Objective. To identify homing peptides specific for human synovium that could be used as targeting devices for delivering therapeutic/diagnostic agents to human joints.Methods. Human synovium and skin were transplanted into SCID mice. A disulfide-constrained 7-amino acid peptide phage display library was injected intravenously into the animals and synovial homing phage recovered from synovial grafts. Following 3-4 cycles of enrichment, DNA sequencing of homing phage clones allowed the identification of specific peptides that were synthesized by a-fluorenylmethyloxycarbonyl chemistry and used in competitive in vivo assays and immunohistochemistry analyses.Results. We isolated synovial homing phages displaying specific peptides that distinctively bound to synovial but not skin or mouse microvascular endothelium (MVE). They retained their tissue homing specificity in vivo, independently from the phage component, the original pathology of the transplanted tissue, and the degree of human/murine graft vascularization. One such peptide (CKSTHDRLC) maintained synovial homing specificity both when presented by the phage and as a free synthetic peptide. The synthetic peptide also competed with and inhibited in vivo the binding of the parent phage to the cognate synovial MVE ligand.Conclusion. This is the first report describing peptides with homing properties specific for human synovial MVE. This was demonstrated using a novel approach targeting human tissues, transplanted into SCID mice, directly by in vivo phage display selection. The identification of such peptides opens the possibility of using these sequences to construct joint-specific drug delivery systems that may have considerable impact in the treatment of arthritic conditions.

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Antibiotics select for resistant bacteria whose existence and emergence is more likely in populations with high phenotypic and genetic diversity. Identifying the mechanisms that generate this diversity can thus have clinical consequences for drug-resistant pathogens. We show here that intermediate levels of antibiotics are associated with higher levels of phenotypic diversity in size of colony forming units (cfus), within a single bacterial population. We examine experimentally thousands of populations of bacteria subjected to different disturbance levels that are created by varying antibiotic concentrations. Based on colony sizes, we find that intermediate levels of antibiotics always result in the highest phenotypic variation of this trait. This result is supported across bacterial densities and in the presence of three different antibiotics with two different mechanisms of action. Our results suggest intermediate levels of a stressor (as opposed to very low or very high levels) could affect the phenotypic diversity of a population, at least with regards to the single trait measured here. While this study is limited to a single phenotypic trait within a single species, the results suggest examining phenotypic and genetic variation created by disturbances and stressors could be a promising way to understand and limit variation in pathogens.

Drone operations in low-altitude urban airspace might be influenced by weather conditions such as wind and rainfall. Severe weather conditions may exceed the threshold of drone tolerability and cause crash accidents, posing risks to people and property. To investigate the influence of weather conditions on drone operation, this paper presents a data-driven method for analysis of weather data and to identify different levels of risk for safe drone operations. Obtained weather data is first collected across Singapore's 63 weather stations, and trend analysis are conducted to test if there are any significant trends in the yearly weather data. The risk standards in low-altitude urban environments are then classified based on the risk cost model. Similar levels of risk are clustered depending on the geographical location. Preliminary results show that the present weather data can be used to model our simulations as past historical weather data have no significant deviations. The results also concluded that low to high rainfall usually occurs at low wind speeds while high wind speeds tend to have low rainfall. The weather data analysis results can be used to generate an environmental risk-map for safe airspace planning and UAV path optimization.