Identification of a new class of activators of the Hippo pathway with antitumor activity in vitro and in vivo.

The Hippo pathway is a key regulator of tissue growth, organ size, and tumorigenesis. Activating the Hippo pathway by gene editing or pharmaceutical intervention has been proven to be a new therapeutic strategy for treatment of the Hippo pathway-dependent cancers. To now, a number of compounds that directly target the downstream effector proteins of Hippo pathway, including YAP and TEADs, have been disclosed, but very few Hippo pathway activators are reported. Here, we discovered a new class of Hippo pathway activator, YL-602, which inhibited CTGF expression in cells irrespective of cell density and the presence of serum. Mechanistically, YL-602 activates the Hippo pathway via MST1/2, which is different from known activators of Hippo pathway. In vitro, YL-602 significantly induced tumor cell apoptosis and inhibited colony formation of tumor cells. In vivo, oral administration of YL-602 substantially suppressed the growth of cancer cells by activation of Hippo pathway. Overall, YL-602 could be a promising lead compound, and deserves further investigation for its mechanism of action and therapeutic applications.

Solar-Blind Photodetector Arrays Fabricated by Weaving Strategy.

The quest for solar-blind photodetectors (SBPDs) with exceptional optoelectronic properties for imaging applications has prompted the investigation of SBPD arrays. Ga2O3, characterized by its ultrawide bandgap and low growth cost, has emerged as a promising material for solar-blind detection. In this study, SBPD arrays were fabricated by weaving Sn-doped ß-Ga2O3 microbelts (MBs). These MBs, which have a conductive core surrounded by a high-resistivity depletion surface layer resulting from the segregation of Sn and oxygen, are woven into a grid structure. Each intersection of the MBs functions as a photodetector pixel, with the intersecting MBs serving as the output electrodes of the pixel. This design simplifies the readout circuit for the photodetector array. The solar-blind photodetector array demonstrates superior solar-blind detection performance, including a dark current of 0.5 pA, a response time of 38.8 µs, a light/dark current ratio of 108, and a responsivity of 300 A/W. This research may provide a feasible strategy for the fabrication of photodetector arrays, thus pushing forward the application of photodetectors in imaging.

A Multi-Task Fusion Strategy-Based Decision-Making and Planning Method for Autonomous Driving Vehicles.

The autonomous driving technology based on deep reinforcement learning (DRL) has been confirmed as one of the most cutting-edge research fields worldwide. The agent is enabled to achieve the goal of making independent decisions by interacting with the environment and learning driving strategies based on the feedback from the environment. This technology has been widely used in end-to-end driving tasks. However, this field faces several challenges. First, developing real vehicles is expensive, time-consuming, and risky. To further expedite the testing, verification, and iteration of end-to-end deep reinforcement learning algorithms, a joint simulation development and validation platform was designed and implemented in this study based on VTD-CarSim and the Tensorflow deep learning framework, and research work was conducted based on this platform. Second, sparse reward signals can cause problems (e.g., a low-sample learning rate). It is imperative for the agent to be capable of navigating in an unfamiliar environment and driving safely under a wide variety of weather or lighting conditions. To address the problem of poor generalization ability of the agent to unknown scenarios, a deep deterministic policy gradient (DDPG) decision-making and planning method was proposed in this study in accordance with a multi-task fusion strategy. The main task based on DRL decision-making planning and the auxiliary task based on image semantic segmentation were cross-fused, and part of the network was shared with the main task to reduce the possibility of model overfitting and improve the generalization ability. As indicated by the experimental results, first, the joint simulation development and validation platform built in this study exhibited prominent versatility. Users were enabled to easily substitute any default module with customized algorithms and verify the effectiveness of new functions in enhancing overall performance using other default modules of the platform. Second, the deep reinforcement learning strategy based on multi-task fusion proposed in this study was competitive. Its performance was better than other DRL algorithms in certain tasks, which improved the generalization ability of the vehicle decision-making planning algorithm.

Al-Doped Core-Shell-Structured Ni@Mesoporous Silica for Highly Selective Hydrodeoxygenation of Lignin-Derived Aldehydes.

Selective deoxygenation of chemicals using non-noble metal-based catalysts poses a significant challenge toward upgrading biomass-derived oxygenates into advanced fuels and fine chemicals. Herein, we report a bifunctional core-shell catalyst (Ni@Al3-mSiO2) consisting of Ni nanoparticles closely encapsulated by the Al-doped mesoporous silica shell that achieves 100% vanillin conversion and >99% yield of 2-methoxy-4-methylphenol under 1 MPa H2 at 130 °C in water. Due to the unique mesoporous core-shell structure, no significant decrease in catalytic activity was observed after 10 recycles. Furthermore, incorporating Al atoms into the silica shell significantly increased the number of acidic sites. Density functional theory calculations reveal the reaction pathway of the vanillin hydrodeoxygenation process and uncover the intrinsic influence of the Al sites. This work not only provides an efficient and cost-effective bifunctional hydrodeoxygenation catalyst but also offers a new synthetic protocol to rationally design promising non-noble metal catalysts for biomass valorization or other widespread applications.

Discovery of [1,2,3]Triazolo[4,5-c]quinoline Derivatives as a New Class of Ataxia-Telangiectasia Mutated Kinase Inhibitors.

Ataxia-telangiectasia mutated (ATM) is an atypical serine/threonine protein kinase which is implicated in the repair of DNA double-strand breaks. Numerous reports have shown that ATM inhibition is an attractive target for radiotherapy and chemotherapy sensitization. Herein we report a new series of ATM kinase inhibitors containing the 1H-[1,2,3]triazolo[4,5-c]quinoline scaffold, which was obtained by virtual screening, structural optimization, and structure-activity relationship studies. Among the inhibitors, A011 was one of the most potent, with an IC50 value of 1.0 nM against ATM. In colorectal cancer cells (SW620 and HCT116), A011 was able to inhibit activation of ATM signaling induced by irinotecan (CPT-11) and ionizing radiation and then increased the sensitivity of colorectal cancer cells to irinotecan and ionizing radiation through increasing G2/M arrest and inducing apoptosis. In the SW620 human colorectal adenocarcinoma tumor xenograft model, A011 sensitized SW620 to CPT-11 by inhibiting ATM activity. Collectively, this work has identified a promising lead in the discovery of potent inhibitors against ATM.

UnVELO: Unsupervised Vision-Enhanced LiDAR Odometry with Online Correction.

Due to the complementary characteristics of visual and LiDAR information, these two modalities have been fused to facilitate many vision tasks. However, current studies of learning-based odometries mainly focus on either the visual or LiDAR modality, leaving visual-LiDAR odometries (VLOs) under-explored. This work proposes a new method to implement an unsupervised VLO, which adopts a LiDAR-dominant scheme to fuse the two modalities. We, therefore, refer to it as unsupervised vision-enhanced LiDAR odometry (UnVELO). It converts 3D LiDAR points into a dense vertex map via spherical projection and generates a vertex color map by colorizing each vertex with visual information. Further, a point-to-plane distance-based geometric loss and a photometric-error-based visual loss are, respectively, placed on locally planar regions and cluttered regions. Last, but not least, we designed an online pose-correction module to refine the pose predicted by the trained UnVELO during test time. In contrast to the vision-dominant fusion scheme adopted in most previous VLOs, our LiDAR-dominant method adopts the dense representations for both modalities, which facilitates the visual-LiDAR fusion. Besides, our method uses the accurate LiDAR measurements instead of the predicted noisy dense depth maps, which significantly improves the robustness to illumination variations, as well as the efficiency of the online pose correction. The experiments on the KITTI and DSEC datasets showed that our method outperformed previous two-frame-based learning methods. It was also competitive with hybrid methods that integrate a global optimization on multiple or all frames.

Spatial distribution characteristics of heavy metal(loid)s health risk in soil at scale on town level.

In order to reveal the Spatial distribution characteristics of heavy metal(loid)s health risk in soil on the town-scale, 788 topsoil samples were collected from a town in the hinterland of Chengdu Plain, with 5 subordinate communities and 17 administrative villages as the research sub regions. The USEPA health risk assessment model was used to assess the health risks of heavy metal(loid)s Cd, Hg, As, Cu, Pb, Cr, Zn and Ni in the soil, the health risk analysis method system based on the geographic detector and the optimized rank-size theory model are used to clarify the spatial differentiation and risk level difference of health risk in the study area. The results showed that the average values of Cd, Hg, As, Pb, Cr, Cu, Ni and Zn in the soil of the study area were 0.221, 0.155, 9.76, 32.2, 91.9, 35.2, 37.1 and 108.8 mg/kg, respectively. The health risks of heavy metal(loid)s in soil to adults and children are generally within the acceptable range, but the maximum hazard index of children and the maximum non-carcinogenic risk value of Cr to children are 2.653303 and 1.213098 respectively, which were exceeding the acceptable range. The carcinogenic risk of Cr and As to adults and children and the carcinogenic risk of Cd to children are at 1 × 10-4 to 1 × 10-6, exceeding the 10-6 health threshold. The q-value range of heavy metal(loid)s health risk spatial differentiation of soil in the study area is 0.016-0.425. The spatial differentiation of non-carcinogenic risk of Hg, As, Ni, Pb, Cd and Cr and the spatial differentiation of carcinogenic risk of Cr and As are larger, which needs further attention. The strictly controlled area of heavy metal(loid)s health risk in the soil of the study area (R ≥ 1.1) is mainly concentrated in the central, western and northeast sub regions, and most sub regions belong to the safe utilization area (0.9 < R < 1.1). The health risk assessment of heavy metal(loid)s in soil on a town scale is of positive significance for enriching health risk research methods, measuring health risk levels at different scales, and formulating refined risk management and control strategies.

Livin promotes Th2-type immune response in airway allergic diseases.

OBJECTIVES: To investigate the effects of livin on the Th2 immune response in airway allergic diseases (AAD) and explore the interaction among livin, GATA3, IL-4 in peripheral blood CD4+ T cells of AAD patients. METHODS: WT mice and livin KO mice were developed for model of AAD. Th2 cell levels in the lung tissues and spleen were assessed by flow cytometry. Also, it was assessed in the culture after exposing to livin inhibitor (Lp-15); the protein and mRNA levels of livin, GATA3 and IL-4 in peripheral blood CD4+ T cells isolated from patients with or without AAD were measured by real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting, respectively. Finally, Co-immunoprecipitation (Co-IP) was employed to identify the interaction between livin and GATA3. RESULTS: Compared with WT mouse, Th2 cell frequency in lung tissues and spleen was significantly decreased in livin KO mouse; after adding Lp-15, the differentiation from Naive CD4+T cells in spleen to Th2 cells was blocked; the protein and mRNA levels of livin, GATA3 and IL-4 in AAD group were higher than that in control group. The levels of livin were positively correlated with IL-4, and GATA3 was also positively correlated with IL-4 and livin. GATA3 was detected in the protein complex co-precipitated with livin antibody, and livin was also detected in the protein complex co-precipitated by GATA3 antibody. CONCLUSION: Livin increases the expression of IL-4 and facilitates naive CD4+ T cells to differentiate into Th2 cells, which triggers airway allergy.

Lane Mark Detection with Pre-Aligned Spatial-Temporal Attention.

Lane mark detection plays an important role in autonomous driving under structural environments. Many deep learning-based lane mark detection methods have been put forward in recent years. However, most of current methods limit their solutions within one single image and do not make use of the de facto successive image input during the driving scene, which may lead to inferior performance in some challenging scenarios such as occlusion, shadows, and lane mark degradation. To address the issue, we propose a novel lane mark detection network which takes pre-aligned multiple successive frames as inputs to produce more stable predictions. A Spatial-Temporal Attention Module (STAM) is designed in the network to adaptively aggregate the feature information of history frames to the current frame. Various structure of the STAM is also studied to ensure the best performance. Experiments on Tusimple and ApolloScape datasets show that our method can effectively improve lane mark detection and achieve state-of-the-art performance.

Photocatalytic Conversion of Lignin into Chemicals and Fuels.

Lignin, an underutilized component of lignocellulosic biomass, is regarded as a rich reservoir for the production of aromatic chemicals and fuels. Despite extensive research in recent years, lignin's potential is far from being fully unlocked. Photocatalysis that uses sustainable solar energy to drive lignin conversion under mild conditions has been identified as a promising strategy and received growing research interest. This review aims to present a critical introduction to the photocatalytic conversion of lignin, including a summary of lignin conversion pathways and mechanisms, as well as the latest cutting-edge innovations on photocatalyst design and reactor construction. Moreover, the screening of solvents and regulation of other key factors that are involved in photocatalytic lignin conversion are also elucidated and future perspectives and challenges for photocatalytic conversion of lignin into valuable products are discussed.

Simultaneous Semantic Segmentation and Depth Completion with Constraint of Boundary.

As the core task of scene understanding, semantic segmentation and depth completion play a vital role in lots of applications such as robot navigation, AR/VR and autonomous driving. They are responsible for parsing scenes from the angle of semantics and geometry, respectively. While great progress has been made in both tasks through deep learning technologies, few works have been done on building a joint model by deeply exploring the inner relationship of the above tasks. In this paper, semantic segmentation and depth completion are jointly considered under a multi-task learning framework. By sharing a common encoder part and introducing boundary features as inner constraints in the decoder part, the two tasks can properly share the required information from each other. An extra boundary detection sub-task is responsible for providing the boundary features and constructing cross-task joint loss functions for network training. The entire network is implemented end-to-end and evaluated with both RGB and sparse depth input. Experiments conducted on synthesized and real scene datasets show that our proposed multi-task CNN model can effectively improve the performance of every single task.

Discovery of 1,8-disubstituted-[1,2,3]triazolo[4,5-c]quinoline derivatives as a new class of Hippo signaling pathway inhibitors.

Inhibitors of the Hippo signaling pathway have been demonstrated to have a potential clinical application in cases such as tissue repair and organ regeneration. However, there is a lack of potent Hippo pathway inhibitors at present. Herein we report the discovery of a series of 1,8-disubstituted-[1,2,3]triazolo[4,5-c]quinoline derivatives as a new class of Hippo pathway inhibitors by utilizing a cell line-based screening model (A549-CTGF). Structure-activity relationship (SAR) of these compounds was also discussed. The most potent compound in the A549-CTGF cell assay, 11g, was then evaluated by real-time PCR and immunofluorescence assays. Overall, this study provides a starting point for later drug discovery targeting the Hippo signaling pathway.

Metal-Free Aerobic Oxidative Selective C-C Bond Cleavage in Heteroaryl-Containing Primary and Secondary Alcohols.

A transition-metal-free aerobic oxidative selective C-C bond-cleavage reaction in primary and secondary heteroaryl alcohols is reported. This reaction was highly efficient and tolerated various heteroaryl alcohols, generating a carboxylic acid derivative and a neutral heteroaromatic compound. Experimental studies combined with density functional theory calculations revealed the mechanism underlying the selective C-C bond cleavage. This strategy also provides an alternative simple approach to carboxylation reaction.

Compact omnidirectional multi-stereo vision system for 3D reconstruction.

Omnidirectional stereovision is beneficial in wide applications due to its large field of view. Traditional implementation of such a system employs two separate cameras looking at separate mirrors to achieve 3D stereo reconstruction. In this paper, a compact omnidirectional multi-stereo system that consists of only one camera and multiple mirrors is proposed. The novelty lies in the designing of a principal mirror above the array of sub-mirrors to achieve multiple omnidirectional stereo pairs that have appropriate baselines and multiple viewpoints in a compact manner. The system's imaging projection and error propagation model are presented. Experiments based on simulation and real data verify the effectiveness of our system.

A L0 sparse analysis prior for blind poissonian image deconvolution.

This paper proposes a new approach for blindly deconvolving images that are contaminated by Poisson noise. The proposed approach incorporates a new prior, that is the L0 sparse analysis prior, together with the total variation constraint into the maximum a posteriori (MAP) framework for deconvolution. A greedy analysis pursuit numerical scheme is exploited to solve the L0 regularized MAP problem. Experimental results show that our approach not only produces smooth results substantially suppressing artifacts and noise, but also preserves intensity changes sharply. Both quantitative and qualitative comparisons to the specialized state-of-the-art algorithms demonstrate its superiority.

Noncentral catadioptric camera calibration using a generalized unified model.

This Letter proposes a generalized unified model (GUM) for the calibration of noncentral catadioptric cameras. Releasing the constraint on the projection center and the orientation of the imaging plane that the traditional unified projection model has, the new model is able to well compensate the misalignment between the mirror and the camera. Being a compact and approximate central model, the GUM inherits the flexibility and simplicity from the unified model while maintaining accuracy even under severe misalignment. The calibration algorithm to compute the describing parameters of the model is also given. With the GUM, the calibration of central or noncentral systems could be treated with equal simplicity (or complexity). Experiments on both synthetic data and real images proved our success.

The camera itself as a calibration pattern: a novel self-calibration method for non-central catadioptric cameras.

A novel and practical self-calibration method for misaligned non-central catadioptric cameras is proposed in this paper. Without the aid of any special calibration patterns in the scene, the developed method is able to automatically estimate the pose parameters of the mirror with respect to the perspective camera. First it uses the ellipse corresponding to the mirror boundary in the image to compute the possible solutions for mirror postures. With two pose candidates, thereafter we propose a novel selection method to find the correct solution by using the image of the lens boundary. The whole calibration process is automatic and convenient to carry out since nothing more than a single image acquired from the catadioptric camera is needed. Experimental results both on synthesized and real images prove our success.