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1,997 results for “Rice”
Figure 3 in Purification and Characterization of Midgut α-Glucosidase from Larvae of the Rice Green Caterpillar, Naranga aenescens Moore
Figure 3. Effect of pH (a) and (b) temperature on the activity of N. aenescens α -glucosidase.Different letters indicate that the relative activity of enzymes is significantly different from each other by Tukey's test (P <0.05).
Figure 2 in Purification and Characterization of Midgut α-Glucosidase from Larvae of the Rice Green Caterpillar, Naranga aenescens Moore
Figure 2. Analysis of purified αglucosidase by SDS-PAGE. Lanes 1 and 2: Active fraction after ion exchange chromatography stained with histochemical and general staining, respectively; Lane 3: Molecular weight markers.
Figure 1 in Purification and Characterization of Midgut α-Glucosidase from Larvae of the Rice Green Caterpillar, Naranga aenescens Moore
Figure 1. Elution profile of N. aenescens α-glucosidase on DEAE-sepharose column. The active peak is indicated. Arrow is pointing to the fifth peak, eluted around 0.4 M salt, corresponding to the α-glucosidase activity.
RICE WHEAT CROPPING SYSTEMS-CONSTRAINTS AND STRATEGIES : A REVIEW
<p>The rice-wheat cropping system (RWCS) in the Indo-Gangetic plains (IGP) of South Asia with the help of Green<br> Revolution in the early 1970’s greatly contributed to India's food self-sufficiency and livelihood of millions of<br> peoplethus, became the country's primary source of food-grain production. However, deterioration of soil health and<br> quality, ground water depletion, water stress, labour shortage, introduction of new weeds and pests particularly<br> Phalaris minor, Scirpophaga incertulas and climate change have all contributed to a major production standstill and<br> deterioration in recent years by which the sustainability of rice wheat cropping system is now at jeopardy. Traditional<br> agronomic practices had various negative implications on the sustainability of rice wheat cropping system with the<br> introduction of HYVs. So, a paradigm shift is required to achieve long-term productivity, sustainability and allow<br> farmers to minimise inputs, optimise yields, enhance profitability, maintain the natural resource base and reduce risk<br> owing to both environmental and economic issues through resource-conserving technologies (RCTs) including<br> zero/minimaltillage, PUSA decomposer, bed planting, crop residue management, mechanical rice transplanter (MRT)<br> and crop diversification. This article focuses some of the issues that need to be addressed in the RWCS in order to<br> achieve the goal of increasing regional productivity and assuring food security while maximising the effective use of<br> natural resources, enhancing rural livelihoods and aiding in poverty alleviation.</p>
Determination of traits responding to iron toxicity stress at different stages and genome-wide association analysis for iron toxicity tolerance in rice (Oryza sativa L.)
<p>This vcf file constitute underlying raw data material for the manuscript "Determination of traits responding to iron toxicity stress at different stages and genome-wide association analysis for iron toxicity tolerance in rice (Oryza sativa L.)". <br> The SNP genotype data came from a whole-genome resequencing and were called using the Nipponbare IRGSP 1.0 rice reference genome. SNPs with a miss rate greater than 30% and minor allele frequency (MAF) less than 5% were removed. Heterozygous alleles were also excluded. Finally, 160,498 SNPs were selected and used in the GWAS analysis. </p>
Paddy rice methane emissions across Monsoon Asia
<p>Although rice cultivation is one of the most important agricultural sources of methane and contributes ~8 % of total global anthropogenic emissions, large discrepancies remain among estimates of global methane emissions from rice cultivation due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5 degrees) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combined paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning, and produced gridded up-scaling estimates of rice methane emissions at 5000-m resolution at 8-day intervals across Monsoon Asia, where ~87% of global rice area is cultivated and ~90% of global rice production occurs.<br> </p>
Figure 1 in Sensitivity to salinity at the emergence and seedling stages of barnyardgrass (EchinochloQ crus-gQlli), weedy rice (OryzQ sQtivQ), and rice with different tolerances to ALS-inhibiting herbicides
Figure 1. Dose–response emergence curve with the average data points of the different Echinochloa crus-galli populations against the salt concentration. Curve parameter estimates (Equation 1): s1 (b = 1.94, d = 55.85, e = 287.76), s2 (b = 1.52, d = 89.91, e = 222.71), s3 (b = 1.13, d = 89.91, e = 282.58), r1(b = 3.96, d = 67.67, e = 196.55), r2 (b = 6.85, d = 94.64, e = 123.58). The salt concentration required to reduce emergence by 50% (EC50) is shown below the graph. Only the significant pairwise comparisons between EC50 (SI index) are shown (Equation 2).
Figure 2 in Sensitivity to salinity at the emergence and seedling stages of barnyardgrass (EchinochloQ crus-gQlli), weedy rice (OryzQ sQtivQ), and rice with different tolerances to ALS-inhibiting herbicides
Figure 2. Dose–response emergence curve with the average data points of the different Oryza sativa (weedy rice) populations and rice varieties against the salt concentration. Curve parameter estimates (Equation 1): wr1 (b = 9.40, d = 87.14, e = 195.80), wr2 (b = 9.40, d = 87.14, e = 160.19), wr3 (b = 8.49, d = 89.73, e = 173.01), Baldo (b = 4.81, d = 87.12, e = 146.49), CL80 (b = 3.08, d = 60.89, e = 140.04). The salt concentration required to reduce the emergence by 50% (EC50) and the significant pairwise comparisons between EC50 (SI index) are shown below the graph (Equation 2).
Figure 3 in Relationship between weedy rice (Oryzo sotivo) infestation level and agronomic practices in Italian rice farms
Figure 3. Relative importance of each variable in the clustering, as identified by the two-step cluster analysis. Variable scoring 1 represents the most important variable in the cluster formation.
Figure 2 in Relationship between weedy rice (Oryzo sotivo) infestation level and agronomic practices in Italian rice farms
Figure 2. Percentage of rice farms with different Oryzo sotivo infestation levels (low, medium, and high) on the basis of the adopted cultivation practices. (A) Total farm area and average farm area per class; (B) total farm area cultivated with ClearfieldṜ (CL) varieties and average farm area per class; (C) tillage; (D) sowing; (E) water management; (F) seed origin; (G) crop rotation; (H) stale seedbed; (I) imazamox use; and (J) O. sotivo resistance to imazamox.
Figure 4 in Relationship between weedy rice (Oryzo sotivo) infestation level and agronomic practices in Italian rice farms
Figure 4. The three clusters identified in the two-step cluster analysis and the composition of each cluster for all the variables that contributed to clustering. The size of the circle and percentages close to each circle represent the proportion of farms pertaining to a certain category of each variable. The percentage of farms belonging to each cluster is reported in parentheses following the cluster name. (A) Cluster 1; (B) cluster 2; and (C) cluster 3.
Figure 1 in Molecular profiling of bacterial blight resistance in Malaysian rice cultivars
Figure 1. UPGMA dendrogram showed the patterns of resistance to bacterial blight in Malaysia rice varieties, based on scoring of 13 SSR and 1 STS.
Figure 1 in Antioxidant extract of black rice prevents renal dysfunction and renal fibrosis caused by ethanol-induced toxicity
Figure 1. Histopathological changes of kidney sections of 1) NC (Normal Control group) Control section of kidney showing cortical parenchyma to consist of dense rounded structures, the glomeruli (G), surrounded by narrow Bowman's capsular spaces (BCS); 2) PC (Positive Control group) showing glomeruli with mild mesangial proliferation (G), moderate degree of chronic interstitial inflammatory infiltrate and tubular epithelial cells focal degeneration, Cloudy swelling tubular cells with narrow (arrow) or obliterated (yellow arrow); 3) 100 mg/kg bw BREE showing mild interstitial inflammation in the interstitium (arrow); 4) 200 mg/kg bw BREE showing normal appearance of glomerular capillary tuft (G) and Bowman's capsule basement membrane (BCS) and a clear improvement in the general shape of tubes and cells. H&E (Mag. X400).
Figure 2 in Antioxidant extract of black rice prevents renal dysfunction and renal fibrosis caused by ethanol-induced toxicity
Figure 2. Histopathology of nephropathy in 1) NC (Normal Control group) showing minimal amount of collagen around renal tubules, capillary tuft and Bowman's capsules of glomerulus; 2) PC (Positive Control group) showing an increase of the collagen fibers around Bowman's capsule, capillary loops and convoluted tubules of glomerulus and both necrotic and apoptotic changes in the renal tubules; 3) 100 mg/kg bw BREE showing mild to moderate increased collagen fibres; 4) 200 mg/kg bw BREE.Showing a little amount of collagen similar or close to NC. (Masson's Trichrome × 400).
Figure 4 in A comparative study on the virulence of entomopathogenic fungi against Trogoderma granarium (Everts) (Coleoptera: Dermestidae) in stored grains rice
Figure 4. Mortality (%) of T. granarium grubs exposed to Metarhizium anisopliae at different concentrations. Different letters above the bars represent the significant difference at P=0.05.
Figure 5 in A comparative study on the virulence of entomopathogenic fungi against Trogoderma granarium (Everts) (Coleoptera: Dermestidae) in stored grains rice
Figure 5. Mortality (%) of T. granarium grubs exposed to Isaria fumosoroseus at different concentrations. Different letters above the bars represent the significant difference at P=0.05.
Figure 3 in A comparative study on the virulence of entomopathogenic fungi against Trogoderma granarium (Everts) (Coleoptera: Dermestidae) in stored grains rice
Figure 3. Mortality (%) of T. granarium grubs exposed to Immersion and Food mix method at a conidial concentration of 1x108. Different letters above the bars represent the significant difference at P=0.05.
Figure 7 in A comparative study on the virulence of entomopathogenic fungi against Trogoderma granarium (Everts) (Coleoptera: Dermestidae) in stored grains rice
Figure 7. Mortality (%) of T. granarium grubs exposed to three different fungal strains at different conidial concentrations. Different letters above the bars represent the significant difference at P=0.05.
Figure 2 in A comparative study on the virulence of entomopathogenic fungi against Trogoderma granarium (Everts) (Coleoptera: Dermestidae) in stored grains rice
Figure 2. Mortality (%) of T. granarium grubs exposed to food mix method at 1x108 conidia mL-1. Different letters above the bars represent the significant difference at P=0.05.
Figure 1 in A comparative study on the virulence of entomopathogenic fungi against Trogoderma granarium (Everts) (Coleoptera: Dermestidae) in stored grains rice
Figure 1. Mortality (%) of T. granarium grubs exposed to immersion method at 1x108 conidia mL-1. Different letters above the bars represent the significant difference at P=0.05.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.