Nobel Prize-winning American scientist James Watson has died aged 97. His co-discovery of the structure of DNA opened the door to help explain how DNA replicates and carries genetic information, setting the stage for rapid advances in molecular biology. But his honorary titles were stripped in 2019 after he repeated comments about race and intelligence. In a TV programme, he made a reference to a view that genes cause a difference on average between blacks and whites on IQ tests. The death of Watson, who co-discovered the double-helix structure of DNA in 1953, was confirmed to the BBC by Cold Spring Harbor Laboratory, where he worked and researched for decades.

In February 1953, two men walked into a pub in Cambridge and announced they had found the secret of life. It was not an idle boast. One was James Watson, an American biologist from the Cavendish laboratory; the other was his British research partner, Francis Crick. The full Promethean power of their achievement would slowly emerge over decades of research by fellow geneticists. It also opened a Pandora's Box of controversial scientific and ethical issues - including human cloning, designer babies and Frankenstein foods.

Financial bubbles often arrive without much warning, but create long-lasting economic effects. For example, during the dot-com bubble, innovative technologies created market disruptions through excitement for a promised bright future. Such technologies originated from research where scientists had developed them for years prior to their entry into the markets. That raises a question on the possibility of analyzing scientific publishing data (e.g. citation networks) leading up to a bubble for signals that may forecast the rise and fall of similar future bubbles. To that end, we utilized temporal SNAs to detect possible relationships between the publication citation networks of scientists and financial market data during two modern eras of rapidly shifting technology: 1) dot-com era from 1994 to 2001 and 2) AI era from 2017 to 2024. Results showed that the patterns from the dot-com era (which did end in a bubble) did not definitively predict the rise and fall of an AI bubble. While yearly citation networks reflected possible changes in publishing behavior of scientists between the two eras, there was a subset of AI era scientists whose publication influence patterns mirrored those during the dot-com era. Upon further analysis using multiple analysis techniques (LSTM, KNN, AR X/GARCH), the data seems to suggest two possibilities for the AI era: unprecedented form of financial bubble unseen or that no bubble exists. In conclusion, our findings imply that the patterns present in the dot-com era do not effectively translate in such a manner to apply them to the AI market.

Uncertainty of scientific findings are typically reported through statistical metrics such as $p$-values, confidence intervals, etc. The magnitude of this objective uncertainty is reflected in the language used by the authors to report their findings primarily through expressions carrying uncertainty-inducing terms or phrases. This language uncertainty is a subjective concept and is highly dependent on the writing style of the authors. There is evidence that such subjective uncertainty influences the impact of science on public audience. In this work, we turned our focus to scientists themselves, and measured/analyzed the subjective uncertainty and its impact within scientific communities across different disciplines. We showed that the level of this type of uncertainty varies significantly across different fields, years of publication and geographical locations. We also studied the correlation between subjective uncertainty and several bibliographical metrics, such as number/gender of authors, centrality of the field's community, citation count, etc. The underlying patterns identified in this work are useful in identification and documentation of linguistic norms in scientific communication in different communities/societies.

How many times must a phenomenon occur before it graduates from a coincidence to a pattern? Usually, the answer depends on how unlikely, how far from the ordinary, and how (seemingly) inexplicable the phenomenon is. The more so, the lower the threshold. I was very surprised (and pleased) to read of this year's winners of the Nobel Prize in Physics: John Hopfield, a professor of Molecular Biology and earlier of Chemistry and Biology, together with Geoffrey Hinton, a professor of Computer Science. Their affiliations name three major scientific fields, none of them being Physics!

As one gains seniority, there is a presumption--dubious, perhaps--that one also gains wisdom. Thus, I find myself asked, not infrequently, to share some wisdom with junior researchers who seek insight that can foster success in their careers or life in general. I offer one cautionary bit of advice: "Life is not linear." Linearity is one of the greatest success stories in mathematics. According to Encyclopedia Britannica, "Unlike other parts of mathematics that are frequently invigorated by new ideas and unsolved problems, linear algebra is very well understood." In the U.S., in eighth grade, pupils learn how to analyze and represent linear functions and solve linear equations and systems of linear equations.

Scientific social stratification is a classic theme in the sociology of science. The deep integration of social media has bridged the gap between scientometrics and sociology of science. This study comprehensively analyzes the impact of social media on scientific stratification and mobility, delving into the complex interplay between academic status and social media activity in the digital age. [Research Method] Innovatively, this paper employs An Explainable Logistic Attribution Analysis from a meso-level perspective to explore the correlation between social media behaviors and scientific social stratification. It examines the impact of scientists' use of social media in the digital age on scientific stratification and mobility, uniquely combining statistical methods with machine learning. This fusion effectively integrates hypothesis testing with a substantive interpretation of the contribution of independent variables to the model. [Research Conclusion] Empirical evidence demonstrates that social media promotes stratification and mobility within the scientific community, revealing a nuanced and non-linear facilitation mechanism. Social media activities positively impact scientists' status within the scientific social hierarchy to a certain extent, but beyond a specific threshold, this impact turns negative. It shows that the advent of social media has opened new channels for academic influence, transcending the limitations of traditional academic publishing, and prompting changes in scientific stratification. Additionally, the study acknowledges the limitations of its experimental design and suggests future research directions.

BACKGROUND David A. Shamma is a distinguished industry scientist researching people, AI, and HCI. As a curious child armed with a screwdriver, I disassembled things. I was interested in seeing how devices clicked together. On more than one occasion, a random spring would jettison out of the device and get me in trouble. Other times, I hoped my experiments went unnoticed.

Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advent of large language models affect the practice of science? For this opinion piece, we have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate. Schulz et al. make the argument that working with LLMs is not fundamentally different from working with human collaborators, while Bender et al. argue that LLMs are often misused and over-hyped, and that their limitations warrant a focus on more specialized, easily interpretable tools. Marelli et al. emphasize the importance of transparent attribution and responsible use of LLMs. Finally, Botvinick and Gershman advocate that humans should retain responsibility for determining the scientific roadmap. To facilitate the discussion, the four perspectives are complemented with a response from each group. By putting these different perspectives in conversation, we aim to bring attention to important considerations within the academic community regarding the adoption of LLMs and their impact on both current and future scientific practices.

NASA's James Webb Space Telescope has detected what were believed to be fabled'dark stars' that could solve one of the universe's biggest mysteries. A team of astronomers led by The University of Texas (UT) at Austin identified three potential'dark stars' that formed about 320 million years after the Big Bang, making them the earliest stars ever seen by human eyes. The image shows three fuzzy dots glowing in the blackness of space, but astronomers believe the tiny specs could lead to uncovering the elusive dark matter. Dark stars could only exist if dark matter creates heat at the core, preventing the stars from collapsing and causing them to puff up, which the team found in JWST's observations. Although dark matter makes up about 85 percent of the universe, its nature has eluded scientists.