Design, synthesis, and biological evaluation of NAD(P)H: quinone oxidoreductase (NQO1)-targeted oridonin prodrugs possessing indolequinone moiety for hypoxia-selective activation

The enzyme NQO1 is a potential target for selective cancer therapy due to its overexpression in certain hypoxic tumors. A series of prodrugs possessing a variety of cytotoxic diterpenoids (oridonin and its analogues) as the leaving groups activated by NQO1 were synthesized by functionalization of 3-(hydroxymethyl)indolequinone, which is a good substrate of NQO1. The target compounds (29a-m) exhibited relatively higher antiproliferative activities against NQO1-rich human colon carcinoma cells (HT-29) and human lung carcinoma (A549) cells (IC50 ¼ 0.263e2.904 mM), while NQO1-defficient lung adenosquamous carcinoma cells (H596) were less sensitive to these compounds, among which, compound 29h exhibited the most potent antiproliferative activity against both A549 and HT-29 cells, with IC50 values of 0.386 and 0.263 mM, respectively. Further HPLC and docking studies demonstrated that 29h is a good substrate of NQO1. Moreover, the investigation of anticancer mechanism showed that the representative compound 29h affected cell cycle and induced NQO1 dependent apoptosis through an oxidative stress triggered mitochondria-related pathway in A549 cells. Besides, the antitumor activity of 29h was also verified in a liver cancer xenograft mouse model. Biological evaluation of these compounds concludes that there is a strong correlation between NQO1 enzyme and induction of cancer cell death. Thus, this suggests that some of the target compounds activated by NQO1 are novel prodrug candidates potential for selective anticancer therapy

An Adaptive Orthogonal Basis Method for Computing Multiple Solutions of Differential Equations with polynomial nonlinearities

This paper presents an innovative approach, the Adaptive Orthogonal Basis Method, tailored for computing multiple solutions to differential equations characterized by polynomial nonlinearities. Departing from conventional practices of predefining candidate basis pools, our novel method adaptively computes bases, considering the equation's nature and structural characteristics of the solution. It further leverages companion matrix techniques to generate initial guesses for subsequent computations. Thus this approach not only yields numerous initial guesses for solving such equations but also adapts orthogonal basis functions to effectively address discretized nonlinear systems. Through a series of numerical experiments, this paper demonstrates the method's effectiveness and robustness. By reducing computational costs in various applications, this novel approach opens new avenues for uncovering multiple solutions to differential equations with polynomial nonlinearities

Integrating Maker Education into Primary Science: A Case Study of the "Ship Research" Curriculum with 3D Printing and Sensor Technology

This study develops an interdisciplinary primary science curriculum—"Ship Research"—guided by maker education principles, integrating 3D printing, graphical programming (Scratch/Arduino), and sensor technologies (water level/obstacle avoidance). A "knowledge-practice-iteration" pedagogical framework was implemented through contextualized project-based learning, supported by a multidimensional evaluation system (self-assessment, peer review, and artifact evaluation). Post-intervention assessments revealed significant improvements: students' confidence in hands-on skills increased by 7.5%, interest in scientific inquiry rose by 10%, and collaborative problem-solving capabilities were enhanced. The findings demonstrate that maker curricula enhanced by digital tools effectively foster innovation competencies in elementary students, offering a replicable model for transforming science education through technology-enabled creative practices

Data poisoning attacks against conformal prediction

The efficient and theoretically sound uncertainty quantification is crucial for building trust in deep learning models. This has spurred a growing interest in conformal prediction (CP), a powerful technique that provides a model-agnostic and distribution-free method for obtaining conformal prediction sets with theoretical guarantees. However, the vulnerabilities of such CP methods with regard to dedicated data poisoning attacks have not been studied previously. To bridge this gap, for the first time, we in this paper propose a new class of black-box data poisoning attacks against CP, where the adversary aims to cause the desired manipulations of some specific examples' prediction uncertainty results (instead of misclassifications). Additionally, we design novel optimization frameworks for our proposed attacks. Further, we conduct extensive experiments to validate the effectiveness of our attacks on various settings (e.g., the full and split CP settings). Notably, our extensive experiments show that our attacks are more effective in manipulating uncertainty results than traditional poisoning attacks that aim at inducing misclassifications, and existing defenses against conventional attacks are ineffective against our proposed attacks

A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer

Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset introduced by NTIRE challenges. One of the reasons for such failures is that non-homogeneous haze does not obey one of the assumptions that is required for modeling homogeneous haze. In addition, a large number of pairs of non-homogeneous hazy image and the clean counterpart is required using traditional end-to-end training approaches, while NH-HAZE23 dataset is of limited quantities. Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing approach that reduces the distribution gaps between the target dataset and the augmented one. This finding indeed aligns with the essence of data-centric AI. With a novel network architecture and a principled data-preprocessing approach that systematically enhances data quality, we present an innovative dehazing method. Specifically, we apply RGB-channel-wise transformations on the augmented datasets, and incorporate the state-of-the-art transformers as the backbone in the two-branch framework. We conduct extensive experiments and ablation study to demonstrate the effectiveness of our proposed method.Comment: Accepted by CVPRW 202

ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representation

State-of-the-art vision-language models (VLMs) still have limited performance in structural knowledge extraction, such as relations between objects. In this work, we present ViStruct, a training framework to learn VLMs for effective visual structural knowledge extraction. Two novel designs are incorporated. First, we propose to leverage the inherent structure of programming language to depict visual structural information. This approach enables explicit and consistent representation of visual structural information of multiple granularities, such as concepts, relations, and events, in a well-organized structured format. Second, we introduce curriculum-based learning for VLMs to progressively comprehend visual structures, from fundamental visual concepts to intricate event structures. Our intuition is that lower-level knowledge may contribute to complex visual structure understanding. Furthermore, we compile and release a collection of datasets tailored for visual structural knowledge extraction. We adopt a weakly-supervised approach to directly generate visual event structures from captions for ViStruct training, capitalizing on abundant image-caption pairs from the web. In experiments, we evaluate ViStruct on visual structure prediction tasks, demonstrating its effectiveness in improving the understanding of visual structures. The code is public at \url{https://github.com/Yangyi-Chen/vi-struct}.Comment: Accepted to EMNLP 202

A case of complicated parapneumonic pleural effusion caused by paragonimus infection in a child was successfully treated by intrapleural injection of urokinase: a case report and literature review

ObjectiveThe clinical data of a child with complex parapneumonic effusion (PPE) caused by pneumofluke infection were analyzed, and the diagnosis and treatment of the disease were discussed through literature review. The effectiveness and safety of urokinase in the treatment of complex PPE and empyema caused by multiple pathogens were emphasized.MethodsA 3-year-old male child with pneumofluke infection was admitted to the pediatric department of Mianyang Central Hospital. Chest CT and chest ultrasound showed a right pleural effusion with dense septum formation. The condition was relieved after treatment with praziquantel for anti-infection, thoracic catheter drainage, and urokinase injection into pleural cavity. Through systematic literature search of Pubmed, Embase, CNKI, Wanfang and VIP Chinese databases, no cases of urokinase treatment of pleural effusion caused by pneumofluke infection were found.ResultsA total of 150 ml thick yellow turbidous fluid was drained out of the pleural cavity, and the patient's symptoms and signs were significantly relieved. Reexamination of the chest CT showed that the right pleural effusion was significantly reduced and the right lung was significantly reexpanded. There were no complications such as bronchopleural fistula, pneumothorax, abnormal coagulation function, bleeding and fever during treatment.ConclusionThoracic catheter drainage combined with injection of urokinase is an effective and safe method for the treatment of complex PPE and empyema caused by pneumofluke infection in children. At the same time, the literature review showed that urokinase injection into pleural cavity was effective in the treatment of complex PPE and empyema caused by infection, trauma, tumor and other causes, and no obvious side effects occurred

Effects of spinal-epidural anesthesia combined with intravenous etomidate on adrenocortical and immune stress in elderly patients undergoing anorectal surgery: A retrospective analysis

The management of anesthesia in elderly patients undergoing surgery presents unique challenges, particularly in mitigating stress responses and ensuring stability. Etomidate may alleviate adrenocortical and immune stress. This study aims to investigate the anesthetic effects of combined spinal-epidural anesthesia (CSEA) supplemented with etomidate during anorectal surgery in elderly patients. The medical records of 49 cases treated with CSEA and etomidate (ETO group) and 48 cases treated with CSEA alone (control group) were reviewed and analyzed. All patients received ropivacaine hydrochloride for anesthesia, with the ETO group additionally receiving an infusion of etomidate for sedation. Parameters such as arterial blood gas, visual analog scale (VAS), Ramsay sedation scale (RSS), serum cortisol and norepinephrine levels, pro-inflammatory cytokines, and lymphocyte ratios were assessed at different time points. Compared to the control group, the ETO group showed increased mean arterial pressure, decreased heart rate, and elevated arterial SpO2 30 minutes after anesthesia. The ETO group also had higher RSS scores, lower VAS scores, and reduced serum cortisol and norepinephrine levels. Additionally, decreased levels of pro-inflammatory cytokines, such as interleukin (IL)-6, tumor necrosis factor (TNF)-α, and IL-8, were observed, along with an increase in the regulatory cytokine IL-10. An increased proportion of CD4+ T cells and a higher CD4/CD8 ratio were also noted. This study demonstrates the benefits of using etomidate to mitigate adrenocortical and immune stress in elderly patients undergoing anorectal surgery

Executable Code Actions Elicit Better LLM Agents

Large Language Model (LLM) agents, capable of performing a broad range of actions, such as invoking tools and controlling robots, show great potential in tackling real-world challenges. LLM agents are typically prompted to produce actions by generating JSON or text in a pre-defined format, which is usually limited by constrained action space (e.g., the scope of pre-defined tools) and restricted flexibility (e.g., inability to compose multiple tools). This work proposes to use executable Python code to consolidate LLM agents' actions into a unified action space (CodeAct). Integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations through multi-turn interactions. Our extensive analysis of 17 LLMs on API-Bank and a newly curated benchmark shows that CodeAct outperforms widely used alternatives (up to 20% higher success rate). The encouraging performance of CodeAct motivates us to build an open-source LLM agent that interacts with environments by executing interpretable code and collaborates with users using natural language. To this end, we collect an instruction-tuning dataset CodeActInstruct that consists of 7k multi-turn interactions using CodeAct. We show that it can be used with existing data to improve models in agent-oriented tasks without compromising their general capability. CodeActAgent, finetuned from Llama2 and Mistral, is integrated with Python interpreter and uniquely tailored to perform sophisticated tasks (e.g., model training) using existing libraries and autonomously self-debug.Comment: Accepted by ICML 2024; Code, data, model, and demo are available at https://github.com/xingyaoww/code-ac