New tylophorine analogs as potential antitumor agents

Tylophorine and related phenanthroindolizidine alkaloids isolated principally from Asclepiadaceae have been targets of synthetic modification because of their profound cytotoxic antitumor activity. As part of our interest in plant-derived antitumor agents, novel water-soluble phenanthrene-based tylophorine derivatives (PBTs) were designed, synthesized and evaluated for anticancer activity. Several PBTs showed superior activity profiles with EC50 values in the sub-micromolar range, which are comparable to those of currently used antitumor drugs. A structure-activity relationship (SAR) study was also explored to facilitate the further development of this new compound class. Subsequently, C9-substituted PBTs were designed and synthesized using 2, 3 methylenedioxy-6-methoxyphenanthrene as a common skeleton based on our prior SAR findings. The C-9 site is an ideal position for introducing more polar, water-solubility-enhancing moieties. We also extended the in vitro antitumor screening to include additional significant tumor types [A549 (lung), DU-145 (prostate), ZR-751 (breast), KB (nasopharyngeal)] as well as a multi-drug resistant cancer cell subline [KB-Vin (multi-drug resistant KB subline)]. Most of the compounds showed fairly uniform and potent cytotoxic activity with EC50 approximately equal to10-7 M against both wild type and matched multi-drug resistant KB cell lines, and displayed notable selectivity toward DU-145 (prostate) and ZR-751 (breast) cancer cell lines. A combination of QSAR modeling and database mining was used to facilitate further design and discovery of novel anticancer PBTs. MolConnZ 2D topological descriptors were applied to a dataset of 52 chemically diverse PBTs and variable selection models were generated using the kappa nearest neighbor (kappa-NN) method. The derived kappa-NN QSAR models have high internal accuracy, with leave-one-out cross-validated R2 (q2) values ranging between 0.6 and 0.8. The original dataset was then divided into several training and test sets to provide highly predictive models with q2 values greater than 0.5 for the training sets and R2 values greater than 0.6 for the test sets. The ten best models were capable of mining the commercially available ChemDiv Database (450,000 compounds) and resulted in 34 consensus hits. Of these 34 compounds, 10 compounds were tested and 8 were confirmed to be active with a best EC50 of 1.8 ?M. These models were further validated by predicting the activity of four new PBTs compounds with reasonable accuracy and 11 consensuses hits with R2 of 0.52. These results indicate that this approach can be successfully applied to further design and discovery of anticancer drug candidates from this compound class

Entropy Weight Measure Model of Online Influential Users’ Relative Social Capital

Based on the perspectives of information resource management and social capital measurement, this paper studies how influential users acquire, accumulate, and use their social capital in social networks to explore the general rules, which enterprises use influential users’ relative competitiveness in their topic areas of expertise to advertise precisely. The paper describes the social capital differences among influential users by introducing and calculating users’ relative social capital. Results show that user’s social capital values in different fields are dissimilar, and the scope and intensity of social capital among different users are relative. The proposed method is proved to be effective and reasonable

Information Entropy-based Social Capital Measure Method of Online Influential Users

Measuring online user influence is a major research topic in social marketing performance maximization. In this study, we comprehensively investigate how online influential users gain, accumulate, and use their social capital from the perspective of information resource management and social capital measurement. First, we define the social capital of online influential users and the attribute characters and relationships reflected fully by personality and sociality index data. We then construct a social capital measurement indicator system and information entropy model of online users. After the calculations of this model, we finally forma social capital measure method of online influential users. The rationality and validity of proposed model are tested by experimental study on real datasets

A convex dual programming for the rational minimax approximation and Lawson's iteration

Computing the discrete rational minimax approximation in the complex plane is challenging. Apart from Ruttan's sufficient condition, there are few other sufficient conditions for global optimality. The state-of-the-art rational approximation algorithms, such as the adaptive Antoulas-Anderson (AAA), AAA-Lawson, and the rational Krylov fitting (RKFIT) method, perform highly efficiently, but the computed rational approximants may be near-best. In this paper, we propose a convex programming approach, the solution of which is guaranteed to be the rational minimax approximation under Ruttan's sufficient condition. Furthermore, we present a new version of Lawson's iteration for solving this convex programming problem. The computed solution can be easily verified as the rational minimax approximant. Our numerical experiments demonstrate that this updated version of Lawson's iteration generally converges monotonically with respect to the objective function of the convex programming. It is an effective competitive approach for the rational minimax problem, compared to the highly efficient AAA, AAA-Lawson, and the stabilized Sanathanan-Koerner iteration.Comment: 38 pages, 10 figure

Supervised Knowledge Makes Large Language Models Better In-context Learners

Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The recent progress in large-scale generative models has further expanded their use in real-world language applications. However, the critical challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. While previous in-context learning research has focused on enhancing models to adhere to users' specific instructions and quality expectations, and to avoid undesired outputs, little to no work has explored the use of task-Specific fine-tuned Language Models (SLMs) to improve LLMs' in-context learning during the inference stage. Our primary contribution is the establishment of a simple yet effective framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks. Using our proposed plug-in method, enhanced versions of Llama 2 and ChatGPT surpass their original versions regarding generalizability and factuality. We offer a comprehensive suite of resources, including 16 curated datasets, prompts, model checkpoints, and LLM outputs across 9 distinct tasks. The code and data are released at: https://github.com/YangLinyi/Supervised-Knowledge-Makes-Large-Language-Models-Better-In-context-Learners. Our empirical analysis sheds light on the advantages of incorporating discriminative models into LLMs and highlights the potential of our methodology in fostering more reliable LLMs.Comment: Accepted to ICLR 202

PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts. In response to this vital need, we introduce PromptBench, a robustness benchmark designed to measure LLMs' resilience to adversarial prompts. This study uses a plethora of adversarial textual attacks targeting prompts across multiple levels: character, word, sentence, and semantic. These prompts are then employed in diverse tasks, such as sentiment analysis, natural language inference, reading comprehension, machine translation, and math problem-solving. Our study generates 4,032 adversarial prompts, meticulously evaluated over 8 tasks and 13 datasets, with 567,084 test samples in total. Our findings demonstrate that contemporary LLMs are vulnerable to adversarial prompts. Furthermore, we present comprehensive analysis to understand the mystery behind prompt robustness and its transferability. We then offer insightful robustness analysis and pragmatic recommendations for prompt composition, beneficial to both researchers and everyday users. We make our code, prompts, and methodologies to generate adversarial prompts publicly accessible, thereby enabling and encouraging collaborative exploration in this pivotal field: https://github.com/microsoft/promptbench.Comment: Technical report; 23 pages; code is at: https://github.com/microsoft/promptbenc

PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization

Instruction tuning large language models (LLMs) remains a challenging task, owing to the complexity of hyperparameter selection and the difficulty involved in evaluating the tuned models. To determine the optimal hyperparameters, an automatic, robust, and reliable evaluation benchmark is essential. However, establishing such a benchmark is not a trivial task due to the challenges associated with evaluation accuracy and privacy protection. In response to these challenges, we introduce a judge large language model, named PandaLM, which is trained to distinguish the superior model given several LLMs. PandaLM's focus extends beyond just the objective correctness of responses, which is the main focus of traditional evaluation datasets. It addresses vital subjective factors such as relative conciseness, clarity, adherence to instructions, comprehensiveness, and formality. To ensure the reliability of PandaLM, we collect a diverse human-annotated test dataset, where all contexts are generated by humans and labels are aligned with human preferences. Our results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM enables the evaluation of LLM to be fairer but with less cost, evidenced by significant improvements achieved by models tuned through PandaLM compared to their counterparts trained with default Alpaca's hyperparameters. In addition, PandaLM does not depend on API-based evaluations, thus avoiding potential data leakage. All resources of PandaLM are released at https://github.com/WeOpenML/PandaLM

On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective

ChatGPT is a recent chatbot service released by OpenAI and is receiving increasing attention over the past few months. While evaluations of various aspects of ChatGPT have been done, its robustness, i.e., the performance to unexpected inputs, is still unclear to the public. Robustness is of particular concern in responsible AI, especially for safety-critical applications. In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective. To do so, we employ the AdvGLUE and ANLI benchmarks to assess adversarial robustness and the Flipkart review and DDXPlus medical diagnosis datasets for OOD evaluation. We select several popular foundation models as baselines. Results show that ChatGPT shows consistent advantages on most adversarial and OOD classification and translation tasks. However, the absolute performance is far from perfection, which suggests that adversarial and OOD robustness remains a significant threat to foundation models. Moreover, ChatGPT shows astounding performance in understanding dialogue-related texts and we find that it tends to provide informal suggestions for medical tasks instead of definitive answers. Finally, we present in-depth discussions of possible research directions.Comment: Technical report; code is at: https://github.com/microsoft/robustlear

Antitumor Agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents

A combined approach of validated QSAR modeling and virtual screening was successfully applied to the discovery of novel tylophrine derivatives as anticancer agents. QSAR models have been initially developed for 52 chemically diverse phenanthrine-based tylophrine derivatives (PBTs) with known experimental EC50 using chemical topological descriptors (calculated with the MolConnZ program) and variable selection k nearest neighbor (kNN) method. Several validation protocols have been applied to achieve robust QSAR models. The original dataset was divided into multiple training and test sets, and the models were considered acceptable only if the leave-one-out cross-validated R2 (q2) values were greater than 0.5 for the training sets and the correlation coefficient R2 values were greater than 0.6 for the test sets. Furthermore, the q2 values for the actual dataset were shown to be significantly higher than those obtained for the same dataset with randomized target properties (Y-randomization test), indicating that models were statistically significant. Ten best models were then employed to mine a commercially available ChemDiv Database (ca. 500K compounds) resulting in 34 consensus hits with moderate to high predicted activities. Ten structurally diverse hits were experimentally tested and eight were confirmed active with the highest experimental EC50 of 1.8µM implying an exceptionally high hit rate (80%). The same ten models were further applied to predict EC50 for four new PBTs, and the correlation coefficient (R2) between the experimental and predicted EC50 for these compounds plus eight active consensus hits was shown to be as high as 0.57. Our studies suggest that the approach combining validated QSAR modeling and virtual screening could be successfully used as a general tool for the discovery of novel biologically active compounds

Extracellular Matrix Elasticity Regulates Osteocyte Gap Junction Elongation: Involvement of Paxillin in Intracellular Signal Transduction

Background/Aims: Osteocytes can sense and respond to extracellular stimuli, including biochemical factors throughout the cell body, dendritic processes, and cilia bending. However, further exploration is required of osteocyte function in response to substrate stiffness, an important passive mechanical cue at the interface between osteocytes and the extracellular matrix, and the deep bio-mechanism in osteocytes involving mechanosensing of cell behavior. Methods: We fabricated silicon-based elastomer polydimethylsiloxane substrates with different stiffnesses but with the same surface topologies. We then seeded osteocytes onto the substrates to examine their responses. Methodologies used included scanning electron microscopy (SEM) for cell morphology, confocal laser scanning microscopy (CLSM) for protein distribution, western blot for protein levels, co-immunoprecipitation for protein interactions, and quantitative real-time polymerase chain reaction for gene expression. Results: SEM images revealed that substrate stiffness induced a change in osteocyte morphology, and CLSM of F-actin staining revealed that substrate stiffness can alter the cytoskeleton. These results were accompanied by changes in focal adhesion capacity in osteocytes, determined via characterization of vinculin expression and distribution. Furthermore, on the exterior of the cell membrane, fibronectin was altered by substrate stiffness. The fibronectin then induced a change in paxillin on the inner membrane of the cell via protein–protein interaction through transmembrane processing. Paxillin led to changes in connexin 43 via protein–protein binding, thereby influencing osteocyte gap junction elongation. Conclusion: This process -from mechanosensing and mechanotransduction to cell function - not only indicates that the effects of mechanical factors on osteocytes can be directly sensed from the cell body, but also indicates the involvement of paxillin transduction