While your MagSafe Battery Pack is charging, the status light might flash. If it flashes green, your battery pack is fully charged. If it flashes amber, your battery pack might need more time to charge.
In order to get 7.5W charging on the go, update your MagSafe Battery Pack to the latest firmware. The firmware update begins automatically after you attach your battery pack to your iPhone. The firmware update can take about one week.
To update the firmware using a Mac or an iPad model with a USB-C port, detach the battery pack from your iPhone, plug one end of a Lightning to USB cable into the Lightning connector on your battery pack, and the other end into your Mac or iPad. The firmware update will take about 5 minutes.
What is a coil pack? A coil pack is like a large capacitor that generates spark energy for each individual spark plug during each engine revolution. Stock or OEM Coils are weak and don't deliver an efficient amount of energy, they work at a minimum requirement from the factory and perform poorly. The RIPP High Performance Coils are totally redesigned and re-engineered to add much needed power and throttle response to your 2TR-FE.
Every 2023 Silverado comes with standard Chevy Safety Assist, a package of six advanced safety and driver assistance features designed to help give you added peace of mind every time you get in your truck.
MetCirc, designed for the annotation of MS/MS features in untargeted metabolomics data, visualizes the spectral similarity matrix (e.g. the normalized dot product) between MS/MS spectra in a Circos-like interactive shiny application. Within the shiny application, similarity scores can be thresholded, MS/MS spectra can be interactively explored and annotated based on expert knowledge given the similarity score and displayed spectral features. MetCirc relies on the MSnbase framework to store MS/MS spectral data and to calculate similarities between spectra. Similarly, CluMSID employs spectral similarity matching to guide annotation of MS/MS spectra, incorporates functionality to calculate a correlation networks and for hierarchical and density-based clustering. compMS2Miner is another R package for MS/MS feature annotation and offers functionality for noise filtering, MS/MS substructure annotation, calculation of correlation- and spectral similarity-based networks and interactive visualization.
Several R packages implement the functionality to generate metabolic networks. These networks can afterwards be analysed by their topological properties, be used to identify motifs that differ between experimental conditions or queried to find associations between metabolic features. MetaMapR generates metabolic networks by integrating enzymatic transformation, structural similarity between metabolites, mass spectral similarity and empirical correlation information. Hereby, MetaMapR queries biochemical reactions in KEGG and molecular fingerprints for structural similarities in PubChem. Furthermore, MetaMapR aims at incorporating metabolites with unknown biochemistry and unknown structures, and integrates other data sources (genomic, proteomic, clinical data). The package Metabox offers a pipeline for metabolomics data analysis, including functionality for data-driven network construction using correlation, estimation of chemical structure similarity networks using substructure fingerprints. Its statistical analysis highlights metabolites that are altered based on the experimental design group, which can be further interrogated by network and pathway analysis tools. Furthermore, the package MetabNet includes functionality to perform targeted metabolome-wide association studies (MWAS) and to guide the association of unknowns to a specific metabolic pathway, followed by mapping a target metabolite to the metabolic network structure.
Several R packages enable pathway analysis that uses quantitative data of metabolites and maps these to biological pathways. The Bioconductor package pwOmics analyses proteomics, transcriptomics and other -omics data in combination to highlight molecular mechanisms for single-point and time-series experiments. In downstream analyses, pwOmics allows for pathway, transcription factor and target gene identification.
Many R packages guide the discovery of biomarkers for specific phenotypes. Among these is lilikoi, that maps features to pathways by using standardized HMDB IDs, transforms metabolomic profiles to pathway-based profiles using pathway deregulation scores, a measure how much a sample deviates from a normal level, followed by feature selection, classification and prediction. INDEED (INtegrated DiffErential Expression and Differential network analysis) aims to detect biomarkers by performing a differential expression analysis, which is combined with a differential network analysis based on partial correlation and followed by a network topology analysis. Subsequently, activity scores are calculated based on differences detected in the differential expression and the topology of the differential network that will guide the selection of biomarkers. Another R package for biomarker and feature selection is MoDentify which finds regulated modules, groups of correlating molecules that can span from few metabolites to entire pathways, to a given phenotype. These groups are possibly functionally coordinated, coregulated or driven by a similar or same biological process. Score maximization using a multivariable linear regression model with the candidate module as dependent and the phenotype and optional covariates as independent variables identifies the modules. Furthermore, MoDentify implements Gaussian graphical models, where depending on the resolution nodes reflect metabolites or entire pathways.
R offers packages to analyze metabolic systems and to estimate biochemical reaction rates in metabolic networks using flux balance analysis, e.g. BiGGR, abcdeFBA, sybil, and fbar. For example, BiGGR interfaces with the BiGG databases that contains reconstructions of metabolic networks. After importing pathways from the database, flux balance and downstream routines can be performed, e.g. linear optimization routines or likelihood-based ensembles of calculated flux distributions fitting experimental data.
The package MetaboLouise simulates longitudinal metabolomics data. The simulation builds on a mathematical representation that is parameterized according to underlying biological networks, i.e. by defining metabolites and relation between them by initializing enzyme rates. Optionally, the package implements functionality to vary the rates depending on the network state, to add external fluxes and to analyze results based on different parameters.
A plethora of pathway resources exist, aptly aggregated by Pathguide.org. A number of these resources can be accessed by R packages, which were partly reviewed in : rBiopaxParser, graphite, NCIgraph, pathview, KEGGgraph, SBMLR, rsbml, gaggle, and PSICQUIC. Of these, graphite stores pathway information for proteins and metabolites of currently fourteen species (version 1.28.0). Available databases are KEGG, Biocarta, Reactome, NCI/Nature Pathway Interaction Database, HumanCyc, Panther, SMPDB and PharmGKB. graphite offers in addition topological and statistical pathway analysis tools for metabolomics data by interfaces with the Bioconductor packages SPIA and clipper and supports functionality to build own pathways. Furthermore, RPathVisio enables creating and editing biological pathways. RPathVisio enables to visualise data on pathways, to perform statistics on pathway data, and provides an interface to WikiPathways. KEGGREST allows to access the KEGG REST API via a client interface. The package provides utility to search keywords, convert identifiers and link across databases. The package also allows to return amino acid sequences as AAStringSet or nucleotide sequences as DNAStringSet objects (from the Biostrings  package).Another package, paxtoolsr, provides literature-curated pathway using the Biological Pathway Exchange (BioPAX) format by providing an interface to the Pathway Commons database (including data from the NCI Pathway Interaction Database (PID), PantherDB, HumanCyc, Reactome, PhosphoSitePlus and HPRD). rWikiPathways is an interface between R and WikiPathways.org. Pathways can be queried, interrogated and downloaded to the R session. Furthermore, rWikiPathways associates metabolite information to pathways when providing the system code of a chemical database (e.g. from HMDB, ChEBI, or ChemSpider).RaMP provides a relational database of Metabolomics Pathways, integrates pathway, gene, and metabolite annotations from KEGG, HMDB, Reactome, and WikiPathways. The database is downloadable as a standalone MySQL dump, for integration with other software, and is also accessible through an R package, and includes a shiny  web interface that supports four basic queries: 1) retrieve analytes (genes of metabolites) given a pathway name; 2) retrieve a pathways for one or more analytes; 3) retrieve analytes involved in the same reaction; 4) retrieve ontologies (cellular location, biofluid locations, etc.) from metabolites. The web interface also supports pathway overrepresentation analysis on genes, metabolites, or genes and metabolites combined (query 3) and includes clustering of significantly enriched pathways according to the percent of overlapping analytes between pathways. Further, the web interface provides network visualization of gene-metabolites relationships (query 4).
The Python 2 package contains the Python development environment. It is useful for object-oriented programming, writing scripts, prototyping large programs or developing entire applications. This version is for backward compatibility with other dependent packages.
--with-ensurepip=yes : This switch enables building pip and setuptools packaging programs. setuptools is needed for building some Python modules. This switch is not recommended because this version of Python is no longer maintained. 2b1af7f3a8