Bacteria-Responsive Self-Assembly of Antimicrobial Peptide Nanonets for Trap-and-Kill of Antibiotic-Resistant Strains

Bacterial trapping using nanonets is a ubiquitous immune defense mechanism against infectious microbes. These nanonets can entrap microbial cells, effectively arresting their dissemination and rendering them more vulnerable to locally secreted microbicides. Inspired by this evolutionarily conserved anti-infective strategy, a series of 15 to 16 residue-long synthetic β-hairpin peptides is herein constructed with the ability to self-assemble into nanonets in response to the presence of bacteria, enabling spatiotemporal control over microbial killing. Using amyloid-specific K114 assay and confocal microscopy, the membrane components lipoteichoic acid and lipopolysaccharide are shown to play a major role in determining the amyloid-nucleating capacity as triggered by Gram-positive and Gram-negative bacteria respectively. These nanonets displayed both trapping and killing functionalities, hence offering a direct improvement from the trap-only biomimetics in literature. By substituting a single turn residue of the non-amyloidogenic BTT1 peptide, the nanonet-forming BTT1-3A analog is produced with comparable antimicrobial potency. With the same sequence manipulation approach, BTT2-4A analog modified from BTT2 peptide showed improved antimicrobial potency against colistin-resistant clinical isolates. The peptide nanonets also demonstrated robust stability against proteolytic degradation, and promising in vivo efficacy and biosafety profile. Overall, these bacteria-responsive peptide nanonets are promising clinical anti-infective alternatives for circumventing antibiotic resistance.     

Commodified science and social wellbeing

This paper explores the increasing trend towards the commodification of public research and development (R&D) and the impact of this on social wellbeing. In many developed countries, the changes introduced by governments to funding mechanisms for universities and public research institutions has led to a fundamental shift in the focus of public R&D. The focus has shifted from creating useful public, codifiable knowledge to creating a knowledge commodity driven by commercial imperatives. Although there may be an economic argument to be made for the virtues of such change, we argue here that the potential costs to social wellbeing have been largely, and dangerously, ignored.

A new SiO/C anode composition for lithium-ion battery

A new anode composition comprising SiO and graphite(C) is prepared through a high-energy ball milling process. During the first cycle, the anode delivers high discharge and charge capacity values of 1556 and 693 mAh g⁻¹, respectively. The electrode shows a reversible charge capacity value of 688 mAh g⁻¹ at the 30th cycle with 99% Coulombic efficiency. X-ray diffraction analysis reveals that ball milling does not produce any new compound, but only causes a reduction in particle size. The irreversible and reversible capacities appear to be interdependent.     

Characterizing temporal development of biofilm porosity using artificial neural networks

We used artificial neural networks (ANN) to compute parameters characterising biofilm structure from biofilm images and to interpolate a limited number of experimental data characterising the effects of nutrient concentration and flow velocity on the areal porosity of biofilms. ANN were trained using a set of experimental data characterising structural parameters of biofilms of Pseudomonas aeruginosa (ATCC #700829), Pseudomonas fluorescens (ATCC #700830) and Klebsiella pneumoniae (ATCC #700831) for various flow velocities and glucose concentrations. We used 80% of the data to train ANN and 10% of the data to validate the results, which is routinely carried out as a countermeasure against overtraining. Trained ANN were used to interpolate into the data set and evaluate the missing 10% of the data. To compare ANN accuracy in evaluating the missing data with the accuracies achieved using other interpolation algorithms, we used spline, cubic, linear and nearest-neighbour interpolation algorithms to evaluate the missing data. ANN estimates were consistently closer to the experimental data than the estimates made using the other methods.     

The Role of Autophagy in Chemical Proteasome Inhibition Model of Retinal Degeneration

We recently demonstrated that chemical proteasome inhibition induced inner retinal degeneration, supporting the pivotal roles of the ubiquitin–proteasome system in retinal structural integrity maintenance. In this study, using beclin1-heterozygous (Becn1-Het) mice with autophagic dysfunction, we tested our hypothesis that autophagy could be a compensatory retinal protective mechanism for proteasomal impairment. Despite the reduced number of autophagosome, the ocular tissue morphology and intraocular pressure were normal. Surprisingly, Becn1-Het mice experienced the same extent of retinal degeneration as was observed in wild-type mice, following an intravitreal injection of a chemical proteasome inhibitor. Similarly, these mice equally responded to other chemical insults, including endoplasmic reticulum stress inducer, N-methyl-D-aspartate, and lipopolysaccharide. Interestingly, in cultured neuroblastoma cells, we found that the mammalian target of rapamycin-independent autophagy activators, lithium chloride and rilmenidine, rescued these cells against proteasome inhibition-induced death. These results suggest that Becn1-mediated autophagy is not an effective intrinsic protective mechanism for retinal damage induced by insults, including impaired proteasomal activity; furthermore, autophagic activation beyond normal levels is required to alleviate the cytotoxic effect of proteasomal inhibition. Further studies are underway to delineate the precise roles of different forms of autophagy, and investigate the effects of their activation in rescuing retinal neurons under various pathological conditions.     

Improvement of cycle behaviour of SiO/C anode composite by thermochemically generated Li4SiO4 inert phase for lithium batteries

A new anode composite material is prepared by thermal treatment of a blend made of silicon monoxide (SiO) and lithium hydroxide (LiOH) at 550 °C followed by ball milling with graphite. X-ray diffraction pattern confirms the presence of Li₄SiO₄ in the thermally treated (SiO + LiOH) material. The electrode appears to be smooth and glassy as evident from observation with a scanning electron microscope (SEM), possibly due to the presence of nano-silicon and Li₄SiO₄ particles, and exhibits superior performance with a charge capacity of ∼333 mAh g⁻¹ at the 100th cycle with a low-capacity fade on cycling. Cyclic voltammograms of the electrode predict high power capability. On the other hand, the electrode comprising of only SiO and C prepared through ball milling, devoid of Li₄SiO_4, shows hard crust particulates in the electrode exhibiting low charge–discharge capacities with cycling.     

Cyclic voltammetric study of zinc and zinc oxide electrodes in 5.3 M KOH

The behaviour of zinc and zinc oxide in 5.3 M KOH in the presence of alkaline earth oxides, SnO, Ni(OH)₂ and Co(OH)₂ was examined by cyclic voltammetry. The influence of the alkaline earth oxides was compared with additives of established effects (Bi₂O₃, LiOH, Na₂CO₃ and CdO). The alkaline earth oxide each exhibits a distinct behaviour towards zincate. Whereas, a single process of interaction with zincate was shown by CaO; two modes of reaction were obtained with SrO and BaO. Solid solution formation was noticed with BeO and MgO. The other additives forming solid solution with ZnO were CdO, SnO. The ionic sizes of Ni(OH)₂ and Co(OH)₂ allow solid solution formation with Zn(OH)₂. Both Bi₂O₃ and Na₂CO₃ enter into complexation with zincate. LiOH forms two distinct zincates, of which one is an oxo zincate leaching the `hydroxyl' functionality. Cyclic voltammetry revealed the deposition of the oxide/hydroxide additives as metal prior to the onset of zinc deposition and the potential range for this additive metal deposition is almost the same for different additives (SnO, CdO, Ni(OH)₂). The beneficial action of these additives to zinc alkaline cells is associated with a substrate effect. The implication of this electrocatalytic deposition of metals on a zinc oxide electrode is also discussed.     

Procedure for determining maximum sustainable power generated by microbial fuel cells

Power generated by microbial fuel cells is computed as a product of current passing through an external resistor and voltage drop across this resistor. If the applied resistance is very low, then high instantaneous power generated by the cell is measured, which is not sustainable; the cell cannot deliver that much power for long periods of time. Since using small electrical resistors leads to erroneous assessment of the capabilities of microbial fuel cells, a question arises: what resistor should be used in such measurements? To address this question, we have defined the sustainable power as the steady state of power delivery by a microbial fuel cell under a given set of conditions and the maximum sustainable power as the highest sustainable power that a microbial fuel cell can deliver under a given set of conditions. Selecting the external resistance that is associated with the maximum sustainable power in a microbial fuel cell (MFC) is difficult because the operator has limited influence on the main factors that control power generation: the rate of charge transfer at the current-limiting electrode and the potential established across the fuel cell. The internal electrical resistance of microbial fuel cells varies, and it depends on the operational conditions of the fuel cell. We have designed an empirical procedure to predict the maximum sustainable power that can be generated by a microbial fuel cell operated under a given set of conditions. Following the procedure, we change the external resistors incrementally, in steps of 500 omega every 10, 60, or 180 s and measure the anode potential, the cathode potential, and the cell current. Power generated in the microbial fuel cell that we were using was limited by the anodic current. The anodic potential was used to determine the condition where the maximum sustainable power is obtained. The procedure is simple, microbial fuel cells can be characterized within an hour, and the results of the measurements can serve many purposes, such as: (1) estimating power generation in various MFCs, (2) comparing power generation in MFCs using different electroactive reactants, (3) quantifying the effects of the operational regime on the power generation in MFCs, and finally, (4) the purpose for which the procedure was designed, optimizing the performance of existing MFCs.     

Thermal and electrochemical behaviour of C/LixCoO2 cell during safety test

Thermal and electrochemical processes in a 1000 mAh lithium-ion pouch cell with a graphite anode and a Li_xCoO₂ cathode during a safety test are examined. In overcharge tests, the forced current shifts the cell voltage to above 4.2 V. This causes a cell charged at the 1 C rate to lose cycleability and a cell charged at the 3 C rate to undergo explosion. In nail penetration and impact tests, a high discharge current passing through the cells gives rise to thermal runaway. These overcharge and high discharge currents promote joule heat within the cells and leads to decomposition and release of oxygen from the de-lithiated Li_xCoO₂ and combustion of carbonaceous materials. X-ray diffraction analysis reveals the presence of Co₃O₄ in the cathode material of a 4.5 V cell heated to 400 °C. The major cathode product formed after the combustion process cells abused by forced current is Co₃O₄ and by discharge current the products are LiCoO₂ and Co₃O₄. The formation of a trace quantity of CoO through the reduction of Co₃O₄ by virtue of the reducing power of the organic solvent is also discussed.     

MRI brain image enhancement using brightness preserving adaptive fuzzy histogram equalization

In this article, fuzzy logic based adaptive histogram equalization (AHE) is proposed to enhance the contrast of MRI brain image. Medical image plays an important role in monitoring patient's health condition and giving an effective diagnostic. Mostly, medical images suffer from different problems such as poor contrast and noise. So it is necessary to enhance the contrast and to remove the noise in order to improve the quality of a various medical images such as CT, X‐ray, MRI, and MAMOGRAM images. Fuzzy logic is a useful tool for handling the ambiguity or uncertainty. Brightness Preserving Adaptive Fuzzy Histogram Equalization technique is proposed to improve the contrast of MRI brain images by preserving brightness. Proposed method comprises of two stages. First, fuzzy logic is applied to an input image and then it's output is given to AHE technique. This process not only preserves the mean brightness and but also improves the contrast of an image. A huge number of highly MRI brain images are taken in the proposed method. Performance of the proposed method is compared with existing methods using the parameters namely entropy, feature similarity index, and contrast improvement index and the experimental results show that the proposed method overwhelms the previous existing methods.