Thermophoresis of synthetic and biological systems

This work focuses on the thermal diffusion behavior of biological and synthetic bio-compatible systems using a holographic grating technique Thermal Diffusion Forced Rayleigh Scattering (TDFRS). Most biological systems are water-based and the thermodiffusion behavior of aqueous systems is less understood than organic systems, which can often be described by empirical correlations. Therefore, our study focuses on the investigation of four different aqueous systems in order to get a deeper microscopic understanding of the thermophoretic behavior. The thesis starts with small bio-molecules nucleotides, the building blocks of nucleic acids. Five nucleotides with systematically varied structures are investigated to make the connections between the thermodiffusion and their physical properties such as acidity, solubility, hydrophobicity and their capability to form hydrogen bonds. We find a correlation between the thermal diffusion coefficient and the ratio of the thermal expansion coefficient and the kinematic viscosity. Besides the capability to form hydrogen bonds, the charge effect is another important parameter that complicates the situation in aqueous systems. In this context aqueous electrolyte solutions are a simple model system, which can be investigated by computer simulations and experimentally. We study alkali halide aqueous solutions (Na+/K+-Cl-) by both non-equilibrium molecular dynamics simulations and TDFRS measurements. We find that the Soret coefficient decreases with increasing concentration at temperatures higher than 315 K, whereas it increases at lower temperatures. In agreement with previous experiments, we find a sign inversion. We use computer simulations as a microscopic approach to establish a correlation between sign and magnitude of the Soret coefficient and ionic solvation and hydrogen bond structure of the solutions. Additionally, the analysis of heat transport in ionic solutions by quantifying the thermal conductivity as a function of concentration is provided. The simulations accurately reproduce the decrease of the thermal conductivity with increasing salt concentration that is observed in experiments. Charged colloids and proteins in aqueous electrolyte solutions are always surrounded by an electric double layer. Their contribution to the thermodiffusion of colloids and proteins have also to be considered. We investigate the charge effect using a charge stabilized rod-like colloid (fd-virus), which can be modeled theoretically. The Soret coefficient of the charged rods is measured as a function of the Debye screening length, as well as the rod-concentration. The Soret coefficient of the rods increases monotonically with increasing Debye length, while there is a relatively weak dependence on the rod-concentration when the ionic strength is kept constant. An existing theory for thermodiffusion of charged spheres is extended to describe the thermodiffusion of long and thin charged rods, leading to an expression for the Soret coefficient in terms of the Debye length, the rod-core dimensions, and the surface charge density. The thermal diffusion coefficient of a charged colloidal rod is shown to be accurately represented, for arbitrary Debye lengths, by the thermal diffusion coefficient of a superposition of spherical beads with the same diameter and the same surface charge density as the colloidal rod. Additionally, a fd-virus grafted with a water soluble polymer polyethylene oxide (PEO) is investigated as a function of ionic strength. For a low ionic strength corresponding to a large Debye length we find the same behavior as in the case of the bare virus, while with increasing ionic strength the Soret coefficient increases, which is the opposite trend compared to the bare fd-virus. PEO is a non-ionic water soluble, bio-compatible polymer with the capability to form hydrogen bonds with water. Only low molar mass ethylene oligomer is soluble in ethanol, while water is a good solvent for the whole molar mass range. To study the solvent quality effect we investigate the thermodiffusion of ethylene oxide oligomers and PEO at different temperatures in water, ethanol and in a water/ethanol mixture with a water content of 70% in a molar mass range from monomer up to M_w=180000 g/mol. The specific water/ethanol concentration has been chosen, because the thermal diffusion coefficient of the water/ ethanol mixture vanishes, so that the system can be treated as a pseudo binary mixture. In pure ethanol, a sign change of the Soret and thermal diffusion coefficient as a function of molar mass from positive to negative is found for a molar mass around 2200 g/mol. In water/ethanol mixtures, PEO of all molar masses accumulate at the warm side, while in pure water they all enrich at the cold side. The interaction energies e_s (solvent-solvent) and e_p (polymer-solvent) are determined from a theoretical model derived by Würger [Phys. Rev. Lett., 102, 078302, 2009]. e_s and e_p are positive under good solvent conditions and negative otherwise

Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization

Global covariance pooling in convolutional neural networks has achieved impressive improvement over the classical first-order pooling. Recent works have shown matrix square root normalization plays a central role in achieving state-of-the-art performance. However, existing methods depend heavily on eigendecomposition (EIG) or singular value decomposition (SVD), suffering from inefficient training due to limited support of EIG and SVD on GPU. Towards addressing this problem, we propose an iterative matrix square root normalization method for fast end-to-end training of global covariance pooling networks. At the core of our method is a meta-layer designed with loop-embedded directed graph structure. The meta-layer consists of three consecutive nonlinear structured layers, which perform pre-normalization, coupled matrix iteration and post-compensation, respectively. Our method is much faster than EIG or SVD based ones, since it involves only matrix multiplications, suitable for parallel implementation on GPU. Moreover, the proposed network with ResNet architecture can converge in much less epochs, further accelerating network training. On large-scale ImageNet, we achieve competitive performance superior to existing counterparts. By finetuning our models pre-trained on ImageNet, we establish state-of-the-art results on three challenging fine-grained benchmarks. The source code and network models will be available at http://www.peihuali.org/iSQRT-COVComment: Accepted to CVPR 201

Dilemma and Breakthrough: Innovation on Models of Public Legal Education in China Based on Knowledge Graph

In over 30 years, the forms of public legal education activities have become increasingly rich. However, with the technology refresh, the traditional public legal education model characterized by one-way communication has gradually become out of touch, which can not adapt to the return of the people’s subjectivity and meet the personalized needs of different groups of people. As an important part of advancing the Rule of Law in China, public legal education should be timely innovated with the help of new technology. By combining the knowledge graph technology in the era of artificial intelligence with the work of public legal education, this paper studies how to use the knowledge graph technology to build public legal education network platform, introduce customized legal education content, and establish a sound mechanism for intelligent public legal education work, so that users can complete the important transformation from the object of legal education to the subject of law learning. This will enrich the theoretical research results of public legal education

Coriolis Force Compensation and Laser Beam Delivery for 100-Meter Baseline Atom Interferometry

The Coriolis force is a significant source of systematic phase errors and dephasing in atom interferometry and is often compensated by counter-rotating the interferometry laser beam against Earth's rotation. We present a novel method for performing Coriolis force compensation for long-baseline atom interferometry which mitigates atom-beam misalignment due to beam rotation, an effect which is magnified by the long lever arm of the baseline length. The method involves adjustment of the angle of the interferometer beam prior to a magnifying telescope, enabling the beam to pivot around a tunable position along the interferometer baseline. By tuning the initial atom kinematics, and adjusting the angle with which the interferometer beam pivots about this point, we can ensure that the atoms align with the center of the beam during the atom optics laser pulses. This approach will be used in the MAGIS-100 atom interferometer and could also be applied to other long-baseline atom interferometers. An additional challenge associated with long baseline interferometry is that since long-baseline atom interferometers are often located outside of typical laboratory environments, facilities constraints may require lasers to be housed in a climate-controlled room a significant distance away from the main experiment. Nonlinear effects in optical fibers restrict the use of fiber-based transport of the high-power interferometry beam from the laser room to the experiment. We present the design of and prototype data from a laser transport system for MAGIS-100 that maintains robustness against alignment drifts despite the absence of a long fiber

Maintaining human fetal pancreatic stellate cell function and proliferation require β1 integrin and collagen I matrix interactions

Pancreatic stellate cells (PaSCs) are cells that are located around the acinar, ductal, and vasculature tissue of the rodent and human pancreas, and are responsible for regulating extracellular matrix (ECM) turnover and maintaining the architecture of pancreatic tissue. This study examines the contributions of integrin receptor signaling in human PaSC function and survival. Human PaSCs were isolated from pancreata collected during the 2nd trimester of pregnancy and identified by expression of stellate cell markers, ECM proteins and associated growth factors. Multiple integrins are found in isolated human PaSCs, with high levels of β1, a3 and a5. Cell adhesion and migration assays demonstrated that human PaSCs favour collagen I matrix, which enhanced PaSC proliferation and increased TGFβ1, CTGF and a3β1 integrin. Significant activation of FAK/ERK and AKT signaling pathways, and up-regulation of cyclin D1 protein levels, were observed within PaSCs cultured on collagen I matrix. Blocking β1 integrin significantly decreased PaSC adhesion, migration and proliferation, further complementing the aforementioned findings. This study demonstrates that interaction of β1 integrin with collagen I is required for the proliferation and function of human fetal PaSCs, which may contribute to the biomedical engineering of the ECM microenvironment needed for the efficient regulation of pancreatic development

Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning

Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms. Notably, employing simple observation transformations alone can yield outstanding performance without extra auxiliary representation tasks or pre-trained encoders. However, it remains unclear which attributes of DA account for its effectiveness in achieving sample-efficient visual RL. To investigate this issue and further explore the potential of DA, this work conducts comprehensive experiments to assess the impact of DA's attributes on its efficacy and provides the following insights and improvements: (1) For individual DA operations, we reveal that both ample spatial diversity and slight hardness are indispensable. Building on this finding, we introduce Random PadResize (Rand PR), a new DA operation that offers abundant spatial diversity with minimal hardness. (2) For multi-type DA fusion schemes, the increased DA hardness and unstable data distribution result in the current fusion schemes being unable to achieve higher sample efficiency than their corresponding individual operations. Taking the non-stationary nature of RL into account, we propose a RL-tailored multi-type DA fusion scheme called Cycling Augmentation (CycAug), which performs periodic cycles of different DA operations to increase type diversity while maintaining data distribution consistency. Extensive evaluations on the DeepMind Control suite and CARLA driving simulator demonstrate that our methods achieve superior sample efficiency compared with the prior state-of-the-art methods.Comment: NeurIPS 2023 poste