maximum a posteriori (MAP) inference and Markov Chain-IRoTS (MC-IRoTS) for conditional inference in the new framework. We show how MC-IRoTS can also be used for discriminative weight learning. We describe how sequences of protein secondary structure can be modeled through the proposed language and show through some preliminary experiments the promise of our approach for the problem of protein fold recognition from these sequences