All raw data were centroided and converted to 32-bit uncompressed mzXML file using Bruker Data Analysis. A script was developed to select all possible MS/MS spectra in each LC-MS/MS run that could correspond to a compound present in the sample. For each compound, we calculated the theoretical mass M from its chemical composition and searched for the M+H, M+2H, M+K, and M+Na adducts. Putative identifications included all MS/MS spectra whose precursor m/z had a ppm error <50 compared to the theoretical mass of each possible precursor m/z; all tandem MS/MS spectra with an MS1 precursor intensity of <1E4 were ignored. All candidate identifications were manually inspected and the most abundant representative spectrum for each compound was added to the corresponding library at the gold or bronze level based upon an expert evaluation of the spectrum quality. The best MS/MS spectrum per compound as added to the GNPS-Collections library without filtering or alteration from the mzXML files.
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Chemical composition
Chemical composition
Cheemical composition refers to the specific arrangement and proportion of elements within a chemical substance.
Understanding chemical composition is crucial for accurately characterizing and studying the properties of materials, drugs, and other substances.
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By streamlining this process, PubCompare.ai helps elevate the accuracy and efficiency of chemical composition studies, leading to more robust and reliable findings.
Understanding chemical composition is crucial for accurately characterizing and studying the properties of materials, drugs, and other substances.
Researchers can leverage cutting-edge AI tools like PubCompare.ai to easily locate relevant protocols from scientific literature and patents, then use AI-driven comparisons to identify the optimal approaches for their chemical composition research.
By streamlining this process, PubCompare.ai helps elevate the accuracy and efficiency of chemical composition studies, leading to more robust and reliable findings.
Most cited protocols related to «Chemical composition»
cDNA Library
chemical composition
Gold
Tandem Mass Spectrometry
Antioxidant (DPPH and ABTS radical scavenging, reducing power (CUPRAC and FRAP), phosphomolybdenum, and metal chelating (ferrozine method)) and enzyme inhibitory activities [cholinesterase (ChE) Elmann’s method], tyrosinase (dopachrome method), α-amylase (iodine/potassium iodide method), and α -glucosidase (chromogenic PNPG method)) were determined using the methods previously described by Zengin et al. (2014) (link) and Dezsi et al. (2015) (link).
For the DPPH (1,1-diphenyl-2-picrylhydrazyl) radical scavenging assay: Sample solution (1 mg/mL; 1 mL) was added to 4 mL of a 0.004% methanol solution of DPPH. The sample absorbance was read at 517 nm after a 30 min incubation at room temperature in the dark. DPPH radical scavenging activity was expressed as millimoles of trolox equivalents (mg TE/g extract).
For ABTS (2,2′-azino-bis(3-ethylbenzothiazoline) 6-sulfonic acid) radical scavenging assay: Briefly, ABTS+ was produced directly by reacting 7 mM ABTS solution with 2.45 mM potassium persulfate and allowing the mixture to stand for 12–16 in the dark at room temperature. Prior to beginning the assay, ABTS solution was diluted with methanol to an absorbance of 0.700 ± 0.02 at 734 nm. Sample solution (1 mg/mL; 1 mL) was added to ABTS solution (2 mL) and mixed. The sample absorbance was read at 734 nm after a 30 min incubation at room temperature. The ABTS radical scavenging activity was expressed as millimoles of trolox equivalents (mmol TE/g extract) (Mocan et al., 2016a (link)).
For CUPRAC (cupric ion reducing activity) activity assay: Sample solution (1 mg/mL; 0.5 mL) was added to premixed reaction mixture containing CuCl2 (1 mL, 10 mM), neocuproine (1 mL, 7.5 mM) and NH4Ac buffer (1 mL, 1 M, pH 7.0). Similarly, a blank was prepared by adding sample solution (0.5 mL) to premixed reaction mixture (3 mL) without CuCl2. Then, the sample and blank absorbances were read at 450 nm after a 30 min incubation at room temperature. The absorbance of the blank was subtracted from that of the sample. CUPRAC activity was expressed as milligrams of trolox equivalents (mg TE/g extract).
For FRAP (ferric reducing antioxidant power) activity assay: Sample solution (1 mg/mL; 0.1 mL) was added to premixed FRAP reagent (2 mL) containing acetate buffer (0.3 M, pH 3.6), 2,4,6-tris(2-pyridyl)-S-triazine (TPTZ) (10 mM) in 40 mM HCl and ferric chloride (20 mM) in a ratio of 10:1:1 (v/v/v). Then, the sample absorbance was read at 593 nm after a 30 min incubation at room temperature. FRAP activity was expressed as milligrams of trolox equivalents (mg TE/g extract).
For phosphomolybdenum method: Sample solution (1 mg/mL; 0.3 mL) was combined with 3 mL of reagent solution (0.6 M sulfuric acid, 28 mM sodium phosphate and 4 mM ammonium molybdate). The sample absorbance was read at 695 nm after a 90 min incubation at 95°C. The total antioxidant capacity was expressed as millimoles of trolox equivalents (mmol TE/g extract) (Mocan et al., 2016c (link)).
For metal chelating activity assay: Briefly, sample solution (1 mg/mL; 2 mL) was added to FeCl2 solution (0.05 mL, 2 mM). The reaction was initiated by the addition of 5 mM ferrozine (0.2 mL). Similarly, a blank was prepared by adding sample solution (2 mL) to FeCl2 solution (0.05 mL, 2 mM) and water (0.2 mL) without ferrozine. Then, the sample and blank absorbances were read at 562 nm after 10 min incubation at room temperature. The absorbance of the blank was sub-tracted from that of the sample. The metal chelating activity was expressed as milligrams of EDTA (disodium edetate) equivalents (mg EDTAE/g extract).
For ChE inhibitory activity assay: Sample solution (1 mg/mL; 50 μL) was mixed with DTNB (5,5-dithio-bis(2-nitrobenzoic) acid, Sigma, St. Louis, MO, United States) (125 μL) and AChE [acetylcholines-terase (Electric ell AChE, Type-VI-S, EC 3.1.1.7, Sigma)], or BChE [BChE (horse serum BChE, EC 3.1.1.8, Sigma)] solution (25 μL) in Tris–HCl buffer (pH 8.0) in a 96-well microplate and incubated for 15 min at 25°C. The reaction was then initiated with the addition of acetylthiocholine iodide (ATCI, Sigma) or butyrylthiocholine chloride (BTCl, Sigma) (25 μL). Similarly, a blank was prepared by adding sample solution to all reaction reagents without enzyme (AChE or BChE) solution. The sample and blank absorbances were read at 405 nm after 10 min incubation at 25°C. The absorbance of the blank was subtracted from that of the sample and the cholinesterase inhibitory activity was expressed as galanthamine equivalents (mgGALAE/g extract) (Mocan et al., 2016b (link)).
For Tyrosinase inhibitory activity assay: Sample solution (1 mg/mL; 25 μL) was mixed with tyrosinase solution (40 μL, Sigma) and phosphate buffer (100 μL, pH 6.8) in a 96-well microplate and incubated for 15 min at 25°C. The reaction was then initiated with the addition of L-DOPA (40 μL, Sigma). Similarly, a blank was prepared by adding sample solution to all reaction reagents without enzyme (tyrosinase) solution. The sample and blank absorbances were read at 492 nm after a 10 min incubation at 25°C. The absorbance of the blank was subtracted from that of the sample and the tyrosinase inhibitory activity was expressed as kojic acid equivalents (mgKAE/g extract) (Mocan et al., 2017 (link)).
For α-amylase inhibitory activity assay: Sample solution (1 mg/mL; 25 μL) was mixed with α-amylase solution (ex-porcine pancreas, EC 3.2.1.1, Sigma) (50 μL) in phosphate buffer (pH 6.9 with 6 mM sodium chloride) in a 96-well microplate and incubated for 10 min at 37°C. After pre-incubation, the reaction was initiated with the addition of starch solution (50 μL, 0.05%). Similarly, a blank was prepared by adding sample solution to all reaction reagents without enzyme (α-amylase) solution. The reaction mixture was incubated 10 min at 37°C. The reaction was then stopped with the addition of HCl (25 μL, 1 M). This was followed by addition of the iodine-potassium iodide solution (100 μL). The sample and blank absorbances were read at 630 nm. The absorbance of the blank was subtracted from that of the sample and the α-amylase inhibitory activity was expressed as acarbose equivalents (mmol ACE/g extract) (Savran et al., 2016 (link)).
For α-glucosidase inhibitory activity assay: Sample solution (1 mg/mL; 50 μL) was mixed with glutathione (50 μL), α-glucosidase solution (from Saccharomyces cerevisiae, EC 3.2.1.20, Sigma) (50 μL) in phosphate buffer (pH 6.8) and PNPG (4-N-trophenyl-α-D -glucopyranoside, Sigma) (50 μL) in a 96-well microplate and incubated for 15 min at 37°C. Similarly, a blank was prepared by adding sample solution to all reaction reagents without enzyme (α-glucosidase) solution. The reaction was then stopped with the addition of sodium carbonate (50 μL, 0.2 M). The sample and blank absorbances were read at 400 nm. The absorbance of the blank was subtracted from that of the sample and the α-glucosidase inhibitory activity was expressed as acarbose equivalents (mmol ACE/g extract) (Llorent-Martínez et al., 2016 (link)).
All the assays were carried out in triplicate. The results are expressed as mean values and standard deviation (SD). The differences between the different extracts were analyzed using one-way analysis of variance (ANOVA) followed by Tukey’s honestly significant difference post hoc test with α = 0.05. This treatment was carried out using SPSS v. 14.0 program.
For the DPPH (1,1-diphenyl-2-picrylhydrazyl) radical scavenging assay: Sample solution (1 mg/mL; 1 mL) was added to 4 mL of a 0.004% methanol solution of DPPH. The sample absorbance was read at 517 nm after a 30 min incubation at room temperature in the dark. DPPH radical scavenging activity was expressed as millimoles of trolox equivalents (mg TE/g extract).
For ABTS (2,2′-azino-bis(3-ethylbenzothiazoline) 6-sulfonic acid) radical scavenging assay: Briefly, ABTS+ was produced directly by reacting 7 mM ABTS solution with 2.45 mM potassium persulfate and allowing the mixture to stand for 12–16 in the dark at room temperature. Prior to beginning the assay, ABTS solution was diluted with methanol to an absorbance of 0.700 ± 0.02 at 734 nm. Sample solution (1 mg/mL; 1 mL) was added to ABTS solution (2 mL) and mixed. The sample absorbance was read at 734 nm after a 30 min incubation at room temperature. The ABTS radical scavenging activity was expressed as millimoles of trolox equivalents (mmol TE/g extract) (Mocan et al., 2016a (link)).
For CUPRAC (cupric ion reducing activity) activity assay: Sample solution (1 mg/mL; 0.5 mL) was added to premixed reaction mixture containing CuCl2 (1 mL, 10 mM), neocuproine (1 mL, 7.5 mM) and NH4Ac buffer (1 mL, 1 M, pH 7.0). Similarly, a blank was prepared by adding sample solution (0.5 mL) to premixed reaction mixture (3 mL) without CuCl2. Then, the sample and blank absorbances were read at 450 nm after a 30 min incubation at room temperature. The absorbance of the blank was subtracted from that of the sample. CUPRAC activity was expressed as milligrams of trolox equivalents (mg TE/g extract).
For FRAP (ferric reducing antioxidant power) activity assay: Sample solution (1 mg/mL; 0.1 mL) was added to premixed FRAP reagent (2 mL) containing acetate buffer (0.3 M, pH 3.6), 2,4,6-tris(2-pyridyl)-S-triazine (TPTZ) (10 mM) in 40 mM HCl and ferric chloride (20 mM) in a ratio of 10:1:1 (v/v/v). Then, the sample absorbance was read at 593 nm after a 30 min incubation at room temperature. FRAP activity was expressed as milligrams of trolox equivalents (mg TE/g extract).
For phosphomolybdenum method: Sample solution (1 mg/mL; 0.3 mL) was combined with 3 mL of reagent solution (0.6 M sulfuric acid, 28 mM sodium phosphate and 4 mM ammonium molybdate). The sample absorbance was read at 695 nm after a 90 min incubation at 95°C. The total antioxidant capacity was expressed as millimoles of trolox equivalents (mmol TE/g extract) (Mocan et al., 2016c (link)).
For metal chelating activity assay: Briefly, sample solution (1 mg/mL; 2 mL) was added to FeCl2 solution (0.05 mL, 2 mM). The reaction was initiated by the addition of 5 mM ferrozine (0.2 mL). Similarly, a blank was prepared by adding sample solution (2 mL) to FeCl2 solution (0.05 mL, 2 mM) and water (0.2 mL) without ferrozine. Then, the sample and blank absorbances were read at 562 nm after 10 min incubation at room temperature. The absorbance of the blank was sub-tracted from that of the sample. The metal chelating activity was expressed as milligrams of EDTA (disodium edetate) equivalents (mg EDTAE/g extract).
For ChE inhibitory activity assay: Sample solution (1 mg/mL; 50 μL) was mixed with DTNB (5,5-dithio-bis(2-nitrobenzoic) acid, Sigma, St. Louis, MO, United States) (125 μL) and AChE [acetylcholines-terase (Electric ell AChE, Type-VI-S, EC 3.1.1.7, Sigma)], or BChE [BChE (horse serum BChE, EC 3.1.1.8, Sigma)] solution (25 μL) in Tris–HCl buffer (pH 8.0) in a 96-well microplate and incubated for 15 min at 25°C. The reaction was then initiated with the addition of acetylthiocholine iodide (ATCI, Sigma) or butyrylthiocholine chloride (BTCl, Sigma) (25 μL). Similarly, a blank was prepared by adding sample solution to all reaction reagents without enzyme (AChE or BChE) solution. The sample and blank absorbances were read at 405 nm after 10 min incubation at 25°C. The absorbance of the blank was subtracted from that of the sample and the cholinesterase inhibitory activity was expressed as galanthamine equivalents (mgGALAE/g extract) (Mocan et al., 2016b (link)).
For Tyrosinase inhibitory activity assay: Sample solution (1 mg/mL; 25 μL) was mixed with tyrosinase solution (40 μL, Sigma) and phosphate buffer (100 μL, pH 6.8) in a 96-well microplate and incubated for 15 min at 25°C. The reaction was then initiated with the addition of L-DOPA (40 μL, Sigma). Similarly, a blank was prepared by adding sample solution to all reaction reagents without enzyme (tyrosinase) solution. The sample and blank absorbances were read at 492 nm after a 10 min incubation at 25°C. The absorbance of the blank was subtracted from that of the sample and the tyrosinase inhibitory activity was expressed as kojic acid equivalents (mgKAE/g extract) (Mocan et al., 2017 (link)).
For α-amylase inhibitory activity assay: Sample solution (1 mg/mL; 25 μL) was mixed with α-amylase solution (ex-porcine pancreas, EC 3.2.1.1, Sigma) (50 μL) in phosphate buffer (pH 6.9 with 6 mM sodium chloride) in a 96-well microplate and incubated for 10 min at 37°C. After pre-incubation, the reaction was initiated with the addition of starch solution (50 μL, 0.05%). Similarly, a blank was prepared by adding sample solution to all reaction reagents without enzyme (α-amylase) solution. The reaction mixture was incubated 10 min at 37°C. The reaction was then stopped with the addition of HCl (25 μL, 1 M). This was followed by addition of the iodine-potassium iodide solution (100 μL). The sample and blank absorbances were read at 630 nm. The absorbance of the blank was subtracted from that of the sample and the α-amylase inhibitory activity was expressed as acarbose equivalents (mmol ACE/g extract) (Savran et al., 2016 (link)).
For α-glucosidase inhibitory activity assay: Sample solution (1 mg/mL; 50 μL) was mixed with glutathione (50 μL), α-glucosidase solution (from Saccharomyces cerevisiae, EC 3.2.1.20, Sigma) (50 μL) in phosphate buffer (pH 6.8) and PNPG (4-N-trophenyl-α-
All the assays were carried out in triplicate. The results are expressed as mean values and standard deviation (SD). The differences between the different extracts were analyzed using one-way analysis of variance (ANOVA) followed by Tukey’s honestly significant difference post hoc test with α = 0.05. This treatment was carried out using SPSS v. 14.0 program.
Generation rate, composition, and physical and chemical nature of both feces and urine were recorded as of Table 1 . Each recorded datum was the mean of the data from the reported study. Some published papers reported two or more independent studies so these papers contributed more than one value to the data set. The mean and median of each variable were both calculated as measures of central tendency and data were checked for normality by calculating a coefficient of skewness (Young, 1962 ):
Measured variables for feces and urine
No outliers were removed from the data set but were identified in the graphical output. Full statistical calculations were only conducted on variables that had at least seven values but a median value is given for data when there were less than seven values.
A summary of studies used in the statistical analysis are outlined inTable 2 , including the location and number of studies. A large proportion (80%) of the data set was from studies conducted in Europe and North America. A distinction was therefore made between low and high income countries by the measure of development; using the Human Development Index (HDI), a composite index measuring average achievement in three basic dimensions of human development; life expectancy, education, and income (UNDP, 2011 ).
The geographical location and human development index ranking of studies used in statistical analysis
| Feces unit | Urine unit | |
|---|---|---|
| Variable | of measure | of measure |
| Generation | g/cap/day | L/cap/day |
| Frequency of defecation | motions/24 hr | urinations/24 hr |
| Water content | % total mass | % total mass |
| Organic composition | % total mass | % dry mass |
| Components of solids | % total mass | % total mass |
| Inorganic composition | % dry mass | % dry mass |
| Daily excretion of elements | g/cap/day | g/cap/day, mg/L |
| Chemical nature | ||
| pH | pH | pH |
| COD and BOD | mg/g wet mass | mg/L |
| Physical form | ||
| Bristol stool form | Linear scale (1–7) | |
| Diarrhea prevalence | % of population |
No outliers were removed from the data set but were identified in the graphical output. Full statistical calculations were only conducted on variables that had at least seven values but a median value is given for data when there were less than seven values.
A summary of studies used in the statistical analysis are outlined in
| Country | n | HDI* | References |
|---|---|---|---|
| Africa | 2 | 3/4a | Cranston and Burkitt (1975 (link)), Burkitt et al. (1980 (link)) |
| Australia | 2 | 1 | Birkett et al. (1996 (link)), Hovey et al. (2003 (link)) |
| Burma | 1 | 4 | Myo-Kin et al. (1994 (link)) |
| Canada | 3 | 1 | Burkitt et al. (1980 (link)), Vuksan et al. (1999 (link)) |
| China | 3 | 2 | Jie et al. (2000 (link)), Chen et al. (2008 (link)), Bai and Wang (2010 ) |
| Denmark | 2 | 1 | Maclennan and Jensen (1977 (link)), Jensen et al. (1982 (link)) |
| Developing countries | 2 | 3/4a | Feachem et al. (1978 ) |
| Europe and North America | 1 | 1/2b | Feachem et al. (1978 ) |
| European | 1 | 1b | Mykkänen et al. (1998 (link)) |
| Finland | 4 | 1 | Reddy et al. (1975 (link)), Reddy et al. (1978 (link)), Jensen et al. (1982 (link)), Mykkänen et al. (1998 (link)) |
| Germany | 1 | 1 | Erhardt et al. (1997 (link)) |
| Guatemala | 1 | 3 | Calloway and Kretsch (1978 (link)) |
| Holland | 4 | 1 | Stasse-Wolthuis et al. (1980 (link)), Van Faassen et al. (1993 (link)), Gaillard (2002 ), Wierdsma et al. (2011 (link)) |
| India | 1 | 3 | Shetty and Kurpad (1986 (link)) |
| Iran | 1 | 2 | Adibi et al. (2007 (link)) |
| Japan | 7 | 1 | Glober et al. (1977 ), Polprasert and Valencia (1981 ), Tarida et al. (1984 (link)), Saitoh et al. (1999 (link)), Danjo et al. (2008 (link)), Shinohara et al. (2010 (link)), Hotta and Funamizu (2009 (link)) |
| Kenya | 1 | 4 | Cranston and Burkitt (1975 (link)) |
| New Zealand | 1 | 1 | Pomare et al. (1981 ) |
| North America | 1 | 1b | Vuksan et al. (2008 (link)) |
| Peru | 1 | 2 | Crofts (1975 (link)) |
| Singapore | 1 | 1 | Chen et al. (2000 (link)) |
| South Africa | 2 | 3 | Burkitt et al. (1972 ), Walker (1975 (link)) |
| Spain | 1 | 1 | Roig et al. (1993 (link)) |
| Sweden | 4 | 1 | Reddy et al. (1978 (link)), Vinneras (2002 ), Vinnerås et al. (2006 ) |
| Thailand | 2 | 2 | Danivat et al. (1988 ), Schouw et al. (2002 (link)) |
| Tonga | 1 | 2 | Pomare et al. (1981 ) |
| UK | 26 | 1 | Olmsted et al. (1934 ), Connell et al. (1965 (link)), Southgate and Durnin (1970 (link)), Burkitt et al. (1972 ), Goy et al. (1976 (link)), Wyman et al. (1978 (link)), Prynne and Southgate (1979 (link)), Stephen and Cummings (1980 (link)), Eastwood et al. (1984 (link)), Eastwood et al. (1986 (link)), Davies et al. (1986 (link)), Cummings et al. (1987 (link)), Sandler and Drossman (1987 (link)), Cummings et al. (1992 (link)), Murphy et al. (1993 (link)), Cummings et al. (1996 (link)), Lewis and Heaton (1997 (link)), Chen et al. (1998 (link)), Reddy et al. (1998 (link)), Rivero-Marcotegui et al. (1998 (link)), Aichbichler et al. (1998 (link)), Almeida et al. (1999 ), Magee et al. (2000 (link)), Chaplin et al. (2000 (link)), Woodmansey et al. (2004 (link)), Silvester et al. (2011 (link)) |
| USA | 18 | 1 | Canfield et al. (1963 ), Watts et al. (1963 (link)), Diem and Lentner (1970 ), Goldsmith and Burkitt (1975 (link)), Cummings et al. (1978 ), Glober et al. (1977 ), Goldberg et al. (1977 (link)), Beyer and Flynn (1978 (link)), Reddy et al. (1978 (link)), Calloway and Kretsch (1978 (link)), Kien et al. (1981 (link)), Polprasert and Valencia (1981 ), Tucker et al. (1981 (link)), Schubert et al. (1984 (link)), Parker and Gallagher (1988 ), Zuckerman, et al. (1995 (link)), Aichbichler et al. (1998 (link)), McRorie et al. (2000) |
*Human Development Index Classifications (UNDP, 2011 ): 1. Very high, 2. High, 3. Medium, 4. Low.aClassification not available, presumed to be ranking 3 or 4.bClassification not available, presumed to be ranking 1 or 2.
The sequences of all the PPRs were identified with reference to the 11,938 sequences of Orthohepevirus A (including 338 complete HEV genomes) available in the Virus Pathogen Resource (VIPR) database.5 Selected sequences were systematically searched to identify insertions so that they could be used, together with those identified by PacBio sequencing, for further analysis. The compositions of HEV PPR insertions/duplications were determined and their post-translational modifications predicted by analyzing a range of parameters. Potential ubiquitination sites were identified using the BDM-PUB server6 with a threshold of >0.3 average potential score. Potential phosphorylation sites were identified using the NetPhos 3.1 server7 with a threshold of >0.5 average potential score. Potential acetylation sites were identified using the Prediction of Acetylation on Internal Lysines (PAIL) server8 with a threshold of >0.2 average potential score. Potential N-linked glycosylation sites were identified using the NetNGlyc 1.0 server9 with a threshold of >0.5 average potential score. Potential methylation sites were identified using the BPB-PPMS server10 with a threshold of >0.5 average potential score. Nuclear export signal (NES) sites were identified using the Wregex server11 with parameters NES/CRM1 and Relaxed. Nuclear localization signal (NLS) sites were identified using SeqNLS12 with a 0.86 cut-off. The amino acid composition (proportions of amino acids), physico-chemical composition, and net load were analyzed with R. Principal component analysis (PCA) is a mathematical algorithm that reduces the dimensionality of the data while retaining most of the variation in a data set. PCA allows to identify new variables, the principal components, which are linear combinations of the original variables (Ringner, 2008 (link)). PCA was done (excluding the amino acid composition due to redundancy with physico-chemical properties) to summarize and visualize the information on the variables in our data set (Abdi and Williams, 2010 (link)); each variable was then studied independently. An in-house R-pipeline based on the amino acid sequences and the results of each analysis was used to generate bar plots for amino acid composition. The amino acid compositions were assigned to one of two categories: sequences with insertions/duplications (including insertions of human genome and HEV genome duplications) and sequences without insertions/duplications. The other parameters were assigned to one of three categories: sequences with insertions, those with duplications, and sequences without insertion/duplication.
Acetylation
Amino Acids
Amino Acid Sequence
chemical composition
chemical properties
DNA Insertion Elements
Genome
Genome, Human
Insertion Mutation
Lysine
Methylation
Nuclear Export Signals
Nuclear Localization Signals
Pathogenicity
Phosphorylation
Protein Glycosylation
Sequence Insertion
Ubiquitination
Virus
Most recents protocols related to «Chemical composition»
The chemical composition of the scaffolds was determined using X-ray diffractometry and Rietveld analysis. To determine the qualitative phase composition, three wedges of each composition were finely ground and placed in a cuvette of the diffractometer (D8 Advance, Bruker Corporations, Karlsruhe, Germany). The measurements were carried out under the following conditions: Cu-Kα radiation, measurement angle 2θ = 10–40°, measurement speed of 0.5 s/step and rotation of the cuvette of 15 rpm.
The phase composition was determined using DIFFRAC.EVA V.5.1.0.5. (Bruker Corporations, Billerica, MA, USA) based on ICDD reference patterns: MgHPO4·3H2O (newberyite; PDF Ref. 00-020-0153), CaHPO4·2H2O (brushite; PDF Ref. 00-009-0077), NH4MgPO4·6H2O (struvite; PDF Ref. 00-015-0762), Mg3(PO4)2 (farringtonite; PDF Ref. 00-033-0876), Ca4Mg5(PO4)6 (stanfieldite; PDF Ref. 00-011-0231) and MgO (magnesium oxide/periclase; PDF Ref. 00-004-0829). A quantitative phase analysis was carried out using the Rietveld method. The TOPAS V6 software (Bruker Corporations, Billerica, MA, USA) was used for this purpose.
The phase composition was determined using DIFFRAC.EVA V.5.1.0.5. (Bruker Corporations, Billerica, MA, USA) based on ICDD reference patterns: MgHPO4·3H2O (newberyite; PDF Ref. 00-020-0153), CaHPO4·2H2O (brushite; PDF Ref. 00-009-0077), NH4MgPO4·6H2O (struvite; PDF Ref. 00-015-0762), Mg3(PO4)2 (farringtonite; PDF Ref. 00-033-0876), Ca4Mg5(PO4)6 (stanfieldite; PDF Ref. 00-011-0231) and MgO (magnesium oxide/periclase; PDF Ref. 00-004-0829). A quantitative phase analysis was carried out using the Rietveld method. The TOPAS V6 software (Bruker Corporations, Billerica, MA, USA) was used for this purpose.
The chemical composition of the test sample was estimated using a JEOL JSM-6610LV (Tokyo, Japan) SEM (Scanning Electron Microscope) with an Oxford Instruments X-MAX 80 (Abingdon, Oxfordshire, England) module installed for energy dispersive X-ray (EDS) measurements using an accelerating voltage of 5 kV. Measurements were performed on pure Si samples, with a low accelerating voltage applied to minimize the depth from which the characteristic radiation originated, which was then used to measure qualitative and quantitative chemical composition.
Due to the limitations of the EDS method (in particular, the inability to accurately quantify light and heavy elements simultaneously, as well as to accurately analyze the carbon content and the possibility of carbon contamination of both the walls of the vacuum chamber of the microscope and the surface of the samples), the results of the proportion of carbon relative to the other elements can be subject to large error. To minimize errors, an analysis of the relative ratio of carbon to silicon was carried out, comparing the results obtained with those obtained for reference samples of SiN coatings, for which the measured carbon content was considered to be the contamination standard noise. All coatings in terms of their chemical composition were measured during a single test, to obtain the same carbon contamination conditions. For each group of coatings, two samples of monocrystalline silicon with deposited coatings were examined by SEM-EDS [36 (link),37 ].
Due to the limitations of the EDS method (in particular, the inability to accurately quantify light and heavy elements simultaneously, as well as to accurately analyze the carbon content and the possibility of carbon contamination of both the walls of the vacuum chamber of the microscope and the surface of the samples), the results of the proportion of carbon relative to the other elements can be subject to large error. To minimize errors, an analysis of the relative ratio of carbon to silicon was carried out, comparing the results obtained with those obtained for reference samples of SiN coatings, for which the measured carbon content was considered to be the contamination standard noise. All coatings in terms of their chemical composition were measured during a single test, to obtain the same carbon contamination conditions. For each group of coatings, two samples of monocrystalline silicon with deposited coatings were examined by SEM-EDS [36 (link),37 ].
The basic chemical composition of the wines is shown below in Table 2. There were signi cant differences across all variables measured. In particular, alcohol %, TA and RS are deemed to have sensory implications. The difference in the alcohol of the wines were driven primarily by the brix of the grapes that was harvested (data not shown).
The main chemical composition items of fish musles were estimated according to the guidelines of AOAC (2000), including the percentages of moisture and ash, in addition to the crude lipid content (Ether extract) which was analyzed using the Soxhlet system with ether as a solvent (SRPS ISO 1443 :1997) . Moreover, the crude protein was determined by Micro Klejdahl.
Physio-chemical methods were used according to Porto and others (15) to determine the chemical composition of fish, feces, and feed ingredients. In an oven with a 105 °C setting, weight loss was used to measure moisture. Ash was produced by burning a known quantity of the sample at 550 °C to a constant weight. Crude protein was calculated by converting total nitrogen, which was measured using the Kjeldahl method, into protein. The 6.25 factor was applied. Using the Soxhlet procedure, petroleum ether was used to extract the total lipids. The total calorie content was determined using the equivalent caloric values for proteins, lipids, and carbohydrates, which were 5.5, 9.1, and 4.1 kcal/g, respectively. Carbohydrates were estimated using the following equation:
Carbohydrates = 100 -(moisture % + protein% + lipid% + ash%)
Carbohydrates = 100 -(moisture % + protein% + lipid% + ash%)
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The ESCALAB 250Xi is a high-performance X-ray photoelectron spectroscopy (XPS) system designed for surface analysis. It provides precise and reliable data for the characterization of materials at the nanoscale level.
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The S-4800 is a high-resolution scanning electron microscope (SEM) manufactured by Hitachi. It provides a range of imaging and analytical capabilities for various applications. The S-4800 utilizes a field emission electron gun to generate high-quality, high-resolution images of samples.
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The D8 Advance is a versatile X-ray diffractometer (XRD) designed for phase identification, quantitative analysis, and structural characterization of a wide range of materials. It features advanced optics and a high-performance detector to provide accurate and reliable results.
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The K-Alpha is a high-performance X-ray photoelectron spectroscopy (XPS) system designed for surface analysis. It provides detailed information about the chemical composition and bonding of materials at the surface. The K-Alpha system features advanced technology to deliver accurate and reproducible data for a wide range of sample types and applications.
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The JEM-2100F is a transmission electron microscope (TEM) designed and manufactured by JEOL. It is capable of high-resolution imaging and analytical capabilities. The JEM-2100F is used for a variety of research and industrial applications that require advanced electron microscopy techniques.
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DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.
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Gallic acid is a naturally occurring organic compound that can be used as a laboratory reagent. It is a white to light tan crystalline solid with the chemical formula C6H2(OH)3COOH. Gallic acid is commonly used in various analytical and research applications.
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The JEM-2100 is a transmission electron microscope (TEM) manufactured by JEOL. It is designed to provide high-quality imaging and analysis of a wide range of materials at the nanoscale level. The instrument is equipped with a LaB6 electron source and can operate at accelerating voltages up to 200 kV, allowing for the investigation of a variety of samples.
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DPPH is a chemical compound used as a free radical scavenger in various analytical techniques. It is commonly used to assess the antioxidant activity of substances. The core function of DPPH is to serve as a stable free radical that can be reduced, resulting in a color change that can be measured spectrophotometrically.
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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.
More about "Chemical composition"
Chemical composition refers to the specific arrangement and proportion of elements within a chemical substance.
Understanding this is crucial for accurately characterizing and studying the properties of materials, drugs, and other substances.
Researchers can leverage cutting-edge AI tools like PubCompare.ai to easily locate relevant protocols from scientific literature, pre-prints, and patents, then use AI-driven comparisons to identify the optimal approaches for their chemical composition research.
By streamlining this process, PubCompare.ai helps elevate the accuracy and efficiency of chemical composition studies, leading to more robust and reliable findings.
This is particularly important for advanced analytical techniques like ESCALAB 250Xi, S-4800, and D8 Advance, which provide detailed insights into the chemical composition of samples.
Similarly, mass spectrometry methods like K-Alpha and JEM-2100F can be used to precisely determine the elemental composition and molecular structure of compounds.
Other key subtopics in chemical composition research include the use of solvents like DMSO, the analysis of antioxidant activity using DPPH, and the quantification of compounds like Gallic acid using techniques like JEM-2100 and Methanol extraction.
Ultimately, by optimizing the protocols and approaches used in chemical composition studies, researchers can elevate the accuracy, efficiency, and reliability of their findings, leading to advancements in fields ranging from materials science to pharmaceutical development.
Understanding this is crucial for accurately characterizing and studying the properties of materials, drugs, and other substances.
Researchers can leverage cutting-edge AI tools like PubCompare.ai to easily locate relevant protocols from scientific literature, pre-prints, and patents, then use AI-driven comparisons to identify the optimal approaches for their chemical composition research.
By streamlining this process, PubCompare.ai helps elevate the accuracy and efficiency of chemical composition studies, leading to more robust and reliable findings.
This is particularly important for advanced analytical techniques like ESCALAB 250Xi, S-4800, and D8 Advance, which provide detailed insights into the chemical composition of samples.
Similarly, mass spectrometry methods like K-Alpha and JEM-2100F can be used to precisely determine the elemental composition and molecular structure of compounds.
Other key subtopics in chemical composition research include the use of solvents like DMSO, the analysis of antioxidant activity using DPPH, and the quantification of compounds like Gallic acid using techniques like JEM-2100 and Methanol extraction.
Ultimately, by optimizing the protocols and approaches used in chemical composition studies, researchers can elevate the accuracy, efficiency, and reliability of their findings, leading to advancements in fields ranging from materials science to pharmaceutical development.