A survey of filamentous organisms at the deer Island treatment plant
Abstract The Deer Island Treatment Plant (DITP) treating both domestic and industrial wastewater, has a peak flow capacity of up to 1270 million gallons per day (MGD) (3342 m3 min−1). The DITP contains a Pilot Plant, which consists of two identical pure oxygen activated sludge treatment trains, each with a maximum capacity of 1 MGD (2.63 m3 min−1) to simulate the maximum flow of the full size facility. This study documents the community of filamentous organisms living in the activated sludge under various operational conditions in the Pilot Plant. Sixteen types of filamentous organisms were identified. The major filamentous organisms present in the Pilot Plant were Type 1701, Type 0041, Sphaerotilus natans, Microthrix parvicella, and Nocardia sp. Nocardia sp. was quite dominant in the early stages of the experimental period during high ambient temperatures (summer), indicating that temperature is one determining factor in the distribution of Nocardia sp. Thiothrix sp. was very sensitive to the dissolved oxygen (DO) level, with low DO values favoring the growth of the organisms. Microbiological observations made during the start‐up and stable operation of the full‐scale secondary treatment plant (maximum capacity 780 MGD) are also reported. No dominant filamentous species existed during the start‐up period. Type 0803 and Type 1701, indicators of low oxygen level for the applied food to microorganism ratio, confirmed the low DO conditions of activated sludge tanks.
Phylogenetic in situ/ex situ analysis of a sulfur mat microbial community from a thermal sulfide spring in the north Caucasus
A phylogenetic in situ/ex situ analysis of a sulfur mat formed by colorless filamentous sulfur bacteria in a thermal sulfide spring (northern spur of the main Caucasian ridge) was carried out. Nine phylotypes were revealed in the mat. Thiothrix sp. and Sphaerotilus sp. were the dominant phylotypes (66.3% and 26.3%, respectively). The 16S rRNA gene nucleotide sequence of Sphaerotilus sp. phylotype from the clone library was identical to the sequences of the seven Sphaerotilus strains isolated from the same source. A very high degree of similarity of Sphaerotilus strains revealed by ERIC-PCR fingerprints indicated little or no population diversity of this species in the mat. Thiothrix phylotype from the clone library and two Thiothrix strains isolated from the same mat sample differed in one to three nucleotides of 16S rRNA genes; this is an indication of this organism’s population variability in the mat. 16S rRNA genes of the strains and clones of Thiothrix sp. exhibited the highest similarity (ca. 99%) with Thiothrix unzii; the strains and clones of Sphaerotilus had 99% similarity with the type species Sphaerotilus natans (the only species of this genus) and therefore can be assigned to this species. The minor seven components belong to the phylotypes from the Proteobacteria (3%), as well as the Chlorobia, Cyanobacteria, Clostridia, and Bacteroidetes phylogenetic groups, each of them constituting not more than 1%. Intracellular accumulation of elemental sulfur by Sphaerotilus similar to other filamentous sulfur bacteria was demonstrated for the first time (both in the population of the sulfur spring and in cultures with sulfide). Although mass growth of Sphaerotilus and Thiothrix is typical of bacterial populations of anthropogenic ecosystems (the activated sludge of treatment facilities), stable communities of these bacteria have not been previously found in the sulfur mats or “threads” of natural sulfide springs.
Integrating Local CNN and Global CNN for Script Identification in Natural Scene Images
Script identification in natural scene images is a key pre-step for text recognition and is also an indispensable condition for automatic text understanding systems that are designed for multi-language environments. In this paper, we present a novel framework integrating Local CNN and Global CNN both of which are based on ResNet-20 for script identification. We first obtain a lot of patches and segmented images based on the aspect ratios of the images. Subsequently, these patches and segmented images are used as inputs to Local CNN and Global CNN for training, respectively. Finally, to get the final results, the Adaboost algorithm is used to combine the results of Local CNN and Global CNN for decision-level fusion. Benefiting from such a strategy, Local CNN fully exploits the local features of the image, effectively revealing subtle differences among the scripts that are difficult to distinguish such as English, Greek, and Russian. Moreover, Global CNN mines the global features of the image to improve the accuracy of script identification. The experimental results demonstrate that our approach has a good performance on four public datasets.