Altering peptide fibrillization by polymer conjugation

A strategy is presented that exploits the ability of synthetic polymers of different nature to disturb the strong selfassembly capabilities of amyloid based β-sheet forming peptides. Following a convergent approach, the peptides of interest were synthesized via solid-phase peptide synthesis (SPPS) and the polymers via reversible addition−fragmentation chain transfer (RAFT) polymerization, followed by a copper(I) catalyzed azide− alkyne cycloaddition (CuAAC) to generate the desired peptide− polymer conjugates. This study focuses on a modified version of the core sequence of the β-amyloid peptide (Aβ), Aβ(16−20) (KLVFF). The influence of attaching short poly(Nisopropylacrylamide) and poly(hydroxyethylacrylate) to the peptide sequences on the self-assembly properties of the hybrid materials were studied via infrared spectroscopy, TEM, circular dichroism and SAXS. The findings indicate that attaching these polymers disturbs the strong self-assembly properties of the biomolecules to a certain degree and permits to influence the aggregation of the peptides based on their β-sheets forming abilities. This study presents an innovative route toward targeted and controlled assembly of amyloid-like fibers to drive the formation of polymeric nanomaterials.

A thermoresponsive supramolecular polymer network comprising pyrene-functionalized gold nano-particles and a chain-folding polydiimide.

A thermoresponsive, supramolecular nanocomposite has been prepared by the addition of pyrenyl functionalized gold nanoparticles (AuNPs) to a polydiimide that contains receptor residues designed to form defined complexes with pyrene. The novel pyrenyl-functionalized AuNPs (P-AuNPs) were characterized by transmission electron microscopy, with surface functionalization confirmed by infrared and UV–visible spectroscopic analyses. Mixing solutions of the P-AuNPs and a π-electron-deficient polydiimide resulted in the formation of electronically complementary, chain-folded and π–π-stacked complexes, so affording a new supramolecular nanocomposite network which precipitated from solution. The P-AuNPs bind to the polydiimide via π–π stacking interactions to create supramolecular cross-links. UV–visible spectroscopic analysis confirmed the thermally reversible nature of the complexation process, and transmission electron microscopy (TEM), infrared spectroscopy (IR), and differential scanning calorimetry (DSC) were used to characterize the supramolecular-nanocomposite material. The supramolecular polymer network is insoluble at room temperature, yet may be dissolved at temperatures above 60 °C. The thermal reversibility of this system is maintained over five heat/cool cycles without diminishment of the network characteristics. In contrast to the individual components, the nanocomposite formed self-supporting films, demonstrating the benefit of the supramolecular network in terms of mechanical properties. Control experiments probing the interactions between a model diimide compound that can also form a π-stacked complex with the π-electron rich pyrene units on P-AuNPs showed that, while complexation was readily apparent, precipitation did not occur because a supramolecular cross-linked network system could not be formed with this system.

Cloud droplet size and liquid water path retrievals from zenith radiance measurements: examples from the Atmospheric Radiation Measurement Program and the Aerosol Robotic Network

The ground-based Atmospheric Radiation Measurement Program (ARM) and NASA Aerosol Robotic Net- work (AERONET) routinely monitor clouds using zenith ra- diances at visible and near-infrared wavelengths. Using the transmittance calculated from such measurements, we have developed a new retrieval method for cloud effective droplet size and conducted extensive tests for non-precipitating liquid water clouds. The underlying principle is to combine a liquid-water-absorbing wavelength (i.e., 1640 nm) with a non-water-absorbing wavelength for acquiring information on cloud droplet size and optical depth. For simulated stratocumulus clouds with liquid water path less than 300 g m−2 and horizontal resolution of 201 m, the retrieval method underestimates the mean effective radius by 0.8μm, with a root-mean-squared error of 1.7 μm and a relative deviation of 13%. For actual observations with a liquid water path less than 450 g m−2 at the ARM Oklahoma site during 2007– 2008, our 1.5-min-averaged retrievals are generally larger by around 1 μm than those from combined ground-based cloud radar and microwave radiometer at a 5-min temporal resolution. We also compared our retrievals to those from combined shortwave flux and microwave observations for relatively homogeneous clouds, showing that the bias between these two retrieval sets is negligible, but the error of 2.6 μm and the relative deviation of 22 % are larger than those found in our simulation case. Finally, the transmittance-based cloud effective droplet radii agree to better than 11 % with satellite observations and have a negative bias of 1 μm. Overall, the retrieval method provides reasonable cloud effective radius estimates, which can enhance the cloud products of both ARM and AERONET.

An effective approach for land-cover classification from airborne lidar fused with co-registered data

Airborne lidar provides accurate height information of objects on the earth and has been recognized as a reliable and accurate surveying tool in many applications. In particular, lidar data offer vital and significant features for urban land-cover classification, which is an important task in urban land-use studies. In this article, we present an effective approach in which lidar data fused with its co-registered images (i.e. aerial colour images containing red, green and blue (RGB) bands and near-infrared (NIR) images) and other derived features are used effectively for accurate urban land-cover classification. The proposed approach begins with an initial classification performed by the Dempster–Shafer theory of evidence with a specifically designed basic probability assignment function. It outputs two results, i.e. the initial classification and pseudo-training samples, which are selected automatically according to the combined probability masses. Second, a support vector machine (SVM)-based probability estimator is adopted to compute the class conditional probability (CCP) for each pixel from the pseudo-training samples. Finally, a Markov random field (MRF) model is established to combine spatial contextual information into the classification. In this stage, the initial classification result and the CCP are exploited. An efficient belief propagation (EBP) algorithm is developed to search for the global minimum-energy solution for the maximum a posteriori (MAP)-MRF framework in which three techniques are developed to speed up the standard belief propagation (BP) algorithm. Lidar and its co-registered data acquired by Toposys Falcon II are used in performance tests. The experimental results prove that fusing the height data and optical images is particularly suited for urban land-cover classification. There is no training sample needed in the proposed approach, and the computational cost is relatively low. An average classification accuracy of 93.63% is achieved.

Generalised Bayesian cloud detection for satellite imagery. part 2: technique and validation for day-time imagery

Numerical Weather Prediction (NWP) fields are used to assist the detection of cloud in satellite imagery. Simulated observations based on NWP are used within a framework based on Bayes' theorem to calculate a physically-based probability of each pixel with an imaged scene being clear or cloudy. Different thresholds can be set on the probabilities to create application-specific cloud masks. Here, the technique is shown to be suitable for daytime applications over land and sea, using visible and near-infrared imagery, in addition to thermal infrared. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 89% and 73% for ocean and land, respectively using the Bayesian technique, compared to 90% and 70%, respectively for the threshold-based techniques associated with the validation dataset.