Unlocking Low-Light-Rainy Image Restoration by Pairwise Degradation Feature Vector Guidance

Rain in the dark is a common natural phenomenon. Photos captured in such a condition significantly impact the performance of various nighttime activities, such as autonomous driving, surveillance systems, and night photography. While existing methods designed for low-light enhancement or deraining show promising performance, they have limitations in simultaneously addressing the task of brightening low light and removing rain. Furthermore, using a cascade approach, such as ``deraining followed by low-light enhancement'' or vice versa, may lead to difficult-to-handle rain patterns or excessively blurred and overexposed images. To overcome these limitations, we propose an end-to-end network called $L^{2}RIRNet$ which can jointly handle low-light enhancement and deraining. Our network mainly includes a Pairwise Degradation Feature Vector Extraction Network (P-Net) and a Restoration Network (R-Net). P-Net can learn degradation feature vectors on the dark and light areas separately, using contrastive learning to guide the image restoration process. The R-Net is responsible for restoring the image. We also introduce an effective Fast Fourier - ResNet Detail Guidance Module (FFR-DG) that initially guides image restoration using detail image that do not contain degradation information but focus on texture detail information. Additionally, we contribute a dataset containing synthetic and real-world low-light-rainy images. Extensive experiments demonstrate that our $L^{2}RIRNet$ outperforms existing methods in both synthetic and complex real-world scenarios

RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors

Existing dehazing approaches struggle to process real-world hazy images owing to the lack of paired real data and robust priors. In this work, we present a new paradigm for real image dehazing from the perspectives of synthesizing more realistic hazy data and introducing more robust priors into the network. Specifically, (1) instead of adopting the de facto physical scattering model, we rethink the degradation of real hazy images and propose a phenomenological pipeline considering diverse degradation types. (2) We propose a Real Image Dehazing network via high-quality Codebook Priors (RIDCP). Firstly, a VQGAN is pre-trained on a large-scale high-quality dataset to obtain the discrete codebook, encapsulating high-quality priors (HQPs). After replacing the negative effects brought by haze with HQPs, the decoder equipped with a novel normalized feature alignment module can effectively utilize high-quality features and produce clean results. However, although our degradation pipeline drastically mitigates the domain gap between synthetic and real data, it is still intractable to avoid it, which challenges HQPs matching in the wild. Thus, we re-calculate the distance when matching the features to the HQPs by a controllable matching operation, which facilitates finding better counterparts. We provide a recommendation to control the matching based on an explainable solution. Users can also flexibly adjust the enhancement degree as per their preference. Extensive experiments verify the effectiveness of our data synthesis pipeline and the superior performance of RIDCP in real image dehazing.Comment: Acceptted by CVPR 202

AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation

We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional correlation volumes for all pairs of pixels, and use the predicted bilateral flows to retrieve correlations for updating both flows and the interpolated content feature. Second, we derive multiple groups of fine-grained flow fields from one pair of updated coarse flows for performing backward warping on the input frames separately. Combining these two designs enables us to generate promising task-oriented flows and reduce the difficulties in modeling large motions and handling occluded areas during frame interpolation. These qualities promote our model to achieve state-of-the-art performance on various benchmarks with high efficiency. Moreover, our convolution-based model competes favorably compared to Transformer-based models in terms of accuracy and efficiency. Our code is available at https://github.com/MCG-NKU/AMT.Comment: Accepted to CVPR202

Interrogating cell culture populations for the selection of production cell lines using microfluidic culturing, single cell analysis, and predictive modelling

Cell line development for manufacturing is a lengthy, multi-step, resource intensive, critical path activity. Attempts to perform in silico modelling and prediction of cell culture has been difficult due to complexities around heterogeneous cell culture populations that rapidly shift over generations under changing selective conditions. For example, early populations will often change as response to media and culturing conditions from a static colony culturing in microtiter plates, to small scale suspension culturing, and finally in a controlled bioreactor processes. As a result, it is challenging to make the final cell line selection early, while predicting future bioprocess performance, and ultimately estimate the protein product quality. We address this challenge by drastically increasing the amount of early cell culture population data obtained through use of emerging single cell technologies. Data obtained is combined with modelling approaches to select the best cell lines upfront to reduce timelines and processing steps. To achieve this, we have implemented a platform from Berkeley Lights that effectively digitalizes most aspects of cell culture. Thousands of individual cell lines can be manipulated, cultured and interrogated on a perfusion nanofluidic chip resulting in extensive data on cell behavior on an individual cell level as well as the populations. Through multivariate predictive modeling of this data, we can predict the performance of candidate clonal cell lines in larger scale production runs. Incorporation of additional single cell analysis such as digital droplet RT-PCR and next generation sequencing further predicts product quality, such as heterogeneity of bispecifics and sequence variant detection. Similar approaches can further be used to then study the stability and integrity of a final CHO cell banks. When combined, single cell interrogation of early culture populations allow for the dematerialization of the CLD process, make better predictions of bioprocess performance, and reduce select the final production clone earlier

On-stack replacement, distilled

On-stack replacement (OSR) is essential technology for adaptive optimization, allowing changes to code actively executing in a managed runtime. The engineering aspects of OSR are well-known among VM architects, with several implementations available to date. However, OSR is yet to be explored as a general means to transfer execution between related program versions, which can pave the road to unprecedented applications that stretch beyond VMs. We aim at filling this gap with a constructive and provably correct OSR framework, allowing a class of general-purpose transformation functions to yield a special-purpose replacement. We describe and evaluate an implementation of our technique in LLVM. As a novel application of OSR, we present a feasibility study on debugging of optimized code, showing how our techniques can be used to fix variables holding incorrect values at breakpoints due to optimizations

FSD-C10: A more promising novel ROCK inhibitor than Fasudil for treatment of CNS autoimmunity.

Rho-Rho kinase (Rho-ROCK) triggers an intracellular signalling cascade that regulates cell survival, death, adhesion, migration, neurite outgrowth and retraction and influences the generation and development of several neurological disorders. Although Fasudil, a ROCK inhibitor, effectively suppressed encephalomyelitis (EAE), certain side effects may limit its clinical use. A novel and efficient ROCK inhibitor, FSD-C10, has been explored. In the present study, we present chemical synthesis and structure of FSD-C10, as well as the relationship between compound concentration and ROCK inhibition. We compared the inhibitory efficiency of ROCKI and ROCK II, the cell cytotoxicity, neurite outgrowth and dendritic formation, neurotrophic factors and vasodilation between Fasudil and FSD-C10. The results demonstrated that FSD-C10, like Fasudil, induced neurite outgrowth of neurons and dendritic formation of BV-2 microglia and enhanced the production of neurotrophic factor brain-derived neurotrophic factor (BDNF), glial cell line-derived neurotrophic factor (GDNF) and neurotrophin-3 (NT-3). However, the cell cytotoxicity and vasodilation of FSD-C10 were relatively small compared with Fasudil. Although Fasudil inhibited both ROCK I and ROCK II, FSD-C10 more selectively suppressed ROCK II, but not ROCK I, which may be related to vasodilation insensitivity and animal mortality. Thus, FSD-C10 may be a safer and more promising novel ROCK inhibitor than Fasudil for the treatment of several neurological disorders

Efficient and accurate simulations of deformable particles immersed in a fluid using a combined immersed boundary lattice Boltzmann finite element method

The deformation of an initially spherical capsule, freely suspended in simple shear flow, can be computed analytically in the limit of small deformations [D. Barthes-Biesel, J. M. Rallison, The Time-Dependent Deformation of a Capsule Freely Suspended in a Linear Shear Flow, J. Fluid Mech. 113 (1981) 251-267]. Those analytic approximations are used to study the influence of the mesh tessellation method, the spatial resolution, and the discrete delta function of the immersed boundary method on the numerical results obtained by a coupled immersed boundary lattice Boltzmann finite element method. For the description of the capsule membrane, a finite element method and the Skalak constitutive model [R. Skalak et al., Strain Energy Function of Red Blood Cell Membranes, Biophys. J. 13 (1973) 245-264] have been employed. Our primary goal is the investigation of the presented model for small resolutions to provide a sound basis for efficient but accurate simulations of multiple deformable particles immersed in a fluid. We come to the conclusion that details of the membrane mesh, as tessellation method and resolution, play only a minor role. The hydrodynamic resolution, i.e., the width of the discrete delta function, can significantly influence the accuracy of the simulations. The discretization of the delta function introduces an artificial length scale, which effectively changes the radius and the deformability of the capsule. We discuss possibilities of reducing the computing time of simulations of deformable objects immersed in a fluid while maintaining high accuracy.Comment: 23 pages, 14 figures, 3 table

Effects of tumor metabolic microenvironment on regulatory T cells

Recent studies have shown that on one hand, tumors need to obtain a sufficient energy supply, and on the other hand they must evade the body’s immune surveillance. Because of their metabolic reprogramming characteristics, tumors can modify the physicochemical properties of the microenvironment, which in turn affects the biological characteristics of the cells infiltrating them. Regulatory T cells (Tregs) are a subset of T cells that regulate immune responses in the body. They exist in large quantities in the tumor microenvironment and exert immunosuppressive effects. The main effect of tumor microenvironment on Tregs is to promote their differentiation, proliferation, secretion of immunosuppressive factors, and chemotactic recruitment to play a role in immunosuppression in tumor tissues. This review focuses on cell metabolism reprogramming and the most significant features of the tumor microenvironment relative to the functional effects on Tregs, highlighting our understanding of the mechanisms of tumor immune evasion and providing new directions for tumor immunotherapy