ASMF: Ambient social media forensics chain of custody with an intelligent digital investigation process using federated learning.

Ambient Intelligence is a concept that relates to a new paradigm of pervasive computing and has the objective of automating responses from the system to humans without any human intervention. In social media forensics, gathering, analyzing, storing, and validating relevant evidence for investigation in a heterogeneous environment is still questionable. There is no hierarchy for automation, even though standardization and secure processes from data collection to validation have not yet been discussed. This poses serious issues for the current investigation procedures and future evidence chain of custody management. This paper contributes threefold. First, it proposes a framework using a blockchain network with a dual chain of data transmission for privacy protection, such as on-chain and off-chain. Second, a protocol is designed to detect and separate local and global cyber threats and undermine multiple federated principles to personalize search space broadly. Third, this study manages personalized updates by means of optimizing backtracking parameters and automating replacements, which directly affects the reduction of negative influence on the social networking environment in terms of imbalanced and distributed data issues. This proposed framework enhances stability in digital investigation. In addition, the simulation uses an extensive social media dataset in different cyberspaces with a variety of cyber threats to investigate. The proposed work outperformed as compared to traditional single-level personalized search and other state-of-the-art schemes.

Polygenic associations and causal inferences between serum immunoglobulins and amyotrophic lateral sclerosis.

BACKGROUND: Due to the limitations like reverse causation and residual confounding commonly seen in the observational studies, the relationship between serum immunoglobulins and amyotrophic lateral sclerosis (ALS) remains unclear. METHODS: Summary statistics from large-scale genome-wide association studies (GWAS) among European ancestry populations (~15,000 individuals for serum immunoglobulins, and more than 36,000 individuals for ALS) were accessed and used in the discovery and replication phase, respectively. Polygenic risk score analysis was performed to test the polygenic association, and Mendelian randomization analysis was used to infer the causality. RESULTS: An inverse polygenic association was discovered between IgA and ALS, as well as between IgM and ALS. Such associations were however not replicated using a larger GWAS of ALS, and no causal association was observed for either IgA-ALS or IgM-ALS. For IgG and ALS, a positive polygenic association was both discovered [odds ratio (OR) = 1.18, 95% confidence interval (CI): 1.12-1.25, P = 5.9x10-7] and replicated (OR = 1.13, 95% CI: 1.06-1.20, P = 0.001). A causal association between IgG and ALS was suggested in the discovery analysis (OR = 1.06, 95 %CI: 1.02-1.10, P = 0.009), but it was not statistically significant in the replication analysis (OR = 1.07, 95 %CI: 0.90-1.24, P = 0.420). CONCLUSION: This study suggests a positive polygenic association between serum IgG and ALS.