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AI-driven drug discovery accelerates anti-addiction treatment by enhancing precision and targeting key neurochemical systems. 

Imbalanced data, where certain classes are significantly underrepresented in a dataset, is a widespread machine learning (ML) challenge across various fields of chemistry, yet it remains inadequately addressed. 

Data sets with imbalanced class sizes, where one class size is much smaller than that of others, occur exceedingly often in many applications, including those with biological foundations, such as disease diagnosis and drug discovery. Therefore, it is extremely important to be able to identify data elements of classes of various sizes, as a failure to do so can result in heavy costs. Nonetheless, many data classification procedures do not perform well on imbalanced data sets as they often fail to detect elements belonging to underrepresented classes. In this work, we propose the BTDT-MBO algorithm, incorporating Merriman–Bence–Osher (MBO) approaches and a bidirectional transformer, as well as distance correlation and decision threshold adjustments, for data classification tasks on highly imbalanced molecular data sets, where the sizes of the classes vary greatly. The proposed technique not only integrates adjustments in the classification threshold for the MBO algorithm in order to help deal with the class imbalance, but also uses a bidirectional transformer procedure based on an attention mechanism for self-supervised learning. In addition, the model implements distance correlation as a weight function for the similarity graph-based framework on which the adjusted MBO algorithm operates. The proposed method is validated using six molecular data sets and compared to other related techniques. The computational experiments show that the proposed technique is superior to competing approaches even in the case of a high class imbalance ratio. 

Cybersickness – discomfort caused by virtual reality (VR) – remains a significant problem that negatively affects the user experience. Research on individual differences in cybersickness has typically focused on overall sickness intensity, but a detailed understanding should include whether individuals differ in the relative intensity of cybersickness symptoms. This study used latent profile analysis (LPA) to explore whether there exist groups of individuals who experience common patterns of cybersickness symptoms. Participants played a VR game for up to 20 min. LPA indicated three groups with low, medium, and high overall cybersickness. Further, there were similarities and differences in relative patterns of nausea, disorientation, and oculomotor symptoms between groups. Disorientation was lower than nausea and oculomotor symptoms for all three groups. Nausea and oculomotor were experienced at similar levels within the high and low sickness groups, but the medium sickness group experienced more nausea than oculomotor. Characteristics of group members varied across groups, including gender, virtual reality experience, video game experience, and history of motion sickness. These findings identify distinct individual experiences in symptomology that go beyond overall sickness intensity, which could enable future interventions that target certain groups of individuals and specific symptoms. 

Abstract Increases in the concentration of dissolved organic matter (DOM) have been documented in many inland waters in recent decades, a process known as “browning”. Previous studies have often used space‐for‐time substitution to examine the direct consequences of increased DOM on lake ecosystems. However, browning often occurs concomitant with other ecologically important water chemistry changes that may interact with or overwhelm any potential ecological response to browning itself. Here we examine a long‐term (~20 year) dataset of 28 lakes in the Adirondack Park, New York, USA, that have undergone strong browning in response to recovery from acidification. With these data, we explored how primary producer and zooplankton consumer populations changed during this time and what physical and chemical changes best predicted these long‐term ecosystem changes. Our results indicate that changes in primary producers are likely driven by reduced water clarity due to browning, independent of changes in nutrients, counter to previously hypothesized primary producer response to browning. In contrast, declines in calcium concomitant with browning play an important role in driving long‐term declines in zooplankton biomass. Our results indicate that responses to browning at different trophic levels are decoupled from one another. Concomitant chemical changes have important implications for our understanding of the response of aquatic ecosystems to browning.