Sludge Bulking Control with Ozone

O zonation was studied as a non-specific means of sludge bulking control. Direct ozonation at 6 g/m³ influent, i.e. 4 gO₃/kg sludge per day into the aeration basin of a 150 l/d Phoredox system controlled the growth of filamentous organisms and improved the settleability of the sludge. Bulking control with ozone is not significantly more expensive than with chlorine or with larger settler capacity.     

Trihalomethane Formation during Sludge Bulking Control with Chlorine

Effluent quality implications of bulking control with chlorine in activated-sludge treatment have been studied on laboratory scale. Batch chlorination of sludge samples led to the formation of trihalomethanes, mainly as chloroform (in hundreds of ùg/l) and even more non-specified organohalogens. Most organohalogens were formed from substances in the liquid effluent, and fewer out of the sludge solids. The presence of sludge lowered the concentration of extractable trihalomethanes in the effluent. Continuous chlorination in a laboratory-scale activated-sludge plant did not produce measurable quantities of any organohalogen compounds.     

Improved Sewage Treatment with Ozonated Activated Sludge

A ctivated SLUDGE SEWAGE treatment processes are often inadequate to comply with effluent criteria as promulgated in anti-pollution legislation. Ozonation was studied as a means of improving nutrient removal activated sludge processes without the salinity and other disadvantages of the often used polyvalent metal salt addition. Direct ozonation into the aeration basin of a 150 I/d Phoredox (modified Bardenpho) system led to improved removal of organic substances. Nitrification was improved while phosphate removal was not adversely affected. Beneficial biological activity in the activated sludge was enhanced. The trihalomethane formation potential was reduced. Pollutional loads could be lessened, while an effluent with increased reuse potential was produced.     

Identification and analysis of 2-chloro-6-methylphenol, 2,6-dichlorophenol and indole: causes of taints and off-flavours in wines

Background and Aims: The aim of this study was to determine the cause of taints and off-flavours in a number of commercial wines and to develop methods for quantitative analysis of the compounds responsible. Methods and Results: Three compounds, 2-chloro-6-methylphenol (i.e. 6-chloro-ortho-cresol), 2,6-dichlorophenol and indole, were identified by gas chromatography (GC)/mass spectrometry (MS)/olfactometry as causes of taints or off-flavours in the wines. New methods for quantitative analysis of these three compounds, as well as some other chloro- and bromophenols were developed. The methods employed GC/MS and stable isotope dilution analysis, and confirmed the presence of 2-chloro-6-methylphenol, 2,6-dichlorophenol and indole in some wines at concentrations well above their odour and taste detection thresholds. Conclusions: 2-Chloro-6-methylphenol, 2,6-dichlorophenol and indole were confirmed as causes of taints and off-flavours in commercial wines. Significance of the Study: The development of new analytical methods will make it possible to determine whether chlorophenol (and perhaps also bromophenol) taint in wine is more widespread than previously recognised. Similarly, the simple, rapid and accurate method for quantifying indole in wine will allow large numbers of wine and ferment samples to be studied to determine those factors that can result in unacceptably high concentrations in commercial wines.     

General phenological model to characterise the timing of flowering and veraison of Vitis vinifera L.

Background and Aims: Phenological models, which are based on responses of the plant to temperature, are useful tools to predict grapevine (Vitis vinifera L.) phenology in various climate conditions. This study aimed to develop a single process‐based phenological model at the species level to predict two important stages of development for V. vinifera L.: flowering and veraison. Methods and Results: Three different phenological models were tested and the model that gave the best results was optimised for its parameters. The chosen model Spring Warming was found optimal with regard to the trade‐off between parsimony of input parameters and efficiency. The base temperature (Tb) of 0°C calculated from the 60th day (t₀) of the year (for the Northern hemisphere) was found to be the most optimal parameter set tested. This model henceforth referred to as the Grapevine Flowering Veraison model (GFV) was successfully validated at the varietal level using an independent dataset. Conclusions: A general phenological model, GFV, has been successfully developed to characterise the timing of flowering and veraison for the grapevine. Significance of the Study: The model is simple for the user, can be successfully applied to many varieties and can be used as an easy predictor of phenology for different varieties under climate change scenarios.     

Efficient nucleus detector in histopathology images

In traditional cancer diagnosis, (histo)pathological images of biopsy samples are visually analysed by pathologists. However, this judgment is subjective and leads to variability among pathologists. Digital scanners may enable automated objective assessment, improved quality and reduced throughput time. Nucleus detection is seen as the corner stone for a range of applications in automated assessment of (histo)pathological images. In this paper, we propose an efficient nucleus detector designed with machine learning. We applied colour deconvolution to reconstruct each applied stain. Next, we constructed a large feature set and modified AdaBoost to create two detectors, focused on different characteristics in appearance of nuclei. The proposed modification of AdaBoost enables inclusion of the computational cost of each feature during selection, thus improving the computational efficiency of the resulting detectors. The outputs of the two detectors are merged by a globally optimal active contour algorithm to refine the border of the detected nuclei. With a detection rate of 95% (on average 58 incorrectly found objects per field‐of‐view) based on 51 field‐of‐view images of Her2 immunohistochemistry stained breast tissue and a complete analysis in 1 s per field‐of‐view, our nucleus detector shows good performance and could enable a range of applications in automated assessment of (histo)pathological images.