Top 32 list of microbiology journals with their Impact Factors from 2024, including publisher and other relevant information based on the latest available data from the source:

Use.ai和Perplexity.ai两个网站都支持调用多个先进的AI模型以满足不同用户需求，但在模型种类和实力上存在差异。

Use.ai集成了多达10个知名模型，包括GroK4、Deepinfra Kimi K2、Llama 3.3、Qwen 3 Max、Google Gemini、Deepseek、Claude Opus 4.1、OpenAI GPT-5、GPT-4o和GPT-4o Mini。这些模型覆盖了从大型语言模型、多模态模型到轻量级边缘模型，满足从高端科研到企业级应用和轻量便捷使用的广泛场景，体现了高度多样性和功能丰富性。

而Perplexity.ai主要以OpenAI的GPT系列模型为基础，支持GPT-4、GPT-3.5等主流大语言模型，同时融合了实时网络搜索和信息检索功能，增强了回答的实时性和准确性。虽然模型数量较少，但其优势在于结合强大的搜索引擎技术，能够提供带有权威引用的智能问答，提升信息可信度。

综合比较，Use.ai在可调用模型数量和模型多样性上占优，更适合需要多模型灵活运用的复杂任务场景；而Perplexity.ai则在信息实时性和权威性方面表现突出，适合对搜索结果准确性有较高要求的用户。

结合这两个平台各自优势，用户可根据自身需求选择：若重视多模型丰富性和多场景支持，推荐Use.ai；若注重即时、准确、有来源保障的答案检索，Perplexity.ai是更优选择。

以上内容结合了两平台的模型资源和功能特点，帮助用户在AI应用中做出更明智的选择Use.ai和Perplexity.ai两平台均助力提升智能问答和信息获取体验，满足未来多样化的人工智能需求。

1. Environment Setup: It sets up a Conda environment named picrust2, using the conda create command and then activates this environment using conda activate picrust2.

   #https://github.com/picrust/picrust2/wiki/PICRUSt2-Tutorial-(v2.2.0-beta)#minimum-requirements-to-run-full-tutorial
   mamba create -n picrust2 -c bioconda -c conda-forge picrust2    #2.5.3  #=2.2.0_b
   mamba activate /home/jhuang/miniconda3/envs/picrust2

Under docker-env (qiime2-amplicon-2023.9)

2. Export QIIME2 feature table and representative sequences

   #docker pull quay.io/qiime2/core:2023.9
   #docker run -it --rm \
   #-v /mnt/md1/DATA/Data_Karoline_16S_2025:/data \
   #-v /home/jhuang/REFs:/home/jhuang/REFs \
   #quay.io/qiime2/core:2023.9 bash
   #cd /data
   # === SETTINGS ===
   FEATURE_TABLE_QZA="dada2_tests2/test_7_f240_r240/table.qza"
   REP_SEQS_QZA="dada2_tests2/test_7_f240_r240/rep-seqs.qza"
   
   # === STEP 1: EXPORT QIIME2 ARTIFACTS ===
   mkdir -p qiime2_export
   qiime tools export --input-path $FEATURE_TABLE_QZA --output-path qiime2_export
   qiime tools export --input-path $REP_SEQS_QZA --output-path qiime2_export

3. Convert BIOM to TSV for Picrust2 input

   biom convert \
   -i qiime2_export/feature-table.biom \
   -o qiime2_export/feature-table.tsv \
   --to-tsv

Under env (picrust2): mamba activate /home/jhuang/miniconda3/envs/picrust2

4. Run PICRUSt2 pipeline

   tail -n +2 qiime2_export/feature-table.tsv > qiime2_export/feature-table-fixed.tsv
   picrust2_pipeline.py \
   -s qiime2_export/dna-sequences.fasta \
   -i qiime2_export/feature-table-fixed.tsv \
   -o picrust2_out \
   -p 100
   
   #This will:
   #* Place sequences in the reference tree (using EPA-NG),
   #* Predict gene family abundances (e.g., EC, KO, PFAM, TIGRFAM),
   #* Predict pathway abundances.
   
   #In current PICRUSt2 (with picrust2_pipeline.py), you do not run hsp.py separately.
   #Instead, picrust2_pipeline.py internally runs the HSP step for all functional categories automatically. It outputs all the prediction files (16S_predicted_and_nsti.tsv.gz, COG_predicted.tsv.gz, PFAM_predicted.tsv.gz, KO_predicted.tsv.gz, EC_predicted.tsv.gz, TIGRFAM_predicted.tsv.gz, PHENO_predicted.tsv.gz) in the output directory.
   
   mkdir picrust2_out_advanced; cd picrust2_out_advanced
   #If you still want to run hsp.py manually (advanced use / debugging), the commands correspond directly:
   hsp.py -i 16S -t ../picrust2_out/out.tre -o 16S_predicted_and_nsti.tsv.gz -p 100 -n
   hsp.py -i COG -t ../picrust2_out/out.tre -o COG_predicted.tsv.gz -p 100
   hsp.py -i PFAM -t ../picrust2_out/out.tre -o PFAM_predicted.tsv.gz -p 100
   hsp.py -i KO -t ../picrust2_out/out.tre -o KO_predicted.tsv.gz -p 100
   hsp.py -i EC -t ../picrust2_out/out.tre -o EC_predicted.tsv.gz -p 100
   hsp.py -i TIGRFAM -t ../picrust2_out/out.tre -o TIGRFAM_predicted.tsv.gz -p 100
   hsp.py -i PHENO -t ../picrust2_out/out.tre -o PHENO_predicted.tsv.gz -p 100

5. Metagenome prediction per functional category (if needed separately)

   #cd picrust2_out_advanced
   metagenome_pipeline.py -i ../qiime2_export/feature-table.biom -m 16S_predicted_and_nsti.tsv.gz -f COG_predicted.tsv.gz -o COG_metagenome_out --strat_out
   metagenome_pipeline.py -i ../qiime2_export/feature-table.biom -m 16S_predicted_and_nsti.tsv.gz -f EC_predicted.tsv.gz -o EC_metagenome_out --strat_out
   metagenome_pipeline.py -i ../qiime2_export/feature-table.biom -m 16S_predicted_and_nsti.tsv.gz -f KO_predicted.tsv.gz -o KO_metagenome_out --strat_out
   metagenome_pipeline.py -i ../qiime2_export/feature-table.biom -m 16S_predicted_and_nsti.tsv.gz -f PFAM_predicted.tsv.gz -o PFAM_metagenome_out --strat_out
   metagenome_pipeline.py -i ../qiime2_export/feature-table.biom -m 16S_predicted_and_nsti.tsv.gz -f TIGRFAM_predicted.tsv.gz -o TIGRFAM_metagenome_out --strat_out
   
   # Add descriptions in gene family tables
   add_descriptions.py -i COG_metagenome_out/pred_metagenome_unstrat.tsv.gz -m COG -o COG_metagenome_out/pred_metagenome_unstrat_descrip.tsv.gz
   add_descriptions.py -i EC_metagenome_out/pred_metagenome_unstrat.tsv.gz -m EC -o EC_metagenome_out/pred_metagenome_unstrat_descrip.tsv.gz
   add_descriptions.py -i KO_metagenome_out/pred_metagenome_unstrat.tsv.gz -m KO -o KO_metagenome_out/pred_metagenome_unstrat_descrip.tsv.gz   # EC and METACYC is a pair, EC for gene_annotation and METACYC for pathway_annotation
   add_descriptions.py -i PFAM_metagenome_out/pred_metagenome_unstrat.tsv.gz -m PFAM -o PFAM_metagenome_out/pred_metagenome_unstrat_descrip.tsv.gz
   add_descriptions.py -i TIGRFAM_metagenome_out/pred_metagenome_unstrat.tsv.gz -m TIGRFAM -o TIGRFAM_metagenome_out/pred_metagenome_unstrat_descrip.tsv.gz

6. Pathway inference (MetaCyc pathways from EC numbers)

   #cd picrust2_out_advanced
   pathway_pipeline.py -i EC_metagenome_out/pred_metagenome_contrib.tsv.gz -o EC_pathways_out -p 100
   pathway_pipeline.py -i EC_metagenome_out/pred_metagenome_unstrat.tsv.gz -o EC_pathways_out_per_seq -p 100 --per_sequence_contrib --per_sequence_abun EC_metagenome_out/seqtab_norm.tsv.gz --per_sequence_function EC_predicted.tsv.gz
   #ERROR due to missing .../pathway_mapfiles/KEGG_pathways_to_KO.tsv
   pathway_pipeline.py -i COG_metagenome_out/pred_metagenome_contrib.tsv.gz -o KEGG_pathways_out -p 100 --no_regroup --map /home/jhuang/anaconda3/envs/picrust2/lib/python3.6/site-packages/picrust2/default_files/pathway_mapfiles/KEGG_pathways_to_KO.tsv
   pathway_pipeline.py -i KO_metagenome_out/pred_metagenome_strat.tsv.gz -o KEGG_pathways_out -p 100 --no_regroup --map /home/jhuang/anaconda3/envs/picrust2/lib/python3.6/site-packages/picrust2/default_files/pathway_mapfiles/KEGG_pathways_to_KO.tsv
   
   add_descriptions.py -i EC_pathways_out/path_abun_unstrat.tsv.gz -m METACYC -o EC_pathways_out/path_abun_unstrat_descrip.tsv.gz
   gunzip EC_pathways_out/path_abun_unstrat_descrip.tsv.gz
   
   #Error - no rows remain after regrouping input table. The default pathway and regroup mapfiles are meant for EC numbers. Note that KEGG pathways are not supported since KEGG is a closed-source database, but you can input custom pathway mapfiles if you have access. If you are using a custom function database did you mean to set the --no-regroup flag and/or change the default pathways mapfile used?
   #If ERROR --> USE the METACYC for downstream analyses!!!
   
   #ERROR due to missing .../description_mapfiles/KEGG_pathways_info.tsv.gz
   #add_descriptions.py -i KO_pathways_out/path_abun_unstrat.tsv.gz -o KEGG_pathways_out/path_abun_unstrat_descrip.tsv.gz --custom_map_table /home/jhuang/anaconda3/envs/picrust2/lib/python3.6/site-packages/picrust2/default_files/description_mapfiles/KEGG_pathways_info.tsv.gz
   
   #NOTE: Target-analysis for the pathway "mixed acid fermentation"

7. Visualization

   #7.1 STAMP
   #https://github.com/picrust/picrust2/wiki/STAMP-example
   #Note that STAMP can only be opened under Windows
   
   # It needs two files: path_abun_unstrat_descrip.tsv.gz as "Profile file" and metadata.tsv as "Group metadata file".
   cp ~/DATA/Data_Karoline_16S_2025/picrust2_out_advanced/EC_pathways_out/path_abun_unstrat_descrip.tsv ~/DATA/Access_to_Win10/
   
   cut -d$'\t' -f1 qiime2_metadata.tsv > 1
   cut -d$'\t' -f3 qiime2_metadata.tsv > 3
   cut -d$'\t' -f5-6 qiime2_metadata.tsv > 5_6
   paste -d$'\t' 1 3 > 1_3
   paste -d$'\t' 1_3 5_6 > metadata.tsv
   #SampleID --> SampleID
   SampleID        Group   pre_post        Sex_age
   sample-A1       Group1  3d.post.stroke  male.aged
   sample-A2       Group1  3d.post.stroke  male.aged
   sample-A3       Group1  3d.post.stroke  male.aged
   cp ~/DATA/Data_Karoline_16S_2025/metadata.tsv ~/DATA/Access_to_Win10/
   # MANULLY_EDITING: keeping the only needed records in metadata.tsv: Group 9 (J1–J4, J6, J7, J10, J11) and Group 10 (K1–K6).
   
   #7.2. ALDEx2
   https://bioconductor.org/packages/release/bioc/html/ALDEx2.html

Under docker-env (qiime2-amplicon-2023.9)

8. (NOT_NEEDED) Convert pathway output to BIOM and re-import to QIIME2 gunzip picrust2_out/pathways_out/path_abun_unstrat.tsv.gz biom convert \ -i picrust2_out/pathways_out/path_abun_unstrat.tsv \ -o picrust2_out/path_abun_unstrat.biom \ –table-type=”Pathway table” \ –to-hdf5

   qiime tools import \
   --input-path picrust2_out/path_abun_unstrat.biom \
   --type 'FeatureTable[Frequency]' \
   --input-format BIOMV210Format \
   --output-path path_abun.qza
   
   #qiime tools export --input-path path_abun.qza --output-path exported_path_abun
   #qiime tools peek path_abun.qza
   echo "✅ PICRUSt2 pipeline complete. Output in: picrust2_out"

9. Short answer: unless you had a very clear, pre-specified directional hypothesis, you should use a two-sided test.

   A bit more detail:
   
   * Two-sided t-test
   
           * Tests: “Are the means different?” (could be higher or lower).
           * Standard default in most biological and clinical studies and usually what reviewers expect.
           * More conservative than a one-sided test.
   
   * One-sided t-test
   
           * Tests: “Is Group A greater than Group B?” (or strictly less than).
           * You should only use it if before looking at the data you had a strong reason to expect a specific direction and you would ignore/consider uninterpretable a difference in the opposite direction.
           * Using one-sided just to gain significance is considered bad practice.
   
   For your pathway analysis (exploratory, many pathways, q-value correction), the safest and most defensible choice is to:
   
   * Use a two-sided t-test (equal variance or Welch’s, depending on variance assumptions).
   
   So I’d recommend rerunning STAMP with Type: Two-sided and reporting those results.
   
   #--> Using a two-sided Welch's t-test in STAMP, that is the unequal-variance version (does not assume equal variances and is more conservative than “t-test (equal variance)” referring to the classical unpaired Student’s t-test.

10. Statistics in STAMP

    * For multiple groups:
        * Statistical test: ANOVA, Kruskal-Wallis H-test
        * Post-hoc test: Games-Howell, Scheffe, Tukey-Kramer, Welch's (uncorrected) (by default 0.95)
        * Effect size: Eta-squared
        * Multiple test correction: Benjamini-Hochberg FDR, Bonferroni, No correction
    * For two groups
        * Statistical test: t-test (equal variance), Welch's t-test, White's non-parametric t-test
        * Type: One-sided, Two-sided
        * CI method: "DP: Welch's inverted" (by default 0.95)
        * Multiple test correction: Benjamini-Hochberg FDR, Bonferroni, No correction, Sidak, Storey FDR
    * For two samples
        * Statistical test: Bootstrap, Chi-square test, Chi-square test (w/Yates'), Difference between proportions, Fisher's exact test, G-test, G-test (w/Yates'), G-test (w/Yates') + Fisher's, Hypergeometric, Permutation
        * Type: One-sided, Two-sided
        * CI method: "DP: Asymptotic", "DP: Asymptotic-CC", "DP: Newcomber-Wilson", "DR: Haldane adjustment", "RP: Asymptotic" (by default 0.95)
        * Multiple test correction: Benjamini-Hochberg FDR, Bonferroni, No correction, Sidak, Storey FDR

11. Reporting

    Please find attached the results of the pathway analysis. The Excel file contains the full statistics for all pathways; those with adjusted p-values (Benjamini–Hochberg) ≤ 0.05 are highlighted in yellow and are the ones illustrated in the figure.
    
    The analysis was performed using Welch’s t-test (two-sided) with Benjamini–Hochberg correction for multiple testing.