CRL4 antagonizes SCFFbxo7-mediated turnover of cereblon and BK channel to regulate learning and memory.

Intellectual disability (ID), one of the most common human developmental disorders, can be caused by genetic mutations in Cullin 4B (Cul4B) and cereblon (CRBN). CRBN is a substrate receptor for the Cul4A/B-DDB1 ubiquitin ligase (CRL4) and can target voltage- and calcium-activated BK channel for ER retention. Here we report that ID-associated CRL4CRBN mutations abolish the interaction of the BK channel with CRL4, and redirect the BK channel to the SCFFbxo7 ubiquitin ligase for proteasomal degradation. Glioma cell lines harbouring CRBN mutations record density-dependent decrease of BK currents, which can be restored by blocking Cullin ubiquitin ligase activity. Importantly, mice with neuron-specific deletion of DDB1 or CRBN express reduced BK protein levels in the brain, and exhibit similar impairment in learning and memory, a deficit that can be partially rescued by activating the BK channel. Our results reveal a competitive targeting of the BK channel by two ubiquitin ligases to achieve exquisite control of its stability, and support changes in neuronal excitability as a common pathogenic mechanism underlying CRL4CRBN-associated ID.

The effect of jet lag on the human brain: A neuroimaging study.

Jet lag is commonly experienced when travelers cross multiple time zones, leaving the wake-sleep cycle and intrinsic biological "clocks" out of synchrony with the current environment. The effect of jet lag on intrinsic cortical function remains unclear. Twenty-two healthy individuals experiencing west-to-east jet lag flight were recruited. Brain structural and functional magnetic resonance studies, as well as psychological and neurohormonal tests, were carried out when participants returned from travel over six time zones and 50 days later when their jet lag symptoms had resolved. During jet lag, the functional brain network exhibited a small-world topology that was shifted toward regularity. Alterations during jet lag relative to recovery included decreased basal ganglia-thalamocortical network connections and increased functional connectivity between the medial temporal lobe subsystem and medial visual cortex. The lower melatonin and higher thyroid hormone levels during jet lag showed the same trend as brain activity in the right lingual gyrus. Although there was no significant difference between cortisol measurements during and after jet lag, cortisol levels were associated with temporal lobe activity in the jet lag condition. Brain and neuroendocrine changes during jet lag were related to jet lag symptoms. Further prospective studies are needed to explore the time course over which jet lag acts on the human brain.

Improving color constancy by selecting suitable set of training images.

With very simple implementation, regression-based color constancy (CC) methods have recently obtained very competitive performance by applying a correction matrix to the results of some low level-based CC algorithms. However, most regression-based methods, e.g., Corrected Moment (CM), apply a same correction matrix to all the test images. Considering that the captured image color is usually determined by various factors (e.g., illuminant and surface reflectance), it is obviously not reasonable enough to apply a same correction to different test images without considering the intrinsic difference among images. In this work, we first mathematically analyze the key factors that may influence the performance of regression-based CC, and then we design principled rules to automatically select the suitable training images to learn an optimal correction matrix for each test image. With this strategy, the original regression-based CC (e.g., CM) is clearly improved to obtain more competitive performance on four widely used benchmark datasets. We also show that although this work focuses on improving the regression-based CM method, a noteworthy aspect of the proposed automatic training data selection strategy is its applicability to several representative regression-based approaches for the color constancy problem.

Activation of c-Abl Kinase Potentiates the Anti-myeloma Drug Lenalidomide by Promoting DDA1 Protein Recruitment to the CRL4 Ubiquitin Ligase.

Cullin-RING ligase 4 (CRL4), a complex of Cul4 and DDB1, regulates the cell cycle, DNA damage repair, and chromatin replication by targeting a variety of substrates for ubiquitination. CRL4 is also hijacked by viral proteins or thalidomide-derived compounds to degrade host restriction factors. Here we report that the c-Abl non-receptor kinase phosphorylates DDB1 at residue Tyr-316 to recruit a small regulatory protein, DDA1, leading to increased substrate ubiquitination. Pharmacological inhibition or genetic ablation of the Abl-DDB1-DDA1 axis decreases the ubiquitination of CRL4 substrates, including IKZF1 and IKZF3, in lenalidomide-treated multiple myeloma cells. Importantly, panobinostat, a recently approved anti-myeloma drug, and dexamethasone enhance lenalidomide-induced substrate degradation and cytotoxicity by activating c-Abl, therefore providing a mechanism underlying their combination with lenalidomide to treat multiple myeloma.

Improving color constancy by discounting the variation of camera spectral sensitivity.

It is an ill-posed problem to recover the true scene colors from a color biased image by discounting the effects of scene illuminant and camera spectral sensitivity (CSS) at the same time. Most color constancy (CC) models have been designed to first estimate the illuminant color, which is then removed from the color biased image to obtain an image taken under white light, without the explicit consideration of CSS effect on CC. This paper first studies the CSS effect on illuminant estimation arising in the inter-dataset-based CC (inter-CC), i.e., training a CC model on one dataset and then testing on another dataset captured by a distinct CSS. We show the clear degradation of existing CC models for inter-CC application. Then a simple way is proposed to overcome such degradation by first learning quickly a transform matrix between the two distinct CSSs (CSS-1 and CSS-2). The learned matrix is then used to convert the data (including the illuminant ground truth and the color-biased images) rendered under CSS-1 into CSS-2, and then train and apply the CC model on the color-biased images under CSS-2 without the need of burdensome acquiring of the training set under CSS-2. Extensive experiments on synthetic and real images show that our method can clearly improve the inter-CC performance for traditional CC algorithms. We suggest that, by taking the CSS effect into account, it is more likely to obtain the truly color constant images invariant to the changes of both illuminant and camera sensors.

Natural Image Reconstruction from fMRI Based on Node-Edge Interaction and Multi-Scale Constraint.

Reconstructing natural stimulus images using functional magnetic resonance imaging (fMRI) is one of the most challenging problems in brain decoding and is also the crucial component of a brain-computer interface. Previous methods cannot fully exploit the information about interactions among brain regions. In this paper, we propose a natural image reconstruction method based on node-edge interaction and a multi-scale constraint. Inspired by the extensive information interactions in the brain, a novel graph neural network block with node-edge interaction (NEI-GNN block) is presented, which can adequately model the information exchange between brain areas via alternatively updating the nodes and edges. Additionally, to enhance the quality of reconstructed images in terms of both global structure and local detail, we employ a multi-stage reconstruction network that restricts the reconstructed images in a coarse-to-fine manner across multiple scales. Qualitative experiments on the generic object decoding (GOD) dataset demonstrate that the reconstructed images contain accurate structural information and rich texture details. Furthermore, the proposed method surpasses the existing state-of-the-art methods in terms of accuracy in the commonly used n-way evaluation. Our approach achieves 82.00%, 59.40%, 45.20% in n-way mean squared error (MSE) evaluation and 83.50%, 61.80%, 46.00% in n-way structural similarity index measure (SSIM) evaluation, respectively. Our experiments reveal the importance of information interaction among brain areas and also demonstrate the potential for developing visual-decoding brain-computer interfaces.

Explaining Orientation Adaptation in V1 by Updating the State of a Spatial Model.

In this work, we extend an influential statistical model based on the spatial classical receptive field (CRF) and non-classical receptive field (nCRF) interactions (Coen-Cagli et al., 2012) to explain the typical orientation adaptation effects observed in V1. If we assume that the temporal adaptation modifies the "state" of the model, the spatial statistical model can explain all of the orientation adaptation effects in the context of neuronal output using small and large grating observed in neurophysiological experiments in V1. The "state" of the model represents the internal parameters such as the prior and the covariance trained on a mixed dataset that totally determine the response of the model. These two parameters, respectively, reflect the probability of the orientation component and the connectivity among neurons between CRF and nCRF. Specifically, we have two key findings: First, neural adapted results using a small grating that just covers the CRF can be predicted by the change of the prior of our model. Second, the change of the prior can also predict most of the observed results using a large grating that covers both CRF and nCRF of a neuron. However, the prediction of the novel attractive adaptation using large grating covering both CRF and nCRF also necessitates the involvement of a connectivity change of the center-surround RFs. In addition, our paper contributes a new prior-based winner-take-all (WTA) working mechanism derived from the statistical-based model to explain why and how all of these orientation adaptation effects can be predicted by relying on this spatial model without modifying its structure, a novel application of the spatial model. The research results show that adaptation may link time and space by changing the "state" of the neural system according to a specific adaptor. Furthermore, different forms of stimulus used for adaptation can cause various adaptation effects, such as an a priori shift or a connectivity change, depending on the specific stimulus size.

Key regulators of sensitivity to immunomodulatory drugs in cancer treatment.

Immunomodulatory drugs (IMiDs) include thalidomide, lenalidomide, and pomalidomide, which have shown significant efficacy in the treatment of multiple myeloma (MM), myelodysplastic syndrome (MDS) with deletion of chromosome 5q (del(5q)) and other hematological malignancies. IMiDs hijack the CRL4CRBN ubiquitin ligase to target cellular proteins for ubiquitination and degradation, which is responsible for their clinical activity in MM and MDS with del(5q). However, intrinsic and acquired resistance frequently limit the efficacy of IMiDs. Recently, many efforts have been made to explore key regulators of IMiD sensitivity, resulting in great advances in the understanding of the regulatory networks related to this class of drugs. In this review, we describe the mechanism of IMiDs in cancer treatment and summarize the key regulators of IMiD sensitivity. Furthermore, we introduce genome-wide CRISPR-Cas9 screenings, through which the regulatory networks of IMiD sensitivity could be identified.

ERK signaling mediates resistance to immunomodulatory drugs in the bone marrow microenvironment.

Immunomodulatory drugs (IMiDs) have markedly improved patient outcome in multiple myeloma (MM); however, resistance to IMiDs commonly underlies relapse of disease. Here, we identify that tumor necrosis factor (TNF) receptor-associated factor 2 (TRAF2) knockdown (KD)/knockout (KO) in MM cells mediates IMiD resistance via activation of noncanonical nuclear factor κB (NF-κB) and extracellular signal-regulated kinase (ERK) signaling. Within MM bone marrow (BM) stromal cell supernatants, TNF-α induces proteasomal degradation of TRAF2, noncanonical NF-κB, and downstream ERK signaling in MM cells, whereas interleukin-6 directly triggers ERK activation. RNA sequencing of MM patient samples shows nearly universal ERK pathway activation at relapse on lenalidomide maintenance therapy, confirming its clinical relevance. Combination MEK inhibitor treatment restores IMiD sensitivity of TRAF2 KO cells both in vitro and in vivo. Our studies provide the framework for clinical trials of MEK inhibitors to overcome IMiD resistance in the BM microenvironment and improve patient outcome in MM.

Recent advances in the molecular mechanism of thalidomide teratogenicity.

Thalidomide was first marketed in 1957 but soon withdrawn because of its notorious teratogenicity. Studies on the mechanism of action of thalidomide revealed the pleiotropic properties of this class of drugs, including their anti-inflammatory, antiangiogenic and immunomodulatory activities. Based on their notable activities, thalidomide and its analogues, lenalidomide and pomalidomide, have been repurposed to treat erythema nodosum leprosum, multiple myeloma and other haematological malignancies. Thalidomide analogues were recently found to hijack CRL4CRBN ubiquitin ligase to target a number of cellular proteins for ubiquitination and proteasomal degradation. Thalidomide-mediated degradation of SALL4 and p63, transcription factors essential for embryonic development, very likely plays a critical role in thalidomide embryopathy. In this review, we provide a brief retrospective summary of thalidomide-induced teratogenesis, the mechanism of thalidomide activity, and the latest advances in the molecular mechanism of thalidomide-induced birth malformations.

Novel immunomodulatory drugs and neo-substrates.

Thalidomide, lenalidomide and pomalidomide are immunomodulatory drugs (IMiDs) effective in the treatment of multiple myeloma, myelodysplastic syndrome (MDS) with deletion of chromosome 5q and other hematological malignancies. Recent studies showed that IMiDs bind to CRBN, a substrate receptor of CRL4 E3 ligase, to induce the ubiquitination and degradation of IKZF1 and IKZF3 in multiple myeloma cells, contributing to their anti-myeloma activity. Similarly, lenalidomide exerts therapeutic efficacy via inducing ubiquitination and degradation of CK1α in MDS with deletion of chromosome 5q. Recently, novel thalidomide analogs have been designed for better clinical efficacy, including CC-122, CC-220 and CC-885. Moreover, a number of neo-substrates of IMiDs have been discovered. Proteolysis-targeting chimeras (PROTACs) as a class of bi-functional molecules are increasingly used as a strategy to target otherwise intractable cellular protein. PROTACs appear to have broad implications for novel therapeutics. In this review, we summarized new generation of immunomodulatory compounds, their potential neo-substrates, and new strategies for the design of novel PROTAC drugs.

Combining Bottom-Up and Top-Down Visual Mechanisms for Color Constancy Under Varying Illumination.

Multi-illuminant-based color constancy (MCC) is quite a challenging task. In this paper, we proposed a novel model motivated by the bottom-up and top-down mechanisms of human visual system (HVS) to estimate the spatially varying illumination in a scene. The motivation for bottom-up based estimation is from our finding that the bright and dark parts in a scene play different roles in encoding illuminants. However, handling the color shift of large colorful objects is difficult using pure bottom-up processing. Thus, we further introduce a top-down constraint inspired by the findings in visual psychophysics, in which high-level information (e.g., the prior of light source colors) plays a key role in visual color constancy. In order to implement the top-down hypothesis, we simply learn a color mapping between the illuminant distribution estimated by bottom-up processing and the ground truth maps provided by the dataset. We evaluated our model on four datasets and the results show that our method obtains very competitive performance compared with the state-of-the-art MCC algorithms. Moreover, the robustness of our model is more tangible considering that our results were obtained using the same parameters for all the datasets or the parameters of our model were learned from the inputs, that is, mimicking how HVS operates. We also show the color correction results on some real-world images taken from the web.

Underwater Image Enhancement Using Adaptive Retinal Mechanisms.

We propose an underwater image enhancement model inspired by the morphology and function of the teleost fish retina. We aim to solve the problems of underwater image degradation raised by the blurring and nonuniform color biasing. In particular, the feedback from color-sensitive horizontal cells to cones and a red channel compensation are used to correct the nonuniform color bias. The center-surround opponent mechanism of the bipolar cells and the feedback from amacrine cells to interplexiform cells then to horizontal cells serve to enhance the edges and contrasts of the output image. The ganglion cells with color-opponent mechanism are used for color enhancement and color correction. Finally, we adopt a luminance-based fusion strategy to reconstruct the enhanced image from the outputs of ON and OFF pathways of fish retina. Our model utilizes the global statistics (i.e., image contrast) to automatically guide the design of each low-level filter, which realizes the self-adaption of the main parameters. Extensive qualitative and quantitative evaluations on various underwater scenes validate the competitive performance of our technique. Our model also significantly improves the accuracy of transmission map estimation and local feature point matching using the underwater image. Our method is a single image approach that does not require the specialized prior about the underwater condition or scene structure.

Restriction of hepatitis B virus replication by c-Abl-induced proteasomal degradation of the viral polymerase.

About 257 million people with chronic infection of hepatitis B virus (HBV) worldwide are at high risk of developing terminal liver diseases. Reactivation of virus replication has been frequently reported in those patient populations receiving imatinib (an Abl kinase inhibitor) or bortezomib (a proteasome inhibitor) to treat concurrent diseases, but the underlying mechanism for this reactivation is unknown. We report that the HBV polymerase protein is recruited by Cdt2 to the cullin-RING ligase 4 (CRL4) for ubiquitination and proteasome degradation and that this process is stimulated by the c-Abl nonreceptor tyrosine kinase. Genetic ablation of the Abl-CRL4Cdt2 axis or pharmaceutical inhibition of this process stabilizes HBV polymerase protein and increases viral loads in HBV-infected liver cancer cell lines. Our study reveals a kinase-dependent activation of CRL4 ubiquitin ligase that can be targeted for blocking HBV replication.

A portable image-based cytometer for rapid malaria detection and quantification.

Increasing resistance by malaria parasites to currently used antimalarials across the developing world warrants timely detection and classification so that appropriate drug combinations can be administered before clinical complications arise. However, this is often challenged by low levels of infection (referred to as parasitemia) and presence of predominantly young parasitic forms in the patients' peripheral blood. Herein, we developed a simple, inexpensive and portable image-based cytometer that detects and numerically counts Plasmodium falciparum infected red blood cells (iRBCs) from Giemsa-stained smears derived from infected blood. Our cytometer is able to classify all parasitic subpopulations by quantifying the area occupied by the parasites within iRBCs, with high specificity, sensitivity and negligible false positives (~ 0.0025%). Moreover, we demonstrate the application of our image-based cytometer in testing anti-malarial efficacy against a commercial flow cytometer and demonstrate comparable results between the two methods. Collectively, these results highlight the possibility to use our image-based cytometer as a cheap, rapid and accurate alternative for antimalarial testing without compromising on efficiency and minimal processing time. With appropriate filters applied into the algorithm, to rule out leukocytes and reticulocytes, our cytometer may also be used for field diagnosis of malaria.

A Retinal Mechanism Inspired Color Constancy Model.

In this paper, we propose a novel model for the computational color constancy, inspired by the amazing ability of the human vision system (HVS) to perceive the color of objects largely constant as the light source color changes. The proposed model imitates the color processing mechanisms in the specific level of the retina, the first stage of the HVS, from the adaptation emerging in the layers of cone photoreceptors and horizontal cells (HCs) to the color-opponent mechanism and disinhibition effect of the non-classical receptive field in the layer of retinal ganglion cells (RGCs). In particular, HC modulation provides a global color correction with cone-specific lateral gain control, and the following RGCs refine the processing with iterative adaptation until all the three opponent channels reach their stable states (i.e., obtain stable outputs). Instead of explicitly estimating the scene illuminant(s), such as most existing algorithms, our model directly removes the effect of scene illuminant. Evaluations on four commonly used color constancy data sets show that the proposed model produces competitive results in comparison with the state-of-the-art methods for the scenes under either single or multiple illuminants. The results indicate that single opponency, especially the disinhibitory effect emerging in the receptive field's subunit-structured surround of RGCs, plays an important role in removing scene illuminant(s) by inherently distinguishing the spatial structures of surfaces from extensive illuminant(s).

A Retina Inspired Model for Enhancing Visibility of Hazy Images.

The mammalian retina seems far smarter than scientists have believed so far. Inspired by the visual processing mechanisms in the retina, from the layer of photoreceptors to the layer of retinal ganglion cells (RGCs), we propose a computational model for haze removal from a single input image, which is an important issue in the field of image enhancement. In particular, the bipolar cells serve to roughly remove the low-frequency of haze, and the amacrine cells modulate the output of cone bipolar cells to compensate the loss of details by increasing the image contrast. Then the RGCs with disinhibitory receptive field surround refine the local haze removal as well as the image detail enhancement. Results on a variety of real-world and synthetic hazy images show that the proposed model yields results comparative to or even better than the state-of-the-art methods, having the advantage of simultaneous dehazing and enhancing of single hazy image with simple and straightforward implementation.

Color Constancy Using Double-Opponency.

The double-opponent (DO) color-sensitive cells in the primary visual cortex (V1) of the human visual system (HVS) have long been recognized as the physiological basis of color constancy. In this work we propose a new color constancy model by imitating the functional properties of the HVS from the single-opponent (SO) cells in the retina to the DO cells in V1 and the possible neurons in the higher visual cortexes. The idea behind the proposed double-opponency based color constancy (DOCC) model originates from the substantial observation that the color distribution of the responses of DO cells to the color-biased images coincides well with the vector denoting the light source color. Then the illuminant color is easily estimated by pooling the responses of DO cells in separate channels in LMS space with the pooling mechanism of sum or max. Extensive evaluations on three commonly used datasets, including the test with the dataset dependent optimal parameters, as well as the intra- and inter-dataset cross validation, show that our physiologically inspired DOCC model can produce quite competitive results in comparison to the state-of-the-art approaches, but with a relative simple implementation and without requiring fine-tuning of the method for each different dataset.