X-ray induced photodynamic therapy (PDT) with a mitochondria-targeted liposome delivery system.

In this study, we constructed multifunctional liposomes with preferentially mitochondria-targeted feature and gold nanoparticles-assisted synergistic photodynamic therapy. We systemically investigated the in vitro X-ray triggered PDT effect of these liposomes on HCT 116 cells including the levels of singlet oxygen, mitochondrial membrane potential, cell apoptosis/necrosis and the expression of apoptosis-related proteins. The results corroborated that synchronous action of PDT and X-ray radiation enhance the generation of cytotoxic reactive oxygen species produced from the engineered liposomes, causing mitochondrial dysfunction and increasing the levels of apoptosis

Trapped lipopolysaccharide and LptD intermediates reveal lipopolysaccharide translocation steps across the Escherichia coli outer membrane

Lipopolysaccharide (LPS) is a main component of the outer membrane of Gram-negative bacteria, which is essential for the vitality of most Gram-negative bacteria and plays a critical role for drug resistance. LptD/E complex forms a N-terminal LPS transport slide, a hydrophobic intramembrane hole and the hydrophilic channel of the barrel, for LPS transport, lipid A insertion and core oligosaccharide and O-antigen polysaccharide translocation, respectively. However, there is no direct evidence to confirm that LptD/E transports LPS from the periplasm to the external leaflet of the outer membrane. By replacing LptD residues with an unnatural amino acid p-benzoyl-L-phenyalanine (pBPA) and UV-photo-cross-linking in E.coli, the translocon and LPS intermediates were obtained at the N-terminal domain, the intramembrane hole, the lumenal gate, the lumen of LptD channel, and the extracellular loop 1 and 4, providing the first direct evidence and “snapshots” to reveal LPS translocation steps across the outer membrane

The Influence of Physiological Status on age Prediction of Anopheles Arabiensis Using Near Infra-red spectroscopy

Determining the age of malaria vectors is essential for evaluating the impact of interventions that reduce the survival of wild mosquito populations and for estimating changes in vectorial capacity. Near infra-red spectroscopy (NIRS) is a simple and non-destructive method that has been used to determine the age and species of Anopheles gambiae s.l. by analyzing differences in absorption spectra. The spectra are affected by biochemical changes that occur during the life of a mosquito and could be influenced by senescence and also the life history of the mosquito, i.e., mating, blood feeding and egg-laying events. To better understand these changes, we evaluated the influence of mosquito physiological status on NIR energy absorption spectra. Mosquitoes were kept in individual cups to permit record keeping of each individual insect’s life history. Mosquitoes of the same chronological age, but at different physiological stages, were scanned and compared using cross-validations. We observed a slight trend within some physiological stages that suggest older insects tend to be predicted as being physiologically more mature. It was advantageous to include mosquitoes of different chronological ages and physiological stages in calibrations, as it increases the robustness of the model resulting in better age predictions. Progression through different physiological statuses of An. arabiensis influences the chronological age prediction by the NIRS. Entomologists that wish to use NIR technology to predict the age of field-caught An. gambiae s.l from their study area should use a calibration developed from their field strain using mosquitoes of diverse chronological ages and physiological stages to increase the robustness and accuracy of the predictions.\u

Dynamics of Kv1 Channel Transport in Axons

Concerted actions of various ion channels that are precisely targeted along axons are crucial for action potential initiation and propagation, and neurotransmitter release. However, the dynamics of channel protein transport in axons remain unknown. Here, using time-lapse imaging, we found fluorescently tagged Kv1.2 voltage-gated K+ channels (YFP-Kv1.2) moved bi-directionally in discrete puncta along hippocampal axons. Expressing Kvβ2, a Kv1 accessory subunit, markedly increased the velocity, the travel distance, and the percentage of moving time of these puncta in both anterograde and retrograde directions. Suppressing the Kvβ2-associated protein, plus-end binding protein EB1 or kinesin II/KIF3A, by siRNA, significantly decreased the velocity of YFP-Kv1.2 moving puncta in both directions. Kvβ2 mutants with disrupted either Kv1.2-Kvβ2 binding or Kvβ2-EB1 binding failed to increase the velocity of YFP-Kv1.2 puncta, confirming a central role of Kvβ2. Furthermore, fluorescently tagged Kv1.2 and Kvβ2 co-moved along axons. Surprisingly, when co-moving with Kv1.2 and Kvβ2, EB1 appeared to travel markedly faster than its plus-end tracking. Finally, using fission yeast S. pombe expressing YFP-fusion proteins as reference standards to calibrate our microscope, we estimated the numbers of YFP-Kv1.2 tetramers in axonal puncta. Taken together, our results suggest that proper amounts of Kv1 channels and their associated proteins are required for efficient transport of Kv1 channel proteins along axons

Surface adsorption and lubrication properties of plant and dairy proteins: A comparative study

The aim of this work was to compare the surface adsorption and lubrication properties of plant and dairy proteins. Whey protein isolate (WPI) and pea protein isolate (PPI) were chosen as model animal and plant proteins, respectively, and various protein concentrations (0.1–100 mg/mL) were studied with/without heat treatment (90 °C/60 min). Quartz crystal microbalance with dissipation monitoring (QCM-D) experiments were performed on hydrophilic (gold) and hydrophobic polydimethylsiloxane (PDMS) sensors, with or without a mucin coating, latter was used to mimic the oral surface. Soft tribology using PDMS tribopairs in addition to wettability measurements, physicochemical characterization (size, charge, solubility) and gel electrophoresis were performed. Soluble fractions of PPI adsorbed to significantly larger extent on PDMS surfaces, forming more viscous films as compared to WPI regardless of heat treatment. Introducing a mucin coating on a PDMS surface led to a decrease in binding of the subsequent dietary protein layers, with PPI still adsorbing to a larger extent than WPI. Such large hydrated mass of PPI resulted in superior lubrication performance at lower protein concentration (≤10 mg/mL) as compared to WPI. However, at 100 mg/mL, WPI was a better lubricant than PPI, with the former showing the onset of elastohydrodynamic lubrication. Enhanced lubricity upon heat treatment was attributed to the increase in apparent viscosity. Fundamental insights from this study reveal that pea protein at higher concentrations demonstrates inferior lubricity than whey protein and could result in unpleasant mouthfeel, and thus may inform future replacement strategies when designing sustainable food products

Understanding and examining teacher resilience from multiple perspectives

In this chapter, I argue that differing conceptualisations of the construct of resilience shape and enrich the research questions and methodology used to examine it. In addition, the conceptual focus has implications for questions such as whose responsibility it is for the development of resilience. Research conducted within two Australian projects, Keeping Cool and BRiTE (Building Resilience in Teacher Education) is used as an illustration of the impact of a changing conceptual focus. For example, beginning with a psychological perspective led to an examination of risk and protective factors for individuals. More contextual approaches involved a comparison of countries. Recent systemic views support a model that encompasses both personal and contextual characteristics, as well as strategies used and outcomes achieved. It is argued that taking multiple perspectives in this programme of work has enabled the incorporation of a broad range of research methods and findings, and contributed to a deeper understanding of the construct of teacher resilience

Comparative Evaluation of Light-Trap Catches, Electric Motor Mosquito Catches and Human Biting Catches of Anopheles in the Three Gorges Reservoir

The mosquito sampling efficiency of light-trap catches and electric motor mosquito catches were compared with that of human biting catches in the Three Gorges Reservoir. There was consistency in the sampling efficiency between light-trap catches and human biting catches for Anopheles sinensis (r = 0.82, P<0.01) and light-trap catches were 1.52 (1.35–1.71) times that of human biting catches regardless of mosquito density (r = 0.33, P>0.01), while the correlation between electric motor mosquito catches and human biting catches was found to be not statistically significant (r = 0.43, P>0.01) and its sampling efficiency was below that of human biting catches. It is concluded that light-traps can be used as an alternative to human biting catches of Anopheles sinensis in the study area and is a promising tool for sampling malaria vector populations

Exploring matrix factorization techniques for significant genes identification of Alzheimer’s disease microarray gene expression data

<p>Abstract</p> <p>Background</p> <p>The wide use of high-throughput DNA microarray technology provide an increasingly detailed view of human transcriptome from hundreds to thousands of genes. Although biomedical researchers typically design microarray experiments to explore specific biological contexts, the relationships between genes are hard to identified because they are complex and noisy high-dimensional data and are often hindered by low statistical power. The main challenge now is to extract valuable biological information from the colossal amount of data to gain insight into biological processes and the mechanisms of human disease. To overcome the challenge requires mathematical and computational methods that are versatile enough to capture the underlying biological features and simple enough to be applied efficiently to large datasets.</p> <p>Methods</p> <p>Unsupervised machine learning approaches provide new and efficient analysis of gene expression profiles. In our study, two unsupervised knowledge-based matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF) are integrated to identify significant genes and related pathways in microarray gene expression dataset of Alzheimer’s disease. The advantage of these two approaches is they can be performed as a biclustering method by which genes and conditions can be clustered simultaneously. Furthermore, they can group genes into different categories for identifying related diagnostic pathways and regulatory networks. The difference between these two method lies in ICA assume statistical independence of the expression modes, while NMF need positivity constrains to generate localized gene expression profiles.</p> <p>Results</p> <p>In our work, we performed FastICA and non-smooth NMF methods on DNA microarray gene expression data of Alzheimer’s disease respectively. The simulation results shows that both of the methods can clearly classify severe AD samples from control samples, and the biological analysis of the identified significant genes and their related pathways demonstrated that these genes play a prominent role in AD and relate the activation patterns to AD phenotypes. It is validated that the combination of these two methods is efficient.</p> <p>Conclusions</p> <p>Unsupervised matrix factorization methods provide efficient tools to analyze high-throughput microarray dataset. According to the facts that different unsupervised approaches explore correlations in the high-dimensional data space and identify relevant subspace base on different hypotheses, integrating these methods to explore the underlying biological information from microarray dataset is an efficient approach. By combining the significant genes identified by both ICA and NMF, the biological analysis shows great efficient for elucidating the molecular taxonomy of Alzheimer’s disease and enable better experimental design to further identify potential pathways and therapeutic targets of AD.</p