The power of storytelling in STEM.

Elea-Maria Abisamra is an honors undergraduate student and research fellow at Virginia Tech. She is majoring in Cognitive and Behavioral Neuroscience and has passions for STEM, writing, and entrepreneurship. In June 2022, Elea acted on her dream and founded Kids Can Write, becoming a CEO of a global nonprofit organization helping turn kids into published authors while teaching them STEM in an innovative and unique way. This is her story.

Here to stay: Writing lasting epigenetic memories.

In this issue of Cell, Nuñez et al. develop CRISPRoff, a programmable epigenetic memory writer capable of establishing specific gene silencing programs that are stably maintained across cell division and differentiation. The singular dCas9 fusion offers a simple, reliable, and general tool for genome-wide screens, multiplexed editing, and potential therapeutics.

Writing to the Mind's Eye of the Blind.

The implantation of electrodes on the visual cortex of blind individuals could lead to the restoration of a rudimentary form of sight. In this issue of Cell, Beauchamp et al. use electrical stimulation of the visual cortex to create visual perception of shapes.

A Scientist and a Journalist Walk into a Bar .

Who are science journalists, and how can journalists and research scientists work together to improve science communication?

More writing: mTORC1 promotes m6A mRNA methylation.

Cho et al. (2021) and Villa et al. (2021) demonstrate that mTORC1 stimulates m6A mRNA methylation via WTAP expression and SAM synthesis. Increased mRNA methylation in turn promotes cell growth by enhancing mRNA degradation or translation.

Restoring and attributing ancient texts using deep neural networks.

Ancient history relies on disciplines such as epigraphy-the study of inscribed texts known as inscriptions-for evidence of the thought, language, society and history of past civilizations1. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. Here we present Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian's workflow. The architecture of Ithaca focuses on collaboration, decision support and interpretability. While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool. Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history. This research shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history.

Help biocurators to maximize the reach of your data.

Curated scientific databases catalogue and amplify research findings to maximize their reach. Authors should write their papers with this in mind, ensuring that data are accurate, easy to extract, and presented in standardized formats.

The gender agency gap in fiction writing (1850 to 2010).

Works of fiction play a crucial role in the production of cultural stereotypes. Concerning gender, a widely held presumption is that many such works ascribe agency to men and passivity to women. However, large-scale diachronic analyses of this notion have been lacking. This paper provides an assessment of agency attributions in 87,531 fiction works written between 1850 and 2010. It introduces a syntax-based approach for extracting networks of character interactions. Agency is then formalized as a dyadic property: Does a character primarily serve as an agent acting upon the other character or as recipient acted upon by the other character? Findings indicate that female characters are more likely to be passive in cross-gender relationships than their male counterparts. This difference, the gender agency gap, has declined since the 19th century but persists into the 21st. Male authors are especially likely to attribute less agency to female characters. Moreover, certain kinds of actions, especially physical and villainous ones, have more pronounced gender disparities.

Even laypeople use legalese.

Whereas principles of communicative efficiency and legal doctrine dictate that laws be comprehensible to the common world, empirical evidence suggests legal documents are largely incomprehensible to lawyers and laypeople alike. Here, a corpus analysis (n = 59) million words) first replicated and extended prior work revealing laws to contain strikingly higher rates of complex syntactic structures relative to six baseline genres of English. Next, two preregistered text generation experiments (n = 286) tested two leading hypotheses regarding how these complex structures enter into legal documents in the first place. In line with the magic spell hypothesis, we found people tasked with writing official laws wrote in a more convoluted manner than when tasked with writing unofficial legal texts of equivalent conceptual complexity. Contrary to the copy-and-edit hypothesis, we did not find evidence that people editing a legal document wrote in a more convoluted manner than when writing the same document from scratch. From a cognitive perspective, these results suggest law to be a rare exception to the general tendency in human language toward communicative efficiency. In particular, these findings indicate law's complexity to be derived from its performativity, whereby low-frequency structures may be inserted to signal law's authoritative, world-state-altering nature, at the cost of increased processing demands on readers. From a law and policy perspective, these results suggest that the tension between the ubiquity and impenetrability of the law is not an inherent one, and that laws can be simplified without a loss or distortion of communicative content.

New paradigm for teaching scientific writing in STEM.

Written and oral communication are skills graduate students often request training in and supervisors often bemoan the lack of. We describe an approach to address this training gap using an instructional model that integrates experienced research-active PIs with an expert in the study and teaching of technical writing.

Nobel and novice: Author prominence affects peer review.

Peer review is a well-established cornerstone of the scientific process, yet it is not immune to biases like status bias, which we explore in this paper. Merton described this bias as prominent researchers getting disproportionately great credit for their contribution, while relatively unknown researchers get disproportionately little credit [R. K. Merton, Science 159, 56-63 (1968)]. We measured the extent of this bias in the peer-review process through a preregistered field experiment. We invited more than 3,300 researchers to review a finance research paper jointly written by a prominent author (a Nobel laureate) and by a relatively unknown author (an early career research associate), varying whether reviewers saw the prominent author's name, an anonymized version of the paper, or the less-well-known author's name. We found strong evidence for the status bias: More of the invited researchers accepted to review the paper when the prominent name was shown, and while only 23% recommended "reject" when the prominent researcher was the only author shown, 48% did so when the paper was anonymized, and 65% did when the little-known author was the only author shown. Our findings complement and extend earlier results on double-anonymized vs. single-anonymized review [R. Blank, Am. Econ. Rev. 81, 1041-1067 (1991); M. A. Ucci, F. D'Antonio, V. Berghella, Am. J. Obstet. Gynecol. MFM 4, 100645 (2022)].

Artificial Intelligence: A Help or Hindrance to Scientific Writing?

We have assessed the chatbot Generative Pretrained Transformer, a type of artificial intelligence software designed to simulate conversations with human users, in an experiment designed to test its relevance to scientific writing. chatbot Generative Pretrained Transformer could become a promising and powerful tool for tasks such as automated draft generation, which may be useful in academic activities to make writing work faster and easier. However, the use of this tool in scientific writing raises some ethical concerns and therefore there have been calls for it to be regulated. It may be difficult to recognize whether an abstract or paper is written by a chatbot or a human being because chatbots use advanced techniques, such as natural language processing and machine learning, to generate text that is similar to human writing. To detect the author is a complex task and requires thorough critical reading to reach a conclusion. The aim of this paper is, therefore, to explore the pros and cons of the use of chatbots in scientific writing.

Quantifying Radiology Residents' Learning Curves in Report Writing Performance Through Report Comparison and Jaccard Similarity.

Background Report writing skills are a core competency to be acquired during residency, yet objective tools for tracking performance are lacking. Purpose To investigate whether the Jaccard index, derived from report comparison, can objectively illustrate learning curves in report writing performance throughout radiology residency. Materials and Methods Retrospective data from 246 984 radiology reports written from September 2017 to November 2022 in a tertiary care radiology department were included. Reports were scored using the Jaccard similarity coefficient (ie, a quantitative expression of the amount of edits performed; range, 0-1) of residents' draft (unsupervised initial attempt at a complete report) or preliminary reports (following joint readout with attending physicians) and faculty-reviewed final reports. Weighted mean Jaccard similarity was compared between years of experience using Welch analysis of variance with post hoc testing overall, per imaging division, and per modality. Relationships with years and quarters of resident experience were assessed using Spearman correlation. Results This study included 53 residents (mean report count, 4660 ± 3546; 1-5 years of experience). Mean Jaccard similarity of preliminary reports increased by 6% from 1st-year to 5th-year residents (0.86 ± 0.22 to 0.92 ± 0.15; P < .001). Spearman correlation demonstrated a strong relationship between residents' experience and higher report similarity when aggregated for years (rs = 0.99 [95% CI: 0.85, 1.00]; P < .001) or quarters of experience (rs = 0.90 [95% CI: 0.73, 0.96]; P < .001). For residents' draft reports, Jaccard similarity increased by 14% over the course of the 5-year residency program (0.68 ± 0.27 to 0.82 ± 0.23; P < .001). Subgroup analysis confirmed similar trends for all imaging divisions and modalities (eg, in musculoskeletal imaging, from 0.77 ± 0.31 to 0.91 ± 0.16 [P < .001]; rs = 0.98 [95% CI: 0.72, 1.00] [P < .001]). Conclusion Residents' report writing performance increases with experience. Trends can be quantified with the Jaccard index, with a 6% improvement from 1st- to 5th-year residents, indicating its effectiveness as a tool for evaluating training progress and guiding education over the course of residency. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bruno in this issue.

Performance of Artificial Intelligence Content Detectors Using Human and Artificial Intelligence-Generated Scientific Writing.

BACKGROUND: Few studies have examined the performance of artificial intelligence (AI) content detection in scientific writing. This study evaluates the performance of publicly available AI content detectors when applied to both human-written and AI-generated scientific articles. METHODS: Articles published in Annals of Surgical Oncology (ASO) during the year 2022, as well as AI-generated articles using OpenAI's ChatGPT, were analyzed by three AI content detectors to assess the probability of AI-generated content. Full manuscripts and their individual sections were evaluated. Group comparisons and trend analyses were conducted by using ANOVA and linear regression. Classification performance was determined using area under the curve (AUC). RESULTS: A total of 449 original articles met inclusion criteria and were evaluated to determine the likelihood of being generated by AI. Each detector also evaluated 47 AI-generated articles by using titles from ASO articles. Human-written articles had an average probability of being AI-generated of 9.4% with significant differences between the detectors. Only two (0.4%) human-written manuscripts were detected as having a 0% probability of being AI-generated by all three detectors. Completely AI-generated articles were evaluated to have a higher average probability of being AI-generated (43.5%) with a range from 12.0 to 99.9%. CONCLUSIONS: This study demonstrates differences in the performance of various AI content detectors with the potential to label human-written articles as AI-generated. Any effort toward implementing AI detectors must include a strategy for continuous evaluation and validation as AI models and detectors rapidly evolve.