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Indoor air quality (IAQ) in vehicles can be important to people's health, especially for those whose occupations require them to spend extensive time in vehicles. To date, research on vehicle IAQ has primarily focused on direct emissions as opposed to chemistry happening in vehicle cabins. In this work, we conducted time-resolved measurements of the oxidants and oxidant precursors ozone (O3), nitric oxide (NO), nitrogen dioxide (NO2), and nitrous acid (HONO) inside the cabin of a 2012 Toyota Rav4 under varying ventilation conditions (i.e., car off, car on with passive ventilation, car on with mechanical ventilation via the recirculating fan, and car on with mechanical ventilation via the direct fan). Ozone levels inside the vehicle were significantly lower than outdoors under most conditions, and were approximately half the outdoor levels when the direct fan was in operation. Nitric oxide and NO2 concentrations were very low both inside the vehicle and outdoors. Nitrous acid levels in the vehicle were lower than reported values in other indoor environments, though much higher than expected outdoor levels. We also investigated the potential for photochemical production of radicals in the vehicle. Time- and wavelength-resolved solar irradiance spectra were collected, and steady state hydroxyl radical (OH) and nitrate radical (NO3) concentrations were calculated. Steady state OH concentrations were predicted to be similar to those in air masses in residences illuminated by sunlight, suggesting the importance of HONO photolysis in vehicles. Conversely, nitrate radicals (NO3) were not considered significant indoor oxidants in our study due to rapid titration by NO. Overall, our findings emphasize the importance of both air exchange and photochemistry in shaping the composition of air inside vehicles.

Precordial normal variant ST elevation (NV-STE), previously often called "early repolarization," may be difficult to differentiate from subtle ischemic STE due to left anterior descending (LAD) occlusion. We previously derived and validated a logistic regression formula that was far superior to STE alone for differentiating the two entities on the ECG. The tool uses R-wave amplitude in lead V4 (RAV4), ST elevation at 60 ms after the J-point in lead V3 (STE60V3) and the computerized Bazett-corrected QT interval (QTc-B). The 3-variable formula is: 1.196 x STE60V3 + 0.059 × QTc-B - 0.326 × RAV4 with a value ≥23.4 likely to be acute myocardial infarction (AMI). Adding QRS voltage in V2 (QRSV2) would improve the accuracy of the formula. 355 consecutive cases of proven LAD occlusion were reviewed, and those that were obvious ST elevation myocardial infarction were excluded. Exclusion was based on one straight or convex ST segment in V2-V6, 1 millimeter of summed inferior ST depression, any anterior ST depression, Q-waves, "terminal QRS distortion," or any ST elevation >5 mm. The NV-STE group comprised emergency department patients with chest pain who ruled out for AMI by serial troponins, had a cardiologist ECG read of "NV-STE," and had at least 1 mm of STE in V2 and V3. R-wave amplitude in lead V4 (RAV4), ST elevation at 60 ms after the J-point in lead V3 (STE60V3) and the computerized Bazett-corrected QT interval (QTc-B) had previously been measured in all ECGs; physicians blinded to outcome then measured QRSV2 in all ECGs. A 4-variable formula was derived to more accurately classify LAD occlusion vs. NV-STE and optimize area under the curve (AUC) and compared with the previous 3-variable formula. There were 143 subtle LAD occlusions and 171 NV-STE. A low QRSV2 added diagnostic utility. The derived 4-variable formula is: 0.052*QTc-B - 0.151*QRSV2 - 0.268*RV4 + 1.062*STE60V3. The 3-variable formula had an AUC of 0.9538 vs. 0.9686 for the 4-variable formula (p = 0.0092). At the same specificity as the 3-variable formula [90.6%, at which cutpoint (≥23.4), 123 of 143 MI were correctly classified for 86% sensitivity], the sensitivity of the new formula at cutpoint ≥17.75 is 90.2%, with 129/143 correctly classified MI, identifying an additional 6 cases. The cutpoint with the highest accuracy (92.0%) was at a cutoff value ≥18.2, with 88.8% sensitivity, 94.7% specificity, and a positive and negative likelihood ratio of 16.9 (95% CI: 8.9-32) and 0.12 (95% CI: 0.07-0.19). At this cutpoint, it correctly classified an additional 11 cases (289 of 315, vs. 278 of 315): 127/143 for MI (an additional 4 cases) and 162/171 for NV-STE (an additional 7 cases). On the ECG, a 4-variable formula was derived which adds QRSV2; it differentiates subtle LAD occlusion from NV-STE better than the 3-variable formula. At a value ≥18.2, the formula (0.052*QTc-B - 0.151*QRSV2 - 0.268*RV4 + 1.062*STE60V3) was very accurate, sensitive, and specific, with excellent positive and negative likelihood ratios. This formula needs to be validated.

This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.

The authors show a surgical technique of trapping/resection of ruptured dominant vertebral artery aneurysm in conjunction with reconstruction of vertebral artery by V3-radial artery (RA) graft-V4 bypass through suboccipital craniotomy and far lateral approach. Step by step muscle dissection in posterior fossa enable fine exposure of occipital artery for possible OA-PICA bypass and V3 portion of vertebral artery. Extradural drilling of posterior one-third condyle and condylar fossa facilitate exposure of triangular surgical corridor made by medulla, spinal root of 11th nerve and lower cranial nerves, and thus enabling aneurismal resection and RA-V4 anastomosis. The video can be found here: http://youtu.be/LxsARGdHSVw .

Measuring greenhouse gas (GHG) source emissions provides data for validation of GHG inventories, which provide the foundation for climate change mitigation. Two Toyota RAV4 electric vehicles were outfitted with high-precision instrumentation to determine spatial and temporal resolution of GHGs (e.g., nitrous oxide, methane [CH4], and carbon dioxide [CO2]), and other gaseous species and particulate metrics found near emission sources. Mobile measurement platform (MMP) analytical performance was determined over relevant measurement time scales. Pollutant residence times through the sampling configuration were measured, ranging from 3 to 11 sec, enabling proper time alignment for spatial measurement of each respective analyte. Linear response range for GHG analytes was assessed across expected mixing ratio ranges, showing minimal regression and standard error differences between 5, 10, 30, and 60 sec sampling intervals and negligible differences between the two MMPs. GHG instrument drift shows deviation of less than 0.8% over a 24-hr measurement period. These MMPs were utilized in tracer-dilution experiments at a California landfill and natural gas compressor station (NGCS) to quantify CH4 emissions. Replicate landfill measurements during October 2009 yielded annual CH4 emissions estimates of 0.10±0.01, 0.11±0.01, and 0.12±0.02 million tonnes of CO2 equivalent (MTCO2E). These values compare favorably to California GHG Emissions Inventory figures for 2007, 2008, and 2009 of 0.123, 0.125, and 0.126 MTCO2E/yr, respectively, for this facility. Measurements to quantify NGCS boosting facility-wide emissions, during June 2010 yielded an equivalent of 5400±100 TCO2E/yr under steady-state operation. However, measurements during condensate transfer without operational vapor recovery yield an instantaneous emission rate of 2-4 times greater, but was estimated to only add 12 TCO2E/yr overall. This work displays the utility for mobile GHG measurements to validate existing measurement and modeling approaches, so emission inventory values can be confirmed and associated uncertainties reduced. Measuring greenhouse gas (GHG) source emissions provides data and validation for GHG inventories, the foundation for climate change mitigation. Mobile measurement platforms with robust analytical instrumentation completed tracer-dilution experiments in California at a landfill and natural gas compressor station (NGCS) to quantify CH4 emissions. Data collected for landfill CH4 agree with the current California emissions inventory, while NGCS data show the possible variability from this type of facility. This work displays the utility of mobile GHG measurements to validate existing measurement and modeling approaches, such that emission inventory values can be confirmed, associated uncertainties reduced, and mitigation efforts quantified.

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