Polymerization-Induced Polymersome Fusion

The dynamic interactions of membranes, particularly their fusion and fission, are critical for the transmission of chemical information between cells. Fusion is primarily driven by membrane tension built up through membrane deformation. For artificial polymersomes, fusion is commonly induced via the external application of a force field. Herein, fusion-promoted development of anisotropic tubular polymersomes (tubesomes) was achieved in the absence of an external force by exploiting the unique features of aqueous ring-opening metathesis polymerization-induced self-assembly (ROMPISA). The out-of-equilibrium tubesome morphology was found to arise spontaneously during polymerization, and the composition of each tubesome sample (purity and length distribution) could be manipulated simply by targeting different core-block degrees of polymerization (DPs). The evolution of tubesomes was shown to occur via fusion of “monomeric” spherical polymersomes, evidenced most notably by a step-growth-like relationship between the fraction of tubular to spherical nano-objects and the average number of fused particles per tubesome (analogous to monomer conversion and DP, respectively). Fusion was also confirmed by Förster resonance energy transfer (FRET) studies to show membrane blending and confocal microscopy imaging to show mixing of the polymersome lumens. We term this unique phenomenon polymerization-induced polymersome fusion, which operates via the buildup of membrane tension exerted by the growing polymer chains. Given the growing body of evidence demonstrating the importance of nanoparticle shape on biological activity, our methodology provides a facile route to reproducibly obtain samples containing mixtures of spherical and tubular polymersomes, or pure samples of tubesomes, of programmed length. Moreover, the capability to mix the interior aqueous compartments of polymersomes during polymerization-induced fusion also presents opportunities for its application in catalysis, small molecule trafficking, and drug delivery

A comparison of visual and haltere-mediated equilibrium reflexes in the fruit fly Drosophila melanogaster

Flies exhibit extraordinary maneuverability, relying on feedback from multiple sensory organs to control flight. Both the compound eyes and the mechanosensory halteres encode angular motion as the fly rotates about the three body axes during flight. Since these two sensory modalities differ in their mechanisms of transduction, they are likely to differ in their temporal responses. We recorded changes in stroke kinematics in response to mechanical and visual rotations delivered within a flight simulator. Our results show that the visual system is tuned to relatively slow rotation whereas the haltere-mediated response to mechanical rotation increases with rising angular velocity. The integration of feedback from these two modalities may enhance aerodynamic performance by enabling the fly to sense a wide range of angular velocities during flight

L\'{e}vy scaling: the Diffusion Entropy Analysis applied to DNA sequences

We address the problem of the statistical analysis of a time series generated by complex dynamics with a new method: the Diffusion Entropy Analysis (DEA) (Fractals, {\bf 9}, 193 (2001)). This method is based on the evaluation of the Shannon entropy of the diffusion process generated by the time series imagined as a physical source of fluctuations, rather than on the measurement of the variance of this diffusion process, as done with the traditional methods. We compare the DEA to the traditional methods of scaling detection and we prove that the DEA is the only method that always yields the correct scaling value, if the scaling condition applies. Furthermore, DEA detects the real scaling of a time series without requiring any form of de-trending. We show that the joint use of DEA and variance method allows to assess whether a time series is characterized by L\'{e}vy or Gauss statistics. We apply the DEA to the study of DNA sequences, and we prove that their large-time scales are characterized by L\'{e}vy statistics, regardless of whether they are coding or non-coding sequences. We show that the DEA is a reliable technique and, at the same time, we use it to confirm the validity of the dynamic approach to the DNA sequences, proposed in earlier work.Comment: 24 pages, 9 figure

Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.Comment: 39 pages, 10 figure

Towards Synthetic Life: Establishing a Minimal Segrosome for the Rational Design of Biomimetic Systems

DNA segregation is a fundamental life process, crucial for renewal, reproduction and propagation of all forms of life. Hence, a dedicated segregation machinery, a segrosome, must function reliably also in the context of a minimal cell. Conceptionally, the development of such a minimal cell follows a minimalistic approach, aiming at engineering a synthetic entity only consisting of the essential key elements necessary for a cell to survive. In this thesis, various prokaryotic segregation systems were explored as possible candidates for a minimal segrosome. Such a minimal segrosome could be applied for the rational design of biomimetic systems including, but not limited to, a minimal cell. DNA segregation systems of type I (ParABS) and type II (ParMRC) were compared for ensuring genetic stabilities in vivo using vectors derived from the natural secondary chromosome of Vibrio cholerae. The type II segregation system R1-ParMRC was chosen as the most promising candidate for a minimal segrosome, and it was characterized and reconstituted in vitro. This segregation system was encapsulated into biomimetic micro-compartments and its lifetime prolonged by coupling to ATP-regenerating as well as oxygen-scavenging systems. The segregation process was coupled to in vitro DNA replication using DNA nanoparticles as a mimic of the condensed state of chromosomes. Furthermore, another type II segregation system originating from the pLS20 plasmid from Bacillus subtilis (Alp7ARC) was reconstituted in vitro as a secondary orthogonal segrosome. Finally, a chimeric RNA segregation system was engineered that could be applied for an RNA-based protocell. Overall, this work demonstrates successful bottom-up assemblies of functional molecular machines that could find applications in biomimetic systems and lead to a deeper understanding of living systems

Giant Dipole Resonance Width in near-Sn Nuclei at Low Temperature and High Angular Momentum

High energy gamma-rays in coincidence with low energy yrast gamma-rays have been measured from 113Sb, at excitation energies of 109 and 122 MeV, formed by bombarding 20Ne on 93Nb at projectile energies of 145 and 160 MeV respectively to study the role of angular momentum (J) and temperature (T) over Giant Dipole Resonance (GDR) width. The maximum populated angular momenta for fusion were 67hbar and 73hbar respectively for the above-mentioned beam energies. The high energy photons were detected using a Large Area Modular BaF2 Detector Array (LAMBDA) along with a 24-element multiplicity filter. After pre-equilibrium corrections, the excitation energy E* was averaged over the decay steps of the compound nucleus (CN). The average values of temperature, angular momentum, CN mass etc. have been calculated by the statistical model code CASCADE. Using those average values, results show the systematic increase of GDR width with T which is consistent with Kusnezov parametrization and the Thermal Shape Fluctuation Model. The rise of GDR width with temperature also supports the assumptions of adiabatic coupling in the Thermal Shape Fluctuation Model. But the GDR widths and corresponding reduced plots with J are not consistent with the theoretical model at high spins.Comment: 19 pages, 10 figures, Submitted to Physics Review