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Family

Family: A fundamental social group consisting of parents and their children.
Families provide individuals with identity, social interaction, status, and a way of life.
This term can also be used metaphorically to denote a group of people with common interests or features.
Optimixe your family research protocols for reproducibility and accuracy using PubCompare.ai, an AI-driven platform that allows you to effortlessly locate the best protocols and products from literature, pre-prints, and patents by comparing them side-by-side.
Streamline your family research workflow and achieve reliable, data-driven results.

Most cited protocols related to «Family»

1
Simulating 16S Sequence Alignments
The simulated protein alignments and the genuine COG alignments were described previously [2] (link). The 16S alignment with 237,882 distinct sequences was taken from GreenGenes [33] (link) (http://greengenes.lbl.gov). The 16S alignment with 15,011 distinct “families” is a non-redundant subset of these sequences ( identical). 16S alignments with 500 sequences are also non-redundant random subsets ( identical). Other large 16S alignments are from [11] (link).
For the 16S-like simulations with 78,132 distinct sequences, we used a maximum-likelihood tree inferred from a non-redundant aligned subset of the full set of 16S sequences ( % identity) by an earlier version of FastTree (1.9) with the Jukes-Cantor model (no CAT). To ensure that the simulated trees were resolvable, which facilitates comparison of methods (but inflates the accuracy of all methods), branch lengths of less than 0.001 were replaced with values of 0.001, which corresponds to roughly one substitution across the internal branch, as the 16S alignment has 1,287 positions. Evolutionary rates for each site were randomly selected from 16 rate categories according to a gamma distribution with a coefficient of variation of 0.7. Given the tree and the rates, sequences were simulated with Rose [34] (link) under the HKY model and no transition bias. To allow Rose to handle branch lengths of less than 1%, we set “MeanSubstitution = 0.00134” and multiplied the branch lengths by 1,000.
Price M.N., Dehal P.S, & Arkin A.P. (2010). FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE, 5(3), e9490.
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Publication 2010
Biological Evolution Cantor Gamma Rays Proteins Sequence Alignment Trees
2
Simulated Evolutionary Alignments Generator
We obtained sequences of members of Clusters of Orthologous Groups (COG) gene families (Tatusov et al. 2001) and members of Pfam PF00005 (Finn et al. 2006 ) from the fall 2007 release of the MicrobesOnline database (http://www.microbesonline.org/). We aligned the sequences to the family's profile, using reverse position-specific Blast for the COG alignment (Schaffer et al. 2001 (link)) and hmmalign for the PF00005 alignment (http://hmmer.janelia.org/). As the profiles only include positions that are present in many members of the family, these alignments do not contain all positions from the original sequences. The 16S rRNA alignment is from greengenes and is trimmed with the greengenes mask (DeSantis et al. 2006 (link); http://greengenes.lbl.gov).
To simulate alignments with realistic phylogenies and realistic gaps, we used the COG alignments. In each simulation, we selected the desired number of sequences from a COG alignment, we removed positions that were over 25% gaps, we estimated a topology and branch lengths with PhyML (Guindon and Gascuel 2003 (link)), we estimated evolutionary rates across sites with PHYLIP's proml (http://evolution.genetics.washington.edu/phylip.htm), we simulated sequences with Rose (Stoye et al. 1998 (link)), and we reintroduced the gaps from the original alignment. For simulations of 5,000 sequences, we used FastTree instead of PhyML and we assigned evolutionary rates at random. For N = 10, we simulated 3,100 alignments (10 independent runs per family); for N = 50, we simulated 3,099 alignments; for N = 250, we simulated 308 alignments; for N = 1,250, we simulated only 92 alignments because some PhyML jobs did not complete, and for N = 5,000, we simulated 7 alignments, as only seven families contained enough nonredundant sequences. See supplementary note 2 (Supplementary Material online) for technical details.
Price M.N., Dehal P.S, & Arkin A.P. (2009). FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix. Molecular Biology and Evolution, 26(7), 1641-1650.
Publication 2009
Biological Evolution Family Member Genes RNA, Ribosomal, 16S
3
Evaluation of Protein Sequence Alignment Algorithms
An up-to-date version of HOM39 (26 (link)) was extracted from the July 2004 release of HOMSTRAD (17 (link)) () based on two criteria used in (26 (link)). HOMSTRAD is a curated database of structural alignments of homologous proteins whose coordinates are available. Each entry of HOMSTRAD, a structural alignment, is extended by introducing homologous sequences with CLUSTAL W. Only the alignments based on structural superposition were used in this study. Out of 1033 entries of the HOMSTRAD, 55 entries (19.7% pairwise identity, 7.69 sequences and 159 aligned residues on average) were extracted for the evaluation of alignment accuracy. This dataset is referred to as ‘HOM+0’ in this paper.
We made the ‘HOM+20,’ ‘HOM+50’ and ‘HOM+100’ datasets by extending each entry of HOM+0 in a way similar to PREFAB (11 (link)). Amino acid sequences similar (E-value < 10−10) to each member of an entry were collected from the SwissProt database (rel. 43) using BLAST (27 (link)) and added to the entry. If more than n (=20, 50 or 100) sequences were collected, we randomly selected n sequences to be added. Only amino acid positions of the sequences that were reported to show significant similarity by BLAST were added. The accuracy of an alignment was measured by the fraction of columns aligned identically to the reference alignment. When we evaluated the accuracy, the n sequences added to the HOM+n were removed.
SABmark (18 (link)) version 1.65 was downloaded from . SABmark is designed to assess the performance of protein sequence alignment algorithms and consists of two parts, the Twilight Zone set (with ‘very low’ similarity; referred to as the TWI set in this paper) and the Superfamily set (with ‘low’ similarity; referred to as SUP). The TWI set was mainly used in the present study to examine the abilities of algorithms for aligning distantly related sequences. The TWI set was also extended in the same manner as described above. These are hereafter referred to as ‘TWI+n’ (n = 0, 20 and 50). The accuracy value fD, the ratio of the number of correctly aligned residues divided by the length of reference alignment, was calculated using the score.pl script provided by the authors of SABmark. The accuracies were separately considered for two subsets. One subset (denoted as TWIf+n) includes only the sequence pairs classified to the same family by Van Walle et al. (18 (link)), and the other subset (denoted as TWIs+n) consists of the sequence pairs classified not to the same family but to the same superfamily.
The PREFAB (11 (link)) version 3 dataset was downloaded from . The accuracy was measured using Q, the number of correctly aligned residue pairs divided by the number of residue pairs in the reference alignment (11 (link)).
Katoh K., Kuma K.I., Toh H, & Miyata T. (2005). MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Research, 33(2), 511-518.
Publication 2005
Amino Acids Amino Acid Sequence Homologous Sequences Proteins Sequence Alignment
4
Negative Binomial Generalized Linear Models for Transcriptome Analysis
GLMs are an extension of classical linear models to non-normally distributed response data (42 ,43 ). GLMs specify probability distributions according to their mean–variance relationship, for example the quadratic mean–variance relationship specified above for read counts. Assuming that an estimate is available for ϕg, so the variance can be evaluated for any value of μgi, GLM theory can be used to fit a log-linear model

for each gene (32 (link),41 ). Here xi is a vector of covariates that specifies the treatment conditions applied to RNA sample i, and βg is a vector of regression coefficients by which the covariate effects are mediated for gene g. The quadratic variance function specifies the negative binomial GLM distributional family. The use of the negative binomial distribution is equivalent to treating the πgi as gamma distributed.
McCarthy D.J., Chen Y, & Smyth G.K. (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research, 40(10), 4288-4297.
Publication 2012
Cloning Vectors Gamma Rays Genes Genes, vif RNA, immune RNA I
5
Microbiome Data Analysis with Hypothesis Testing
Although useful for exploring and summarizing microbiome data, many of the graphics and ordination methods discussed here are not formal tests of any particular hypothesis. The most common framework for testing in microbiome studies is the comparisons of samples from different categories (e.g. healthy and obese; control and treated; different environments). Standard test statistics include the t-test, the paired permutation t-test, and ANOVA type tests based on F or pseudo-F statistics. However, microbiome data have two particularities. First, the raw abundance counts are never normally distributed, so the preferred methods are nonparametric. Second, there is contiguous information available about the relationships between OTUs, as well as for variables measured on the samples, so testing is sometimes more elaborate than a two-sample test. The hypergeometric test, also known as Fisher's exact test, is used in cases when we have a test statistic for each of the different OTUs. The goal is to confirm that a certain property of these significant OTUs is overrepresented compared to the general population of OTUs, often called “the universe”. For instance in Holmes et al [65] (link) and Nelson et al [68] several phyla were shown to be significantly over-abundant in IBS rats as compared to healthy controls using this hypergeometric test.
An organizing principle in many nonparametric testing protocols is that the repetition of an analysis multiple times enables the user to control for multiple testing, or to evaluate the quality of estimators or the optimal values of tuning parameters. Modern confirmatory analyses currently depend on these repeated analyses under various data perturbation schemes, of which resampling, permutations, and Monte Carlo simulations are the most common. For instance the bootstrap uses many thousands of analyses of resampled data to address problems such as statistical stability or bias estimation [69] , and can even provide confidence regions [69] for nonstandard parameters, such as phylogenetic trees [70] . Repeating analyses on permuted data can allow for control of the probability of encountering 1 or more false positives (falsely rejected nulls) among your group of simultaneous hypotheses, also called the Family Wise Error Rate (FWER). For instance, Westfall and Young's permutation-based minP procedure controls the FWER [71] and is implemented within the multtest package [72] . The phyloseq package interfaces with minP in multtest through a wrapper function, called mt. In the following example code we use the mt wrapper to control the FWER while simultaneously testing whether each OTU correlates with the “Enterotypes” classification of the samples. Note that we first remove samples that were not assigned an enterotype by the original authors (Table 1).
McMurdie P.J, & Holmes S. (2013). phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE, 8(4), e61217.
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Publication 2013
Microbiome neuro-oncological ventral antigen 2, human Obesity Rattus norvegicus

Most recents protocols related to «Family»

1
MXene Antennas: Flexible, Transparent, and High-Performing
Not available on PMC !

Example 3

The following features are relevant to the disclosed invention(s):

This work demonstrated the fabrication of dipole antennas made from different MXene compositions of Ti3C2, Ti2C, Mo2TiC2 as exemplars of the general MXene family.

The films exemplified here were binder free and fabricated simply from the MXene colloidal solutions in water (MXene ink). Since MXenes can be made in colloidal aqueous and non-aqueous (e.g., organic solvent) solutions, they can be used as ink to print, spray paint, etc. any shape, design and thickness to fabricate very thin, flexible and transparent antennas in one simple step.

Any kind of antenna fabrication method can be employed, for example printing, spraying, coating, painting, rolling MXene clay into films, cutting complicated shapes for different antenna designs.

MXene return loss and peak gain outperformed any synthetic materials. Although MXenes are theoretically not as conductive as copper, the present work showed that MXene outperforms copper, the mostly used and very well-known antenna material. The as synthesized binder free titanium carbide (Ti3C2) MXene film dipole antenna showed a return loss of about 50 dB. The MXene antenna's radiation pattern measurements showed a peak gain similar to the copper dipole antenna. Such a high antenna performance has never been reported for any nanomaterials.

With the variety of MXene composition, it was and will be possible to tune the antenna for different applications.

By controlling the flake size, the bandwidth of the antenna can further be controlled.

Fabricating MXene-polymer composites can protect MXene from oxidation and can further improve its flexibility. In order to make MXenes films mechanically more robust, 2D MXene flakes can be embedded in polymer matrices. Moreover, using a polymer as a matrix can further improve the oxidation resistance of MXenes.

US11862847B2. Antennas comprising MX-ENE films and composites (2024-01-02). Drexel University [US]. Inventors: Yury Gogotsi [US], Babak Anasori [US].
Full text: Click here
Patent 2024
Clay Copper Electric Conductivity One-Step dentin bonding system Polymers Radiation Solvents titanium carbide
2
Simultaneous CD40 and CEACAM5 Targeting
Not available on PMC !

Example 4

Aim

The aim of the study was to evaluate the ability of selected CD40 and CEACAM5 targeting RUBY™ bsAbs to bind both their targets simultaneously as well as their potential cross-reactivity with additional members of the CEA protein family was evaluated by ELISA.

Materials and Methods

96-well plates were coated with 0.5 μg/mL antigen, hCEACAM-1 (2244-CM-050, R&D Systems), hCEACAM-5 (4128-CM-050, R&D Systems), hCEACAM-6 (3934-CM-050, R&D Systems) or CEACAM-8 (9639-CM-050, R&D Systems) in PBS over night at 4° C. After washing in PBS/0.05% Tween 20 (PBST), the plates were blocked with PBST, 2% BSA for at least 30 minutes at room temperature before a second round of washing. RUBY bsAbs, diluted in PBST, 0.5% BSA, were then added and allowed to bind for at least 1 hour at room temperature. After washing, plates were incubated with either 50 μl detection antibody (0.5 μg/ml HRP conjugated goat anti human-kappa light chain, #STAR127P, AbD Serotec) for analysis of binding to CEACAM protein family proteins or 0.5 μg/ml biotinylated hCD40-muIg (504-030, Ancell) followed by HRP conjugated streptavidin (21126, Pierce) for confirmation of dual antigen binding. Finally, a final round of washing was performed and bound complexes detected using SuperSignal Pico Luminescent as substrate and luminescence signals were measured using Fluostar Optima.

Results and Conclusions

All evaluated RUBY™ bsAbs was indeed able to bind to both CD40 and human CEACAM5 simultaneously (FIG. 2), although with varying potency. In general, bsAbs carrying 1132 as CD40 binding antibody (Multi46-Multi49) displayed lower potency in the dual target ELISA, as compared to bsAbs carrying G12_mut. Also, Multi38 displayed reduced dual target binding compared to other G12_mut based bsAbs, likely due to lower CEACAM5 binding of Fab6 than other evaluated CEACAM5 binding antibodies.

As can be seen in FIG. 3, a majority of the evaluated CD40 and CEACAM5 targeting RUBY™ bsAbs did not cross react with any of the other CEA family members evaluated. However, a limited number of the assayed bsAb did show significant cross-reactivity with CEACAM1 (Multi38, Multi39, Multi45 and Multi 49) or CEACAM6 (Multi40).

All in all, it can be concluded that all evaluated RUBY™ bsAbs have the ability to bind CD40 and CEACAM5 simultaneously and a majority of the set was specific for CEACAM5, with no or little detectable binding to other evaluated members of the CEA protein family.

US11873348B2. Peptides (2024-01-16). ALLIGATOR BIOSCIENCE AB [SE]. Inventors: Peter Ellmark [SE], Karin Hagerbrand [SE], Laura Varas [SE], Mattias Levin [SE], Anna Säll [SE], Laura von Schantz [SE].
Full text: Click here
Patent 2024
Antibodies Antigens biliary glycoprotein I Binding Proteins Carcinoembryonic Antigen carcinoembryonic antigen-related cell adhesion molecule 6, human Cross Reactions Enzyme-Linked Immunosorbent Assay Family Member Gene Products, Protein Goat Homo sapiens Immunoglobulin kappa-Chains Immunoglobulins Luminescence Streptavidin Tween 20 Vision
3
VLC-PUFA and Elovanoids Regulate Bid in RPE
Not available on PMC !

Example 4

FIG. 6—(A) VLC-PUFA and elovanoids ELV1 and ELV2 mediated effect on Bid upregulation in ARPE-19 cells under stress. This figure displays the downregulation of the proapoptotic protein of the Bcl2 family Bid by western blot analysis by VLC-PUFA and elovanoids in RPE cells in culture under oxidative-stress. Results indicate that upregulated Bid protein by OS, as evident from the figure, was inhibited by both elovanoids and VLC-PUFA. It is interesting to see that the sodium salts of the elovaniod precursors are more effective than the methyl ester forms. (B) VLC-PUFA and ELV1 and ELV2 compounds mediated upregulation of Bid in ARPE-19 cells under stress. This Figure shows the quantification of Bid downregulation.

US11858888B2. Compounds, compositions, and methods for the treatment of inflammatory, degenerative, and neurodegenerative diseases (2024-01-02). Board of Supervisors of Louisiana State University and Agricultural and Mechanical College [US], University of Southern California [US]. Inventors: Nicolas G. Bazan [US], Nicos A. Petasis [US].
Full text: Click here
Patent 2024
Apoptosis Inducing Proteins bcl-2 Gene Bid Protein Cells Down-Regulation Esters Inflammation Neurodegenerative Disorders Oxidative Stress Polyunsaturated Fatty Acids Salts Sodium Therapeutics Up-Regulation (Physiology) Western Blot
4
Atypical Hemolytic Uremic Syndrome: Complement Regulation and Treatment
Not available on PMC !

Example 19

Atypical hemolytic uremic syndrome (aHUS) is characterized by hemolytic anemia, thrombocytopenia, and renal failure caused by platelet thrombi in the microcirculation of the kidney and other organs. aHUS is associated with defective complement regulation and can be either sporadic or familial. aHUS is associated with mutations in genes coding for complement activation, including complement factor H, membrane cofactor B and factor I, and well as complement factor H-related 1 (CFHR1) and complement factor H-related 3 (CFHR3). Zipfel, P. F., et al., PloS Genetics 3(3):e41 (2007).

The effect of the exemplary fusion protein construct of this disclosure to treat aHUS is determined by obtaining and lysing red blood cells from aHUS patients treated with the exemplary fusion protein construct. It is observed that treatment with the exemplary fusion protein construct is effective in blocking lysis of red blood cells in the patients suffering from aHUS, compared to treatment with a sham control.

US11879008B2. Methods of treating complement mediated diseases with fusion protein constructs comprising anti-C3d antibody and a complement modulator (2024-01-23). Q32 Bio Inc. [US]. Inventors: Michael Steven Curtis [US], Michael Storek [US], Shelia Marie Violette [US], Susan L. Kalled [US], Kelly C. Fahnoe [US], Cheng Ran Huang [US], Ellen Garber Stark [US], Frederick Robbins Taylor [US], Justin Andrew Caravella [US], Vernon Michael Holers [US].
Full text: Click here
Patent 2024
Anemia, Hemolytic Atypical Hemolytic Uremic Syndrome Blood Platelets Complement Activation Complement C1 Complement factor H Erythrocytes Fibrinogen Genes Kidney Kidney Failure Microcirculation Mutation Patients Proteins Thrombocytopenia Thrombus Tissue, Membrane
5
Elovanoids Upregulate SIRT1 in RPE Cells
Not available on PMC !

Example 8

FIG. 10—(A) Effect of NPD1 and VLC-PUFA C32:6 and C34:6 in mediating upregulation of SIRT1 in ARPE-19 cells. (B) Quantification of SIRT1 upregulation by NPD1, C32:6 and C34:6. SIRT1 (Sirtuin1) belongs to a family of highly conserved proteins linked to caloric restriction beneficial outcomes and aging by regulating energy metabolism, genomic stability and stress resistance. SIRT1 is a potential therapeutic target in several diseases including cancer, diabetes, inflammatory disorders, and neurodegenerative diseases or disorders. Elovanoids induce cell survival involving the upregulation of the Bcl2 class of survival proteins and the downregulation of pro-apoptotic Bad and Bax under oxidative stress (OS) in RPE cells. The data in this Figure suggest that elovanoids upregulate SIRT1 abundance in human RPE cells when confronted with OS. As a consequence, remarkable cell survival takes place. This target of elovanoids might be relevant to counteract consequences of several diseases associated with SIRT1.

US11858888B2. Compounds, compositions, and methods for the treatment of inflammatory, degenerative, and neurodegenerative diseases (2024-01-02). Board of Supervisors of Louisiana State University and Agricultural and Mechanical College [US], University of Southern California [US]. Inventors: Nicolas G. Bazan [US], Nicos A. Petasis [US].
Full text: Click here
Patent 2024
Anastasis B-Cell Leukemia 2 Family Proteins Caloric Restriction Cells Cell Survival Diabetes Mellitus Energy Metabolism Genomic Stability Homo sapiens Inflammation Malignant Neoplasms Neurodegenerative Disorders Oxidative Stress Polyunsaturated Fatty Acids Sirtuin 1 Staphylococcal Protein A Therapeutics Up-Regulation (Physiology)

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2
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.

More about "Family"

Household: A fundamental social unit consisting of parents and their children.
Households provide individuals with identity, social interaction, status, and a way of life.
This term can also be used metaphorically to denote a group of people with common interests or features.
Optimize your household research protocols for reproducibility and accuracy using PubCompare.ai, an AI-driven platform that allows you to effortlessly locate the best protocols and products from literature, pre-prints, and patents by comparing them side-by-side.
Streamline your household research workflow and achieve reliable, data-driven results.
Kinship: The relationship between family members, such as parents, children, siblings, and extended relatives.
Kinship provides a sense of belonging and shared identity within a family unit.
Leverage PubCompare.ai to identify the most effective protocols and tools for studying kinship patterns and dynamics within households.
Genealogy: The study of family lineages and ancestral histories.
Genealogical research can provide insights into the genetic, cultural, and social factors that shape family structures over generations.
Utilize PubCompare.ai to access a comprehensive database of family research methodologies, including techniques for DNA analysis (e.g., SAS 9.4, HiSeq 2000, TRIzol reagent) and data management (e.g., Stata 15, SPSS version 20, Prism 8).
Domestic Life: The daily activities, routines, and practices that occur within a household or family setting.
Understanding domestic life can offer valuable insights into family dynamics, gender roles, and societal norms.
PubCompare.ai can help you identify the most effective observational and ethnographic methods for studying domestic life, such as the use of FBS (Fetal Bovine Serum) for cell culture experiments.
By leveraging the power of PubCompare.ai, researchers can streamline their family-related studies, access the latest protocols and products, and ensure the reproducibility and accuracy of their findings.
This AI-driven platform empowers researchers to make informed decisions, optimize their workflows, and deliver reliable, data-driven results that advance our understanding of the fundamental social unit known as the family.