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关键词： Systems biology   Computer science   Bioengineering  

摘要： Gene regulatory networks (GRNs) are critically important for efforts in biomedicine and biotechnology. Here, we introduce the Regulatory Network Machine (RNM) framework, demonstrating how GRNs behave as analog computers capable of sophisticated information processing. Our RNM framework encapsulates: (1) a dissipative dynamic system with a focus on GRNs, (2) a set of inputs to the system, (3) system output states with identifiable relevance to biotechnological or biomedical objectives, and (4) Network Finite State Machines (NFSMs), which are maps detailing how the system changes equilibrium state in response to patterns of applied inputs. As an extension to attractor landscape analysis, the NFSMs map the sequential logic inherent in the GRN and, therefore, embody the "software-like" nature of the system, providing easy identification of specific applied interventions necessary to achieve desired, stable biological outcomes. We illustrate the use of our RNM framework in important biological examples, including in cancer renormalization.

关键词： Students  

摘要： As artificial intelligence (AI) further embeds itself into many settings across personal and professional contexts, increasing attention must be paid not only to AI ethics, but also to the governance and regulation of AI technologies through AI policy. However, the prevailing post-secondary computing curriculum is currently ill-equipped to prepare future AI practitioners to confront increasing demands to implement abstract ethical principles and normative policy preferences into the design and development of AI systems. We believe that familiarity with the ‘AI policy landscape’ and the ability to translate ethical principles to practices will in the future constitute an important responsibility for even the most technically-focused AI engineers. Toward preparing current computer science (CS) students for these new expectations, we developed an AI Policy Module to introduce discussions of AI policy into the CS curriculum. Building on a successful pilot in fall 2024, in this innovative practice full paper we present an updated and expanded version of the module, including a technical assignment on "AI regulation". We present the findings from our pilot of the AI Policy Module 2.0, evaluating student attitudes towards AI ethics and policy through pre- and post-module surveys. Following the module, students reported increased concern about the ethical impacts of AI technologies while also expressing greater confidence in their abilities to engage in discussions about AI regulation. Finally, we highlight the AI Regulation Assignment as an effective and engaging tool for exploring the limits of AI alignment and emphasizing the role of ‘policy’ in addressing ethical challenges. © 2025, CC BY-NC-ND.

关键词： Palbociclib   bovine serum albumin   interplay   spectral analysis   dynamic simulation  

摘要： Spectroscopic and molecular simulation techniques are increasingly becoming powerful tools for studying the drug-protein binding acting. In this article, the conjugation features of palbociclib, a cell cycle-dependent kinase 4/6 (CDK4/6) inhibitor primarily used for HR+/HER2- breast cancer treatment, with bovine serum albumin (BSA) was examined in a physiomimetic setting. Based on the experimental results, the incorporation of a 1:1 palbociclib-BSA complex resulted in the intrinsic fluorescence quenching of BSA by palbociclib via static quenching. The results of competition experiments suggested that palbociclib has a higher probability for entering the BSA target Site III as compared to site I and site II. The thermodynamic and competition experiments yielded evidence to suggest that van der Waals forces, hydrogen bonding and hydrophobic interactions were responsible for the binding of palbociclib to BSA. Structural alterations resulting from palbociclib administration to BSA illustrated a minor influence of palbociclib on the advanced conformations of BSA. However, it led to an increase in hydrophobicity surrounding the tryptophan (Trp) and tyrosine (Tyr) residues. In addition, the experimental findings underwent a validation via the application of molecular docking and molecular dynamics simulations.

摘要： Two-year institutions in the United States, commonly known as Community and Technical Colleges, play a vital role in computing education by offering a range of programs that awards credentials and degrees. These programs address regional workforce needs, support socioeconomic development, and provide pathways for students to transition to four-year institutions. The Computer Science Transfer Associate degree, a widely recognized award, requires alignment with four-year programs through Memoranda of Understanding (MOUs) and adherence to established curriculum guidelines, such as those provided by ACM/IEEE-CS. In 2023, the ACM, IEEE-CS, and AAAI released the CS2023 Curricula, a comprehensive framework defining 17 Knowledge Areas for computer science education. The ACM Committee for Computing Education in Community Colleges (CCECC) builds on this foundation through the "CS202x Transfer" initiative, aiming to map these Knowledge Areas to two-year programs. This initiative involves collaboration with four-year institutions to validate content and refine curriculum models, ensuring their relevance to both academic and workforce needs. Key focus areas include cybersecurity, artificial intelligence (AI), and society, ethics, and the profession (SEP), which are integrated across curricula to address emerging technologies and ethical considerations.

关键词： Biographies  

摘要： In his autobiographic essay written in 1999, "From logic to computer science and back", Martin David Davis (3/8/1928–1/1/2023) indicated that he viewed himself as a logician and a computer scientist. He expanded the essay in 2016 and expressed a new perspective through a changed title, "My life as a logician". He points out that logic was the unifying theme underlying his scientific career. Our paper attempts to provide a consistent vision that illuminates Davis’ successive contributions leading to his landmark writings on computability, unsolvable problems, automated reasoning, as well as the history and philosophy of computing. © 2025, CC BY-SA.

摘要： The rapid evolution of AI and the increased accessibility of scientific articles through open access marks a pivotal moment in research. AI-driven tools are reshaping how scientists explore, interpret, and contribute to the body of scientific knowledge, offering unprecedented opportunities. Nonetheless, a significant challenge remains: dealing with the overwhelming number of papers published every year. A promising solution is the use of knowledge graphs, which provide structured, interconnected, and formalized frameworks that improve the capabilities of AI systems to integrate information from the literature. This paper presents the last version of the Computer Science Knowledge Graph (CS-KG 2.0), an extensive knowledge base generated from 15 million research papers. CS-KG 2.0 describes 25 million entities linked by 67 million relationships, offering a nuanced representation of the scientific knowledge within the field of computer science. This innovative resource facilitates new research opportunities in key areas such as analysis and forecasting of research trends, hypothesis generation, smart literature search, automatic production of literature review, and scientific question-answering.

关键词： Noise reduction   Recommender systems   Noise   Collaborative filtering   Noise measurement   Training   Contrastive learning   Reinforcement learning   Computer science   Adaptation models   Dynamic denoising   graph learning   implicit feedback   recommender system  

摘要： Due to the inherent challenges in acquiring explicit feedback, graph collaborative filtering (GCF) models often resort to implicit feedback. However, there exists noise in implicit feedback that may not accurately reflect users' preferences. This noise will be amplified by the aggregating and propagating operations of GCF, thereby affecting the performance of GCF. Existing noise mitigation methods attempt to filter noisy samples from implicit feedback data, yet they face limitations such as dependency on side information for sample selection, neglect of false-negative noise, and disregard for the impact of previous selections on current iteration. To overcome these challenges, we propose a dynamic graph learning framework to denoise implicit feedback for GCF (DGLD). DGLD comprises a graph learning module and a reinforcement learning module. The graph learning module evaluates the confidence degrees of interactions by leveraging user-item cosine similarity and global user preferences, updating user-item interaction graph without relying on side information. Meanwhile, the reinforcement learning module employs a policy network to select "false-positive denoising" and "false-negative denoising" actions based on performance of the recommendation model and state of interaction graph obtained from previous iterations. These modules work synergistically to dynamically denoise implicit feedback. Experimental results on three benchmark datasets underscore the superiority of our approach over state-of-the-art general and denoising recommendation models.

关键词： Computing activities   K-12 computing education   computational thinking   emerging technologies   computer science   learning  

摘要： The interest in computing learning activities is continuously increasing, keeping pace with the evolution of educational technologies. This rise in engagement is evident within approaches grounded in constructivism and constructionism, which provide an ideal setting for the spectrum of skills, literacies, and competencies that global initiatives capitalise on in K-12 education. Due to recent advancements in technology and societal changes, we are addressing the need for an updated perspective focussed on designing and implementing computational activities. To achieve this, we have analysed forty-six peer-reviewed empirical studies from the last decade to summarise technology-supported learning strategies' current direction and future potential. The main findings describe programming software and IoT as the preferred digital tools, often presented in novel combinations, extensions, and contextualisations. Implementation primarily occurs within the domain of computer science but exhibits a high degree of interdisciplinarity. Furthermore, there is an increase in activities referring to real-life scenarios, encouraging learners to develop computational skills beyond the classroom and expand their digital awareness through interaction with emerging technologies. We leverage these trends and significant patterns by proposing a summary of focal points for future research and practice, with a focus on activity design.