Occurrence and survival of Campylobacter spp. in dairy matrices investigated by viability qPCR

The aim of this study was to investigate the survival of Campylobacter jejuni in dairy matrices by viability qPCR. Survival in UHT milk was examined first and the cheese outbreak strain, C. jejuni 11 218 showed enhanced tendency to become viable but nonculturable (VBNC). It survived for long at different pH values, in raw and fermented milk and cheese. Viability qPCR was also used to examine the occurrence of viable Campylobacter spp. in raw milk, detecting the bacteria in 40.9% of 25 mL samples negative on culture. Results showed that viability qPCR is valuable in estimating Campylobacter risk in dairy products.     

Recyclability Studies on Poly(lactic acid)/Poly(butylene succinate-co-adipate) (PLA/PBSA) Biobased and Biodegradable Films

Poly(lactic acid)/poly(butylene succinate-co-adipate) (PLA/PBSA) blends are found promising for film packaging applications because of their flexibility, resistance, and compostability. Industrially extruded granules and films based on PLA and containing different amounts of PBSA are reprocessed through mini-extrusion, to simulate recycling, and tested in terms of their melt flow rate as a function of PBSA content. Moreover, pure PLA commercial granules and the film produced extruding the PLA/PBSA 60/40 blend are reprocessed several times by injection molding and characterized in terms of melt flow rate, mechanical properties, thermal properties, and color as a function of injection molding cycles. The variation in melt fluidity and thermo-mechanical properties is negligible up to 3 injection molding cycles for both pure PLA granules and PLA/PBSA blend. In the case of blend the change in color (yellowing and darkening) is more evident and slight local compositional change in injection molded items can be evidenced as well as a slight decrease in PBS crystallinity as a function of injection molding cycles. Nevertheless, in applications where these aspects are not critical, these materials can be recycled by extrusion or injection molding before being composted, thus prolonging their life cycle and storing carbon in them as longer as possible.     

Tools for monitoring toxicological and genotoxicological changes in a drinking water treatment plant in Northeast Italy

Drinking water quality can be compromised at different stages, from raw water to treated one. This research aimed to evaluate the toxicity and genotoxicity of groundwater contaminated by fluorinated compounds treated in a drinking water treatment plant, through several bioassays. Water samples underwent chemical analyses and were assayed on Daphnia magna, Pseudokirchneriella subcapitata, Allium cepa, human leukocytes and Salmonella typhimurium. Physical–chemical parameters were always within the Italian legislation limits. Water after filtration and disinfection caused slight toxicity in D. magna; the sample after filtration inhibited the proliferation of P. subcapitata. None of the water samples exerted toxicity in A. cepa. All the analysed samples had genotoxic effects on A. cepa and human leucocytes, while only disinfected water caused mutations in S. typhimurium. A battery composed of tests on D. magna, P. subcapitata, S. typhimurium and A. cepa could represent a useful tool to verify the toxicity/genotoxicity through the water treatment stages and to improve drinking water quality management.     

Autoencoders for unsupervised real-time bridge health assessment

Over the last decades, the rising number of aging infrastructures has progressively fueled much interest toward the field of structural health monitoring. Following the increasing popularity of artificial intelligence algorithms, an autoencoder-based damage detection technique within the context of unsupervised learning is proposed in this paper to provide support for practical engineering applications. The developed methodology uses the autoencoder to reconstruct raw acceleration sequences of user-defined length collected from a healthy structure. To quantify the errors between the original input and the reconstructed output, which may be representative of damage occurrence, two indexes of reconstruction loss are selected as damage-sensitive features. To support damage detection, a selected number of short-time sequences are finally grouped into a unique macrosequence. The novel procedure can effectively both work at the single sensor level, as well as combine the predictive models using an ensemble learning strategy. Avoiding system identification, results obtained in the Z24 bridge demonstrate that the proposed method is quite effective for local damage detection with limited computational effort and using a limited number of sensors, thereby suitable to be easily applicable in the context of real-time bridge assessment.