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Higher education students, formally adults, are nevertheless subject to adultism. The co-authors of this article--the director of the Students as Teachers and Learners (SaLT) program and three undergraduates who have worked in pedagogical partnership with faculty through SaLT--discuss how this program counters adultism on three levels: conceptually, structurally, and personally/interpersonally. We conclude with implications of this work for others interested in creating structures, practices, and relationships that counter adultism in higher education.

Artificial Intelligence (AI) and, more specifically, Machine Learning (ML) methodologies have successfully tailored commercial applications for decades. However, the recent profound success of large language models like ChatGPT and the enormous subsequent funding from governments and investors have positioned ML to emerge as a paradigm-shifting technology across numerous domains in the coming years. To cultivate a competent workforce and prepare students for success in this new AI-focused evolving world, the integration of ML is proposed to begin in compulsory education rather than in college courses or expensive boot camps. Unfortunately, ML is a complex and intimidating topic for high school teachers to engage with, let alone high school students. Based on our experiences hosting Machine Learning for High School Teachers (ML4HST) workshops for teachers teaching ML topics at our institution, we present in this paper various considerations for educating educators on the topic of ML. In particular, we discuss (a) overarching pedagogic strategies, (b) accessibility of resources such as computational hardware and datasets, (c) balancing theory and implementation, (d) appropriate selection of topics and activities for fostering understanding and engagement, and perhaps most importantly, (e) a compilation of pitfalls to avoid. Synthesizing these insights, we propose a framework for successfully empowering educators to introduce ML in the classroom.

Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy. However, the actual goals still need to be attained, like embedding essays into vectors with cohesion and coherence, and providing student feedback is still challenging. In this paper, we proposed coherence-based embedding of an essay into vectors using sentence-Bidirectional Encoder Representation for Transformers. We trained these vectors on Long short-term memory and bidirectional long short-term memory to capture sentence connectivity with other sentences' semantics. We used two datasets: standard ASAP Kaggle and a domain-specific dataset with almost 2500 responses from 650 students. Our model performed well on both datasets, with an average quadratic weighted kappa score of 0.76. Furthermore, we achieved good results compared to other prescribed models, and we also tested our model on adversarial responses of both datasets and observed decent outcomes.

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.

Biomedical Quality Engineers (QEs) ensure that medical devices are safe, reliable, and consistent. To better understand career pathways for biomedical QEs, we (1) quantified the prevalence of QE skills in entry-level biomedical engineering (BME) job listings and (2) interviewed seven biomedical Quality Engineers with a BME bachelor's degree. We mapped participant responses to the mechanisms in Social Cognitive Career Theory and identified common career pathways for biomedical QEs. To our surprise, over 40% of entry-level BME jobs were QE positions and 70% required QE-related skills. The interview participants were unaware of careers in QE careers as undergraduates and learned about QE while entering the job market--a surprising finding given the prevalence of entry-level QE jobs. The participants had low outcome expectations for QE careers and higher outcome expectations for research and development positions; instructors should be aware that a design-focused curriculum can bias students against QE careers. BME departments should introduce QE topics and experiences to help students make informed career decisions and be competitive in the sizable biomedical QE job market.

Forms of immobility both limit unqualified human agency and enable diverse channels of mobility. In this sense, mobility and immobility work together. Certain philosophical movements such as Southern theories and disability studies treat constraints, sedentariness, and boundaries as needing to be respected and accommodated in any inquiry. This article draws from these schools to theorize disruptions and constraints as resources in the circulation of languages, texts, and meanings. To index this generative role of constraints in communication, I adopt the term "crip" from theorizations in disability studies. "Crip" invokes the paradoxical reality that while being crippled poses disruptions in mobility, this rupture also generates new knowledge and possibilities into the flow of life (McRuer, 2006). This article explains how "crip translingualism" would treat ruptures, constraints, and boundaries as resourceful for meaning making. This is a corrective to certain previous theorizations that have treated translingualism as based on unrestricted flows and fluidities, influenced by dominant orientations to mobility. I illustrate from a classroom literacy interaction where the ruptures posed by the heritage languages of multilingual students motivated everyone to adopt creative strategies to expand the meaning of "meaning," redefine literacy as negotiated, and develop ethical dispositions to collaborate in communicating across language boundaries. I argue that the incomprehensions and vulnerabilities created by language diversity actually motivate everyone to develop strategies to creatively read and write. In this manner, constraints don't stifle the text or students, but mobilize new flows of meanings and interactions.

Technological tools that provide automated feedback on classroom teaching afford a unique opportunity for educators to engage in self-reflection and work towards improvement goals, in particular to ensure that their instructional environment is equitable and productive for students. More information is needed about how teachers experience automated professional learning tools, including what they perceive as relevant and impactful for their everyday teaching. This mixed-methods study explored the perceptions and engagement of 21 math teachers who used an AI-based tool that generates information about their discourse practices from classroom recordings. Findings indicate that teachers perceived the tool to have a high utility value, especially those who elected to use it over two school years. These teachers increased their use of talk moves over time, suggesting that they were making intentional changes due to their review and uptake of the personalized feedback. These results from this study speak to promising directions for developing AI-based professional learning tools that can support teacher learning and instructional improvement, particularly tools with robust perceived utility.

Abstract: Terrestrial groundwater travels through subterranean estuaries before reaching the sea. Groundwater‐derived nutrients drive coastal water quality, primary production, and eutrophication. We determined how dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and dissolved organic nitrogen (DON) are transformed within subterranean estuaries and estimated submarine groundwater discharge (SGD) nutrient loads compiling > 10,000 groundwater samples from 216 sites worldwide. Nutrients exhibited complex, nonconservative behavior in subterranean estuaries. Fresh groundwater DIN and DIP are usually produced, and DON is consumed during transport. Median total SGD (saline and fresh) fluxes globally were 5.4, 2.6, and 0.18 Tmol yr−1 for DIN, DON, and DIP, respectively. Despite large natural variability, total SGD fluxes likely exceed global riverine nutrient export. Fresh SGD is a small source of new nutrients, but saline SGD is an important source of mostly recycled nutrients. Nutrients exported via SGD via subterranean estuaries are critical to coastal biogeochemistry and a significant nutrient source to the oceans.

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

INTRODUCTION: Testosterone replacement therapy (TRT) can improve quality of life for men with hypogonadism. However, it is generally avoided in patients with a history of prostate cancer or breast cancer as there is uncertainty about risks. This case illustrates an example of synchronous metastatic prostate cancer and male breast cancer following TRT. PRESENTATION OF CASE: A 72-year-old man with previously treated intermediate-risk prostate adenocarcinoma experienced a gradual rise in prostate-specific antigen (PSA) while self-administering testosterone replacement. He was later found to have recurrent metastatic prostate cancer and prior to initiating androgen deprivation therapy (ADT), he was also diagnosed with male breast cancer. His treatment has consisted of continued ADT for metastatic castration-sensitive prostate cancer (mCSPC) as well as surgical resection of his breast cancer. DISCUSSION: ADT plays a role in treatment of male breast cancer and prostate cancer. TRT remains relatively contraindicated in patients with a history of these malignancies, but the evidence supporting this recommendation is somewhat limited. CONCLUSION: This case highlights the potential risk for synchronous recurrent prostate and new male breast cancer following TRT. Further studies are needed to better elucidate the increased risks of these malignancies with TRT.

SUMMARY: perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. AVAILABILITY AND IMPLEMENTATION: The software is coded in Python. Compiled executables and source code are available at https://github.com/AitchisonLab/perox-per-cell. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sensory adaptation is the process whereby brain circuits adjust neuronal activity in response to redundant sensory stimuli. Although sensory adaptation has been extensively studied for individual neurons on timescales of tens of milliseconds to a few seconds, little is known about it over longer timescales or at the population level. We investigated population-level adaptation in the barrel field of the mouse somatosensory cortex (S1BF) using in vivo two-photon calcium imaging and Neuropixels recordings in awake mice. Among stimulus-responsive neurons, we found both adapting and facilitating neurons, which decreased or increased their firing, respectively, with repetitive whisker stimulation. The former outnumbered the latter by 2:1 in layers 2/3 and 4; hence, the overall population response of mouse S1BF was slightly adapting. We also discovered that population adaptation to one stimulus frequency (5 Hz) does not necessarily generalize to a different frequency (12.5 Hz). Moreover, responses of individual neurons to repeated rounds of stimulation over tens of minutes were strikingly heterogeneous and stochastic, such that their adapting or facilitating response profiles were not stable across time. Such representational drift was particularly striking when recording longitudinally across 8-9 days, as adaptation profiles of most whisker-responsive neurons changed drastically from one day to the next. Remarkably, repeated exposure to a familiar stimulus paradoxically shifted the population away from strong adaptation and toward facilitation. Thus, the adapting vs. facilitating response profile of S1BF neurons is not a fixed property of neurons but rather a highly dynamic feature that is shaped by sensory experience across days.

Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols.