Optimization of medium composition for apple rootstocks

Impact of medium composition (plant growth regulators, mineral nutrients) on multiplication rate, shoot elongation, callusing and rooting of apple rootstocks ('M9', 'M27', and 'MM106') cultured on gelled basal Murashige and Skoog (MS) medium were investigated. Multiplication rate was mainly dependent upon kind of plant growth regulators especially, 6-benzylaminopurine (BA), mineral concentration and genotypes. The best shoot production in terms of shoot number and shoot quality was obtained using 4.4 μM BA and 2.27 μM thidiazuron (TZD) during the shoot multiplication phase, but 8.8 μM BA + 1.14 μM TZD and 2.8 μM gebberllic acid (GA3) during the shoot elongation phase for all genotypes. Application of high (2.8 μM) concentration of GA3 increased the elongation of adventitious shoots than low concentrations. The highest multiplication rate (5.7 No.//shoot) and the highest amount of total fresh weight (2.25 g/jar), as growth rate, were produced by applying 4.4 μM BA + 2.27 μM TDZ for ‘M27’ genotype. Micropropagation potential of ‘M27’ genotype was higher than other genotypes. 'MM106' genotype had the lowest multiplication rate (0.7 No./month), when 0 μM BA+9.08 μM TDZ was applied. Multiplication of explants from the 1st subculture was more sensitive to BA than that from the 3th or 4th subculture. The rooting of explants was promoted by indole-3-butyric acid (IBA) significantly and the best result for rooting was achieved in the half-strength MS medium containing 5.4 μM IBA and 1.2 μM 2,4-dichlorophenoxyacetic acid (2, 4-D). The highest percentage (64%) rooting was produced for ‘MM106’ genotype and the lowest (11%) for ‘M9' after 3 months. Root formation was increased with decreasing concentrations in cytokinins, but increasing auxins (IBA). Rooting percentage of shoot cultures in the low 1/2X-MS medium was significantly more than shoot cultures in the high 2X-MS medium.Key words: Apple rootstocks, medium composition, multiplication rate, plant growth regulators (PGRs)

Chemical composition, antibacterial and antifungal activity of three ecotypes of Thymus fallax Fisch. volatile oils

Background and Aims: Thymus fallax Fisch. is an aromatic plant belonging to the Lamiaceae family, used for medicinal and spice purposes almost everywhere in the world. In this investigation volatile oils from three ecotypes of T. fallax have isolated using a Clevenger-type apparatus. Methods: The quantitative and qualitative analysis was performed by gas chromatography/Mass spectroscopy (GC and GC/Mass). Antibacterial activity of compounds was assayed using the disc diffusion method against G- and G+ bacteria and some fungi pathogenesis. Results: Final results shows that Thymol & carvacrol constitute of the main elements present in the essential oil of T. fallax. In Lamiaceae plants, thymol is always accompanied by its isomer carvacrol. Both compounds are biologically active and have potent antibacterial (gram+ and gram-) and anti fungal activity. The essential oil exhibited strong antioxidant activity. Conclusions: Recognized compounds of Thymus fallax Fisch. volatile oils are biologically active and have potent antibacterial (gram+ and gram-) and anti fungal activity. This study also affirmed three ecotypes volatiale oil had significant effects against G- and G+ bacteria and some fungi pathogenesis

The prediction model for additively manufacturing of NiTiHf high-temperature shape memory alloy

NiTi-based alloys are one of the most well-known alloys among shape memory alloys having a wide range of applications from biomedical to aerospace areas. Adding a third element to the binary alloys of NiTi changes the thermomechanical properties of the material remarkably. Two unique features of stability and high transformation temperature have turned NiTiHf as a suitable ternary shape memory alloys in various applications. Selective laser melting (SLM) as a layer-based fabrication method addresses the difficulties and limitations of conventional methods. Process parameters of SLM play a prominent role in the properties of the final parts so that by using the different sets of process parameters, different thermomechanical responses can be achieved. In this study, different sets of process parameters (PPs) including laser power, hatch space, and scanning speed were defined to fabricate the NiTiHf samples. Changing the PPs is a powerful tool for tailoring the thermomechanical response of the fabricated parts such as transformation temperature (TTs), density, and mechanical response. In this work, an artificial neural network (ANN) was developed to achieve a prediction tool for finding the effect of the PPs on the TTs and the size deviation of the printed parts

Numerical study for prediction of optimum operational parameters in laser welding of NiTi alloy

Laser welding of NiTi alloy is a challenging process since it strongly affects the functionality of the material in the heat affected and fusion zones. In fact, the inherent thermal process can remarkably change the transformation temperature of NiTi alloy in the welding zone because of variation in the material composition. Accordingly, the laser parameters such as laser power and velocity effectively determine the quality of the welded component. The functional and mechanical behavior of the resulting welded NiTi parts can also be effectively improved by controlling laser parameters, and consequently, improve the weldability quality. The purpose of the present study was to establish a reliable finite element model to predict the thermal behavior induced by the laser welding process. To this end, a numerical model was employed to estimate the optimum laser parameters, which can reduce the heat affected and the fusion regions and thus result in a better weld. The results of the finite element model show good accuracy compared to the experimental results including the transient temperature and the dimension of the heat affected and fusion zones. In addition, an Artificial Neural Network (ANN)approach was applied, as a predictable tool, to perform a nonlinear mapping between inputs and outputs of the welding process in order to find the optimum laser parameters

Differentiation by simplified AFLP of Pseudomonas syringe pv. syringae isolates from fields, panicles and nurseries of the Guilan province – Iran

Pseudomonas syringae pv. syringae is a plant pathogen bacterium in rice which causes bacterial sheath rot. In this study, simplified AFLP (Amplified fragment length polymorphism) was tested in order to evaluate genetic diversity of 38 Pseudomonas syringae pv. syringae strains isolated from rice fields of the Guilan province, by 12 AFLP primers. The products resulted from AFLP were separated using agarose gel electrophoresis. The maximum number of PIC among all tested primers belonged to primer 36. Based on the Unweighted Pair Group Method with Arithmetic (UPGMA) method, using NTSISpc Software, all Pseudomonas syringae pv. syringae strains were divided into three distinct clusters which had a 70% similarity level. Cluster analysis of studied populations (isolates of fields, panicles, and nurseries) determined that a high genetic identity could be seen among isolates obtained from the panicles and nurseries, while strains isolated from the fields and nurseries had the most genetic distance with each other. The result of this study showed that sampling site and weather conditions play an important role in genetic evolution of strains. It was also found that AFLP is an effective marker in evaluating genetic diversity within and among isolates being studied, while all of them had the same host and pathogenesis characteristics

A variable stiffness transverse mode shape memory alloy actuator as a minimally invasive organ positioner

Smart materials have gained a great deal of attention in recent years because of their unique actuation properties. Actuators are needed in the medical field where space is limited. Presented within this work is an organ positioner used to position the esophagus away from the left atrium to avoid the development of an esophageal fistula during atrial fibrillation (afib) ablation procedures. Within this work, a subroutine was implemented into the finite element framework to predict the midspan load capacity of a near equiatomic NiTi specimen in both the super elastic and shape memory regimes. The purpose of the simulations and experimental results was to develop a design envelope for the organ positioning device. The transverse loading experiments were conducted at several different temperatures leading to the ability to design a variable stiffness actuator. This is essential because the actuator must not be too stiff to injure the organ it is positioning. Extended further, geometric perturbations were applied in the virtual model and the entire design envelope was developed. Further, nitinol was tested for safety in the radio-frequency environment (to ensure that local heating will not occur in the ablation environment). With the safety of the device confirmed, a primitive prototype was manufactured and successfully tested in a cadaver. The design of the final device is also presented. The contribution of this work is the presentation of a new type of positoning device for medical purposes (NiTiBOP). In the process a comprehensive model for transverse actuation of an SMA actuator was developed and experimentally verified

A prediction model for finding the optimal laser parameters in additive manufacturing of NiTi shape memory alloy

Shape memory alloys (SMAs) have been applied for various applications in the fields of aerospace, automotive, and medical. Nickel-titanium (NiTi) is the most well-known alloy among the others due to its outstanding functional characteristics including superelasticity (SE) and shape memory effect (SME). These particular properties are the result of the reversible martensite-to-austenite and austenite-to-martensite transformations. In recent years, additive manufacturing (AM) has provided a great opportunity for fabricating NiTi products with complex shapes. Many researchers have been investigating the AM process to set the optimal operational parameters, which can significantly affect the properties of the end-products. Indeed, the functional and mechanical behavior of printed NiTi parts can be tailored by controlling laser power, laser scan speed, and hatch spacing having them a crucial role in properties of 3D-printed parts. In particular, the effect of the input parameters can significantly alter the mechanical properties such as strain recovery rates and the transformation temperatures; therefore, using suitable parameter combination is of paramount importance. In this framework, the present study develops a prediction model based on artificial neural network (ANN) to generate a nonlinear map between inputs and outputs of the AM process. Accordingly, a prototyping tool for the AM process, also useful for dealing with the settings of the optimal operational parameters, will be built, tested, and validated