Mesh-Free Hydrodynamic Stability

A specialized mesh-free radial basis function-based finite difference (RBF-FD) discretization is used to solve the large eigenvalue problems arising in hydrodynamic stability analyses of flows in complex domains. Polyharmonic spline functions with polynomial augmentation (PHS+poly) are used to construct the discrete linearized incompressible and compressible Navier-Stokes operators on scattered nodes. Rigorous global and local eigenvalue stability studies of these global operators and their constituent RBF stencils provide a set of parameters that guarantee stability while balancing accuracy and computational efficiency. Specialized elliptical stencils to compute boundary-normal derivatives are introduced and the treatment of the pole singularity in cylindrical coordinates is discussed. The numerical framework is demonstrated and validated on a number of hydrodynamic stability methods ranging from classical linear theory of laminar flows to state-of-the-art non-modal approaches that are applicable to turbulent mean flows. The examples include linear stability, resolvent, and wavemaker analyses of cylinder flow at Reynolds numbers ranging from 47 to 180, and resolvent and wavemaker analyses of the self-similar flat-plate boundary layer at a Reynolds number as well as the turbulent mean of a high-Reynolds-number transonic jet at Mach number 0.9. All previously-known results are found in close agreement with the literature. Finally, the resolvent-based wavemaker analyses of the Blasius boundary layer and turbulent jet flows offer new physical insight into the modal and non-modal growth in these flows

High-throughput, Efficient, and Unbiased Capture of Small RNAs from Low-input Samples for Sequencing.

MicroRNAs hold great promise as biomarkers of disease. However, there are few efficient and robust methods for measuring microRNAs from low input samples. Here, we develop a high-throughput sequencing protocol that efficiently captures small RNAs while minimizing inherent biases associated with library production. The protocol is based on early barcoding such that all downstream manipulations can be performed on a pool of many samples thereby reducing reagent usage and workload. We show that the optimization of adapter concentrations along with the addition of nucleotide modifications and random nucleotides increases the efficiency of small RNA capture. We further show, using unique molecular identifiers, that stochastic capture of low input RNA rather than PCR amplification influences the biased quantitation of intermediately and lowly expressed microRNAs. Our improved method allows the processing of tens to hundreds of samples simultaneously while retaining high efficiency quantitation of microRNAs in low input samples from tissues or bodily fluids

Suppression of Exosomal PD-L1 Induces Systemic Anti-tumor Immunity and Memory.

PD-L1 on the surface of tumor cells binds its receptor PD-1 on effector T cells, thereby suppressing their activity. Antibody blockade of PD-L1 can activate an anti-tumor immune response leading to durable remissions in a subset of cancer patients. Here, we describe an alternative mechanism of PD-L1 activity involving its secretion in tumor-derived exosomes. Removal of exosomal PD-L1 inhibits tumor growth, even in models resistant to anti-PD-L1 antibodies. Exosomal PD-L1 from the tumor suppresses T cell activation in the draining lymph node. Systemically introduced exosomal PD-L1 rescues growth of tumors unable to secrete their own. Exposure to exosomal PD-L1-deficient tumor cells suppresses growth of wild-type tumor cells injected at a distant site, simultaneously or months later. Anti-PD-L1 antibodies work additively, not redundantly, with exosomal PD-L1 blockade to suppress tumor growth. Together, these findings show that exosomal PD-L1 represents an unexplored therapeutic target, which could overcome resistance to current antibody approaches

Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation

Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for highly subjective tasks, such as large-mask image inpainting or style transfer. On the other hand, GAN-based diverse image generative methods require retraining/fine-tuning the network or designing complex noise injection functions, which is computationally expensive, task-specific, or struggle to generate high-quality results. Given that many deterministic conditional image generative models have been able to produce high-quality yet fixed results, we raise an intriguing question: is it possible for pre-trained deterministic conditional image generative models to generate diverse results without changing network structures or parameters? To answer this question, we re-examine the conditional image generation tasks from the perspective of adversarial attack and propose a simple and efficient plug-in projected gradient descent (PGD) like method for diverse and controllable image generation. The key idea is attacking the pre-trained deterministic generative models by adding a micro perturbation to the input condition. In this way, diverse results can be generated without any adjustment of network structures or fine-tuning of the pre-trained models. In addition, we can also control the diverse results to be generated by specifying the attack direction according to a reference text or image. Our work opens the door to applying adversarial attack to low-level vision tasks, and experiments on various conditional image generation tasks demonstrate the effectiveness and superiority of the proposed method.Comment: 9 pages, 7 figures, accepted by AAAI2

PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style Mapping

3D scene stylization refers to transform the appearance of a 3D scene to match a given style image, ensuring that images rendered from different viewpoints exhibit the same style as the given style image, while maintaining the 3D consistency of the stylized scene. Several existing methods have obtained impressive results in stylizing 3D scenes. However, the models proposed by these methods need to be re-trained when applied to a new scene. In other words, their models are coupled with a specific scene and cannot adapt to arbitrary other scenes. To address this issue, we propose a novel 3D scene stylization framework to transfer an arbitrary style to an arbitrary scene, without any style-related or scene-related re-training. Concretely, we first map the appearance of the 3D scene into a 2D style pattern space, which realizes complete disentanglement of the geometry and appearance of the 3D scene and makes our model be generalized to arbitrary 3D scenes. Then we stylize the appearance of the 3D scene in the 2D style pattern space via a prompt-based 2D stylization algorithm. Experimental results demonstrate that our proposed framework is superior to SOTA methods in both visual quality and generalization.Comment: Accepted to AAAI 202

CONSTITUTIONAL AND INSTITUTIONAL STRUCTURAL DETERMINANTS OF POLICY RESPONSIVENESS TO PROTECT CITIZENS FROM EXISTENTIAL THREATS: COVID-19 AND BEYOND

A multitude of government forms and institutional variations have the same aims of serving their countries and citizens but vary in outcomes. What it means to best serve the citizens is, however, a matter of broad interpretation and so the disagreements persist. The ongoing COVID-19 pandemic creates new metrics for comparing government performance – the metrics of human deaths, or, alternatively and as we pursue it here, the metrics of the speed of government response in preventing human deaths through policy adoption. We argue in this essay that institutional and government systems with more authority redundancies are more likely to rapidly generate policy in response to crisis and find better policy solutions compared to centralized systems with minimal authority redundancies. This is due to a multiplicity of access points to policy making, which increase the chances of a policymaker crafting the “correct” response to crisis, which can be replicated elsewhere. Furthermore, citizens in centralized and unitary governments must rely on national policymakers to get the correct response as subnational policymakers are highly constrained compared to their counterparts in decentralized systems. As policy authority is institutionally defined, these policy authority redundancies correspond to specific institutional and constitutional forms. In this paper, we provide a mathematical/formal model where we specifically analyze the contrast in the speed of policy response between more centralized and autocratic states versus democratic federations

In vivo CRISPR screens identify a dual function of MEN1 in regulating tumor–microenvironment interactions

Functional genomic screens in two-dimensional cell culture models are limited in identifying therapeutic targets that influence the tumor microenvironment. By comparing targeted CRISPR–Cas9 screens in a two-dimensional culture with xenografts derived from the same cell line, we identified MEN1 as the top hit that confers differential dropout effects in vitro and in vivo. MEN1 knockout in multiple solid cancer types does not impact cell proliferation in vitro but significantly promotes or inhibits tumor growth in immunodeficient or immunocompetent mice, respectively. Mechanistically, MEN1 knockout redistributes MLL1 chromatin occupancy, increasing H3K4me3 at repetitive genomic regions, activating double-stranded RNA expression and increasing neutrophil and CD8+ T cell infiltration in immunodeficient and immunocompetent mice, respectively. Pharmacological inhibition of the menin–MLL interaction reduces tumor growth in a CD8+ T cell-dependent manner. These findings reveal tumor microenvironment-dependent oncogenic and tumor-suppressive functions of MEN1 and provide a rationale for targeting MEN1 in solid cancers

Generalized Contour Dynamics: A Review

Contour dynamics is a computational technique to solve for the motion of vortices in incompressible inviscid flow. It is a Lagrangian technique in which the motion of contours is followed, and the velocity field moving the contours can be computed as integrals along the contours. Its best-known examples are in two dimensions, for which the vorticity between contours is taken to be constant and the vortices are vortex patches, and in axisymmetric flow for which the vorticity varies linearly with distance from the axis of symmetry. This review discusses generalizations that incorporate additional physics, in particular, buoyancy effects and magnetic fields, that take specific forms inside the vortices and preserve the contour dynamics structure. The extra physics can lead to time-dependent vortex sheets on the boundaries, whose evolution must be computed as part of the problem. The non-Boussinesq case, in which density differences can be important, leads to a coupled system for the evolution of both mean interfacial velocity and vortex sheet strength. Helical geometry is also discussed, in which two quantities are materially conserved and whose evolution governs the flow