Continuous cultivation of dry seeded rice in addition to adverse environmental factors make the rice soils deficient of nitrogen more than any other essential nutrient. The study was conducted under ...greenhouse conditions in pots by varying soil N during seedling growth. Six cultivars with diverse genetic background, CL152, R4100412, Antonio, Bowman, Nipponbare, and Rex, were seeded in 6 × 12 inch PVC pots filled with 3:1 (sand: soil) as the rooting medium. All plants were irrigated with full-strength Hoagland nutrient solution, delivered three times for 28 days after sowing. Then, half the plants in each cultivar were supplied with 100% of control nutrient solution minus N for another 4 weeks. Agronomic, physiological and root parameters measurements recorded at optimum interval. Cumulative indices were calculated by summing each response index value as a ratio of treatment by control values. Cultivars were classified based on total cumulative N stress response index (TCNSRI), the sum of all the traits measured, into different groups based on mean and standard error. The reductions in most of the measured traits were less in CL152 and R4100412 than other rice cultivars to soil N stress. Based on total cumulative N stress response indices, the cultivars were classified. The cultivar R41004122 was identified as tolerant, CL152 as intermediate, and Antonio, Bowman, Nipponbare and Rex were sensitive to soil N stress. The identified soil N stress tolerant and intermediately tolerant cultivars will be useful to breeders to develop new genotypes and farmers to select cultivars best suited for dry seeded rice.
Upland cotton (
L.) growth and development during the pre-and post-flowering stages are susceptible to high temperature and drought. We report the field-based characterization of multiple ...morpho-physiological and reproductive stress resilience traits in 11 interspecific chromosome substitution (CS) lines isogenic to each other and the inbred
line TM-1. Significant genetic variability was detected (
< 0.001) in multiple traits in CS lines carrying chromosomes and chromosome segments from CS-B (
) and CS-T (
). Line CS-T15sh had a positive effect on photosynthesis (13%), stomatal conductance (33%), and transpiration (24%), and a canopy 6.8 °C cooler than TM-1. The average pollen germination was approximately 8% greater among the CS-B than CS-T lines. Based on the stress response index, three CS lines are identified as heat- and drought-tolerant (CS-T07, CS-B15sh, and CS-B18). The three lines demonstrated enhanced photosynthesis (14%), stomatal conductance (29%), transpiration (13%), and pollen germination (23.6%) compared to TM-1 under field conditions, i.e., traits that would expectedly enhance performance in stressful environments. The generated phenotypic data and stress-tolerance indices on novel CS lines, along with phenotypic methods, would help in developing new cultivars with improved resilience to the effects of global warming.
The ability to determine the optimal nitrogen (N) content in maize plants needed to obtain maximum growth is important to the management of the crop. It has been shown that N content declines as a ...function of aboveground biomass accumulation (W): N = 3.4W
-0.37
. The goal of this study is to evaluate the applicability of relating chlorophyll meter readings with the optimal N content relationship to provide a tool for whole-plant N-status diagnosis in maize without the necessity of measuring N content. Biomass of shoot and specific organs, N concentration, and chlorophyll meter measurement of specific leaves were measured over several sites and years. Nitrogen-concentration measurements indicated that whole-plant N status can be represented by the N concentration of the topmost fully expanded leaf. A quantitative relationship between N concentration and chlorophyll meter measurement on the uppermost expanded leaf was established and validated.
Background
Pooling datasets from multiple studies can significantly improve statistical power: larger sample sizes can enable the identification of otherwise weak disease‐specific patterns. When ...modern learning methods are utilized (e.g., for predicting progression to dementia), differences in data acquisition‐methods / scanner‐protocols can enable the model to “cheat”, i.e. utilizes site‐specific artifacts rather than disease‐specific features. In this study, we develop a method to harmonize the performance of DNN classifiers across scanners/sites, via so‐called fairness constraints, thereby encouraging consistent behavior while controlling for site‐specific nuisance variables.
Method
We conducted two studies: (a) to demonstrate feasibility of pooling across sites (Site‐Pooling) and (b) to pool data across scanners (Scanner‐Pooling). For Site‐Pooling, our analysis included summaries from Freesurfer processed T1‐weighted images of the Wisconsin Alzheimer's Disease Research Center (ADRC) and German Center for Neurodegenerative Diseases (DZNE). The Freesurfer summaries were used to train a two layer neural network classifier and five‐fold cross‐validation performance was assessed. For Scanner‐Pooling experiments, Freesurfer processed MR images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to train a deep 3D convolutional network. Performance average on a held‐out test dataset was evaluated. In both cases, a constraint to equalize the performance of the trained classifier across the domains (sites/scanners) was incorporated during training.
Result
Table 1 shows the results of AD/MCI classification for site‐pooling analysis. Our proposed method is compared against a naive pooling approach which does not incorporate the “harmonization constraint”. As shown, the proposed method improves the “difference of errors” measure by 8% / 7% and with only a small drop in overall error rates. Figure 1 illustrates the results from our scanner‐pooling analysis. The performance across the three scanners, GE, Siemens and Philips, is evaluated pair‐wise. A consistent improvement in harmonization is observed and only ∼2% drop in overall error rate is seen.
Conclusion
We provide a harmonization constraint based algorithm to mitigate site specific differences when performing analysis of pooled brain imaging datasets in AD studies. In contrast to a method which modifies the data, we achieve harmonization by constraining the classifier to perform similarly across sites/groups/scanners, improving reproducibility.
In the Smart City, the Integrated Renewable Energy System (IRES) is playing a crucial role. Integrating the available renewable energy sources is useful in solving energy supply and demand-related ...issues. For a stable state of energy supply and energy demand, their proper size is needed to adapt to integrated renewable energy sources in the future. To address technical, economic and sizing problems, different algorithms needed to implement the integrated renewable energy scheme, as suggested by various authors. This paper provides a comprehensive review of various topics related to power generation for Smart City based on Integrated Renewable Energy System (IRES). It discusses in detail issues related to the integration of different energy sources, use of smart grids for integration, methods of IRES sizing using software followed by methods of sizing using artificial intelligence algorithms. This article reviews different AI algorithms that focus on the sizing of integrated renewable energy systems in smart cities.
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