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Hybrids

Hybrids are combinations of two or more distinct elements or components, often used to enhance or optimize research and development.
This can include hybrid methods, models, or approaches that leverage the strengths of multiple techniques to improve accuracy, reproducibility, and overall research quality.
Effectively managing and comparing hybrid approaches is crucial for researchers seeking to maximize the impact of their work across a range of disciplines.
PubCompare.ai's AI-driven platform helps identify the best hybrid protocols from literature, pre-prints, and patents, enabling researchers to make informed decisions and enhance the rigor of their hybrid research projects.

Most cited protocols related to «Hybrids»

1
Enhancing RNA-Seq Read Alignment Accuracy
Exon-spanning reads sometimes have very small anchors (defined here as 1–7 bp) in one of the exons. Correctly aligning these reads is extremely difficult because a 1- to 7-bp anchor will align to numerous locations, even in a local FM index. Arguably the most effective approach to align such short-anchored reads is to use splice site information to remove the introns computationally before alignment. We can identify and collect splice site locations when aligning reads with long anchors and then rerun HISAT for the short-anchored reads (Supplementary Fig. 9). This two-step approach is very similar to the two-step algorithm in TopHat2.
More specifically, in the two-step HISATx2 method, we use the first run of HISAT (HISATx1) to generate a list of splice sites supported by reads with long anchors. In the second run we then use the splice sites to align reads with small anchors. For example, consider the unmapped read spanning exons e2 and e3 (the upper portion of Supplementary Fig. 9). The right part of the read will be mapped to exon e3 using the global search and extension operations, leaving a short, 3-bp segment unmapped. We then check the splice sites found in the first run of HISAT to find any splice sites near this partial alignment. In this example, we find a splice site supported by a read spanning exons e2 and e3 with long anchors in each exon. On the basis of this information, we directly compare the 3 bp of the read and the corresponding genomic sequence in exon e2. If it matches, we combine the 3-bp alignment with the alignment of the rest of the read. This ‘junction extension’ procedure that makes use of previously identified splice sites is represented by brown arrows in the figure.
As we show in our experiments on simulated reads, this two-step strategy produces accurate alignment of reads with anchors as small as 1 bp (see Results). Although HISATx2 has considerably better sensitivity, it takes twice as long to run as HISATx1. As an alternative, we developed a hybrid method, HISAT, which has sensitivity almost equal to that of HISATx2 but with the speed of HISATx1. HISAT collects splice sites as it processes the reads, similarly to the first run of HISATx2. However, as it is processing, it uses the splice sites collected thus far to align short-anchored reads. In the vast majority of cases, it can align even the shortest anchors because it has seen the associated splice sites earlier. This result follows from the observation that most splice sites can be discovered within the first few million reads, and most RNA-seq data sets contain tens of millions of reads. As our results show, HISAT provides alignment sensitivity that very nearly matches the two-step HISATx2 algorithm, with a run time nearly as fast as the one-step HISAT method.
The hybrid approach is also effective in aligning reads spanning more than two exons, which are more likely to have small anchors. The alignment sensitivity for such reads increases from 53% using HISATx1 to 95% using HISAT (Supplementary Fig. 2).
Kim D., Langmead B, & Salzberg S.L. (2015). HISAT: a fast spliced aligner with low memory requirements. Nature methods, 12(4), 357-360.
Publication 2015
Exons Genome Hybrids Hypersensitivity Introns RNA-Seq Toxic Epidermal Necrolysis Vision
2
Compiling Yeast Protein Interaction Data
All protein interaction data sets from MIPS [13 (link)], Gene Ontology [43 (link)] and PreBIND were collected as described previously [6 (link)]. The YPD protein interaction data are from March 2001 and were originally requested from Proteome, Inc. . Other interaction data sets are from BIND . A BIND yeast import utility was developed to integrate data from SGD [12 (link)], RefSeq [44 (link)], Gene Registry , the list of essential genes from the yeast deletion consortium [11 (link)] and GO terms [43 (link)]. This database ensures proper matching of yeast gene names among the multiple data sets that may use different names for the same genes. The yeast proteome used here is defined by SGD and RefSeq and contains 6,334 ORFs including the mitochondrial chromosome. Before performing comparisons, the various interaction data sets were entered into a local instance of BIND as pairwise protein interaction records. The MIPS complex catalogue was downloaded in February 2002.
The protein interaction data sets used here were composed as follows. 'Gavin Spoke' is the spoke model of the raw purifications from Gavin et al [7 (link)]. 'Y2H' is all known large-scale [2 (link)-5 (link),10 (link)] combined with normal yeast two-hybrid results from MIPS. 'HTP Only' is only high-throughput or large-scale data [2 (link)-7 (link),10 (link)] The 'Benchmark' set was constructed from MIPS, YPD and PreBIND as previously described [6 (link)]. 'Pre HTMS' was composed of all yeast sets except the recent large-scale mass spectrometry data sets [6 (link),7 (link)]. 'AllYeast' was the combination of all above data sets. All data sets are non-redundant.
Bader G.D, & Hogue C.W. (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4, 2.
Publication 2003
Deletion Mutation DNA, Mitochondrial Genes Genes, Essential Hybrids Macrophage Inflammatory Protein-1 Mass Spectrometry Open Reading Frames Proteins Proteome Saccharomyces cerevisiae SET protein, human
3
Modeling Challenges in Imputation Datasets
In order to understand the modeling choices underlying our new imputation algorithm, it is crucial to consider the statistical issues that arise in imputation datasets. For simplicity, we will discuss these issues in the context of Scenario A, although we will also extend them to Scenario B in the Results section. Fundamentally, imputation is very similar to phasing, so it is no surprise that most imputation algorithms are based on population genetic models that were originally used in phasing methods. The most important distinction between phasing and imputation datasets is that the latter include large proportions of systematically missing genotypes.
Large amounts of missing data greatly increase the space of possible outcomes, and most phasing algorithms are not able to explore this space efficiently enough to be useful for inference in large studies. A standard way to overcome this problem with HMMs [6] (link),[11] (link) is to make the approximation that, conditional on the reference panel, each study individual's multilocus genotype is independent of the genotypes for the rest of the study sample. This transforms the inference problem into a separate imputation step for each study individual, with each step involving only a small proportion of missing data since the reference panel is assumed to be missing few, if any, genotypes.
In motivating our new imputation methodology, we pointed out that modeling the study individuals independently, rather than jointly, sacrifices phasing accuracy at typed SNPs; this led us to propose a hybrid approach that models the study haplotypes jointly at typed SNPs but independently at untyped SNPs. We made the latter choice partly to improve efficiency – it is fast to impute untyped alleles independently for different haplotypes, which allows us to use all of the information in large reference panels – but also because of the intuition that there is little to be gained from jointly modeling the study sample at untyped SNPs.
By contrast, the recently published BEAGLE [13] (link) imputation approach fits a full joint model to all individuals at all SNPs. To overcome the difficulties caused by the large space of possible genotype configurations, BEAGLE initializes its model using a few ad-hoc burn-in iterations in which genotype imputation is driven primarily by the reference panel. The intuition is that this burn-in period will help the model reach a plausible part of parameter space, which can be used as a starting point for fitting a full joint model.
This alternative modeling strategy raises the question of whether, and to what extent, it is advantageous to model the study sample jointly at untyped SNPs. One argument [20] (link) holds that there is no point in jointly modeling such SNPs because all of the linkage disequilibrium information needed to impute them is contained in the reference panel. A counterargument is that, as with any statistical missing data problem, the “correct” inference approach is to create a joint model of all observed and missing data. We have found that a full joint model may indeed improve accuracy on small, contrived imputation datasets (data not shown), and this leads us to believe that joint modeling could theoretically increase accuracy in more realistic datasets.
However, a more salient question is whether there is any useful information to be gained from jointly modeling untyped SNPs, and whether this information can be obtained with a reasonable amount of computational effort. Most imputation methods, including our new algorithm, implicitly assume that such information is not worth pursuing, whereas BEAGLE assumes that it is. We explore this question further in the sections that follow.
Howie B.N., Donnelly P, & Marchini J. (2009). A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies. PLoS Genetics, 5(6), e1000529.
Full text: Click here
Publication 2009
Alleles Genotype Haplotypes Hybrids Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Intuition Joints Seizures Single Nucleotide Polymorphism
4
ERCC Spike-in Control Quantification
The Ambion(textregistered) External RNA Controls Consortium (ERCC) spike-in control includes 92 spike-in transcripts, which are spiked in difference concentrations in each of the two mixes (Mix 1 and Mix 2) (http://www.lifetechnologies.com, 2013). The transcripts in these two mixes are present at defined Mix 1:Mix 2 molar concentration ratios, described by four subgroups (log fold changes of 2, 0, −0.58 and −1, respectively). Each group contains 23 transcripts spanning a 106-fold concentration range, with approximately the same transcript size and GC content. The median length of the spike-in transcript sequence is 994 bp.
The ERCC spike-in control sequencing data used in this study were created as part of the SEQC study. Mix 1 and Mix 2 were pooled with SEQC sample A (UHRR) and sample B (HBRR), respectively, before library preparation was performed. Spike-in transcript sequences were combined with human genome so that a hybrid index can be built by each aligner. Spike-in reads and human reads were then mapped to the hybrid index.
To compute fold changes for each spike-in transcript, read counts were normalized by total number of mapped spike-in reads and by the transcript length (reads per 1 kb transcript per 10 000 mapped spike-in reads). An offset count of 0.5 was added to the raw read counts to avoid taking the log of zero.
Liao Y., Smyth G.K, & Shi W. (2013). The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Research, 41(10), e108.
Publication 2013
DNA Library Genome, Human Homo sapiens Hybrids Molar PER1 protein, human
5
Solvent Boundary Potential Simulation Methods
One approach for simulating a small part of a large system (e.g.,
the enzyme active site region of a large protein) uses a solvent boundary
potential (SBP). In SBP simulations, the macromolecular system is separated
into an inner and an outer region. In the outer region, part of the
macromolecule may be included explicitly in a fixed configuration, while the
solvent is represented implicitly as a continuous medium. In the inner
region, the solvent molecules and all or part of the macromolecule are
included explicitly and are allowed to move using molecular or stochastic
dynamics. The SBP aims to “mimic” the average
influence of the surroundings, which are not included explicitly in the
simulation.27 ,28 There are several implementations of the SBP
method in CHARMM. The earliest implementation, called the stochastic
boundary potential (SBOU), uses a soft nonpolar restraining potential to
help maintain a constant solvent density in the inner or
“simulation” region while the molecules in a shell
or buffer region are propagated using Langevin dynamics.27 By virtue of its simplicity, this treatment
remains attractive and it is sufficient for many applications.320 (link),321 (link) To improve the treatment of systems with irregular
boundaries in which part of the protein is in the outer region, a refinement
of the method has been developed that first scales the exposed charges to
account for solvent shielding and then corrects for the scaling by
post-processing.307
The Spherical Solvent Boundary Potential (SSBP), which is part of
the Miscellaneous Mean Field Potential (MMFP) module (see Section III F), is
designed to simulate a molecular solute completely surrounded by an
isotropic bulk aqueous phase with a spherical boundary.28 In SSBP the radius of the spherical region is
allowed to fluctuate dynamically and the influence of long-range
electrostatic interactions is incorporated by including the dielectric
reaction field response of the solvent.28 ,29 This approach has
been used to study several systems.322 –325
Because SSBP incorporates the long-range electrostatic reaction field
contribution, the method is particularly useful in free energy calculations
that involve introducing charges.322 –325
Like the SBOU charge-scaling method,307 the Generalized Solvent Boundary Potential (GSBP) is
designed for irregular boundaries when part of the protein is outside the
simulation region.29 However, unlike
SBOU, GSBP includes long-range electrostatic effects and reaction fields. In
the GSBP approach, the influence of the outer region is represented in terms
of a solvent-shielded static field and a reaction field expressed in terms
of a basis set expansion of the charge density in the inner region, with the
basis set coefficients corresponding to generalized electrostatic
multipoles.29 ,326 The solvent-shielded static field from the
outer macromolecular atoms and the reaction field matrix representing the
coupling between the generalized multipoles are both invariant with respect
to the configuration of the explicit atoms in the inner region. They are
calculated only once (with the assumption that the size and shape of inner
region does not change during the simulation) using the finite-difference
Poisson-Boltzmann (PB) equation of the PBEQ module. This formulation is an
accurate and computationally efficient hybrid MD/continuum method for
simulating a small region of a large macromolecular system,326 and is also used in QM/MM approaches.281 (link),327 (link)
Brooks B.R., Brooks CL I.I.I., MacKerell AD J.r., Nilsson L., Petrella R.J., Roux B., Won Y., Archontis G., Bartels C., Boresch S., Caflisch A., Caves L., Cui Q., Dinner A.R., Feig M., Fischer S., Gao J., Hodoscek M., Im W., Kuczera K., Lazaridis T., Ma J., Ovchinnikov V., Paci E., Pastor R.W., Post C.B., Pu J.Z., Schaefer M., Tidor B., Venable R.M., Woodcock H.L., Wu X., Yang W., York D.M, & Karplus M. (2009). CHARMM: The Biomolecular Simulation Program. Journal of computational chemistry, 30(10), 1545-1614.
Publication 2009
Buffers Dietary Fiber Electrostatics Enzymes Hybrids Proteins Radius Solvents Staphylococcal Protein A

Most recents protocols related to «Hybrids»

1
Photocatalytic CO2 Fixation on Olefins
Not available on PMC !

Example 8

Cyclohexene (1a) and polar organic solvent (as mentioned in Table 1) in (1:2 to 1:10 weight ratio with respect to the substrate) was taken in to a 60 ml vessel. Further, the hybrid photocatalyst was added and the resulting mixture was saturated with CO2 by purging at 1 atm pressure. The reaction vessel was sealed and irradiated with 20 W LED light (Model No. HP-FL-20 W-F, Hope LED Opto-Electric CO., Ltd) for 24 h. The conversion of the olefin and selectivity of the α,β-unsaturated hydroxyl or carbonyl compound as determined by GC-FID and GC-MS is mentioned in the Table 1 (entry 8-13).

US11872547B2. Process for the photocatalytic allylic oxidation of olefins using carbon dioxide (2024-01-16). COUNCIL OF SCIENTIFIC & INDUSTRIAL RESEARCH [IN]. Inventors: Suman Lata Jain [IN], Sandhya Saini [IN], Shafuir Rehman Khan [IN], Praveen Kumar Khatri [IN], Anjan Ray [IN].
Full text: Click here
Patent 2024
Alkenes Blood Vessel cyclohexene Electricity Gas Chromatography-Mass Spectrometry Genetic Selection Hybrids Hydroxyl Radical Light Pressure Solvents
2
Quantifying Glycated CD59 Peptide
Not available on PMC !

Example 4

A sandwich-type ELISA assay is prepared that specifically quantifies GCD59 relative to the glycated CD59 (GCD59) peptide hybrid surrogate. Antibody H9 is used as a capture antibody to capture CD59 in samples tested. The detection antibody is developed using a peptide antigen containing a glucitollysine residue in the equivalent position of K41 in CD59. For this reason, these assays detect the reduced form of GCD59. These anti-glucitollysine GCD59 ELISA kit assays also include a sample preparation step that utilizes NaBH4 as the reducing agent for transformation of the glycated form of GCD59 into the reduced glycated form of GCD59. Kits include solutions of reducing agent in organic solvent to carry out this step.

Sandwich-type Amadori-modified GCD59 ELISA assays do not need sample preparation by NaBH4 reduction due to detection antibody specificity for the non-reduced Amadori-modified form of GCD59. Sample treatment with DTT may, however, improve antibody affinity.

US11866506B2. Anti-CD59 antibodies (2024-01-09). Mellitus, LLC [US]. Inventors: Michael Chorev [US], Jose A. Halperin [US].
Full text: Click here
Patent 2024
Antibody Affinity Antibody Specificity Antigens Biological Assay CD59 protein, human Enzyme-Linked Immunosorbent Assay glucitollysine Hybrids Immunoglobulins Peptides Reducing Agents Solvents Specimen Handling
3
Graphene Oxide Polymer Composite Synthesis
Not available on PMC !

Example 1

Graphene oxide (GO) was suspended in dimethylformamide (DMF) and thoroughly dispersed using a VWR Scientific Model 75T Aquasonic (sonic power ˜90 W, frequency ˜40 kHz) for 24 hours. The concentration of GO in the reaction mixture was held at 1 wt %. To the dispersed GO, a range of weight percentages 1-12 wt % Bisphenol F ethoxylate (2 EO/phenol) diacrylate (BisF) with average molar mass (Mn)˜484 (Sigma) and 4-11.5 wt % PEGDA of various Mn, 700 and 575, were added so that the total amount of polymer added equaled 12 wt %. To this mixture 0.02 g of the photo-initiator lithium phenyl(2,4,6-trimethylbenzoyl)phosphinate) (TPO-Li, Colorado Photopolymer Solutions) was dissolved.

US11880133B2. Architected three dimensional graphene via additive manufacturing (2024-01-23). LAWRENCE LIVERMORE NATIONAL SECURITY, LLC [US]. Inventors: Marcus A Worsley [US], Patrick G. Campbell [US], Eric B. Duoss [US], James S. Oakdale [US], Christopher M. Spadaccini [US], Ryan Hensleigh [US].
Full text: Click here
Patent 2024
Aquasonic ARID1A protein, human bisphenol A Dimethylformamide Graphene graphene oxide Hybrids Lithium Molar Phenol poly(ethylene glycol)diacrylate Polymers Resins, Plant
4
Amphiphilic Dendrimer Hybrids for Nucleic Acid Delivery
Not available on PMC !

Example 2

In the current invention, we have synthesized sugar and deoxy sugar-modified amphiphilic dendrimer hybrids. These amphiphilic molecules have hydrophobic units, thus should be able to self-assemble in solution whereas the hydrophilic sugar moieties should improve biocompatibility and loading capacity of the nucleic acid carrier.

[Figure (not displayed)]
where A is an amine linker, B is a hydrophobic unit, Y is a sugar moiety and Z is a deoxy sugar moiety

US11859201B2. Nanoparticle compositions containing sugar functionalized nucleic acid carriers (2024-01-02). TIBA Biotech LLC [US]. Inventors: Poulami Talukder [US], Jasdave Chahal [US].
Full text: Click here
Patent 2024
Amines Dendrimers Deoxy Sugars Hybrids Nucleic Acids Sugars
5
Hybrid Neurostimulation for Peripheral Neuropathy
Not available on PMC !

Example 3

Other neurostimulation applications are also considered including, but not limited to, Myography stimulators. The Myography stimulators can be designed to implement the hybrid electro-plasmonic methodology for stimulation and treatment of peripheral neuropathy as an alternative to current electromyography.

Electromyography (EMG) generally measures muscle response or electrical activity in response to a nerve's stimulation of muscle. In general, a small needle electrode is inserted into different muscles to stimulate the muscle fibers. Electrical activity is measured when the muscle contracts and relaxes. The hybrid neurostimulation technology described herein can be used to replace the electrode with a hybrid device capable of both electrical and optical stimulation.

The disclosures of all publications cited above are expressly incorporated herein by reference, each in its entirety, to the same extent as if each were incorporated by reference individually.

It is also to be understood that the following claims are intended to cover all of the generic and specific features of the invention herein described, and all statements of the scope of the invention which, as a matter of language, might be said to fall there between. Now that the invention has been described,

US11878182B1. Hybrid electro-plasmonic modulation of neural activity with visible-light-sensitive gold nanotransducer interface (2024-01-23). University of South Florida [US]. Inventors: Ratka Damnjanovic [US], Parveen Bazard [US], Robert Dana Frisina [US], Venkat Rama Bhethanabotla [US].
Full text: Click here
Patent 2024
Electricity Electromyography Generic Drugs Hybrids Muscle Tissue Myography Needles Peripheral Nervous System Diseases Photic Stimulation

Top products related to «Hybrids»

1
Synergy h1 hybrid reader by Agilent Technologies
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2
Q exactive hybrid quadrupole orbitrap mass spectrometer by Thermo Fisher Scientific
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3
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Synergy h1 hybrid multi mode reader by Agilent Technologies
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Synergy h4 hybrid microplate reader by Agilent Technologies
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More about "Hybrids"

Hybrids are innovative combinations of two or more distinct elements or components, often utilized to enhance and optimize research and development efforts.
These hybrid methods, models, or approaches leverage the strengths of multiple techniques to improve accuracy, reproducibility, and overall research quality.
Effectively managing and comparing hybrid approaches is crucial for researchers seeking to maximize the impact of their work across a range of disciplines.
PubCompare.ai's AI-driven platform helps identify the best hybrid protocols from literature, pre-prints, and patents, enabling researchers to make informed decisions and enhance the rigor of their hybrid research projects.
This includes hybrid instruments like the Synergy H1 Hybrid Reader, Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer, Synergy H1 Hybrid Multi-Mode Microplate Reader, Synergy H1 Hybrid Multi-Mode Reader, Synergy H4 Hybrid Reader, Synergy H1, Synergy H4 Hybrid Multi-Mode Microplate Reader, HiSeq 2500, and Q Exactive Plus Hybrid Quadrupole-Orbitrap mass spectrometer.
Leveraging accurate comparisons of hybrid approaches can help enhance reproducibility and accuracy across a wide range of research disciplines, from life sciences to engineering.
By identifing the most effective hybrid protocols, researchers can maximize the impact of their work and drive innovation forward.
Wether you're working on hybrid methods, models, or instruments, PubCompare.ai's AI-powered platform can provide the insights you need to optimize your hybrid research projects.