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Recent advances in Artificial Intelligence (AI) have brought society closer to the long-held dream of creating machines to help with both common and complex tasks and functions. From recommending movies to detecting disease in its earliest stages, AI has become an aspect of daily life many people accept without scrutiny. Despite its functionality and promise, AI has inherent security risks that users should understand and programmers must be trained to address. The ICE (integrity, confidentiality, and equity) cybersecurity labs developed by a team of cybersecurity researchers addresses these vulnerabilities to AI models through a series of hands-on, inquiry-based labs. Through experimenting with and manipulating data models, students can experience firsthand how adversarial samples and bias can degrade the integrity, confidentiality, and equity of deep learning neural networks, as well as implement security measures to mitigate these vulnerabilities. This article addresses the pedagogical approach underpinning the ICE labs, and discusses both sample activities and technological considerations for teachers who want to implement these labs with their students. 

Recent advances in Artificial Intelligence (AI) have brought society closer to the long-held dream of creating machines to help with both common and complex tasks and functions. From recommending movies to detecting disease in its earliest stages, AI has become an aspect of daily life many people accept without scrutiny. Despite its functionality and promise, AI has inherent security risks that users should understand and programmers must be trained to address. The ICE (integrity, confidentiality, and equity) cybersecurity labs developed by a team of cybersecurity researchers addresses these vulnerabilities to AI models through a series of hands-on, inquiry-based labs. Through experimenting with and manipulating data models, students can experience firsthand how adversarial samples and bias can degrade the integrity, confidentiality, and equity of deep learning neural networks, as well as implement security measures to mitigate these vulnerabilities. This article addresses the pedagogical approach underpinning the ICE labs, and discusses both sample activities and technological considerations for teachers who want to implement these labs with their students. 

Recent advances in Artificial Intelligence (AI) have brought society closer to the long-held dream of creating machines to help with both common and complex tasks and functions. From recommending movies to detecting disease in its earliest stages, AI has become an aspect of daily life many people accept without scrutiny. Despite its functionality and promise, AI has inherent security risks that users should understand and programmers must be trained to address. The ICE (integrity, confidentiality, and equity) cybersecurity labs developed by a team of cybersecurity researchers addresses these vulnerabilities to AI models through a series of hands-on, inquiry-based labs. Through experimenting with and manipulating data models, students can experience firsthand how adversarial samples and bias can degrade the integrity, confidentiality, and equity of deep learning neural networks, as well as implement security measures to mitigate these vulnerabilities. This article addresses the pedagogical approach underpinning the ICE labs, and discusses both sample activities and technological considerations for teachers who want to implement these labs with their students.

 

Physical inactivity is a scourge to human health, promoting metabolic disease and muscle wasting. Interestingly, multiple ecological niches have relaxed investment into physical activity, providing an evolutionary perspective into the effect of adaptive physical inactivity on tissue homeostasis. One such example, the Mexican cavefishAstyanax mexicanus,has lost moderate-to-vigorous activity following cave colonization, reaching basal swim speeds ~3.7-fold slower than their river-dwelling counterpart. This change in behavior is accompanied by a marked shift in body composition, decreasing total muscle mass and increasing fat mass. This shift persisted at the single muscle fiber level via increased lipid and sugar accumulation at the expense of myofibrillar volume. Transcriptomic analysis of laboratory-reared and wild-caught cavefish indicated that this shift is driven by increased expression ofpparγ—the master regulator of adipogenesis—with a simultaneous decrease in fast myosin heavy chain expression. Ex vivo and in vivo analysis confirmed that these investment strategies come with a functional trade-off, decreasing cavefish muscle fiber shortening velocity, time to maximal force, and ultimately maximal swimming speed. Despite this, cavefish displayed a striking degree of muscular endurance, reaching maximal swim speeds ~3.5-fold faster than their basal swim speeds. Multi-omic analysis suggested metabolic reprogramming, specifically phosphorylation of Pgm1-Threonine 19, as a key component enhancing cavefish glycogen metabolism and sustained muscle contraction. Collectively, we reveal broad skeletal muscle changes following cave colonization, displaying an adaptive skeletal muscle phenotype reminiscent to mammalian disuse and high-fat models while simultaneously maintaining a unique capacity for sustained muscle contraction via enhanced glycogen metabolism.

 

Small ponds—farm ponds, detention ponds, or impoundments below 0.01 km²—serve important human needs throughout most large river basins. Yet the role of small ponds in regional nutrient and sediment budgets is essentially unknown, currently making it impossible to evaluate their management potential to achieve water quality objectives. Here we used new hydrography data sets and found that small ponds, depending on their spatial position within both their local catchments and the larger river network, can dominate the retention of nitrogen, phosphorus, and sediment compared to rivers, lakes, and reservoirs. Over 300,000 small ponds are collectively responsible for 34%, 69%, and 12% of the mean annual retention of nitrogen, phosphorus, and sediment in the Northeastern United States, respectively, with a dominant influence in headwater catchments (54%, 85%, and 50%, respectively). Small ponds play a critical role among the many aquatic features in long‐term nutrient and sediment loading to downstream waters.