Endocytic and secretory traffic in Arabidopsis merge in the trans-Golgi network/early endosome, an independent and highly dynamic organelle.

Plants constantly adjust their repertoire of plasma membrane proteins that mediates transduction of environmental and developmental signals as well as transport of ions, nutrients, and hormones. The importance of regulated secretory and endocytic trafficking is becoming increasingly clear; however, our knowledge of the compartments and molecular machinery involved is still fragmentary. We used immunogold electron microscopy and confocal laser scanning microscopy to trace the route of cargo molecules, including the BRASSINOSTEROID INSENSITIVE1 receptor and the REQUIRES HIGH BORON1 boron exporter, throughout the plant endomembrane system. Our results provide evidence that both endocytic and secretory cargo pass through the trans-Golgi network/early endosome (TGN/EE) and demonstrate that cargo in late endosomes/multivesicular bodies is destined for vacuolar degradation. Moreover, using spinning disc microscopy, we show that TGN/EEs move independently and are only transiently associated with an individual Golgi stack.

Effects of traffic control on the soil physical quality and the cultivation of sugarcane

The cultivation of sugarcane with intensive use of machinery, especially for harvest, induces soil compaction, affecting the crop development. The control of agricultural traffic is an alternative of management in the sector, with a view to preserve the soil physical quality, resulting in increased sugarcane root growth, productivity and technological quality. The objective of this study was to evaluate the physical quality of an Oxisol with and without control traffic and the resulting effects on sugarcane root development, productivity and technological quality. The following managements were tested: no traffic control (NTC), traffic control consisting of an adjustment of the track width of the tractor and sugarcane trailer (TC1) and traffic control consisting of an adjustment of the track width of the tractor and trailer and use of an autopilot (TC2). Soil samples were collected (layers 0.00-0.10; 0.10-0.20 and 0.20-0.30 m) in the plant rows, inter-row center and seedbed region, 0.30 m away from the plant row. The productivity was measured with a specific weighing scale. The technological variables of sugarcane were measured in each plot. Soil cores were collected to analyze the root system. In TC2, the soil bulk density and compaction degree were lowest and total porosity and macroporosity highest in the plant row. Soil penetration resistance in the plant row, was less than 2 MPa in TC1 and TC2. Soil aggregation and total organic carbon did not differ between the management systems. The root surface and volume were increased in TC1 and TC2, with higher productivity and sugar yield than under NTC. The sugarcane variables did not differ between the managements. The soil physical quality in the plant row was preserved under management TC1 and TC2, with an improved root development and increases of 18.72 and 20.29 % in productivity and sugar yield, respectively.

Evaluating the Effectiveness of Red Light Running Camera Enforcement in Cedar Rapids and Developing Guidelines For Selection and use of Red Light Running Countermeasures, Midwest Transportation Consortium, Final Report November 2011.

Red light running (RLR) is a problem in the US that has resulted in 165,000 injuries and 907 fatalities annually. In Iowa, RLR-related crashes make up 24.5 percent of all crashes and account for 31.7 percent of fatal and major injury crashes at signalized intersections. RLR crashes are a safety concern due to the increased likelihood of injury compared to other types of crashes. One tool used to combat red light running is automated enforcement in the form of RLR cameras. Automated enforcement, while effective, is often controversial. Cedar Rapids, Iowa installed RLR and speeding cameras at seven intersections across the city. The intersections were chosen based on crash rates and whether cameras could feasibly be placed at the intersection approaches. The cameras were placed starting in February 2010 with the last one becoming operational in December 2010. An analysis of the effect of the cameras on safety at these intersections was determined prudent in helping to justify the installation and effectiveness of the cameras. The objective of this research was to assess the safety effectiveness of the RLR program that has been implemented in Cedar Rapids. This was accomplished by analyzing data to determine changes in the following metrics:  Reductions in red light violation rates based on overall changes, time of day changes, and changes by lane  Effectiveness of the cameras over time  Time in which those running the red light enter the intersection  Changes in the average headway between vehicles entering the intersection

Vehicular Traffic Flow Map, 1988

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1990

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1992

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1994

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1996

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1998

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2000

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2002

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2004

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2006

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2008

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.