Processes of glacial lake changes in areas and numbers from 1990 to 2010.

<p>Processes of glacial lake changes in areas and numbers from 1990 to 2010.</p

Annual mean temperature (a) and annual precipitation (b) change in the central Himalayas from 1981 to 2010.

<p>Annual mean temperature (a) and annual precipitation (b) change in the central Himalayas from 1981 to 2010.</p

The second complete mitochondrial genome of <i>Capillidium rhysosporum</i> within the family Capillidiaceae, Entomophthorales

The complete mitochondrial genome of the entomophthoroid fungus Capillidium rhysosporum (strain no.: ATCC 12588) was sequenced using next-generation sequencing technology. The assembled circular genome has a length of 46,756 base pairs with a GC content of 27.06%. Gene prediction identified 15 core protein-coding genes (PCGs), two rRNA genes, and 27 tRNA genes. Phylogenetic analysis confirmed that C. rhysosporum belongs to the Zoopagomycota clade and is closely related to C. heterosporum. This study presents the second complete mitochondrial genome within the family Capillidiaceae, contributing to the mitochondrial DNA database of entomophthoroid fungi.</p

Changes to pro- and supra-glacial lakes from 1990 to 2010.

<p>Changes to pro- and supra-glacial lakes from 1990 to 2010.</p

Additional file 2 of Predicting microbial community compositions in wastewater treatment plants using artificial neural networks

Additional  file 2: Figure S1. Ranking of importance weights ofenvironmental factors in different alpha-diversities predictive models. FigureS2. a. Comparison ofintra- and inter-group Bray-Curtis similarity between predicted and observedcommunities. b. Average prediction accuracy R21:1of microbial taxa at different taxonomic levels. Figure S3. Environmental factor importance weights andPearson’s correlation coefficients. FigureS4. Correlation ofcorrelation coefficients of environment factors with ASVs>10%subcommunity, skewness, and kurtosis of normalized environment variables withtheir Garson’s connection weights. Figure S5. a. Comparison of predictiveaccuracy R21:1 between low,medium, and high abundance taxa. b. Comparison of predictiveaccuracy R21:1 between low,medium, and high-frequency taxa. c. Correlation of relative abundance with the occurrencefrequency of ASVs. d. Correlation of the R21:1in test sets with the coefficient of variation of ASVs. Figure S6. Comparison ofaverage relative abundance and occurrencefrequency between above, neutral, and below partitions. Figure S7. Fit of theneutral community model (NCM) of above, neutral, and below partitions. Figure S8.The taxonomic composition, average relative abundance, occurrence frequency,and estimated migration rate of core and non-core taxa. Figure S9. Predictionof functional groups with 10 high-weight environmental factors. Figure S10. Fitof the neutral community model (NCM) of high abundance, medium abundance, andlow abundance subcommunities. Figure S11. Changes of mean square errors (MSE) andcoefficients of determination (R2) on the validation set with epochswhen training the model

Distribution of glacial lakes in the central Himalayas.

<p>Distribution of glacial lakes in the central Himalayas.</p

Rapidly expanding glacial lakes in different river basins.

<p>Rapidly expanding glacial lakes in different river basins.</p

Distribution and change of glacial lakes at different altitudinal zones on the north (a and c) and south sides (b and d) of the main central Himalayan range.

<p>Distribution and change of glacial lakes at different altitudinal zones on the north (a and c) and south sides (b and d) of the main central Himalayan range.</p

GLOF events and changes in typical critical glacial lakes within the study area.

<p>GLOF events and changes in typical critical glacial lakes within the study area.</p

Additional file 3 of Predicting microbial community compositions in wastewater treatment plants using artificial neural networks

Additional  file 3: Table S1. Alpha-diversitiesof AS system. Table S2. Summary of ASVs belonging ASVs>10% sub-community.Table S3. Summary of microbial taxa at different taxonomic levels. Table S4. Averageimportance weights of environmental factors in different ASVs predictivemodels. Table S5. Summary of different environment variables. Table S6. Summaryof genera belonging to major functional groups. TableS7. Abbreviations, meanings, and types of environment variables. Table S8. Numericaland normalized environmental data