Whole-genome protein sequences and gene positions for Arabidopsis thaliana, Populus trichocarpa, Vitis vinifera, Glycine max, Oryza sativa and Brachypodium distachyon were retrieved from Phytozome v7.0 (http://www.phytozome.net/ ). Whole-genome protein sequences and gene positions for Sorghum bicolor and Zea mays were retrieved from EnsemblPlants (http://plants.ensembl.org/index.html ) and MaizeSequence Release 5b.60 (http://www.maizesequence.org/index.html ) respectively. If a gene had more than one transcript, only the first transcript in the annotation was used. To search for homology, the protein-coding genes from each genome was compared against itself and other genomes using BLASTP (49 (link)). For a protein sequence, the best five non-self hits in each target genome that met an E-value threshold of 10−5 were reported.
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Living Beings
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Sorghum bicolor
Sorghum bicolor
Sorghum bicolor is a species of grass commonly known as sorghum, a cereal grain that is cultivated for food, feed, and fuel.
It is an important crop in many parts of the world, particularly in arid and semi-arid regions.
Sorghum bicolor is a resilient plant that can tolerate drought and high temperatures, making it a valuable resource in areas with limited water availibility.
The plant produces edible grains that can be used for a variety of food and industrial applications.
Researchers studying Sorghum bicolor can leverage the PubCompare.ai platform to enhance their work, locating protocols from literature, pre-prints, and patents, and using AI-driven comparisons to identify the best protocols and products.
This can improve reproducibility and acuracy in Sorghum bicolor studies.
It is an important crop in many parts of the world, particularly in arid and semi-arid regions.
Sorghum bicolor is a resilient plant that can tolerate drought and high temperatures, making it a valuable resource in areas with limited water availibility.
The plant produces edible grains that can be used for a variety of food and industrial applications.
Researchers studying Sorghum bicolor can leverage the PubCompare.ai platform to enhance their work, locating protocols from literature, pre-prints, and patents, and using AI-driven comparisons to identify the best protocols and products.
This can improve reproducibility and acuracy in Sorghum bicolor studies.
Most cited protocols related to «Sorghum bicolor»
Amino Acid Sequence
Arabidopsis thalianas
Brachypodium distachyon
Gene Products, Protein
Genes
Genome
Oryza sativa
Plants
Populus
Sorghum bicolor
Soybeans
Staphylococcal Protein A
Vitis
Zea mays
eQuilibrator source code is freely available on Google Code (http://code.google.com/p/milo-lab/ ). The eQuilibrator web interface is composed of a back-end implemented in Python and a user interface implemented with HTML, CSS and JavaScript. A wide range of open software and databases significantly aid the development and maintenance of eQuilibrator. Non-thermodynamic data including compound names, masses and formulae, enzyme names, EC classes and catalyzed reactions are drawn from KEGG (17 (link)) and stored in a MySQL database. Thermodynamic data is drawn from several sources (11 ,23 ,24 (link)) and is stored in the same database. The Django framework is used to simplify database setup and querying, HTML generation, and web serving. Search queries are parsed via the pyparsing library and search results are ranked according to the degree to which they match the search query, where the degree of matching is computed using the edit-distance algorithm. The user interface is implemented using standard HTML and CSS. The dynamic portions of the interface, including real-time search suggestions, are implemented in JavaScript using the jQuery framework.
Thermodynamic data is available for download in standard formats—JavaScript Object Notation (JSON) and Comma-Separated Values (CSV)—and is provided at several levels of granularity. Files containing compound ΔfG′° and reaction ΔrG′° values at various combinations of pH and ionic strength are available. For those interested in detailed analyses involving varying cellular pH and ionic strength, files containing ΔfG° values for the various protonation states of KEGG compounds are also available.
Thermodynamic data is available for download in standard formats—JavaScript Object Notation (JSON) and Comma-Separated Values (CSV)—and is provided at several levels of granularity. Files containing compound ΔfG′° and reaction ΔrG′° values at various combinations of pH and ionic strength are available. For those interested in detailed analyses involving varying cellular pH and ionic strength, files containing ΔfG° values for the various protonation states of KEGG compounds are also available.
cDNA Library
Cells
Cytoplasmic Granules
Enzymes
Python
Sorghum bicolor
LCN genes were identified based on OrthoFinder26 (link) results. The orthologues were obtained from six monocots (Spirodela polyrhiza, Zostera marina, Musa acuminata, Ananas comosus, Sorghum bicolor and Oryza sativa) and six eudicots (Nelumbo nucifera, Vitis vinifera, Populus trichocarpa, A. thaliana, Solanum lycopersicum and Beta vulgaris), N. colorata, Amborella, and the gymnosperms G. biloba, P. abies and P. taeda. LCN genes needed to meet the following requirements: strictly single-copy in N. colorata, Amborella, G. biloba, P. abies or P. taeda, and single-copy in at least five of the 12 eudicots or monocots. With G. biloba, P. abies or P. taeda as the outgroup, we identified 2,169, 1,535 and 1,515 orthologous LCN genes, respectively. Furthermore, we trimmed the sites with less than 90% coverage. LCN gene trees were estimated from the remaining sites using RAxML v.7.7.8 using the GTR+G+I model for nucleotide sequences (Fig. 1c ) and the JTT+G+I model for amino acid sequences (Supplementary Note 4.1 ). To account for incomplete lineage sorting and different substitution rates, we applied the multispecies coalescent model and a supermatrix method, respectively, to the LCN genes and found further support for the sister relationship between Amborella and all other extant flowering plants (Supplementary Note 4.2 ).
We further carefully selected five LCN gene sets (1,167, 834, 683, 602 and 445) from 115 species and applied both a supermatrix method27 (link)–29 (link) and the multi-species coalescent model to infer the phylogeny of angiosperms (Supplementary Note4.2 ). The phylogeny inferred from 1,167 LCN genes is shown in Fig. 1d , with different support values from the multi-species coalescent analyses of the other four LCN gene sets.
To estimate the evolutionary timescale of angiosperms, we calibrated a relaxed molecular clock using 21 fossil-based age constraints7 (link) throughout the tree, including the earliest fossil tricoplate pollen (approximately 125 Ma) associated with eudicots30 . We concatenated 101 selected genes (205,185 sites) and fixed the tree topology to that inferred from our coalescent-based analysis of 1,167 genes from 115 taxa. We performed a Bayesian phylogenomic dating analysis of the 101 selected genes in MCMCtree, part of the PAML package31 (link),32 (link), and used approximate likelihood calculation for the branch lengths33 (link). Molecular dating was performed using an auto-correlated model of among-lineage rate variation, the GTR substitution model, and a uniform prior on the relative node times. Posterior distributions of node ages were estimated using Markov chain Monte Carlo sampling, with samples drawn every 250 steps over 10 million steps following a burn-in of 500,000 steps. We checked for convergence by running the analysis in duplicate and checked for sufficient sampling.
We also implemented the penalized likelihood method under a variable substitution rate using TreePL34 (link) and r8s35 (link), as a constant substitution rate across the phylogenetic tree was rejected (P < 0.01) for all cases by likelihood-ratio tests in PAUP36 . Three fossil calibrations, corresponding to the crown groups of Lamiales, Cornales and Laurales, were implemented as minimum age constraints in our penalized likelihood dating analysis, except that the earliest appearance of tricolpate pollen grains (about 125 Ma)30 was used to fix the age of crown eudicots. We determined the best smoothing parameter value of the concatenated 101 LCN genes as 0.32 by performing cross-validations of a range of smooth parameters from 0.01 to 10,000 (algorithm = TN; crossv = yes; cvstart = −2; cvinc = 0.5; cvnum = 15). We used 100 bootstrap trees with branch lengths generated by RAxML37 (link) to infer the 95% confidence intervals of age estimates (Supplementary Note4.2 ).
We further carefully selected five LCN gene sets (1,167, 834, 683, 602 and 445) from 115 species and applied both a supermatrix method27 (link)–29 (link) and the multi-species coalescent model to infer the phylogeny of angiosperms (Supplementary Note
To estimate the evolutionary timescale of angiosperms, we calibrated a relaxed molecular clock using 21 fossil-based age constraints7 (link) throughout the tree, including the earliest fossil tricoplate pollen (approximately 125 Ma) associated with eudicots30 . We concatenated 101 selected genes (205,185 sites) and fixed the tree topology to that inferred from our coalescent-based analysis of 1,167 genes from 115 taxa. We performed a Bayesian phylogenomic dating analysis of the 101 selected genes in MCMCtree, part of the PAML package31 (link),32 (link), and used approximate likelihood calculation for the branch lengths33 (link). Molecular dating was performed using an auto-correlated model of among-lineage rate variation, the GTR substitution model, and a uniform prior on the relative node times. Posterior distributions of node ages were estimated using Markov chain Monte Carlo sampling, with samples drawn every 250 steps over 10 million steps following a burn-in of 500,000 steps. We checked for convergence by running the analysis in duplicate and checked for sufficient sampling.
We also implemented the penalized likelihood method under a variable substitution rate using TreePL34 (link) and r8s35 (link), as a constant substitution rate across the phylogenetic tree was rejected (P < 0.01) for all cases by likelihood-ratio tests in PAUP36 . Three fossil calibrations, corresponding to the crown groups of Lamiales, Cornales and Laurales, were implemented as minimum age constraints in our penalized likelihood dating analysis, except that the earliest appearance of tricolpate pollen grains (about 125 Ma)30 was used to fix the age of crown eudicots. We determined the best smoothing parameter value of the concatenated 101 LCN genes as 0.32 by performing cross-validations of a range of smooth parameters from 0.01 to 10,000 (algorithm = TN; crossv = yes; cvstart = −2; cvinc = 0.5; cvnum = 15). We used 100 bootstrap trees with branch lengths generated by RAxML37 (link) to infer the 95% confidence intervals of age estimates (Supplementary Note
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A-101
Abies
Amino Acid Sequence
Ananas
Base Sequence
Beta vulgaris
Biological Evolution
Cycadopsida
Genes
Lamiales
Laurales
Lycopersicon esculentum
Magnoliopsida
Musa
Nelumbo nucifera
Oryza sativa
Pollen
Populus
Sorghum bicolor
Trees
Vitis
Zostera
The OrthoFinder97 (link) clustering method was used to classify complete proteomes of 23 sequenced green plant genomes, including A. filiculoides and S. cucullata (Supplementary Table 5 ), into orthologous gene lineages (that is, orthogroups). We selected taxa that represented all of the major land plant and green algal lineages, including six core eudicots (A. thaliana, Lotus japonicus, Populus trichocarpa, Solanum lycopersicum, Erythranthe guttata and Vitis vinifera), five monocots (O. sativa, Sorghum bicolor, Musa acuminata, Zostera marina and Spirodella polyrhiza), one basal angiosperm (A. trichopoda), two gymnosperms (Pinus taeda and Picea abies), two ferns (A. filiculoides and S. cucullata), one lycophyte (S. moellendorffii), four bryophytes (Sphagnum fallax, P. patens, Marchantia polymorpha and Jungermannia infusca) and two green algae (Klebsormidium flaccidum and C. reinhardtii). In total, 16,817 orthogroups containing at least two genes were circumscribed, 8,680 of which contain at least one gene from either A. filiculoides or S. cucullata. Of the 20,203 annotated A. filiculoides genes and the 19,780 annotated S. cucullata genes, 17,941 (89%) and 16,807 (84%) were classified into orthogroups, respectively. The details for each orthogroup, including gene counts, secondary clustering of orthogroups (that is, super-orthogroups)110 (link) and functional annotations, are reported in Supplementary Table 5 .
We used Wagner parsimony implemented in the program Count111 (link) with a weighted gene gain penalty of 1.2 to reconstruct the ancestral gene content at key nodes in the phylogeny of the 23 land plants and green algae species (Supplementary Table5 ). The ancestral gene content dynamics—gains, losses, expansions and contractions—are depicted in Supplementary Fig. 5 . Complete details of orthogroup dynamics for the key ancestral nodes that include seed plants, such as Salviniaceae, euphyllophytes and vascular plants, are reported in Supplementary Table 5 .
We used Wagner parsimony implemented in the program Count111 (link) with a weighted gene gain penalty of 1.2 to reconstruct the ancestral gene content at key nodes in the phylogeny of the 23 land plants and green algae species (Supplementary Table
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Arabidopsis thalianas
Chlorophyta
Cycadopsida
Embryophyta
Ferns
Genes
Genome
Green Plants
Jungermanniae
Lotus japonicus
Lycopersicon esculentum
Magnoliopsida
Marchantia
Mosses
Musa
Pinus abies
Pinus taeda
Plant Embryos
Populus
Proteome
Sorghum bicolor
Sphagnum
Tracheophyta
Vitis
Zostera
We analytically derived an approximate lower bound for the number of switching-steps to be performed by the switching-algorithm, when applied to a bipartite network (where V is the set of vertices and E the set of links, with ). This bound is equal to
where d is the edge density of the original network, defined as the ratio between and the number of edges of a fully connected bipartite graph with the same number of nodes in the two classes: . With respect to the empirical bound proposed in (Milo et al., 2003 ) (i.e. ), our bound can be expressed as
at least for bipartite graphs.
In what follows, we will denote with a rewired version of the bipartite network G obtained with the switching-algorithm through k switching-steps. We assume intuitively that is a rewired version of G if
Supplementary Materials , and the R package igraph (Csardi and Nepusz, 2006 ).
where d is the edge density of the original network, defined as the ratio between and the number of edges of a fully connected bipartite graph with the same number of nodes in the two classes: . With respect to the empirical bound proposed in (Milo et al., 2003 ) (i.e. ), our bound can be expressed as
at least for bipartite graphs.
In what follows, we will denote with a rewired version of the bipartite network G obtained with the switching-algorithm through k switching-steps. We assume intuitively that is a rewired version of G if
The average similarity between G and its rewired version
The average similarity between G and
Satisfaction
Sorghum bicolor
Most recents protocols related to «Sorghum bicolor»
The primary research observer and one secondary observer were trained on how to score the texting conversations for content and how to score the ancillary measures. To assist with scoring, checklists were provided that contained the observational definitions. The secondary observer reviewed the permanent products of 33% of the texting conversations across conditions, along with 33% of the videotapes of baseline, intervention training, generalization texting partner probes, FaceTime® probes, and follow-up sessions for each participant. The primary and secondary observers then compared scores to each other to determine interobserver agreement. If scorers disagreed, they re-watched the videotapes and re-examined the permanent product of that session to resolve discrepancies. Interrater reliability was high across both participants and phases of the study, ranging from 88 to 100%. A summary of interrater reliability across participants can be seen below in Table 4 .
Additionally, two observers who did not participate in the texting intervention assessed procedural integrity in 33% of the sessions across conditions and participants. This was done using the videotapes of the baseline, intervention training, generalization texting partner probes, FaceTime® probes, and follow-up sessions for each participant. The observers received training on how procedures were implemented and were each given a check sheet to use when scoring the presence and absence of each step in the procedure across sessions and participants. Procedural fidelity for all participants ranged from 94 to 100% on average. Mean procedural fidelity for Bennett was 95%, Milo = 95%, Anna = 94%, Veronica = 94%, and Levi = 100%. The only errors involved the experimenter not ending a few of the sessions at exactly ten minutes.
Interrater reliability
Texting steps | Texting content | FaceTime® content | Gen-probe Steps | Gen-probe content | |
---|---|---|---|---|---|
Bennett | 100% | 98% | 88% | 93% | 100% |
Milo | 100% | 93% | 88% | 100% | 95% |
Anna | 100% | 95% | 88% | 100% | 90% |
Veronica | 100% | 95% | 94% | 100% | 90% |
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Generalization, Psychological
Sorghum bicolor
Veronica
Vision
Bennett was 10 years and 9 months old at the start of the study (see Table 1 ). In addition to his autism spectrum disorder (ASD) diagnosis, Bennett also had an ADHD diagnosis. Bennett had difficulty maintaining a back and forth conversation. He usually engaged in monologues about inappropriate topics. Bennett and his texting partner Milo often partnered together in social skill sessions and shared similar interests. Milo, an 8-year, 7-month-old boy, primarily discussed preferred topics and communicated more with therapists as opposed to peers.
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Autism Spectrum Disorders
Diagnosis
Disorder, Attention Deficit-Hyperactivity
Sorghum bicolor
Sorghum (Sorghum bicolor) inbred line “407B” was used in this study. Healthy seeds were immersed in distilled water and germinated in petri dishes on filter papers. Then, seedlings with uniform growth were selected and cultivated in hydroponic boxes with 1 L 1/2 Murashige and Skoog (MS) solution (pH 5.8) (Adhikari et al., 2020 (link)). All plants were cultivated in a growth room maintained at 28 ± 2°C, with a photoperiod of 16/8 (day/night). The seedlings were grown in hydroponic medium for 7 days, and then these seedlings were subjected to different concentrations of CdCl2 (0, 10, 20, 50, 100 and 150 μM) (AR, AladdinBio-Chem Technology Co., Ltd, Shanghai, China) for 3 days. After treatment, sorghum plants were collected, and the plant height, root length and lateral density were measured. All samples were ground in liquid nitrogen and stored at -80°C.
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Chloride, Cadmium
Hyperostosis, Diffuse Idiopathic Skeletal
Nitrogen
Plant Embryos
Plant Roots
Plants
Seedlings
Sorghum
Sorghum bicolor
Soro is one of the administrative districts of Hadiyya zone which is located in south central Ethiopia. It is situated approximately 272 km southwest of Addis Ababa and in a close proximity to the Gimicho town. Sibiya Arera is geographically located in 7° 9′ 0″–7° 11′ 0″ N latitude and 37° 52′ 30″–37° 54′ 0″ E longitude (Figure 1 ).
Rainfall distribution in the study area is bimodal, characterized by heavy rainy season from June to September, and light rainy season from March to May. The annual long term average rainfall is 1,107 mm and peak rainfall in September. The long term average annual temperature is 17.2°C [10 (link)]. The mean monthly temperature ranges from 15.98°C in December to 18.91°C in March (Figure 2 ). These favorable climatic conditions and high population have made the district to be one of the intensively cultivated areas in the south central highlands of Ethiopia. Rain-fed agriculture is the only source of livelihood for the majority of population. It is characterized by a smallholder mixed crop-livestock production.
Soil is a good indicator of the influence of soil parent material and the spatial variability in the degree of weathering, geological, and other factors are responsible for soil formation and development [11 ]. The dominant soil type of the study area is Nitisols that cover extensive areas of agricultural fields are highly suitable for crop production. The local geology is characterized by volcanic basalt flows and Cenozoic pyroclastic fall deposits [10 (link)].
The major land use/land cover types in the district include cultivated land, grazing land, forest land, and built-up areas. Cultivated land is the dominant land use type with 50,454 hectares (73.3% of the total area). At the present time, the local community has been implementing different practices to protect the adverse effect of erosion on their farmland and to improve soil fertility. Sibiya Arera is one of the areas with better implementation of soil and water conservation practices. Model farmers adopted biological and physical conservation practices, however, there is still land without conservation technologies which owned by reluctant farmers showing no willingness to implement soil and water conservation measures.
The farming system of the study area is mainly subsistence farming based on mixed crop-livestock production. Major crops grown in the area include wheat (Triticum aestivum L.), maize (Zea mays L.), barley (Hordeum vulgare L.), sorghum (Sorghum bicolor (L.) Moench), and teff (Eragrostis tef (Zucc.) Trotter). All farmers of the area have been practicing rain-fed agriculture based on continuous cultivation. Previously, diammonium phosphate (DAP) and urea were the main fertilizer types used by a large number of people. However, currently, farmers in the study area have started to use blended fertilizers such as nitrogen, phosphorus, sulfur (NPS), and nitrogen, phosphorus, sulfur, and boron (NPSB).
Arable lands are composed of the landscape without conservation practice, physical soil and water conservation structures (fanya juu), and physical soil and water conservation structures combined with biological practices (fanya juu stabilized with desho grass). Soil and water conservation practices are mechanisms used to reduce erosion and associated nutrient loss, reducing the risk of production; however, are not constructed in some agricultural lands in the study area. As a result soil erosion is major deterioration processes which lead to soil degradation and declining agricultural productivity in nonconserved agricultural land. Fanya juu structures integrated with biological practices are permanent features made of earth, designed to protect the soil from uncontrolled runoff and erosion and retain water where needed. It seeks to increase the amount of water seeping into the soil, reducing the speed and amount of water running off. Erosion is prevented by keeping enough vegetation cover on embankment to protect the soil surface and binds the soil together and maintains soil structure.
Rainfall distribution in the study area is bimodal, characterized by heavy rainy season from June to September, and light rainy season from March to May. The annual long term average rainfall is 1,107 mm and peak rainfall in September. The long term average annual temperature is 17.2°C [10 (link)]. The mean monthly temperature ranges from 15.98°C in December to 18.91°C in March (
Soil is a good indicator of the influence of soil parent material and the spatial variability in the degree of weathering, geological, and other factors are responsible for soil formation and development [11 ]. The dominant soil type of the study area is Nitisols that cover extensive areas of agricultural fields are highly suitable for crop production. The local geology is characterized by volcanic basalt flows and Cenozoic pyroclastic fall deposits [10 (link)].
The major land use/land cover types in the district include cultivated land, grazing land, forest land, and built-up areas. Cultivated land is the dominant land use type with 50,454 hectares (73.3% of the total area). At the present time, the local community has been implementing different practices to protect the adverse effect of erosion on their farmland and to improve soil fertility. Sibiya Arera is one of the areas with better implementation of soil and water conservation practices. Model farmers adopted biological and physical conservation practices, however, there is still land without conservation technologies which owned by reluctant farmers showing no willingness to implement soil and water conservation measures.
The farming system of the study area is mainly subsistence farming based on mixed crop-livestock production. Major crops grown in the area include wheat (Triticum aestivum L.), maize (Zea mays L.), barley (Hordeum vulgare L.), sorghum (Sorghum bicolor (L.) Moench), and teff (Eragrostis tef (Zucc.) Trotter). All farmers of the area have been practicing rain-fed agriculture based on continuous cultivation. Previously, diammonium phosphate (DAP) and urea were the main fertilizer types used by a large number of people. However, currently, farmers in the study area have started to use blended fertilizers such as nitrogen, phosphorus, sulfur (NPS), and nitrogen, phosphorus, sulfur, and boron (NPSB).
Arable lands are composed of the landscape without conservation practice, physical soil and water conservation structures (fanya juu), and physical soil and water conservation structures combined with biological practices (fanya juu stabilized with desho grass). Soil and water conservation practices are mechanisms used to reduce erosion and associated nutrient loss, reducing the risk of production; however, are not constructed in some agricultural lands in the study area. As a result soil erosion is major deterioration processes which lead to soil degradation and declining agricultural productivity in nonconserved agricultural land. Fanya juu structures integrated with biological practices are permanent features made of earth, designed to protect the soil from uncontrolled runoff and erosion and retain water where needed. It seeks to increase the amount of water seeping into the soil, reducing the speed and amount of water running off. Erosion is prevented by keeping enough vegetation cover on embankment to protect the soil surface and binds the soil together and maintains soil structure.
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ammonium phosphate, dibasic
basalt
Biopharmaceuticals
Boron
Climate
Crop, Avian
Eragrostis
Farmers
Fertility
Forests
Hordeum vulgare
Light
Livestock
Nitrogen
Nutrients
Parent
Phosphorus
Physical Examination
Poaceae
Rain
Soil Erosion
Sorghum
Sorghum bicolor
Sulfur
Triticum aestivum
Urea
Zea mays
The experiment was conducted at Jiangxiaobai Red Sticky Sorghum Planting Demonstration Base (29°07′N, 106°16′E), located in Chongqing, China. The area has a mean temperature of 17.9°C, and a mean annual rainfall of 1,100 mm during the last 30 years (Chongqing Meteorological Bureau). The soil was a neutral yellow soil with a pH 7.5, alkali-dispelled nitrogen 96.15 mg/kg, available phosphorus (Olsen-P) 17.83 mg/kg, and available potassium 90.15 mg/kg.
The red sticky Sorghum bicolor cv. Chuannuoliang 2 is the principal variety planting in this base for Baijiu (distilled liquor) production. Sorghum-Canola rotation system is used in this base. Generally, the sorghum will be sown in May after the canola is harvested, and be harvested in August, then the canola will be sown in October. From August to October, there is about 2 months’ fallow period, which is suitable for the regrowth of the harvested sorghum, called ratooning sorghum. Currently, the ratooning sorghum is chopped and returned to soils or discarded. In this study, on 2th October, 2020 and 10th October, 2021, the ratooning sorghum at their heading and flowering stage were harvested for whole plant silage (above 5 cm from the ground). These ratooning sorghums have the potential to be used as fodder or silage.
A. niger was bought from Beijing Beina Chuanglian Biotechnology Research Institute. Before used as additives, the A. niger was cultured on Potato Dextrose Agar medium (PDA) at 28°C for 72 h, then, the activated A. niger was inoculated into Potato Dextrose Broph liquid medium (PDB) at 32°C for 3 days at 170 ~ 180 r/min (Wang et al., 2007 (link)). When the concentration of A. niger solution reached 2.56 × 107 CFU/mL, it was evenly sprayed on the silage as additives.
The red sticky Sorghum bicolor cv. Chuannuoliang 2 is the principal variety planting in this base for Baijiu (distilled liquor) production. Sorghum-Canola rotation system is used in this base. Generally, the sorghum will be sown in May after the canola is harvested, and be harvested in August, then the canola will be sown in October. From August to October, there is about 2 months’ fallow period, which is suitable for the regrowth of the harvested sorghum, called ratooning sorghum. Currently, the ratooning sorghum is chopped and returned to soils or discarded. In this study, on 2th October, 2020 and 10th October, 2021, the ratooning sorghum at their heading and flowering stage were harvested for whole plant silage (above 5 cm from the ground). These ratooning sorghums have the potential to be used as fodder or silage.
A. niger was bought from Beijing Beina Chuanglian Biotechnology Research Institute. Before used as additives, the A. niger was cultured on Potato Dextrose Agar medium (PDA) at 28°C for 72 h, then, the activated A. niger was inoculated into Potato Dextrose Broph liquid medium (PDB) at 32°C for 3 days at 170 ~ 180 r/min (Wang et al., 2007 (link)). When the concentration of A. niger solution reached 2.56 × 107 CFU/mL, it was evenly sprayed on the silage as additives.
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Agar
Alkalies
Amniotic Fluid
Fodder
Glucose
Nitrogen-15
Phosphorus
Plants
Potassium
Silage
Solanum tuberosum
Sorghum
Sorghum bicolor
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Xylobiose is a type of disaccharide composed of two xylose molecules. It is a naturally occurring compound found in various plant materials.
More about "Sorghum bicolor"
Sorghum (Sorghum bicolor) is a resilient and widely-cultivated cereal grain that thrives in arid and semi-arid regions.
This C4 grass species is an important food, feed, and fuel crop, particularly in water-scarce areas.
Sorghum's drought and heat tolerance make it a valuable resource in regions with limited water availability.
The sorghum plant produces edible grains that can be utilized for a variety of food and industrial applications.
Researchers studying this versatile crop can leverage powerful tools like the PubCompare.ai platform to enhance their work.
PubCompare.ai enables researchers to locate relevant protocols from literature, preprints, and patents, and use AI-driven comparisons to identify the most effective protocols and products.
This can improve the reproducibility and accuracy of sorghum studies, which may involve techniques such as RNeasy Plant Mini Kit, TRIzol reagent, and HiSeq X Ten sequencing for RNA extraction and analysis.
Other relevant kits and reagents include the RNAprep Pure Plant Kit, Genome Analyzer IIx, HiSeq 2500, DNeasy Plant Mini Kit, and Power SYBR Green PCR Master Mix.
Additionally, enzymes like Celluclast 1.5 L and substrates like xylobiose may be utilized in sorghum research and processing.
By leveraging the power of PubCompare.ai and incorporating a range of techniques and tools, researchers can deepen their understanding of this important cereal crop and drive innovation in sorghum-related fields, ultimately enhancing food security and sustainable development in arid and semi-arid regions.
This C4 grass species is an important food, feed, and fuel crop, particularly in water-scarce areas.
Sorghum's drought and heat tolerance make it a valuable resource in regions with limited water availability.
The sorghum plant produces edible grains that can be utilized for a variety of food and industrial applications.
Researchers studying this versatile crop can leverage powerful tools like the PubCompare.ai platform to enhance their work.
PubCompare.ai enables researchers to locate relevant protocols from literature, preprints, and patents, and use AI-driven comparisons to identify the most effective protocols and products.
This can improve the reproducibility and accuracy of sorghum studies, which may involve techniques such as RNeasy Plant Mini Kit, TRIzol reagent, and HiSeq X Ten sequencing for RNA extraction and analysis.
Other relevant kits and reagents include the RNAprep Pure Plant Kit, Genome Analyzer IIx, HiSeq 2500, DNeasy Plant Mini Kit, and Power SYBR Green PCR Master Mix.
Additionally, enzymes like Celluclast 1.5 L and substrates like xylobiose may be utilized in sorghum research and processing.
By leveraging the power of PubCompare.ai and incorporating a range of techniques and tools, researchers can deepen their understanding of this important cereal crop and drive innovation in sorghum-related fields, ultimately enhancing food security and sustainable development in arid and semi-arid regions.