A Semi-supervised Learning Application for Hand Posture Classification

The rapid growth of HCI applications results in increased data size and complexity. For this, advanced machine learning techniques and data analysis solutions are used to prepare and process data patterns. However, the cost of data pre-processing, labelling, and classification can be significantly increased if the dataset is huge, complex, and unlabelled. This paper aims to propose a data pre-processing approach and semi-supervised learning technique to prepare and classify a big Motion Capture Hand Postures dataset. It builds the solutions via Tri-training and Co-forest techniques and compares them to figure out the best-fitted approach for hand posture classification. According to the results, Co-forest outperforms Tri-training in terms of Accuracy, Precision, recall, and F1-score

Transcriptomic effects of paternal cocaine-seeking on the reward circuitry of male offspring

Abstract It has been previously established that paternal development of a strong incentive motivation for cocaine can predispose offspring to develop high cocaine-seeking behavior, as opposed to sole exposure to the drug that results in drug resistance in offspring. However, the adaptive changes of the reward circuitry have not been fully elucidated. To infer the key nuclei and possible hub genes that determine susceptibility to addiction in offspring, rats were randomly assigned to three groups, cocaine self-administration (CSA), yoked administration (Yoke), and saline self-administration (SSA), and used to generate F1. We conducted a comprehensive transcriptomic analysis of the male F1 offspring across seven relevant brain regions, both under drug-naïve conditions and after cocaine self-administration. Pairwise differentially expressed gene analysis revealed that the orbitofrontal cortex (OFC) exhibited more pronounced transcriptomic changes in response to cocaine exposure, while the dorsal hippocampus (dHip), dorsal striatum (dStr), and ventral tegmental area (VTA) exhibited changes that were more closely associated with the paternal voluntary cocaine-seeking behavior. Consistently, these nuclei showed decreased dopamine levels, elevated neuronal activation, and elevated between-nuclei correlations, indicating dopamine-centered rewiring of the midbrain circuit in the CSA offspring. To determine if possible regulatory cascades exist that drive the expression changes, we constructed co-expression networks induced by paternal drug addiction and identified three key clusters, primarily driven by transcriptional factors such as MYT1L, POU3F4, and NEUROD6, leading to changes of genes regulating axonogenesis, synapse organization, and membrane potential, respectively. Collectively, our data highlight vulnerable neurocircuitry and novel regulatory candidates with therapeutic potential for disrupting the transgenerational inheritance of vulnerability to cocaine addiction