Oxidative conversion of 5-hydroxymethylfurfural (HMF) is of biotechnological interest for the production of renewable (lignocellulose-based) platform chemicals, such as 2,5-furandicarboxylic acid (FDCA). To the best of our knowledge, the ability of fungal aryl-alcohol oxidase (AAO) to oxidize HMF is reported here for the first time, resulting in almost complete conversion into 2,5-formylfurancarboxylic acid (FFCA) in a few hours. The reaction starts with alcohol oxidation, yielding 2,5-diformylfuran (DFF), which is rapidly converted into FFCA by carbonyl oxidation, most probably without leaving the enzyme active site. This agrees with the similar catalytic efficiencies of the enzyme with respect to oxidization of HMF and DFF, and its very low activity on 2,5-hydroxymethylfurancarboxylic acid (which was not detected by GC-MS). However, AAO was found to be unable to directly oxidize the carbonyl group in FFCA, and only modest amounts of FDCA are formed from HMF (most probably by chemical oxidation of FFCA by the H 2 O 2 previously generated by AAO). As aldehyde oxidation by AAO proceeds via the corresponding geminal diols (aldehyde hydrates), the various carbonyl oxidation rates may be related to the low degree of hydration of FFCA compared with DFF. The conversion of HMF was completed by introducing a fungal unspecific heme peroxygenase that uses the H 2 O 2 generated by AAO to transform FFCA into FDCA, albeit more slowly than the previous AAO reactions. By adding this peroxygenase when FFCA production by AAO has been completed, transformation of HMF into FDCA may be achieved in a reaction cascade in which O 2 is the only co-substrate required, and water is the only byproduct formed.

The Actively Heated Fiber Optic (AHFO) method is shown to be capable of measuring soil water content several times per hour at 0.25 m spacing along cables of multiple kilometers in length. AHFO is based on distributed temperature sensing (DTS) observation of the heating and cooling of a buried fiberoptic cable resulting from an electrical impulse of energy delivered from the steel cable jacket. The results presented were collected from 750 m of cable buried in three 240 m colocated transects at 30, 60, and 90 cm depths in an agricultural field under center pivot irrigation. The calibration curve relating soil water content to the thermal response of the soil to a heat pulse of 10 W m 21 for 1 min duration was developed in the lab. This calibration was found applicable to the 30 and 60 cm depth cables, while the 90 cm depth cable illustrated the challenges presented by soil heterogeneity for this technique. This method was used to map with high resolution the variability of soil water content and fluxes induced by the nonuniformity of water application at the surface.

Fruits of `Granny Smith' and `Yellow Newtown' apples (Malus domestica Borkh), `20th Century' pear (Pyrus serotina L.), and `Angeleno' plum (Prunus domestica L.) were kept in air and in 0.25% or 0.02% O2 at 0, 5, or 10C for 3, 7, 14, 25, or 35 days to study the effects of low-O2 atmospheres on their postharvest physiology and quality attributes. Soluble solids content (SSC), pH, and external appearance were not significantly influenced, but resistance to CO2 diffusion was increased by the low-O2 treatments. Exposures to the low-O2 atmospheres inhibited ripening, including reduction in ethylene production rate, retardation of skin color changes and flesh softening, and maintenance of titratable acidity. The most important detrimental effect of the low-O2 treatments was development of an alcoholic off-flavor that had a logarithmic relation with ethanol content of the fruits. The ethanol content causing slight off-flavor (Eo) increased with SSC of the commodity at the ripe stage, and it could be estimated using the following formula: (Log Eo)/SSC = 0.228. Using SSC of ripe fruits and average ethanol accumulation rate per day (VE) from each low-O2 treatment, the tolerance limit (Tl) of fruits to low-O2 atmospheres could be predicted as follows: Tl = Eo/VE = (100.228SSC)/VE.

Tipping bucket rain gauges (TBR) are widely used worldwide because they are simple, cheap, and have low-energy consumption. However, their main disadvantage lies in measurement errors, such as those caused by rainfall intensity (RI) variation, which results in data underestimation, especially during extreme rainfall events. This work aims to understand these types of errors, identifying some of their causes through an analysis of water behavior and its effect on the TBR mechanism when RI increases. The mechanical biases of TBR effects on data were studied using 13 years of data measured at 10 TBRs in a mountain basin, and two semi-analytical approaches based on the TBR mechanism response to RI have been proposed, validated in the laboratory, and contrasted with a simple linear regression dynamic calibration and a static calibration through a root-mean-square error analysis in two different TBR models. Two main sources of underestimation were identified: one due to the cumulative surplus during the tipping movement and the other due to the surplus water contributed by the critical drop. Moreover, a random variation, not related to RI, was also observed, and three regions in the calibration curve were identified. Proposed calibration methods have proved to be an efficient alternative for TBR calibration, reducing data error by more than 50% in contrast with traditional static calibration.