Molecular phylogeny of the genus Corchorus (Grewioideae, Malvaceae s.l.) based on nuclear rDNA ITS sequences

A molecular phylogenetic analysis of the genus Corchorus (Grewioideae, Malvaceae s.l.) is presented, based on sequences of the nuclear ribosomal DNA internal transcribed spacer (ITS) region for 144 accessions representing 47 species. Several other genera from the subfamily Grewioideae, namely Pseudocorchorus, Triumfetta, Sparrmannia, Entelea, and Grewia, were included as outgroups. The monophyly of the genus was well supported by all phylogenetic analyses (maximum likelihood, Bayesian approaches, and parsimony), and Corchorus was divided into four major clades. The majority of African species formed a statistically highly supported and distinct clade separated from the other pantropically distributed species. Several endemic species from Australia, New Caledonia, and tropical America were nested within this distinct clade, indicating dispersal from Africa to the rest of the pantropics. Based on the taxa included in this study, the two cultivated species (C. olitorius and C. capsularis) shared a common ancestry with wild species of C. africanus, C. brevicornatus, C. pseudocapsularis, C. pseudo-olitorius, C. urticifolius, C. pilosus, C. orinocensis, and C. cunninghamii. Pseudocorchorus, previously considered an accepted genus, was nested within the genus Corchorus and shared a common ancestry especially with C. depressus and C. siliquosus. Based on morphological and anatomical similarity as well as the results of the present molecular findings, inclusion of the six Pseudocorchorus species into Corchorus is proposed, with Pseudocorchorus as a synonym of Corchorus. Of the included outgroup taxa, Triumfetta is the closest sister to Corchorus, while the common ancestor of Corchorus/Pseudocorchorus, Triumfetta, Sparrmannia, and Entelea is Grewia. A further phylogenetic study with more taxa mainly from Australia, together with additional molecular markers and morphological investigation, would help to test the hypothesis on the biogeography and growth form evolution of the genus Corchorus. Keywords: Corchorus, Jute, Malvaceae s.l., Phylogeny, Pseudocorchoru

Folk Classification of Sorghum (Sorghum Bicolor (L.) Moench) Land Races and its Ethnobotanical Implication: A Case Study in Northeastern Ethiopia

Ethiopia is one of the centers of origin and diversity for a number of crop species, among which sorghum has a wide range of distribution in the country. Farmers' knowledge about sorghum crop such as types, names, uses, croppingsystems, cultivation methods, and so on has been handed down inter-generationally, primarily through oral tradition.During the 1998 and 1999 cropping seasons, sorghum landrace collection was conducted in Ethiopia to document farmers' indigenous knowledge, take conservation measures, and incorporate potential landraces into future breedingprograms. The collection strategy was non-random accession collection, incorporating farmers and their rich indigenous knowledge and experience into the collection team. The wealth of genetic diversity in the explored areaconsisted of drought-tolerant, striga-tolerant, and bird-resistant species. Farmers refer to discrete sorghum types by different names, which vary for several characters. For instance, the name wotet-begunche designates a matured sorghum seed with milky taste; ahyo and wof-aybelash mean bird-resistant, in the case where not a single grain was damaged by birds. Farmers' indigenous knowledge also designates striga-tolerant landraces such as mera, mognayfere, minchiro, and ckerekit. Pot and field experiments were conducted later to corroborate the indigenous knowledge of bird-resistant and striga-tolerant sorghum landraces. We recommend that the pest-tolerant landracesconfirmed by these experiments be incorporated into breeding programs

Bioremediation of brewery wastewater using hydroponics planted with vetiver grass in Addis Ababa, Ethiopia

Abstract Background Bioremediation is the use of biological interventions for mitigation of the noxious effects caused by pollutants in the environment including wastewater. It is very useful approach for a variety of applications in the area of environmental protection. It has become an attractive alternative to the conventional cleanup technologies that employ plants and their associated microorganisms to remove, contain, or render harmless environmental contaminants. Methods Three parallel hydroponic treatment systems (each 2 m long × 0.75 m wide × 0.65 m deep) and one control unit were filled with brewery wastewater to an effective depth of 0.5 m. Two sets of floating polystyrene platform were used for each treatment unit to support vetiver tillers for conducting bioremediation study. The wastewater was fed to the hydroponic treatment units at hydraulic loading rate of 10 cm d−1 and hydraulic residence time of 5 days. Influent and effluent samples were collected once a month for 7 months, and analyzed to determine the various parameters relating to the water quality including plant growth and nutrient analyses. Results Vetiver grass grew and established with well-developed root and shoots in the hydroponics under fluctuations of brewery wastewater loads and showed phytoremedial capacity to remove pollutants. Removal efficiencies for BOD5 and COD were significant (p < 0.05), up to 73% (748–1642 mg l−1 inlet), and up to 58% (835–2602 mg l−1 inlet), respectively. Significant removal efficiencies (p < 0.05) ranged from 26 to 46% (14–21 mg l−1 inlet) for TKN, 28–46% (13–19 mg l−1 inlet) for NH4 +-N, 35–58% (4–11 mg l−1 inlet) for NO3 −-N, and 42–63% (4–8 mg l−1 inlet) for PO4−3-P were recorded. Nutrient accumulation in the samples harvested were varied between 7.4 and 8.3 g N kg−1 dry weight and 6.4–7.5 g P kg−1 dry weight in the hydroponic treatment units during the study period. Conclusions This study has shown suitability of vetiver grass for organics and nutrient removal in the bioremediation of brewery wastewater using hydroponics technique in addition to production of valuable biomass. Bioremediation using hydroponics is green and environmentally sustainable approach that offers promising alternative for wastewater treatment in developing countries including Ethiopia

Advanced modeling and optimizing for surface sterilization process of grape vine (Vitis vinifera) root stock 3309C through response surface, artificial neural network, and genetic algorithm techniques

In vitro, sterilization is one of the key components for proceeding with plant tissue cultures. Since the effectiveness of sterilization has a direct impact on the culture's final outcomes, there is a crucial need for optimization of the sterilization process. However, compared with traditional optimizing methods, the use of computational approaches through artificial intelligence-based process modeling and optimization algorithms provides a precise optimal condition for in vitro culturing. This study aimed to optimise in vitro sterilization of grape rootstock 3309C using RSM, ANN, and genetic algorithm (GA) techniques. In this context, two output responses, namely, Clean Culture and Explant Viability, were optimised using the models developed by RSM and ANN, followed by a GA, to obtain a globally optimal solution. The most influential independent factors, such as HgCl2, NaOCl, AgNO3, and immersion time, were considered input variables. The significance of the developed models was investigated with statistical and non-statistical techniques and was optimised to determine the significance of selected inputs. The optimal clean culture of 91%, and the explant viability of 89% can be obtained from 1.62% NaOCl at a 13.96 min immersion time, according to MLP-NSGAII. Sensitivity analysis revealed that the clean culture and explant viability were less sensitive to AgNO3 and more sensitive to immersion time. Results showed that the differences between the GA predicted and validation data were significant after the performance validation of predicted and optimised sterilising agents with immersion time combinations were tested. In general, GA, a potent methodology, may open the door to the development of new computational methods in plant tissue culture

Optimisation of culture conditions for gesho (Rhamnus prinoides.L) callus differentiation using Artificial Neural Network-Genetic Algorithm (ANN-GA) Techniques

Abstract Gesho (Rhamnus prinoides) is a medicinal plant with antioxidant and anti-inflammatory activities commonly used in the ethnomedicinal systems of Africa. Using a three-layer neural network, four culture conditions viz., concentration of agar, duration of light exposure, temperature of culture, and relative humidity were used to calculate the callus differentiation rate of gesho. With the ability to quickly identify optimal solutions using high-speed computers, synthetic neural networks have emerged as a rapid, reliable, and accurate fitting technique. They also have the self-directed learning capability that is essential for accurate prediction. The network's final architecture for four selected variables and its performance has been confirmed with high correlation coefficient (R2, 0.9984) between the predicted and actual outputs and the root-mean-square error of 0.0249, were developed after ten-fold cross validation as the training function. In vitro research had been conducted using the genetic algorithm’s suggestions for the optimal culture conditions. The outcomes demonstrated that the actual gesho differentiation rate was 93.87%, which was just 1.86% lesser than the genetic algorithm's predicted value. The projected induced differentiation rate was 87.62%, the actual value was 84.79%, and the predicted value was 2.83% higher than Response Surface Methods optimisation. The environment for the growth of plant tissue can be accurately and efficiently optimised using a genetic algorithm and an artificial neural network. Further biological investigations will presumably utilise this technology