Search Results (3)

The model haloarchaeon Haloferax volcanii is polyploid with about 20 copies of its major chromosome. Recently it has been described that highly efficient intermolecular gene conversion operates in H. volcanii to equalize the chromosomal copies. In the current study, 24 genes were selected that encode proteins with orthologs involved in gene conversion or homologous recombination in archaea, bacteria, or eukaryotes. Single gene deletion strains of 22 genes and a control gene were constructed in two parent strains for a gene conversion assay; only radA and radB were shown to be essential. Protoplast fusions were used to generate strains that were heterozygous for the gene HVO_2528, encoding an enzyme for carotinoid biosynthesis. It was revealed that a lack of six of the proteins did not influence the efficiency of gene conversion, while sixteen mutants had severe gene conversion defects. Notably, lack of paralogous proteins of gene families had very different effects, e.g., mutant Δrad25b had no phenotype, while mutants Δrad25a, Δrad25c, and Δrad25d were highly compromised. Generation of a quadruple rad25 and a triple sph deletion strain also indicated that the paralogs have different functions, in contrast to sph2 and sph4, which cannot be deleted simultaneously. There was no correlation between the severity of the phenotypes and the respective transcript levels under non-stressed conditions, indicating that gene expression has to be induced at the onset of gene conversion. Phylogenetic trees of the protein families Rad3/25, MutL/S, and Sph/SMC/Rad50 were generated to unravel the history of the paralogous proteins of H. volcanii. Taken together, unselected intermolecular gene conversion in H. volcanii involves at least 16 different proteins, the molecular roles of which can be studied in detail in future projects. Full article

The objective of this study was to identify the most salient driver faults that cause crashes on some Jordanian rural and suburban roadway segments, to examine crashes with distracted driving as the driver’s fault, and to investigate the differences between crashes caused by distracted driving. Data for more than 10,200 crashes on nine roadway segments (five rural and four suburban) were accessed from the relevant government agency, but only n = 2472 were used for analysis after controlling for crashes specified as being caused by drivers’ distracted driving. IBM SPSS version 22 was used to perform descriptive analysis and independent samples’ t-tests. The results revealed that distracted driving was the second most common driver fault to cause crashes and the second main cause of fatalities and injuries on both rural and suburban roadways. Distracted driving on rural highways appears to be more fatal, whereas it caused more crashes with severe injuries on suburban roads. The variables at junction, road grade, number of lanes, weather condition, crash type, and number of vehicles involved were found to be statistically significant but with a small effect size. The following categories showed high percentages of distracted driving crashes on rural and suburban roadways: males, drivers 25–39 years old, non-holidays, weekdays, tangent sections, two-way divided roads, not at junction, level roads, two-lane roads, clear weather, dry surface, daylight, and automobile vehicles showed high percentages of distracted driving crashes on rural and suburban roadways. Differences between crashes on rural and suburban roadways caused by distracted driving were found to be small. Full article

Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies in Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper. Full article