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Biological Evolution

Biological evolution is the gradual process of change in the inherited characteristics of biological populations over successive generations.
It encompasses the origin and descent of species, as well as adaptations and diversification of life forms over time.
This AI-driven platform helps researchers locate the best protocols and products for their biological evolution studies by comparing literature, preprints, and patents.
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Most cited protocols related to «Biological Evolution»

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.
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Publication 2010
Biological Evolution Cantor Gamma Rays Proteins Sequence Alignment Trees
ESTIMATE outputs stromal, immune and ESTIMATE scores by performing ssGSEA13 (link)23 (link)37 (link). For a given sample, gene expression values were rank-normalized and rank-ordered. The empirical cumulative distribution functions of the genes in the signature and the remaining genes were calculated. A statistic was calculated by an integration of the difference between the empirical cumulative distribution function, which is similar to the one used in gene set-enrichment analysis but based on absolute expression rather than differential expression.
We defined ssGSEA based on the signatures related to stromal tissue and immune cell infiltration as stromal and immune scores and combined the stromal and immune scores as the ‘ESTIMATE score’. The formula for calculating ESTIMATE-based tumour purity was developed in TCGA Affymetrix data (n=1,001) including both the ESTIMATE score and ABSOLUTE-based tumour purity. To develop a precise prediction model for tumour purity, we excluded six outliers from all Affymetrix data by computing a multivariate outlier criterion based on the generalized extreme studentized deviate test57 58 using the Bioconductor Parametric and Resistant Outlier Detection (PARODY) package (Supplementary Fig. S8a). Next, we entered both the ESTIMATE score and tumour purity to Eureqa Formuliza 0.97 Beta using the default setting59 . Eureqa attempts to design a mathematical formula that fits observed data employing an evolutionary algorithm60 (link). We obtained a fitted formula to predict tumour purity based on the ESTIMATE score. Finally, we applied this formula to the nonlinear least squares method (nls function for stats package) to determine the final formula for predicting tumour purity, as follows:
Tumour purity=cos (0.6049872018+0.0001467884 × ESTIMATE score). (1)
Publication 2013
Biological Evolution Gene Expression Genes Neoplasms Operator, Genetic Stromal Cells Tissues
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.
Publication 2009
Biological Evolution Family Member Genes RNA, Ribosomal, 16S
In comparative modelling, a 3D protein model of a target sequence is generated by extrapolating experimental information from an evolutionary related protein structure that serves as a template. In SWISS-MODEL, the default modelling workflow consists of the following main steps:

Input data: The target protein can be provided as amino acid sequence, either in FASTA, Clustal format or as a plain text. Alternatively, a UniProtKB accession code (34 (link)) can be specified. If the target protein is heteromeric, i.e. it consists of different protein chains as subunits, amino acid sequences or UniProtKB accession codes must be specified for each subunit.

Template search: Data provided in step 1 serve as a query to search for evolutionary related protein structures against the SWISS-MODEL template library SMTL (30 (link)). SWISS-MODEL performs this task by using two database search methods: BLAST (35 (link),36 (link)), which is fast and sufficiently accurate for closely related templates, and HHblits (37 (link)), which adds sensitivity in case of remote homology.

Template selection: When the template search is complete, templates are ranked according to expected quality of the resulting models, as estimated by Global Model Quality Estimate (GMQE) (30 (link)) and Quaternary Structure Quality Estimate (QSQE) (23 ). Top-ranked templates and alignments are compared to verify whether they represent alternative conformational states or cover different regions of the target protein. In this case, multiple templates are selected automatically and different models are built accordingly. To provide the user with the option to use alternative templates than those selected automatically, all templates are shown in a tabular form with a descriptive set of features. In addition, interactive graphical views facilitate the analysis and comparison of available templates in terms of their three-dimensional structures, sequence similarity and quaternary structure features.

Model building: For each selected template, a 3D protein model is automatically generated by first transferring conserved atom coordinates as defined by the target-template alignment. Residue coordinates corresponding to insertions/deletions in the alignment are generated by loop modelling and a full-atom protein model is obtained by constructing the non-conserved amino acid side chains. SWISS-MODEL relies on the OpenStructure computational structural biology framework (38 (link)) and the ProMod3 modelling engine to perform this step. For more detailed information on model building we refer to a dedicated section in Results.

Model quality estimation: To quantify modelling errors and give estimates on expected model accuracy, SWISS-MODEL relies on the QMEAN scoring function (31 (link)). QMEAN uses statistical potentials of mean force to generate global and per residue quality estimates. The local quality estimates are enhanced by pairwise distance constraints that represent ensemble information from all template structures found. For more information on quality estimation we refer to a dedicated section in Results.

SWISS-MODEL allows for further customization of steps 1 and 3. Expert users can directly upload custom target-template sequence alignments, template structures or DeepView project files (26 (link)) in separate input forms.
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Publication 2018
Amino Acids Amino Acid Sequence Biological Evolution cDNA Library Hypersensitivity INDEL Mutation Protein Domain Proteins Protein Subunits Sequence Alignment
Since its release in 199010 (link), AutoDock has proven to be an effective tool capable of quickly and accurately predicting bound conformations and binding energies of ligands with macromolecular targets9 ,11 (link)–14 . In order to allow searching of the large conformational space available to a ligand around a protein, AutoDock uses a grid-based method to allow rapid evaluation of the binding energy of trial conformations. In this method, the target protein is embedded in a grid. Then, a probe atom is sequentially placed at each grid point, the interaction energy between the probe and the target is computed, and the value is stored in the grid. This grid of energies may then be used as a lookup table during the docking simulation.
The primary method for conformational searching is a Lamarckian genetic algorithm, described fully in Morris et al.9 . A population of trial conformations is created, and then in successive generations these individuals mutate, exchange conformational parameters, and compete in a manner analogous to biological evolution, ultimately selecting individuals with lowest binding energy. The “Lamarckian” aspect is an added feature that allows individual conformations to search their local conformational space, finding local minima, and then pass this information to later generations. A simulated annealing search method and a traditional genetic algorithm search are also available in AutoDock4.
AutoDock4 uses a semiempirical free energy force field to predict binding free energies of small molecules to macromolecular targets. Development and testing of the force field has been described elsewhere11 (link). The force field is based on a comprehensive thermodynamic model that allows incorporation of intramolecular energies into the predicted free energy of binding. This is performed by evaluating energies for both the bound and unbound states. It also incorporates a new charge-based desolvation method that uses a typical set of atom types and charges. The method has been calibrated on a set of 188 diverse protein-ligand complexes of known structure and binding energy, showing a standard error of about 2–3 kcal/mol in prediction of binding free energy in cross-validation studies.
Publication 2009
Biological Evolution Childbirth Ligands Proteins SET protein, human Staphylococcal Protein A

Most recents protocols related to «Biological Evolution»

Example 1

95 g of manganese (purity: 99.95%; purchased from Taewon Scientific Co., Ltd.) and 5 g of high-purity graphite (purity: 99.5%; purchased from Taewon Scientific Co., Ltd.) were placed in a water-cooled copper crucible of an argon plasma arc melting apparatus (manufactured by Labold AG, Germany, Model: vacuum arc melting furnace Model LK6/45), and melted at 2,000 K under an argon atmosphere. The melt was cooled to room temperature at a cooling rate of 104 K/min to obtain an alloy ingot. The alloy ingot was crushed to a particle size of 1 mm or less by hand grinding. Thereafter, the obtained powders were magnetically separated using a Nd-based magnet to remove impurities repeatedly, and the Mn4C magnetic powders were collected. The collected Mn4C magnetic powders were subjected to X-ray diffraction (XRD) analysis (measurement system: D/MAX-2500 V/PO, Rigaku; measurement condition: Cu—Kα ray) and energy-dispersive X-ray spectroscopy (EDS) using FE-SEM (Field Emission Scanning Electron Microscope, MIRA3 LM).

FIGS. 2(a) and 2 (b) show an X-ray diffraction pattern and an energy-dispersive X-ray spectroscopy graph of the Mn4C magnetic material produced according to Example 1 of the present disclosure, respectively.

As can be seen in FIG. 2(a), the Mn4C magnetic material showed diffraction peaks of (111), (200), (220), (311) and (222) crystal planes at 2θ values of 40°, 48°, 69°, 82° and 88°, respectively, in the XRD analysis. Thus, it can be seen that the XRD patterns of the Mn4C magnetic material produced according to Example 1 are well consistent with the patterns of the cubic perovskite Mn4C. In addition, the Mn4C magnetic material shows several very weak diffraction peaks that can correspond to Mn23C6 and Mn. That is, the diffraction peak intensity at 2θ values of 43° and 44°, which correspond to Mn and Mn23C6 impurities, is as very low as about 2.5% of the diffraction intensity of the peak corresponding to the (111) plane. Through this, it can be seen that the powders obtained in Example 1 have high-purity Mn4C phase. The lattice parameter of the Mn4C is estimated to be about 3.8682 Å.

FIG. 2(b) shows the results of analyzing the atomic ratio of Mn:C in the powder by EDS. The atomic ratio of Mn:C is 80.62:19.38, which is very close to 4:1 within the experimental uncertainties. Thus, it can be seen that the powder is also confirmed to be Mn4C.

The M-T curve of the field aligned Mn4C powder obtained in Example 1 was measured under an applied field of 4 T and at a temperature ranging from 50 K to 400 K. Meanwhile, the M-T curve of the randomly oriented Mn4C powder was measured under an applied field of 1 T. The Curie temperature of Mn4C was measured under 10 mT while decreasing temperature from 930 K at a rate of 20 K/min.

FIGS. 3(a) to 3(c) show the M-T curves of the Mn4C magnetic material, produced according to Example 1 of the present disclosure, under magnetic fields of 4 T, 1 T, and 10 mT, respectively.

FIG. 3 shows magnetization-temperature (M-T) curves indicating the results of measuring the temperature-dependent magnetization intensity of the Mn4C magnetic material, produced in Example 1, using the vibrating sample magnetometer (VSM) mode of Physical Property Measurement System (PPMS®) (Quantum Design Inc.).

According to the Néel theory, the ferrimagnets that contain nonequivalent substructures of magnetic ions may have a number of unusual forms of M-T curves below the Curie temperature, depending on the distribution of magnetic ions between the substructures and on the relative value of the molecular field coefficients. The anomalous M-T curves of Mn4C, as shown in FIG. 3(a), can be explained to some extent by the Néel's P-type ferrimagnetism, which appears when the sublattice with smaller moment is thermally disturbed more easily. For Mn4C with two sublattices of MnI and MnII, as shown in FIG. 1, the MnI sublattice might have smaller moment.

FIG. 3(a) shows the temperature dependence of magnetization of the Mn4C magnetic material produced in Example 1. The magnetization of Mn4C measured at 4.2K is 6.22 Am2/kg (4 T), corresponding to 0.258μB per unit cell. The magnetization of the Mn4C magnetic material varies little at temperatures below 50 K, and is quite different from that of most magnetic materials, which undergo a magnetization deterioration with increasing temperature due to thermal agitation. Furthermore, the magnetization of the Mn4C magnetic material increases linearly with increasing temperature at temperatures above 50 K. The linear fitting of the magnetization of Mn4C at 4 T within the temperature range of 100 K to 400 K can be written as M=0.0072T+5.6788, where M and T are expressed in Am2/kg and K, respectively. Thus, the temperature coefficient of magnetization of Mn4C is estimated to be about ˜2.99*10−4μB/K per unit cell. The mechanisms of the anomalous thermomagnetic behaviors of Mn4C may be related to the magnetization competition of the two ferromagnetic sublattices (MnI and MnII) as shown in FIG. 1.

FIG. 3(b) shows the M-T curves of the Mn4C powders at temperatures within the range of 300 K to 930 K under 1 T. The linear magnetization increment stops at 590 K, above which the magnetization of Mn4C starts to decrease slowly first and then sharply at a temperature of about 860 K. The slow magnetization decrement at temperatures above 590 K is ascribed to the decomposition of Mn4C, which is proved by further heat-treatment of Mn4C as described below.

According to one embodiment of the present disclosure, the saturation magnetization of Mn4C increases linearly with increasing temperature within the range of 50 K to 590 K and remains stable at temperatures below 50 K. The increases in anomalous magnetization of Mn4C with increasing temperature can be considered in terms of the Néel's P-type ferrimagnetism. At temperatures above 590 K, the Mn4C decomposes into Mn23C6 and Mn, which are partially oxidized into the manganosite when exposed to air. The remanent magnetization of Mn4C varies little with temperature. The Curie temperature of Mn4C is about 870 K. The positive temperature coefficient (about 0.0072 Am2/kgK) of magnetization in Mn4C is potentially important in controlling the thermodynamics of magnetization in magnetic materials.

The Curie temperature Te of Mn4C is measured to be about 870 K, as shown in FIG. 3(c). Therefore, the sharp magnetization decrement of Mn4C at temperatures above 860 K is ascribed to both the decomposition of Mn4C and the temperature near the Tc of Mn4C.

FIG. 4 is a graph showing the magnetic hysteresis loops of the Mn4C magnetic material, produced according to Example 1 of the present disclosure, at 4.2 K, 200 K and 400 K. The magnetic hysteresis loops were measured by using the PPMS system (Quantum Design) under a magnetic field of 7 T while the temperature was changed from 4 K to 400 K.

As shown in FIG. 4, the positive temperature coefficient of magnetization was further proved by the magnetic hysteresis loops of Mn4C as shown in FIG. 4. The Mn4C shows a much higher magnetization at 400 K than that at 4.2 K. Moreover, the remanent magnetization of Mn4C varies little with temperature and is Δ3.5 Am2/kg within the temperature range of 4.2 K to 400 K. The constant remanent magnetization of Mn4C within a wide temperature range indicates the high stability of magnetization against thermal agitation. The coercivities of Mn4C at 4.2 K, 200 K, and 400 K were 75 mT, 43 mT, and 33 mT, respectively.

The magnetic properties of Mn4C measured are different from the previous theoretical results. A corner MnI moment of 3.85μB antiparallel to three face-centered MnII moments of 1.23μB in Mn4C was expected at 77 K. The net moment per unit cell was estimated to be 0.16μB. In the above experiment, the net moment in pure Mn4C at 77 K is 0.26μB/unit cell, which is much larger than that expected by Takei et al. It was reported that the total magnetic moment of Mn4C was calculated to be about 1μB, which is almost four times larger than the 0.258μB per unit cell measured at 4.2 K, as shown in FIG. 4.

FIG. 5 is an enlarged view of the temperature-dependent XRD patterns of the Mn4C magnetic material produced according to Example 1 of the present disclosure.

The thermomagnetic behaviors of Mn4C are related to the variation in the lattice parameters of Mn4C with temperature. It is known that the distance of near-neighbor manganese atoms plays an important role in the antiferro- or ferro-magnetic configurations of Mn atoms. Ferromagnetic coupling of Mn atoms is possible only when the Mn—Mn distance is large enough. FIG. 5 shows the diffraction peaks of the (111) and (200) planes of Mn4C at temperatures from 16 K to 300 K. With increasing temperature, both (111) and (200) peaks of Mn4C shifted to a lower degree at temperatures between 50 K and 300 K, indicating an enlarged distance of Mn—Mn atoms in Mn4C. No peak shift is obviously observed for Mn4C at temperatures below 50 K. The distance of nearest-neighbor manganese atoms plays an important role in the antiferro- or ferro-magnetic configurations of Mn atoms and thus has a large effect on the magnetic properties of the compounds.

Thus, it can be seen that the abnormal increase in magnetization of Mn4C with increasing temperature occurs due to the variation in the lattice parameters of Mn4C with temperature.

The powder produced in Example 1 was annealed in vacuum for 1 hour at each of 700 K and 923 K, and then subjected to X-ray spectroscopy, and the results thereof are shown in FIG. 6.

The magnetization reduction of Mn4C at temperatures above 590 K is ascribed to the decomposition of Mn4C, which is proved by the XRD patterns of the powders after annealing Mn4C at elevated temperatures. FIG. 6 shows the structural evolution of Mn4C at elevated temperatures. When Mn4C is annealed at 700 K, a small fraction of Mn4C decomposes into a small amount of Mn23C6 and Mn. The presence of manganosite is ascribed to the spontaneous oxidation of the Mn precipitated from Mn4C when exposed to air after annealing. The fraction of Mn23C6 was enhanced significantly for Mn4C annealed at 923 K, as shown in FIG. 6.

These results prove that the metastable Mn4C decomposes into stable Mn23C6 at temperatures above 590 K. The presence of Mn4C in the powder annealed at 923 K indicates a limited decomposition rate of Mn4C, from which the Tc of Mn4C can be measured. Both Mn23C6 and Mn are weak paramagnets at ambient temperature and elevated temperatures. Therefore, the magnetic transition of the Mn4C magnetic material at 870 K is ascribed to the Curie point of the ferrimagnetic Mn4C.

The Mn4C shows a constant magnetization of 0.258μB per unit cell below 50 K and a linear increment of magnetization with increasing temperature within the range of 50 K to 590 K, above which Mn23C6 precipitates from Mn4C. The anomalous M-T curves of Mn4C can be considered in terms of the Néel's P-type ferrimagnetism.

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Patent 2024
Alloys Argon Atmosphere Biological Evolution Cells Copper Cuboid Bone Debility Energy Dispersive X Ray Spectroscopy Face Fever fluoromethyl 2,2-difluoro-1-(trifluoromethyl)vinyl ether Graphite Ions Magnetic Fields Manganese perovskite Physical Processes Plasma Powder Radiography Scanning Electron Microscopy Spectrum Analysis Vacuum Vision X-Ray Diffraction
Not available on PMC !

Example 1

The authors of the invention have identified 3 micropeptides corresponding to sequences SEQ ID NO: 1, 2 and 3.

The micropeptide of SEQ ID NO 1 is a highly conserved 87 aa micropeptide whose sequence is:

(FIG. 1A)
MEGLRRGLSRWKRYHIKVHLADEALLLPLTVRPRDTLSDLRAQLVGQGVSS
WKRAFYYNARRLDDHQTVRDARLQDGSVLLLVSDPR.

In silico analysis of the amino acid sequence predicts a 3D structure resembling the protein UBIQUITIN (FIG. 1B). SEQ ID NO 1 micropeptide is coded by the lncRNA TINCR (LINC00036 in humans and Gm20219 in mice).

The micropeptide of SEQ ID NO: 2 is a 64-amino acid micropeptide whose sequence is:

(FIG. 2A)
MVRRKSMKKPRSVGEKKVEAKKQLPEQTVQKPRQECREAGPLFLQSRRETR
DPETRATYLCGEG.

It is encoded by ZEB2 antisense 1 (ZEB2AS1) long non-coding RNA (lncRNA). ZEB2AS1 is a natural antisense transcript corresponding to the 5′ untranslated region (UTR) of zinc finger E-box binding homeobox 2 (ZEB2). The ORF encoding the micropeptide spams part of the second and third exons of the lncRNA. I-Tasser, a 3D protein structure predictor, has been used in order to build a model of SEQ ID NO: 2 micropeptide 3D structure (FIG. 2B). Further in-silico analysis has revealed high amino acidic sequence conservation across the species and a potential cytoplasmatic localization of the micropeptide of SEQ ID NO: 2.

The micropeptide of SEQ ID NO: 3 is a 78-amino acid micropeptide encoded by the first exon of LINC0086 lncRNA. Its sequence, highly conserved across evolution is:

(FIG. 3A)
MAASAALSAAAAAAALSGLAVRLSRSAAARGSYGAFCKGLTRTLLTFFDLA
WRLRMNFPYFYIVASVMLNVRLQVRIE.

In silico analysis of this sequence predicted a tertiary structure (FIG. 3B) with a transmembrane domain at C-terminal of the protein and a signal peptide in the first 25 amino acids.

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Patent 2024
Amino Acids Amino Acid Sequence Biological Evolution Cytoplasm Exons Homo sapiens Integral Membrane Proteins Mice, House Protein Domain Proteins RNA, Long Untranslated Sequence Analysis Sequence Analysis, Protein Signal Peptides Ubiquitin Zinc Finger E-box Binding Homeobox 2

Example 3

Effectiveness of Newly Evolved TpH Background Strain Using Schistosoma mansoni TpH

One of the 7 evolved high 5HTP-producers was selected to further evaluate if the mutations identified were only specifically beneficial to hsTpH2 or could be widely applicable to others. The chosen evolved strain was first cured to lose the evolution plasmid (e.g. the hsTpH gene) and this was immediately followed by re-introducing the E. coli tyrA gene. Upon restoration of the strain's tyrosine auxotrophy, the resulting strain was transformed with pHM2, which is identical to pHM1 used in the earlier evolution study except that the hsTpH gene was replaced with a Schistosoma mansoni TpH gene (SEQ ID NO:9). The 5HTP production of the resulting strain was compared to a wild-type strain carrying pHM2 in the presence of 100 mg/l tryptophan. Results showed the wild-type transformants could only produce ˜0.05 mg/l 5HTP while the newly evolved background strain transformants accumulated >20 mg/l. These production results demonstrated that the mutations acquired in the evolved background strain were not only beneficial to hsTpH but also to other TpHs; possibly applicable also to other aromatic amino acid hydroxylases (e.g. tyrosine hydroxylase).

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Patent 2024
5-Hydroxytryptophan Aromatic Amino Acids Biological Evolution Cells Escherichia coli Genes Melatonin Mixed Function Oxygenases Mutation Plasmids Schistosoma mansoni Strains Tryptophan Tyrosine Tyrosine 3-Monooxygenase

Example 7

As evidence of the ability of MD to delay resistance evolution, a model was run with simulations for mating disruption in soy, but not corn. Recall that resistance in the default simulations always evolved first to the single gene Bt-soy and then more slowly to the dual gene Bt-corn. When mating disruption is instead used on soy but not corn, this pattern is reversed, and resistance first evolves to the dual gene corn (FIG. 6). The figure emphasizes that there is benefit in mating disruption, but it is difficult to assess because the overall refuge size is minimal. Thus, mating disruption can improve the efficiency of a refuge, but it cannot replace a refuge. When refuges are small and routinely sprayed, mating disruption makes large differences in durability only when conditions are optimal and mating disruption is applied to 100% of fields planted to the transgenes one is trying to protect.

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Patent 2024
Agricultural Crops Animals, Transgenic Biological Evolution Genes Insecticides Mental Recall Zea mays

Example 2

40 grams of zinc bromide was dissolved in 100 ml of 98% formic acid. After 1 hour, all of the salt had dissolved and the solution was heated to 80° C. and hydrogen bromide gas evolved. Once evolution of hydrogen bromide gas ceased, the solution was cooled to 15° C. and 2 grams of cotton, a source of native cellulose with a high degree of polymerisation, was dissolved in the mixture.

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Patent 2024
Biological Evolution Cellulose formic acid Gossypium Hydrobromic acid Polymerization Sodium Chloride zinc bromide

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