Lumen is a handheld device and app that measures metabolism through a single breath, and provides you with data and personalized insights to optimize your life.Connect your Lumen account to your Garmin watch and see the real-time impact of your activities on your metabolism, and receive first of it's kind notification about the ideal times to measure your metabolism, based on your unique life and activities. The days of training without understanding the impact of the efforts on your body are over. With Lumen you no longer need to guess. It's science, technology and data at its finest.Don't yet have a Lumen? Use an exclusive $50 discount code available only to the Garmin community with code garmin50 at checkout. Visit www.lumen.me\/garmin for more information."; var appDescriptionMoreLabel = "More"; It's no secret that optimizing your metabolism has tremendous benefits to your performance and health. Up until now you just couldn't measure metabolism on a daily basis to use it to positively impact your well-being. Lumen is a handheld device and app that measures metabolism through a single breath, and provides you with data and personalized insights to optimize your life.Connect your Lumen account to your Garmin watch and see the real-time impact of your activities on your metabolism, and receive first of it's kind notification about the ideal times to measure your metabolism, based on your unique life and activities.
I'm glad to see that Lumen has produced an app for Garmin as it shows its listening to its users. Pulling Garmin data such as steps per day to the Lumen interface is very useful.However, I don't think the app itself is providing anything additional. The app is feeding us the information we already know - our lumen score after breathing with the use of the Lumen App in the morning. A suggestion might be that we can record to the Garmin watch App when we start eating and when we finish our last meal of the day to record our fasting time. This would be useful rather than trying to think or guess the previous day's last mealtime when we open the app the next morning
Lumen 1.0.3
I am facing issue of 500 Error on every API call, my Php version is 8.1.5, and update dependencies as below for Laravel project, can some one please help to solve this issue. Also attchaed error logs of laravel206993-lumen-2022-05-31-1.log.
Hello, I am using laravel+Lumen ("php": ">7.3","laravel/lumen-framework": ">7.0" ) for 2 months its working fine right now it is giving every API 500 error after the latest deployment using pipeline in Github.
Set the same package version on deploying to Azure App Service as the package version that worked locally; for local environment. (Thanks to Nico Hasse for pointing this to you) " fixed issue using below line set in composer.json, "php": "^7.3^8.0", "laravel/lumen-framework": "^8.3.1"", its resolved my issue"
I am still facing issue of 500 Error on every API call, my Php version is 8.1.5, and update dependencies as below for Laravel project, can some one please help to solve this issue. Also attchaed error logs of laravel206993-lumen-2022-05-31-1.log.
Alcohol-related liver disease is among the most prevalent liver diseases in the United States and Europe1,2. Excessive alcohol consumption causes a range of liver injuries, progressing from steatosis, to steatohepatitis, fibrosis, and ultimately cirrhosis. During alcohol consumption, alcohol is rapidly absorbed by diffusion, mainly in the upper gastrointestinal tract and then enters the liver via the portal vein. The effect of alcohol on the distal small intestine and colon largely comes from circulatory alcohol during the equilibration process between the lumen of the gastrointestinal tract and vascular space3. Alcohol consumption has been shown to alter the stool microbiota composition4,5 and function6, but how relatively small concentrations of ethanol in the large intestine cause the profound changes of the stool microbiota with which they have been associated is currently poorly understood.
Raw paired-end reads from the MiSeq platform were initially trimmed and filtered with Trimmomatic (v. 0.39)58. Adapter trimmed reads were host-filtered with Bowtie2 (v. 2.3.2)59. Paired-end reads were then merged using FLASH (v. 1.2.11)60 and co-assembled by the treatment group using metaSPAdes (v. 3.13.1) with kmer length 21, 33, 55, 77, 99, and 12761. The coverage depth across all contigs was calculated by aligning raw reads from each sample against the co-assembled contigs using Bowtie2 (v. 2.3.2). The resulting coverage depth was used to bin metagenomic contigs into draft genomes with MetaBAT (v. 2.12.1)62, MaxBin (v. 2.2.6)63, and CONCOCT (v. 1.0.0)64. MetaWRAP (v. 1.2.1)65 was used to consolidate multiple binning methods into a final optimal set of draft genomes. CheckM (v. 1.0.3) was used to estimate the contamination and completeness of each draft genome66. The draft bacterial genomes were annotated using PROKKA (v. 1.12)67. The abundance of each draft genome in each sample was generated using Salmon (v. 0.13.1)68. The taxonomic classification of draft genomes was performed through MetaWRAP (v. 1.2.1) with MegaBLAST (v. 2.2.28)69 and taxator-tk (v. 1.3.3)70. Phylogenetic analysis of the draft genomes was performed through phylophlan (v. 3.0.2) within the phylum level with the set of bins within that phylum by lowest common ancestor71, subsequent species level similarity between draft genomes and closest known genomes was compared by orthologous average nucleotide identity72.
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