Nematicidal Volatiles from Bacillus atrophaeus GBSC56 Promote Growth and Stimulate Induced Systemic Resistance in Tomato against Meloidogyne incognita

Bacillus volatiles to control plant nematodes is a topic of great interest among researchers due to its safe and environmentally friendly nature. Bacillus strain GBSC56 isolated from the Tibet region of China showed high nematicidal activity against M. incognita, with 90% mortality as compared with control in a partition plate experiment. Pure volatiles produced by GBSC56 were identified through gas chromatography and mass spectrometry (GC-MS). Among 10 volatile organic compounds (VOCs), 3 volatiles, i.e., dimethyl disulfide (DMDS), methyl isovalerate (MIV), and 2-undecanone (2-UD) showed strong nematicidal activity with a mortality rate of 87%, 83%, and 80%, respectively, against M. incognita. The VOCs induced severe oxidative stress in nematodes, which caused rapid death. Moreover, in the presence of volatiles, the activity of antioxidant enzymes, i.e., SOD, CAT, POD, and APX, was observed to be enhanced in M. incognita-infested roots, which might reduce the adverse effect of oxidative stress-induced after infection. Moreover, genes responsible for plant growth promotion SlCKX1, SlIAA1, and Exp18 showed an upsurge in expression, while AC01 was downregulated in infested plants. Furthermore, the defense-related genes (PR1, PR5, and SlLOX1) in infested tomato plants were upregulated after treatment with MIV and 2-UD. These findings suggest that GBSC56 possesses excellent biocontrol potential against M. incognita. Furthermore, the study provides new insight into the mechanism by which GBSC56 nematicidal volatiles regulate antioxidant enzymes, the key genes involved in plant growth promotion, and the defense mechanism M. incognita-infested tomato plants use to efficiently manage root-knot disease.     

Green biocides,a promising technology: current and future applications to industry and industrial processes

The study of biofilms has skyrocketed in recent years due to increased awareness of the pervasiveness and impact of biofilms. It costs the USA literally billions of dollars every year in energy losses, equipment damage, product contamination and medical infections. But biofilms also offer huge potential for cleaning up hazardous waste sites, filtering municipal and industrial water and wastewater, and forming biobarriers to protect soil and groundwater from contamination. The complexity of biofilm activity and behavior requires research contributions from many disciplines such as biochemistry, engineering, mathematics and microbiology. The aim of this review is to provide a comprehensive analysis of emerging novel antimicrobial techniques, including those using myriad organic and inorganic products as well as genetic engineering techniques, the use of coordination complex molecules, composite materials and antimicrobial peptides and the use of lasers as such or their modified use in combination treatments. This review also addresses advanced and recent modifications, including methodological changes, and biocide efficacy enhancing strategies. This review will provide future planners of biofilm control technologies with a broad understanding and perspective on the use of biocides in the field of green developments for a sustainable future. © 2013 Society of Chemical Industry     

Life cycle assessment to evaluate the green house gas emission from oil palm bio-oil based power plant

The objective of this study is to assess the green house gas (GHG) emission for the production of bio-oil from oil palm biomass and its utilization for 10 MW power generation by evaluating the life cycle carbon footprint analysis. The life cycle GHG emission assessment includes four main stages, which cover the oil-palm cultivation, palm oil mill operation, biomass transportation and pyrolysis process for the production of bio-oil and its utilization for 10MW power generation. The results obtained suggest that the palm bio-oil has potential as a carbon neutral renewable energy source in the future. More importantly, it has no negative impact on the environment as the amount of CO₂ emitted to the atmosphere during combustion of this fuel is lower than that of the CO₂ absorbed from the atmosphere during cultivation stage.     

Intelligent Forecasting Model of COVID-19 Novel Coronavirus Outbreak Empowered with Deep Extreme Learning Machine

An epidemic is a quick and widespread disease that threatens many lives and damages the economy. The epidemic lifetime should be accurate so that timely and remedial steps are determined. These include the closing of borders schools, suspension of community and commuting services. The forecast of an outbreak effectively is a very necessary but difficult task. A predictive model that provides the best possible forecast is a great challenge for machine learning with only a few samples of training available. This work proposes and examines a prediction model based on a deep extreme learning machine (DELM). This methodology is used to carry out an experiment based on the recent Wuhan coronavirus outbreak. An optimized prediction model that has been developed, namely DELM, is demonstrated to be able to make a prediction that is fairly best. The results show that the new methodology is useful in developing an appropriate forecast when the samples are far from abundant during the critical period of the disease.During the investigation, it is shown that the proposed approach has the highest accuracy rate of 97.59% with 70% of training, 30% of test and validation. Simulation results validate the prediction effectiveness of the proposed scheme.     

Simulating oriental brush character considered with aerial action of pen tablet

Multimedia Tools and Applications - The calligraphy is widely known in the oriental countries and has received art and high evaluation in history. The calligraphy is not only intended as a means of...     

The stimulus role of lithium sulfate (Li2SO4) on the electrical and mechanical properties of poly (vinyl alcohol)-MWCNTS-based thin film composites

Journal of Materials Science: Materials in Electronics - This study encompasses the evaluation of mechanical, and electrical properties of PVA-MWCNT-based thin film composites (TFC) in the presence...     

Link failure and congestion-aware reliable data delivery mechanism for mobile ad hoc networks

Link failures and packet drops due to congestion are two frequently occurring problems in mobile ad hoc networks, degrading the network performance significantly. In this paper, we propose a link failure and congestion-aware reliable data delivery (LCRDD) mechanism that jointly exploits local packet buffering and multilevel congestion detection and control approaches for increasing the data delivery performance. On the detection of link failure or congestive state, an LCRDD intermediate node buffers the incoming data packets at the transport layer queue and resumes transmission when the route is repaired locally. In addition, LCRDD’s multilevel congestion detection helps it to take the most appropriate action proactively. Thus, it offers increased reliability and throughput and decreased end-to-end packet delivery delay and routing overhead compared to state-of-the-art protocols, as shown in results of performance evaluations carried out in network simulator v-2.34.