Alzheimer's Disease (AD) is a chronic neurodegenerative disease. Early diagnosis will considerably decrease the risk of further deterioration. Unfortunately, current studies mainly focus on classifying the states of disease in its current stage, instead of predicting the possible development of the disease. Long short-term memory (LSTM) is a special kind of recurrent neural network, which might be able to connect previous information to the present task. Noticing that the temporal data for a patient are potentially meaningful for predicting the development of the disease, we propose a predicting model based on LSTM. Therefore an LSTM network, with fully connected layer and activation layers, is built to encode the temporal relation between features and the next stage of Alzheimer's Disease. The Experiments show that our model outperforms most of the existing models.
The class of cographs, or complement-reducible graphs, arises naturally in many different areas of applied mathematics and computer science. In this paper, we present an optimal algorithm for determining a minimum path cover for a cograph G. In case G has a Harniltonian path (cycle) our algorithm exhibits the path (cycle) as well.
Shewanella xiamenensis BC01 (SXM) was isolated from sediment collected off Xiamen, China and was identified based on the phylogenetic tree of 16S rRNA sequences and the gyrB gene. This strain showed high activity in the decolorization of textile azo dyes, especially methyl orange, reactive red 198, and recalcitrant dye Congo red, decolorizing at rates of 96.2, 93.0, and 87.5%, respectively. SXM had the best performance for the specific decolorization rate (SDR) of azo dyes compared to Proteus hauseri ZMd44 and Aeromonas hydrophila NIU01 strains and had an SDR similar to Shewanella oneidensis MR-1 in Congo red decolorization. Luria-Bertani medium was the optimal culture medium for SXM, as it reached a density of 4.69 g-DCW L(-1) at 16 h. A mediator (manganese) significantly enhanced the biodegradation and flocculation of Congo red. Further analysis with UV-VIS, Fourier Transform Infrared spectroscopy, and Gas chromatography-mass spectrometry demonstrated that Congo red was cleaved at the azo bond, producing 4,4'-diamino-1,1'-biphenyl and 1,2'-diamino naphthalene 4-sulfonic acid. Finally, SEM results revealed that nanowires exist between the bacteria, indicating that SXM degradation of the azo dyes was coupled with electron transfer through the nanowires. The purpose of this work is to explore the utilization of a novel, dissimilatory manganese-reducing bacterium in the treatment of wastewater containing azo dyes.
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