Occupational exposure to polycyclic aromatic hydrocarbons and DNA damage by industry: a nationwide study in Germany

Exposure to polycyclic aromatic hydrocarbons (PAH) and DNA damage were analyzed in coke oven (n = 37), refractory (n = 96), graphite electrode (n = 26), and converter workers (n = 12), whereas construction workers (n = 48) served as referents. PAH exposure was assessed by personal air sampling during shift and biological monitoring in urine post shift (1-hydroxypyrene, 1-OHP and 1-, 2 + 9-, 3-, 4-hydroxyphenanthrenes, SigmaOHPHE). DNA damage was measured by 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodGuo) and DNA strand breaks in blood post shift. Median 1-OHP and SigmaOHPHE were highest in converter workers (13.5 and 37.2 microg/g crea). The industrial setting contributed to the metabolite concentrations rather than the air-borne concentration alone. Other routes of uptake, probably dermal, influenced associations between air-borne concentrations and levels of PAH metabolites in urine making biomonitoring results preferred parameters to assess exposure to PAH. DNA damage in terms of 8-oxo-dGuo and DNA strand breaks was higher in exposed workers compared to referents ranking highest for graphite-electrode production. The type of industry contributed to genotoxic DNA damage and DNA damage was not unequivocally associated to PAH on the individual level most likely due to potential contributions of co-exposures.

Asynchronous ECG time sampling: Saving bits with Golomb-Rice encoding

We present a technique for online compression of ECG signals using the Golomb-Rice encoding algorithm. This is facilitated by a novel time encoding asynchronous analog-to-digital converter targeted for low-power, implantable, long-term bio-medical sensing applications. In contrast to capturing the actual signal (voltage) values the asynchronous time encoder captures and encodes the time information at which predefined changes occur in the signal thereby minimizing the sensor's energy use and the number of bits we store to represent the information by not capturing unnecessary samples. The time encoder transforms the ECG signal data to pure time information that has a geometric distribution such that the Golomb-Rice encoding algorithm can be used to further compress the data. An overall online compression rate of about 6 times is achievable without the usual computations associated with most compression methods.