The effect of cationic groups on the stability of 19F MRI contrast agents in nanoparticles

Fluorine‐19 (¹⁹F)‐based contrast agents are increasingly used for magnetic resonance imaging. Conjugated to polymers, they provide an excellent quantitative imaging tool to detect the movement of the polymeric nanoparticles in vivo as there is no background signal in tissue. One of the challenges is the decline in signal intensity when the conjugated hydrophobic fluorinated functionalities aggregate. Therefore, a new fluorinated monomer was prepared from l ‐arginine that carries a 2,2,2‐trifluoroethyl functional group for imaging. The resulting monomer, 2,2,2‐trifluoroethylamide l ‐arginine methacrylamide (3FArgMA), was copolymerized with poly(ethylene glycol) methyl ether methacrylate (PEGMEMA), [2‐(2,3,4,6‐tetra‐O‐acetyl‐α‐d ‐mannopyranosyloxy)ethyl methacrylate or 1‐O‐methacryloyl‐2,3:4,5‐di‐O‐isopropylidene‐β‐d ‐fructopyranose, respectively, using poly(methyl methacrylate) macro‐reversible addition–fragmentation chain transfer polymerization agent. The resulting block copolymers, which varied in 3FArgMA content, were self‐assembled into micelles of hydrodynamic diameters from 25 to 60 nm. The permanently positively charged arginine functionality on the 3FArgMA displayed repulsive forces against aggregation enabling high spin–spin relaxation times (T₂) in acidic as well as alkaline solutions. However, the longer poly(ethylene glycol) side functionality in PEGMEMA enabled better steric stabilization (T₂~30 ms) while the short fructose side chain was not enough to maintain high T₂ values, in particular when a higher 3FArgMA content was used. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2019, 57, 1994–2001     

Hierarchically Structured Allotropes of Phosphorus from Data-Driven Exploration

The discovery of materials is increasingly guided by quantum-mechanical crystal-structure prediction, but the structural complexity in bulk and nanoscale materials remains a bottleneck. Here we demonstrate how data-driven approaches can vastly accelerate the search for complex structures, combining a machine-learning (ML) model for the potential-energy surface with efficient, fragment-based searching. We use the characteristic building units observed in Hittorf's and fibrous phosphorus to seed stochastic (“random”) structure searches over hundreds of thousands of runs. Our study identifies a family of hierarchically structured allotropes based on a P8 cage as principal building unit, including one-dimensional (1D) single and double helix structures, nanowires, and two-dimensional (2D) phosphorene allotropes with square-lattice and kagome topologies. These findings yield new insight into the intriguingly diverse structural chemistry of phosphorus, and they provide an example for how ML methods may, in the long run, be expected to accelerate the discovery of hierarchical nanostructures.     

Kernel regression estimation for continuous spatial processes

We investigate here a kernel estimate of the spatial regression function r(x) = E(Y _u | X _u = x), x ∈ ℝ^d, of a stationary multidimensional spatial process { Z _u = (X _u, Y _u), u ∈ ℝ ^N }. The weak and strong consistency of the estimate is shown under sufficient conditions on the mixing coefficients and the bandwidth, when the process is observed over a rectangular domain of ℝ^N. Special attention is paid to achieve optimal and suroptimal strong rates of convergence. It is also shown that this suroptimal rate is preserved by using a suitable spatial sampling scheme.      

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.     

Order series of labelled posets

Order series of labelled posets are multi-analogues of the more familiar order polynomials; the corresponding multi-analogues of the related representation polynomials are calledE-series. These series can be used to describe the effect of composition of labelled posets on their order polynomials, whence their interest for us. We give a reciprocity theorem forE-series, and show that for an unlabelled poset the form of theE-series depends only upon the comparability graph of the poset. We also prove that theE-series of any labelled poset is a rational power series (in many indeterminates) and give an algorithm for computing it which runs in polynomial time when the poset is strictly labelled and of bounded width. Finally, we give an explicit product formula for theE-series of strictly labelled interval posets.This research was supported by the Natural Sciences and Engineering Research Council of Canada under operating grant #0105392.     

Theoretical investigation of EPR and optical spectra of Mo(V) in [Mo6O19][N(C4H9)4]3 salt

The electron paramagnetic resonance (EPR) spectral data (the g factors and hyperfine structure constants) and d–d transition spectra for the tetragonal Mo⁵⁺ centre in [Mo₆O₁₉][N(C₄H₉)₄]₃ salt are theoretically investigated from the complete diagonalization method (CDM) for a 4d¹ ion in tetragonally compressed octahedron. The theoretical results are in good agreement with the experimental data. The dependency of the g factors of the ground state on the R_∥(MoO bond length) has been studied. It is shown that the g factors varied with the R_∥ approximately in a linear way.     

Identification of amyloidogenic peptide sequences using a coarse-grained physicochemical model

Cross-beta amyloid is implicated in over 20 human diseases. Experiments suggest that specific sequence elements within amyloidogenic proteins play a major role in seeding amyloid formation. Identifying these seeding sequences is important for rationalizing the molecular mechanisms of amyloid formation and for elaborating therapeutic strategies that target amyloid. Theoretical techniques play an important role in facilitating the identification and structural characterization of putative seeding sequences; most amyloid species are not amenable to high resolution experimental structure techniques. In this study we have combined a coarse-grained physicochemical protein model with a highly efficient Monte Carlo sampling technique to identify amyloidogenic sequences in four proteins for which respective experimental peptide fragmentation data exist. Peptide sequences were defined as amyloidogenic if the ensemble structure predicted for three interacting peptides described a stable and regular three-stranded beta-sheet. For such peptides, free energies were calculated to provide a measure of amyloid propensity. The overall agreement between the experimental and predicted data is good, and we correctly identify several self-recognition motifs proposed to define the cross-beta amyloid fibril architectures of two of the proteins. Our results compare very favorably with those obtained using atomistic molecular dynamics methods, though our simulations are 30-40 times faster.     

Recent progress in separation prediction of counter‐current chromatography

As a liquid‐liquid partition chromatography, counter‐current chromatography has advantages in large sample loading capacity without irreversible adsorption, which has been widely applied in separation and purification fields. The main factors, including partition coefficient, two‐phase solvent systems, apparatus, and operating parameters greatly affect the separation process of counter‐current chromatography. To promote the applications of counter‐current chromatography, it is essential to develop theoretical research to master the principles of counter‐current chromatographic separations so as to achieve predictions before laborious trials. In this article, recent progress about separation prediction methods are reviewed from a point of the steady and unsteady state of the mass transfer process of counter‐current chromatography and its mass transfer characteristics, and then it is divided into three aspects: prediction of partition coefficient, modeling the thermodynamic process of counter‐current chromatography, and modeling the dynamic process of counter‐current chromatography.