Copy number variation (CNV) is an essential genetic driving factor of cancer formation and progression, making intelligent classification based on CNV feasible. However, there are a few challenges in the current machine learning and deep learning methods, such as the design of base classifier combination schemes in ensemble methods and the selection of layers of neural networks, which often result in low accuracy. Therefore, an adaptive bilinear dynamic cascade model (Adap-BDCM) is developed to further enhance the accuracy and applicability of these methods for intelligent classification on CNV datasets. In this model, a feature selection module is introduced to mitigate the interference of redundant information, and a bilinear model based on the gated attention mechanism is proposed to extract more beneficial deep fusion features. Furthermore, an adaptive base classifier selection scheme is designed to overcome the difficulty of manually designing base classifier combinations and enhance the applicability of the model. Lastly, a novel feature fusion scheme with an attribute recall submodule is constructed, effectively avoiding getting stuck in local solutions and missing some valuable information. Numerous experiments have demonstrated that our Adap-BDCM model exhibits optimal performance in cancer classification, stage prediction, and recurrence on CNV datasets. This study can assist physicians in making diagnoses faster and better.

Natural disasters are common and have potentially deleterious impacts on individuals, as well as on the relationships among family members (Adams et al., 2015; Paul, 2015). Additionally, caregiver-, offspring-, and family-level outcomes are often correlated following disaster.
Thus, longitudinal work is needed to clarify the prospective associations among such constructs following severe disasters.
The current study included 1,271 adolescents and investigated whether disaster exposure impacted adolescent posttraumatic stress disorder (PTSD) symptoms, parent distress, and family parent-child conflict and communication, as well as whether/how these factors influenced one another over time. This study used a dynamic cascade model and included adolescents (ages 12-17) and caregivers present for tornadoes in Missouri and Alabama in 2011. These participants were part of a larger study involving a web-based intervention.
Over and above covariates (i.e., adolescent age, gender, race, treatment, prior trauma, adolescent alcohol use and depressive symptoms, and household income), families who experienced greater severity of disaster exposure had adolescents who reported more baseline PTSD symptoms and caregivers who reported more distress at baseline.
Providing tangible resources (e.g., housing, food, transportation, essential possessions) to families post-disaster may reduce parent distress and adolescent PTSD symptoms. Additionally, reducing adolescent PTSD symptoms may prospectively improve relationships between parents and adolescents.