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##Diet design  Diets were designed by a nutritional biologist to deliver the average number of Calories consumed by an average American per day. The average American woman is 63 inches in height and weighs 166 pounds, and the average American man is 69 inches in height and weighs 195 pounds with an average age of 35 [link_text](http://www.cdc.gov/nchs/data/nhanes/databriefs/adultweight.pdf), which translates to a total daily Calorie intake range of 2,000 to 2,600 Calories per day respectively to maintain weight, as determined using the USDA MyPlate SuperTracker tool [link_text](https://www.supertracker.usda.gov/MyWeightManager.aspx. 2010). Therefore an intermediate daily Calorie intake of about 2,200 Calories was chosen as the target.   Meal plans were created using the NutriHand program (Nutrihand Inc., Soraya, CA). Diet nutrient composition was calculated by the NutriHand program from reference nutrient data for individual foods using the USDA National Nutrient Database for Standard Reference [link_text](http://www.nal.usda.gov/ba/bhnrc/ndl). [link_text](http://ndb.nal.usda.gov).  Three one-day meal plans were created to be representative of three typical dietary patterns that are consumed by Americans: 1) the Average American dietary pattern (AA), which includes meat and dairy and focuses on convenience foods, 2) the USDA recommended dietary pattern (USDA), which emphasizes fresh fruits and vegetables, lean meats, whole grains and whole grain products, and dairy, and 3) the Vegan dietary pattern (VEG), which excludes all animal products. The AA meal plan totaled 2268 Calories, which consisted of 35% fat, 53% carbohydrates of which 16.6 g was fiber, and 12% protein. The USDA meal plan totaled 2260 Calories, consisting of 25% fat, 49% carbohydrates of which 45 g was fiber, and 27% protein. The VEG meal plan totaled 2264 Calories and consisted of 31% fat, 54% carbohydrates of which 52 g was fiber, and 15% protein. ##Microbial community analysis  Microbial plate counts were performed by Covance Laboratories (Covance Inc., Madison, WI). Aerobic plate counts were performed according to SPCM:7 [link_text](http://www.fda.gov/Food/FoodScienceResearch/LaboratoryMethods/ucm063346.htm), anaerobic plate counts were performed according to APCM:5 [link_text](http://www.fda.gov/food/foodscienceresearch/laboratorymethods/ucm073598.htm)and [link_text](http://www.fda.gov/food/foodscienceresearch/laboratorymethods/ucm073598.htm) and  the yeast and mold counts were performed according to [link_text](http://www.fda.gov/food/foodscienceresearch/laboratorymethods/ucm073598.htm). Plate counts were reported as colony forming units (CFU) per gram for each meal composite. The CFU/g values were multiplied by the total number of grams in each meal to obtain the CFU per meal, and the values for meals for each day were added to obtain the CFU per day for each dietary pattern. The taxonomic composition of each meal microbiome was assessed via amplification and sequencing of 16S rDNA from the homogenized meals. DNA was extracted from homogenized food samples with the Power Food Microbial DNA Isolation Kit (MoBio Laboratories, Inc.) according to the manufacturer’s protocol. Bacterial DNA was amplified by a two-step PCR enrichment of the 16S rRNA gene (V4 region) using primers 515F and 806R, modified by addition of Illumina adaptor and barcodes sequences. All primer sequences are provided in Supplementary Table 2. Libraries were sequenced using an Illumina MiSeq system, generating 150bp paired-end amplicon reads. The amplicon data was multiplexed using dual barcode combinations for each sample. We used a custom script (Supplementary Datafile 1) to assign each pair of reads to their respective samples when parsing the raw data. This script allows for 1 base pair difference per barcode used (2 per sample) to accommodate for read errors from the machine. The paired reads were then aligned and a consensus was computed using FLASH (InsertReference) with maximum overlap of 120 and a minimum overlap of 70 (other parameters were left as default). The custom script automatically demultiplexes the data into fastq files, executes FLASH, and parses its results to reformat the sequences with appropriate naming conventions for QIIME v. 1.7.0 (InsertReference) in fasta format. The resulting consensus sequences were analyzed using the QIIME pipeline.  ##Statistical Analyses