Genetic and Biochemical Diversity among Valeriana jatamansi Populations from Himachal Pradesh

Valeriana jatamansi Jones is an important medicinal plant that grows wild in Himachal Pradesh, India. Molecular and biochemical diversity among 13 natural populations from Himachal Pradesh was assessed using RAPD and GC-MS to know the extent of existing variation. A total of seven genetically diverse groups have been identified based on RAPD analysis which corroborated well with the analysis based on chemical constituents. The essential oil yield ranged from 0.6% to 1.66% (v/w). A negative correlation between patchouli alcohol and viridiflorol, the two major valued constituents, limits the scope of their simultaneous improvement. However, other few populations like Chamba-II and Kandi-I were found promising for viridiflorol and patchouli alcohol, respectively. The analysis of chemical constitution of oil of the populations from a specific region revealed predominance of specific constituents indicating possibility of their collection/selection for specific end uses like phytomedicines. The prevalence of genetically diverse groups along with sufficient chemical diversity in a defined region clearly indicates the role of ecology in the maintenance of evolution of this species. Sufficient molecular and biochemical diversity detected among natural populations of this species will form basis for the future improvement

Genetic and Biochemical Diversity among Valeriana jatamansi Populations from Himachal Pradesh

Valeriana jatamansi Jones is an important medicinal plant that grows wild in Himachal Pradesh, India. Molecular and biochemical diversity among 13 natural populations from Himachal Pradesh was assessed using RAPD and GC-MS to know the extent of existing variation. A total of seven genetically diverse groups have been identified based on RAPD analysis which corroborated well with the analysis based on chemical constituents. The essential oil yield ranged from 0.6% to 1.66% (v/w). A negative correlation between patchouli alcohol and viridiflorol, the two major valued constituents, limits the scope of their simultaneous improvement. However, other few populations like Chamba-II and Kandi-I were found promising for viridiflorol and patchouli alcohol, respectively. The analysis of chemical constitution of oil of the populations from a specific region revealed predominance of specific constituents indicating possibility of their collection/selection for specific end uses like phytomedicines. The prevalence of genetically diverse groups along with sufficient chemical diversity in a defined region clearly indicates the role of ecology in the maintenance of evolution of this species. Sufficient molecular and biochemical diversity detected among natural populations of this species will form basis for the future improvement

A novel framework for optimizing the edge network node for wearable devices

The Multi-access edge computing (MEC) server would provide context-aware capabilities. When edge computing uses high-quality computing performance to supplement edge applications with vast IoT-based data services, substantial constraints are placed on the collaboration of edge nodes. Conversely to cloud computing, situational circumstances in the edge network are more complicated. In this paper, we provide a novel Edge Network (EDN) optimization (EDN-Opt) to boost the efficiency of edge computing jobs. In particular, we initially specify the parameters for cooperative assessment through the Internet of Things (IoT). Furthermore, the effectiveness of the proposed architecture is shown using real datasets collected from elderly individuals and various activity trackers. A comprehensive study on QoC intended with EDN is used to assess collaboration effectiveness. The cooperative optimization method developed provides improved efficiency To assess the effectiveness of EDN optimization, the discrepancy between the proposed equivalent and the real equivalent is examined. Investigation in this sector analyses several practical cases. The Spearman rank correlation factor is +1 or −1 when a perfect monotonic association is attained with no identifying data. The examination of this article demonstrates that trials show that our proposed edge cooperation optimization technique can quickly assess the EDN and then provide information on the collaborative relationship's replacement occurrences that can help the EDN's design

Effect of cultivation methods on physiological parameters of wheat genotypes at flowering stage (mean data of 2 years).

Effect of cultivation methods on physiological parameters of wheat genotypes at flowering stage (mean data of 2 years).</p

Root attributes as influenced by cultivation methods and wheat genotypes (mean of 2 years).

Root attributes as influenced by cultivation methods and wheat genotypes (mean of 2 years).</p

Effect of cultivation methods on lodging resistant on wheat genotypes (mean data of 2 years); *SWI: System of wheat intensification; *RP: Rectangle planting.

Effect of cultivation methods on lodging resistant on wheat genotypes (mean data of 2 years); *SWI: System of wheat intensification; *RP: Rectangle planting.</p

Standard agronomic management practices followed during the experimental period (2014–16).

Standard agronomic management practices followed during the experimental period (2014–16).</p

S1 Graphical abstract -

Intense cultivation with narrow row spacing in wheat, a common practice in the Indo-Gangetic plains of South Asia, renders the crop more susceptible to lodging during physiological maturity. This susceptibility, compounded by the use of traditional crop cultivars, has led to a substantial decline in overall crop productivity. In response to these challenges, a two-year field study on the system of wheat intensification (SWI) was conducted. The study involved three different cultivation methods in horizontal plots and four wheat genotypes in vertical plots, organized in a strip plot design. Our results exhibited that adoption of SWI at 20 cm × 20 cm resulted in significantly higher intercellular CO2 concentration (5.9–6.3%), transpiration rate (13.2–15.8%), stomatal conductance (55–59%), net photosynthetic rate (126–160%), and photosynthetically active radiation (PAR) interception (1.6–25.2%) over the existing conventional method (plant geometry 22.5 cm × continuous plant to plant spacing) of wheat cultivation. The lodging resistance capacity of both the lower and upper 3rd nodes was significantly higher in the SWI compared to other cultivation methods. Among different genotypes, HD 2967 demonstrated the highest recorded value for lodging resistance capacity, followed by HD 2851, HD 3086, and HD 2894. In addition, adoption of the SWI at 20 cm × 20 cm enhanced crop grain yield by 36.9–41.6%, and biological yield by 27.5–29.8%. Significantly higher soil dehydrogenase activity (12.06 μg TPF g-1 soil hr-1), arylsulfatase activity (82.8 μg p-nitro phenol g-1 soil hr-1), alkaline phosphatase activity (3.11 n moles ethylene g-1 soil hr-1), total polysaccharides, soil microbial biomass carbon, and soil chlorophyll content were also noted under SWI over conventional method of the production. Further, increased root volumes, surface root density and higher NPK uptake were recorded under SWI at 20×20 cm in comparison to rest of the treatments. Among the tested wheat genotypes, HD-2967 and HD-3086 had demonstrated notable increases in grain and biological yields, as well as improvements in the photosynthetically active radiation (PAR) and chlorophyll content. Therefore, adoption of SWI at 20 cm ×20 cm (square planting) with cultivars HD 2967 might be the best strategy for enhancing crop productivity and resource-use efficiency under the similar wheat growing conditions of India and similar agro-ecotypes of the globe.</div

Effect of cultivation methods on intercellular CO2 concentration and transpiration rate of wheat at flowering stage (mean data of 2 years); *SWI: System of wheat intensification.

Effect of cultivation methods on intercellular CO2 concentration and transpiration rate of wheat at flowering stage (mean data of 2 years); *SWI: System of wheat intensification.</p

Effect of cultivation methods and genotypes on NPK uptake of wheat (mean data of 2 years).

Effect of cultivation methods and genotypes on NPK uptake of wheat (mean data of 2 years).</p