The method performances have been tested on the same four datasets of curated pairwise structural alignments of RNAs presented in (7 (link)). In brief, the four datasets (namely BRAliBase, RNAspa, RNA STRAND and RRS) were obtained by merging together structural and alignment information from different curated databases (1 (link),15 (link)–18 (link)). The different level of sequence identity in the pairwise alignments and the many classes of non-coding RNA represented in the datasets assure a good level of variability reducing the possibility of biased results. Moreover, the secondary structures are not derived from consensus secondary structures, therefore there is not perfect agreement among the aligned structures. In (7 (link)) we showed that these reference datasets contain alignments of pairs of RNAs often different in terms of sequence identity and length (Supplementary Table S1). We used these alignments as reference to assess the performances of our method using the SPS (Sum of PairS) score (1 (link)). SPS is defined as the fraction of aligned nucleotides in the reference alignment that are correctly aligned (Supplementary Materials, Section 3). Firstly, we computed the best parameters for gap opening, gap extension and sequence bonus by running an exhaustive search on the datasets. In particular, we run the algorithm several times on each dataset using different sets of parameters. The goal was to find a common set of parameters that can be used independently from the characteristics of the input RNAs. By comparing all the results together we identified a generic set of parameters maximising the accuracy across the datasets. Note that each dataset was used independently (i.e. the best set of parameters computed on one dataset was then tested on all the other ones), and that the datasets are non-redundant, in order to avoid over-fitting (Supplementary Material, Section 2). We compared the performances of our method with those obtained using other state-of-the-art tools, namely LocARNA (19 (link)), gardenia (3 (link)), RNAStrAT (2 ), RNAdistance (11 (link)), RNAforester (20 (link)) and with those from the sequence-only Needleman-Wunsch algorithm (using the needle implementation in the Emboss package) (21 (link)). Our method shows a better overall accuracy than other state-of-the-art methods especially when the sequence identity between the input RNAs is <65% (Supplementary Table S1). Additionally, Web-Beagle has lower computational complexity and running time than the other methods. As an additional test, we decided to test the performances of the methods on the same datasets but using the predicted secondary structures obtained using RNAfold (11 (link)) (Supplementary Table S2). As expected, all methods are less accurate (except for needle that does not use structural information), and we found a positive correlation between the accuracy of the predicted secondary structure and the alignment quality. LocARNA offers the best overall performances in this case, since it does not use the provided structures but recomputes them while aligning. Nevertheless, Web-Beagle performances are not far from those of LocARNA and better than any other method and still maintaining lower computational time (Supplementary Table S3).
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Living Beings
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Plant
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Gardenia
Gardenia
Gardenia is a genus of flowering plants in the family Rubiaceae, native to tropical and subtropical regions of Africa, Asia, and Oceania.
These evergreen shrubs or small trees are prized for their fragrant, white or yellow flowers and glossy leaves.
Gardenias are widely cultivated as ornamental plants and have a variety of uses in traditional medicine.
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These evergreen shrubs or small trees are prized for their fragrant, white or yellow flowers and glossy leaves.
Gardenias are widely cultivated as ornamental plants and have a variety of uses in traditional medicine.
Reserchers can leverage PubCompare.ai's AI-driven platform to locate protocols from literature, preprints, and patents, and identify the best protocols and products for Gardenia research.
Discover Gardenia's secrets and experience a smothless research jounrey with PubCompare.ai.
Most cited protocols related to «Gardenia»
Gardenia
Generic Drugs
Needles
Nucleotides
RNA
RNA, Untranslated
RNA Sequence
Seizures
The medicinal plants used to prepare Yueju are Cyperus rotundus L. (Xiang Fu), Ligusticum chuanxiong Hort. (Chuan Xiong), Gardenia jasminoides Ellis. (Zhi Zi), Atractylodes lancea (Thunb.) DC. (Chang Zu), and Massa Fermentata (Shen Qu). All the medicinal plants were purchased from Nanjing Guoyi Clinical, Medicinal Material Department (Nanjing, China) and authenticated by Dr. Yang Lianyun, Department of Chinese Materia Medica, Nanjing University of Chinese Medicine. According to the preparation of Yueju formula, five plant materials weighted 200.0 g were powdered into 100 meshes. The power was immersed with 2 L 95% ethanol for 2 days at room temperature, and the dissolved solution was collected. This procedure was repeated twice. The collected extracts were combined, filtered, and dried under reduced pressure at a temperature below 55°C. The yielding product weighted about 48.4 g. Yueju ethanol extract (YJ-E) was dispersed in Tween 80 solution (0.5%, w/v in saline). Vehicle solvent (0.5%, w/v Tween 80 in saline) served as the negative control. The solutions of the herb preparation and vehicle were administered to the mice via intragastric administration of the solution at a dosage of 0.2 mL/20 g (body weight), and the concentration of the solution was 300 mg/mL. Ketamine HCl (Gutian Pharmaceuticals, China), dissolved in saline, was administered intraperitoneally.
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Atractylodes lancea
Body Weight
Chinese
Cyperus
Ethanol
Gardenia
jianqu
Ketamine Hydrochloride
Lovage, Alpine
Materia Medica
Mus
Pharmaceutical Preparations
Plant Preparations
Plants
Plants, Medicinal
Pressure
Saline Solution
Solvents
Tween 80
The reference O. sativa genome [47–51 (link)], was selected for testing the software due to its high-quality assembly, small genome size (389 Mb) and quality of its genes and TEs annotations. The O. sativa genome was identical to the one used by [33 (link)] to compare the results with benchmarking tools in this study. This work used the standard library v6.9.5 created by [33 (link)] based on the O. sativa L. ssp. japonica cv. ‘Nipponbare’ v. MSU7 genome and RepeatMasker v4.0.8 [52 ] with the following parameters ‘-pa 36 -q -no_is -norna -nolow -div 40 -cutoff 225’.
Additionally, six different plant genomes (Table1 ) were used to test the execution times of Inpactor2 by assessing different genome sizes and TE compositions. The genomes were downloaded from NCBI and analyzed with Inpactor2 using the following parameters (-m 15000 -n 1000, -i no, -d no, -C 1, -c yes -a no), as suggested in [53 (link)]. Finally, EDTA was run with the same genomes to compare its execution times with Inpactor2. EDTA was executed using EDTA_raw.py script, –type ltr, and the other parameters by default.
Libraries of LTR-RTs of the species shown in Table1 were then created using Inpactor2 (with and without filtering with the -c flag) and EDTA. In addition, two species that were not contained in the training data were used, such as Coffea humblotiana [54 (link)] and Gardenia jasminoides [55 (link)]. These libraries were then annotated using repeatMasker and compared with the proportion of genomes corresponding to LTR-RTs according to the papers where the genomes were reported. A workstation with AMD Ryzen Threadripper 3970X 32-Core Processor, 128 Gb in RAM memory and a GPU Nvidia RTX 2080 super was used to perform all the experiments.
To evaluate the performance of Inpactor2 compared with other software, a similar methodology to the one proposed in [33 (link)] was followed. First, Inpactor v.1.0 [34 ], TEsorter v.1.3 [45 ], Transposon Ultimate v.1.0 [28 ], LTR_retriever v.2.9 [56 (link)] and LTRharvest [57 ] were selected for benchmarking given their methodologies for classifying LTR-RTs to the superfamily level. A workflow was established for each software, initially using LTR_FINDER v.1.0.7 as the LTR-RTs detector. Then, the O. sativa genome was annotated with RepeatMasker and performance metrics were extracted for each workflow. The metrics evaluated were: accuracy, precision, specificity, sensitivity, FDR and F1-score. Figure1 shows the schematic representation of the benchmarking metrics. In this study, TP, FN, TN and FP are the number of nucleotides belonging to each category (Figure 2 ).
The script called ‘lib-test.pl’, included in the EDTA toolkit [33 (link)], was used to extract the six metrics. Since this study only focused on the LTR-RT category, so the script was executed using the -cat ltr parameter to perform the comparative evaluation.
Additionally, six different plant genomes (Table
Libraries of LTR-RTs of the species shown in Table
To evaluate the performance of Inpactor2 compared with other software, a similar methodology to the one proposed in [33 (link)] was followed. First, Inpactor v.1.0 [34 ], TEsorter v.1.3 [45 ], Transposon Ultimate v.1.0 [28 ], LTR_retriever v.2.9 [56 (link)] and LTRharvest [57 ] were selected for benchmarking given their methodologies for classifying LTR-RTs to the superfamily level. A workflow was established for each software, initially using LTR_FINDER v.1.0.7 as the LTR-RTs detector. Then, the O. sativa genome was annotated with RepeatMasker and performance metrics were extracted for each workflow. The metrics evaluated were: accuracy, precision, specificity, sensitivity, FDR and F1-score. Figure
The script called ‘lib-test.pl’, included in the EDTA toolkit [33 (link)], was used to extract the six metrics. Since this study only focused on the LTR-RT category, so the script was executed using the -cat ltr parameter to perform the comparative evaluation.
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Caffea
DNA Library
Edetic Acid
Gardenia
Genes
Genome
Genome, Plant
Hypersensitivity
Jumping Genes
Memory
Nucleotides
Protein sequences of the CCD, ALDH, and UGT family members in A. thaliana were downloaded from the TAIR database, then were used as queries in BLASTP searches against the G. jasminoides protein sequences to identify homologous sequences. Full-length protein sequences were corrected and aligned with ClustalW2 [61 (link)]. Phylogenetic trees were constructed using the maximum likelihood method with the Jones-Taylor-Thornton (JTT) model and 1000 Bootstrap replicates [62 (link)]. Further analyses incorporated blast searches (using Gardenia proteins as queries) of a number of other genomes to identify more CCD, ALDH, and UGT genes. For NMTs, the Coffea canephora XMT protein was used as a query (NCBI accession ABD90685.1). Species considered were Gardenia jasminoides (CoGe genome ID 53980), Coffea canephora (CoGe genome ID 19443), Arabidopsis thaliana (CoGe genome ID 16911), Calotropis gigantea (CoGe genome ID 36623), Catharanthus roseus (CoGe genome ID 36703), Vitis vinifera (CoGe genome ID 19990), Gelsemium sempervirens (CoGe genome ID 53941), and Solanum lycopersicum (CoGe genome ID 12289). Gene model IDs from the respective CoGe-uploaded genomes were retained as leaf IDs for phylogenetic analysis, with the exception that “:” when it appeared in a gene model ID was replaced by “_”. Several additional anchoring protein sequences from NCBI were incorporated in the NMT tree (MTL,_AFV60456.1; DXMT,_ABD90686.1; MXMT,_AFV60445.1; XMT,_ABD90685.1). Searches were run on the CoGe platform using default parameters and saving 100 Blast HSPs per species. Unique translated sequences were then downloaded, duplicates were excluded using BBedit, sequences with internal stop codons were excluded, and then trees were run using PASTA [63 (link)] with MAFFT [64 (link)] to align the protein sequences and FastTree [65 (link)] to create an approximately maximum likelihood tree. Trees were visualized and edited using FigTree (http://tree.bio.ed.ac.uk/software/figtree/ ) (Additional file 4 : Fig. S27, Additional file 5 : Fig. S28, Additional file 6 : Fig. S29, Additional file 7 : Fig. S30). To interpret the supplemental figures, pink branches represent gentianalean clades, green branches represent Rubiaceae clades, and orange gene model IDs represent Gardenia genes. Coffee-specific clades are shown in red. In the NMT supplemental tree (Additional file 4 : Fig. S27), the anchoring protein sequences are shown in red.
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Amino Acid Sequence
Arabidopsis thalianas
Caffea
Calotropis gigantea
Catharanthus roseus
Codon, Terminator
Coffee
Family Member
Gardenia
Gelsemium
Genes
Genome
Homologous Sequences
Lycopersicon esculentum
Paste
Plant Leaves
Proteins
Rubiaceae
Trees
Vitis
We built four different data sets to test the performance of our implementation of the Needleman–Wunsch algorithm based on the BEAR encoding and the MBR. We retrieved known RNA secondary structures from the RNA STRAND (40 ) and RNAspa data sets (41 ). RNA STRAND integrates information about known RNA secondary structure of any type and from different organisms retrieved from several public databases. Instead, RNAspa data set is a collection of curated secondary structures from Rfam.
Data sets of curated sequence alignments were retrieved from RNASTAR (42 (link)) and BRAliBase II (14 (link)). BRAliBase II is a collection of RNA alignment data sets proposed for benchmarking of alignment algorithms. Among the available data sets supplied by BRAliBase, we selected the data set 2 since it is the only one that includes pairwise alignments. RNASTAR includes refined Rfam alignments that were manually curated using structural information from the PDB (Protein Data Bank) (43 (link)). Since these curated data sets of alignments do not provide secondary structure annotation, we combined together structural data sets and secondary structure data sets to obtain a collection of curated alignments of known RNA structures. In particular, we used RNA STRAND secondary structure for RNASTAR alignments, RNAspa secondary structure for BRAliBase alignments and finally the remaining RNAs in RNAspa for Rfam alignments (Supplementary Table S3 in the Supplementary materials reports the number of RNAs and alignments in each data set).
Moreover, we randomly selected pairwise alignments from the RNA families, filtered and folded as described above (excluding those used to compute the substitution matrix), to create a fourth additional data set called RRS (Rfam Random Sampling).
We compared the results obtained using the Needleman–Wunsch + MBR with those obtained using other six alignment methods, namely a sequence-only version of the Needleman–Wunsch, as implemented in the ‘needle’ tool from the EMBOSS package (44 (link)), LocARNA (18 (link)), RNASTraT (25 ), RNAdistance and RNAforester (both included in Vienna package) and gardenia (24 ).
We used the sum-of-pairs score (SPS) (14 (link)) as a measure to evaluate the performances of the alignment methods. SPS is defined as the number of ‘correct pairs’ (pairs found in the reference alignment) over the total number of ‘predicted pairs’ (pairs found in the alignment computed by one of the tested algorithms), and it can be considered as a measure of the sensitivity of a pairwise alignment method. An SPS score of 0 indicates two completely different alignments; conversely, a score of 1 indicates an identical alignment.
Data sets of curated sequence alignments were retrieved from RNASTAR (42 (link)) and BRAliBase II (14 (link)). BRAliBase II is a collection of RNA alignment data sets proposed for benchmarking of alignment algorithms. Among the available data sets supplied by BRAliBase, we selected the data set 2 since it is the only one that includes pairwise alignments. RNASTAR includes refined Rfam alignments that were manually curated using structural information from the PDB (Protein Data Bank) (43 (link)). Since these curated data sets of alignments do not provide secondary structure annotation, we combined together structural data sets and secondary structure data sets to obtain a collection of curated alignments of known RNA structures. In particular, we used RNA STRAND secondary structure for RNASTAR alignments, RNAspa secondary structure for BRAliBase alignments and finally the remaining RNAs in RNAspa for Rfam alignments (Supplementary Table S3 in the Supplementary materials reports the number of RNAs and alignments in each data set).
Moreover, we randomly selected pairwise alignments from the RNA families, filtered and folded as described above (excluding those used to compute the substitution matrix), to create a fourth additional data set called RRS (Rfam Random Sampling).
We compared the results obtained using the Needleman–Wunsch + MBR with those obtained using other six alignment methods, namely a sequence-only version of the Needleman–Wunsch, as implemented in the ‘needle’ tool from the EMBOSS package (44 (link)), LocARNA (18 (link)), RNASTraT (25 ), RNAdistance and RNAforester (both included in Vienna package) and gardenia (24 ).
We used the sum-of-pairs score (SPS) (14 (link)) as a measure to evaluate the performances of the alignment methods. SPS is defined as the number of ‘correct pairs’ (pairs found in the reference alignment) over the total number of ‘predicted pairs’ (pairs found in the alignment computed by one of the tested algorithms), and it can be considered as a measure of the sensitivity of a pairwise alignment method. An SPS score of 0 indicates two completely different alignments; conversely, a score of 1 indicates an identical alignment.
Bears
Gardenia
Hypersensitivity
Needles
Sequence Alignment
Most recents protocols related to «Gardenia»
Xin'an Jianpi Tongbi prescription is a compound preparation of TCM based on the Xin'an medical theory. It contains Xinfeng capsule [22 (link)] (Z20050062 from Wanyao Pharmaceutical Co., Ltd., patent number: ZL 2013 1 0011369.8), Huangqin Qingre Chubi capsule [24 ] (Z20200001 from Wanyao Pharmaceutical Co., Ltd., patent number: ZL 2011 1 0095718.X), and Wuwei Wentong Chubi capsule [25 (link)] (patent number: ZL 2020 10714863.0). The Xinfeng capsule is composed of Astragalus membranaceus, Semen coicis, Tripterygium wilfordii, and Scolopendra spp. These four medicinal materials were extracted by refluxing twice with 75% ethanol. In the first step, ten times the amount of ethanol was added for extraction for 2 h, after which eight times the amount of ethanol was added and allowed to extract for 1.5 h. The drug residues were boiled with eight times the amount of water and extracted for 1.5 h. This was then filtered and allowed to stand. The supernatant was collected and combined with the alcohol extract under pressure to concentrate, and the paste was collected. The sample was dried, crushed, mixed with dextrin, and granulated with ethanol. This was followed by drying, whole granulating, sterilizing, filling, and outsourcing. The Huangqin Qingre Chubi capsule is composed of Scutellaria baicalensis, Prunus persica, Gardenia jasminoides, Semen coicis, and Clematis chinensis. These five medicinal materials were decocted and extracted three times as follows: ten times the amount of water was added for the first time and extracted for 1.5 h; eight times the amount of water was added for the second and third times and extracted for 1 h. The mixture was strained and allowed to stand. Then, the supernatant was absorbed and concentrated under pressure, and the paste was collected; this was then vacuum-dried, the dry extract was crushed, and dextrin was added. Ethanol was used to soften the materials, which were screened, granulated, dried, whole-grained, and filled into capsules. The Wuwei Wentong Chubi capsule is composed of Poria cocos, Epimedium brevicornu, Cinnamomum cassia, Curcumae Longae, and Scutellaria baicalensis. These five medicinal materials were decocted and extracted three times as follows: ten times the amount of water was added for the first time and extracted for 1.5 h; eight times the amount of water was added for the second and third times and extracted for 1 h. This mixture was strained and allowed to settle. Then, the supernatant was absorbed and concentrated under reduced pressure, and the paste was collected. This was then vacuum-dried, the dry extract was crushed, and dextrin was added. Ethanol was used to soften the material, which was then sieved using no. 12 mesh, granulated, dried, whole-grained, filled into capsules, and outsourced. All capsules were produced by the preparation center of the First Affiliated Hospital of Anhui University of Chinese medicine, and the variation range of each capsule was ±10%.
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11-dehydrocorticosterone
Astragalus membranaceus
Capsule
Chinese
Cinnamomum cassia
Clematis
Dextrin
Epimedium
Ethanol
Gardenia
Huangqin
Paste
Pharmaceutical Preparations
Plant Embryos
Pressure
Prunus persica
Scolopendra
Scutellaria baicalensis extract
Tripterygium wilfordii
Vacuum
Wolfiporia extensa
xinfeng
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Albizzia
Animal Model
Animals
Animals, Laboratory
Antidepressive Agents
Behavior Test
Chinese
Common Cold
Disorder, Depressive
Ethics Committees
Fruit
Gardenia
Homo sapiens
hyperici herba
Kidney Cortex
Males
Peony
Pharmaceutical Preparations
Plant Roots
Rattus norvegicus
Saline Solution
Tail
Tube Feeding
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Albizzia
formic acid
Fruit
Gardenia
High-Performance Liquid Chromatographies
Kidney Cortex
paeoniae radix alba
Peony
The herbal medicines used in this experiment, Coptidis Rhizoma, Phellodendri Cortex, Scutellariae Radix, Gardeniae Fructus, Rhei Rhizoma, Salviae miltiorrhizae Radix, and Notoginseng Radix, were purchased from Kyunghee Herbal Medicine (Wonju, Korea). Experimental drugs A and B were prepared using the following method: Herbal medicines (340 g) composed of Coptidis Rhizoma, Phellodendri Cortex, Scutellariae Radix, Gardeniae Fructus, and Rhei Rhizoma (4:4:4:4:1) were reflux-extracted twice with 80% ethanol in boiling water for 2 h, filtered, evaporated in a rotary vacuum evaporator, and lyophilized with a freeze dryer to produce experimental drug A. The dry weight yield was 26.5%. The basis for standardization of the preparation of experimental drug A has been suggested previously [17 (link)]. Experimental drug B (extract of Salviae miltiorrhizae Radix and Notoginseng Radix) was produced using the same method as above, using 315 g of herbal medicines composed of Salviae miltiorrhizae Radix and Notoginseng Radix 17.5:3.4, with a yield of 33.3%. The basis for standardization of the preparation of experimental drug B was suggested previously [22 ] (Table 1 ). BÜCHI Rotavapor R-220 (BÜCHI Labortechnik, Flawil, Switzerland) and deep freezer (IlShin BioBase, Dongducheon, Korea) were used to prepare the raw material extracts. For experimental drug B, five types of Salviae miltiorrhizae Radix (C1, C2, K1, K2, and K3) according to different cultivation regions were purchased from markets and used to prepare five different kinds of drug B (BC1 and BC2 were made using two Chinese Salviae miltiorrhizae Radix C1 and C2, respectively; BK1, BK2, and BK3 were prepared using three Korean Salviae miltiorrhizae Radix K1, K2, and K3, respectively). C1 and C2 were purchased in 2018.Feb and 2019.Jan, respectively. K1, K2, and K3 were purchased in 2019 from Yeongyang, in 2019 from Jangheung, and in 2020 from Yeongyang, respectively.
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Chinese
Coptis chinensis root
Cortex, Cerebral
Drug Compounding
Ethanol
Freezing
Fruit
Gardenia
Huangqin
Investigational New Drugs
Koreans
Medicinal Herbs
Pharmaceutical Preparations
Plant Roots
Rhizome
San-Chi
Vacuum
The herbal material of QYD is resourced from the First Affiliated Hospital of Dalian Medical University (Dalian, Liaoning, China). QYD consists of Bupleurum chinense DC (Chai Hu in Chinese, 15 g), Corydalis yanhusuo (Yan Hu Suo in Chinese, 15 g), Scutellaria baicalensis Georgi (Huang Qin in Chinese, 12 g), Gardenia jasminoides J. Ellis (Zhi Zi in Chinese, 15 g), Paeonia lactiflora Pall. (Bai Shao in Chinese, 15 g), Rheum officinale Baill. (Da Huang in Chinese, 20 g), Aucklandia costus Falc. (Mu Xiang in Chinese, 15 g), and Na2SO4·10 H2O (Mang Xiao in Chinese, 10 g). Professor Aijing Leng certified the authenticity of all the herbs. The QYD extract (1 g crude herb/ml) was made according to the reported methods. Briefly, the herbs are weighed, combined with 10 times their weight in water, steeped for 0.5 h, boiled for 1 h, and filtered while still hot. The residue is boiled again in 8 times the quantity of water used for the herbs and decocted for 0.5 h, and then, the Rheum officinale Baill. is added and simmered for an additional 0.5 h. Filter when hot, add Na2SO4·10 H2O, blend the two filtrates, concentrate to 1 g/ml, sterile package, and store at 4°C for future use. The chemical profiles of QYD mapped using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry are presented in the previous study [10 (link)].
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Bupleurum chinense
Chai Hu
Chinese
Corydalis
Gardenia
Huangqin
Liquid Chromatography
Mass Spectrometry
Paeonia
Rheum officinale
Saussurea costus
Sterility, Reproductive
Top products related to «Gardenia»
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Formic acid is a colorless, pungent-smelling liquid chemical compound. It is the simplest carboxylic acid, with the chemical formula HCOOH. Formic acid is widely used in various industrial and laboratory applications.
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Methanol is a clear, colorless, and flammable liquid that is widely used in various industrial and laboratory applications. It serves as a solvent, fuel, and chemical intermediate. Methanol has a simple chemical formula of CH3OH and a boiling point of 64.7°C. It is a versatile compound that is widely used in the production of other chemicals, as well as in the fuel industry.
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Dulbecco's modified Eagle's medium (DMEM) is a widely used cell culture medium that provides essential nutrients and growth factors for the maintenance and proliferation of various cell types. It is a balanced salt solution that supports the growth and survival of cells in vitro.
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Phosphate-buffered saline (PBS) is an aqueous buffer solution commonly used in biological research. It is a balanced salt solution that maintains a stable pH and osmotic pressure, providing a physiologically compatible environment for cells and tissues. PBS is used to wash, dilute, or suspend biological samples, such as cells or proteins, to maintain their structural and functional integrity.
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Ethanol is a clear, colorless liquid chemical compound commonly used in laboratory settings. It is a key component in various scientific applications, serving as a solvent, disinfectant, and fuel source. Ethanol has a molecular formula of C2H6O and a range of industrial and research uses.
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HPLC-grade acetonitrile is a high-purity organic solvent commonly used as a mobile phase component in high-performance liquid chromatography (HPLC) applications. It is a colorless, volatile liquid with a characteristic odor. The product meets the specifications required for HPLC-grade solvents, ensuring consistency and reliability in analytical procedures.
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The LPS laboratory equipment is a high-precision device used for various applications in scientific research and laboratory settings. It is designed to accurately measure and monitor specific parameters essential for various experimental procedures. The core function of the LPS is to provide reliable and consistent data collection, ensuring the integrity of research results. No further details or interpretations can be provided while maintaining an unbiased and factual approach.
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The Geminin C18 column is a high-performance liquid chromatography (HPLC) column designed for the separation and analysis of a wide range of organic compounds. The column features a silica-based stationary phase with chemically bonded C18 alkyl chains, providing a reversed-phase separation mechanism. This column is suitable for a variety of applications, including the analysis of pharmaceuticals, environmental pollutants, and natural products.
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The Milli-Q system is a water purification system designed to produce high-quality ultrapure water. It utilizes a multi-stage filtration process to remove impurities, ions, and organic matter from the input water, resulting in water that meets the strict standards required for various laboratory applications.
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2,4-dinitrofluorobenzene (DNFB) is a chemical compound used as a reagent in various laboratory procedures. It functions as a fluorinating agent, allowing for the introduction of fluorine atoms into organic molecules. DNFB is commonly utilized in synthetic organic chemistry, biochemical research, and analytical techniques.
More about "Gardenia"
Gardenia, a captivating genus of the Rubiaceae family, has long been revered for its fragrant blossoms and striking foliage.
These evergreen shrubs and small trees, native to tropical and subtropical regions of Africa, Asia, and Oceania, have captured the hearts of botanists, horticulturists, and traditional medicine practitioners alike.
Researchers exploring the secrets of Gardenia can leverage the power of PubCompare.ai's AI-driven platform to uncover a wealth of protocols from the literature, preprints, and patents.
This innovative tool empowers scientists to identify the most effective protocols and products for their Gardenia research, ensuring a seamless and efficient journey.
Beyond the captivating floral display, Gardenia species have a rich history in traditional medicine, with applications ranging from skin care to respiratory ailments.
Compounds like formic acid, methanol, and ethanol have been investigated for their potential therapeutic properties in Gardenia-based formulations.
To support these research endeavors, protocols involving Dulbecco's modified Eagle's medium (DMEM), phosphate-buffered saline (PBS), and high-performance liquid chromatography (HPLC) with acetonitrile have been utilized to analyze and characterize the phytochemical constituents of Gardenia.
Additionally, techniques like 2,4-dinitrofluorobenzene (DNFB) assays have been employed to assess the anti-inflammatory potential of Gardenia-derived compounds.
Through the integration of cutting-edge AI-driven technologies and a comprehensive understanding of Gardenia's botanical and medicinal attributes, researchers can uncover new insights, develop innovative products, and embark on a smothless research journey with the help of PubCompare.ai.
These evergreen shrubs and small trees, native to tropical and subtropical regions of Africa, Asia, and Oceania, have captured the hearts of botanists, horticulturists, and traditional medicine practitioners alike.
Researchers exploring the secrets of Gardenia can leverage the power of PubCompare.ai's AI-driven platform to uncover a wealth of protocols from the literature, preprints, and patents.
This innovative tool empowers scientists to identify the most effective protocols and products for their Gardenia research, ensuring a seamless and efficient journey.
Beyond the captivating floral display, Gardenia species have a rich history in traditional medicine, with applications ranging from skin care to respiratory ailments.
Compounds like formic acid, methanol, and ethanol have been investigated for their potential therapeutic properties in Gardenia-based formulations.
To support these research endeavors, protocols involving Dulbecco's modified Eagle's medium (DMEM), phosphate-buffered saline (PBS), and high-performance liquid chromatography (HPLC) with acetonitrile have been utilized to analyze and characterize the phytochemical constituents of Gardenia.
Additionally, techniques like 2,4-dinitrofluorobenzene (DNFB) assays have been employed to assess the anti-inflammatory potential of Gardenia-derived compounds.
Through the integration of cutting-edge AI-driven technologies and a comprehensive understanding of Gardenia's botanical and medicinal attributes, researchers can uncover new insights, develop innovative products, and embark on a smothless research journey with the help of PubCompare.ai.