Small-molecule PROTAC mediates targeted protein degradation to treat STAT3-dependent epithelial cancer

The aberrant activation of STAT3 is associated with the etiology and progression in a variety of malignant epithelial-derived tumors, including head and neck squamous cell carcinoma (HNSCC) and colorectal cancer (CRC). Due to the lack of an enzymatic catalytic site or a ligand-binding pocket, there are no small-molecule inhibitors directly targeting STAT3 that have been approved for clinical translation. Emerging proteolysis targeting chimeric (PROTAC) technology-based approach represents a potential strategy to overcome the limitations of conventional inhibitors and inhibit activation of STAT3 and downstream genes. In this study, the heterobifunctional small-molecule-based PROTACs are successfully prepared from toosendanin (TSN), with 1 portion binding to STAT3 and the other portion binding to an E3 ubiquitin ligase. The optimized lead PROTAC (TSM-1) exhibits superior selectivity, potency, and robust antitumor effects in STAT3-dependent HNSCC and CRC - especially in clinically relevant patient-derived xenografts (PDX) and patient-derived organoids (PDO). The following mechanistic investigation identifies the reduced expression of critical downstream STAT3 effectors, through which TSM-1 promotes cell cycle arrest and apoptosis in tumor cells. These findings provide the first demonstration to our knowledge of a successful PROTAC-targeting strategy in STAT3-dependent epithelial cancer

Triple Regression for Camera Agnostic Sim2Real Robot Grasping and Manipulation Tasks

Sim2Real (Simulation to Reality) techniques have gained prominence in robotic manipulation and motion planning due to their ability to enhance success rates by enabling agents to test and evaluate various policies and trajectories. In this paper, we investigate the advantages of integrating Sim2Real into robotic frameworks. We introduce the Triple Regression Sim2Real framework, which constructs a real-time digital twin. This twin serves as a replica of reality to simulate and evaluate multiple plans before their execution in real-world scenarios. Our triple regression approach addresses the reality gap by: (1) mitigating projection errors between real and simulated camera perspectives through the first two regression models, and (2) detecting discrepancies in robot control using the third regression model. Experiments on 6-DoF grasp and manipulation tasks (where the gripper can approach from any direction) highlight the effectiveness of our framework. Remarkably, with only RGB input images, our method achieves state-of-the-art success rates. This research advances efficient robot training methods and sets the stage for rapid advancements in robotics and automation

G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification

Pathological glomerulus classification plays a key role in the diagnosis of nephropathy. As the difference between different subcategories is subtle, doctors often refer to slides from different staining methods to make decisions. However, creating correspondence across various stains is labor-intensive, bringing major difficulties in collecting data and training a vision-based algorithm to assist nephropathy diagnosis. This paper provides an alternative solution for integrating multi-stained visual cues for glomerulus classification. Our approach, named generator-to-classifier (G2C), is a two-stage framework. Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has). We optimize these two stages in a joint manner. To provide a reasonable initialization, we pre-train the generators in an unlabeled reference set under an unpaired image-to-image translation task, and then fine-tune them together with the classifier. We conduct experiments on a glomerulus type classification dataset collected by ourselves (there are no publicly available datasets for this purpose). Although joint optimization slightly harms the authenticity of the generated patches, it boosts classification performance, suggesting more effective visual cues are extracted in an automatic way. We also transfer our model to a public dataset for breast cancer classification, and outperform the state-of-the-arts significantly.Comment: Accepted by AAAI 201

A pyrene-functionalized triazole-linked hexahomotrioxacalix[3]arene as a fluorescent chemosensor for Zn²⁺ ions

A new pyrenyl appended hexahomotrioxacalix[3]arene L featuring 1,2,3-triazole linkers was synthesized as a fluorescent chemosensor for Zn²⁺ in mixed aqueous media. It exhibited high affinity toward Zn²⁺, and the monomer and excimer emission of the pyrene moieties could be adjusted. The binding stoichiometry of the L·Zn²⁺ complex was determined to be 1:1, and the association constant (Ka) was found to be 7.05 × 10⁴ M⁻¹. The binding behavior with Zn²⁺ has been confirmed by ¹H NMR spectroscopic analysis

Synthesis and evaluation of a novel fluorescent sensor based on hexahomotrioxacalix[3]arene for Zn²+ and Cd²+

A novel type of selective and sensitive fluorescent sensor having triazole rings as the binding sites on the lower rim of a hexahomotrioxacalix[3]arene scaffold in a cone conformation is reported. This sensor has desirable properties for practical applications, including selectivity for detecting Zn²⁺ and Cd²⁺ in the presence of excess competing metal ions at low ion concentration or as a fluorescence enhancement type chemosensor due to the cavity of calixarene changing from a ‘flattened-cone’ to a more-upright form and inhibition of PET. In contrast, the results suggested that receptor 1 is highly sensitive and selective for Cu²⁺ and Fe³⁺ as a fluorescence quenching type chemosensor due to the photoinduced electron transfer (PET) or heavy atom effect

SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes

Multi-object tracking in sports scenes plays a critical role in gathering players statistics, supporting further analysis, such as automatic tactical analysis. Yet existing MOT benchmarks cast little attention on the domain, limiting its development. In this work, we present a new large-scale multi-object tracking dataset in diverse sports scenes, coined as \emph{SportsMOT}, where all players on the court are supposed to be tracked. It consists of 240 video sequences, over 150K frames (almost 15\times MOT17) and over 1.6M bounding boxes (3\times MOT17) collected from 3 sports categories, including basketball, volleyball and football. Our dataset is characterized with two key properties: 1) fast and variable-speed motion and 2) similar yet distinguishable appearance. We expect SportsMOT to encourage the MOT trackers to promote in both motion-based association and appearance-based association. We benchmark several state-of-the-art trackers and reveal the key challenge of SportsMOT lies in object association. To alleviate the issue, we further propose a new multi-object tracking framework, termed as \emph{MixSort}, introducing a MixFormer-like structure as an auxiliary association model to prevailing tracking-by-detection trackers. By integrating the customized appearance-based association with the original motion-based association, MixSort achieves state-of-the-art performance on SportsMOT and MOT17. Based on MixSort, we give an in-depth analysis and provide some profound insights into SportsMOT. The dataset and code will be available at https://deeperaction.github.io/datasets/sportsmot.html