The field measurements were conducted in April 2006 as described in detail by Niinemets et al. (2009b). In brief, plant material for gas-exchange measurements and foliage chemical and structural analyses was sampled from healthy individuals of 32 Australian evergreen tree and shrub species. Sampling was primarily spread across four sites (HRHN, high rain, high nutrients; HRLN, high rain, low nutrients; LRHN, low rain, high nutrients; LRLN, low rain, low nutrients) selected in native forests and shrublands near Sydney, Australia (see Table 2 for species sampled at each site). The open- or closed-forest vegetation supported at each site is characterized by broad-leaved canopy trees (e.g. Eucalyptus spp., Corymbia spp., Syncarpia glomulifera), with a mix of broad-leaved, needle-leaved, and microphyllous species in the understorey. To get a broader range of leaf structures and photosynthetic capacities, additional samples for five species were taken from Macquarie University campus (MQ; Table 2 ). In all cases, mature individuals were used. All species, except Acacia spp., possessed true mature leaves. Acacia spp. possessed foliage phyllodes that are analogues of broad leaves. It is noted that important structural changes occur in leaves upon transition from the juvenile to the mature leaf form and upon replacement of juvenile foliage by phyllodes in Acacia species (Ullmann, 1989 ; Groom et al., 1997 ). Ontogenetic modifications in leaf form are also associated with alterations in leaf anatomy and mesophyll diffusion conductance (Mullin et al., 2009 (link); Steppe et al., 2011 ). Such modifications constitute a potentially highly important source of species differentiation among different habitats during ontogeny, but such modifications were not studied here. Further details about sites, species, and sampling procedures can be found in Niinemets et al. (2009b).
>
Living Beings
>
Plant
>
Eucalyptus
Eucalyptus
Eucalyptus is a genus of tall, evergreen trees native to Australia and nearby islands.
These trees are known for their distinctive bark, leaves, and flowers, and have a wide range of uses in industries such as timber, pulp, and essential oils.
Eucalyptus trees are also important in many ecosystems, providing habitat and resources for a variety of wildlife.
Reserchers can use PubCompare.ai's innovative AI-driven platform to optimize their Eucalyptus studies, easily locating protocols from literature, preprints, and patents, and leveraging advanced comparisons to identify the most accurate and reproducbile methods.
This solution can enhance efficiency and accuracy in Eucalyptus research.
These trees are known for their distinctive bark, leaves, and flowers, and have a wide range of uses in industries such as timber, pulp, and essential oils.
Eucalyptus trees are also important in many ecosystems, providing habitat and resources for a variety of wildlife.
Reserchers can use PubCompare.ai's innovative AI-driven platform to optimize their Eucalyptus studies, easily locating protocols from literature, preprints, and patents, and leveraging advanced comparisons to identify the most accurate and reproducbile methods.
This solution can enhance efficiency and accuracy in Eucalyptus research.
Most cited protocols related to «Eucalyptus»
Acacia
Diffusion
Eucalyptus
Forests
Needles
Nutrients
Photosynthesis
Plant Leaves
Plants
Rain
Trees
Data were collected by interviewer-administered questionnaires. The questionnaire includes socio-demographic variables, obstetrics history, dietary related variables, food security status, maternal perception, and socio-cultural issues. Six female nurses and three male public health professionals were recruited as data collectors and supervisors, respectively. Additionally, three female laboratory technicians were recruited to do pregnancy tests. Data collectors administered the questionnaire through a face-to-face interviews at the participants' homes. To maintain the optimal privacy of the mothers, other family members didn’t have free access to the place where the interviews were conducted.
Food frequency questionnaire (FFQ), which was taken from previously validated questionnaires [24 –26 (link)], containing 54 food items was used to collect dietary data. The questionnaire was also validated after the assessment of locally available foodstuffs. For the food frequency questions, the women were asked about the frequency of consumption of each food per day, per week or per month in the prior 3 months by taking the variation of dietary intakes within days of the week into consideration [25 (link), 26 (link)].
Food items of the FFQs were grouped into nine food groups: 1. cereals, roots, and tubers; 2. Vitamin-A-rich fruits and vegetables; 3. other fruits; 4. other vegetables; 5. legumes and nuts; 6. meat, poultry, and fish; 7. fats and oils; 8.dairy; and 9. Eggs [24 ].The consumers of a food item were defined as the consumption of a food item at least once a week [26 (link)]. The number of food groups the women ate within a week were counted to analyze dietary diversity score (DDS).
Food variety score (FVS) was computed by counting the individual food items the women consumed within a week. Then, the mean FVS was analyzed. The utilization of animal source food (ASF) was assessed by counting the frequency of each animal source foods the women took within the days of a week. Finally, the frequency of ASF consumption was divided into terciles (three parts). Dietary diversity score, FVS, ASF consumption, and frequency of meal were used to assess dietary practices.
The food security status of the household was assessed using 27 questions, which were adapted from the household food insecurity access scale. The questions were previously validated for use in developing countries [27 (link)]. Food secure households experienced fewer than the first 2 food insecurity indicators. Whereas, a household which experienced from 2 to 10, 11–17, and > 17 food insecurity indicators were considered as mildly, moderately, and severely food insecure households, respectively.
The wealth index of the household was determined using Principal Component Analysis (PCA) by considering latrine, water source, household assets, livestock, and agricultural land ownership. The responses of all non-dummy variables were classified into three parts. The highest score was coded as 1. Whereas, the two lower values were given code 0. In PCA, those variables having a commonality value of greater than 0.5 were used to produce factor scores. Lastly, the score for each household on the first principal component was retained to create the wealth score. Quintiles of the wealth score were created to categorize households as poorest, poor, medium, rich, and richest.
To determine edible crop and vegetable production, each crop and vegetable species cultivated by the household were counted. The number of crops and vegetables produced was classified into three parts. The highest value was labeled as high production, while the two lower values were considered as low production. In the study area, khat, eucalyptus, “Gesho” (a local plant used to make tella or local beer and areki), pepper, and onion are the common cash crops. Count of these cash crops was used to decide cash crop production.
The total ownership of livestock was measured by Tropical Livestock Units (TLUs). There is no TLU index created specifically for Ethiopia, therefore, the indexes for Tropical Africa was used in this study. The TLUs were calculated using the following weighted index factors: cattle = 0.7, horses = 0.5, mules = 0.5, donkey = 0.5, sheep = 0.1, goats = 0.1, chickens = 0.01 [28 ].
Women’s autonomy was assessed using eight questions. For each question, code one was given when a decision was made by the woman alone or jointly with her husband, otherwise zero. The mean was used to classify a woman’s decision making power [16 ].
Maternal knowledge on diet during pregnancy was assessed using 12 questions. Code one was given for each question when the response was correct, or else zero. The attitude was assessed by 20 Likert scale questions using PCA. The factor scores were summed and ranked into terciles (three parts). Then the highest tercile was labeled as a favorable attitude, if not unfavorable attitude.
Subjective norms, intention, perceived susceptibility, perceived severity, perceived benefit, and perceived barriers were assessed using their respective composite questions. Mean was computed and women who scored above the mean for each variable were categorized as having subjective norms, intention, perceived susceptibility, perceived severity, perceived benefit, and perceived barriers, otherwise no.
Food frequency questionnaire (FFQ), which was taken from previously validated questionnaires [24 –26 (link)], containing 54 food items was used to collect dietary data. The questionnaire was also validated after the assessment of locally available foodstuffs. For the food frequency questions, the women were asked about the frequency of consumption of each food per day, per week or per month in the prior 3 months by taking the variation of dietary intakes within days of the week into consideration [25 (link), 26 (link)].
Food items of the FFQs were grouped into nine food groups: 1. cereals, roots, and tubers; 2. Vitamin-A-rich fruits and vegetables; 3. other fruits; 4. other vegetables; 5. legumes and nuts; 6. meat, poultry, and fish; 7. fats and oils; 8.dairy; and 9. Eggs [24 ].The consumers of a food item were defined as the consumption of a food item at least once a week [26 (link)]. The number of food groups the women ate within a week were counted to analyze dietary diversity score (DDS).
Food variety score (FVS) was computed by counting the individual food items the women consumed within a week. Then, the mean FVS was analyzed. The utilization of animal source food (ASF) was assessed by counting the frequency of each animal source foods the women took within the days of a week. Finally, the frequency of ASF consumption was divided into terciles (three parts). Dietary diversity score, FVS, ASF consumption, and frequency of meal were used to assess dietary practices.
The food security status of the household was assessed using 27 questions, which were adapted from the household food insecurity access scale. The questions were previously validated for use in developing countries [27 (link)]. Food secure households experienced fewer than the first 2 food insecurity indicators. Whereas, a household which experienced from 2 to 10, 11–17, and > 17 food insecurity indicators were considered as mildly, moderately, and severely food insecure households, respectively.
The wealth index of the household was determined using Principal Component Analysis (PCA) by considering latrine, water source, household assets, livestock, and agricultural land ownership. The responses of all non-dummy variables were classified into three parts. The highest score was coded as 1. Whereas, the two lower values were given code 0. In PCA, those variables having a commonality value of greater than 0.5 were used to produce factor scores. Lastly, the score for each household on the first principal component was retained to create the wealth score. Quintiles of the wealth score were created to categorize households as poorest, poor, medium, rich, and richest.
To determine edible crop and vegetable production, each crop and vegetable species cultivated by the household were counted. The number of crops and vegetables produced was classified into three parts. The highest value was labeled as high production, while the two lower values were considered as low production. In the study area, khat, eucalyptus, “Gesho” (a local plant used to make tella or local beer and areki), pepper, and onion are the common cash crops. Count of these cash crops was used to decide cash crop production.
The total ownership of livestock was measured by Tropical Livestock Units (TLUs). There is no TLU index created specifically for Ethiopia, therefore, the indexes for Tropical Africa was used in this study. The TLUs were calculated using the following weighted index factors: cattle = 0.7, horses = 0.5, mules = 0.5, donkey = 0.5, sheep = 0.1, goats = 0.1, chickens = 0.01 [28 ].
Women’s autonomy was assessed using eight questions. For each question, code one was given when a decision was made by the woman alone or jointly with her husband, otherwise zero. The mean was used to classify a woman’s decision making power [16 ].
Maternal knowledge on diet during pregnancy was assessed using 12 questions. Code one was given for each question when the response was correct, or else zero. The attitude was assessed by 20 Likert scale questions using PCA. The factor scores were summed and ranked into terciles (three parts). Then the highest tercile was labeled as a favorable attitude, if not unfavorable attitude.
Subjective norms, intention, perceived susceptibility, perceived severity, perceived benefit, and perceived barriers were assessed using their respective composite questions. Mean was computed and women who scored above the mean for each variable were categorized as having subjective norms, intention, perceived susceptibility, perceived severity, perceived benefit, and perceived barriers, otherwise no.
Full text: Click here
Agricultural Crops
Allium cepa
Beer
Catha edulis
Cattle
Cereals
Chickens
Crop, Avian
Diet
Domestic Sheep
Eggs
Equus asinus
Equus caballus
Eucalyptus
Fabaceae
Face
Family Member
Fats
Feeds, Animal
Females
Fishes
Food
Fowls, Domestic
Fruit
Goat
Health Personnel
Households
Husband
Interviewers
Laboratory Technicians
Livestock
Males
Meat
Mothers
Mules
Nurses
Nuts
Oils
Piper nigrum
Plant Roots
Plants
Plant Tubers
Pregnancy
Pregnancy Tests
Susceptibility, Disease
Vegetables
Vitamin A
Woman
The protocol of Lee & Taylor
(1990 ) was used to isolate
genomic DNA from fungal mycelium, grown on MEA in Petri dishes. The primers
ITS1 and ITS4 (White et al.
1990 ) were used to amplify part of the nuclear rRNA operon
spanning the 3' end of the 18S rRNA gene, the first internal transcribed
spacer (ITS1), the 5.8S rRNA gene, the second ITS region and the 5' end of the
28S rRNA gene. The PCR reaction mixture and conditions were the same as those
used by Crous et al.
(2004b ).
The ITS nucleotide sequences generated in this study were added to other
sequences obtained from GenBank
(http://www.ncbi.nlm.nih.gov )
and the alignment was assembled using Sequence Alignment Editor v. 2.0a11
(Rambaut 2002 ) with manual
adjustments for visual improvement where necessary. Due to the size and the
complexity of the original alignment, the sequences were split over four
smaller alignments, each containing genetically similar sequences. The four
datasets were each treated identically. Phylogenetic analyses of sequence data
were done using PAUP (Phylogenetic Analysis Using Parsimony) v. 4.0b10
(Swofford 2002 ). Phylogenetic
analysis of the aligned ITS sequence data consisted of neighbour-joining
analysis with the uncorrected (“p”), the Kimura 2-parameter and
the HKY85 substitution model in PAUP. Alignment gaps were treated as missing
data and all characters were unordered and of equal weight. When they were
encountered, ties were broken randomly. Sequence data were deposited in
GenBank (Table 1 ) and the
alignments in TreeBASE.
(1990 ) was used to isolate
genomic DNA from fungal mycelium, grown on MEA in Petri dishes. The primers
ITS1 and ITS4 (
1990
spanning the 3' end of the 18S rRNA gene, the first internal transcribed
spacer (ITS1), the 5.8S rRNA gene, the second ITS region and the 5' end of the
28S rRNA gene. The PCR reaction mixture and conditions were the same as those
used by Crous et al.
(2004b ).
The ITS nucleotide sequences generated in this study were added to other
sequences obtained from GenBank
(
and the alignment was assembled using Sequence Alignment Editor v. 2.0a11
(Rambaut 2002 ) with manual
adjustments for visual improvement where necessary. Due to the size and the
complexity of the original alignment, the sequences were split over four
smaller alignments, each containing genetically similar sequences. The four
datasets were each treated identically. Phylogenetic analyses of sequence data
were done using PAUP (Phylogenetic Analysis Using Parsimony) v. 4.0b10
(Swofford 2002 ). Phylogenetic
analysis of the aligned ITS sequence data consisted of neighbour-joining
analysis with the uncorrected (“p”), the Kimura 2-parameter and
the HKY85 substitution model in PAUP. Alignment gaps were treated as missing
data and all characters were unordered and of equal weight. When they were
encountered, ties were broken randomly. Sequence data were deposited in
GenBank (
alignments in TreeBASE.
Mycosphaerella and anamorph isolates included in this study for
sequence analysis and morphological comparison.
no. | ||||||
|---|---|---|---|---|---|---|
| Mycosphaerella communis | Dissoconium commune | CPC 11700 | Eucalyptus globulus | Spain | P. Mansilla | DQ302948 |
| CPC 11703 | Eucalyptus globulus | Spain | P. Mansilla | DQ302949 | ||
| CPC 11792 | Eucalyptus sp. | Portugal | A.J.L. Phillips | DQ302950 | ||
| Mycosphaerella cryptica | Colletogloeopsis nubilosum | 1576 | Eucalyptus nitens | Australia | M.J. Wingfield | DQ302951 |
| Mycosphaerella endophytica | Pseudocercosporella endophytica | 1191 | Eucalyptus sp. | South Africa | P.W. Crous | DQ302952 |
1193 | Eucalyptus | South Africa | P.W. Crous | DQ302953 | ||
| Mycosphaerella eucalyptorum | — | 11174 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ302954 |
| Mycosphaerella flexuosa | Stenella sp. | Eucalyptus globulus | Colombia | M.J. Wingfield | DQ302955 | |
1200 | Eucalyptus grandis | Colombia | M.J. Wingfield | DQ302956 | ||
1201 | Eucalyptus grandis | Colombia | M.J. Wingfield | DQ302957 | ||
| CPC 10995 | Eucalyptus sp. | Colombia | M.J. Wingfield | DQ302958 | ||
| Mycosphaerella gamsii | — | 11138 | Eucalyptus sp. | India | W. Gams | DQ302959 |
| Mycosphaerella gracilis | Pseudocercospora gracilis | 1315 | Eucalyptus urophylia | Indonesia | M.J. Wingfield | DQ302960 |
| CPC 11144 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ302961 | ||
| CPC 11181 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ302962 | ||
| Mycosphaerella heimii | Pseudocercospora heimii | CPC 11441 | Eucalyptus sp. | Brazil | A.C. Alfenas | DQ302963 |
| CPC 11453 | Eucalyptus sp. | Brazil | A.C. Alfenas | DQ302964 | ||
| CPC 11548 | Eucalyptus sp. | Brazil | A.C. Alfenas | DQ302965 | ||
| CPC 11716 | — | Brazil | A.C. Alfenas | DQ302966 | ||
| CPC 11879 | Eucalyptus sp. | Portugal | A.J.L. Phillips | DQ302967 | ||
| Mycosphaerella jonkershoekensis | — | 3116 | Protea lepidocarpodendron | Australia | P.W. Crous | DQ302968 |
| Mycosphaerella lateralis | Dissoconium dekkeri | CPC 11218 | Eucalyptus comaldulensis | Bolivia | M.J. Wingfield | DQ302969 |
| CPC 11293 | Eucalyptus tereticornis | Bolivia | M.J. Wingfield | DQ302970 | ||
| CPC 11484 | Eucalyptus sp. | Spain | P. Mansilla | DQ302971 | ||
| CPC 11706 | Eucalyptus globulus | Spain | P. Mansilla | DQ302972 | ||
| CPC 11729 | Eucalyptus globulus | Spain | P. Mansilla | DQ302973 | ||
| CPC 11732 | Eucalyptus globulus | Spain | P. Mansilla | DQ302974 | ||
| CPC 11789 | Eucalyptus sp. | Portugal | J.P. Sampaio | DQ302975 | ||
| Mycosphaerella madeirae | — | CPC 3746 | Eucalyptus grandis | Madeira | S. Denman | DQ302976 |
| Mycosphaerella marksii | ? Pseudocercospora epispermogoniana | 1073 | Eucalyptus sp. | Tanzania | M.J. Wingfield | DQ302977 |
1499 | Eucalyptus globulus | Uruguay | M.J. Wingfield | DQ302978 | ||
5358 | Leucadendron tinctum | Madeira | S. Denman | DQ302979 | ||
3715 | Eucalyptus deglupha | Ecuador | M.J. Wingfield | DQ302980 | ||
| CPC 11215 | Eucalyptus comaldulensis | Bolivia | M.J. Wingfield | DQ302981 | ||
| CPC 11221 | Eucalyptus grandis | Bolivia | M.J. Wingfield | DQ302982 | ||
| CPC 11222 | Eucalyptus grandis | Bolivia | M.J. Wingfield | DQ302983 | ||
| CPC 11795 | Vepris reflexa | South Africa | P.W. Crous | DQ302984 | ||
| Mycosphaerella molleriana | Colletogloeopsis molleriana | CPC 11187 | Eucalyptus sp. | Spain | M.J. Wingfield | DQ302985 |
| CPC 11685 | Eucalyptus globulus | Spain | P. Mansilla | DQ302986 | ||
| CPC 11688 | Eucalyptus globulus | Spain | P. Mansilla | DQ302987 | ||
| CPC 11709 | Eucalyptus globulus | Spain | P. Mansilla | DQ302988 | ||
| CPC 11842 | Eucalyptus sp. | Portugal | A.J.L. Phillips | DQ302989 | ||
| CPC 11845 | Eucalyptus sp. | Portugal | A.J.L. Phillips | DQ302990 | ||
| CPC 12056 | Eucalyptus sp. | Uruguay | M.J. Wingfield | DQ302991 | ||
| Mycosphaerella nubilosa | ? Uwebraunia juvenis | CPC 11246 | Eucalyptus globulus | Spain | M.J. Wingfield | DQ302992 |
| CPC 11249 | Eucalyptus globulus | Spain | M.J. Wingfield | DQ302993 | ||
| CPC 11487 | Eucalyptus sp. | Spain | P. Mansilla | DQ302994 | ||
| CPC 11559 | Eucalyptus sp. | Spain | P. Mansilla | DQ302995 | ||
| CPC 11723 | Eucalyptus globulus | Portugal | A.C. Alfenas | DQ302996 | ||
| CPC 11761 | Eucalyptus globulus | Spain | P. Mansilla | DQ302997 | ||
| CPC 11767 | Eucalyptus globulus | Portugal | L.P. Phillips | DQ302998 | ||
| CPC 11882 | Eucalyptus globulus | Portugal | A.J.L. Phillips | DQ302999 | ||
| CPC 11885 | Eucalyptus sp. | Portugal | A.J.L. Phillips | DQ303000 | ||
| Mycosphaerella parva | — | CPC 11273 | Eucalyptus globulus | Spain | M.J. Wingfield | DQ303001 |
| CPC 11758 | Eucalyptus globulus | Spain | P. Mansilla | DQ303002 | ||
| CPC 11759 | Eucalyptus globulus | Spain | P. Mansilla | DQ303003 | ||
| CPC 11764 | Eucalyptus globulus | Spain | P. Mansilla | DQ303004 | ||
| CPC 11888 | Eucalyptus sp. | Portugal | A.J.L. Phillips | DQ303005 | ||
| Mycosphaerella perpendicularis | — | 10983 | Eucalyptus sp. | Colombia | M.J. Wingfield | DQ303006 |
| Mycosphaerella pluritubularis | — | 11697 | Eucalyptus globulus | Spain | P. Mansilla | DQ303007 |
| Mycosphaerella pseudafricana | — | 1230 | Eucalyptus globulus | Zambia | T.A. Coutinho | DQ303008 |
| Mycosphaerella pseudocryptica | Colletogloeopsis sp. | CPC 11264 | Eucalyptus sp. | New Zealand | J.A. Stalpers | DQ303009 |
11267 | Eucalyptus sp. | New Zealand | J.A. Stalpers | DQ303010 | ||
| Mycosphaerella pseudosuberosa | Trimmatostroma sp. | 12085 | Eucalyptus sp. | Uruguay | M.J. Wingfield | DQ303011 |
| Mycosphaerella quasicercospora | — | 1098 | Eucalyptus sp. | Tanzania | M.J. Wingfield | DQ303012 |
| Mycosphaerella readeriellophora | Readeriella readeriellophora | CPC 11711 | Eucalyptus globulus | Spain | P. Mansilla | DQ303013 |
| Mycosphaerella scytalidii | — | Eucalyptus globulus | Brazil | F.A. Ferreira | DQ303014 | |
| CPC 10988 | Eucalyptus sp. | Colombia | M.J. Wingfield | DQ303015 | ||
10998 | Eucalyptus sp. | Colombia | M.J. Wingfield | DQ303016 | ||
| Mycosphaerella secundaria | — | 1112 | Eucalyptus grandis | Colombia | M.J. Wingfield | DQ303017 |
| Eucalyptus grandis | Brazil | A.C. Alfenas | DQ303018 | |||
| CPC 10989 | Eucalyptus sp. | Colombia | M.J. Wingfield | DQ303019 | ||
11551 | Eucalyptus sp. | Brazil | A.C. Alfenas | DQ303020 | ||
| Mycosphaerella sp. | Stenella pseudoparkii | 1090 | Eucalyptus grandis | Colombia | M.J. Wingfield | DQ303021 |
1092 | Eucalyptus grandis | Colombia | M.J. Wingfield | DQ303022 | ||
1087 | Eucalyptus grandis | Colombia | M.J. Wingfield | DQ303023 | ||
1088 | Eucalyptus grandis | Colombia | M.J. Wingfield | DQ303024 | ||
1089 | Eucalyptus grandis | Colombia | M.J. Wingfield | DQ303025 | ||
| Mycosphaerella sp. | Stenella xenoparkii | 1299 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ303026 |
1301 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ303027 | ||
1300 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ303028 | ||
| Mycosphaerella sp. | — | 727 | Eucalyptus grandis | Indonesia | A.C. Alfenas | DQ303029 |
| Mycosphaerella sp. | — | 728 | Eucalyptus grandis | Indonesia | A.C. Alfenas | DQ303030 |
| Mycosphaerella sp. | — | Eucalyptus globulus | Brazil | F.A. Ferreira | DQ303031 | |
| Mycosphaerella sp. | — | Eucalyptus globulus | Brazil | F.A. Ferreira | DQ303032 | |
| Mycosphaerella sp. | — | Eucalyptus globulus | Brazil | F.A. Ferreira | DQ303033 | |
| Mycosphaerella sp. | — | 1093 | Eucalyptus grandis | Colombia | M.J. Wingfield | DQ303034 |
| Mycosphaerella sp. | — | 1091 | Eucalyptus grandis | Colombia | M.J. Wingfield | DQ303035 |
| Mycosphaerella sp. | — | 1101 | Eucalyptus grandis | Colombia | M.J. Wingfield | DQ303036 |
| Mycosphaerella sp. | — | CPC 10986 | Eucalyptus sp. | Colombia | M.J. Wingfield | DQ303037 |
| Mycosphaerella sp. | — | CPC 11002 | Eucalyptus sp. | Colombia | M.J. Wingfield | DQ303038 |
| Mycosphaerella sp. | — | CPC 11004 | Eucalyptus sp. | Colombia | M.J. Wingfield | DQ303039 |
| Mycosphaerella sp. | — | CPC 12200 | Eucalyptus sp. | South Africa | Z.A. Pretorius | DQ303040 |
| Mycosphaerella sp. | — | CPC 12147 | Acacia mangium | Thailand | W. Himaman | DQ303041 |
| Mycosphaerella stramenti | — | 11545 | Eucalyptus sp. | Brazil | A.C. Alfenas | DQ303042 |
| Mycosphaerella stramenticola | — | 11438 | Eucalyptus sp. | Brazil | A.C. Alfenas | DQ303043 |
| Mycosphaerella suberosa | — | CPC 11032 | Eucalyptus sp. | Colombia | M.J. Wingfield | DQ303044 |
| CPC 11190 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ303045 | ||
| CPC 11276 | Eucalyptus comaldulensis | Spain | M.J. Wingfield | DQ303046 | ||
| CPC 12193 | Eucalyptus sp. | — | A.C. Alfenas | DQ303047 | ||
| Mycosphaerella sumatrensis | — | 11171 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ303048 |
11175 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ303049 | ||
11178 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ303050 | ||
| Mycosphaerella suttonii | Kirramyces epicoccoides | 1550 | Eucalyptus grandis | Australia | M.J. Wingfield | DQ303051 |
1409 | Eucalyptus sp. | Brazil | P.W. Crous | DQ303052 | ||
| Eucalyptus grandis | South Africa | P.W. Crous | DQ303053 | |||
1581 | Eucalyptus grandis | Australia | M.J. Wingfield | DQ303054 | ||
| CPC 11279 | Eucalyptus tereticornis | Bolivia | M.J. Wingfield | DQ303055 | ||
| Mycosphaerella verrucosiafricana | — | 11167 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ303056 |
11169 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ303057 | ||
11170 | Eucalyptus sp. | Indonesia | M.J. Wingfield | DQ303058 | ||
| Mycosphaerella vespa | Colletogloeopsis sp. | CMW 11558 | Eucalyptus sp. | Australia | — | DQ303059 |
| CMW 11559 | Eucalyptus sp. | Australia | — | DQ303060 | ||
| CMW 11560 | Eucalyptus sp. | Australia | — | DQ303061 | ||
| CMW 11563 | Eucalyptus sp. | Australia | — | DQ303062 | ||
| CMW 11564 | Eucalyptus sp. | Australia | — | DQ303063 | ||
| Mycosphaerella walkeri | Sonderhenia eucalypticola | CPC 11252 | Eucalyptus globulus | Spain | M.J. Wingfield | DQ303064 |
| — | Colletogloeopsis zuluensis | CPC 11780 | Eucalyptus sp. | South Africa | P.W. Crous | DQ303065 |
| CPC 11783 | Eucalyptus sp. | South Africa | P.W. Crous | DQ303066 | ||
| CPC 11962; CMW 17322 | Eucalyptus sp. | South Africa | M.J. Wingfield | DQ303067 | ||
| CPC 11965; CMW 17326 | Eucalyptus sp. | Uruguay | M.J. Wingfield | DQ303068 | ||
| CPC 12059 | Eucalyptus sp. | Uruguay | M.J. Wingfield | DQ303069 | ||
| — | Colletogloeopsis sp. | CPC 11786 | Eucalyptus sp. | South Africa | P.W. Crous | DQ303070 |
| — | Pseudocercospora basitruncata | 1202 | Eucalyptus grandis | Colombia | M.J. Wingfield | DQ303071 |
| — | Pseudocercospora clematidis | CPC 11657 | Clematis sp. | U.S.A. | M.A. Palm | DQ303072 |
| — | Pseudocercospora epispermogoniana | Eucalyptus grandis | South Africa | G. Kemp | DQ303073 | |
| — | Eucalyptus grandis | South Africa | G. Kemp | DQ303074 | ||
| Eucalyptus grandis | South Africa | G. Kemp | DQ303075 | |||
| — | Pseudocercospora fatouae | CPC 11648 | Fatoua villosa | Korea | H.D. Shin | DQ303076 |
| — | Pseudocercospora natalensis | 1263 | Eucalyptus nitens | South Africa | T.A. Coutinho | DQ303077 |
| — | Pseudocercospora pseudoeucalyptorum | 3751 | Eucalyptus sp. | Madeira | S. Denman | DQ303078 |
| CPC 10916 | Eucalyptus sp. | South Africa | P.W. Crous | DQ303079 | ||
| CPC 11713 | Eucalyptus globulus | Spain | P. Mansilla | DQ303080 | ||
| — | Pseudocercospora robusta | 1269 | Eucalyptus robur | Malaysia | M.J. Wingfield | DQ303081 |
| — | Pseudocercospora sp. | 1266 | Eucalyptus pellita | Thailand | M.J. Wingfield | DQ303082 |
1493 | Eucalyptus globulus | Uruguay | M.J. Wingfield | DQ303083 | ||
| CPC 11591 | Brachybotrys paridiformis | Korea | H.D. Shin | DQ303084 | ||
| CPC 11592 | Zelkova serrata | Korea | H.D. Shin | DQ303085 | ||
| CPC 11654 | Morus bombycis | Korea | H.D. Shin | DQ303086 | ||
| CPC 11668 | Pilea hamaoi | Korea | H.D. Shin | DQ303087 | ||
| CPC 11680 | Ampelopsis brevipenduncula var. heterophylla | Korea | H.D. Shin | DQ303088 | ||
| CPC 11726 | Platanus occidentalis | Korea | H.D. Shin | DQ303089 | ||
| — | Pseudocercospora subulata | 10849 | Eucalyptus botryoides | New Zealand | M. Dick | DQ303090 |
| — | Pseudocercosporella capsellae | CPC 11677 | Draba nemorosa var. hebecarpa | Korea | H.D. Shin | DQ303091 |
| — | Readeriella sp. | CPC 11186 | Eucalyptus globulus | Spain | M.J. Wingfield | DQ303092 |
| CPC 11735 | Eucalyptus globulus | Spain | P. Mansilla | DQ303093 | ||
| — | Readeriella mirabilis | CPC 11712 | Eucalyptus globulus | Spain | P. Mansilla | DQ303094 |
| — | Septoria eucalyptorum | 11282 | Eucalyptus sp. | India | W. Gams | DQ303095 |
| — | Septoria provencialis | 12226 | Eucalyptus sp. | France | P.W. Crous | DQ303096 |
| — | Stenella sp. | CPC 11671 | Lonicera japonica | Korea | H.D. Shin | DQ303097 |
CBS: Centraalbureau voor Schimmelcultures, Utrecht, The Netherlands; CPC:
Culture collection of Pedro Crous, housed at CBS; CMW: Culture collection of
Mike Wingfield, housed at FABI, Pretoria, South Africa
Acacia
Ampelopsis
Arecaceae
Austroafricana parva
Base Sequence
Character
Clematis
DNA, Fungal
Eucalyptus
Eucalyptus globulus
Euteratosphaeria verrucosiafricana
gamma-glutamylaminomethylsulfonic acid
Genes
Genome
Hyperostosis, Diffuse Idiopathic Skeletal
Lonicera
Morus
Mycelium
Mycodiella sumatrensis
Mycosphaerella
Mycosphaerella grandis
Neopseudocercosporella capsellae
Oligonucleotide Primers
Pallidocercospora heimii
Paramycosphaerella marksii
Phaeophleospora scytalidii
Phaeophleospora stramenti
Pseudocercospora
Pseudocercospora clematidis
Pseudocercospora fatouae
Pseudoteratosphaeria gamsii
Pseudoteratosphaeria perpendicularis
Pseudoteratosphaeria secundaria
Pseudoteratosphaeria stramenticola
Readeriella mirabilis
Ribosomal RNA Genes
RNA, Ribosomal, 5.8S
rRNA Operon
Sequence Alignment
Sequence Analysis
Stenella
Suberoteratosphaeria suberosa
Teratosphaeria molleriana
Teratosphaeria nubilosa
Teratosphaeria pluritubularis
Teratosphaeria quasicercospora
Teratosphaeria readeriellophora
Teratosphaeria zuluensis
Teratosphaericola pseudafricana
Vepris
Xenoteratosphaeria jonkershoekensis
Zasmidium eucalyptorum
Zelkova
Homology searches were performed against public sequence databases. The newest versions as of February 2010 of the protein sequences of Arabidopsis (TAIR 9), Vitis (Sept 2009 build) and Populus (version 2.0, Phytozome) were used to construct the individual BLAST datasets. The Eucalyptus public dataset (EucAll) consisted of 45,442 entries in Genbank (downloaded March 2010), 13,930 entries from the Eucalyptus Wood unigenes and ESTs [33 (link)], E. grandis leaf tissue ESTs (120,661 entries from DOE-JGI-produced 454 sequences, http://eucalyptusdb.bi.up.ac.za/ ) and 190,106 Unigenes and singlets from E. grandis 454 data [15 (link)]. The BLAST e-value threshold was set at 1e-10, with a minimum alignment length of 100 nucleotides (33 amino acids). Functional annotation (GO and KEGG) was performed using BLAST2GO [54 (link)], using the default annotation parameters (BLAST e-value threshold of 1e-06, Gene Ontology annotation threshold of 55). InterPro annotations were performed using InterProScan (http://www.ebi.ac.uk/Tools/InterProScan/ ).
Full text: Click here
Amino Acids
Amino Acid Sequence
Arabidopsis
Eucalyptus
Expressed Sequence Tags
Nucleotides
Plant Leaves
Populus
Tissues
Vitis
The 61 genes included in the analyses of Goremykin et al. [2 (link),3 (link)] and Leebens-Mack et al. [5 (link)] were extracted from our new chloroplast genome sequences of Vitis using the organellar genome annotation program DOGMA [76 (link)]. The same set of 61 genes was extracted from chloroplast genome sequences of six other recently sequenced angiosperm chloroplast genomes, including tomato, potato, soybean, cotton, cucumber, and Eucalyptus (see Table 3 for complete list of genomes examined). In general, alignment of the DNA sequences was straightforward and simply involved adding the 61 genes for the new angiosperms to the aligned data matrix from Leebens-Mack et al. [5 (link)]. In some cases, small in-frame insertions or deletions were required for correct alignment. For two genes, ccsA and matK, the DNA sequences were more divergent, requiring alignment using ClustalX [78 ] followed by manual adjustments. The complete nucleotide alignment is available online at [79 (link)].
Phylogenetic analyses using maximum parsimony (MP) and maximum likelihood (ML) were performed using PAUP* version 4.10 [80 ] on two data sets, one including 28 taxa and a second including 29 taxa by the addition of Gossypium. Phylogenetic analyses excluded gap regions. All MP searches included 100 random addition replicates and TBR branch swapping with the Multrees option. Modeltest 3.7 [81 (link)] was used to determine the most appropriate model of DNA sequence evolution for the combined 61-gene dataset. Hierarchical likelihood ratio tests and the Akaikle information criterion were used to assess which of the 56 models best fit the data, which was determined to be GTR + I + Γ by both criteria. For ML analyses we performed an initial parsimony search with 100 random addition sequence replicates and TBR branch swapping, which resulted in a single tree. Model parameters were optimized onto the parsimony tree. We fixed these parameters and performed a ML analysis with three random addition sequence replicates and TBR branch swapping. The resulting ML tree was used to re-optimize model parameters, which then were fixed for another ML search with three random addition sequence replicates and TBR branch swapping. This successive approximation procedure was repeated until the same tree topology and model parameters were recovered in multiple, consecutive iterations. This tree was accepted as the final ML tree (Figs.3B , 4B ). Successive approximation has been shown to perform as well as full-optimization analyses for a number of empirical and simulated datasets [82 (link)]. Non-parametric bootstrap analyses [83 ] were performed for MP analyses with 1000 replicates with TBR branch swapping, 1 random addition replicate, and the Multrees option and for ML analyses with 100 replicates with NNI branch swapping, 1 random addition replicate, and the Multrees option.
Phylogenetic analyses using maximum parsimony (MP) and maximum likelihood (ML) were performed using PAUP* version 4.10 [80 ] on two data sets, one including 28 taxa and a second including 29 taxa by the addition of Gossypium. Phylogenetic analyses excluded gap regions. All MP searches included 100 random addition replicates and TBR branch swapping with the Multrees option. Modeltest 3.7 [81 (link)] was used to determine the most appropriate model of DNA sequence evolution for the combined 61-gene dataset. Hierarchical likelihood ratio tests and the Akaikle information criterion were used to assess which of the 56 models best fit the data, which was determined to be GTR + I + Γ by both criteria. For ML analyses we performed an initial parsimony search with 100 random addition sequence replicates and TBR branch swapping, which resulted in a single tree. Model parameters were optimized onto the parsimony tree. We fixed these parameters and performed a ML analysis with three random addition sequence replicates and TBR branch swapping. The resulting ML tree was used to re-optimize model parameters, which then were fixed for another ML search with three random addition sequence replicates and TBR branch swapping. This successive approximation procedure was repeated until the same tree topology and model parameters were recovered in multiple, consecutive iterations. This tree was accepted as the final ML tree (Figs.
Full text: Click here
Biological Evolution
Cucumis
DNA Replication
Eucalyptus
Figs
Gene Deletion
Genes
Genome
Genome, Chloroplast
Gossypium
Insertion Mutation
Lycopersicon esculentum
Magnoliopsida
MATK protein, human
Nucleotides
Organelles
Reading Frames
Sequence Alignment
Sequence Analysis
Solanum tuberosum
Soybeans
Trees
Vitis
Most recents protocols related to «Eucalyptus»
Example 3
A study is presented herein which demonstrates the redispersion effect of the fibre composition of the invention.
Tested formulations represent MFC fibre compositions without whitened eucalyptus kraft cellulose additivation; compositions of MFC fibres additivated with 5%, 10% and 20% of whitened eucalyptus kraft cellulose; and formulation with 100% of cellulose.
The morphological and mechanical properties of the formulations were analyzed before and after the pressing step.
The morphological properties analyzed were: fines content (%), fibre length (mm), fibre width (μm) and number of fibres per mass of the composition (millions of fibres/gram).
The analyzed mechanical properties were: tensile index (Nm/g), elongation (%), bursting index (KPam2/g), Scott Bond (ft·lb/in2), body, also referred to as volume-to-mass ratio, (cm3/g) and air passage resistance (s/100 mL air).
The obtained results are presented in the graphs from
Through the obtained results, it is concluded that there is retention of cellulose in the MFC maintaining the properties of the fibre proportion in the composition with regard to its morphology. Furthermore, no significant differences were observed in formulations before and after pressing.
Full text: Click here
Cellulose
Eucalyptus
Fibrosis
Figs
Human Body
Retention (Psychology)
S100 Proteins
Bleached eucalyptus kraft
pulp, unrefined (15 °SR), was provided by Ence (Navia, Spain).
2,2,6,6-Tetramethylpiperidine-1-oxy radical (TEMPO), NaBr, NaOH, NaClO
(15%), copper(II) ethylenediamine, and DTZ (≥98%) were purchased
from Sigma-Aldrich (Schnelldorf, Germany). Glacial acetic acid was
purchased from Scharlab (Sentmenat, Barcelona, Spain). All organic
solvents (reagent grade) were received from Thermo Fisher Scientific
(Loughborough, U.K.). Preliminary results indicated that amylene-stabilized
chloroform is preferred over ethanol-stabilized chloroform.
Distilled water was used for nanocellulose production, but metal
salts were dissolved in Milli-Q water. These metal salts were lead(II)
nitrate, lead(II) chloride, cadmium(II) nitrate, cadmium(II) chloride,
copper(II) chloride, nickel(II) chloride, chromium(III) chloride,
chromium(III) nitrate, and magnesium chloride from Panreac Applichem
(Castellar del Vallès, Barcelona, Spain); potassium nitrate,
iron(III) chloride, and manganese(II) chloride from Scharlab; and
mercury(II) nitrate 1-hydrate, mercury(II) chloride, silver nitrate,
and zinc chloride from Sigma-Aldrich.
pulp, unrefined (15 °SR), was provided by Ence (Navia, Spain).
2,2,6,6-Tetramethylpiperidine-1-oxy radical (TEMPO), NaBr, NaOH, NaClO
(15%), copper(II) ethylenediamine, and DTZ (≥98%) were purchased
from Sigma-Aldrich (Schnelldorf, Germany). Glacial acetic acid was
purchased from Scharlab (Sentmenat, Barcelona, Spain). All organic
solvents (reagent grade) were received from Thermo Fisher Scientific
(Loughborough, U.K.). Preliminary results indicated that amylene-stabilized
chloroform is preferred over ethanol-stabilized chloroform.
Distilled water was used for nanocellulose production, but metal
salts were dissolved in Milli-Q water. These metal salts were lead(II)
nitrate, lead(II) chloride, cadmium(II) nitrate, cadmium(II) chloride,
copper(II) chloride, nickel(II) chloride, chromium(III) chloride,
chromium(III) nitrate, and magnesium chloride from Panreac Applichem
(Castellar del Vallès, Barcelona, Spain); potassium nitrate,
iron(III) chloride, and manganese(II) chloride from Scharlab; and
mercury(II) nitrate 1-hydrate, mercury(II) chloride, silver nitrate,
and zinc chloride from Sigma-Aldrich.
Full text: Click here
Acetic Acid
Cadmium
Chlorides
Chloroform
Chromium
Copper
Ethanol
Ethylenediamines
Eucalyptus
Iron
Magnesium Chloride
Manganese
Mercury
Metals
Nickel
Nitrates
potassium nitrate
Salts
Silver Nitrate
zinc chloride
Protocol full text hidden due to copyright restrictions
Open the protocol to access the free full text link
Albumins
Anesthesia
Animals
Cytokine
Diet
Eosin
Eucalyptus
Food
Histones
Inhalation
Injections, Intraperitoneal
Injuries
Institutional Animal Care and Use Committees
Isoflurane
Ketamine
Lung
Lung Injury
Males
Mice, Inbred C57BL
Mus
Obstetric Delivery
Oropharynxs
physiology
Rivers
Rodent
Saline Solution
Smoke
Tissue Harvesting
Xylazine
In this study, we first used terpene synthase protein sequences from fully sequenced genomes of A. thaliana100 and E. grandis29 (link), to classify the putative genes found in P. cattleyanum according to the previous classification in the subfamilies TPS-a,-b,-c,-e/f, and -g by sequence similarity26 (link).
To examine the evolutionary history of TPS genes, a second analysis including more species (E. grandis, E. globulus, A. thaliana, P. trichocarpa, V. vinifera, C. citriodora, and M. alternifolia) was carried out. We generated a tree with TPS sequences related to primary metabolism (subfamilies -c, -e, and -f) with a total of 45 sequences and a second tree related to secondary metabolism (subfamilies a, b, g) including 360 sequences29 (link),32 (link),55 (link).
The functionally characterized pinene (RtTPS1 and RtTPS2 accession number AXY92166 and AXY92167, respectively) and caryophyllene synthases (RtTPS3 and RtTPS4 accession numbers AXY92168 and AXY92169) from Rhodomyrtus tomentosa52 (link), pinene synthase (EpTPS1 accession number MK873024) and 1,8-cineole synthases (EpTPS2 and EpTPS3 accession numbers MK873025 and QCQ05478) from Eucalyptus polybractea56 (link), beta cayophyllene synthase (Eucgr. J01451) from E. grandis29 (link), myrcene synthase from Antirrhium majus (AAO41727)101 (link), two isoprene synthase genes from E. globulus (EglobTPS106), E. grandis (Eucgr. K00881)29 (link) and five linalool synthases from Oenothera californica (AAD19841)63 (link), Clarkia breweri (AAD19840), Clarkia concinna (AAD19839), and Fragaria x ananassa (CAD57106)102 (link) were also included in the phylogenetic analysis to assess the homology of known TPS to Psidium genes.
For each dataset used to construct the trees, we first aligned the amino acid sequences of putative TPS genes using ClustalW implemented within MEGA v7.0 software package103 (link). Due to high levels of variation and variable exon counts between taxa, we trimmed the alignment using Gblocks104 (link) with the following parameters: smaller final blocks, gap positions within the final blocks, and less strict flanking positions. We used the maximum-likelihood method implemented in PhyML v2.4.4105 (link) online web server106 (link) to perform the phylogenetic analysis. The JTT + G + F was the best-fit substitution model selected with ModelGenerator for protein analyses107 (link). The confidence values in the tree topology were assessed by running 100 bootstrap replicates. Trees were visualized using Figtree v1.4.4108 .
To examine the evolutionary history of TPS genes, a second analysis including more species (E. grandis, E. globulus, A. thaliana, P. trichocarpa, V. vinifera, C. citriodora, and M. alternifolia) was carried out. We generated a tree with TPS sequences related to primary metabolism (subfamilies -c, -e, and -f) with a total of 45 sequences and a second tree related to secondary metabolism (subfamilies a, b, g) including 360 sequences29 (link),32 (link),55 (link).
The functionally characterized pinene (RtTPS1 and RtTPS2 accession number AXY92166 and AXY92167, respectively) and caryophyllene synthases (RtTPS3 and RtTPS4 accession numbers AXY92168 and AXY92169) from Rhodomyrtus tomentosa52 (link), pinene synthase (EpTPS1 accession number MK873024) and 1,8-cineole synthases (EpTPS2 and EpTPS3 accession numbers MK873025 and QCQ05478) from Eucalyptus polybractea56 (link), beta cayophyllene synthase (Eucgr. J01451) from E. grandis29 (link), myrcene synthase from Antirrhium majus (AAO41727)101 (link), two isoprene synthase genes from E. globulus (EglobTPS106), E. grandis (Eucgr. K00881)29 (link) and five linalool synthases from Oenothera californica (AAD19841)63 (link), Clarkia breweri (AAD19840), Clarkia concinna (AAD19839), and Fragaria x ananassa (CAD57106)102 (link) were also included in the phylogenetic analysis to assess the homology of known TPS to Psidium genes.
For each dataset used to construct the trees, we first aligned the amino acid sequences of putative TPS genes using ClustalW implemented within MEGA v7.0 software package103 (link). Due to high levels of variation and variable exon counts between taxa, we trimmed the alignment using Gblocks104 (link) with the following parameters: smaller final blocks, gap positions within the final blocks, and less strict flanking positions. We used the maximum-likelihood method implemented in PhyML v2.4.4105 (link) online web server106 (link) to perform the phylogenetic analysis. The JTT + G + F was the best-fit substitution model selected with ModelGenerator for protein analyses107 (link). The confidence values in the tree topology were assessed by running 100 bootstrap replicates. Trees were visualized using Figtree v1.4.4108 .
Full text: Click here
Amino Acid Sequence
caryophyllene
Clarkia
Eucalyptol
Eucalyptus
Evolution, Molecular
Exons
Fragaria
Genes
Genome
isoprene synthase
linalool
Metabolism
myrcene
Nitric Oxide Synthase
Oenothera
Proteins
Psidium
Secondary Metabolism
terpene synthase
Trees
Young Eucalyptus leaves from around 40 years old eucalyptus tree were collected from GT Road near Pindi Bypass, Gujranwala produce silver nanoparticles. Eucalyptus leaves were collected randomly, washed with tap water, and distilled water respectively. The small pieces of leaves were dried in shade to obtain the fine powder. Finally, the leaf powder was autoclaved under the pressure of 15Ib/sq inch and temperature of 121°C for 5 min [21 (link)]. Leaf powder of 5 g was boiled in 500 mL of distilled water (10 mg/mL) for 10 minutes and filtered by Whatman filter paper. The extract was stored at 4°C for further use. For silver nanoparticle synthesis, 2 mM silver nitrate solution was mixed with ELE (10 mg/mL) in different ratio. The change in color from yellow to dark brown represented the formation of AgNPs [21 (link)].
Full text: Click here
colloidal silver
Eucalyptus
Plant Leaves
Powder
Pressure
Silver
Silver Nitrate
Trees
Top products related to «Eucalyptus»
Sourced in Germany, United States, India, United Kingdom, Italy, China, Spain, France, Australia, Canada, Poland, Switzerland, Singapore, Belgium, Sao Tome and Principe, Ireland, Sweden, Brazil, Israel, Mexico, Macao, Chile, Japan, Hungary, Malaysia, Denmark, Portugal, Indonesia, Netherlands, Czechia, Finland, Austria, Romania, Pakistan, Cameroon, Egypt, Greece, Bulgaria, Norway, Colombia, New Zealand, Lithuania
Sodium hydroxide is a chemical compound with the formula NaOH. It is a white, odorless, crystalline solid that is highly soluble in water and is a strong base. It is commonly used in various laboratory applications as a reagent.
Sourced in United Kingdom, Germany, United States, Switzerland, India, Japan, China, Australia, France, Italy, Brazil
Whatman No. 1 filter paper is a general-purpose cellulose-based filter paper used for a variety of laboratory filtration applications. It is designed to provide reliable and consistent filtration performance.
Sourced in Germany, United States, Italy, India, United Kingdom, China, France, Poland, Spain, Switzerland, Australia, Canada, Sao Tome and Principe, Brazil, Ireland, Japan, Belgium, Portugal, Singapore, Macao, Malaysia, Czechia, Mexico, Indonesia, Chile, Denmark, Sweden, Bulgaria, Netherlands, Finland, Hungary, Austria, Israel, Norway, Egypt, Argentina, Greece, Kenya, Thailand, Pakistan
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.
Sourced in Germany
Maltitol is a sugar alcohol commonly used as a sweetener and bulking agent in various food and pharmaceutical products. It is derived from the hydrogenation of maltose, a disaccharide found in starch. Maltitol has a similar sweetness profile to sucrose but with a lower caloric content.
Sourced in United States, Germany, United Kingdom, India, Italy, Spain, France, Canada, Switzerland, China, Australia, Brazil, Poland, Ireland, Sao Tome and Principe, Chile, Japan, Belgium, Portugal, Netherlands, Macao, Singapore, Sweden, Czechia, Cameroon, Austria, Pakistan, Indonesia, Israel, Malaysia, Norway, Mexico, Hungary, New Zealand, Argentina
Chloroform is a colorless, volatile liquid with a characteristic sweet odor. It is a commonly used solvent in a variety of laboratory applications, including extraction, purification, and sample preparation processes. Chloroform has a high density and is immiscible with water, making it a useful solvent for a range of organic compounds.
Sourced in United States, Germany, Italy, United Kingdom, India, Spain, Japan, Poland, France, Switzerland, Belgium, Canada, Portugal, China, Sweden, Singapore, Indonesia, Australia, Mexico, Brazil, Czechia
Toluene is a colorless, flammable liquid with a distinctive aromatic odor. It is a common organic solvent used in various industrial and laboratory applications. Toluene has a chemical formula of C6H5CH3 and is derived from the distillation of petroleum.
Sourced in United States, Germany, Canada, Belgium, United Kingdom
Xylitol is a type of lab equipment produced by Merck Group. It is a sugar alcohol that can be used in various laboratory applications. The core function of Xylitol is to serve as a sweetener and humectant in experimental and research settings.
Sourced in United States, Germany, Italy, India, France, Spain, United Kingdom, Australia, Switzerland, Poland, Portugal, China, Canada, Sao Tome and Principe, Brazil, Ireland, Mexico, Sweden, Hungary, Singapore, Malaysia, Pakistan, Thailand, Cameroon, Japan, Chile
Sodium carbonate is a water-soluble inorganic compound with the chemical formula Na2CO3. It is a white, crystalline solid that is commonly used as a pH regulator, water softener, and cleaning agent in various industrial and laboratory applications.
Sourced in United States, Germany, United Kingdom, China, Italy, Sao Tome and Principe, France, Macao, India, Canada, Switzerland, Japan, Australia, Spain, Poland, Belgium, Brazil, Czechia, Portugal, Austria, Denmark, Israel, Sweden, Ireland, Hungary, Mexico, Netherlands, Singapore, Indonesia, Slovakia, Cameroon, Norway, Thailand, Chile, Finland, Malaysia, Latvia, New Zealand, Hong Kong, Pakistan, Uruguay, Bangladesh
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.
Sourced in Germany, United States, United Kingdom, Italy, India, Spain, China, Poland, Switzerland, Australia, France, Canada, Sweden, Japan, Ireland, Brazil, Chile, Macao, Belgium, Sao Tome and Principe, Czechia, Malaysia, Denmark, Portugal, Argentina, Singapore, Israel, Netherlands, Mexico, Pakistan, Finland
Acetone is a colorless, volatile, and flammable liquid. It is a common solvent used in various industrial and laboratory applications. Acetone has a high solvency power, making it useful for dissolving a wide range of organic compounds.
More about "Eucalyptus"
Eucalyptus, a genus of evergreen trees native to Australia and nearby islands, is a versatile and valuable natural resource.
These majestic trees, known for their distinctive bark, leaves, and aromatic flowers, are widely utilized in various industries including timber, pulp, and essential oil production.
Beyond their commercial applications, Eucalyptus trees play a crucial role in many ecosystems, providing habitat and resources for a diverse array of wildlife.
Researchers studying Eucalyptus can leverage innovative AI-driven platforms like PubCompare.ai to optimize their research workflows.
These solutions enable scientists to easily locate relevant protocols from scientific literature, preprints, and patents, and leverage advanced comparison tools to identify the most accurate and reproducible methods.
This enhanced efficiency and accuracy can significantly benefit Eucalyptus research, leading to improved understanding of this remarkable genus.
Complementary compounds like Sodium hydroxide, Whatman No. 1 filter paper, Methanol, Maltitol, Chloroform, Toluene, Xylitol, Sodium carbonate, DMSO, and Acetone may also play important roles in Eucalyptus-related studies, depending on the specific research objectives.
By incorporating these relevant terms and leveraging the power of AI-driven platforms, researchers can elevate the quality and impact of their Eucalyptus investigations.
Typo: Reserchers can use PubCompare.ai's innovative AI-driven platform to optimize their Eucalyptus studies, easily locating protocols from literature, preprints, and patents, and leveraging advanced comparisons to identify the most accurate and reproducbile methods.
These majestic trees, known for their distinctive bark, leaves, and aromatic flowers, are widely utilized in various industries including timber, pulp, and essential oil production.
Beyond their commercial applications, Eucalyptus trees play a crucial role in many ecosystems, providing habitat and resources for a diverse array of wildlife.
Researchers studying Eucalyptus can leverage innovative AI-driven platforms like PubCompare.ai to optimize their research workflows.
These solutions enable scientists to easily locate relevant protocols from scientific literature, preprints, and patents, and leverage advanced comparison tools to identify the most accurate and reproducible methods.
This enhanced efficiency and accuracy can significantly benefit Eucalyptus research, leading to improved understanding of this remarkable genus.
Complementary compounds like Sodium hydroxide, Whatman No. 1 filter paper, Methanol, Maltitol, Chloroform, Toluene, Xylitol, Sodium carbonate, DMSO, and Acetone may also play important roles in Eucalyptus-related studies, depending on the specific research objectives.
By incorporating these relevant terms and leveraging the power of AI-driven platforms, researchers can elevate the quality and impact of their Eucalyptus investigations.
Typo: Reserchers can use PubCompare.ai's innovative AI-driven platform to optimize their Eucalyptus studies, easily locating protocols from literature, preprints, and patents, and leveraging advanced comparisons to identify the most accurate and reproducbile methods.