Chemical, Electrochemical and Spectral Characterization of Water Leachates from Biomass

To develop pretreatment strategies for better industrial utilization of biomass materials, six types of biomass were washed with deionized water at 303, 333, and 363 K, and the leachate was characterized by chemical, electrochemical, and spectral analysis. The results show that K+ is the most abundant cation in the leachates. An increase in the washing temperature leads to an increase in the cation concentration mainly because of the increment of K+. The chemical oxygen demand (COD) and the charge difference between inorganic cations and anions for leachate suggest that, in addition to inorganic ions, a few organic compounds and organic anions are released from biomass during washing. Fourier transform infrared (FTIR) spectra of the dry leachate samples reveal that carbohydrates and carboxylates are the major components of the organic compounds and organic salts, respectively. Except for the leachate of rice straw, the charge difference and COD increase with increasing washing temperature because of the increment of carboxylates for all of the other leachates

Additional file 5 of CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Additional file 5: Disease feature matrix

Additional file 1 of Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk

The lncRNA2GO-55 dataset. Additional file 1 includes the Gene Ontology (GO) annotations and the associated PubMed IDs for 55 lncRNAs. (DOCX 26 kb

Additional file 1 of Interactions between Blastocystis subtype ST4 and gut microbiota in vitro

Additional file 1: Table S1. qPCR primers used in this study

Additional file 2 of CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Additional file 2: circRNA comprehensive similarity matrix

Additional file 1 of CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Additional file 1: Adjacency matrix A. The adjacency matrix A constructed from circR2Disease

Additional file 4 of CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Additional file 4: circRNA feature matrix

Additional file 3 of CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Additional file 3: Disease comprehensive similarity

Comparison of parallel and sequential processing policies with different arrival rate.

Comparison of parallel and sequential processing policies with different arrival rate.</p

Additional file 6 of CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Additional file 6: Prediction of the top 40 predicted circRNAs associated with Breast cancer