The Dilemma of Fair Use of Artificial Intelligence Painting and Its Regulations

At present, artificial intelligence painting technology is in an era of prosperity and development. The “transformation” of artificial intelligence painting works has brought challenges to the traditional fair use system, and characterization as fair use may lead to an imbalance of interests. It is necessary to re-examine the rationality of qualifying the use of artificial intelligence paintings as fair use. At the same time, analyze the infringement risk in the painting process in combination with the painting principles of artificial intelligence, and put forward some regulatory suggestions

The epidemiological patterns of non-Hodgkin lymphoma: global estimates of disease burden, risk factors, and temporal trends

BackgroundThe incidence of non-Hodgkin’s lymphoma (NHL) has increased steadily over the past few decades. Elucidating its global burden will facilitate more effective disease management and improve patient outcomes. We explored the disease burden, risk factors, and trends in incidence and mortality in NHL globally.MethodsThe up-to-date data on age-standardized incidence and mortality rates of NHL were retrieved from the GLOBOCAN 2020, CI5 volumes I-XI, WHO mortality database, and Global Burden of Disease (GBD) 2019, focusing on geographic disparities worldwide. We reported incidence and mortality by sex and age, along with corresponding age-standardized rates (ASRs), the average annual percentage change (AAPC), and future burden estimates to 2040.ResultsIn 2020, there were an estimated 545,000 new cases and 260,000 deaths of NHL globally. In addition, NHL resulted in 8,650,352 age-standardized DALYs in 2019 worldwide. The age-specific incidence rates varied drastically across world areas, at least 10-fold in both sexes, with the most pronounced increase trend found in Australia and New Zealand. By contrast, North African countries faced a more significant mortality burden (ASR, 3.7 per 100,000) than highly developed countries. In the past decades, the pace of increase in incidence and mortality accelerated, with the highest AAPC of 4.9 (95%CI: 3.6-6.2) and 6.8 (95%CI: 4.3-9.2) in the elderly population, respectively. Considering risk factors, obesity was positively correlated with age-standardized incidence rates (P< 0.001). And North America was the high-risk region for DALYs due to the high body mass index in 2019. Regarding demographic change, NHL incident cases are projected to rise to approximately 778,000 by 2040.ConclusionIn this pooled analysis, we provided evidence for the growing incidence trends in NHL, particularly among women, older adults, obese populations, and HIV-infected people. And the marked increase in the older population is still a public health issue that requires more attention. Future efforts should be directed at cultivating health awareness and formulating effective and locally tailored cancer prevention strategies, especially in most developing countries

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.Comment: Accepted by Briefings in Bioinformatic