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Dietary Fiber

Dietary fiber refers to the indigestible portion of food derived from plants.
It includes soluble and insoluble components that provide numerous health benefits.
Soluble fibers, such as pectin and gums, can help lower cholesterol and blood sugar levels.
Insoluble fibers, like cellulose and hemicellulose, promote regular bowel movements and gut health.
Consuming a diet rich in dietary fiber has been linked to reduced risk of cardiovascular disease, type 2 diabetes, and certain cancers.
PubCompare.ai can optimize your dietary fiber research by helping you easily locate the most reliable and reproducbile protocols from literature, preprints, and patents using AI-driven comparisons.

Most cited protocols related to «Dietary Fiber»

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Publication 2008

Administrators Dietary Fiber Lanugo Secure resin cement Systolic Pressure TNFSF10 protein, human

Expanding Biomolecular Force Fields with QM Data

Class I additive force fields (see equation 1), which do not explicitly treat electronic polarization, have been designed for use in polar environments typically found in proteins and in solution. To achieve this, the use of experimental target data, supplemented by QM data, was strongly emphasized during optimization of the nonbonded parameters in the biomolecular CHARMM force fields, in order to ensure physical behavior in the bulk phase. However, reproducing experimental data requires molecular dynamics (MD) simulations, which have to be set up carefully and repeated multiple times in the course of the parametrization, making the usage of experimental target data non-trivial and time-consuming. In addition, for many functional groups that may occur in drug-like molecules experimental data may not be available. Due to this lack of data, and since one of the main goals of CGenFF is easy and fast extensibility, a slightly different philosophy was adapted, with more emphasis on QM results as target data for parameter optimization. This is possible due to the wide range of functionalities already available whose parameters were optimized based largely on experimental data, along with the establishment of empirical scaling factors that can be applied to QM data in order to make them relevant for the bulk phase.
The only cases where experimental data would be required are situations where novel atom types are present for which LJ parameters are not already available in CGenFF. These cases would require optimization of the LJ parameters, supplemented with Hartree-Fock (HF) model compound-water minimum interaction energies and distances (see step 2.a under “Generation of target data for parameter validation and optimization” and step 1 under “Parametrization procedure”), based on the reproduction of bulk phase properties, typically pure solvent molecular volumes and heats of vaporization or crystal lattice parameters and heats of sublimation. Descriptions of the optimization protocol have been published previously.7 ,9 ,25 (link) However, it should be noted that CGenFF has been designed to cover the majority of atom types in pharmaceutical compounds, such that optimization of LJ parameters is typically not required.
The remainder of this section includes 1) the procedure to add new model compounds and chemical groups to the force field, 2) the procedure for generating the QM target data, and 3) the procedure for application of the QM information to parametrize new molecules. To put these procedures in better context, example systems including pyrollidine, the addition of substituents to pyrollidine and the development of a linker between pyrollidine and benzene are presented.

Vanommeslaeghe K., Hatcher E., Acharya C., Kundu S., Zhong S., Shim J., Darian E., Guvench O., Lopes P., Vorobyov I, & MacKerell AD J.r. (2010). CHARMM General Force Field (CGenFF): A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. Journal of computational chemistry, 31(4), 671-690.

Publication 2010

Benzene Dietary Fiber Pharmaceutical Preparations Physical Examination Proteins Reproduction Solvents Vaporization

Developing Efficient Research Data Collection Protocols

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Publication 2008

Dietary Fiber Secure resin cement

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

Restrained Macromolecular Structure Refinement

Model parameters, such as coordinates and ADPs, are not refined simultaneously but at separate steps (see §2.2 for details). phenix.refine uses the following refinement target function for restrained refinement of individual coordinates, A similar function is used in restrained ADP refinement, Here, T_exp is the crystallographic term that relates the experimental data to the model structure factors. It can be a least-squares target (LS; for example, as defined in Afonine et al., 2005a ▶ ), an amplitude-based maximum-likelihood target (ML; for example, as defined in Afonine et al., 2005a ▶ ) or a phased maximum-likelihood target (MLHL; Pannu et al., 1998 ▶ ). For refinement of coordinates, T_exp can also be defined in real space (see below).
T_{xyz_restraints} and T_{adp_restraints} are restraint terms that introduce a priori knowledge, thus helping to compensate for the insufficient amount of experimental data owing to finite resolution or incompleteness of the data set typically observed in macromolecular crystallography. Note that the restraint terms are not used in certain situations, for example rigid-body coordinate refinement, TLS refinement, occupancy refinement, f′/f′′ refinement or if the data-to-parameter ratio is extremely high. In these cases the total refinement target is reduced to T_exp.
The weights wxc_scale, wxc and wc (or wxu_scale, wxu and wu, correspondingly) are used to balance the relative contributions of experimental and restraints terms. The automatic weight-estimation procedure is implemented as described in Brünger et al. (1989 ▶ ) and Adams et al. (1997 ▶ ) with some variations and is used by default to calculate wxc and wxu. The long-term experience of using a similar scheme in CNS and PHENIX indicates that it is typically robust and provides a good estimate of weights in most cases, especially at medium to high resolution. In cases where this procedure fails to produce optimal weights, a more time-intensive automatic weight-optimization procedure may be used, as originally described by Brünger (1992 ▶ ) and further adopted by Afonine et al. (2011 ▶ ), in which an array of wxc_scale or wxu_scale values is systematically tested in order to find the value that minimizes R_free while keeping the overall model geometry deviations from ideality within a predefined range. The weight wc (or wu, correspondingly) is used to scale the restraints contribution, mostly duplicating the function of wxc_scale (or wxu_scale), while allowing an important unique option of excluding the restraints if necessary (for example, at subatomic resolution). Setting wc = 0 (or wu = 0) reduces the total refinement target to T_exp.
In maximum-likelihood (ML)-based refinement (Pannu & Read, 1996 ▶ ; Bricogne & Irwin, 1996 ▶ ; Murshudov et al., 1997 ▶ ; Adams et al., 1997 ▶ ; Pannu et al., 1998 ▶ ) the calculation of the ML target (Lunin & Urzhumtsev, 1984 ▶ ; Read, 1986 ▶ , 1990 ▶ ; Lunin & Skovoroda, 1995 ▶ ) requires an estimation of model error parameters, which depend on the current atomic parameters and bulk-solvent model and scales. Since the atomic parameters and the bulk-solvent model are updated during refinement, the ML error model has to be updated correspondingly, as described in Lunin & Skovoroda (1995 ▶ ), Urzhumtsev et al. (1996 ▶ ) and Afonine et al. (2005a ▶ ).

Afonine P.V., Grosse-Kunstleve R.W., Echols N., Headd J.J., Moriarty N.W., Mustyakimov M., Terwilliger T.C., Urzhumtsev A., Zwart P.H, & Adams P.D. (2012). Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallographica Section D: Biological Crystallography, 68(Pt 4), 352-367.

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Publication 2012

Crystallography Dietary Fiber Human Body Muscle Rigidity Solvents

Most recents protocols related to «Dietary Fiber»

Lyophilized Silk Powder Formulations

Not available on PMC !

Example 19

TABLE 37

Embodiments of lyophilized silk powders

Silk SolutionTreatmentSoluble

~60 kDa silk, 6% silk, pH = 7-8lyopholize and cut withno

blender

~60 kDa silk, 6% silk, pH = 10lyopholize and cut withno

blender

~25 kDa silk, 6% silk, pH = 7-8lyopholize and cut withyes

blender

~25 kDa silk, 6% silk, pH = 10lyopholize and cut withyes

blender

The above silk solutions were transformed to a silk powder through lyophilization to remove bulk water and chopping to small pieces with a blender. pH was adjusted with sodium hydroxide. Low molecular weight silk (−25 kDa) was soluble while high molecular weight silk (−60 kDa) was not.

The lyophilized silk powder can be advantageous for enhanced storage control ranging from 10 days to 10 years depending on storage and shipment conditions. The lyophilized silk powder can also be used as a raw ingredient in the pharmaceutical, medical, consumer, and electronic markets. Additionally, lyophilized silk powder can be re-suspended in water, HFIP, or an organic solution following storage to create silk solutions of varying concentrations, including higher concentration solutions than those produced initially.

In an embodiment, aqueous pure silk fibroin-based protein fragment solutions of the present disclosure comprising 1%, 3%, and 5% silk by weight were each dispensed into a 1.8 L Lyoguard trays, respectively. All 3 trays were placed in a 12 ft²lyophilizer and a single run performed. The product was frozen with a shelf temperature of ≤−40° C. and held for 2 hours. The compositions were then lyophilized at a shelf temperature of −20° C., with a 3 hour ramp and held for 20 hours, and subsequently dried at a temperature of 30° C., with a 5 hour ramp and held for about 34 hours. Trays were removed and stored at ambient conditions until further processing. Each of the resultant lyophilized silk fragment compositions were able to dissolve in aqueous solvent and organic solvent to reconstitute silk fragment solutions between 0.1 wt % and 8 wt %. Heating and mixing were not required but were used to accelerate the dissolving rate. All solutions were shelf-stable at ambient conditions.

In an embodiment, an aqueous pure silk fibroin-based protein fragment solution of the present disclosure, fabricated using a method of the present disclosure with a 30 minute boil, has a molecular weight of about 57 kDa, a polydispersity of about 1.6, inorganic and organic residuals of less than 500 ppm, and a light amber color.

In an embodiment, an aqueous pure silk fibroin-based protein fragment solution of the present disclosure, fabricated using a method of the present disclosure with a 60 minute boil, has a molecular weight of about 25 kDa, a polydispersity of about 2.4, inorganic and organic residuals of less than 500 ppm, and a light amber color.

US11878070B2. Silk-based moisturizer compositions and methods thereof (2024-01-23). Evolved By Nature, Inc. [US]. Inventors: Gregory H. Altman [US], Dylan S. Haas [US], Kevin T. Healy [US].

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Patent 2024

Amber ARID1A protein, human Dietary Fiber Fibroins Freeze Drying Freezing Furuncles Light Pharmaceutical Preparations Powder Proteins Silk Sodium Hydroxide Solvents

Infrasound Detection using Fiber Flow Sensors

Not available on PMC !

Example 2

In some applications, an infrasonic sensor is desired, with a frequency response f_lthat extends to an arbitrarily low frequency, such as a tenth of hundredth of a Hertz. Such a sensor might be useful for detecting fluid flows associated with movement of objects, acoustic impulses, and the like. Such an application works according to the same principles as the sonic sensor applications, though the length of individual runs of fibers might have to be greater.

In addition, the voltage response of the electrode output to movements is proportional to the velocity of the fiber, and therefore one would typically expect that the velocity of movement of fluid particles at infrasonic frequencies would low, leading to low output voltages. However, in some applications, the fluid movement is macroscopic, and therefore velocities may be appreciable. For example, in wake detection applications, the amplitude may be quite robust.

Generally, low frequency sound is detected by sensors which are sensitive to pressure such as infrasound microphones and microbarometers. As pressure is a scaler, multiple sensors should be used to identify the source location. Meanwhile, due to the long wave length of low frequency sound, multiple sensors have to be aligned far away to distinguish the pressure difference so as to identify the source location. As velocity is a vector, sensing sound flow can be beneficial to source localization. There is no available flow sensor that can detect infrasound flow in a broad frequency range with a flat frequency response currently. However, as discussed above, thin fibers can follow the medium (air, water) movement with high velocity transfer ratio (approximate to 1 when the fiber diameter is in the range of nanoscale), from zero Hertz to tens of thousands Hertz. If a fiber is thin enough, it can follow the medium (air, water) movement nearly exactly. This provides an approach to detect low frequency sound flow directly and effectively, with flat frequency response in a broad frequency range. This provides an approach to detect low frequency sound flow directly. The fiber motion due to the medium flow can be transduced by various principles such as electrodynamic sensing of the movement of a conductive fiber within a magnetic field, capacitive sensing, optical sensing and so on. Application example based on electromagnetic transduction is given. It can detect sound flow with flat frequency response in a broad frequency range.

For the infrasound detection, this can be used to detect manmade and natural events such as nuclear explosion, volcanic explosion, severe storm, chemical explosion. For the source localization and identification, the fiber flow sensor can be applied to form a ranging system and noise control to find and identify the low frequency source. For the low frequency flow sensing, this can also be used to detect air flow distribution in buildings and transportations such as airplanes, land vehicles, and seafaring vessels.

The infrasound pressure sensors are sensitive to various environmental parameters such as pressure, temperature, moisture. Limited by the diaphragm of the pressure sensor, there is resonance. The fiber flow sensor avoids the key mentioned disadvantages above. The advantages include, for example: Sensing sound flow has inherent benefit to applications which require direction information, such as source localization. The fiber flow sensor is much cheaper to manufacture than the sound pressure sensor. Mechanically, the fiber can follow the medium movement exactly in a broad frequency range, from infrasound to ultrasound. If the fiber movement is transduced to the electric signal proportionally, for example using electromagnetic transduction, the flow sensor will have a flat frequency response in a broad frequency range. As the flow sensor is not sensitive to the pressure, it has a large dynamic range. As the fiber motion is not sensitive to temperature, the sensor is robust to temperature variation. The fiber flow sensor is not sensitive to moisture. The size of the flow sensor is small (though parallel arrays of fibers may consume volume). The fiber flow sensor can respond to the infrasound instantly.

Note that a flow sensor is, or would be, sensitive to wind. The sensor may also respond to inertial perturbances. For example, the pressure in the space will be responsive to acceleration of the frame. This will cause bulk fluid flows of a compressible fluid (e.g., a gas), resulting in signal output due to motion of the sensor, even without external waves. This can be advantages and disadvantages depends on the detailed applications. For example, it can be used to detect flow distribution in the buildings. If used to detect infrasound, the wind influence be overcome by using an effective wind noise reduction approach.

US11869476B2. Acoustic metamaterial (2024-01-09). The Research Foundation for The State University of New York [US]. Inventors: Steven M. Hoffberg [US].

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Patent 2024

Acceleration Acoustics A Fibers Blast Injuries Blood Vessel Cloning Vectors Dietary Fiber Electric Conductivity Electricity Electromagnetics Fibrosis Magnetic Fields Movement Pressure Reading Frames Sound Sound Waves Toxic Epidermal Necrolysis Ultrasonics Vaginal Diaphragm Vibration Water Movements Wind

Microbial Seed Treatments for Enhanced Crop Performance

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Example 2

A. Seed Treatment with Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the isolated microbe as a seed coating, the corn will be planted and cultivated in the standard manner.

A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

B. Seed Treatment with Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the microbial consortium as a seed coating, the corn will be planted and cultivated in the standard manner.

A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

C. Treatment with Agricultural Composition Comprising Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

D. Treatment with Agricultural Composition Comprising Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

A. Seed Treatment with Isolated Microbe

A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.

The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.

B. Seed Treatment with Microbial Consortia

A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.

C. Treatment with Agricultural Composition Comprising Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the with the agricultural composition will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.

D. Treatment with Agricultural Composition Comprising Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

A. Seed Treatment with Isolated Microbe

A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.

B. Seed Treatment with Microbial Consortia

A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

C. Treatment with Agricultural Composition Comprising Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

D. Treatment with Agricultural Composition Comprising Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

The inoculants were prepared from isolates grown as spread plates on R2A incubated at 25° C. for 48 to 72 hours. Colonies were harvested by blending with sterile distilled water (SDW) which was then transferred into sterile containers. Serial dilutions of the harvested cells were plated and incubated at 25° C. for 24 hours to estimate the number of colony forming units (CFU) in each suspension. Dilutions were prepared using individual isolates or blends of isolates (consortia) to deliver 1×10⁵cfu/microbe/seed and seeds inoculated by either imbibition in the liquid suspension or by overtreatment with 5% vegetable gum and oil.

Seeds corresponding to the plants of table 15 were planted within 24 to 48 hours of treatment in agricultural soil, potting media or inert growing media. Plants were grown in small pots (28 mL to 200 mL) in either a controlled environment or in a greenhouse. Chamber photoperiod was set to 16 hours for all experiments on all species. Air temperature was typically maintained between 22-24° C.

Unless otherwise stated, all plants were watered with tap water 2 to 3 times weekly. Growth conditions were varied according to the trait of interest and included manipulation of applied fertilizer, watering regime and salt stress as follows:

- Low N—seeds planted in soil potting media or inert growing media with no applied N fertilizer
- Moderate N—seeds planted in soil or growing media supplemented with commercial N fertilizer to equivalent of 135 kg/ha applied N
- Insol P—seeds planted in potting media or inert growth substrate and watered with quarter strength Pikovskaya's liquid medium containing tri-calcium phosphate as the only form phosphate fertilizer.
- Cold Stress—seeds planted in soil, potting media or inert growing media and incubated at 10° C. for one week before being transferred to the plant growth room.
- Salt stress—seeds planted in soil, potting media or inert growing media and watered with a solution containing between 100 to 200 mg/L NaCl.

Untreated (no applied microbe) controls were prepared for each experiment. Plants were randomized on trays throughout the growth environment. Between 10 and 30 replicate plants were prepared for each treatment in each experiment. Phenotypes were measured during early vegetative growth, typically before the V3 developmental stage and between 3 and 6 weeks after sowing. Foliage was cut and weighed. Roots were washed, blotted dry and weighed. Results indicate performance of treatments against the untreated control.

TABLE 15

StrainShootRoot

Microbe sp.IDCropAssayIOC (%)IOC (%)

Bosea thiooxidans123EfficacyEfficacy

overall100%100%

Bosea thiooxidans54522WheatEarly vigor - insol P30-40 —

Bosea thiooxidans54522RyegrassEarly vigor50-60 50-60

Bosea thiooxidans54522RyegrassEarly vigor - moderate P0-100-10

Duganella violaceinigra111EfficacyEfficacy

overall100%100%

Duganella violaceinigra66361TomatoEarly vigor0-100-10

Duganella violaceinigra66361TomatoEarly vigor30-40 40-50

Duganella violaceinigra66361TomatoEarly vigor20-30 20-30

Herbaspirillum huttiense222Efficacy—

overall100%

Herbaspirillum huttiense54487WheatEarly vigor - insol P30-40 —

Herbaspirillum huttiense60507MaizeEarly vigor - salt stress0-100-10

Janthinobacterium sp.222Efficacy—

Overall100%

Janthinobacterium sp.54456WheatEarly vigor - insol P30-40 —

Janthinobacterium sp.54456WheatEarly vigor - insol P0-10—

Janthinobacterium sp.63491RyegrassEarly vigor - drought0-100-10

stress

Massilia niastensis112EfficacyEfficacy

overall80%80%

Massilia niastensis55184WheatEarly vigor - salt stress0-1020-30

Massilia niastensis55184WinterEarly vigor - cold stress0-1010-20

wheat

Massilia niastensis55184WinterEarly vigor - cold stress20-30 20-30

wheat

Massilia niastensis55184WinterEarly vigor - cold stress10-20 10-20

wheat

Massilia niastensis55184WinterEarly vigor - cold stress<0<0

wheat

Novosphingobium rosa211EfficacyEfficacy

overall100%100%

Novosphingobium rosa65589MaizeEarly vigor - cold stress0-100-10

Novosphingobium rosa65619MaizeEarly vigor - cold stress0-100-10

Paenibacillus amylolyticus111EfficacyEfficacy

overall100%100%

Paenibacillus amylolyticus66316TomatoEarly vigor0-100-10

Paenibacillus amylolyticus66316TomatoEarly vigor10-20 10-20

Paenibacillus amylolyticus66316TomatoEarly vigor0-100-10

Pantoea agglomerans323EfficacyEfficacy

33%50%

Pantoea agglomerans54499WheatEarly vigor - insol P40-50 —

Pantoea agglomerans57547MaizeEarly vigor - low N<00-10

Pantoea vagans55529MaizeEarly vigor<0<0

(formerly P. agglomerans)

Polaromonas ginsengisoli111EfficacyEfficacy

66%100%

Polaromonas ginsengisoli66373TomatoEarly vigor0-100-10

Polaromonas ginsengisoli66373TomatoEarly vigor20-30 30-40

Polaromonas ginsengisoli66373TomatoEarly vigor<010-20

Pseudomonas fluorescens122Efficacy—

100%

Pseudomonas fluorescens54480WheatEarly vigor - insol P>100 —

Pseudomonas fluorescens56530MaizeEarly vigor - moderate N0-10—

Rahnella aquatilis334EfficacyEfficacy

80%63%

Rahnella aquatilis56532MaizeEarly vigor - moderate N10-20 —

Rahnella aquatilis56532MaizeEarly vigor - moderate N0-100-10

Rahnella aquatilis56532WheatEarly vigor - cold stress0-1010-20

Rahnella aquatilis56532WheatEarly vigor - cold stress<00-10

Rahnella aquatilis56532WheatEarly vigor - cold stress10-20 <0

Rahnella aquatilis57157RyegrassEarly vigor<0—

Rahnella aquatilis57157MaizeEarly vigor - low N0-100-10

Rahnella aquatilis57157MaizeEarly vigor - low N0-10<0

Rahnella aquatilis58013MaizeEarly vigor0-1010-20

Rahnella aquatilis58013MaizeEarly vigor - low N0-10<0

Rhodococcus erythropolis313Efficacy—

66%

Rhodococcus erythropolis54093MaizeEarly vigor - low N40-50 —

Rhodococcus erythropolis54299MaizeEarly vigor - insol P>100 —

Rhodococcus erythropolis54299MaizeEarly vigor<0<0

Stenotrophomonas chelatiphaga611EfficacyEfficacy

60%60%

Stenotrophomonas chelatiphaga54952MaizeEarly vigor0-100-10

Stenotrophomonas chelatiphaga47207MaizeEarly vigor<0 0

Stenotrophomonas chelatiphaga64212MaizeEarly vigor0-1010-20

Stenotrophomonas chelatiphaga64208MaizeEarly vigor0-100-10

Stenotrophomonas chelatiphaga58264MaizeEarly vigor<0<0

Stenotrophomonas maltophilia612EfficacyEfficacy

43%66%

Stenotrophomonas maltophilia54073MaizeEarly vigor - low N50-60 —

Stenotrophomonas maltophilia54073MaizeEarly vigor<00-10

Stenotrophomonas maltophilia56181MaizeEarly vigor0-10<0

Stenotrophomonas maltophilia54999MaizeEarly vigor0-100-10

Stenotrophomonas maltophilia54850MaizeEarly vigor 00-10

Stenotrophomonas maltophilia54841MaizeEarly vigor<00-10

Stenotrophomonas maltophilia46856MaizeEarly vigor<0<0

Stenotrophomonas rhizophila811EfficacyEfficacy

12.5%37.5%

Stenotrophomonas rhizophila50839MaizeEarly vigor<0<0

Stenotrophomonas rhizophila48183MaizeEarly vigor<0<0

Stenotrophomonas rhizophila45125MaizeEarly vigor<0<0

Stenotrophomonas rhizophila46120MaizeEarly vigor<00-10

Stenotrophomonas rhizophila46012MaizeEarly vigor<0<0

Stenotrophomonas rhizophila51718MaizeEarly vigor0-100-10

Stenotrophomonas rhizophila66478MaizeEarly vigor<0<0

Stenotrophomonas rhizophila65303MaizeEarly vigor<00-10

Stenotrophomonas terrae221EfficacyEfficacy

50%50%

Stenotrophomonas terrae68741MaizeEarly vigor<0<0

Stenotrophomonas terrae68599MaizeEarly vigor<00-10

Stenotrophomonas terrae68599Capsicum *Early vigor20-30 20-30

Stenotrophomonas terrae68741Capsicum *Early vigor10-20 20-30

The data presented in table 15 describes the efficacy with which a microbial species or strain can change a phenotype of interest relative to a control run in the same experiment. Phenotypes measured were shoot fresh weight and root fresh weight for plants growing either in the absence of presence of a stress (assay). For each microbe species, an overall efficacy score indicates the percentage of times a strain of that species increased a both shoot and root fresh weight in independent evaluations. For each species, the specifics of each independent assay is given, providing a strain ID (strain) and the crop species the assay was performed on (crop). For each independent assay the percentage increase in shoot and root fresh weight over the controls is given.

US11871752B2. Agriculturally beneficial microbes, microbial compositions, and consortia (2024-01-16). BIOCONSORTIA, INC. [US]. Inventors: Peter Wigley [NZ], Susan Turner [US], Caroline George [NZ], Thomas Williams [US], Kelly Roberts [US], Graham Hymus [US], Kelvin Lau [NZ].

Full text: Click here

Patent 2024

Ammonia Asparagine Aspartic Acid Biological Assay Bosea thiooxidans Calcium Phosphates Capsicum Cells Chlorophyll Cold Shock Stress Cold Temperature Crop, Avian Dietary Fiber DNA Replication Droughts Drought Tolerance Embryophyta Environment, Controlled Farmers Fertilization Glutamic Acid Glutamine Glycine Growth Disorders Herbaspirillum Herbaspirillum huttiense Leucine Lolium Lycopersicon esculentum Lysine Maize Massilia niastensis Methionine Microbial Consortia Nitrates Nitrites Nitrogen Novosphingobium rosa Paenibacillus Paenibacillus amylolyticus Pantoea agglomerans Pantoea vagans Phenotype Phosphates Photosynthesis Plant Development Plant Embryos Plant Leaves Plant Proteins Plant Roots Plants Polaromonas ginsengisoli Pseudoduganella violaceinigra Pseudomonas Pseudomonas fluorescens Rahnella Rahnella aquatilis Retention (Psychology) Rhodococcus erythropolis Rosa Salt Stress Sodium Chloride Sodium Chloride, Dietary Stenotrophomonas chelatiphaga Stenotrophomonas maltophilia Stenotrophomonas rhizophila Stenotrophomonas terrae Sterility, Reproductive Strains Technique, Dilution Threonine Triticum aestivum Tryptophan Tyrosine Vegetables Zea mays

Optimizing FAAH Inhibitor Potency

Not available on PMC !

Example 37

To improve inhibition potency relative to FAAH, various portions of the t-TUCB molecule were modified to identify potential FAAH pharmacophores. The 4-trifluoromethoxy group on t-TUCB was modified to the unsubstituted ring (A-3), 4-fluorophenyl (A-2) or 4-chlorophenyl (A-26). Potency on both sEH and FAAH increased as the size and hydrophobicity of the para position substituent increased, with 4-trifluoromethoxy being the most potent on both enzymes. Substituting the aromatic ring for a cyclohexane (A-3) or adamantane (A-4) resulted in a complete loss in activity against FAAH. Results are summarized in Table 1 below.

TABLE 1

Modification of the 4-trifluoromethoxy group of t-TUCB

[Figure (not displayed)]

Stereo-IC₅₀(nM)

R²—N(R³)—L¹—chemistryhsEHhFAAH

t-TUCB[Figure (not displayed)]
[Figure (not displayed)]
trans0.8140

A1-[Figure (not displayed)]
[Figure (not displayed)]
trans309,200

A-2[Figure (not displayed)]
[Figure (not displayed)]
trans184,600

A-26[Figure (not displayed)]
[Figure (not displayed)]
trans7380

A-3[Figure (not displayed)]
[Figure (not displayed)]
trans6>1,000

A-4[Figure (not displayed)]
[Figure (not displayed)]
trans3>10,000

A-10[Figure (not displayed)]
[Figure (not displayed)]
81,800

Next, the center portion of the molecule was modified to further investigate the specificity of t-TUCB on FAAH. Switching the cyclohexane linker to a cis conformation (A-5) resulted in a 20-fold loss of potency while removing the ring and replacing it with a butane chain (A-6) resulted in a completely inactive compound. While this suggests the compound must fit a relatively specific conformation in the active site to be active, we found the aromatic linker had essentially the same potency on FAAH (A-7). Although many potent urea-based FAAH inhibitors have a piperidine as the carbamoylating nitrogen, the modification to piperidine here reduced potency 13-fold. Results are summarized in Table 2 below.

TABLE 2

Modification of the central portion of t-TUCB

[Figure (not displayed)]

Stereo-IC₅₀(nM)

R²—N(R³)—L¹—chemistryhsEHhFAAH

t-TUCB[Figure (not displayed)]
[Figure (not displayed)]
trans0.8140

A-5[Figure (not displayed)]
[Figure (not displayed)]
cis22,800

A-6[Figure (not displayed)]
[Figure (not displayed)]
15>10,000

A-7[Figure (not displayed)]
[Figure (not displayed)]
7170

Since none of the modifications at this point improved potency towards FAAH, we focused on the benzoic acid portion of the molecule as shown in Table 3. To determine the importance of the terminal acid, the corresponding aldehyde (A-20) and alcohol (A-24) in addition to the amide (A-19) and nitrile (A-11) were tested. While the amide had slightly improved potency, the more reduced forms of the acid (A-20 and A-24) and amide (A-11) had substantially less activity on FAAH. Converting the benzoic acid to a phenol (A-21) increased potency while the anisole (A-22) was completely inactive. Since the amide and acid appeared to be active, the amide bioisostere oxadiazole (A-25) was tested and had 38-fold less potency than the initial compound.

TABLE 3

Modification of the benzoic acid portion of t-TUCB

[Figure (not displayed)]

IC₅₀(nM)

R¹hsEHhFAAH

t-TUCB[Figure (not displayed)]
0.8140

A-11[Figure (not displayed)]
5>10,000

A-19[Figure (not displayed)]
270

A-20[Figure (not displayed)]
41,100

A-24[Figure (not displayed)]
35,800

A-21[Figure (not displayed)]
2120

A-22[Figure (not displayed)]
3>10,000

A-25[Figure (not displayed)]
45,300

Since the substrates for FAAH tend to be relatively hydrophobic lipids, we speculated that conversion of the acid and primary amide to the corresponding esters or substituted amides would result in improved potency. The methyl ester (A-12) had 4-fold improved potency relative to the acid. Improving the bulk of the ester with an isopropyl group (A-13) results in a 11-fold loss in potency relative to the methyl ester. However, the similar potency of the benzyl ester (A-14) to the methyl ester demonstrates the bulk but not the size affects potency. Reversing the orientation of the ester (A-23) reduces the potency 3.4-fold. Relative to the primary amide, the methyl (A-18), ethanol (A-15) and glycyl (A-16) amides were all slightly less potent; however, the benzyl amide (A-27) was substantially less potent (16-fold). Generating the methyl ester of the glycyl amide (A-17) increased the potency 4-fold compared to the corresponding acid.

TABLE 4

Potency of ester and amide conjugates of t-TUCB

[Figure (not displayed)]

IC₅₀(nM)

R¹hsEHhFAAH

t-TUCB[Figure (not displayed)]
0.8140

A-12[Figure (not displayed)]
735

A-13[Figure (not displayed)]
5400

A-14[Figure (not displayed)]
324

A-23[Figure (not displayed)]
4120

A-18[Figure (not displayed)]
2170

A-15[Figure (not displayed)]
2100

A-16[Figure (not displayed)]
2130

A-17[Figure (not displayed)]
330

A-27[Figure (not displayed)]
51,100

US11873288B2. Inhibitors for soluble epoxide hydrolase (sEH) and fatty acid amide hydrolase (FAAH) (2024-01-16). The Regents of the University of California [US]. Inventors: Bruce Hammock [US], Sean Kodani [US].

Full text: Click here

Patent 2024

Acids Adamantane Aldehydes Amides anisole Benzoic Acid Butanes Cyclohexane Dietary Fiber Enzymes Esters Ethanol inhibitors Lipids Nitriles Nitrogen Oxadiazoles Phenol piperidine Psychological Inhibition SOCS2 protein, human Urea

Formulation of Levothyroxine Sodium Solution

Not available on PMC !

Example 1

S. NoIngredientsQuantity

1Levothyroxine sodium0.01-1 mg

2Arginine0.01-4 mg

3Propylene glycol0.01-1 ml

4Sodium hydroxideq.s

5Ultrapure waterq.s to 0.1-2 ml

Manufacturing Process

Ultrapure water was taken in a compounding vessel and arginine was added and stirred. Propylene glycol was added to the solution and stirred. pH of the solution was adjusted to 11±0.5 by the addition of sodium hydroxide solution. Then the bulk solution was cooled to 2° C. to 8° C. Levothyroxine sodium was added and stirred till a clear solution was obtained, while maintaining the temperature at 5±3° C. The solution was filtered, followed by filling into suitable containers.

US11865180B2. Levothyroxine formulations for oral use (2024-01-09). LEIUTIS PHARMACEUTICALS LLP [IN]. Inventors: Kocherlakota Chandrashekhar [IN], Banda Nagaraju [IN].

Full text: Click here

Patent 2024

Arginine Blood Vessel Dietary Fiber hydroxide ion Levothyroxine Sodium Propylene Glycol Sodium Hydroxide Thyroxine

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The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.

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More about "Dietary Fiber"

Dietary fiber, also known as roughage or bulk, refers to the indigestible components of plant-based foods that provide numerous health benefits.
It includes both soluble and insoluble fibers, each with distinct roles in maintaining physiological well-being.
Soluble fibers, such as pectin and gums, can help lower cholesterol and blood sugar levels, making them particularly beneficial for individuals with cardiovascular disease or type 2 diabetes.
Insoluble fibers, like cellulose and hemicellulose, promote regular bowel movements and overall gut health, reducing the risk of certain gastrointestinal issues.
Consuming a diet rich in dietary fiber has been linked to a reduced risk of various chronic conditions, including cardiovascular disease, type 2 diabetes, and certain types of cancer.
This is attributed to the fiber's ability to modulate metabolic processes, reduce inflammation, and support a healthy gut microbiome.
Researchers can optimize their dietary fiber studies by utilizing cutting-edge technologies and techniques, such as HiSeq 2500, RNeasy Mini Kit, NovaSeq 6000, NextSeq 500, TRIzol, HiSeq 2000, TRIzol reagent, HiSeq 4000, and the Agilent 2100 Bioanalyzer.
These tools can provide enhanced reproducibility and accuracy, allowing for more reliable and impactful findings.
PubCompare.ai can further streamline the research process by helping researchers easily locate the most reliable and reproducbile protocols from literature, preprints, and patents using AI-driven comparisons.
This can optimize the research workflow and ensure that the most effective methods are used for dietary fiber studies.