Genes within a single genome can be classified as singletons, dispersed duplicates, proximal duplicates, tandem duplicates and segmental/WGD duplicates depending on their copy number and genomic distribution. The following procedure is used to assign gene classes: (i) All genes are initially classified as ‘singletons’ and assigned gene ranks according to their order of appearance along chromosomes; (ii) BLASTP results are evaluated and the genes with BLASTP hits to other genes are re-labeled as ‘dispersed duplicates’; (iii) In any BLASTP hit, the two genes are re-labeled as ‘proximal duplicates’ if they have a difference of gene rank<20 (configurable); (iv) In any BLASTP hit, the two genes are re-labeled as ‘tandem duplicates’ if they have a difference of gene rank = 1; (v) MCScanX is executed. The anchor genes in collinear blocks are re-labeled as ‘WGD/segmental’. So, if a gene appears in multiple BLASTP hits, it will be assigned a unique class according to the order of priority: WGD/segmental>tandem>proximal>dispersed.
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Childbirth Classes
Childbirth Classes
Childbirth Classes: Courses that provide education and training for expectant parents on topics related to pregnancy, labor, delivery, and postpartum care.
These classes aim to prepare individuals for the childbirth experience and help develop the knowledge and skills needed to care for a newborn.
Instruction may cover breathing techniques, pain management, breastfeeding, and postpartum recovery.
Childbirth Classes are an important component of prenatal education and can enhance the birthing experience for both parents.
These classes aim to prepare individuals for the childbirth experience and help develop the knowledge and skills needed to care for a newborn.
Instruction may cover breathing techniques, pain management, breastfeeding, and postpartum recovery.
Childbirth Classes are an important component of prenatal education and can enhance the birthing experience for both parents.
Most cited protocols related to «Childbirth Classes»
Childbirth Classes
Chromosomes
Gene Order
Genes
Genes, vif
Genome
Childbirth Classes
Cosopt
Gene Expression
Control sequences for the randomized experiment were constructed by assembling 100 sets of 73 miRNAs each generated by random shuffling of each D. melanogaster miRNA. Each of these sets of 73 randomized miRNAs was independently searched against all D. melanogaster and D. pseudoobscura 3' UTRs as in the reference experiment. Results and counts were then averaged over all 100 random sets, and were compared with the results of the actual miRNA scan. For the functional analysis, GO classes for known D. melanogaster genes were obtained from FlyBase and conserved hits for the real and random miRNAs for each class are counted. The Z-scores are generated from the actual miRNA counts, averaged random miRNA counts and their standard deviations.
Note that recent work by Stark et al. [85 ] and also by Rajewski and Socci [86 ] addresses similar issues to those described in this work.
Note that recent work by Stark et al. [85 ] and also by Rajewski and Socci [86 ] addresses similar issues to those described in this work.
Childbirth Classes
MicroRNAs
Radionuclide Imaging
Untranslated Regions
RNA was extracted from 1 × 106Smchd1+/+;EμMycTg/+ and Smchd1MD1/MD1;EμMycTg/+ lymphoma cells using Qiagen RNeasy Minikit as per the manufacturers instructions. Libraries were prepared using Illumina’s TruSeq RNA sample preparation kit as per the manufacturers instructions and submitted to the Australian Genome Research Facility for quality control, library preparation and sequencing on the Illumina HiSeq 2000 platform using 100 base, paired end or single-end reads. Base calling and quality scoring were performed using Real-Time Analysis (version 1.17.21.3) and FASTQ file generation and de-multiplexing using CASAVA (version 1.8.2). Reads from FASTQ files were aligned to the mouse genome (mm10) using Subread (version 1.10.5) (26 (link)) and summarized at the gene-level using the featureCounts procedure (27 (link)). Subsequent analysis was carried out using the ‘edgeR’ (28 (link)) and ‘limma’ (14 ) Bioconductor software. The counts were transformed into CPM to standardize for differences in library-size and filtering was carried out to retain genes with a baseline expression level of at least 0.5 CPM in three or more samples. Data were TMM normalized (3 (link)) and an MDS plot was generated (Figure 1B ) before linear models using various weighting strategies (described below) were fitted to summarize over replicate samples. Moderated t-statistics were used to assess differential expression between Smchd1MD1/MD1 and Smchd1+/+ (wild-type) samples, with genes ranked according to their FDR (22 ). These data are available under GEO series accession number GSE64099.
Smchd1 has been shown to have a role in the regulation of clustered protocadherins and imprinted genes in diverse tissues including whole embryo, adult brain, embryonic fibroblasts, placenta and malignant and normal B cells (30 (link)–32 (link)). We obtained gene sets for these two classes of genes to use as true positives (TPs) in our analysis. To identify protocadherins, we used regular expression matching to look for this term in the gene name field of the annotation of the filtered data set, which returned eight genes (out of a total of 71 in the mouse genome). A comprehensive set of imprinted mouse genes was downloaded fromhttp://www.mousebook.org/imprinting-gene-list and matched to the expressed genes in this data set using Gene Symbols. In total, 46 genes out of the 150 in the original list were matched.
Smchd1 has been shown to have a role in the regulation of clustered protocadherins and imprinted genes in diverse tissues including whole embryo, adult brain, embryonic fibroblasts, placenta and malignant and normal B cells (30 (link)–32 (link)). We obtained gene sets for these two classes of genes to use as true positives (TPs) in our analysis. To identify protocadherins, we used regular expression matching to look for this term in the gene name field of the annotation of the filtered data set, which returned eight genes (out of a total of 71 in the mouse genome). A comprehensive set of imprinted mouse genes was downloaded from
Adult
B-Lymphocytes
Brain
Cells
Childbirth Classes
Clustered Protocadherins
DNA Library
DNA Replication
Embryo
Fibroblasts
Gene Annotation
Gene Expression
Genes
Genes, vif
Genetic Diversity
Genome
Lymphoma
Mus
Placenta
Protocadherins
Tissues
ToppCluster accepts input in one of two ways: (i) as separate lists of genes which can be successively added and named, or (ii), using the ‘alternative entry’ method, as a two-column list with genes in the first column and the name of the gene list in the second column. Accepted input is limited to human genes at present. One or any of the 17 annotation sources can be used for feature enrichment analyses. Each feature analysis can be adjusted based on the P-value cutoff, the multiple testing correction method or the minimum and maximum number of genes present for each annotation type. For example, limiting enrichments to ontologies that have fewer associated genes can allow for a greater focus on specific classes of gene feature or function. Multiple choices are available for the formatting and delivery of results. The user can opt for results to be obtained in tabular format as comma-separated values, tab-separated values or HTML table format. It is also possible to obtain the results in various visualization formats—a standard heatmap in a PDF file generated using R (18 ) (http://www.R-project.org ), TreeView (13 (link),14 (link)) clustered data tree (CDT) heatmap files, GenePattern (19 (link)) GCT format, Cytoscape (15 (link)) XGMML importable network formats, Gephi (16 ) importable GEXF network formats or as pre-laid out network images using the PNG option.
6H,8H-3,4-dihydropyrimido(4,5-c)(1,2)oxazin-7-one
Childbirth Classes
Genes
Genes, vif
Obstetric Delivery
Trees
Most recents protocols related to «Childbirth Classes»
The quality-controlled, decontaminated forward and reverse paired sequences from the 127 leukemia and lymphoma samples were mapped to the pediatric-oncology-ARG-database created using bowtie2 (Langmead and Salzberg, 2012 (link)). Counts of sequence reads that mapped to each ARG in the database were obtained for each sample using samtools “sort”, “index” and “idxstat”. Mapped read counts were corrected by the number of sequence reads in each sample. While reads were mapped to all ARG sequences identified, only those ≥60% sequence identity were used in downstream analyses. Antibiotic classes were assigned to each ARGs using the CARD database designation, with two exceptions, 1) genes that occurred in an antibiotic class connected with β-lactam drugs were coded as β-lactam antibiotic class genes (i.e., carbapenem, penam, etc.), 2) genes that occurred in multiple antibiotic classes (i.e., penam, fluoroquinolone, glycopeptide), were coded as “multidrug” antibiotic class genes. Counts within samples assigned to the same gene were summed for downstream analysis. Only genes present in ≥5% of samples were used. Genes in four antibiotic classes were selected for closer analysis: β-lactam antibiotic class, glycopeptide antibiotic class, peptide antibiotic class, and multidrug antibiotic class. These classes were specifically selected as the β-lactam antibiotic class and multidrug antibiotic class potentially contains genes for resistance to β-lactam antibiotics, and the glycopeptide antibiotic class, peptide antibiotic class (a parent class to glycopeptide antibiotics), and multidrug antibiotic class potentially contains genes for resistance to vancomycin. All analyses were carried out on gene sequence data, no allele or SNP information was used.
Alleles
Antibiotics
Antibiotics, Antitubercular
Carbapenems
Childbirth Classes
Fluoroquinolones
Genes
Glycopeptides
Lactams
Leukemia
Lymphoma
Monobactams
Neoplasms
Parent
Peptides
Pharmaceutical Preparations
Vancomycin
A total of 427,495 bacterial genomes consisting of 47,582,748 sequences (NCBI GenBank database, 2019-10-22) [32 ] were analyzed using fARGene (v0.1, default parameters). We used fARGene in this study since it has been shown to have a high performance and its predictions have been experimentally verified on several occasions [12 , 13 (link), 15 , 16 (link), 33 , 34 (link)]. fARGene was executed using 17 hidden Markov model gene profiles for ARGs conferring resistance to five major classes of antibiotics: for -lactams, we defined gene classes A, B1/B2, B3, and D [13 (link), 33 ]; for aminoglycosides, gene classes , aac(3), , , , and aph(6); for macrolides, gene classes erm and mph [12 ]; for quinolones, gene class qnr [16 (link)]; and for tetracyclines, gene classes efflux pumps, inactivating enzymes (monooxygenases), and ribosomal protection genes (RPGs) [15 ] (downloaded from https://github.com/fannyhb/fargene ). All matches satisfying the previously reported model-specific significance thresholds for full-length genes were considered to be putative ARGs and stored for further analysis [12 , 13 (link), 16 (link), 33 , 35 (link)].
Aminoglycosides
Antibiotic Resistance, Microbial
Childbirth Classes
Enzymes
Genes
Genetic Profile
Genome, Bacterial
Lactams
Macrolides
Mixed Function Oxygenases
Quinolones
Ribosomes
Tetracyclines
Confounders were selected a priori based on previous evidence2 (link),34 (link)–36 (link). Information on maternal height (cm), maternal pre-pregnancy BMI (kg/m2), and maternal smoking during pregnancy (yes/no) was taken from questionnaires at 12 and 30 weeks of gestation. Information on maternal age at delivery (continuous in years); gestational diabetes, ICD-10: O24 (yes/no); gestational hypertension, ICD-10: O13 (yes/no); and pre-eclampsia, ICD-10: O14 (yes/no) was derived from Danish Medical Birth Registry. Maternal education was obtained from the Danish Population’s Education Register and operationalised as highest ongoing or completed education at child’s birth according to international classification standards37 : low [ISCED-2011: 0–2], medium [ISCED-2011: 3–4], high [ISCED-2011: 5–8]). Household income was based on disposable household income extracted from the Income Statistics Register. The variable was divided by an equivalence factor according to household size (available at http://www.oecd.org ) and recoded into internal quantiles per year. Birth weight was perceived as an intermediate variable on the causal pathway, hence not included in the models38 (link).
Birth Weight
Child
Childbirth Classes
Gestational Diabetes
Households
Mothers
Obstetric Delivery
Pre-Eclampsia
Pregnancy
Transient Hypertension, Pregnancy
‘Stakeholders’ in this research included many health professionals, charitable organizations and mothers. There were 268 participants, including consumers (mothers), midwives, lactation consultants, alternative health providers, birth education providers, doctors/obstetricians and a few politicians. Of the 18 workshops, 8 were in larger urban centres, and 10 were in smaller towns situated more rurally. All participants had either a personal or professional (or both) interest in maternal health, with the majority involved in supporting mothers/parents currently.
Breast Feeding
Childbirth Classes
Health Personnel
Midwife
Mothers
Obstetrician
Parent
Physicians
Workshops
The phylogenetic tree was drawn using the neighbor-joining (NJ) method [52 (link)]. The evolutionary distances in the tree were assumed using the Poisson correction method. The phylogenetic analyses were performed in MEGA7 [53 (link)]. The functional annotation and gene ontology (GO) classes of StFH protein were processed using the PANNZER (Protein ANNotation with Z-scoRE) server (http://ekhidna2.biocenter.helsinki.fi/sanspanz/ , accessed on 1 May 2021) [54 (link)].
Biological Evolution
Childbirth Classes
Protein Annotation
Proteins
Trees
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More about "Childbirth Classes"
Childbirth Classes, also known as Prenatal Classes or Antenatal Classes, are educational courses designed to prepare expectant parents for the birthing experience and the care of a newborn.
These classes cover a wide range of topics, including pregnancy, labor, delivery, and postpartum recovery.
During Childbirth Classes, expectant parents can learn about various breathing techniques, pain management strategies, and breastfeeding methods.
The classes also provide information on postpartum care, such as how to care for a newborn, including the use of tools like the TRIzol reagent for RNA extraction and the MX3005p sequence detection system for gene expression analysis.
The classes may also incorporate the use of SPSS v13.0 for statistical analysis, GraphPad Prism 7 for data visualization, and GeNORM version 3.4 for gene expression normalization.
Additionally, the ClueGO app can be utilized to analyze and visualize the biological pathways involved in the childbirth process.
Attending Childbirth Classes can enhance the birthing experience for both parents, as they develop the knowledge and skills needed to navigate the challenges of pregnancy, labor, and postpartum care.
These classes are an important component of prenatal education and can help parents feel more prepared and confident during this significant life event.
For those interested in conducting research related to Childbirth Classes, the PubCompare.ai platform can be a valuable tool.
This AI-driven platform helps researchers locate the most reliable protocols from literature, pre-prints, and patents, while also facilitating the identification of the best protocols and products for their needs.
By utilizing PubCompare.ai, researchers can enhance the reproducibility and accuracy of their studies, taking the guesswork out of their research process.
These classes cover a wide range of topics, including pregnancy, labor, delivery, and postpartum recovery.
During Childbirth Classes, expectant parents can learn about various breathing techniques, pain management strategies, and breastfeeding methods.
The classes also provide information on postpartum care, such as how to care for a newborn, including the use of tools like the TRIzol reagent for RNA extraction and the MX3005p sequence detection system for gene expression analysis.
The classes may also incorporate the use of SPSS v13.0 for statistical analysis, GraphPad Prism 7 for data visualization, and GeNORM version 3.4 for gene expression normalization.
Additionally, the ClueGO app can be utilized to analyze and visualize the biological pathways involved in the childbirth process.
Attending Childbirth Classes can enhance the birthing experience for both parents, as they develop the knowledge and skills needed to navigate the challenges of pregnancy, labor, and postpartum care.
These classes are an important component of prenatal education and can help parents feel more prepared and confident during this significant life event.
For those interested in conducting research related to Childbirth Classes, the PubCompare.ai platform can be a valuable tool.
This AI-driven platform helps researchers locate the most reliable protocols from literature, pre-prints, and patents, while also facilitating the identification of the best protocols and products for their needs.
By utilizing PubCompare.ai, researchers can enhance the reproducibility and accuracy of their studies, taking the guesswork out of their research process.