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High-altitude conditions on the Tibetan Plateau are often depicted as an inhospitable environment for conventional farming, yet evidence shows that communities in western Tibet grew ecologically hardy crops such as 6-row barley (Hordeum vulgare) by at least the 1^stmillennium BCE, at locations above 4,000 meters above sea level (masl). However, little is known about the specific cultivation strategies and culinary traditions that these agropastoral communities developed. Stable carbon and nitrogen isotope compositions of grains inform growing conditions and provide much needed insight into the cultivation strategies in such a unique environment. We use δ¹³C and δ¹⁵N values of archaeologically recovered barley remains to investigate past watering and soil-management strategies. Our results infer high labor investment in manuring and watering in barley farming. This suggests an intensive cultivation system in Western Tibet, 1,000 BCE −1,000 CE, despite the high-altitude pastoral landscape. 

Esophageal motility disorders can be diagnosed by either high‐resolution manometry (HRM) or the functional lumen imaging probe (FLIP) but there is no systematic approach to synergize the measurements of these modalities or to improve the diagnostic metrics that have been developed to analyze them. This work aimed to devise a formal approach to bridge the gap between diagnoses inferred from HRM and FLIP measurements using deep learning and mechanics. The “mechanical health” of the esophagus was analyzed in 740 subjects including a spectrum of motility disorder patients and normal subjects. The mechanical health was quantified through a set of parameters including wall stiffness, active relaxation, and contraction pattern. These parameters were used by a variational autoencoder to generate a parameter space called virtual disease landscape (VDL). Finally, probabilities were assigned to each point (subject) on the VDL through linear discriminant analysis (LDA), which in turn was used to compare with FLIP and HRM diagnoses. Subjects clustered into different regions of the VDL with their location relative to each other (and normal) defined by the type and severity of dysfunction. The two major categories that separated best on the VDL were subjects with normal esophagogastric junction (EGJ) opening and those with EGJ obstruction. Both HRM and FLIP diagnoses correlated well within these two groups. Mechanics‐based parameters effectively estimated esophageal health using FLIP measurements to position subjects in a 3‐D VDL that segregated subjects in good alignment with motility diagnoses gleaned from HRM and FLIP studies. 

Despite significant advances in machine learning (ML) applications within science, there is a notable gap in its integration into K-12 education to enhance data literacy and scientific inquiry (SI) skills. To address this gap, we enable K-12 teachers with limited technical expertise to apply ML for pattern discovery and explore how ML can empower educators in teaching SI. We design a web-based tool, ML4SI, for teachers to create ML-supported SI learning activities. This tool can also facilitate collecting data about the interaction between ML techniques and SI learning. A pilot study with three K-12 teachers provides insights to prepare the next generation for the era of big data through ML-supported SI learning.