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Unregulated private wells are understudied potential sources of community-acquired Legionnaires’ disease. Here we conducted a comprehensive survey of 44 homes supplied by private wells in Wake County, North Carolina, quantifying Legionella spp. DNA, Legionella pneumophila DNA, and total bacterial 16S rRNA genes via real-time polymerase chain reaction in hot and cold drinking water samples, along with culturable L. pneumophila via IDEXX Legiolert in cold drinking water samples. Legionella spp. DNA, L. pneumophila DNA and culturable L. pneumophila were detected in 100, 65·5 and 15·9% of the 44 homes, respectively, and culturable levels were comparable to some municipal surveys applying the same methods. Total coliforms and Escherichia coli were monitored as representative faecal indicators and were found in 20·4 and 0·0% of homes. Within certain sample types, Legionella spp. and L. pneumophila gene copy numbers were positively associated with total bacteria (i.e. total 16S rRNA genes) and water softener use, but were not associated with faecal indicator bacteria, inorganic water parameters or other well characteristics. These findings confirm that occurrence of Legionella and L. pneumophila is highly variable in private wells.

Legionella is the leading identified cause of waterborne disease outbreaks associated with US municipal water systems. While Legionella is known to occur naturally in groundwater, prior efforts to characterize its occurrence in unregulated private wells are limited to sampling at the wellhead and not in the home plumbing where Legionella can thrive. This work documents much higher levels of Legionella in home plumbing versus water directly from private wells and examines factors associated with higher Legionella occurrence.

 

Efficient storage systems come from the intelligent management of the data units, i.e., disk blocks in local file system level. Block correlations represent the semantic patterns in storage systems. These correlations can be exploited for data caching, pre-fetching, layout optimization, I/O scheduling, etc. to finally realize an efficient storage system. In this paper, we introduce Block2Vec, a deep learning based strategy to mine the block correlations in storage systems. The core idea of Block2Vec is twofold. First, it proposes a new way to abstract blocks, which are considered as multi-dimensional vectors instead of traditional block Ids. In this way, we are able to capture similarity between blocks through the distances of their vectors. Second, based on vector representation of blocks, it further trains a deep neural network to learn the best vector assignment for each block. We leverage the recently advanced word embedding technique in natural language processing to efficiently train the neural network. To demonstrate the effectiveness of Block2Vec, we design a demonstrative block prediction algorithm based on mined correlations. Empirical comparison based on the simulation of real system traces shows that Block2Vec is capable of mining block-level correlations efficiently and accurately. This research and trial show that the deep learning strategy is a promising direction in optimizing storage system performance.