Measures of disease activity in glaucoma

Glaucoma is the leading cause of irreversible blindness globally which significantly affects the quality of life and has a substantial economic impact. Effective detective methods are necessary to identify glaucoma as early as possible. Regular eye examinations are important for detecting the disease early and preventing deterioration of vision and quality of life. Current methods of measuring disease activity are powerful in describing the functional and structural changes in glaucomatous eyes. However, there is still a need for a novel tool to detect glaucoma earlier and more accurately. Tear fluid biomarker analysis and new imaging technology provide novel surrogate endpoints of glaucoma. Artificial intelligence is a post-diagnostic tool that can analyse ophthalmic test results. A detail review of currently used clinical tests in glaucoma include intraocular pressure test, visual field test and optical coherence tomography are presented. The advanced technologies for glaucoma measurement which can identify specific disease characteristics, as well as the mechanism, performance and future perspectives of these devices are highlighted. Applications of AI in diagnosis and prediction in glaucoma are mentioned. With the development in imaging tools, sensor technologies and artificial intelligence, diagnostic evaluation of glaucoma must assess more variables to facilitate earlier diagnosis and management in the future

Data_Sheet_2_Differential assembly of root-associated bacterial and fungal communities of a dual transgenic insect-resistant maize line at different host niches and different growth stages.xlsx

Transgenic technology has been widely applied to crop development, with genetically modified (GM) maize being the world’s second-largest GM crop. Despite the fact that rhizosphere bacterial and fungal populations are critical regulators of plant performance, few studies have evaluated the influence of GM maize on these communities. Plant materials used in this study included the control maize line B73 and the mcry1Ab and mcry2Ab dual transgenic insect-resistant maize line 2A-7. The plants and soils samples were sampled at three growth stages (jointing, flowering, and maturing stages), and the sampling compartments from the outside to the inside of the root are surrounding soil (SS), rhizospheric soil (RS), and intact root (RT), respectively. In this study, the results of alpha diversity revealed that from the outside to the inside of the root, the community richness and diversity declined while community coverage increased. Morever, the different host niches of maize rhizosphere and maize development stages influenced beta diversity according to statistical analysis. The GM maize line 2A-7 had no significant influence on the composition of microbial communities when compared to B73. Compared to RS and SS, the host niche RT tended to deplete Chloroflexi, Gemmatimonadetes and Mortierellomycota at phylum level. Nitrogen-fixation bacteria Pseudomonas, Herbaspirillum huttiense, Rhizobium leguminosarum, and Sphingomonas azotifigens were found to be enriched in the niche RT in comparison to RS and SS, whilst Bacillus was found to be increased and Stenotrophomonas was found to be decreased at the maturing stage as compared to jointing and flowering stages. The nitrogen fixation protein FixH (clusters of orthologous groups, COG5456), was found to be abundant in RT. Furthermore, the pathogen fungus that causes maize stalk rot, Gaeumannomyces radicicola, was found to be abundant in RT, while the beneficial fungus Mortierella hyalina was found to be depleted in RT. Lastly, the abundance of G. radicicola gradually increased during the development of maize. In conclusion, the host niches throughout the soil-plant continuum rather than the Bt insect-resistant gene or Bt protein secretion were primarily responsible for the differential assembly of root-associated microbial communities in GM maize, which provides the theoretical basis for ecological agriculture.</p

Alternative Fuels for Particulate Control in CI Engines

It is widely known that diesel combustion in a compression ignition (CI) engine produces and emits a significant amount of particulate matter (PM) which contributes to degradation in both health and environment. The origin of the soot formation depends on several factors, however, the main source of the soot emissions is the combustion of the diesel fuel itself. To circumvent this issue, studies have been conducted to explore and exploit the advantages of fuels with a lower sooting tendency. Around the world, the utilization of oxygenated biodiesels, such as fatty acid methyl esters (FAME), have been increasing to allow the reduction of PM emissions alongside the net CO2. Due to the FAMEs oxygen content, the fuel is oxidized more readily during the combustion process and thus emitting a significantly lower engine out concentration of PM emission than that of commercial diesel fuel. The utilization of the lighter alcohol fuels, methanol and ethanol neat and blended, is a good option to reduce the soot to zero levels. The reduction of soot to near zero levels introduces another advantage; the soot-NOx trade-off diminishes completely when utilizing exhaust gas recycling (EGR). The issue, however, is that the PN emission of nucleation mode particles is high when utilizing such fuels while ignition is hard to achieve with high octane number fuels in a CI engine