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Photosynthesis

Q: What are the different types of photosynthesis?

There are two main types of photosynthesis: oxygenic photosynthesis and anoxygenic photosynthesis. Oxygenic photosynthesis is the most common type, where plants, algae, and cyanobacteria use carbon dioxide, water, and sunlight to produce glucose and oxygen. Anoxygenic photosynthesis, on the other hand, occurs in some bacteria and does not produce oxygen as a byproduct.

Q: How can PubCompare.ai help with photosynthesis research?

PubCompare.ai can be a valuable tool for optimizing photosynthesis research. The platform allows you to screen protocol literature more efficiently by leveraging AI to pinpoint critical insights. PubCompare.ai's AI-driven analysis can help researchers identify the most effective protocols related to photosynthesis for their specific research goals, enabling them to choose the best option for reproducibility and accuracy. This can lead to enhanced experimental design, improved data quality, and accelerated progress in photosynthesis studies.

Q: What are some practical applications of photosynthesis?

Photosynthesis has a wide range of practical applications, including: 1) Improved crop yields: Understanding and optimizing photosynthesis can lead to the development of more productive crop varieties. 2) Biofuel production: Photosynthetic organisms, such as algae, can be used to produce biofuels, providing a renewable energy source. 3) Carbon sequestration: Enhancing photosynthesis in plants and other organisms can help remove carbon dioxide from the atmosphere, mitigating the effects of climate change. 4) Oxygen production: The oxygen produced by photosynthesis is essential for the survival of many lifeforms on Earth, including humans.

Q: How does photosynthesis differ from respiration?

Photosynthesis and respiration are complementary processes that are essential for life. During photosynthesis, organisms use sunlight, water, and carbon dioxide to produce glucose and oxygen. In contrast, respiration is the process by which organisms use oxygen to break down glucose and produce energy in the form of ATP. While photosynthesis is a anabolic process that builds up complex organic molecules, respiration is a catabolic process that breaks down these molecules to release energy.

Q: What are some common challenges in studying photosynthesis?

Some common challenges in studying photosynthesis include: 1) Complexity of the process: Photosynthesis involves a series of intricate biochemical reactions, making it a challenge to fully understand and optimize. 2) Environmental factors: Photosynthesis is influenced by a variety of environmental factors, such as light intensity, temperature, and nutrient availability, which can be difficult to control in experimental settings. 3) Genetic and molecular regulation: The genes and molecular mechanisms that regulate photosynthesis are not fully elucidated, requiring more research to unravel the underlying complexities. 4) Scalability: Translating insights from lab-scale experiments to real-world applications, such as improving crop yields or biofuel production, can be a significant challenge.

Photosynthesis is the process by which plants and other organisms use sunlight, water, and carbon dioxide to create oxygen and energy in the form of sugar.
This complex biochemical reaction is essential for the survival of many lifeforms on Earth.
Photosynthesis involves a series of intricate steps, including light absorption, electron transport, and the reduction of carbon dioxide to produce organic compounds.
Understanding the mechanisms and regulation of photosynthesis is crucial for advancing research in fields such as plant biology, renewable energy, and environmental science.
Optimizing photosynthesis can lead to improved crop yields, enhanced biofuel production, and innovative approaches to carbon sequestration.
Researchers studying photosynthesis can leverage PubCompare.ai to idetnify the most effective protocols from literature, preprints, and patents, enhancing the reproducibility and accuracy of their work.

Most cited protocols related to «Photosynthesis»

Comprehensive Plant Trait Database (TRY)

The TRY data compilation focuses on 52 groups of traits characterizing the vegetative and regeneration stages of plant life cycle, including growth, reproduction, dispersal, establishment and persistence (Table 2). These groups of traits were collectively agreed to be the most relevant for plant life-history strategies, vegetation modelling and global change responses on the basis of existing shortlists (Grime et al., 1997 ; Weiher et al., 1999 ; Lavorel & Garnier, 2002 ; Cornelissen et al., 2003b; Díaz et al., 2004 ; Kleyer et al., 2008 ) and wide consultation with vegetation modellers and plant ecologists. They include plant traits sensu stricto, but also ‘performances’ (sensuViolle et al., 2007 ), such as drought tolerance or phenology.
Quantitative traits vary within species as a consequence of genetic variation (among genotypes within a population/species) and phenotypic plasticity. Ancillary information is necessary to understand and quantify this variation. The TRY dataset contains information about the location (e.g. geographical coordinates, soil characteristics), environmental conditions during plant growth (e.g. climate of natural environment or experimental treatment), and information about measurement methods and conditions (e.g. temperature during respiration or photosynthesis measurements). Ancillary data also include primary references.
By preference individual measurements are compiled in the database, like single respiration measurements or the wood density of a specific individual tree. The dataset therefore includes multiple measurements for the same trait, species and site. For some traits, e.g. leaf longevity, such data are only rarely available on single individuals (e.g. Reich et al., 2004 ), and data are expressed per species per site instead. Different measurements on the same plant (resp. organ) are linked to form observations that are hierarchically nested. The database structure ensures that (1) the direct relationship between traits and ancillary data and between different traits that have been measured on the same plant (resp. organ) is maintained and (2) conditions (e.g. at the stand level) can be associated with the individual measurements (Kattge et al., 2010 ). The structure is consistent with the Extensible Observation Ontology (OBOE; Madin et al., 2008 (link)), which has been proposed as a general basis for the integration of different data streams in ecology.
The TRY dataset combines several preexisting databases based on a wide range of primary data sources, which include trait data from plants grown in natural environments and under experimental conditions, obtained by a range of scientists with different methods. Trait variation in the TRY dataset therefore reflects natural and potential variation on the basis of individual measurements at the level of single organs, and variation due to different measurement methods and measurement error (random and bias).

Kattge J., Díaz S., Lavorel S., Prentice I.C., Leadley P., Bönisch G., Garnier E., Westoby M., Reich P.B., Wright I.J., Cornelissen J.H., Violle C., Harrison S.P., Van Bodegom P.M., Reichstein M., Enquist B.J., Soudzilovskaia N.A., Ackerly D.D., Anand M., Atkin O., Bahn M., Baker T.R., Baldocchi D., Bekker R., Blanco C.C., Blonder B., Bond W.J., Bradstock R., Bunker D.E., Casanoves F., Cavender-Bares J., Chambers J.Q., Chapin FS I.I.I., Chave J., Coomes D., Cornwell W.K., Craine J.M., Dobrin B.H., Duarte L., Durka W., Elser J., Esser G., Estiarte M., Fagan W.F., Fang J., Fernández-Méndez F., Fidelis A., Finegan B., Flores O., Ford H., Frank D., Freschet G.T., Fyllas N.M., Gallagher R.V., Green W.A., Gutierrez A.G., Hickler T., Higgins S.I., Hodgson J.G., Jalili A., Jansen S., Joly C.A., Kerkhoff A.J., Kirkup D., Kitajima K., Kleyer M., Klotz S., Knops J.M., Kramer K., Kühn I., Kurokawa H., Laughlin D., Lee T.D., Leishman M., Lens F., Lenz T., Lewis S.L., Lloyd J., Llusià J., Louault F., Ma S., Mahecha M.D., Manning P., Massad T., Medlyn B.E., Messier J., Moles A.T., Müller S.C., Nadrowski K., Naeem S., Niinemets Ü., Nöllert S., Nüske A., Ogaya R., Oleksyn J., Onipchenko V.G., Onoda Y., Ordoñez J., Overbeck G., Ozinga W.A., Patiño S., Paula S., Pausas J.G., Peñuelas J., Phillips O.L., Pillar V., Poorter H., Poorter L., Poschlod P., Prinzing A., Proulx R., Rammig A., Reinsch S., Reu B., Sack L., Salgado-Negret B., Sardans J., Shiodera S., Shipley B., Siefert A., Sosinski E., Soussana J.F., Swaine E., Swenson N., Thompson K., Thornton P., Waldram M., Weiher E., White M., White S., Wright S.J., Yguel B., Zaehle S., Zanne A.E, & Wirth C. (2011). TRY – a global database of plant traits. Global Change Biology, 17(9), 2905-2935.

Publication 2011

Cell Respiration Climate Drought Tolerance Genetic Diversity Genotype Growth Disorders Life History Strategies Phenotypic Plasticity Photosynthesis Plant Diseases Plant Leaves Plants Regeneration Reproduction Respiratory Rate Therapies, Investigational Trees

Assessing Vegetation Exposure via Satellite Imagery

Exposure to vegetation around each participant’s home address was estimated using a satellite image–based vegetation index. Chlorophyll in plants absorbs visible light (0.4–0.7 μm) for use in photosynthesis, whereas leaves reflect near-infrared light (0.7–1.1 μm). The Normalized Difference Vegetation Index (NDVI) calculates the ratio of the difference between the near-infrared region and red reflectance to the sum of these two measures and ranges from –1.0 to 1.0, with larger values indicating higher levels of vegetative density (Kriegler et al. 1969 ). For this study, we used data from the Moderate-resolution Imaging Spectroradiometer (MODIS) from NASA’s Terra satellite. MODIS provides images every 16 days at a 250-m resolution (Carroll et al. 2004 ).
We used geographic information systems (GIS) software from ArcMap (ESRI, Redlands, CA) to estimate the mean NDVI value inside radii of 250- and 1,250-m buffers around each participant’s home. We chose the 250-m radius as a measure of greenness directly accessible outside each home and the 1,250-m radius as a measure of greenness within a 10- to 15-min walk based on prior work within the Nurses’ Health Study cohorts on neighborhood environments and health behaviors (James et al. 2014 (link)). We created a seasonally time-varying measure based on the NDVI for a representative month in each season (January, April, July, and October) (Figure 1B–D). Two exposure metrics were calculated for each radius: contemporaneous NDVI (the greenness value for the current season), to reflect short-term exposure to greenness, and cumulative average NDVI (updated based on changes in seasonal NDVI as well as on changes in address), to reflect long-term exposure to greenness. For both exposure metrics, exposures were updated as NDVI changed over time as well as when participants moved to new residential addresses (updated based on the receipt of a biennial questionnaire with a new residential address).

James P., Hart J.E., Banay R.F, & Laden F. (2016). Exposure to Greenness and Mortality in a Nationwide Prospective Cohort Study of Women. Environmental Health Perspectives, 124(9), 1344-1352.

Publication 2016

Buffers Chlorophyll Infrared Rays Light, Visible Nurses Photosynthesis Plants Radius

Growth, Photosynthesis, and Nitric Oxide Estimation

Growth was measured in terms of fresh weight. Seedlings were selected randomly from control and treated samples and then their fresh weight was determined. For the estimation of photosynthetic pigments (total chlorophyll, chlorophyll a + chlorophyll b), the method of Lichtenthaler (1987) (link) was adopted. For the assessment of photosynthetic performance, chlorophyll a fluorescence measurements were taken in the dark adapted leaves of control and treated seedlings using hand held leaf fluorometer (FluorPen FP 100, Photos System Instrument, Czech Republic). The estimation of NO was performed according to the method of Zhou et al. (2005) (link) as described in Singh et al. (2015) (link).

Tripathi D.K., Mishra R.K., Singh S., Singh S., Vishwakarma K., Sharma S., Singh V.P., Singh P.K., Prasad S.M., Dubey N.K., Pandey A.C., Sahi S, & Chauhan D.K. (2017). Nitric Oxide Ameliorates Zinc Oxide Nanoparticles Phytotoxicity in Wheat Seedlings: Implication of the Ascorbate–Glutathione Cycle. Frontiers in Plant Science, 8, 1.

Publication 2017

ARID1A protein, human Chlorophyll Chlorophyll A chlorophyll b Fluorescence Photosynthesis Pigmentation Plant Leaves Seedlings

Root-Associated Microbiome Profiling

Seed sterility was verified by plating and deep-sequencing of homogenates from sterile seedlings (Supplementary Fig. 13). We established seedling growth, harvesting and DNA preparation pipelines as detailed in the specific sections below. We defined the bacterial community within each soil, and the community associated with plant roots across a number of controlled experimental variables: soil type, plant sample fraction, plant age and plant genotype. For plant age, we harvested roots from two developmental stages: at the formation of an inflorescence meristem (yng) and during fruiting when ≥50% of the rosette leaves were senescent (old). The former represents plants at the peak of photosynthetic conversion to carbon, whereas the latter represents a stage well after the source-sink shift has occurred, marking the change in carbon allocation from vegetal to reproductive utilization^{23 (link)}. We prepared two microbial sample fractions from each individual plant: a rhizosphere (bacteria contained in the layer of soil covering the outer surface of the root system that could be washed from roots in a buffer/detergent solution), and EC (bacteria from within the plant root system after sonication-based removal of the rhizoplane; Supplementary Fig. 1). We also collected control soil samples (soil treated in parallel, but without a plant grown in it).

Lundberg D.S., Lebeis S.L., Paredes S.H., Yourstone S., Gehring J., Malfatti S., Tremblay J., Engelbrektson A., Kunin V., del Rio T.G., Edgar R.C., Eickhorst T., Ley R.E., Hugenholtz P., Tringe S.G, & Dangl J.L. (2012). Defining the core Arabidopsis thaliana root microbiome. Nature, 488(7409), 86-90.

Publication 2012

Apicoectomy Bacteria Buffers Carbon Detergents Genotype Inflorescence Meristem Photosynthesis Plant Roots Plants Reproduction Rhizosphere Specimen Collection Sterility, Reproductive

Aiptasia Symbiodinium Transcriptome Library

A clonal line of Aiptasia pallida (clone CC7, available through the Pringle lab) hosting Symbiodinium of clade A was established from a single tiny propagule in a population obtained from Carolina Biological Supply (Burlington, NC) and grown into an abundant stock. Given the Symbiodinium clade harbored by this population, it is likely that the Aiptasia individual originated from the Florida Keys lineage. Approximately 500 anemones of various sizes were harvested from this stock under normal growth conditions (~26°C; salinity, ~33 ppt; light, ~40 μmol m^-2s^-1photosynthetic photon flux; 12-h light-dark cycle), blotted to remove excess water, and immediately frozen in liquid nitrogen. The anemones were then ground to a fine powder under liquid nitrogen using a ceramic mortar and pestle. The powder was weighed (~4 g) while still frozen and mixed with a proportional volume (50 ml) of TRIzol Reagent (Invitrogen, Carlsbad, CA); extraction was then performed in accordance with the manufacturer's instructions yielding ~5 mg of total RNA. This RNA was sent to Open Biosystems (Huntsville, AL), where it was tested for quality; mRNA was then isolated using oligo(dT)-coated magnetic particles (Seradyn, Indianapolis, IN), and cDNA was synthesized. Double-stranded cDNA was size fractionated to enrich for long reads, cloned into the vector pExpress1 (Express Genomics, Frederick, MD), and electroporated into E. coli strain DH10B. The resulting library was determined to contain ~96% recombinants with an average insert size of 1.95 kb. Sequencing was performed on 96-well capillary sequencing platforms (ABI 3700) at the DOE Joint Genome Institute (JGI, Walnut Creek, CA) and at the Genome Core Facility at the University of California, Merced, USA, CA.

Sunagawa S., Wilson E.C., Thaler M., Smith M.L., Caruso C., Pringle J.R., Weis V.M., Medina M, & Schwarz J.A. (2009). Generation and analysis of transcriptomic resources for a model system on the rise: the sea anemone Aiptasia pallida and its dinoflagellate endosymbiont. BMC Genomics, 10, 258.

Publication 2009

Anemone Biopharmaceuticals Capillaries Cloning Vectors DNA, Complementary DNA Library Escherichia coli Freezing Genome Growth Disorders Joints Juglans Light Nitrogen Oligonucleotides Photosynthesis Powder RNA, Messenger Salinity Strains trizol

Most recents protocols related to «Photosynthesis»

Microbial Seed Treatments for Enhanced Crop Performance

Not available on PMC !

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].

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

Herbicide Resistance Mutation Screening

Not available on PMC !

Example 11

Media was selected for use and kill curves developed as specified above. For selection, different techniques were utilized. Either a step wise selection was applied, or an immediate lethal level of herbicide was applied. In either case, all of the calli were transferred for each new round of selection. Selection was 4-5 cycles of culture with 3-5 weeks for each cycle. Cali were placed onto nylon membranes to facilitate transfer (200 micron pore sheets, Biodesign, Saco, Maine). Membranes were cut to fit 100×20 mm Petri dishes and were autoclaved prior to use 25-35 calli (average weight/calli being 22 mg) were utilized in every plate. In addition, one set of calli were subjected to selection in liquid culture media with weekly subcultures followed by further selection on semi-solid media. Mutant lines were selected using saflufenacil, 1,5-dimethyl-6-thioxo-3-(2,2,7-trifluoro-3-oxo-4-(prop-2-ynyl)-3,4-dihydro-2H-benzo[b][1,4]oxazin-6-yl)-1,3,5-triazinane-2,4-dione (CAS 1258836-72-4), flumioxazin, butafenacil, acifluorfen, lactofen, bifenox, sulfentrazone, and photosynthesis inhibitor diuron as negative control. Efficiencies of obtaining mutants was high either based on a percentage of calli that gave rise to a regenerable, mutant line or the number of lines as determined by the gram of tissue utilized.

US11866720B2. Transgenic or non-transgenic plants with mutated protoporphyrinogen oxidase having increased tolerance to herbicides (2024-01-09). BASF AGRO B.V. [NL]. Inventors: Raphael Aponte [DE], Stefan Tresch [DE], Matthias Witschel [DE], Jens Lerchl [DE], Dario Massa [DE], Tobias Seiser [DE], Thomas Mietzner [DE], Jill Marie Paulik [US], Chad Brommer [US].

Patent 2024

acifluorfen bifenox butafenacil Callosities Culture Media Diuron flumioxazin Herbicides Hyperostosis, Diffuse Idiopathic Skeletal lactofen N'-(2-chloro-4-fluoro-5-(3-methyl-2,6-dioxo-4(trifluoromethyl)-3,6-dihydro-1(2H)-pyrimidinyl)benzoyl)-N-isopropyl-N-methylsulfamide N-(2,4-dichloro-5-(4-(difluoromethyl)-4,5-dihydro-3-methyl-5-oxo-1H-1,2,4-triazol-1-yl)phenyl)methanesulfonamide N-methylacetamide-oxotremorine M Nylons Photosynthesis Tissue, Membrane Tissues

Efficient Herbicide Resistance Screening

Not available on PMC !

Example 11

Media is selected for use and kill curves developed as specified above. For selection, different techniques are utilized. Either a step wise selection is applied, or an immediate lethal level of herbicide is applied. In either case, all of the calli are transferred for each new round of selection. Selection is 4-5 cycles of culture with 3-5 weeks for each cycle. Cali are placed onto nylon membranes to facilitate transfer (200 micron pore sheets, Biodesign, Saco, Maine). Membranes are cut to fit 100×20 mm Petri dishes and are autoclaved prior to use 25-35 calli (average weight/calli being 22 mg) are utilized in every plate. In addition, one set of calli are subjected to selection in liquid culture media with weekly subcultures followed by further selection on semi-solid media. Mutant lines are selected using saflufenacil, 1,5-dimethyl-6-thioxo-3-(2,2,7-trifluoro-3-oxo-4-(prop-2-ynyl)-3,4-dihydro-2H-benzo[b][1,4]oxazin-6-yl)-1,3,5-triazinane-2,4-dione (CAS 1258836-72-4/trifludimoxazin), flumioxazin, butafenacil, acifluorfen, lactofen, bifenox, sulfentrazone, and photosynthesis inhibitor diuron as negative control. Efficiencies of obtaining mutants is high either based on a percentage of calli that gave rise to a regenerable, mutant line or the number of lines as determined by the gram of tissue utilized.

US11879132B2. Plants having increased tolerance to herbicides (2024-01-23). BASF AGRO B.V. [NL]. Inventors: Raphael Aponte [DE], Stefan Tresch [DE], Dario Massa [DE], Matthias Witschel [DE], Tobias Seiser [DE], Jill Marie Paulik [US].

Patent 2024

Lettuce Biomass and Metabolite Analysis

Physiological data were analyzed by ANOVA and Tukey post-hoc test where p<0.05. Treatments that share a letter were not significantly different. Physiological data reported in the body of this report were collected from the same replication of the experiments used in the transcriptome analysis. Photosynthetic data were further analyzed by dividing by the PPFD recorded at time of measurement to identify potential effects of small changes in light intensity introduced by filter degradation. To minimize variation between lettuce replications, the biomass and secondary data were normalized relative to the control treatment within each round. Lettuce biomass and secondary metabolite data were normalized by replicate relative to their respective controls. Two replicates were performed simultaneously in the same growth chamber and were normalized together. These normalized data are presented in the supplementary information.

Charles M., Edwards B., Ravishankar E., Calero J., Henry R., Rech J., Saravitz C., You W., Ade H., O’Connor B, & Sederoff H. (2023). Emergent molecular traits of lettuce and tomato grown under wavelength-selective solar cells. Frontiers in Plant Science, 14, 1087707.

Publication 2023

DNA Replication Gene Expression Profiling Human Body Lactuca sativa Light neuro-oncological ventral antigen 2, human Photosynthesis physiology

Photosynthesis Measurement of Lettuce

A LI-6400XT (LI-COR, Inc., USA) was used to collect photosynthetic data from lettuce as previously described (Ravishankar et al., 2021 (link)). Two sample measurements were collected per leaf, two leaves per plant and four plants per treatment beginning five days before the final harvest. Photosynthesis was measured in situ inside the growth boxes to observe the impact of the light intensity and spectrum created by the OSC filters. The chamber door was kept closed during data collection to minimize changes to the environment and a black cloth was used to block ambient white light from entering around the equipment. A CO₂ scrubber was used to prevent elevated CO₂ levels from researcher exhalation. PPFD was monitored as measured by the instrument to ensure lighting conditions remained consistent throughout the data collection for each treatment. Photosynthetic data collection was limited in tomato due to experimental constraints.

Publication 2023

Cardiac Arrest Exhaling Lactuca sativa Light Lycopersicon esculentum Photosynthesis Plant Leaves Plants

Top products related to «Photosynthesis»

Li 6400 by LI COR

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The LI-6400 is a portable photosynthesis system designed for measuring gas exchange in plants. It is capable of measuring net carbon dioxide and water vapor exchange, as well as environmental conditions such as temperature, humidity, and light levels.

Li 6400xt by LI COR

Sourced in United States, Germany, United Kingdom

The LI-6400XT is a portable photosynthesis system designed for measuring gas exchange in plants. It is capable of measuring net photosynthesis, transpiration, stomatal conductance, and other physiological parameters. The system consists of a control unit and a leaf chamber that encloses a portion of a plant leaf.

Li 6800 by LI COR

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The LI-6800 is a portable photosynthesis system designed for field research. It measures gas exchange and fluorescence parameters to study physiological responses in plants. The device features a temperature-controlled leaf chamber and can be used to analyze a variety of plant species and environmental conditions.

Spad 502 by Konica Minolta

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The SPAD-502 is a portable, hand-held spectrophotometer designed to measure the Soil Plant Analysis Development (SPAD) index, which is a relative measure of leaf chlorophyll content. It provides quick and non-destructive measurements of leaf greenness or chlorophyll concentration in plants.

Li 6400 portable photosynthesis system by LI COR

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The LI-6400 portable photosynthesis system is a scientific instrument designed to measure and analyze the gas exchange characteristics of plants. It provides researchers with the ability to quantify photosynthesis, respiration, and transpiration in a controlled environment.

Ciras 3 by PP Systems

Sourced in United States, United Kingdom

The CIRAS-3 is a portable gas exchange system designed for measuring photosynthesis and respiration in plants. It provides accurate measurements of carbon dioxide and water vapor exchange. The CIRAS-3 is a versatile instrument suitable for a wide range of plant and environmental research applications.

Li 6400xt portable photosynthesis system by LI COR

Sourced in United States

The LI-6400XT Portable Photosynthesis System is a laboratory instrument designed to measure photosynthetic processes in plants. It provides accurate and reliable data on gas exchange and environmental conditions in a controlled chamber.

Gfs 3000 by Walz

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The GFS-3000 is a laboratory equipment designed for general scientific applications. It features precise temperature control and monitoring capabilities. The core function of this product is to provide a controlled environment for various experimental and analytical processes.

Pam 2500 by Walz

Sourced in Germany

The PAM-2500 is a laboratory equipment product designed for analytical purposes. It serves as a versatile tool for researchers and scientists in various fields. The core function of the PAM-2500 is to perform precise measurements and analyses, though the specific intended use may vary depending on the application.

Ciras 2 by PP Systems

Sourced in United Kingdom, United States

The CIRAS-2 is a portable gas exchange system designed for measuring the photosynthesis and respiration of plants. It provides precise and reliable measurements of carbon dioxide and water vapor exchange.

What are the different types of photosynthesis?

There are two main types of photosynthesis: oxygenic photosynthesis and anoxygenic photosynthesis. Oxygenic photosynthesis is the most common type, where plants, algae, and cyanobacteria use carbon dioxide, water, and sunlight to produce glucose and oxygen. Anoxygenic photosynthesis, on the other hand, occurs in some bacteria and does not produce oxygen as a byproduct.

How can PubCompare.ai help with photosynthesis research?

PubCompare.ai can be a valuable tool for optimizing photosynthesis research. The platform allows you to screen protocol literature more efficiently by leveraging AI to pinpoint critical insights. PubCompare.ai's AI-driven analysis can help researchers identify the most effective protocols related to photosynthesis for their specific research goals, enabling them to choose the best option for reproducibility and accuracy. This can lead to enhanced experimental design, improved data quality, and accelerated progress in photosynthesis studies.

What are some practical applications of photosynthesis?

Photosynthesis has a wide range of practical applications, including: 1) Improved crop yields: Understanding and optimizing photosynthesis can lead to the development of more productive crop varieties. 2) Biofuel production: Photosynthetic organisms, such as algae, can be used to produce biofuels, providing a renewable energy source. 3) Carbon sequestration: Enhancing photosynthesis in plants and other organisms can help remove carbon dioxide from the atmosphere, mitigating the effects of climate change. 4) Oxygen production: The oxygen produced by photosynthesis is essential for the survival of many lifeforms on Earth, including humans.

How does photosynthesis differ from respiration?

Photosynthesis and respiration are complementary processes that are essential for life. During photosynthesis, organisms use sunlight, water, and carbon dioxide to produce glucose and oxygen. In contrast, respiration is the process by which organisms use oxygen to break down glucose and produce energy in the form of ATP. While photosynthesis is a anabolic process that builds up complex organic molecules, respiration is a catabolic process that breaks down these molecules to release energy.

What are some common challenges in studying photosynthesis?

Some common challenges in studying photosynthesis include: 1) Complexity of the process: Photosynthesis involves a series of intricate biochemical reactions, making it a challenge to fully understand and optimize. 2) Environmental factors: Photosynthesis is influenced by a variety of environmental factors, such as light intensity, temperature, and nutrient availability, which can be difficult to control in experimental settings. 3) Genetic and molecular regulation: The genes and molecular mechanisms that regulate photosynthesis are not fully elucidated, requiring more research to unravel the underlying complexities. 4) Scalability: Translating insights from lab-scale experiments to real-world applications, such as improving crop yields or biofuel production, can be a significant challenge.

More about "Photosynthesis"

Photosynthesis is a crucial biological process that powers the growth and survival of plants, algae, and some bacteria on Earth.
This complex biochemical reaction converts light energy from the sun, carbon dioxide, and water into glucose and oxygen, which are essential for the sustenance of diverse lifeforms.
The key steps in photosynthesis include light absorption, electron transport, and the reduction of carbon dioxide to produce organic compounds like carbohydrates.
Understanding the mechanisms and regulation of photosynthesis is vital for advancements in plant biology, renewable energy, and environmental science.
Researchers can optimize photosynthesis to improve crop yields, enhance biofuel production, and develop innovative carbon sequestration techniques.
Leveraging specialized equipment like the LI-6400, LI-6400XT, LI-6800, SPAD-502, LI-6400 portable photosynthesis system, CIRAS-3, LI-6400XT Portable Photosynthesis System, GFS-3000, PAM-2500, and CIRAS-2 can provide valuable insights into the intricate processes of photosynthesis.
PubCompare.ai is a powerful tool that can help researchers identify the most effective protocols from literature, preprints, and patents, enhancing the reproducibility and accuracy of their photosynthesis studies.
By streamlining the research process and leveraging the latest advancements, scientists can take their photosynthesis research to new heights and contribute to groundbreaking discoveries in fields such as plant biology, renewable energy, and environmental science.