Two-Dimensional Supramolecular Polymerization of a Bis-Urea Macrocycle into a Brick-Like Hydrogen-Bonded Network

We report on a dendronized bis-urea macrocycle 1 self-assembling via a cooperative mechanism into two-dimensional (2D) nanosheets formed solely by alternated urea-urea hydrogen bonding interactions. The pure macrocycle self-assembles in bulk into one-dimensional liquid-crystalline columnar phases. In contrast, its self-assembly mode drastically changes in CHCl₃ or tetrachloroethane, leading to 2D hydrogen-bonded networks. Theoretical calculations, complemented by previously reported crystalline structures, indicate that the 2D assembly is formed by a brick-like hydrogen bonding pattern between bis-urea macrocycles. This assembly is promoted by the swelling of the trisdodecyloxyphenyl groups upon solvation, which frustrates, due to steric effects, the formation of the thermodynamically more stable columnar macrocycle stacks. This work proposes a new design strategy to access 2D supramolecular polymers by means of a single non-covalent interaction motif, which is of great interest for materials development.     

Stereochemical Studies of the Karlotoxin Class Using NMR Spectroscopy and DP4 Chemical‐Shift Analysis: Insights into their Mechanism of Action

After publication of karlotoxin 2 (KmTx2; 1 ), the harmful algal bloom dinoflagellate Karlodinium sp. was collected and scrutinized to identify additional biologically active complex polyketides. The structure of 1 was validated and revised at C49 using computational NMR tools including J‐based configurational analysis and chemical‐shift calculations. The characterization of two new compounds [KmTx8 ( 2 ) and KmTx9 ( 3 )] was achieved through overlaid 2D HSQC NMR techniques, while the relative configurations were determined by comparison to 1 and computational chemical‐shift calculations. The detailed evaluation of 2 using the NCI‐60 cell lines, NMR binding studies, and an assessment of the literature supports a mode of action (MoA) for targeting cancer‐cell membranes, especially of cytostatic tumors. This MoA is uniquely different from that of current agents employed in the control of cancers for which 2 shows sensitivity.     

A study of ignition and combustion of liquid hydrocarbon droplets in premixed fuel/air mixtures in a rapid compression machine

The combustion of two fuels with disparate reactivity such as natural gas and diesel in internal combustion engines has been demonstrated as a means to increase efficiency, reduce fuel costs and reduce pollutant formation in comparison to traditional diesel or spark-ignited engines. However, dual fuel engines are constrained by the onset of uncontrolled fast combustion (i.e., engine knock) as well as incomplete combustion, which can result in high unburned hydrocarbon emissions. To study the fundamental combustion processes of ignition and flame propagation in dual fuel engines, a new method has been developed to inject single isolated liquid hydrocarbon droplets into premixed methane/air mixtures at elevated temperatures and pressures. An opposed-piston rapid compression machine was used in combination with a newly developed piezoelectric droplet injection system that is capable of injecting single liquid hydrocarbon droplets along the stagnation plane of the combustion chamber. A high-speed Schlieren optical system was used for imaging the combustion process in the chamber. Experiments were conducted by injecting diesel droplet of various diameters (50 µm < d_o < 400 µm), into methane/air mixtures with varying equivalence ratios (0 < ϕ < 1.2) over a range of compressed temperatures (700 K < T_c < 940 K). Multiple autoignition modes was observed in the vicinity of the liquid droplets, which were followed by transition to propagating premixed flames. A computational model was developed with CONVERGE™, which uses a 141 species dual-fuel chemical kinetic mechanism for the gas phase along with a transient, analytical droplet evaporation model to define the boundary conditions at the droplet surface. The simulations capture each of the different ignition modes in the vicinity of the injected spherical diesel droplet, along with bifurcation of the ignition event into a propagating, premixed methane/air flame and a stationary diesel/air diffusion flame.     

Comparison of the separation of proteins of wide-ranging molecular weight via trilobal polypropylene capillary-channeled polymer fiber,commercial superficiously porous,and commercial size exclusion columns

Reversed phase and size-exclusion chromatography methods are commonly used for protein separations, although they are based on distinctly different principles. Reversed phase methods yield hydrophobicity-based (loosely-termed) separation of proteins on porous supports, but tend to be limited to proteins with modest molecular weights based on mass transfer limitations. Alternatively, size-exclusion provides complementary benefits in the separation of higher mass proteins based on entropic, not enthalpic, processes, but tend to yield limited peak capacities. In this study, microbore columns packed with a novel trilobal polypropylene capillary-channeled polymer fiber were used in a reversed phase modality for the separation of polypeptides and proteins of molecular weights ranging from 1.4 to 660 kDa. Chromatographic parameters including gradient times, flow rates, and trifluoroacetic acid concentrations in the mobile phase were optimized to maximize resolution and throughput. Following optimization, the performance of the trilobal fiber column was compared to two commercial-sourced columns, a superficially porous C4-derivatized silica and size exclusion, both of which are sold specifically for protein separations and operated according to the manufacturer-specified conditions. In comparison to the commercial columns, the fiber-based column yielded better separation performance across the entirety of the suite, at much lower cost and shorter separation times.     

Structure-based design,synthesis of novel probes for cytochrome P450 OleT

Cytochrome P450 OleT_SA, a new cytochrome P450 enzyme from Staphylococcus aureus, catalyzes the oxidative decarboxylation and hydroxylation of fatty acids to generate terminal alkenes and fatty alcohols. The mechanism of this bifurcative chemistry remains largely unknown. Herein, a class of derivatized fatty acids were synthesized as probes to investigate the effects of substrate structure on the product type of P450 OleT_SA. The results demonstrate that the fine-tuned structure of substrates, even in a remote distance from the carboxyl group, significantly regulates OleT catalyzed decarboxylation/hydroxylation reactions. Molecular docking analysis indicated the potential interactions between the carboxylate groups of different probes and the enzyme active center which was attributed to the bifurcative chemistry.     

RPE65 and the Accumulation of Retinyl Esters in Mouse Retinal Pigment Epithelium

The RPE65 protein of the retinal pigment epithelium (RPE) enables the conversion of retinyl esters to the visual pigment chromophore 11‐cis retinal. Fresh 11‐cis retinal is generated from retinyl esters following photoisomerization of the visual pigment chromophore to all‐trans during light detection. Large amounts of esters accumulate in Rpe65^?/? mice, indicating their continuous formation when 11‐cis retinal generation is blocked. We hypothesized that absence of light, by limiting the conversion of esters to 11‐cis retinal, would also result in the build‐up of retinyl esters in the RPE of wild‐type mice. We used HPLC to quantify ester levels in organic extracts of the RPE from wild‐type and Rpe65^?/? mice. Retinyl ester levels in Sv/129 wild‐type mice that were dark adapted for various intervals over a 4‐week period were similar to those in mice raised in cyclic light. In C57BL/6 mice however, which contain less Rpe65 protein, dark adaptation was accompanied by an increase in ester levels compared to cyclic light controls. Retinyl ester levels were much higher in Rpe65^?/? mice compared to wild type and kept increasing with age. The results suggest that the RPE65 role in retinyl ester homeostasis extends beyond enabling the formation of 11‐cis retinal.     

Synthesis and characterization of poly1-hexene/silica nanocomposites

SiO₂ nano particles, with particle size of 12 nm, were first modified by substituting surface OH groups with O-hexyl moiety. Then, poly1-hexene/modified-SiO₂ composites with various nano-SiO₂ weight fractions were prepared by three different methods: in situ, solution, and melt methods and designated as PH-SiO₂/Insitu, PH-SiO₂/Sol and PH-SiO₂/Melt, respectively. PH-SiO₂/Insitu samples showed highly uniform particle dispersion up to 30 wt. % of silica while in PH-SiO₂/Sol and PH-SiO₂/Melt samples agglomeration of the silica nanoparticles occurred for filler contents ≥5 wt. % (i.e. 5, 10, 20 and 30 wt%). In the synthesized composites, the storage modulus significantly increased as high as 20.7 times when compared with neat poly1-hexene. Maximum decomposition temperature (T_max) and char yield at 600 °C increased with increasing silica level. Rheological results showed that G^ʹ> G^ʺ over the frequency range, illustrating the elastic behavior of the composite samples. In fact, samples showed the characteristic of a non-Newtonian fluid with a strong shear thinning effect in which η* increased with increasing filler weight fraction. From the results, it can be expected that modified silica could replace silica nanoparticles in polyolefin nanocomposite reinforcement.     

Environmentally benign green composites based on epoxy resin/bacterial cellulose reinforced glass fiber: Fabrication and mechanical characteristics

Bio-based bacterial cellulose (BC) epoxy composites were manufactured and their mechanical properties were examined. The BC was initially fabricated from Vietnamese nata de coco by means of alkaline pretreatment followed by solvent exchange. The obtained fibers were dispersed in epoxy resin (EP) by both mechanical stirring and ultrasonic techniques. The resulting blend was used as the matrix for glass-fiber (GF) composite fabrication using a prepreg method followed by multiple hot-press-curing steps. The morphology, mechanical characteristics and mode-I interlaminar fracture toughness of the fabricated composites were investigated. With a 0.3-wt% BC content, the mode-I interlaminar fracture toughness for both crack initiation and crack propagation were improved by 128.8% and 1110%, respectively. The fatigue life was dramatically extended by a factor of 12, relative to the unmodified composite. Scanning electron microscopy images revealed that the BC plays a vital role in increasing the interlaminar fracture toughness of a GF/EP composite via the mechanisms of crack reflection, debonding and fiber-bridging.     

Harmonic Besov and Triebel–Lizorkin Spaces on the Ball

Harmonic Besov and Triebel–Lizorkin spaces on the unit ball in \({\mathbb R}^d\) with full range of parameters are introduced and studied. It is shown that these spaces can be identified with respective Besov and Triebel–Lizorkin spaces of distributions on the sphere. Frames consisting of harmonic functions are also developed and frame characterization of the harmonic Besov and Triebel–Lizorkin spaces is established.     

An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data

To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associations between miRNA and disease using machine learning methods. However, the prediction accuracy of supervised machine learning methods is limited by lacking of experimentally-validated uncorrelated miRNA-disease pairs. Without these negative samples, training a highly accurate model is much more difficult. Different from traditional miRNA-disease prediction models using randomly selected unknown samples as negative training samples, we propose an ensemble learning framework to solve this positive-unlabeled (PU) learning problem. The framework incorporates two steps, i.e., a novel semi-supervised Kmeans (SS-Kmeans) to extract reliable negative samples from unknown miRNA-disease pairs and subagging method to generate diverse training sample sets to make full use of those reliable negative samples for ensemble learning. Combined with effective random vector functional link (RVFL) network as prediction model, the proposed framework showed superior prediction accuracy comparing with other popular approaches. A case study on lung and gastric neoplasms further confirms the framework’s efficacy at identifying miRNA disease associations.